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How Artificial Intelligence (AI) and Machine Learning (ML) ...
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How Artificial Intelligence (AI) and Machine Learning (ML) are expected to transform Recruitment Process Outsourcing (RPO)
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https://www.adastraconsultants.com
|
[] |
This can empower recruiters and help to improve the efficiency of the recruitment process and reduce the time it takes to fill open positions. By reducing the ...
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How Artificial Intelligence (AI) and Machine Learning (ML) are expected to transform Recruitment Process Outsourcing (RPO)
Automation of repetitive tasks: AI and ML can automate repetitive tasks such as creating job descriptions, candidate sourcing, resume screening, candidate matching, interview scheduling, and conducting tests or interviews, while also saving time and reducing errors. This can empower recruiters and help to improve the efficiency of the recruitment process and reduce the time it takes to fill open positions. By reducing the legwork and time-to-hire, AI and ML are also slashing hiring costs which can then be allocated for better areas such as training or employee benefits.
Improving the candidate experience: AI and ML can be used to analyse candidate data and provide a more personalised, unbiased, and efficient candidate experience, which can help to attract and retain top talent. In addition, by reducing skill assessment time from the traditional whole day to a matter of a few seconds or minutes, AI and ML driven assessment and data-gathering techniques to provide instant feedback to candidates and reduce their wait time, thereby providing a wonderful first experience with your company, to job-seekers.
Enhancing decision-making: The AI and ML recruitment software that is built into recruitment tools is used to analyse candidate data and provide insights that can help RPO providers make more informed decisions about which candidates to hire. They also keep a handy database of the best passive talent fitment across the globe, thereby having easily accessible candidates for your company. This can help to improve the overall quality of hires and reduce attrition within the organisation.
Identifying patterns and trends: AI and ML driven Chatbots are becoming increasingly popular for their two-way chat opportunities, between job seekers and employers. These chats are real-time and therefore much faster than traditional email. What is more, they are ideal to identify common trending issues that job seekers want clarity on. AI and ML are also used to analyse large amounts of data and identify patterns and trends that are used to inform recruitment strategies. For example, it can help to identify the best recruiting channels, or the most effective interview questions.
Predictive analytics: AI and ML can be used to predict the success of a candidate based on the data available, such as their qualifications, experience, and interview performance. This can help to identify the most likely candidates to excel in a particular role and can help to reduce the risk of bad hires. AI and ML minimise a company’s risk by using algorithms that can identify the ideal applicant for a role and instantly assess her/ him against a range of criteria and prediction models.
Overall, AI and ML have the potential to significantly improve the efficiency and effectiveness of RPO and can help to reduce costs, improve the candidate experience, and increase the quality of hires.
| 2023-02-14T00:00:00 |
https://www.adastraconsultants.com/how-artificial-intelligence-ai-and-machine-learning-ml-are-expected-to-transform-recruitment-process-outsourcing-rpo/
|
[
{
"date": "2023/02/14",
"position": 55,
"query": "artificial intelligence hiring"
}
] |
|
12 Machine Learning Engineers & Teams for Hire in 2025
|
12 Machine Learning Engineers & Teams for Hire in 2025
|
https://www.devteam.space
|
[
"Ceo",
"Edge Ai World - Fzco",
"Founder",
"Hadom Enterprises",
"Director Of Software Product",
"Keep It Simple Storage",
"Charity Token Pty Ltd",
"Owner",
"Medfeetree",
"Midnight Biotechnologies"
] |
6 Steps to Hiring ML Developers. Here are the 6 essential steps to hire a machine learning engineer: Step 1: Define Project Requirements. Your machine learning ...
|
Aran Davies is a full-stack software development engineer and tech writer with experience in Web and Mobile technologies. He is a tech nomad and has seen it all.
How to Hire ML Engineers – A Guide: 5 Best Companies to Hire ML Engineers in 2025
The fact that you are reading this page means that you likely want to hire a machine learning developer or an ML team. If this is true, you most likely want to use ML technology to address problems that will help your business get ahead, as so many other companies are doing.
You will need ML developers with strong programming abilities, relevant industry expertise, and a proactive can-do mentality. Finding and hiring ML developers can be a real challenge if you don’t know how.
The great news is that you’ve come to the correct location! In this post on how to find ML developers, I will share my deep insight on how to find the best ML developers.
I'll start by breaking down the costs associated with hiring ML engineers and the expenses tied to developing specific ML systems by complexity. After showcasing some ML project examples, I’ll share the top five companies to hire ML developers.
Within computer science, machine learning (ML) is one of the most exciting and rapidly developing fields. Researchers have been enthralled for decades with the question, "Can machines learn and improve from data?" We are finally arriving at the point where we are going to be able to learn just how much computers will be able to learn and improve.
Numerous corporate, governmental, and academic institutions have committed resources to the advancement of machine learning (ML) technologies over the years. With each passing year, more and more companies are jumping on board. PwC estimates that by 2030, ML may boost the world economy by $15.7 trillion.
While this is seeing ML technology rapidly advancing, it does mean that machine learning developers are in very high demand. This is why it is so important to know how and where to hire machine learning engineers.
I will now show you how to do just that.
In this article:
Latest ML Trends
Gartner anticipates a transformation in how we interact with ML and AI technologies. Gartner recently reported that "by 2028, one-third of interactions involving AI will utilize action models and autonomous systems to complete tasks."
These autonomous systems can perform tasks without constant human oversight, utilizing ML and AI technologies to make informed decisions that can give companies the edge on their competitors through a better understanding of their users.
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How Much Does It Cost to Hire a Machine Learning Developer?
When it comes to hiring ML developers, what should you expect to pay? Whether you're hiring full-time or part-time ML engineers, you'll find cost data available on a per-hour basis. These hourly rates can vary based on several factors:
What is your hiring location? Costs tend to be higher in North America and Western Europe.
Costs tend to be higher in North America and Western Europe. Do you want to hire ML engineers from top-tier companies in the US? Expect to pay a premium.
Expect to pay a premium. Will you need in-house ML engineers located in the US? This may come with higher hourly rates.
This may come with higher hourly rates. Are you looking to hire remote ML developers from reputable US-based companies? You can anticipate fair value for your investment.
You can anticipate fair value for your investment. Are you considering freelancers? While hourly rates may be lower, this option carries its own set of risks, which I'll elaborate on shortly.
How Much Does It Cost to Hire a Machine Learning Developer?
If you’re hiring ML engineers in the US, it’s advisable to partner with a US-based company. This will ensure that you can enforce your legal agreements should there be any problems. An added bonus is cultural alignment, which will facilitate smoother collaboration with your ML team.
Hire Machine Learning engineers in the United States
An experienced or expert ML engineer will cost you $55-$300 per hour in the US. Your monthly costs will range from $10,400-$48,000 for a full-time ML developer.
Hire Machine Learning engineers in Western Europe
In Western Europe, the hourly rate of an intermediate-to-expert ML engineer can range from $45-$200. Expect to pay $8,000-$32,000 per month if you hire such an experienced ML engineer full-time.
Eastern Europe
Hiring ML developers at the middle-to-advanced level can cost you from $35 to $150 per hour in Eastern Europe. You will need to pay $5,600-$24,000 for full-time ML specialists.
India
The hourly rate for intermediate-to-expert ML engineers in India is $10-$65. Prepare to pay $1,600-$10,400 per month if you hire such ML developers or ML consultants full-time.
Back to Top
Cost of Hiring ML Developers in the United States by State
When considering the cost of hiring a full-time ML developer in the United States, it’s important to note that these figures represent annual salaries and are generally lower than the rates for outsourcing developers on a temporary basis.
California (San Diego, Los Angeles, San Francisco): $103,677 - $154,250 per year
$103,677 - $154,250 per year Colorado (Denver, Colorado Springs, Greenwood Village): $111,000 - $124,000 per year
$111,000 - $124,000 per year Illinois (Chicago, Buffalo Grove, Deerfield): $106,228 - $123,440 per year
$106,228 - $123,440 per year Maryland (Fort Washington, Baltimore, Annapolis): $90,001 - $115,000 per year
$90,001 - $115,000 per year Massachusetts (Boston, Worcester, Springfield): $112,500 - $113,875 per year
$112,500 - $113,875 per year New York (Albany, New York, Brooklyn): $112,500 - $166,400 per year
$112,500 - $166,400 per year Texas (Austin, Houston, Dallas): $93,917 - $121,811 per year.
Back to Top
How Much Does It Cost to Develop an ML Solution in 2025?
The costs associated with developing ML solutions largely depend on the complexity of the project. For instance, creating a simple ML chatbot will generally be less expensive than developing a medium-complexity ML application, such as a virtual assistant, or a complex ML project, such as a driving assist system. Ultimately, the expenses for complex ML projects can escalate significantly.
In a 2024 post, Elon Musk stated that “Tesla's cumulative investment in its self-driving program would be well over $10 billion this year”. This is how much some companies are spending on ML and AI-based systems.
Simple ML Projects
Consider a scenario where you're developing an ML system using off-the-shelf tools. You would design and deploy models utilizing standard machine learning algorithms backed by organized, high-quality, and labeled datasets. This would be categorized as a simple ML development project. Such a project might range from $5,000 to $50,000 and would likely require between 1 to 3 ML developers. This project would likely take 1 to 8 weeks to complete.
Medium-Complexity ML Software Development Projects
Imagine your ML project involves multiple capabilities, such as supervised learning, natural language processing, and computer vision. If the project requires developing medium-complexity ML models and relies solely on unlabelled data for training, it would fall into the medium-complexity category. Expect costs of somewhere between $50,000 and $250,000 and a timeframe of 1.5 to 6 months. This kind of project typically requires between 2 and 6 machine learning engineers.
Complex ML Software Development Projects
Complex ML projects will involve sophisticated models and advanced techniques like deep learning. They could require extensive data preparation, including unlabelled datasets such as visual recognition data from cameras that make up a driver-assist system. These projects often need specialized expertise and can cost significantly more than medium-complexity ones, requiring a team of seasoned ML engineers over an extended timeframe.
Defining Your ML Application Features
Your app must stand out to succeed in the competitive ML application market. If you created an ML application that mirrored all competitors' features, what incentive would users have to choose yours?
Developing a comprehensive feature list is essential for visualizing your ML app and its goals and, subsequently, allowing you to gauge your budgeting and hiring needs. Document all desired features and goals to create a clear product outline, which will guide you in hiring the right ML development team.
Back to Top
What Kind of ML Developers Do You Need?
If you’re updating an existing ML application that was built in R or need deep integration with R-based libraries, hiring an R developer is necessary. However, many newer developers specialize in Python, which is the preferred language for contemporary ML development due to its extensive libraries and community support.
Python vs. R: Pros and Cons
The primary difference is that Python allows developers to write streamlined code, eliminating the need for verbose syntaxes such as braces and semicolons. This means that a Python developer can focus on creating clean and maintainable code. This simplicity is especially beneficial for junior developers, as it reduces the chances of making errors that can lead to bugs.
Note: I strongly advise against using junior ML developers in your project. Experience is key when it comes to ML development; don’t risk hiring junior ML developers.
Python also includes several innovative features, such as dynamic typing and powerful libraries, which contribute to cleaner code with fewer issues. In contrast, languages like R can complicate memory management, making it a challenging task for developers. Python addresses this with its garbage collection system, which automatically manages memory by deallocating objects that are no longer in use.
Another crucial point to consider is support. While R is still supported by the community, the level of investment has decreased significantly in recent years. This is primarily due to the growing popularity of Python, leading to fewer libraries being developed for R. Over time, this waning interest may result in reduced support for R by its maintainers.
It is for this reason that I strongly recommend using Python.
Backend Service Providers
Selecting a reliable backend service provider is essential for hosting your ML application, along with choosing the best backend developers who possess all the expertise to write the necessary code and set up your system.
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Naturally, you'll want fast performance and reliability from your ML system and supporting web and mobile apps. Fortunately, many service providers meet these needs, though some stand out more than others. Here are my top picks for backend service providers.
Google Cloud Platform (GCP)
Google Cloud Platform offers an extensive range of products and services, including powerful tools like Google Compute Engine and TensorFlow for machine learning projects. Its robust AI and ML capabilities make GCP an excellent choice for those developing machine learning solutions.
Amazon Web Services (AWS)
AWS continues to be the world's leading backend service provider. Thanks to the foresight of its founder, Amazon was an early player in cloud infrastructure, allowing it to dominate the market. AWS provides a vast array of powerful tools, including AWS Lambda, which facilitates scalable machine-learning applications.
IBM Cloud
IBM Cloud is a strong backend provider, particularly for enterprises. It offers a variety of tools, including Watson for AI and machine learning, as well as Red Hat OpenShift for hybrid cloud deployments. These tools allow developers to create scalable ML applications that can be deployed in diverse environments.
Microsoft Azure
Microsoft Azure is widely used by Fortune 500 companies due to its comprehensive suite of tools and technologies. Azure provides IaaS, PaaS, SaaS, and serverless options tailored for both ML and application development, making it a versatile choice for backend developers and ML developers.
Additional ML Developer Skills
Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Knowledge of IDEs like Jupyter Notebook, PyCharm, or Visual Studio Code.
Familiarity with app development frameworks and libraries, such as Flask, FastAPI, and Django.
Understanding of CI/CD pipelines and tools like Jenkins, Azure DevOps, or GitLab.
Knowledge of data visualization tools and libraries, such as Matplotlib or Seaborn.
Familiarity with relevant project management methodologies like Agile and Scrum.
Ability to quickly adapt to your project management tools.
Strong communication skills.
Excellent problem-solving capabilities and critical thinking skills.
Back to Top
6 Steps to Hiring ML Developers
Here are the 6 essential steps to hire a machine learning engineer:
Step 1: Define Project Requirements
Your machine learning project requirements encompass the specific functionalities, features, and goals that your project must meet for success. These requirements are crucial and should be established before entering the design and development phases.
While your project’s requirements may evolve during development, especially after launch, as you gather user feedback, the overall goals and core functionalities are likely to remain constant.
Take the time to mentally walk through the process of using your application, focusing on your specific objectives. If you are building a chatbot, then what level of engagement are you hoping for? Will your users expect short and concise answers or will they want long format and detailed answers, etc?
Create a list of both functional and non-functional requirements, also considering aspects like scalability, performance, and security.
Once your project requirements are clear, you’ll have a better understanding of the type of ML engineers and backend developers you will need.
What Type of Developers Will I Need for a Machine Learning Project?
The intricacy of your project and the tech stack you choose will determine how many and what kind of developers and data engineers you will need.
For a typical machine learning project, you would most likely need one or more mid-level or senior Python engineers with knowledge of machine learning frameworks such as TensorFlow or PyTorch. Multiple developers, a data scientist, and possibly a UI/UX designer may be required for mid- or enterprise-level projects.
You will also need experts in those fields if your project calls for specialist technologies like data engineering, natural language processing, or deep learning.
If you're unsure of your needs, consider speaking with one or more machine learning development specialists. Making contact with ML-focused software development firms will likely yield very insightful information.
Step 2: Software Development Company or Freelancer Platform?
Create a job description template when you've decided on the specifics of your project and post it to freelancing websites or job boards. This template will also make contacting software development companies easier. Let’s now look at how these platforms differ.
If you do not want to hire a machine learning engineer full-time, you can hire from a software development business or a freelancer platform.
Hire Machine Learning Developers from a Software Development Company
The safest option for hiring developers is to work with a dedicated software development company that provides full-time staff on an outsourced (temporary) basis to client companies.
Advantages of hiring developers from a software development company include:
Access to a wide range of expert developers.
Developers who are fully vetted and work for the company full-time.
Experience with similar projects enhances the likelihood of success.
Assignment of an account manager to facilitate communication and project management.
Inclusion of a quality assurance (QA) professional to oversee the project before launch.
Access to project management tools and processes.
Contracts that safeguard your intellectual property (IP) rights.
Risk-free trials allow you to evaluate a developer’s fit without a long-term commitment.
Hire Machine Learning Developers from a Freelancer Platform
Freelancer platforms may be a viable option for small-scale projects on a tight budget.
However, the downsides include:
Freelancers often juggle multiple projects, leading to part-time commitments.
A time-consuming vetting process to check past work.
The risk of freelancers abandoning your project for more lucrative opportunities.
Potential issues with intellectual property security.
Lack of project management tools or oversight.
I recommend being cautious with freelancers, as many companies encounter significant challenges when their hired freelancers lack the necessary skills or leave unexpectedly. Opting for a professional software development company can save you from these headaches.
You should conduct three interviews: a primary interview, a skills interview, and a final interview.
Primary Interview:
Use this initial interview to narrow down your candidate list. Discuss foundational knowledge, communication skills, problem-solving approaches, and motivation for your project.
Important questions to ask include:
Are they familiar with Agile or other development methodologies?
Can they work with your specific tools or task trackers?
What communication platforms do they prefer (e.g., Slack)?
Do they have a GitHub repository, or can they access yours?
Ensure remote developer candidates can overlap with your working hours by at least four hours for effective communication during planning and problem-solving.
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Skills Interview:
This interview assesses the technical skills of your machine learning candidates. If you're not experienced in ML development, consider involving a professional who can help evaluate candidates' technical answers.
You might also consider assigning a coding problem that is relevant to your project. This will allow you to gauge their coding style and familiarity with necessary frameworks.
Final Interview:
By this stage, you should have narrowed it down to three or four candidates. This will be the most in-depth interview, focusing on their ability to meet your project’s specific needs.
For example, ask how they would implement a machine-learning model for a particular feature. Gauge their understanding of the tools and technologies they would employ and discuss potential challenges and solutions.
Step 4: Carefully Review Their Past Projects
It’s critical to thoroughly check references and past projects. Encourage candidates to demonstrate their previous work during the final interview to validate their skills. Look for relevant projects that showcase their expertise in machine learning and their problem-solving abilities.
Step 5: Onboard Your Machine Learning Developer
After you've decided on a candidate, give it some thought to be sure you made the best decision.
Before providing project details, have them sign a non-disclosure agreement (NDA) to start the onboarding process. Give them the equipment they need and give them a tour of your development team. Clearly define your expectations and development approach, as well as communication channels.
Give them all the tools they require, such as project schedules, paperwork, and any pertinent training materials, so they can begin contributing successfully right away.
Step 6: Set Expectations and Goals
Setting clear objectives and goals for the machine learning developer is crucial after onboarding. Specify their roles within the project and include deadlines and deliverables. Plan frequent check-ins to keep an eye on things, handle obstacles, and make sure the project's goals are being met.
By following these procedures, you should be able to effectively incorporate your new machine learning developer into your project, maximizing their contributions and guaranteeing the accomplishment of your machine learning project.
That is all. Your ML developer can immediately begin working on your project.
Back to Top
Top 5 Companies to Hire Machine Learning Developers in 2025
Finding the right machine learning developer for your project can be challenging, but knowing where to look can make the process smoother. Here are the top five companies to consider when hiring machine learning developers in 2025.
1. DevTeam.Space software development company
DevTeam.Space is a community of 1,200 developers and 62 development teams based in the United States and Europe. All its full-time developers are thoroughly vetted by the company and work under NDAs when requested.
The company boasts a remarkable 99% success rate after completing hundreds of projects. Notable clients include Disney, Airbus, and NEC, which have partnered with DevTeam.Space to create next-generation applications using machine learning and artificial intelligence technologies.
DevTeam.Space’s success can be attributed to its unique AI-powered Agile process and the assignment of dedicated account managers to every developer, designer, and development team they outsource. The company also provides a free quality assurance engineer to check the finished product before release.
2. Arc/ freelance platform
Arc is a remote freelance platform that connects freelance software developers, including machine learning specialists, with companies looking to hire them. The platform claims to have 350,000 developers worldwide. However, access to vetted developers requires a subscription.
Current and past employee reviews on Trustpilot are predominantly positive. Arc promises to connect client companies with suitable machine learning developers within 72 hours, making it a fast option for hiring.
3. Fiverr/ freelance platform
Fiverr is an Israeli-based freelancer platform that serves a wide range of freelance skills, including machine learning. Within its structure, there is a dedicated section for machine learning developers, meaning once you post your job description, you're likely to receive multiple applications.
While it provides a broad pool of candidates, Fiverr’s business model means you’ll need to conduct thorough evaluations of the applicants to ensure they meet your project requirements.
4. Upwork/ freelance platform
Upwork is the world's largest freelancer platform, featuring millions of freelancers across various domains, including machine learning and data science.
Setting up an account is straightforward—just post your job description and begin interviewing candidates. Keep in mind that Upwork takes a 10-20% fee from all freelancers, so factor this into your pay rate, as lower pay rates demoralize freelancers.
5. Toptal/ freelance platform
Toptal claims to allow only the top 3% of freelancers onto its platform, providing an extra layer of assurance that you'll be receiving high-quality talent for your machine-learning project.
This software development-specific platform enables you to hire machine learning developers, data scientists, and AI specialists, ensuring you have access to a curated talent pool that can meet your technical needs.
Conclusion
Finding the ideal machine learning developer for your project is not that hard when you know where to look. At DevTeam.Space, there is a vibrant community comprising 62 expert development teams and over 1,200 skilled developers.
The platform has partnered with some of the biggest companies in the world and numerous innovative startups. By submitting your project request, DevTeam.Space will match you with suitable machine learning developers within just 72 hours, allowing you to move forward with your project development.
DevTeam.Space offers transparent pricing details and a risk-free trial to all clients. In the rare instance that you are not 100% satisfied with your developer, they provide a replacement at no extra cost. However, with over 350 projects completed and a success rate of 99%, such instances are highly unlikely.
When you engage machine learning developers from DevTeam.Space, you're assigned a dedicated account manager with extensive experience in managing projects similar to yours. Additionally, a complimentary quality assurance engineer is included to test all new features and pre-release applications, ensuring optimal performance and security of your finished product.
For those in need of expert machine learning development services, simply fill out their user-friendly hiring form. Their dedicated tech account managers will promptly reach out to schedule a complimentary discovery call to answer any questions you may have.
Back to Top
| 2023-02-14T00:00:00 |
https://www.devteam.space/hire-machine-learning-engineers/
|
[
{
"date": "2023/02/14",
"position": 81,
"query": "artificial intelligence hiring"
}
] |
|
Booz Allen is still betting big on tech talent, despite layoffs ...
|
Booz Allen is still betting big on tech talent, despite layoffs in the industry
|
https://fortune.com
|
[
"Sydney Lake",
"Jasmine Suarez",
"Preston Fore",
"Alyse Maguire",
"Booz Allen Is Still Betting Big On Tech Talent",
"Despite Layoffs In The Industry"
] |
... artificial intelligence, and software development continues to grow. Layoffs happening in Big Tech have totaled 263,000-plus jobs in 2022 and the early part ...
|
© 2025 Fortune Media IP Limited. All Rights Reserved. Use of this site constitutes acceptance of our Terms of Use and Privacy Policy | CA Notice at Collection and Privacy Notice | Do Not Sell/Share My Personal Information
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| 2023-02-14T00:00:00 |
https://fortune.com/education/articles/booz-allen-is-still-betting-big-on-tech-talent-despite-layoffs-in-the-industry/
|
[
{
"date": "2023/02/14",
"position": 45,
"query": "artificial intelligence layoffs"
}
] |
|
Tech winter is a misnomer. Call it a tech squall, says ...
|
Tech winter is a misnomer. Call it a tech squall, says Gregory Francis, CEO of Access Partnership
|
https://govinsider.asia
|
[] |
The overriding trend in the tech world coming into 2023 has been massive tech layoffs, with over 100,000 employees laid off in the year thus far according ...
|
The overriding trend in the tech world coming into 2023 has been massive tech layoffs, with over 100,000 employees laid off in the year thus far according to layoffs.fyi . But Gregory Francis, Chief Executive Officer (CEO) of public policy consultancy Access Partnership, is optimistic. In his words, the tech winter may merely be a tech squall.
He points to the cases of Twitter and Meta, which he says are facing unique circumstances — Elon Musk’s controversial takeover of the social media platform and Meta’s unparalleled investment into the metaverse, respectively.
In addition, he highlights that tech companies hired aggressively from 2020 to 2021. According to Bloomberg , Microsoft, Alphabet, Salesforce, and Meta increased their number of hires during that period by 35 per cent, creating over 100,000 new jobs in total. The recent tech layoffs may simply be a correction in the aftermath of this hiring frenzy.
So what are the lasting trends in the tech world to pay attention to this year?
Access Partnership’s new report on the tech policy trends predicts that 2023 will see stronger regulation on thorny issues like cryptocurrency, artificial intelligence and data transfers, while tech players will have to strengthen the case for emerging technology like the metaverse, autonomous vehicles, and web 3.0.
Francis tells GovInsider that in 2023, emerging technologies such as artificial intelligence and quantum computing will drive innovation in ways we can only guess at, and the risks and benefits of such tech remain yet to be seen.
But governments are inherently reactive to the innovations coming out of big tech, he says. Corporate players and tech companies play a vital role in enumerating the risks and benefits their products pose and providing the policy frameworks for governments to consider.
In turn, this will help tech solutions be used in as many markets as possible, and as fairly as possible, says Francis.
Corporate players to pave the way
As tech layoffs and revenue losses hit, big tech will have to adopt a posture “more characterised by humility than audacity,” says Francis.
Given how transformative emerging technology will be, the tech sector will have to be more sensitive to how products are received and provide the policy solutions governments are looking for. This will be key to ensuring that tech adoption proceeds fairly across global markets, from Texas to Borneo.
Francis highlights the case of Microsoft, which helped pave the way for cloud regulation and cloud adoption globally. This enabled economies to quickly pivot to the cloud when the pandemic hit. In 2015, Microsoft published a cloud policy roadmap to help government ICT decision-makers develop frameworks for secure cloud computing adoption.
If corporate players don’t proactively help governments shape regulation, the tendency is for governments to either react slowly or to react in ways that limit access first. This can create a digital divide between countries that have access to new technologies and countries that don’t, and limit how many markets tech companies can reach.
When technology like quantum computing goes mainstream, it will turbocharge advanced economies, he predicts. By exponentially increasing processing capabilities, quantum computing is expected to play key roles in research and development and product design for industries such as transportation, healthcare, and communications, wrote GovInsider .
And this will only further expand digital divides into digital caverns, warns Francis. Corporate entities will have to lead the way in explaining how their technology can be used safely and fairly to regulators to encourage mass adoption and prevent these divides.
How regulators can respond to new technology
Regulators are developing workgroups both internally and through global forums to develop stances in response to new technology, Francis notes. They are speaking to experts, developing pilot projects and sandboxes, and working together to develop global standards.
But there will always be some paralysis, as there is no clear pathway for the most cutting edge of technology. Decision-making will only become slower as the rate of innovation increases.
Regulators need to be nimble and flexible to enable access to new technologies, while being able to quickly respond to unintended consequences, he says.
He adds that one great example of regulators experimenting with new tech policy is Australia’s News Media Bargaining Code, which levels the playing field for independent newsmakers and giant online platforms. To protect the businesses of Australian media companies, the code compels big tech companies to pay local news publishers when news content is made available on their platforms.
Second, governments can convene major companies and ecosystem players to understand the risks and benefits of upcoming technology. This is an opportunity to convince tech players that they will need to develop a vision for how such tech can be used fairly, and to lead the way in self-regulation.
Singapore is well equipped to be a convener of stakeholders for topics such as online security in the metaverse, he says.
Potentially, Singapore could turn to unlikely purveyors of ideas such as companies in the entertainment industry, which have more experience managing the challenges of children going online, he notes. Access Partnership’s tech policy report highlights online security for children as a key concern when it comes to the metaverse and recommends a proactive and collaborative approach to ensure governance concerns do not deter metaverse participation.
Third, governments in ASEAN can work together to develop policy frameworks for new technology, saving individual agencies time and resources from developing regulations independently.
It will remain critical for governments to ensure fair access to the advantages offered by new technology, while guarding against unintended consequences. Proactive decision making and regulation by governments, coupled with the guidance provided by big tech companies on public policy, will determine whether technology serves as an equaliser or entrenches inequality further.
| 2023-02-14T00:00:00 |
https://govinsider.asia/intl-en/article/tech-winter-is-a-misnomer-call-it-a-tech-squall-says-gregory-francis-ceo-of-access-partnership
|
[
{
"date": "2023/02/14",
"position": 50,
"query": "artificial intelligence layoffs"
}
] |
|
How the Cybersecurity Workforce Will Weather a Recession
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How the Cybersecurity Workforce Will Weather a Recession
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https://www.isc2.org
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[] |
... layoffs will be necessary as the economy slows. ... Research Insights. Read all Research Articles · July 1, 2025•Article. July is AI Month – Our Focus on ...
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Layoffs appear imminent, but executives say cybersecurity workers will be less impacted than others
Organizations of all sizes are bracing for staff cuts in 2023, with 85% of respondents in a new ISC2 study saying they believe layoffs will be necessary as the economy slows. Cybersecurity teams, however, will be the least affected by staff reductions, as organizations anticipate an increase in cyber threats in 2023.
The study gathered input from C-suite business leaders – excluding technology executives such as CIOs and CISOs – concerned about the economy. Should layoffs be necessary, respondents expect bigger cuts in other areas of their businesses such as HR, finance, operations, marketing and sales, than in cybersecurity. Only 10% of respondents foresee reductions in cybersecurity teams, compared to an average of 20% in other areas.
The reluctance to lay off cybersecurity professionals suggests that top executives understand the critical role cybersecurity teams play in their organizations, along with the potential for increased cybersecurity threats during a period of economic difficulty. There is also a practical reason; executives recognize how difficult it is to recruit skilled cybersecurity professionals because they have always been in short supply.
When asked why cybersecurity teams were less likely to be impacted by staff reductions than other departments, participants told us: “Because as the economy gets worse, and more people are out of work, cybercrime will increase. We have to be prepared with cybersecurity to combat the threat.” “Cybersecurity is one of the top priorities in my organization. We can't jeopardize our reputation, lose trust and face the penalties due to lack of security.” “They are essential to the business’ welfare and its continuation.”
The Value of Cybersecurity Teams
To assess the impact of a potential economic downturn on cybersecurity teams, ISC2 polled 1,000 C-suite executives in December 2022 across five countries: Germany, Japan, Singapore, the U.K. and U.S. The findings indicate leaders no longer view cybersecurity as a nice-to-have function when budget is available, but rather an essential, critical asset that delivers value.
It is likely this maturing view of cybersecurity has been shaped by a continuing series of high-profile and damaging breaches. Security incidents have left no doubt as to the lengths threat actors will go to steal data or disrupt operations, in some cases even putting lives at risk.
Executives are now aware of the threat, with 87% noting that reductions in their cybersecurity teams would increase risk for their organizations. That awareness appears to be weighing on their minds as they consider staff reductions across their organizations.
Asked to rank business functions most likely be involved in a first round of layoffs, 31% of respondents cited cybersecurity as the least likely to be impacted. In comparison, a far higher number of respondents ranked HR (44%), sales (41%) and operations (40%) higher for likely job cuts.
Moreover, once staff reductions are complete and organizations get ready to rehire personnel, cybersecurity workers are at the top of the list for re-investment.
Just over half of respondents (51%) say cybersecurity professionals would be prioritized for hiring or rehiring. IT is another priority (49%), with research and development (R&D) not far behind (41%). Lower on the rehiring priority list are marketing (35%), finance (34%), operations (31%), sales (30%) and HR (29%).
Further evidence of the importance executives place on cybersecurity teams is a willingness to hire despite uncertain economic conditions. Nearly three quarters of respondents (74%) are open to recruiting cybersecurity talent laid off elsewhere should the opportunity present itself. With reports of job cuts at organizations including Twitter, Meta, Microsoft, Amazon and Google, cybersecurity staff could benefit from proactive hiring targeted towards those recent layoffs. With so many tech jobs impacted by recent layoffs, it is possible that many of those individuals may find opportunity in pursuing a career in cybersecurity, where they can apply related skills and expertise.
The willingness to keep hiring cybersecurity personnel is the continuation of a cybersecurity workforce investment trend. At least 90% of participants from all countries represented in the study said they increased cybersecurity hiring in the last two to three years. The only exception was Germany, where only 78% of participants cited increased hiring over the same period. It is worth noting that the ISC2 Cybersecurity Workforce Study tracked a 165% increase in the size of the German cybersecurity workforce in 2021, when it overtook the U.K. for the first time as Europe’s largest cybersecurity employer.
Rising Threat Awareness
Regardless of the economic outlook, the study reveals a realistic assessment of the threat landscape within the C-suite. A solid majority of respondents (81%) believe threats will rise during 2023. This is hardly surprising, considering the upward trend of recent years combined with current global economic and political issues. Statistics collected by various organizations indicate that 2022 was a banner year for cyberattacks.
Most respondents (80%) believe a weakening economy will increase cyber threats. And a larger majority (87%) think a staff reduction in their cybersecurity teams will further increase risk. Data suggest executives understand the importance of keeping their cybersecurity teams intact even if wider organizational layoffs become unavoidable.
The findings also revealed some regional variations in concerns about risk. When asking respondents if they believed reducing cybersecurity staff would increase risk for their organizations, some clear divides emerged. 73% of participants in Germany were concerned about increased risk due to possible reductions in cybersecurity staffing. Meanwhile, 96% of participants from Singapore and 94% from the U.K. were concerned about increased risk if staff cuts are necessary. German respondents are also least likely to believe cyber threats will rise in 2023 (65%), while more than 80% of participants from the rest of the countries covered in the study believed threats will escalate in 2023.
Fears of increased risk notwithstanding, the overwhelming majority of respondents (91%) express confidence their organizations are prepared for a cyberattack. Should layoffs of cybersecurity professionals occur, that number drops to 83%.
Cybersecurity Layoff Considerations
We asked participants to share how they would determine which staff would be impacted if layoffs within their cybersecurity teams become necessary.
While layoffs would be spread across experience levels, the survey suggests the C-Suite believes junior staff will be impacted at a higher rate (63%) followed by senior team members (41%),managers (29%) and cybersecurity executives (21%).
for other criteria for deciding which staff to layoff, salary emerged as the least important factor. It is cited by only 30% of respondents, compared to other factors such as performance (50%), expertise/skill set (49%), skill redundancy (43%), and diversity/team composition (37%).
As scarce as qualified talent is, the C-suite isn’t willing to overlook performance. That it ranks as the most important factor in deciding whom to include in staff cuts underlines the importance of advocating for one’s self and demonstrating value back to the team and organization, even in a field where qualified professionals are in short supply. Regardless of skill level, commitment and hard work are still considered essential and valuable assets.
Recession Impact
Potential layoffs aren’t the only challenge cybersecurity professionals may face during a recession. In response to uncertain economic times, respondents told us their teams may be impacted by increased adoption of automation (41%) and by asking team members to work longer hours (40%). Other contingencies for softening the impact of an economic downturn include hiring more entry- and junior-level staff (36%) and a freeze on promotions and raises (30%). Only a small minority of respondents (8%) believe a recession would not impact the cybersecurity team at all.
Despite the increased reliance on automation, cybersecurity professionals already work long hours. The bad news for cybersecurity professionals is that those hours might be further extended at a time when raises and promotions are likely off the table.
Conclusion
Whether a global recession occurs in 2023 remains to be seen. Staff reductions are already underway as companies seek to preemptively tighten their belts in the face of near-double-digit inflation across G20 nations.
For cybersecurity professionals, the field as a whole appears it will weather uncertain economic times better than other business functions. Even when considering layoffs across the board, C-suite executives are reluctant to cut their cybersecurity teams and say they will do what they can to retain talent and shield teams from downsizing for as long as they can.
The C-suite, leaders and hiring managers need to take stock and understand the full extent of their cybersecurity workforce. While a headcount reduction in some form may seem inevitable or unavoidable, any such decision needs to consider the cost incurred of acquiring and developing skilled individuals in the first place, along with the cost and likely availability of the same people when economic conditions improve. Our findings suggest that executives are aware demand for cyber talent outpaces supply and believe that they will benefit from retaining people now to avoid chronic shortages later.
Cybersecurity professionals may have less to fear when it comes to layoffs than their colleagues in other departments. And even if they are included in a staff reduction, the prospects of quickly finding other employment are high. Nevertheless, the demand for and high value placed on cybersecurity personnel will not shield the profession completely from the pressures caused by layoffs. The findings of this study highlight that cybersecurity staff and people leaders need to brace themselves for longer hours and greater workloads just as economic challenges may serve as a catalyst for increased cyber threats around the world.
Study Methodology
We surveyed a total of 1,000 business executives in December 2022 from Germany (200), Japan (200), Singapore (200), the U.S. (200) and U.K. (200). Respondents were screened to allow only non-tech/security C-suite professionals to participate. Respondents were also limited to those working within an organization with a cybersecurity team of at least two (2) employees and anticipating economic challenges in 2023. The margin of error for the global descriptive statistics in this research is +/- 3.1 at a 95% confidence level.
About ISC2
ISC2 is an international nonprofit membership association focused on inspiring a safe and secure cyber world. Best known for the acclaimed Certified Information Systems Security Professional (CISSP®) certification, ISC2 offers a portfolio of credentials that are part of a holistic, pragmatic approach to security. Our association of candidates, associates, and members, nearly 330,000 strong, is made up of certified cyber, information, software, and infrastructure security professionals who are making a difference and helping to advance the industry. Our vision is supported by our commitment to educate and reach the general public through our charitable foundation –The Center for Cyber Safety and Education™. For more information on ISC2, visit www.isc2.org, follow us on Twitter, or connect with us on Facebook and LinkedIn.
| 2023-02-14T00:00:00 |
https://www.isc2.org/Insights/2023/02/how-the-cybersecurity-workforce-will-weather-a-recession
|
[
{
"date": "2023/02/14",
"position": 63,
"query": "artificial intelligence layoffs"
}
] |
|
Laid-off tech workers seek leverage on the way out
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Laid-off tech workers seek leverage on the way out
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https://www.foxbusiness.com
|
[] |
... artificial intelligence sectors. For some tech workers, everything is negotiable, including the severance package that comes with a layoff. As tech ...
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For some tech workers, everything is negotiable, including the severance package that comes with a layoff.
As tech companies shed tens of thousands of jobs, staffers at different levels are pushing back on the terms of their departures. Some employees are hiring lawyers or other advisers to press for more money or an extension of their health benefits before agreeing to deals—adopting the type of tactics traditionally used by senior executives haggling over their severance terms.
For years, Rora, a negotiation consulting firm that advises tech professionals, offered guidance on compensation during the job-offer process. Last May, Brian Liou, Rora’s founder, started hearing from laid-off tech workers who wanted help negotiating their severances; the company has since added it to its suite of services.
"People were demanding it," Mr. Liou says.
LAYOFFS 2023: TECH COMPANIES LEAD JOB CUTS AMID ECONOMIC UNCERTAINTY
As recently as two years ago, there wasn’t much demand for assistance in pushing back on a severance offer following a layoff, and asking about such terms during a job negotiation was considered taboo, recruiters and consultants say. The tech labor market was far tighter than it is today, and especially during the pandemic, tech workers could change jobs for better pay with relative ease. While many sectors are still adding jobs at a rapid clip and the unemployment rate is low, tech job growth has slowed from the frenzied pace in the early days of the pandemic.
Since the spring of last year, there has been a steady stream of layoffs from companies including Meta Platforms Inc., Amazon.com Inc. and Salesforce Inc. that collectively have announced cuts to tens of thousands of jobs. There have also been significant cuts at Alphabet Inc.’s Google and Microsoft Corp. Technology jobs throughout the economy fell by 32,000 last month, according to an analysis from CompTIA, an information-technology trade group.
Ticker Security Last Change Change % META META PLATFORMS INC. 720.92 +3.41 +0.48% AMZN AMAZON.COM INC. 225.69 +0.67 +0.30% CRM SALESFORCE INC. 259.55 +1.55 +0.60% GOOG ALPHABET INC. 182.81 +1.50 +0.83% MSFT MICROSOFT CORP. 503.02 -0.30 -0.06%
While the power balance is seen shifting toward companies, tech workers are a group used to getting what they want. Shaped by a decadelong talent war, they are accustomed to having a voice in how their companies are run and enjoying extraordinary benefits, whether it is on-site massages, private concerts or a wellness retreat.
META EMPLOYEES SAY 'ZERO WORK' GETTING DONE AS LAYOFFS LOOM, REPORT
Among the incentives for companies to accommodate a worker who has been laid off are nondisparagement and nondisclosure agreements, says Lisa Buckingham, a former human resources executive who is president of Ellig Group, a leadership advisory and executive search firm. She says companies are increasingly wary of the potential for employees to make the circumstances of their exits public or to post complaints about their treatment on LinkedIn or Twitter that could damage companies’ brands.
"Social media is changing the game," says Ms. Buckingham, who is based in Philadelphia.
Companies also hope that they will be in hiring mode again before long, and might want or need to hire some people back when the economy improves. They also don’t want their current employees getting wind of less than favorable departures because it is bad for morale.
Jenny Dearborn, a former human resources executive in tech who is now on several boards, cautions companies against customizing packages beyond factoring in an employee’s years of service and organizational level.
CLICK HERE TO GET THE FOX BUSINESS APP
"A company should have an openly published severance policy that they do not deviate from," she says. "It’s easy for discrimination to happen when a company varies from a consistently applied severance strategy."
On the employee side, what has unfolded at Twitter Inc. in recent months has served as a "shock and awe" reminder that anything can happen, Ms. Buckingham with Ellig Group says. Since Elon Musk bought the social-media platform in October, he has pared the staff to roughly 2,000 employees from almost 8,000.
Some workers aren’t hesitant to push back on the terms initially offered, says Mr. Liou of Rora, because they say they think their companies over-hired during the pandemic and are trimming excess head count as opposed to being in dire financial straits. Other factors contributing to their mind-set include seeing firms rescind job offers and recruiting talent away from secure jobs only to let them go months later.
"Workers are realizing they need to protect themselves more," says Mr. Liou.
One employee was able to triple the number of months’ pay initially offered, and others have secured smaller perks, such as getting to keep the company laptop and monitor, Mr. Liou said. Last month, a director-level client who was laid off while on an H1B visa for foreign workers got an extension on his termination date by three months in the form of unpaid leave, so he had a longer window to find a job and stay in the country.
Jennifer Schlador, a talent agent for executives in the San Diego area, said she helped a director-level client negotiate his separation agreement from a large tech company in December.
"They asked him to stay and teach someone for two weeks, give them all the information he had on the job he was doing," she says.
The client agreed to train the person taking on his job responsibilities and pass along his business contacts but only if he received a $50,000 bonus he was set to be paid in March, Ms. Schlador says. The agreement was amended to include his bonus.
At online-education provider Coursera Inc., a group of laid-off employees in December wrote a letter to the CEO objecting to the decision not to accelerate vesting schedules for stock-based compensation granted to employees and not waiving some stock-vesting cliffs.
The company gave workers four months’ salary, with potentially more depending on their role or tenure. It also offered employees healthcare coverage and outplacement assistance.
ELON MUSK SAYS TWITTER IS 'TRENDING TO BREAKEVEN,' LAST THREE MONTHS WERE 'EXTREMELY TOUGH'
"We realize this decision impacts employees’ livelihoods and we strove to provide what we believe is meaningful transition assistance," a Coursera spokeswoman previously told The Wall Street Journal.
Phillis Rambsy, an employment lawyer based in Nashville who frequently works with tech employees, says clients will sometimes reach out for help in making sense of all the legalese in the agreements they are presented.
One employee who was within a year of qualifying for Medicare succeeded in getting that coverage added to his package, Ms. Rambsy said. Another employee was able to keep her bio on the company website for a couple of additional months while she job hunted—the company agreed to put her on unpaid leave for that time.
One of the most common terms in a severance package is an employee agreeing to relinquish all future right to sue for claims both known and unknown, says Ms. Rambsy. That commitment in writing is valuable to companies and can be used as leverage to secure additional benefits beyond what it is initially offered.
| 2023-02-14T00:00:00 |
https://www.foxbusiness.com/economy/laid-off-tech-workers-seek-leverage-way-out
|
[
{
"date": "2023/02/14",
"position": 85,
"query": "artificial intelligence layoffs"
}
] |
|
Pixlr 2023: An AI-Powered Experience Unlike Any Other
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Pixlr 2023: An AI-Powered Experience Unlike Any Other
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https://pixlr.com
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[] |
This tool uses AI technology to automatically fill in specific areas of an image with patterns, textures, or other graphics with the use of prompts. This saves ...
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#madewithpixlr Creative Design Inspiration Updates Pixlr 2023: An AI-Powered Experience Unlike Any Other
The field of graphic design has been constantly evolving and adapting to new technologies and has recently entered a new era due to the growth of Artificial Intelligence (AI).
As AI continues to advance, it will bring new opportunities for designers to express their creativity and push the boundaries of what’s possible. However, it may also pose new challenges for designers who need to keep up with the technology and integrate it into their workflow in meaningful ways. With its ability to learn from past designs and create new original ones, AI is changing the way we think about graphic design and the creative process. However, Pixlr 2023 is here to deliver a creative experience unlike any other.
We are thrilled to announce the launch of Pixlr 2023, the latest version of the popular online graphic design tool. With its cutting-edge AI technology, Pixlr 2023 brings a whole new level of convenience and creativity to the graphic design process. Whether you’re a seasoned designer or just starting out, you’ll find every tool you need to bring your ideas to life. From advanced image editing tools to innovative new design features, Pixlr 2023 has been designed to provide you with a design experience that’s more efficient, accurate, and most importantly enjoyable.
In this blog, we’d like to showcase some of the newest features we have added in the latest release so that you can start implementing them into your own creative process.
1. Smart Resize
The Smart Resize tool uses AI technology to automatically resize images and graphics, ensuring that they retain their quality and resolution no matter the size. This makes it easier to create and integrate graphics across many different platforms and devices, from website banners to social media posts, without having to manually adjust the size and aspect ratio of each image.
With its intuitive interface and real-time preview, the Smart Resize tool is a game-changer for designers who want to save time as you no longer need to remember the correct dimensions yourself. Without a need for design skills, users are still able to produce high-quality designs without sacrificing creative control. Whether you’re working on a branding project or just need to resize a few images for your website, Pixlr 2023’s Smart Resize tool is a must-have tool for any graphic designer.
2. AI Image Generation
Another exciting feature of Pixlr 2023 is its AI Image Generation tool. This tool uses cutting-edge AI technology to generate unique images and graphics in real-time, based on your input and preferences. Simply type in keywords and prompts to your liking, and the AI Image Generation tool will generate a custom image or graphic that meets your specifications and provides you with unique box imagery. With its ability to learn from past designs and create new ones, the AI Image Generation tool opens up endless possibilities for graphic designers to express their creativity in a way that is more efficient and seamless.
You can bid farewell to the struggles of searching for specific elements and images on the Internet and shifting between multiple tabs during your editing process. Whether you’re looking for inspiration or need to create an eye-catching design in a short amount of time, the AI Image Generation tool is a valuable asset that can help you achieve your goals in no time.
3. AI Infill
The AI Infill tool in Pixlr 2023 is a game-changer for graphic designers who want to add more creativity and detail to their designs. This tool uses AI technology to automatically fill in specific areas of an image with patterns, textures, or other graphics with the use of prompts. This saves designers plenty of time and effort in the process.
With its intuitive interface and real-time preview, the AI Infill tool makes it easy to add unique and eye-catching elements to your designs, even if you’re working with a limited budget or tight deadline. Whether you’re looking to create a sophisticated brand identity or just want to add some visual interest to your social media posts, the AI Infill tool is the perfect tool for the job with its unlimited possibilities.
4. Text Warp
The Text Warp tool in Pixlr 2023 is a must-have for designers who want to create professional-looking graphics with ease. This tool allows you to warp text around images and other graphics, creating a cohesive and visually appealing design. It will save time for any designer, allowing them to work more efficiently. With its intuitive interface and real-time preview, the Text Warp tool makes it easy to see how your design will look, even before you publish it.
Whether you’re working on a brochure, poster, or social media graphic, the Text Warp tool is the perfect way to add text to your designs in a way that elevates the overall design layout. The tool also allows you to adjust the spacing and alignment of the text, so you can create the exact look you desire.
5. Color Channel
The Color Channel tool in Pixlr 2023 is an advanced color correction tool that gives graphic designers precise control over the colors in their designs. With this tool, you can adjust the brightness, saturation, and hue of each color channel in an image, allowing you to fine-tune the color balance and create the exact look you want. Most importantly, it allows you to adjust a specific part of the image without affecting the entire image. Whether you’re working on a brand identity project or just want to enhance the colors in a photo, the Color Channel tool makes it easy to achieve the perfect imagery.
The tool also includes a real-time preview, so you can see how your changes will affect the overall look of the image. Not only will this tool improve the quality of your designs and save you time, but it is also easy to use. Whether you’re a seasoned designer or starting out, you’ll find that the Color Channel tool is essential in the Pixlr 2023 arsenal.
6. Colorize
The Colorize tool in Pixlr 2023 is a powerful tool that allows you to add color to black-and-white images, giving them a new level of depth and dimension. With this tool, you can choose from a range of colors and shades, and apply them to specific areas of the image to create a customized look.
With just a few clicks, the Colorize tool allows you to transform images into 2 or 3-tone colors to make your image pop. The tool also includes a real-time preview, so you can see how your changes will affect the overall look of the image. With its ability to make popping dynamic imagery and also save you precious time, the Colorize tool is a valuable asset for any graphic designer.
Get a whole new editing experience with Pixlr!
Pixlr Suite 2023 is packed with a range of innovative new features that have changed the game of graphic design. From the AI Image Generation tool, AI Infill, Text Wrap, Color Channel tool, and the Colorize tool, Pixlr 2023 has something new to offer to every designer, regardless of skill level or experience. These tools make it easier and more convenient than ever to create high-quality graphics while saving time and adding unique elements to your designs. With its user-friendly interface, real-time preview capabilities, and powerful AI technology, Pixlr Suite is set to revolutionize the world of graphic design.
Start your 30-day FREE trial today!
We are also offering a 30-Day Free Trial Plan under Pixlr Premium’s subscription package, where you can cancel Auto Renewal at any time through your account settings. Furthermore, you’re also given the choice of a monthly subscription ($7.99/month) or a yearly subscription ($4.90/month) which gives a whopping 38% discount! So what are you waiting for? Sign up now!
| 2023-02-14T00:00:00 |
2023/02/14
|
https://pixlr.com/blog/pixlr-2023-an-ai-powered-experience-unlike-any-other/
|
[
{
"date": "2023/02/14",
"position": 25,
"query": "artificial intelligence graphic design"
}
] |
Human-powered Design vs AI-driven Design: Which is The ...
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Human-powered Design vs AI-driven Design: Which is The Future?
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https://www.algoworks.com
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AI helps sort out ideas faster using attention data and previous data design models. Implementing. Finally, the designer completes developing all design ...
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In the battle of Human-powered Design vs AI-driven Design, the human-powered design relies on the intuition, empathy, and imagination of designers to craft visually appealing and meaningful experiences. On the other corner, AI-driven design utilizes algorithms and data-driven approaches to automate the design process, creating designs faster and more efficiently.
Designing for the future: the age-old debate of human touch vs technology takes a new twist. However, with the advent of AI and machine learning, the question on everyone’s mind is whether machines will take over the creative process or will the human touch continue to reign supreme.
Explaining Modern Web Designing Stages
Today, both human-created Design and AI design processes have to follow the below six stages of web designing:
a) Forming (The creator analyzes client requirements to assess the web design context)
b) Defining (The creator drafts the complete set of requirements to create a development roadmap)
c) Ideating (The creator generates creative ideas to meet the design requirements)
d) Socializing (The creator represents the design ideas to the clients for confirmation)
e) Implementing (The creator implements high-quality designs for deploying)
To understand the impact of AI today, we would have to break down the web designing stages for further examination.
Comparing Web Designing Stages: Human-powered design vs AI-driven design
Let’s compare the theoretical and AI capabilities to perform activities in each web designing stage.
Forming
In this first stage, the human designers have to perform the following activities:
a) Inquire about unique design context to plan the right design solution.
b) Explore different perspectives to form different design strategies.
c) Combine the stakeholders’ vision with the design concept.
Today’s web designers use various AI tools to perform the above tasks. However, AI systems like ChatGPT are bound to offer only generic advice toward functional differentiation. Hence, these fail to consider human emotions that feed the initial design idea.
Defining
Considering the future of design technology, adapting AI systems for this process is crucial for designers to draft an initial project plan. It collects detailed information based on business requirements and end-user expectations.
In this process, AI systems like ChatGPT offer faster solutions than human brains. AI systems utilize digitally collected data to suggest suitable design plans and strategies. It provides faster data processing speed and performs reasonably well than humans.
Ideating
Designers brainstorm their minds to create unique design ideas in this phase. Many designers follow their past experiences to define newer ideas relevant to the client’s expectations. Today’s impact of AI on design industry is that designers use AI systems to generate design ideas based on user inputs. Naturally, the usage of UI and UX designing tools is increasing among developers.
Socializing
Once the design ideas get ready, the designers consider client recommendations and end-user feedback to enhance the design results. Often, designers focus on their aesthetic intuitions to choose the best idea. Today, designers use various tools to create a strong point of view that combines human creativity and AI in design. AI helps sort out ideas faster using attention data and previous data design models.
Implementing
Finally, the designer completes developing all design components. AI systems can boost this step. Designs can use AI tools to fine-tune their implementation by performing continuous reviews. AI enables designers to generate UI or UX development codes with complete syntaxes using the existing codebase.
Pros and Cons of AI-driven Design
Being aware of the capabilities of AI-driven design tools, graphic designers can benefit from the following pros:
Pros: Content Personalization
AI can develop and update creative content, which is challenging for content creators. AI uses data metrics of customer behavior to create relevant and market-centric design concepts. It helps target specific audiences worldwide.
Pros: Developing Variations
The AI-assisted design offers different variations derived from a single idea. AI cultivates alternative data-driven design solutions to create multiple design variations. So the designer can create as many unique instances as he wants!
Pros: Speed and Accuracy
Undoubtedly, AI systems provide more accurate design solutions than humans at the fastest speed. It can collect, analyze, and process vast volumes of data within minutes. Plus, there are no chances of error occurrence.
Cons: No Understanding of Empathy
The role of AI in design process is to improve accuracy, fasten development, and bring variations. AI doesn’t have the understanding of raw feelings or empathy like humans. Naturally, it fails to connect with the idea behind the design!
Cons: Lacks Originality
The primary focus of human creativity is on the ability of designers to create original design contexts. AI cannot deliver originality like humans, as AI will always use the pre-stored data sets to create variations.
Cons: AI is not for non-technical people
Human-centered design and AI design use tools and technologies, but AI is highly technical. Web designers with little technical knowledge cannot use AI systems as per their true potential.
A Glimpse To The Future
So where are we standing in the face of Machine Learning and AI? Today web designers are using various software to reduce the required designing efforts. In each step of the web designing process, human creators depend on multiple tools and technologies. With technological advancement, it’s hard to convey a clear verdict for Human-powered design vs AI-driven design. AI cannot match human creators’ dedication, emotion, and originality. Still, it will be interesting to discover how the world will adopt AI for designing. Consult professional UI UX design experts to choose what’s best for your business!
Design that tells a story, crafted by hand vs Design beyond imagination, powered by AI – Choose wisely!
About Us: Algoworks is a B2B IT firm providing end-to-end product development services. Operating chiefly from its California office, Algoworks is reputed for its partnership with Fortune 500 companies such as Amazon, Dell, Salesforce, and Microsoft. Algoworks is an expert in UI/UX Designs with experience catering to enterprises belonging to all domains. This includes: Brand Strategy, Product Design, UX Research, UI & Animation and Design Testing. For more information, contact us here.
| 2023-02-14T00:00:00 |
https://www.algoworks.com/blog/human-powered-design-vs-ai-driven-design/
|
[
{
"date": "2023/02/14",
"position": 44,
"query": "artificial intelligence graphic design"
}
] |
|
Can AI create your website?
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Can AI create your website?
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https://coconutdesign.eu
|
[
"Csilla Slezsák"
] |
Usually for AI-generated web design, you have to enter keywords on the basis of which the artificial intelligence creates the design(s). The outcome is a ...
|
In the recent weeks and months, AI and AI generated content have become a super popular topic. Of course, web design could not be left out either. AI design tools have appeared, and according to the promises, with their help, a website can be created in a few seconds.
Let’s see how far AI can really take us when it comes to designing and building our website…
What is AI currently good for in website design?
Usually for AI-generated web design, you have to enter keywords on the basis of which the artificial intelligence creates the design(s). The outcome is a visually appealing hero section (the opening part of the website) with a relevant, high quality image and the necessary UI parts: menu, button, and some text. And that’s all.
So technically, AI scans the internet and finds the best solutions, then creates an abstraction and shows you some examples. It’s important to know that it is only going to be a hero image, not the whole website with subpages, functions, properly planned and developed user journeys, etc.
Uniqueness with AI generated Web Design..
Well, creating hero sections for inspiration is great. However, in our opinion AI in this case just shows us the typical website examples for the selected field or keywords. So technically it shows us how to create a good looking website in a certain topic which blends into the crowd of similar pages. If you want your website – your company – to stand out, you will need a little extra sauce, which probably only a human (designer) brain can come up with.
So, Benefits of Using AI for Web Design are:
It helps your designer to do the competition research. To find out the most important elements and styles your competitors use.
It knows all the scientific parts of web design: accessibility, proportions, readability, etc. So the designer can focus on other things.
It creates a beautiful, eye-catching, remarkable image on your topic (if the proper keywords were given).
You still need both of us for a visually appealing and well working website.
Are AI Generated Website Images Perfect?
AI can create pretty cool images & visuals that attract attention – although sometimes you don’t really know what you’re seeing. You can spare time by downloading those AI generated items separately, so you can skip the photoshooting and photoshopping part, and focus more on other stages of the web design process. In our opinion, there are topics that work really well, like gaming, fantasy, technology. However, we find lifestyle images pretty scary in an unhuman sense.
Are AI Generated Websites Effective on Mobile?
Until now, we have only talked about the desktop view, and this is no accident. The extremely important deficiency of these designs is that they are only suitable for desktops at a certain resolution and don’t take into account responsiveness at all. Therefore, they are beautiful, but may require plenty of work to adjust them to real-world needs.
In summary, it can be said that these designs are beautiful and catchy, but from a design perspective they’re difficult to integrate into functional sites.
Can You Design Your Own Website with AI?
Web design is not only about graphic design programs, and coding. Behind a good-looking, effective, and user-friendly website there is actual science. You have to follow certain basic design rules to make sure that your web design will actually work in real life. And you have to combine those rules with the message you want to communicate.
When designing a website you need to be able to define and create a user journey that can lead your users through your website. AI can give you some inspirations on the overall look, but it can’t find out your needs and it is not able to customise all the elements and rules to your own business. This is something that only a real-life experienced designer can do.
Do We Use AI for Web design?
To make a long story short – sure!
We are not afraid of AI and technical development, we embrace it!
We see it as a powerful tool which, of course, helps and speeds up competition analysis. This provides us with more time to add a little twist that makes your business special. Because, at the end of the day, your story is what really matters, and that cannot be told by any AI, only by you.
So, do you want to stay up-to-date on AI and web design topics? Then it’s time to subscribe to our newsletter!
| 2023-02-14T00:00:00 |
2023/02/14
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https://coconutdesign.eu/ai-generated-website-artificial-intelligence-web-design/
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[
{
"date": "2023/02/14",
"position": 91,
"query": "artificial intelligence graphic design"
}
] |
Artificial Intelligence's Role in Supply Chain Risk ...
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Artificial intelligence’s role in supply chain risk management
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https://www.everstream.ai
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... job on their own. That's where artificial intelligence (AI)comes in. Transportation and logistics, a crucial part of the supply chain, has been cited by ...
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Here, we explore how AI is the keystone of today’s supply chain risk management, where human input is critical, and the cost benefits it can offer. If you’re responsible for managing a supply chain, you’ll learn how AI can help you reduce risk and keep your operation running smoothly.
What is artificial intelligence in logistics?
Gartner defines AI as technology that applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. It also says AI can be broken down into two categories: either used to augment human capabilities and reduce errors, or it used to automate key processes. When it comes to transportation and logistics, both use cases apply.
Logistics leaders often lack the tools they need to identify, assess, and mitigate risks, usually because they do not have access to all the data they need to move accurately and rapidly. The tools they do have are disconnected and fail to provide a comprehensive view of their risks, the probability, and severity of those risks, and exactly where, when, and how those risks could impact individual shipments. Legacy systems and manual processes collect and crunch numbers from disjointed systems, all of which takes time, introduces the risk for error, and results in an incomplete view of what matters most.
How artificial intelligence is used in supply chain risk management
AI is the ability of a computer to learn and make increasingly more appropriate decisions without the direct intervention or programming of a human each time. When applied to supply chain risk management, artificial intelligence algorithms can identify patterns and trends that are otherwise difficult to spot, then incorporate personalized guidelines and feedback, and immediately adjust plans to mitigate any potential risks identified.
Risk is inherent in the supply chain but with AI, companies have real-time data to anticipate risk before those risks turn into issues. Leaders can make data-backed decisions early in the shipping process when mitigation efforts have the greatest chance for success.
The key to fully optimizing AI in the supply chain is to use it to forecast and make predictions. Predictive analytics is an important aspect of this advanced and intelligent technology, giving leaders a way to go a step beyond the identification and assessment of risks to predict what could happen based on reliable data.
To respond quickly to identified risks, which is the most important aspect of mitigation efforts, leaders need their decisions to be more automated. As Gartner has said, automating processes, including the decision-making process, is where AI shines. Predictive analytics and machine learning are powerful tools that when combined, deliver unprecedented capabilities at scale.
Ongoing AI management provides insights that help human users monitor their entire supply chain, from top to bottom, with accuracy and precision. AI can provide end-to-end visibility from procurement to logistics, alerting supply chain managers to potential proactive actions before there’s a disruption. It can even rank and score each risk according to a company’s standards.
Automated AI monitoring reduces costs throughout the entire supply network by identifying inefficiencies and continually improving them based on what it has learned.
For example, AI can help businesses make more informed decisions about their inventory levels and pricing strategies. AI analysis can identify potential threats within the supply chain by analyzing data from suppliers or customers to detect any irregularities or changes – like noting anomalies in supplier payments or delivery times.
Track supplier performance over time by monitoring production capabilities, service quality, and cost effectiveness. These insights can help businesses identify new potential suppliers or renegotiate existing contracts with current suppliers. In addition, businesses can use AI tools to analyze customer feedback from previous orders to predict future customer demand or suggest improvements that could help increase sales.
But the biggest advantage of AI monitoring is the computer’s ability to map and monitor an entire supply network, down to the last tier. Instead of waiting for disruption to filter its way up to your operation, AI alerts supply chain managers to risk far down the supply chain, providing valuable days and weeks to shift plans before resources become more expensive or unavailable.
Moving from reactive to proactive
Actionable, predictive intelligence uses machine learning techniques to make risks more predictable, even risks as seemingly unpredictable as the weather and environmental conditions and natural disasters, such as floods, extreme temperatures, and severe storms. Wildfires, social unrest, infrastructure outages, and other risks are all included in the risk assessment. This intelligence also helps with decisioning.
Tools now exist that automate the task of finding lower-risk alternatives to current shipment plans. The technology automatically scans shipments up to three days before and three days after a planned pickup date to compare risk levels. Companies can define their own risk tolerance and set up alerts that notify them when a risk threshold could be met. AI also recommends the lowest-cost equipment options that maintain freight integrity, as well as which mode of transportation and lane are most likely to result in shipments being on time.
It isn’t just real-time data that is important for decision-makers. AI in supply chain management provides historical context so that leaders can identify trends and use those to inform decisions and hone their processes so that they are always learning and improving. The better and faster they become at identifying, assessing, and mitigating risks, the more bite they can make into market share.
These predictive analytics shine a light into potential future events and recommended actions. With past, present, and future perspective, leaders are more aware than ever of their entire ecosystem and able to make decisions with greater confidence, understanding context, dependencies, and scenarios. They can use this data to justify their decisions within the company and externally with customers, setting appropriate delivery expectations, and keeping lines of communication open.
The value of human oversight in artificial intelligence monitoring
By combining human oversight with advanced AI technology, companies can gain insight into potential risks within their supply chains while also gaining greater control over their operations. Human oversight and verification ensure accuracy, precision, and personalization in AI operations.
Human researchers and fact-checkers review and validate the datasets that AI systems use, ensuring that all data points are accurate, relevant, and up to date. Additionally, human review of AI-generated outputs can help to eliminate errors due to misinterpretation or illogical assumptions made by the system. Human experts monitor a system’s performance over time to ensure its accuracy remains consistent as changes occur within the business environment.
Humans are also able to detect patterns or anomalies that may not be immediately apparent to an AI system, allowing for a more comprehensive analysis of potential risks. AI systems may struggle with nuanced, complex scenarios that require a deeper contextual understanding, and human analysts are better suited for detecting subtle relationships between different pieces of information.
On the other hand, human bias must also be taken into consideration when it comes to data review and interpretation. Human reviewers may come from different backgrounds with varying levels of experience. The best supply chain risk monitoring platforms minimize human bias by using protocols and training which promote impartial decision-making, and develop technical safeguards against biased outcomes based on gender, race, or ethnicity.
How artificial intelligence reduces supply chain disruption costs
At Everstream Analytics, we apply our AI algorithms and expert analyst oversight to support the world’s largest supply chains. Our clients have seen significant savings after deploying our risk management platform:
5% reduction in expedited freight costs
10% improvements in on-time performance
30% reduction in revenue losses from disruption
50-70% reduction in time to identify and assess disruption impact
$2+ million annual savings in temperature-sensitive freight costs
AI-supported risk management helps reduce costs primarily by scoring risk by how likely it is to happen, and buying companies more time to adjust operations before disruption hits. For example, organizations can notably reduce unexpected delays and reduce costs while also improving customer service. The use of AI also minimizes manual labor efforts, reduces errors, minimizes data inconsistency, and accelerates communication within the organization making it easier to reduce lead time and delivery costs.
Essentially, AI-driven solutions enable organizations to shorten their supply chain risk management cycle times and increase their ROI via improved process visibility, real-time decision making, automated approval controls and much more. Companies can recognize risks faster and are able to take preventive action earlier than ever before. They can also scenario-plan to anticipate likely outcomes.
For today’s supply chains, artificial intelligence is a powerful risk management tool. If you’re ready to learn more, download our special report with a cost-benefit analysis and real-life case studies.
How to bring artificial intelligence into your supply chain
If you are considering an investment into artificial intelligence for your supply chain, your decision will come down to buying an AI risk-management application from a vendor or building it in-house. Supply Chain Dive points out that, “Bringing the required talent on board can be a struggle. Fifty-six percent of respondents considered hiring a top challenge in the current environment and 78% said there was high competition for the talent available.”
Instead, you can focus your budget on software purposely built for logistics leaders who want to make risk more predictable, actionable, and manageable. With comprehensive risk identification and analysis, dynamic planning, and automated decisioning, all of which utilize AI, leaders can have greater confidence they can deliver more loads on time and in full.
For today’s supply chains, artificial intelligence is a powerful risk management tool. If you’re ready to learn more, download our special report with a cost-benefit analysis and real-life case studies.
| 2023-02-15T00:00:00 |
2023/02/15
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https://www.everstream.ai/articles/artificial-intelligence-role-in-supply-chain-risk-management/
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[
{
"date": "2023/02/15",
"position": 70,
"query": "AI job losses"
}
] |
Top 10 digital workplace trends for 2023
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Top 10 digital workplace trends for 2023
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https://www.clearpeople.com
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Here are the top 10 digital workplace trends in 2023: 1. AI and machine learning The use of AI and machine learning in the digital workplace is growing rapidly.
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The modern workplace has undergone significant changes, with many companies blending their physical office space with a digital workplace. Staying informed about the digital workplace trends is crucial for organizations looking to stay ahead of the curve and unlock new opportunities in 2023 and beyond.
The digital workplace offers flexibility, efficiency and cost savings for both employers and their staff. Employees themselves tend to report greater work/life balance in a remote role1.
However, the advancement of workplace technologies and digital solutions means the workplace – even the digital workplace – is set for another overhaul.
In this blog:
Here are the top 10 digital workplace trends in 2023:
1. AI and machine learning
The use of AI and machine learning in the digital workplace is growing rapidly, with many companies already using AI technologies to automate repetitive tasks and track metrics, such as content utility and employee performance.
As these technologies become increasingly widespread in the workplace, companies with digital workplaces could benefit from AI advancements to facilitate data-driven intranets, enhanced customer service, as well as AI-powered learning resources for employees.
The emergence of ‘chatbot’ technology is also significant for the digital workplace. AI technology designed to simulate human conversations could provide employees and customers with instant replies to support requests, without a lengthy wait from a human customer service advisor. This means an employee can resolve their question and another employee is freed from a repetitive task to focus on something more productive.
2. Data privacy and security
With the growing volume of data being generated and shared in the digital workplace, data privacy and security will be of paramount importance in 2023. Organizations will invest in advanced cybersecurity measures, including multi-factor authentication, encryption, and secure cloud storage solutions. Compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), will be a priority for businesses across industries.
For those operating a company intranet, advanced encryption technologies and secure authentication processes will be essential to ensuring the safety of confidential data. Companies might also look to invest in cyber security training for their employees, helping them spot potential risks and taking proactive steps to protect their systems from malware.
3. Cloud-based collaboration
Cloud-based collaboration is more relevant that ever in the digital workplace. Companies need to invest in robust collaboration tools, video conferencing solutions, project management platforms, and virtual whiteboarding to facilitate seamless digital collaboration and maintain productivity across distributed teams.
This offers a massive productivity boost both for companies using remote working, and companies operating from several branches or cross-country.
Cloud collaboration can also help companies upskill current employees, as the quick distribution of knowledge and AI-generated learning resources helps users learn new skills without the associated costs of re-training.
4. Employee tracking technology
When the pandemic forced employees to carry out their work from home, there was little for employers to use in the way of task-tracking technology – remote work was based on mutual trust and self-discipline. However, this could be set to change as employee tracking technology becomes more widely available.
Employee tracking technology includes digital tools and company intranets that can track productivity, task completion and hours spent working. Companies may think they no longer have to worry about staff productivity outside the office, but they can expect some pushback from employees.
What people loved so much about remote working was the liberty of not being observed while working. Employee tracking technology could be seen as a sneaky return to the old ways of office-based working. There is a middle ground though. Organizations looking to introduce employee tracking technology can demonstrate how this can be used to employees’ advantage by helping to boost productivity and efficiency.
5. Employee well-being and engagement
As organizations embrace remote and hybrid work models, prioritizing employee well-being and engagement will be critical in 2023. Companies will focus on providing flexible work arrangements, mental health support, and resources for work-life balance. Digital workplace platforms will incorporate features such as wellness apps, virtual team-building activities, and real-time feedback mechanisms to foster a positive work environment and ensure employee satisfaction and productivity.
6. Comprehensive knowledge bases
Working in a digital workplace offers exciting opportunities when it comes to expanding employee knowledge and skills, and knowledge management is going to play a significant role in achieving this in 2023 and beyond.
For companies looking at transforming into completely digital workplaces, knowledge is crucial for an immersive digital employee experience.
7. Knowledge culture
Robust digital workplaces of 2023 and beyond are those that foster a knowledge culture.
Knowledge culture is a company culture that promotes people identifying, contributing and sharing knowledge. It’s a culture that prizes the effective, efficient use of knowledge and recognizes that knowledge is the key to a company’s competitive advantage.
Whether this means group video calls, more collaborative decision-making, or online group activities designed to boost brainstorming and productivity, the aim is for ‘purpose-driven’ work.
8. Focus on digital employee experience
As more companies move to digital and automated processes, the spotlight is going to fall on the digital employee experience. Organizations moving to digital operations will need to consider how they run and manage a digital workplace, and what tools can be used to support the transition – and their employees.
Aside from equipping employees with user-friendly digital tools and training, the question of mental health is also at the forefront of these conversations.
After all, remote and digital working – while still largely popular with employees – isn't immune to criticism. Although studies are inconclusive (as it's still too early in the WFH experiment to say), some critics claim that working from home on a permanent basis can enhance feelings of depression, solitude, and loneliness. While this may not be true for all employees, it can be true for young people, or those living alone.
Therefore, the onus is going to be on digital companies to ensure the mental health and wellbeing of remote employees; not just for their productivity, but their overall emotional health. This could mean enhanced HR technologies, virtual support, or greater flexibility over digital/hybrid working.
9. Sustainability
As corporate organizations turn their attention towards sustainability, we can expect to see an increase in eco-friendly efforts, even from those operating digital workplaces. While the digital workplace is more eco-friendly in many ways than running a physical office – with reduced demands on lighting and heating or cooling and less commuting – there are still further gains in sustainability a company with a remote workforce can make.
Digital workplaces can accelerate their sustainability efforts by implementing environmentally friendly policies, such as using instant messaging instead of email, and using their company intranet rather than file-sharing over external platforms. Companies can also look to fully digitizing all operations, from invoices to employee training. Even storing data in the cloud is an operational area where companies can take a more sustainable approach.
10. Digital skills development
The rapid pace of technological advancements demands that employees continually upgrade their digital skills. In 2023, organizations will invest in robust training programs and digital learning platforms to upskill their workforce. This will include initiatives to enhance digital literacy, data analytics proficiency, cybersecurity awareness, and other emerging technologies. Additionally, microlearning and gamification techniques will be employed to make learning engaging and accessible to employees.
The bottom line on digital workplace trends in 2023
The digital workplace is undergoing significant transformations in 2023, driven by the need for flexibility, collaboration, and productivity.
Embracing the trends mentioned above will position organizations for success in this rapidly evolving landscape. By leveraging hybrid work models, adopting AI and automation, prioritizing data privacy and security, focusing on knowledge and employee well-being, and investing in digital skills development, businesses can create a future-ready digital workplace that drives innovation and empowers their workforce to thrive.
More blogs about the digital workplace in 2023 and beyond:
1 Employees Are Happier in The Office? More Research Suggests Otherwise
| 2023-02-15T00:00:00 |
https://www.clearpeople.com/blog/digital-workplace-trends-2023
|
[
{
"date": "2023/02/15",
"position": 11,
"query": "workplace AI adoption"
}
] |
|
The future of technology and its impact on the workforce
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The future of technology and its impact on the workforce
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https://www.axd.agency
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AI and ML systems can automate tedious activities, freeing up human workers to do more creative and meaningful work. They can also help humans make better ...
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"The business changes. The technology changes. The team changes. But the problem isn't change, per se, because change is always going to happen; the problem, rather, is our inability and willingness to cope and adapt with change when it comes," Kent Beck, software engineer and author of the Agile Manifesto.
That change is coming. In fact, it is already here.
The current rate at which technology is transforming society is unparalleled, and its effects on the workplace are no exception. Recent developments in technology, such as artificial intelligence, Machine Learning (ML), 5G, and edge computing, are revolutionising the way we do business and work.
In your roles as CEOs and company leaders, you need to anticipate changes and make smart choices regarding the future of your workforce. This forward-thinking, future-proof approach is vital to ensuring your company, and all your people can maximise the exciting opportunities that are just around the corner.
In the following article, we will provide you with an overview of where technology is right now and where it's headed, highlighting the most important trends and providing insightful forecasts about how they'll affect the workplace.
Most importantly, we'll give you some tips on how to start preparing for the future of work right now.
Technologies trends impacting the future of work
Companies of all sizes are embracing technological advancements in an effort to boost workplace effectiveness, productivity, and creativity. There are now more ways than ever before for businesses to connect with consumers, cooperate with partners, and develop innovative new goods and services thanks to technological advancements like cloud computing, big data, mobile devices, and the Internet of Things.
Here's a look at the technological developments having the most significant impact on the way we work:
Artificial intelligence and machine learning
When combined, artificial intelligence (AI) and machine learning (ML) have the ability to automate many mundane activities and pave the way for new types of labour.
Data analysis, customer care, and even some sales and marketing tasks are just some of the daily operations that can be delegated to AI and ML algorithms.
AI and ML won't replace humans. Instead, they will help us work better.
AI and ML systems can automate tedious activities, freeing up human workers to do more creative and meaningful work. They can also help humans make better decisions by providing insights and information. For example, these smart algorithms can analyse massive volumes of data, revealing insights that would elude the human mind.
AI and ML may also help humans with strenuous activities like repeated actions or complicated computations, meaning people work more comfortably, reducing injury risk and increasing job satisfaction.
Business executives and CEOs that use these technologies and integrate them into their workforces before competitors will create a more efficient and effective workforce.
Remote work technology
The pandemic lockdown speeded up the move towards remote work, with many businesses implementing new technology to allow workers to get the job done from anywhere.
Video conferencing and other collaboration technologies, as well as cloud-based project management systems, helped businesses overcome physical distance and build more adaptable, responsive teams.
As telecommuting and other remote technology become even more widespread, it will undoubtedly alter the professional landscape in important ways. Companies will be able to tap into a larger talent pool, recruiting the best people for the best job from anywhere in the world. They will also be able to build more diverse and inclusive teams.
Edge computing and 5G
5G networks and edge computing present organisations with exciting new possibilities to boost productivity and dependability.
Edge computing puts computational power closer to the end user, lowering latency and enhancing performance, while 5G networks offer faster and more dependable communication.
Companies may now build more connected and smarter workforces with the help of these technologies, opening up new avenues for creativity and progress in the workplace.
Automation via robotics
Robotic automation is another technology revolutionising the workplace, with applications across a wide range of sectors. Robots are being employed in many industries, from manufacturing and logistics to healthcare and retail to automate mundane jobs. Companies will be able to increase productivity, save expenses, and drive innovation as robots and automation technologies continue to develop in the years ahead.
Cybersecurity
We live in a digital world. Every successful business needs a strong online presence and a robust digital infrastructure to deliver services and information.
This has opened up a vast number of opportunities, allowing local businesses to target global markets and audiences. However, it's also made businesses of all sizes more vulnerable to cyberattacks and data breaches, making cybersecurity a pressing issue in the modern digital era.
As technology continues to drive change in the workplace, businesses must take precautions to ensure the security of their information systems and data. Protecting your business and people from online dangers requires taking precautions, like delivering training to staff, staying up-to-date, and investing in security tools.
The current state of technology adoption in the workplace and its effects on employees
Here's a look at the kind (and sizes) of change we can expect to see in the coming years.
The cloud is getting bigger and bigger...
The cloud infrastructure services industry is expected to generate $44 billion by the end of 2023, according to research by Synergy Research Group.
The AI and ML economy
PwC projects that AI will add up to $15.7 billion to the global economy by 2030.
Remote work will continue to be part of the new normal
Gartner estimates that by 2024, more than 40% of workers will be able to do some or all of their jobs remotely, up from 30% before the epidemic.
Automation will create more jobs, not less
According to research by the World Economic Forum, 133 million new jobs will be produced by 2025 to compensate for the 75 million jobs lost to automation.
Upgrading and retraining are critical
According to research by the World Economic Forum, more than half of all workers will require reskilling by 2025. These numbers show how important it is for company executives to anticipate the effects of technology advancements on their staff.
Businesses that engage in reskilling and upskilling programmes and embrace new technology will thrive in the dynamic markets of the very near future.
The benefits of being an early technology adopter
It's hard to overstate the importance of adopting new technologies and embracing change. To say that it could be the difference between future success and total failure is not an exaggeration.
John Thomas Chambers, the former executive chairman and CEO of Cisco Systems, explains:
"At least 40% of all businesses will die in the next 10 years… if they don't figure out how to change their entire company to accommodate new technologies."
You need to move fast and adapt quickly. And here's why...
Gain a crucial advantage over the competition
A survey by Accenture indicated that early adopters gain a massive competitive advantage, with a median gain of 6.9% in terms of revenue growth.
Increase productivity
A survey by McKinsey & Company found that early adopters of technology experience an average productivity improvement of 3-4%, compared to non-adopters.
Increase customer satisfaction
A survey by Gartner indicates that early adopters report an average gain of 10% in customer satisfaction compared to non-adopters.
Improved employee satisfaction
A survey by Deloitte revealed that early adopters of technology experience an average gain of 8% in employee satisfaction compared to non-adopters.
More revenue
Early adopters report a 5-6% revenue increase compared to non-adopters, according to a Harvard Business Review study.
The challenges of adopting new workplace technologies
Adopting new technology in the workplace provides several benefits, including increased efficiency, higher production, and lower expenses. However, there are several challenges to overcome.
The deployment of new technologies may involve substantial expenditure and investment, as well as adjustments to existing processes and systems.
Additionally, adopting new technology can lead to job displacement, requiring individuals to reskill and upskill to stay relevant in the changing work environment.
Companies that want to invest in innovative workplace technologies must also invest in their people. This requires a culture of change and continuous improvement within the business and significant financial investment to cover the cost of skills and knowledge training, including flexible work arrangements that allow people to learn the new skills they need.
The expert angle
Famous business executives and CEOs recognise the necessity of staying ahead of the curve when it comes to technology and its influence on the workforce.
Here's what some of today's most successful leaders and CEOs have to say about the importance of embracing change and tech innovation.
"The greatest way to anticipate the future is to invent it," Alan Kay, computer scientist and inventor.
"We are moving from a world where computing power was rare to a place where it now is nearly infinite, and where the ever-growing capacity of computation can be harnessed to produce value in new and unexpected ways," Marc Andreessen, co-founder of Andreessen Horowitz.
"Technology is not only a tool but a catalyst for transformation in every area of business," Tim Cook, CEO of Apple.
"Every industry and every organisation will have to transform itself in the next few years. What is coming at us is bigger than the original internet, and you need to understand it, get on board with it, and figure out how to transform your business," Tim O'Reilly, Founder & CEO of O'Reilly Media.
Recommendations for CEOs and business leaders: how to prepare for the future of work
As a CEO or company leader, you should:
Stay updated about the newest technical breakthroughs in your sector and implement new technologies that can create a competitive edge.
Invest in reskilling and upskilling programmes for your staff to help them adapt to new technology and work procedures.
Implement effective cybersecurity procedures to safeguard your systems and data from cyber-attacks.
Foster a culture of innovation that encourages your staff to adopt new technology and work practices.
Prepare your people for change. Encourage them to embrace it positively, seeing the adoption of workplace technology as an opportunity to grow, learn, and augment their skillset.
Embrace remote work and other flexible work arrangements to develop a more agile and diversified staff.
Summary
Technology is reshaping the workplace at an unprecedented rate. By remaining updated about the newest advancements, investing in reskilling and upskilling programmes, and embracing new technologies that give a competitive edge, company leaders can prepare for the future of work and remain successful in an ever-changing business landscape.
Let AXD help
We are a B2B copywriting and content marketing agency based in Manchester. Our technologists, business leaders, and copywriters have experience in various technology industries — from early-stage startups to scale-ups and Fortune 500 companies. Whether you're a startup or a global brand, our international experts will give your content the edge, enabling you to open conversations, build relationships, & drive customer loyalty.
We create content that illustrates precisely what you do and why you do it better than anyone else. It's simple. More clicks, more leads, and more sales.
To find out more, book your free discovery call today.
| 2023-02-15T00:00:00 |
https://www.axd.agency/post/the-future-of-technology-and-its-impact-on-the-workforce
|
[
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"date": "2023/02/15",
"position": 22,
"query": "workplace AI adoption"
},
{
"date": "2023/02/15",
"position": 94,
"query": "AI labor market trends"
}
] |
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Adopt the NIST AI RMF with Credo AI
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Start Operationalizing the NIST AI Risk Management Framework with Policy Packs
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https://www.credo.ai
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Adopt the NIST AI RMF with Credo AI · Track and drive the NIST AI RMF adoption across all internal teams. · Demonstrate compliance with customers and the market.
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As the adoption of Artificial Intelligence continues to grow at a rapid pace, organizations must take immediate action to prioritize the responsible use and development of this powerful technology. To support this endeavor, the National Institute of Standards and Technology (NIST) developed the NIST AI Risk Management Framework (NIST AI RMF) to guide organizations in managing and mitigating AI-related risks effectively.
Implementing the NIST AI RMF can be a complex process.
Nonetheless, with Credo AI's Governance software, organizations can tackle the complexities of implementing the critical NIST AI RMF framework with ease. As contributors to the development of the NIST AI Risk Management Framework (NIST AI RMF) and participants at its official launch in the U.S. Department of Commerce, Credo AI has developed a comprehensive solution to help organizations adopt the framework with ease.
Our NIST AI RMF Policy Pack provides a straightforward approach to implementing continuous governance and accountability that aligns with best AI/ML lifecycle practices. With Credo AI, you'll benefit from a seamless solution for managing the NIST AI RMF, including the ability to:
Track and drive the NIST AI RMF adoption across all internal teams.
Demonstrate compliance with customers and the market.
Reduce overall AI risk exposure through adherence to a best-practice risk management framework created by a standard-setting body.
Don’t let uncertainty about where to start with the NIST AI RMF hold you back. Reach out to us and simplify your adoption now!
| 2023-02-15T00:00:00 |
https://www.credo.ai/responsible-ai/nist-ai-rmf
|
[
{
"date": "2023/02/15",
"position": 34,
"query": "workplace AI adoption"
}
] |
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Workforce and Learning Trends 2023
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Workforce and Learning Trends 2023
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https://www.comptia.org
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[] |
... adoption and expansion of talent mobility programs. ... Do the already high strategic stakes of HR get even higher with the emergence of artificial intelligence ( ...
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Introduction
Although the peak of the pandemic seems firmly in the rear-view mirror, a “new normal” has not yet truly emerged, including—and perhaps especially—in the way we work. Even as some prominent CEOs have declared the end of remote work and demanded that their employees return to a centralized office, the headlines are still filled with stories of empty downtown office buildings. Fears of a recession persist, yet the labor market remains historically strong.
Amid such mixed signals and unstable terrain, companies are bracing for continued upheaval and are preparing for a wide range of possibilities. Instead of falling back on traditional solutions, organizations must rely on experimentation and knowledge-sharing to chart a productive path forward. To aid in this effort, CompTIA's Workforce and Learning Trends examines how human resources (HR) and learning and development (L&D) professionals are thinking about these challenges while simultaneously working to move their organizations forward on many fronts.
The economy stands at the forefront of many organizations' concerns. Almost 80% of survey respondents expect that economic conditions will moderately or significantly impact their company's human resources priorities on some level in 2023.
Employees are also feeling the stress. According to a recent iCIMS survey, nearly 80% of workers do not feel fully secure professionally or financially. Some segments of workers are now less likely to quit without a new job lined up than they were one year ago. However, that has not dampened the desire for new or better employment opportunities, and approximately 30% of the workforce continues to actively explore career changes, according to CompTIA's latest Job Seeker Trends research.
Despite the pendulum swings of sentiment, the long-term trends in the labor market continue to point to talent constraints in many occupation categories. A majority of HR professionals in CompTIA's survey expect their organizations will maintain their current pace of hiring in the first half of 2023. At the time of reporting, the national unemployment rate hovered at a historically low 3.6%, while the unemployment rate for technology professionals stood at 2.2%, signaling a very tight labor market for tech talent.
Hiring continues to be driven by the pursuit of cutting-edge skills that can help organizations gain or maintain their competitive edge. In terms of staffing changes anticipated in the first half of 2023, the overwhelming majority of respondents indicated that they were planning to hire to fill new positions or meet the demand for new skills—rather than fill vacated positions or tighten their workforce through layoffs or hiring freezes.
Given sustained economic concerns, paired with an unusually strong labor market, many businesses are turning to their existing resources to find or develop the desired skillsets rather than exclusively trying to attract new external talent. Although skills remain a core focus, flexibility is the new watchword. Employers and employees alike are striving to maximize untapped opportunities within their present situations. Business leaders are pursuing new ways to take stock of their organizations’ existing skills and are expressing more openness to unconventional, shorter-term arrangements that enable employees to contribute across the organization. Employees, in turn, seem more willing to remain within their current situation if they feel like opportunities for advancement are available.
Talent mobility approaches grow in scope and importance
To maintain a competitive advantage in a post-pandemic era marked by rapid change and innovation, companies must be able to develop and then deploy their existing resources effectively, quickly, and responsively. Learning and development programs, aimed at both upskilling and reskilling workers, will be essential—but not sufficient in and of themselves—for success. Companies will also need to maintain a comprehensive and real-time inventory of their current skills and shift those skills throughout the organization to match current needs.
Even before the pandemic, projecting which skills would be valuable over a three-to-five-year horizon was difficult, given the rate of technological change and evolution. However, more recently, as the structure and location of work have changed dramatically, such projections have become even more challenging. The World Economic Forum estimates that within the next five years, up to 50% of all employees will require some level of reskilling to perform their roles effectively and nearly 100 million new roles will be created. A five-year development plan for either an organization or an individual can seem daunting in this context.
The breakneck rate of skills turnover is likely a prime motivator for companies’ adoption and expansion of talent mobility programs. In total, three-quarters of the respondents to the CompTIA survey reported the existence of either a formal or informal talent mobility program within their organization, and 75% reported that their organization plans to increase their program’s scope and their emphasis on talent mobility programs and policies.
When an organization has a well-developed, far-reaching, and successful talent mobility program, it can adapt to changing environments, adroitly identify needed skills, and then deploy resources quickly and effectively across teams, divisions, or even entities. These multi-faceted programs nurture internal talent and leverage the underutilized skills of an existing workforce rather than relying on new hires to fill skills gaps, offering powerful benefits, especially at a time when many are being tasked to do more with less.
Employers can use talent mobility programs to improve outcomes by engaging and developing their current talent. From employees’ perspectives, the moment of the “great resignation” may be fading, and they, too, are more drawn to stability and certainty than they were even a year ago. A recent survey found that of people who left their jobs due to feeling unhappy or unfulfilled, 54% later regretted doing so. This indicates an opening for employers to retain more workers if they can create more advancement opportunities and make those opportunities more visible and accessible across the organization. In fact, LinkedIn found that 41% of employees will stay longer at a company that offers high internal mobility versus companies with low internal hiring. Strong talent mobility programs, therefore, can motivate employees who are already a culture fit to stay with an organization and, therefore, help a company avoid unnecessary hiring and onboarding costs.
Talent mobility programs also make it easier to move current employees within an organization. These employees can begin adding value from their first day on a new job because they don’t require the same start-up time as a new hire. Plus, when employees move across an organization, they can bring deep expertise from one part of the business to another, generating new insights and opportunities. However, to accomplish this, companies must create reward systems, relevant development tools, and coaching and mentoring opportunities to facilitate this exchange of knowledge and experience.
That is why it is so important for talent mobility programs to not only focus on the organization’s strategic goals, but also to create opportunities for employees across the company to pursue their own professional interests. Business leaders in our survey seemed to appreciate this goal. A majority indicated that their talent mobility programs were primarily driven by the desire to empower employees to actively manage their careers. Only 31% of respondents indicated that their talent mobility programs were the product of necessity when external searches failed.
Yet, there may be a gap between what employers think they are providing versus how employees perceive those efforts. According to iCIMS, 70% of workers report not knowing how to progress in their careers, and more than half think it’s difficult to find and apply to open jobs internally. Such beliefs impede retention. Workers recognize that changing roles is necessary to upgrade their skills, raise their income, and demonstrate a track record of evolving and relevant experience. If they do not feel they can change roles internally, they will be driven to look elsewhere.
Talent mobility programs also must reckon with employers and employees’ increasing prioritization of experiential learning and hands-on, performance-based training. Simply moving employees to new projects and teams will not generate meaningful and successful talent mobility within an organization. Individuals are unlikely to do well in new roles without supplemental training and mentoring so that they can rapidly adapt to their new assignments. Flexible work arrangements also require substantial logistical coordination, which means talent mobility programs must provide a holistic but deep view of an organization and the individuals within it. This not only raises the stakes of such programs but also places more responsibility on L&D and HR professionals.
CompTIA believes that talent mobility programs can—and should—extend beyond a single corporation’s efforts. We research, analyze, and advocate for best-in-class training that can empower a broad range of workers to contribute to—and benefit from—the opportunities available across the full breadth of the information technology (IT) workforce. The CompTIA Apprenticeships for Technology Program, for example, offers businesses the opportunity to upskill and reskill workers while connecting people to promising careers in IT. This program uses a competence-driven skills attainment model to help interested workers transition from a job to a fulfilling career pathway. In addition to efforts like the Apprenticeship Program, CompTIA routinely partners with large organizations and local governments to develop scalable training programs that can be implemented for communities at large.
T-shaped skills models remain integral for future-proofing careers
Although the phrase “T-shaped skills” was coined over 30 years ago, it has recently gained more popularity in the professional development industry because it provides a compelling visualization of the unique combination of depth and breadth of skills that can lead to success in today’s workplace. In the CompTIA Workforce survey, 84% of respondents indicated that they use a T-shaped skills model in some capacity to guide talent management. The “T-shaped model” signifies an employee with a significant understanding of a particular field, as well as a wide range of cross-discipline competencies. In this formulation, the vertical bar of the “T” represents the in-depth knowledge of the specialized field, and the horizontal bar of the “T” represents capabilities in a broad range of related skills. An employee with T-shaped skills is particularly valuable today when flexibility is so prized; they can draw on their technical knowledge and apply it to areas of expertise and business segments other than their own. This powerful combination can help future-proof an organization and boost its competitiveness.
Although similar, the T-shaped model connotes some important distinctions from the “specialist versus generalist” formulation or the recent emphasis on soft skills, also referred to as durable skills.
Developing T-shaped employees arguably requires more professional development and continuing education than more traditional skills models do. Mid-career professionals may need as much, or more, training than their lower-level counterparts. That is because to sustain both axes of the “T,” employees need to stay up to date on the technical skills that are central to their core function, but they also need to develop broader capabilities, including creativity, collaboration, and communication skills. A senior business leader, for example, should understand their organization’s operations and strategic goals, as well as the larger industry context.
Such challenges help explain why HR professionals are increasingly turning to skills taxonomies—a strategy employed by 74% of the respondents in the CompTIA survey. Skills taxonomies classify and track the capabilities within an organization and can, therefore, amplify and refine talent mobility efforts. They can also help organizations create training opportunities that enable employees to learn complementary skills. Employees can also benefit from skills taxonomies because they can see what skills they’ve mastered and identify which learning opportunities will help them pursue their objectives. Business leaders can also evaluate the ways skills are learned in their organization and track how those competencies become deeper and broader over time.
Despite the widespread adoption of skills taxonomies, only 36% of survey respondents use a formal tool to do so. One explanation for this is that formal tools require both money and time to create and maintain. However, an added difficulty is the challenge of tracking skills acquisition. Employees often learn via organic, unpredictable, and yet remarkably powerful arrangements. An employee at a call center learns to remain calm with difficult customers by overhearing a veteran supervisor day after day. An IT help desk worker improves her coding skills by asking questions from the programmer sitting next to her. Even though L&D and HR professionals recognize the considerable value of these informal learning opportunities, such arrangements are difficult to incorporate systematically into a structured tool.
Algorithms and platforms exert greater influence over talent development
The concept of human resources technology – commonly referred to as HR tech—continues to evolve. It was not too long ago when HR tech centered around Microsoft Word docs, spreadsheets, and a couple of siloed applications. Today, HR tech encompasses many interconnected systems, spanning software, hardware, platforms, data, and cybersecurity, with the prevailing goal of orchestrating digital and people transformation.
Despite the imprecise market sizing estimates of the multi-billion-dollar HR tech space due to definitional challenges, most projections depict a rapidly growing market fueled by existing players and new market entrants. According to Pitchbook, a capital markets research firm under the parent company Morningstar, VC investment in HR tech startups in 2021 soared by over 250%, year-over-year. Even with the recent cooling of early-stage investments, HR tech remains an attractive market for deal flow and disruption.
CompTIA’s survey of HR professionals confirms this outlook. A majority of organizations reported plans to upgrade or adopt new HR tech in the year ahead. Such a change typically starts with core systems and platforms, designated as HRIS, HRMS, or HCM, where two-thirds of organizations have new or expanded investments on their technology roadmap. As expected, investment activity skews towards larger organizations, but it is reasonable to infer that organizations of all sizes are at some stage of adoption or evaluation.
The most interesting investment from the perspective of “what’s next” is in the emerging areas of talent development. From the survey data, three of the top four new investments in platforms and systems are in areas that focus on developing and deploying talent.
Learning and training platforms
Talent mobility and talent marketplace platforms
Skills inventory, talent intelligence, and career pathways platforms
The old adage “it’s not about the technology” arguably rings truer than ever with regard to HR tech. Advances in technology are undoubtedly important, especially with integrated solutions that prioritize user experience. Achieving desired outcomes, however, inevitably requires much more than just a quick technological fix. For example, the most sophisticated training and development platforms will likely underperform if the learning content, implementation, staff support, and executive leadership commitment fall short.
Despite indications of the growing strategic importance of the HR function, many organizations continue to see room for improvement in fine-tuning processes and policies across CHRO, CIO, CFO, and other C-suite power centers.
Do the already high strategic stakes of HR get even higher with the emergence of artificial intelligence (AI) and the prospect of an algorithmic-driven organization? One school of thought leans into historical context, asserting that wave after wave of technological innovation is more evolutionary and gradual than revolutionary and radical. The counterpoint, of course, rests on the “Is this time different?” question.
Based on the CompTIA survey data, most HR professionals expect some degree of impact (net 72% among US respondents), but like most, others grapple with the timing and magnitude of impact.
The early use cases of generative AI, most notably OpenAI’s ChatGPT, provide a glimpse at the potential. Imagine understaffed HR departments using a generative AI platform to write job descriptions, employee handbooks, conflict of interest disclosures, and the list goes on. However, generative AI tools also open up the possibility that higher-level tasks, such as comprehensive employee engagement analysis, salary reviews, interactive coaching, or candidate interviews, can also become automated. These uses are not merely theoretical; they are happening today.
When ChatGPT itself was asked about potential uses of AI in human resources – a clever approach taken by the HR Exchange Network—the system returned the following categories of functions:
Recruiting and hiring
Employee onboarding
Employee engagement
Performance evaluation
Training and development
Compliance and policy
HR data management
Employee assistance
Diversity and inclusion
While the accompanying chart shows only 10% of HR professionals reporting an initial negative perception of AI, the larger 52% in the “to-be-determined” camp clearly has questions and concerns. The concept of AI bias has been around for many years. Examples of hiring platforms trained on biased data that then produce algorithmic-biased results in candidate selection make the headlines on an all-too-frequent basis. In addition, there are many concerns over accuracy (such as whether AI can really know what it doesn’t know), as well as privacy and cost. It is still early in the generative AI trend cycle, and it will take time and thoughtful refinements before the true impact becomes clear.
Organizations strive to maintain DEIB and ESG momentum
Diversity, Equity, Inclusion, and Belonging (DEIB) continues to be top of mind for HR professionals. In CompTIA’s 2021 Workforce and Learning Trends Survey, more than half of HR professionals expected their organizations to pursue new initiatives related to DEIB in the year ahead. Two years later, the impacts of these initiatives are beginning to emerge—52% reported their organization’s status with DEIB efforts was regaining or generating momentum. Although there’s certainly room for improvement, there is also hope on the horizon: 35% say their DEIB efforts are moving to the next level.
The principles of the Environment, Social, and Governance (ESG) framework have also emerged as a major area of focus, and 83% of respondents reported having some ESG programs and policies in place. As with DEIB efforts, the data suggests movement on the ESG front, but an opportunity for improvement remains. While 57% reported their organization’s ESG efforts as regaining or generating momentum, 29% indicated that they are taking their ESG efforts to the next level.
Companies are facing some significant challenges as they attempt to achieve or maintain this momentum, though. Identifying the right priorities, goals, and metrics of success tops the list; 44% of respondents indicated that this is a challenge. A sizable number, 41%, point to general resistance to change and to budget constraints as the next two challenges, followed by execution (40%), commitment from senior leadership and boards (37%), and bias—both conscious and unconscious (28%).
These results are reflected in other research. Evelyn R. Carter, a social psychologist, and Natalie Johnson, co-founder and managing director of Paradigm, indicated that while 60% of their study’s participants reported having DEI strategies in place, these strategies didn’t “always focus on the right goals or accountability partners.” Only 28% of the companies held C-suite executives accountable for the progress of a DEI strategy, for instance. The study also found that it was difficult for organizations to collect and analyze the right data to guide and assess their efforts.
Other research on ESG progress reflects similar challenges. While more than 90% of S&P 500 companies now publish ESG reports as they’re mandated to do, significant barriers remain. It is difficult to identify which metrics actually measure the effectiveness of ESG programs. In addition, these metrics must be accurately tracked and then broadly reported so that apples-to-apples comparisons can be made. Without this kind of consistent and revealing data, investors, consumers, potential employees, and other key stakeholders are left without critical, actionable insights, according to McKinsey.
Despite these obstacles, our research reveals that DEIB has been an area of interest for at least two years now, and ESG is quickly becoming a focus—even within companies that are not mandated to report on these measures. This demonstrates a concerted effort that is clearly gaining momentum. This year’s responses surrounding both DEIB and ESG means these efforts will likely make continued progress in 2024.
Talent marketplaces add a new dimension to workforce flexibility
Organizations increasingly value the idea of workplace flexibility, but facilitating and maintaining these flexible work arrangements create significant challenges. Robust talent marketplaces have the potential to transform how work is assigned and to revolutionize talent mobility programs. The best-known talent marketplace platforms are the large-scale, broad-based freelancing sites, such as Upwork or freelancer.com, where employers can post jobs and workers can apply for positions that match their interests. In this year’s Workforce and Learning Trends survey, 68% of respondents indicated their companies use these external freelance marketplaces frequently or occasionally. The most often cited motivations for consulting these tools were the need for a specific skill set, followed by the potential cost advantages of using freelance employees.
The stigma that sometimes surrounded these talent platforms—and freelance work more generally—has lifted in recent years. When these platforms first came online, freelancers were often perceived as “stuck” on such sites, resorting to them as stopgap measures until they could secure full-time, stable employment. Today, freelancing is seen as much more lucrative and sustainable. There are more tools to support freelancers’ administrative and back-office needs, so the heavy lifting of running a company doesn’t fall squarely on one person’s shoulders. The pandemic also supercharged the widespread adoption of remote work and normalized productive collaboration regardless of physical location. Finally, the growing scope of the talent marketplaces themselves has enabled freelancers to scale their businesses more easily and to increase their revenue.
As the value of these platforms has become more evident and the tools themselves have become more powerful, companies have begun to develop their own internal talent marketplaces. Notable early adopters include Unilever, Schneider Electric, Standard Charter, Seagate, Societe Generale, and IBM. These companies hope that their internal talent marketplaces will encourage employee engagement and improve retention by making career opportunities more visible and accessible to their workforce.
An internal talent marketplace can facilitate and streamline a wide range of an organization’s talent management needs; many combine aspects of career management, social networking, and a recruiting system in one. A talent marketplace can also provide access to gig work, mentorship, rotation programs, volunteer options, and skill-building experiences that support the organization’s strategic objectives.
As noted in the section covering the “Algorithms and Platforms Exert Greater Influence over Talent Development” trend, internal talent marketplaces are expected to increasingly leverage AI-enabled tools, empowering them to develop an organization-wide skills inventory, identify skills gaps and dynamically match interested workers to new opportunities that they might not otherwise consider.
By improving transparency, talent marketplaces might also enhance DEIB efforts. Research shows that job candidates consistently decide not to apply for positions because they believe they will not be fairly or accurately evaluated. Even when they have the requisite skills, they suspect the person in charge of hiring will perceive them as unqualified. Women are especially reticent to apply for jobs unless they strictly meet all of the criteria set out in the job description. An AI-powered marketplace can foreground relevant opportunities for these workers, which can shift the candidates’ perceptions about their suitability for a role and how they will likely be evaluated. In addition, marketplaces can showcase employees’ skills, experiences, and potential rather than their rank so that hiring managers and team leaders can more easily identify potential alignment with projects and opportunities.
The trend toward talent marketplace platforms seems likely to accelerate. Gen Z and millennial workers are very comfortable with these tools, and they are also more likely to have engaged in the kinds of flexible work arrangements that are often procured through these platforms. In this year’s survey, 46% of Gen Z and Millennials indicated that they “frequently” engaged in freelance or gig work, and 67% of workers in these age groups reported doing so “occasionally.” These workers are primed to think of their work more flexibly—not as filling a specific role but as providing skills for a particular project for a defined period. This perspective can be especially beneficial in a time of continued volatility when rapid adaptation can provide a competitive edge.
Spotlights
Training spending increases, but so does training fatigue
Despite varying degrees of concern around economic uncertainty, half of HR professionals indicate that their organizations plan to increase spending on training in 2023, versus only 10% expecting a decrease in spending. This is in keeping with broader findings. In addition to spending more money on training, 59% of respondents expect that the time devoted to training and development will increase in the next year, while only 10% believe it will decrease. For context, according to the Association for Talent Development’s 2022 State of the Industry report, organizations spent on average $1,280 per employee on workplace learning.
This year’s CompTIA survey found that 82% of HR professionals believe that training and development efforts help improve employee retention and engagement. However, worries about training fatigue have also increased. In our survey, 58% of HR professionals reported some degree of concern related to overtraining or training fatigue among their employees. This is unsurprising, given that the pandemic pushed workers to spend more time online and increased mandatory training and compliance requirements.
To counteract this fatigue, companies are refocusing their training programs to ensure they provide relevant and personalized opportunities that recognize individual achievement and skills acquisition rather than pursuing a top-down model of training that promotes the organization’s needs. This has led to more of a “mix and match” approach to education, including an expansion of self-service formats, such as podcasts, webcasts, video conferences, and e-learning modules, as well as in-person activities that achieve multiple purposes, like instructor-led courses that also devote time to networking and informal mentoring. AI-powered training tools are also improving personalization options, enabling businesses to suggest customized training opportunities based on employee preferences — from content to format to when they participate. This gives employees more freedom to personalize their learning paths and develop new skills.
In light of the ever-increasing training demands, employers can no longer rely on workers’ sense of company duty or even their investment in a long-term career path to sufficiently motivate them to complete hours of online coursework. To address this issue, companies are focusing on shorter-term objectives and more immediate incentives, such as problem-based learning, rewards, badges, and gamification. An ample body of research demonstrates that such strategies increase learner engagement, attendance, participation, and feelings of competence.
Certifications add value to HR toolkit
Keeping up to date with the most in-demand and valuable skills is essential for employers and employees who want to remain competitive. To achieve this, employers want a hiring process that can efficiently and accurately identify candidates who possess the desired skills. Yet many HR professionals do not possess in-depth, technical knowledge of the skills their company is hiring for. Given the breakneck pace of innovation today, even experienced technology managers may find it difficult to identify and evaluate skills requirements. [Design note, brief callout referencing: For more on the topic of over-spec’ing in hiring and a free tool to help overcome it, see the CompTIA Job Posting Optimizer]
The data confirms that industry-recognized technical certifications remain a powerful tool to verify a candidate’s knowledge, skills, and abilities (KSAs).
80% of HR professionals in medium-sized and large organizations rely on industry-recognized technical certifications during the hiring process Net among US HR professionals | Among UK HR professionals, the net figure is 82%
Although companies still value the knowledge gained from a four-year degree, they also recognize that higher education cannot keep pace with the constant evolution in technical skills. Certifications can provide more specific and concrete insights into an employee’s capabilities than a degree can, especially one that was earned years ago or in another field of study.
Despite their value, certifications are also causing confusion, especially given their recent proliferation. The IT industry, in particular, has been inundated with badges, designations, and other forms of credentialing that can make it very difficult to determine their significance. Smaller organizations often do not have an expert on their team who can validate the worthiness of a particular recognition. For these reasons, CompTIA advocates for industry-recognized certifications, which typically require formal coursework and a comprehensive exam. This ensures that the certification reflects not only the completion of a course but also a necessary level of understanding.
Age bias in the workplace has many facets
As many companies continue to face challenges in attracting and retaining top talent, especially in specialized and hard-to-fill positions, there’s one segment of the workforce they may be overlooking—older workers.
The majority of HR professionals in the CompTIA survey indicated that their organization has not implemented a retention program that specifically targets aging employees or those nearing retirement. Only 14% report having a dedicated program or effort, while 32% report an “all of the above” approach in programs and efforts to retain workers.
This is a segment of the workforce that is eager to stay engaged, according to research. In its study, Voya Financial, a subsidiary of ING, used the term “employment extenders” to refer to workers over 50 who had retired but later rejoined the workforce. Some media outlets couldn’t resist referring to this as the “great unretiring,” as segments of workers returned to work after a temporary, pandemic-induced retirement.
Yet, according to an AARP survey, “78% of workers age 50 or older said they’ve seen or experienced age discrimination in the workplace.” Similar to other forms of bias, this may take many forms, from overt stereotypes of older workers being less tech-savvy to subtler forms of unconscious bias, such as assuming an older worker is less interested in learning new skills.
Age bias remains a concern in the tech workforce. Demographic data confirms that the tech workforce skews younger than the overall US workforce. There are many factors at play in age distributions across occupation categories, so the data does not automatically prove bias, but combined with other directional insights, it does suggest employers, managers, and perhaps older workers themselves are acting in ways counter to their interests.
According to the World Economic Forum, older workers are looking for job security, work-life balance, unretirement (the desire to work), and belonging—not so different from employees in other generations. All in all, this cohort may represent key benefits to employers struggling to find talent.
The other facet of this discussion occurs at the other end of the age and bias spectrum. That is what may be characterized as the “positivity bias” of assuming younger age cohorts – the Digital Natives—are experts in workplace technologies. This very issue was raised in the recent article, “Gen Z Has a Tech Skills Gap at Work,” which detailed the challenges that younger workers face as they transition from consumer-friendly apps and videogames to enterprise-level networks, applications, and security requirements.
Despite the questionable practice of grouping large swaths of the population into classifications based on birth year and then assigning labels to them, the reality is that generational cohort discussion will never disappear from workplace discourse. Savvy organizations will look past labels and bias and instead focus on recruiting, retaining, and developing the best possible team they can.
Buzzword watch
By design, buzzwords elicit a reaction. Sometimes, the reaction is an eye-roll in response to the unnecessary labeling of everything that then snowballs out of control due to keyword tagging and SEO. In other situations, it may be a shoulder shrug of confusion at insider jargon or the repackaging of longstanding concepts with new terminology. Putting aside these minor amusements – or annoyances, depending on your perspective—buzzwords do often provide some new kernel of insight into how workers or organizations are thinking and behaving.
Buzzwords and terms making the rounds in news stories and social media references include:
Anti-perks
Career cushioning
Ghost jobs / Ghosting
Hustle culture
Labor hoarding
Micro-stresses
Permacrisis
Productivity paranoia
Proximity bias
Quiet hiring
Rage applying
Surge staffing
For further reading, see articles such as 23 trending HR buzzwords.
Please note this is an excerpt, and the full report contains more detail.
Download the full report
| 2023-02-15T00:00:00 |
https://www.comptia.org/en-us/resources/research/workforce-and-learning-trends-2023/
|
[
{
"date": "2023/02/15",
"position": 90,
"query": "workplace AI adoption"
},
{
"date": "2023/02/15",
"position": 28,
"query": "AI labor market trends"
}
] |
|
Tech to the rescue - Management Perspectives
|
Tech to the rescue
|
http://www.aima.in
|
[] |
AI upskilling isn't just a good HR policy; it drives higher employee engagement, maximises AI adoption, and ensures a competitive edge in an evolving market.
|
Everything you know today you have learnt, and your potential to keep learning is limitless. The only barrier is a self-defeating belief that learning is no longer possible. So, like the bees, keep moving. Keep learning. Keep adapting.
| 2023-02-15T00:00:00 |
http://www.aima.in/management-perspectives/tech-to-the-rescue
|
[
{
"date": "2023/02/15",
"position": 100,
"query": "workplace AI adoption"
}
] |
|
Emerging Software Development Trends - Maryville Online
|
Emerging Software Development Trends
|
https://online.maryville.edu
|
[
"Marcos Andres Bonet Rodriguez"
] |
Employment of software developers, quality assurance analysts, and testers is projected to grow 25% between 2021 to 2031External link:open_in_new, according to ...
|
Software developers serve as the creative and technical masterminds behind many of today’s popular technology tools, from mobile apps for personal use, to data solutions that help address real-world problems such as climate change, to any number of digital breakthroughs.
In developing and testing computer software applications, software developers perform a wide range of activities, including analyzing user needs; applying computer and mathematics principles in their designs; creating specifications; enhancing existing software systems; and collaborating with hardware developers, project managers, and others involved in the software development process. Software developers also monitor software development trends as the demand for technology evolves to meet the global market’s continued appetite for innovation.
The future of the software development role looks bright. Employment of software developers, quality assurance analysts, and testers is projected to grow 25% between 2021 to 2031, according to the U.S. Bureau of Labor Statistics (BLS). This projection far exceeds the predicted growth of the labor market as a whole.
For individuals looking to enter or advance in the software development field, keeping up to date on key software development trends in coding, apps, agile systems, and interface design can open opportunities for an exciting — and rewarding — career. The median annual salary for software developers was $120,730 as of May 2021, according to the BLS.
Software development encompasses various roles, including the following:
Applications Software Developer : Develops, tests, and programs software applications for computers, mobile phones, and other devices using existing development tools
: Develops, tests, and programs software applications for computers, mobile phones, and other devices using existing development tools Software Engineer : Applies engineering principles and programming language knowledge to create software products and systems
: Applies engineering principles and programming language knowledge to create software products and systems Systems Software Developers: Develops software used on a system level, such as operating systems and drivers, to run a computer’s hardware and application programs
These roles are a small representation of the vast number of job titles open to software developers.
Thanks to the global market’s continued appetite for technological innovation, software development’s future has looked bright for years. Amid the changing technological landscape, businesses must stay current on emerging trends in software development, which is why software developers play a pivotal role in helping companies get ahead, or at the least, stay competitive.
Why Tracking Emerging Software Development Trends Is Important
Software development processes and practices are continually evolving because of the fast rate of technological change. Emerging trends such as low-code, no-code, and DevOps are transforming how professionals develop, deploy, and manage software and software platforms.
At the same time, changes in software development processes and practices help drive the accelerated pace of the technology sector itself. An organization that fails to keep track of emerging software development trends runs the risk of becoming obsolete.
Since the software development field is in constant flux thanks to a range of factors — a changing technological landscape, globalization, evolving consumer and business demands, and market trends — software developers must refresh their skills to remain competitive throughout their careers.
Resources: History of Software Development
A look into the history of software development and the evolution of software development roles helps individuals looking to enter the field or advance their careers understand software development trends.
10 Emerging Software Development Trends You Should Know
What will software development look like in the next decade? No one knows for sure. Still, a look at emerging software development trends of today can provide a glimpse of the future. The following are 10 of the most noteworthy software development trends dominating the tech industry.
1. Cloud-Native Application Development
Cloud-native application development is a software development trend that involves building and running applications in distributed computing environments (aka “the cloud”). It provides organizations with a streamlined approach to building and updating apps while ensuring quality and minimizing risk.
A simple definition of a cloud-native app is a program designed for the cloud. For example, a streaming service like Netflix runs on the cloud, but users can interact with it through their smart TVs or other devices.
Cloud-native apps require front-end and back-end architecture. The front end is where the user interacts with the software that lives on the back end via a user interface. Back-end infrastructure consists of storage and hardware at a distant server farm (i.e., in the cloud).
Software developers working on cloud-native apps integrate the front end and back end to ensure they interact seamlessly. Software developers in this space should have programming skills in traditional languages such as C/C++ and C# and web-centric languages such as Java, JavaScript, .NET, Node.js, PHP, Python, and Ruby. Knowledge of cloud-native app development platforms, containers, Kubernetes, APIs, DevOps, and Agile methodology is vital to the role.
Statistics point to the growing adoption of cloud-native app development. Gartner reports about 65% of application software spending will go to cloud technologies in 2025, nearly a 10% rise from 2022.
2. Artificial Intelligence (AI)
A 2021 McKinsey report about the state of artificial intelligence in business says 56% of organizations have adopted AI. The report points to the growing impact of AI technologies in four key areas that can directly impact an organization’s bottom line — service operations, product and/or service development, marketing and sales, and risk.
A primary area driving rising interest in AI-driven applications is machine learning (ML). Machine learning is a subset of AI that can be defined as a machine with the ability to learn from experience without programming, similar to human intelligence.
Software developers working on AI and ML applications use vast amounts of data and build algorithms to train a model to analyze data and predict outcomes. AI and ML can shorten processes in the software development lifecycle, such as testing. Using AI, tests can run automatically, providing more time for software developers to write code.
To build models, software developers working with AI and ML should have programming skills in languages such as Python, R, Java, and C++. Software developers in this space also have knowledge of problem definition, data collection and preparation, and model learning, deployment, integration, and management.
3. Blockchain Technology
A blockchain is a digital ledger distributed across computer network nodes. Blockchains can be described as databases but are structured differently. While a database typically organizes data into tables and grows in size as it stores more information, a blockchain structures data into blocks that hold a limited amount of information. When a block’s storage capacity is reached, it closes. This block then links to the previously filled block, and the blocks are strung together to form the blockchain.
A key feature of blockchain is that its data structure creates a secure and decentralized timeline and record of data and transactions. The structure of the blockchain creates an irreversible record of a transaction, which means that the data in a block is guaranteed to be trustworthy without requiring third-party verification. This makes blockchain ideal for financial uses, such as cryptocurrency systems like Bitcoin and Ethereum.
As blockchain gains publicity and acceptance in financial markets, other businesses are exploring its uses. For example, the use of blockchain technologies to drive supply chain transparency can reduce risk and improve the overall efficiency of the global supply chain. Another example of blockchain use cases and industry applications includes healthcare; specifically, protecting and securing healthcare and genomics data and improving the tracking of disease and outbreaks.
Software developers working in this area focus on building applications from the front end to the back end that maintain a secure and decentralized record of transactions on the blockchain. For software developers interested in becoming blockchain developers, common programming language skills include Java, C++, Solidity, Python, JavaScript, Ruby, and PHP. An understanding of databases, networking, data structures, and cryptography is essential.
4. Automation
The primary benefit of automation is that it minimizes human involvement. Automation technology can help manufacturers uncover anomalies in production or support a financial organization’s fight against fraudulent transactions. This gives individuals more time to work on higher-value activities and improve labor productivity, a key driver of economic growth.
As the world becomes increasingly digital, companies are investing in automation to support their digital transformation and reaping financial rewards. For instance, companies see returns of $5.44 over the first three years for every dollar invested in marketing automation, according to Oracle.
As a software development trend, automation is used in diverse types of activities ― from application development to infrastructure deployment. Automation can also streamline the software development life cycle. Software developers working on building automation solutions typically have knowledge of scripting, source code management, Kubernetes, security, testing, and monitoring. Programming skills typically include Java, Python, Ruby, and Perl.
5. Cybersecurity
The latest digital trends point to worrying signs of increasing cyberattacks. As remote workforces ramp up, vulnerabilities are introduced to data systems ― such as employees connecting to data via unsecured devices and connections, or lackluster password policies. One report cites a 238% increase in cyberattacks among remote workers since the start of the COVID-19 pandemic.
While cybersecurity has long been crucial for businesses in an increasingly globalized, interconnected world, the growing adoption of cybersecurity practices is expected to help business and government organizations defend against cyberattacks.
Software development engineers working in this space build applications and systems that protect organizations from cybercrimes and cyber threats. General technical skills of software developers looking to work in cybersecurity include Java, SQL, and JavaScript. Software developers with knowledge of cryptography, information systems, security operations, and information security can improve their chances of landing a cybersecurity role.
6. Multi-Cloud
Businesses adopting digital strategies are embracing the cloud, which, according to Gartner, will drive digital experiences for more than 85% of organizations by 2025. But what happens to an organization’s data when the cloud infrastructure hosting their data systems goes down? When an organization’s data systems crash ― whether from a cyberattack, power outage, or other cause ― it can mean huge business losses.
Multi-cloud is growing in adoption because it helps organizations improve infrastructure capabilities and cost and minimize their reliance on a single cloud provider. Through multi-cloud technology, organizations can diversify the locations of their data systems, helping guarantee high availability to data. In the simplest terms, multi-cloud works as a backup: If one cloud system goes down, other clouds with the same data systems will remain available to users.
Using multiple clouds may be becoming commonplace, but with every cloud provider comes different requirements. These requirements allow data to work seamlessly across many different clouds. Establishing a multi-cloud infrastructure can be complex, requiring skilled developers to create software that integrates applications and systems across different cloud environments.
Software developers often use Python, Java, Go, and C++ to create algorithms for multi-cloud environments. Knowledge of virtualization, hosting, databases, and storage as well as the major cloud providers including Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure is necessary to effectively develop software on each environment.
7. The Internet of Things
The Internet of Things (IoT) describes the billions of physical devices connected to each other via the internet and sensors. IoT Analytics reports the number of IoT-connected devices will more than double from 12.2 billion in 2021 to 27 billion in 2025.
Advanced IoT analytics help organizations improve efficiency and reduce costs. Software developers in this space build software that enables devices to communicate with others via sensors and the internet. A key component of IoT is empowering business leaders with the ability to access huge volumes of data collected from devices to inform business decision-making.
Skills and technologies used by software developers for building IoT applications include high-level languages used for web and mobile applications such as Java, Swift, and Node.js. Software developers also typically have knowledge of API automation, testing, information security, and UI/UX design.
8. Low-Code and No-Code
Low-code and no-code platforms allow individuals without any programming skills to build innovative software. These tools provide users with a visual interface to build apps and create workflows. Each platform offers different benefits: low-code platforms eliminate most of the coding process, while no-code development platforms eliminate all coding processes.
No-code platforms are typically for business users who have virtually no programming language background to build apps with basic functionality. On the other hand, using low-code platforms requires some knowledge of technology, including .NET, Java, and API tools, to provide enhanced functionality and customization and integrate with other software, including legacy software.
Low- and no-code tools are a rapidly growing software development trend. A Zapier report says 82% of no-code platform users began their use during the pandemic, and 90% of companies attribute faster growth thanks to the adoption of low-code. Time savings, the ability to automate workflows, and increased flexibility were among the primary factors driving the growth of these tools.
9. DevOps and Agile
The success of an organization’s digital transformation hinges on a number of complex processes going right. But 70% of digital transformation efforts come short of goals, according to Boston Consulting Group. Additionally, CIO magazine reports that up to 70% of IT projects fail. Software projects can go off track because of a lack of collaboration between individuals and teams.
DevOps and Agile are two approaches to solving problems and improving collaboration in software development. While both have the primary aim of accelerating software development in collaborative settings without compromising quality, each focuses on distinct parts of the process.
DevOps focuses on improving collaboration between development and operations teams during product development. A subset of DevOps, called DevSecOps, focuses on security. While Agile also focuses on collaboration throughout a project, its primary aim is to facilitate customer feedback in the process and enable rapid releases. Combining the two approaches can enable organizations to optimize the success of products as well as projects.
Software developers with knowledge of Agile methodologies such as Scrum and Kanban can deliver applications far more quickly, improve software quality, and provide better user experiences. Knowledge of Linux fundamentals and scripting languages such as Python is essential to DevOps. DevOps engineers also have experience with Node.js and languages such as Java, JavaScript, Ruby, and PHP.
10. User Experience (UX) Design
A software developer can develop a product with all the bells and whistles, but if users find its user interface lackluster, they simply won’t use the product. Today, good customer experiences are vital to business success. According to PwC, 59% of consumers are willing to walk away after a number of bad experiences, while 17% will leave after only one unpleasant experience.
This is why user experience design (UX) is an increasingly important software development trend. UX design typically takes place separate from the software development process. For example, while UX designers research, design, and test user interfaces, software developers put together a product’s code and architecture. Moreover, while UX designers focus on ensuring the end user is satisfied with how a product operates, a software developer is concerned about the product’s technological performance. However, tying these two processes together makes the system work optimally.
UX encompasses all types of technology products. For example, mobile-responsive design, a subset of UX, focuses on mobile experiences. Software developers who want to build software with end-users in mind should understand UX concepts such as wireframing, prototyping, and using design tools such as Adobe.
Emerge as a Software Development Leader
As the digital transformation accelerates, billions more devices access the IoT, data continues to expand, and new software development trends emerge — there will be a growing need for software development professionals.
Successful software developers are those who can remain at the forefront of helping organizations adapt to digital technology’s rapid changes, which translates into continued career and leadership opportunities. Developers, however, must keep their technical skills fresh and their knowledge up to date to remain competitive.
| 2023-02-15T00:00:00 |
2023/02/15
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https://online.maryville.edu/blog/software-development-trends/
|
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"date": "2023/02/15",
"position": 62,
"query": "AI labor market trends"
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How Will AI Impact Organizations and the Role of Leaders?
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AI, Organizations, & the Role of Leaders
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https://www.giantleapconsulting.com
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[
"Bill Treasurer"
] |
Innovation: Artificial intelligence can help organizations innovate by identifying new opportunities and predicting market trends. However, leaders will ...
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If you have a television or radio, chances are you have heard talk of AI (Artificial Intelligence). Artificial intelligence has the potential to improve workplace efficiency dramatically. It has the potential to take over dangerous or repetitive tasks and allow workers to complete jobs that require creativity or compassion. The impact artificial intelligence is already having on organizations is significant. Here are some of the ways in which AI is expected to continue to impact organizations:
Automation: This technology can automate many tasks that were previously done by humans, such as data entry, analysis, and even decision-making. This can lead to increased efficiency, lower costs, and faster processing times. However, it can also lead to job displacement, so leaders will need to find ways to retrain and redeploy their workforce. Data Analytics: AI can analyze vast amounts of data and extract insights that humans may miss. This can lead to better decision-making and more accurate predictions. However, leaders will need to ensure that the data being used is unbiased and ethical. Customer Service: AI-powered chatbots can provide 24/7 customer service, which can improve customer satisfaction and reduce costs. However, leaders will need to ensure that the chatbots are properly programmed and can handle complex inquiries. Personalization: AI can help personalize products and services for individual customers based on their preferences and behavior. This can improve customer loyalty and increase revenue. However, leaders will need to ensure that the personalization is done in an ethical and transparent manner. Innovation: Artificial intelligence can help organizations innovate by identifying new opportunities and predicting market trends. However, leaders will need to ensure that their organization has a culture of innovation and that their employees are trained to work with AI tools.
How will the Use of AI Impact Leaders?
AI can help managers and leaders make better decisions by providing them with more accurate and relevant data, insights, and recommendations.
By automating routine tasks, such as data entry and analysis, managers and leaders are free to focus on higher-level tasks that require their expertise. Through this data analysis AI can also assist managers and leaders in providing a better customer experience by analyzing customer data and providing personalized recommendations.
Looking to improve productivity in your organization? AI can assist managers and leaders in identifying areas where productivity can be improved, optimizing workflows, and streamlining processes. And while productivity is a goal that directly affects the bottom line, employee engagement and retention do as well. AI can help managers and leaders identify the needs and preferences of their employees. This allows them to create a more engaging and satisfying work environment.
In summary, AI is likely to impact managers and leaders in a range of ways, providing them with more accurate information, greater efficiencies, and new opportunities for innovation and growth.
A Shift in Leadership Style and Skills
Artificial intelligence is here, and if it isn’t at your organization yet, chances are it will be soon. Here are a few steps leaders can take to be prepared:
Understand AI: Leaders will need to have a basic understanding of AI and its potential uses in their organizations. Foster a culture of innovation: Encourage experimentation and risk-taking to discover new uses for AI. Re-skill and retrain employees: Invest in training and education to help employees adapt to working with AI tools. Ensure the ethical use of AI: Ensure that AI is used in an ethical and transparent manner. And that its impact on society is carefully considered. Manage change: Manage the transition to AI in a way that minimizes disruption to their organization and workforce.
In summary, AI will have a significant impact on organizations and the role of leaders. Leaders will need to embrace AI, invest in training and education, and ensure that its impact is carefully managed and monitored.
This post was actually written with a major assist from AI. AI does a pretty good job, right? Except for old-school writing that uses phrases like, “In summary…”
Is your organization using AI? If so, how is it impacting the leaders in your organization?
| 2023-03-15T00:00:00 |
2023/03/15
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https://www.giantleapconsulting.com/courageous-leadership/how-will-ai-impact-organizations-and-the-role-of-leaders/
|
[
{
"date": "2023/02/15",
"position": 67,
"query": "AI labor market trends"
}
] |
Generative AI Market Size, Growth, Trends | Report 2023- ...
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Generative AI Market Size, Growth, Trends
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https://www.visionresearchreports.com
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[] |
The global generative AI market was surpassed at USD 10.77 billion in 2022 and is expected to hit around USD 210.52 billion by 2032, growing at a CAGR of 34.62 ...
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The global generative AI market was surpassed at USD 10.77 billion in 2022 and is expected to hit around USD 210.52 billion by 2032, growing at a CAGR of 34.62% from 2023 to 2032.
Key Pointers
The software segment accounted for the largest revenue share of 65.04% in 2022 and is expected to continue to dominate the market over the forecast period.
The service segment is anticipated to witness the fastest growth rate of 35.54% during the forecast period.
The diffusion networks segment is expected to witness the fastest growth rate of 37.26% during the forecast period.
The media & entertainment segment accounted for the largest revenue of USD 1,527.65 million in 2022 and is projected to grow at a CAGR of 33.58% over the forecast period.
The BFSI segment is expected to witness the fastest growth rate of 36.4% during the forecast period.
North America dominated the market with a share of 40.24% in 2022 and is projected to grow at a CAGR of 34.78% over the forecast period.
Asia Pacific is anticipated to grow at the fastest CAGR of 35.66% during the forecast period.
Report Scope of the Generative AI Market
Report Coverage Details Market Size in 2022 USD 10.77 billion Revenue Forecast by 2032 USD 210.52 billion Growth rate from 2023 to 2032 CAGR of 34.62% Base Year 2022 Forecast Period 2023 to 2032 Regions Covered North America, Europe, Asia Pacific, Latin America, Middle East & Africa Companies Covered Synthesia; MOSTLY AI Inc.; Genie AI Ltd; Amazon Web Services, Inc.; IBM; Google LLC; Microsoft; Adobe, Rephrase.ai; D-ID
Factors such as the rising applications of technologies such as super-resolution, text-to-image conversion, & text-to-video conversion and growing demand to modernize workflow across industries are driving the demand for generative AI applications among industries. The COVID-19 pandemic had a positive impact on the generative AI market as businesses shifted to the online work model, increasing digitalization across industries. As per IBM’s Global AI Adoption Index 2022 report, over 53% of IT professionals commented that they have accelerated the roll-out of Artificial Intelligence (AI) in the last 24 months as a part of their response to the pandemic Moreover, during the pandemic advance diagnosing tools were developed using Artificial Intelligence (AI) to detect the COVID-19 virus and various other imaging systems.
Generative AI makes use of unsupervised learning algorithms for spam detection, image compression, and preprocessing data stage such as removing noise from visual data to improve picture quality. Moreover, supervised learning algorithms is used for medical imaging and image classification. Furthermore, it has applications in various industries such as BFSI, healthcare, automotive & transportation, IT & telecommunications, media & entertainment, and others.
Generative AI is at a developing stage which will require a skilled workforce and high investment in implementation for development. According to IBM’s global AI adoption index 2022 report, 34% of respondents believed that a lack of Artificial Intelligence (AI) skills, expertise, or knowledge was restricting the adoption of Artificial Intelligence (AI) for industries. Hence, the unavailability of a skilled workforce and the high implementation costs are expected to slow down the pace of development of the market.
Major market players such as U.S.-based Microsoft and Apple Inc. are progressively investing in Research & Development (R&D) activities. Moreover, these companies are researching technologies such as Artificial Intelligence (AI) and Machine Learning (ML). For instance, in May 2020, World Wide Technology, a technology service provider started an initiative that is focused on artificial intelligence and machine learning and has some of the highest experiments and work programmed on generative AI. As an increasing number of companies continue to develop and experiment in the field and use generative AI in their products, the market is expected to witness promising growth opportunities.
Regional Insights
North America dominated the market with a share of 40.25% in 2022 and is projected to grow at a CAGR of 34.77% over the forecast period due to factors such as rising pseudo-imagination & medical care and increasing banking frauds. Also, the presence of prominent market players such as U.S.-based Meta, Microsoft, and Google LLC, developed technology organizations, and the presence of experts are likely to drive the growth generative AI market in the region.
Asia Pacific is anticipated to grow at the fastest CAGR of 35.61% during the forecast period. Increasing government initiatives in Artificial Intelligence (AI) in Asia Pacific and the increasing adoption of AI applications are driving the growth of generative AI in the region.
Generative AI Market Segmentations:
By Component By Technology By End-use Software Services Generative Adversarial Networks (GANs) Transformers Variational Auto-encoders Diffusion Networks Media & Entertainment BFSI IT & Telecommunication Healthcare Automotive & Transportation Others
| 2023-02-15T00:00:00 |
https://www.visionresearchreports.com/generative-ai-market/40039
|
[
{
"date": "2023/02/15",
"position": 75,
"query": "AI labor market trends"
}
] |
|
Smart Robots Market Growth, Trends and Forecast to 2032
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Smart Robots Market Growth, Trends and Forecast to 2032
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https://straitsresearch.com
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[
"Straits Research"
] |
The potential of artificial intelligence (AI) and automation superiority have rendered a ripe market for the global smart robots market. Smart robots have ...
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Smart Robots Market Size The global smart robots market size was valued at USD 13631.82 million in 2023 and is projected to reach from USD 16291.39 million in 2024 to USD 64608.40 million by 2032, growing at a CAGR of 19.51% during the forecast period (2024–2032). Smart robots are machines integrated with artificial intelligence to augment and assist humans in various activities. Advancements in AI have brought evolution and better capacity in robots. Smart robots collaborate with humans and learn from their behavior while working alongside them. The number and types of operations that can be automated or augmented with the help of smart robots and other smart machines are rapidly increasing. Smart robots can do manual labor as well as perform cognitive tasks. Smart robots have found their applications in various industries across the supply chain, from pick and pack to sortation and transport. The cost savings associated with smart robots attracts many organizations. Highlights Software dominates the component segment
Industrial dominates the application segment
North America is the highest shareholder in the global market
Smart Robots Market Growth Factors Development in robotic technology The robotics industry is rapidly encouraging the smart robot market due to the fast-paced technological advancements converging in engineering, automation, artificial intelligence, and machine learning. The consequences of the emergence of smart robots have resulted in the transformation of humans' task-performing capacity. As stated by the International Federation of Robotics (IFR), the number of robots is projected to be augmented three-fold over the past two decades. Moreover, the trends introduced in robotics project the international stock of robots to multiply faster in the next twenty years. Furthermore, several governments and private organizations are spending heavily on R&D activities to boost advancements in the field of robotics and AI. For instance, the National Research Foundation of Singapore is anticipated to invest USD 150 million into a new national program in AI. While in the UK, EUR 270 million were invested from 2017 to 2018 to boost science, research, and innovation. This was done with the help of the Government's new industrial strategy, which is expected to fund EUR 4.7 billion by 2021. Similarly, the Intelligent Robot Development and Supply Promotion Act of Korea offers opportunities to develop the robot industry in Korea as a core industry in the fourth industrial revolution. Therefore, such developments in the robotics industry are anticipated to augment the market growth for smart robots. Increasing labor shortage across various regions Several countries across the world are facing a shortage of labor. The falling birth rates and an aging population in many developed economies are leading to negative labor growth. While in developing regions, rapid growth has created a higher demand for labor. However, many emerging economies are expected to reach the final phase of their demographic peak. According to the UN Population Division database and ILO LABORSTA database, Europe is anticipated to be severely affected by a labor shortage. Robotics is a popular type of automation due to its versatility, affordability, and efficiency. Robots have found applications across the supply chain, from pick and pack to sortation and transport. In addition, robots do not require insurance or benefits and can reduce costs related to employee overtime, seasonal and temporary staffing, and skills training. Additionally, ROI for robotic automation is typically achieved in under two years. The labor shortage across various regions and smart robots' monetary advantages augment their adoption across different industries. Restraining Factors Concerns over data privacy and security The rapid growth of robotic systems has implications for various fields. It can be a boon to disparate fields such as healthcare, education, logistics transportation, and several others. Conversely, the robotic system will also bring forth far-reaching employment, economy, and security variations. The primary issue concerning privacy involves data protection and data transfer. Smart robots continuously interact with the environments in which they operate. This includes constantly transmitting large amounts of data in and out of the system. In healthcare, concerning smart robots, the data is not just environmental and contextual data but also those that relate to the particular humans to which these robots are attached. This data could contain sensitive health and financial information. Hence, smart robots' security and discreetness are critical design imperatives. It compromises the qualities of the individual while upsetting the robot's and that person's safety and security. Market Opportunities Technological progress in automation The potential of artificial intelligence (AI) and automation superiority have rendered a ripe market for the global smart robots market. Smart robots have come a long way in the past decade, from performing complex logistics to allowing effortless unmanning of defense vehicles. Furthermore, the technical ability to automate various tasks with changing complexities has encouraged an exponential demand for smart robots in numerous industries, such as agriculture, defense, healthcare, infrastructure, aerospace, logistics, entertainment, research, and development. Therefore, the bourgeoning applications of smart robots in various fields could soon cause these products to become essential.
If you have a specific query, feel free to ask our experts. Speak To Analyst
Regional Insights North America: dominant region with a CAGR of 16.99% North America is the most substantial shareholder in the global smart robots market and is anticipated to grow at a CAGR of 16.99% during the forecast period. North America is expected to proliferate in the smart robot market over the forecast period owing to the early adoption of technically advanced products in the end-user industries. Owing to the rising demand from the medical field for rehabilitation in the region, favorable funding for assistive technologies for research, various hospitals adopting surgical robots, and the availability of technologically smart robots are driving factors for the service robotics market in the region. In addition, around 35% of the producers of smart robots are located in North America, which is the ultimate potential for regional market growth in North America. The smart robot market is driven by rapid commercialization and the progress of its manufacturing industry. Additionally, the early acceptance of technology in this region has encouraged the smart robot market to flourish briskly. The U.S. doubles its robotic investments to establish itself as a world leader in robotics technologies. Further, the National Robotics Initiative (NRI) of the U.S. supports essential research in the United States that will accelerate the growth and ubiquitous use of collaborative robots (co-robots). Europe: fastest growing region with the highest CAGR Europe is estimated to grow at a CAGR of 17.84% over the forecast period. Smart robots are becoming increasingly helpful and can significantly help people in their everyday lives, in private and professional spheres. The European Commission is determined to collaborate at all levels, from the regional to the European level because Europe is a global leader in this field and stands to gain from it. The government's increased focus on energy efficiency and emission control standards has resulted in the proliferation of gas sensors in the European automotive sector. In addition, the growth in end-user industries and increasing avenues for gas sensors are also expected to be significant growth drivers in this region. Some major players operating in the smart robot market are ABB, KUKA, BLUE FROG ROBOTICS, Kongsberg Maritime, and Universal Robots. These companies are active on several fronts of rising and disruptive ICT technologies and are supplying the market with significant products and services. Further, robotics research and education are performed at technical universities and research labs all over Sweden, fueling the market's growth. The Asia-Pacific region has long been seen as an innovator in industrial and consumer robots. High regulatory and financial support by various governments for Asian companies to add service robotics technologies in personal, industrial, and professional applications. Some major players operating in the smart robot market are SoftBank Robotics Group, FANUC, Hanson Robotics, YASKAWA, and Samsung Electronics. The Asia-Pacific is anticipated to be the highest growing regional market owing to the rising economies in the region, including India, Japan, and China. Additionally, the present boom in the automotive sector is predicted to drive the smart robot markets in Asia-Pacific. Over the next decade, China plans to play a leading role, utilizing the strengths of its existing businesses and through a new government plan announced in July 2017 by China's State Council. The program aims for China to be a world leader by 2030 in areas such as self-driving cars and smart robots that can learn from their environment and build on their capabilities based on that knowledge. Furthermore, high costs, end-user difficulties, and poor audio/video and internet connectivity issues in certain countries are the major factors hampering the market's growth in the Asia-Pacific region. The RoW comprises the Middle East and Africa and Latin America region. Latin America, the Middle East, and Africa are anticipated to show moderate growth due to the relatively low technological progress in the region. However, the Latin American Robotics Council has been actively organizing student competitions, symposia, and summer schools, to promote robotics and bring together students and researchers across the region, fueling the market's growth. The focus on robotic urban restructuring in Dubai primarily focuses on attracting overseas participants to engage with the Dubai Future Accelerators in partnership with key government agencies, thereby driving the market in the Middle East and Africa region.
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Smart Robots Market Segmentation Analysis By component The software segment is responsible for the largest market share and is anticipated to grow at a CAGR of 18.31% over the forecast period. The software segment is expected to witness substantial growth during the forecast period owing to the adoption of the Internet of Things, artificial intelligence, and other software technologies. The growing demand for automated technology drives the smart robot's software segment. Moreover, the software provides well-integrated tools that aid developers in incorporating AI into the system to make it possible for the machine to tackle problems such as language interactions. As a result, the demand for the software segment is estimated to continue increasing over the forecast period. Nevertheless, a major restraining factor for the development of the robot software segment is a malware attack on data engulfed by robots. The hardware segment comprises sensors, actuators, power sources, control systems, and other components. In the smart robots market, hardware components are anticipated to gain high traction due to their implementation in numerous robotic systems such as technological, vocational, literature, etc. These vital functioning parts will help robots perform movement and analysis. The hardware components have gained popularity worldwide owing to their efficient human motion detection features with the help of cameras and sensors. In addition, the hardware segment dominated the smart robot market due to the growing demand for components such as actuators, sensors, power modules, and controllers. Smart robotics use high-accuracy sensors for detecting the surrounding environment. Furthermore, LiDAR and RADAR solutions support robotic systems in facial, gesture, and object recognition functions. The microphone is used for the voice and command recognition feature. These features delivered by hardware components lead to rapid development in the global smart robot market demand. By application The industrial segment owns the highest market share and is estimated to grow at a CAGR of 17.72% during the forecast period. The demand for industrial robots is mainly observed in industries such as automobiles and heavy engineering. Industrial robots are being developed to overcome the limits of traditional labor tasks, which is anticipated to be the primary factor driving market growth. Additionally, an increase in the need for automation in microelectronics and the technology set by the government and industry associations are escalating the demand for the industrial robotics market. However, the high initial cost of purchasing this equipment, followed by installation cost and high maintenance costs, are the factors that are likely to restrain the growth of the industrial robotics market. Moreover, using industrial robots to help achieve automation in the manufacturing process provides significant growth opportunities for the key players in the industrial robotics market. Service robots were designed to serve humans and conduct valuable tasks. These service robots are semi-automated or fully- automated. They can perform all kinds of dangerous or monotonous tasks and provide intensifying efficiency and high-security level. It reduces human work pressure and provides results that are efficient and accurate. The revenue generation of industries has increased after the service robots have become used. They provide smooth functioning of repetitive tasks, manage day-to-day tasks very effectively, and carry out critical operations effortlessly. In addition, due to the upsurge in the usage of smartphones, growing application areas and emerging economies provide a growth bar to the service robot market. The industries that seek the support of service robots include healthcare, forestry, logistics, construction, and professional cleaning. The emerging needs of such industries boom the growth of the service robot market.
Market Size By Component Hardware Software Services
List of key players in Smart Robots Market DeLaval ABB Ltd Blue Frog Robotics Samsung Electronics Co Ltd. Intuitive Surgical iRobot Corporation SoftBank Robotics Co Ltd. Hanson Robotics Sanbot Innovation Technology Intelitek Inc.
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Recent Developments February 2023 - Xiaomi plans to launch the CyberOne and CyberDog robots apart from the smartphone at MWC 2023 (Mobile World Congress 2023). CyberOne is a bipedal robot, whereas CyberDog robot is a quadrupedal robot.
- Xiaomi plans to launch the CyberOne and CyberDog robots apart from the smartphone at MWC 2023 (Mobile World Congress 2023). CyberOne is a bipedal robot, whereas CyberDog robot is a quadrupedal robot. February 2023- AAEON, a leader in IoT solutions, is collaborating with PlayRobot to produce an educational robot development kit. The academic community is eager to include the kit in their curriculum after the team demonstrated the gear at National Central University.
Smart Robots Market Segmentations By Component (2020-2032) Hardware
Software
Services By Mobility (2020-2032) Stationary
Mobile By Application (2020-2032) Welding and Painting
Assembling and Disassembling
Material Handling and Sorting
Inspection and Security
Others By End User Industry (2020-2032) Automotive
Manufacturing
Electrical and Electronics
Food and Beverages
Chemical
Residential
Others By Region (2020-2032) North America
Europe
APAC
Middle East and Africa
LATAM
| 2023-02-15T00:00:00 |
https://straitsresearch.com/report/smart-robots-market
|
[
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"date": "2023/02/15",
"position": 81,
"query": "AI labor market trends"
}
] |
|
AI Tools for Voice of the Customer in Banking - Two Use ...
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AI Tools for Voice of the Customer in Banking
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https://emerj.com
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[] |
Its advanced AI goes beyond basic analysis by providing suggestions, multilingual analysis, and automatic grouping. ... Barclays is a leading British universal ...
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Voice of the customer (VoC) is a term used in modern business practices to describe customers’ feedback about their experience with the products and services both inside and outside any official business channels to make their opinions known. Businesses capture the voice of the customer data to provide the best possible customer experiences. The process needs to be proactive and innovative to capture the customers’ changing requirements with time.
Online reviews and feedback have proven to be a reliable source and channel for understanding customer needs for the product or service. It also helps to analyze product competitiveness in the marketplace.
Traditional and classical methodologies in capturing the voice of customers have focused mainly on market surveys and questionnaires. But listening to customers is only beneficial if the feedback data is analyzed and efforts are made to make the course corrections.
This article will explore two of the most prevalent real-world applications of AI to capture the voice of the customer in banking by exploring two use cases:
Personalizing customer conversations: Using NLP in conjunction with conversational AI to make conversations with customers over chat more engaging and relevant to their interests.
Using NLP in conjunction with conversational AI to make conversations with customers over chat more engaging and relevant to their interests. Analyzing customer sentiments: Using deeper data insights to draw the exact sentiment and attitudes from customer feedback.
For each use case, we further discuss the business challenge said technology solves, the data being leveraged in the process, and the distinct benefit that AI applies to the processes therein.
Use Case #1: Conversation AI Using NLP
Conversational AI helps humans to communicate with an automatic system using natural language. The interaction could be text or speech-based. Conversation systems, depending on their capabilities, domain, and level of intelligence, are known as chatbots, conversation agents, virtual assistants, or bots.
Conversational agents such as chatbots have been around for a long time. However, these models’ practical utilization and effectiveness have only increased due to machine learning and NLP advances.
Many advanced chatbots leverage NLP capabilities. Computers are configured to read, process, and analyze large amounts of natural language information. AI bots learn from people’s conversations and interactions to expand their database. Unlike AI-based chatbots, rule-based chatbots no longer respond when they encounter unfamiliar commands and unrecognized phrases.
Another challenge with rule-based chatbots is that they are typically guided by a decision tree, resulting in interactions with the chatbot that feel robotic and not conversational. The customer or the user can be given a set of predefined options that lead to the desired answers. However, doing so often limits their feedback and adversely affects the customer experience.
Rule-based chatbots only answer questions within the predefined limitations, and often they cannot learn independently, requiring businesses to train and improve them manually.
AI-based chatbots offer various services to the company, including automatic customer service. Applying speech recognition and NLP, AI-based chatbots can address even the complex request from the user.
Vendor Example: Active.Ai
Active.Ai is a Singapore-based fintech startup with subsidiaries in the US and India. The company uses AI to deliver conversational banking services. The website mentions that it uses advanced NLP and machine intelligence to enable customers to have natural conversations over messaging, voice, or IoT devices.
Triniti API offers cutting-edge capabilities to financial institutions to provide their customers with a natural channel of engagement via voice and text. The company claims that Triniti’s state-of-the-art AI algorithms enable it to identify and understand intent, interpret discourse and reactions, extract entities and recognize values, and analyze sentiments and emotions in conversations.
The company’s website also claims they have tuned their AI engine specifically for banking and finance. Their technology is built with a preprocessor, NLP, NLU, NLG, and machine comprehension in mind.
Screenshots from customers’ conversations with the chatbot (Source: Active.Ai)
Axis Bank, an Indian banking and financial services company, wanted to deliver easy interactive conversations to its customers through its mobile application. The bank wanted to provide an experience that did not feel robotic and was more than just a rule-based chatbot. In partnership with Active.Ai, the bank launched Axis Aha!, a conversation AI platform.
The conversation platform can process transactions, service requests, and inquiries and help customers to apply for credit cards and loans.
Screenshot from Axis Bank case report (Source: Active.Ai)
The Active.Ai case study report claims that the NLP-based chatbot helped the bank to process 20 million plus utterances with 97 percent data accuracy. It further mentions that the conversational platform has a 40x growth rate by a factor of 40.
In one of the interviews with Express Computers, Praveen Bhatt, Head – of Digital Banking & Customer Experience at Axis Bank, revealed that the initial idea was to convert chatbots into personal relationship managers. The bank was aggressively focusing on personalization to stay relevant to the customer rather than targeting them through just emails that are generic in nature.
The same interview mentions that previously the bank had 300 email agents, which had gone down to 70 agents after implementing Active.Ai’s solution. The solution was better able to evaluate the customer experience, which resulted in a drop in email volumes since enough necessary customer inquiries were moved to the chatbots.
Use Case #2: Analyzing Sentiments from Customer’s Feedback
Sentiment analysis involves techniques, tools, and methods to detect and extract subjective information from languages, such as opinions and attitudes. It helps find the customer’s mood about what he said or wrote.
Also known as opinion mining, sentiment analysis is the study of sentiments that determines the judgment of people’s opinions, evaluations, and emotions concerning entities such as products, services, and emotions.
To help understand language difficulties such as sarcasm, ambiguity, context, and irony, the manual processing of sentiments needs a human component in the analysis. While sentiment analysis can be done manually by humans, the challenge becomes the accurate meaning of the opinion and detecting whether unsuitable sentiment is positive or negative.
Though the manual process helps improve the analysis’s accuracy, it is time-consuming.
AI-based approaches to sentiment analytics denote natural language processing, text analysis, linguistics and biometrics to methodically identify extract, quantity, and the study of expressive states and individual data.
These sentiment analysis systems use natural language Processing (NLP) and machine learning to quantify sentiments by looking for topics, themes, and categories within sentences as a positive number, negative, or zero.
Vendor Example: Keatext
Keatext is a Montreal-based software development company. It provides text and sentiment analysis platforms for a 360-degree view of customer feedback.
Keatext Review Analysis offers sentiment analysis for reviews and feedback from the customer. Its advanced AI goes beyond basic analysis by providing suggestions, multilingual analysis, and automatic grouping. The company claims that the platform unifies insights from customer conversations in multiple languages. It can process data from 50 plus languages to English without missing out on the sentiments.
The company’s website also claims that this platform helps businesses to:
Monitor overall brand perception
Know the root causes of negative reviews
Using data from sources like NPS or CSAT surveys (sources where a rating scale is paired with an open-ended response), Keatext identifies up to 12 topics and ranks the impact of those topics on the associates’ scores.
1. For an analyst or marketing manager, the recommendation module of the platform offers a listicle view of the strengths and weaknesses in the feedback data.
Screenshot from Keatext Demo Video. (Source: Keatext)
2. The SWOT chart (Strengths, Weaknesses, Opportunities, and Threats) provides details into what impacts customer satisfaction so the business can prioritize what needs attention.
Screenshot from Keatext Demo Video. (Source: Keatext)
3. To get a detailed view of why a particular business factor has a negative value, the analyst can get the view of opinions in the list, which displays certain words from customers’ feedback like high, shocking, or outrageous.
Screenshot from Keatext Demo Video. (Source: Keatext)
4.The dashboard provides a heat map of the comments and feedback. For instance, in the picture below, there is a correlation between outrageous and fee with 200 comments.
Screenshot from Keatext Demo Video. (Source: Keatext)
5. The comment overview section divides all the feedback into four main categories:
Praises
Problems
Suggestions
Questions
This feature can help the team to go deep into the pain points and prioritize the efforts.
Screenshot from Keatext Demo Video. (Source: Keatext)
6. The dashboard also provides a sentiment score based on the analysis. This score can be viewed separately for praises, problems, suggestions, and questions or all together for the brand name.
Screenshot from Keatext Demo Video. (Source: Keatext)
While Keatext does not provide public information citing measured, tangible business results for the banking sector, their case study reporting claims that time to insights was reduced by 98% for one of their clients.
It further mentions that it previously used to take the client 8 hours to parse the details of the data in 6,000 reviews, but now it takes just 10 minutes using Keatext.
| 2023-02-15T00:00:00 |
https://emerj.com/ai-tools-for-voc-in-banking-two-use-cases/
|
[
{
"date": "2023/02/15",
"position": 86,
"query": "universal basic income AI"
}
] |
|
We Will Never Fully Understand How AI Works
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We Will Never Fully Understand How AI Works — But That Shouldn’t Stop You From Using It
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https://www.institutionalinvestor.com
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[] |
In their commitment to uncovering ways that AI can contribute to fundamental scientific discoveries that improve human life, computational biologists and other ...
|
The search for alpha can take us to some unusual places — perhaps none more so than the 13th century works of Thomas Aquinas. His philosophical maxim, “Finitum non capax infiniti” — “The finite cannot comprehend the infinite” — makes a compelling argument for an entirely undifferentiated source of alpha: artificial intelligence.
To apply this postulate to AI: While there are some types of AI that humans can comprehend, there are others that, because of their complexity and high dimensionality, are beyond the ken of human intelligence.
There are clear signs that we have reached a tipping point where certain types of AI have surpassed the human mind: The finite (humans) cannot comprehend the infinite (advanced AI).
Yet because of a deep-seated industry bias that investment results must be explainable, investors have been slow to accept the superhuman capabilities of advanced AI and, as a result, are failing to consider unique sources of alpha that could provide better investment outcomes.
An archetype of such an “infinite” AI is OpenAI’s ChatGPT, a chatbot that, as New York Times technology reporter Kevin Roose notes, “can write jokes (some of which are actually funny), working computer code, and college-level essays. It can also guess at medical diagnoses, create text-based Harry Potter games, and explain scientific concepts at multiple levels of difficulty.”
ChatGPT is a large language model (LLM), and a key to its success is its generative AI: It uses deep learning and reinforcement learning to analyze and understand the meaning of language and then generate a relevant response.
While ChatGPT might seem like a parlor game, its underlying AI could prove transformative. For example, in a peer-reviewed paper recently published in Nature Biotechnology, researchers used the same type of LLM that underpins ChatGPT to “generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics.”
Not only could the amazing success of this LLM, called ProGen, radically transform medicine and healthcare, but, according to the authors, such deep-learning-based language models could be used “to solve problems in biology, medicine, and the environment.”
Yet such solutions come with a caveat: While researchers are able to explain their models and inputs and evaluate the quality of the results, a deep-learning model’s complex architecture and recursive nature make it impossible to explain how multiple neurons work together to produce a specific prediction or decision. ProGen, for example, “is a 1.2-billion-parameter neural network trained using a publicly available dataset of 280 million protein sequences,” making it impossible to map specific inputs to specific outputs, the authors write.
Such unexplainability is an endogenous feature of deep learning. In the vernacular, it’s a black box. As Yoshua Bengio, a pioneer of deep-learning research, notes, “As soon as you have a complicated enough machine, it becomes almost impossible to completely explain what it does.”
In their commitment to uncovering ways that AI can contribute to fundamental scientific discoveries that improve human life, computational biologists and other researchers accept the results of the ProGen and other black boxes without the need for explanations of the specific decisions made by the AI (and thereby accede to our philosophical maxim).
Yet investors balk at such acceptance. Instead, they cleave dogmatically to a belief stated by the Investment Association: “Financial institutions must be able to provide clear explanations of how decisions involving AI are made, at every stage of the process. These explanations must be set out in a transparent and accessible way, and made available to employees, customers, regulators, and other relevant stakeholders.”
(Regulators are considering imposing similar explainability requirements on applied AI. For example, the European Union’s General Data Protection Regulation proposes that individuals have a right to an explanation of an algorithmic decision, although precisely what form such explanations might take or how they might be evaluated is unclear.)
This transparency requirement holds investment processes using or based on deep neural networks to a standard that even traditional investment managers and humans generally cannot meet. According to Roman Yampolskiy: “Even to ourselves, we rationalize our decision after the fact and don’t become aware of our decisions or how we made them until after they been made unconsciously. People are notoriously bad at explaining certain decisions, such as how they recognize faces or what makes them attracted to a particular person. These reported limitations in biological agents support [the] idea that unexplainability is a universal impossibility result impacting all sufficiently complex intelligences.”
More importantly, this demand for “clear explanations of how decisions involving AI are made, at every stage of the process” displays a profound ignorance of advanced AI because such individual explanations simply are not possible.
This demand is bolstered by the false claim, again expressed by the Investment Association, that “several techniques are now available to help with interpretability.”
This claim rests on a false hope. A review of the academic literature indicates that while it is possible to provide broad descriptions of how an advanced AI system works, contemporary techniques used to explain individual decisions are “unreliable or, in some instances, only offer superficial levels of explanation” and are “rarely informative with respect to individual decisions.” (It’s worth noting that academic literature sometimes differentiates between interpretability and explainability, but here we are using the terms interchangeably.)
The literature typically divides human-comprehensible explanations of advanced machine-learning decisions into two categories: inherent explainability and post-hoc explainability.
In their paper, “The False Hope of Current Approaches to Explainable Artificial Intelligence in Health Care,” Dr. Marzyeh Ghassemi and her co-authors explain that while many basic machine-learning models are inherently explainable because the relationship between their relatively simple inputs and model output can be quantified, advanced AI models like those using deep neural networks are “too complex and high-dimensional to be easily understood; they cannot be explained by a simple relationship between inputs and outputs.”
As an alternative, some researchers attempt to use post-hoc explanations — but these are, by their nature, problematic. First, current techniques used to produce post-hoc explanations do not reliably capture the relationship between inputs and outputs and may be misleading.
More generally, as Ghassemi et al. point out, post-hoc explanations “are only approximations to the model’s decision procedure and therefore do not fully capture how the underlying model will behave. As such, using post-hoc explanations to assess the quality of model decisions adds an additional source of error — not only can the model be right or wrong, but so can the explanation.”
Professor Cynthia Rudin takes the argument a step further, writing that post-hoc explanations “must be wrong,” that by definition they are not completely faithful to the original model and must be less accurate with respect to the primary task.
In the end, Rudin concludes, “You could have many explanations for what a complex model is doing. Do you just pick the one you ‘want’ to be correct?”
The demand for explanations of individual decisions combined with the inadequacy of existing explainability techniques leads to this flawed solution, with humans left to their own devices to determine which post-hoc explanation is correct. And as Ghassemi et al. conclude, “Unfortunately, the human tendency is to ascribe a positive interpretation: We assume that the feature we would find important is the one that was used.”
This human-centric disposition represents what tech writer Ben Dickson calls “the fallacy of the anthropocentric view of minds,” which, in the case of advanced AI, means we assume that AI makes decisions in the same way as human intelligence. This fallacy robs us of the ability to see that advanced AI is not merely a more powerful type of mind but is, as Wired’s Tom Simonite so eloquently writes, an “alien intelligence, perceiving and processing the world in ways fundamentally different from the way we do.”
By anthropomorphizing AI, we not only trivialize its power but place conditions on it that cannot be met — like requiring clear explanations of particular results — thereby limiting its utility and application.
Yet, as we’ve seen, there are transformative use cases like ChatGPT and ProGen where the demand for explainability is at odds with broader scientific or commercial objectives. In these cases, a black-box model is not only a far better choice than an explainable model; it is the only choice.
Investing presents another well-documented case where the capabilities of traditional investment processes are not good enough to consistently solve the problem — specifically, how to deliver alpha — and would benefit from the use of deep neural network-based models.
However, the deeply entrenched belief that individual investment decisions need to be explainable circumscribes the manager’s choice to human-based methods and basic forms of AI that augment human processes. It indicates that we are choosing explanations over accuracy and predictive power, disqualifying AI that could produce better investment outcomes and effectively dooming clients to a cycle of underperformance.
It’s true that cogent explanations give us the confidence that an investment process works, establishing the trust that is key to any investor-manager relationship. Yet there is a way to trust advanced AI that does not require explanations of individual decisions.
First, managers can clearly explain why a chosen model is appropriate for the use case. Second, they can broadly describe the model’s design and how it works. Third, they can make clear the choice of inputs. Fourth, they can delineate how the particular model is built, validated, and retrained, and empirically demonstrate that it generalizes — in other words, it adapts to new, previously unseen data. Fifth, managers can demonstrate that the model’s live performance is in line with the test period’s performance.
Scientific disciplines have long used such an empirical method to create trust in the black box. Medicine, for example, offers a contemporary application of this method for evaluating and validating black boxes. Consider the drug acetaminophen, “which, despite having been used for more than a century, has a mechanism of action that remains only partially understood,” Ghassemi et al. write. “Despite competing explanations for how acetaminophen works, we know that it is a safe and effective pain medication because it has been extensively validated in numerous randomized controlled trials. RCTs have historically been the gold-standard way to evaluate medical interventions, and it should be no different for AI systems” (emphasis added).
To provide clients with the investment outcomes they seek, we must look beyond our conventional investment approaches, which means renouncing our fetish for explanations, overcoming what professor Zachary Lipton describes as our “concession to institutional biases against new methods,” and accepting “a sufficiently accurate model should be demonstrably trustworthy, and interpretability would serve no purpose.”
But first, we need to accept that the finite cannot comprehend the infinite.
| 2023-02-15T00:00:00 |
https://www.institutionalinvestor.com/article/2bstr1aauoiex25yto45c/opinion/we-will-never-fully-understand-how-ai-works-but-that-shouldnt-stop-you-from-using-it
|
[
{
"date": "2023/02/15",
"position": 92,
"query": "universal basic income AI"
}
] |
|
ChatGPT could upend jobs across Wall Street. Here's how ...
|
ChatGPT could upend jobs across Wall Street. Here's how 6 areas of finance, from investment banking to wealth management, might be impacted.
|
https://www.businessinsider.com
|
[
"Bianca Chan",
"Paige Hagy"
] |
ChatGPT could upend jobs across Wall Street. Here's how 6 areas of finance, from investment banking to wealth management, might be impacted.
|
This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
Experts have predicted ChatGPT's impact on a variety of industries, and it appears Wall Street will be no exception.
OpenAI's ChatGPT has been the subject of public fascination recently. Microsoft poured $10 billion into the startup during its most recent round at the end of January, which reportedly valued the startup at $29 billion.
ChatGPT is a chatbot that generates conversational written responses to a user's questions and prompts by using generative artificial intelligence that recognizes and mimics human speech patterns while dispatching encyclopedic knowledge. From churning out grammatically-correct but substance-lacking school essays to giving sound advice on how to negotiate a raise, playing with the chatbot is a fun pastime, but people have begun to wonder the impact it could have on across a multitude of industries.
It's still early days — ChatGPT was released to the public in late November — but experts already expect ChatGPT and its underlying tech to be utilized as a productivity-enhancing tool in finance.
"It's going to automate select tasks that knowledge workers are engaged in today so that they can focus on higher-value tasks," Dylan Roberts, partner and principal at KPMG, told Insider.
ChatGPT has the capacity eventually to replace humans in certain roles, according to some, but the realization rests in the customers' hands.
"Technology will be able to do it. The question is, do customers accept it or not?" Peter-Jan Van de Venn, vice president of global digital banking at consulting firm Mobiquity, told Insider.
However, ChatGPT does have at least one issue specific to its application to finance. The product is "black-boxed," according to Charles Hearn, the chief technology officer at fintech Alloy, meaning the AI can present answers without being able to trace how it got to those answers. That process can make it difficult to meet some regulatory requirements in financial services.
Others are skeptical of ChatGPT's ability to revolutionize industries and doubt the likelihood that it is the end-all be-all for white-collar workers. One Princeton professor even called the chatbot a "bullshit generator," The Markup reported.
But Dev Patnaik, CEO of strategy and innovation firm Jump Associates, said dismissing tools like ChatGPT so quickly is a mistake.
"You are using all of your brain power to stack up reasons of why the future is not going to happen rather than think and reason about what you're going to do about it when it does," Patnaik told Insider.
But while you mull on whether or not to pay attention to ChatGPT on Wall Street, some are already putting the tech to work. At Sweden private-equity giant EQT, ChatGPT is enhancing the firm's 7-year-old AI platform it built itself, according to Alexandra Lutz, who heads up EQT's AI-powered Motherbrain.
"Now, not only can we bring a perspective on ChatGPT and what it means, we can bring examples to senior leadership, to the portfolio companies to help them understand this is not hype, this is real. There are ways that you can use this and you can start using it tomorrow," Lutz said.
Insider spoke to five industry experts to get their take on how ChatGPT and its underlying tech could be applied to various sectors of financial services.
| 2023-02-15T00:00:00 |
https://www.businessinsider.com/how-chatgpt-could-be-used-on-wall-street-investing-dealmaking-2023-2
|
[
{
"date": "2023/02/15",
"position": 33,
"query": "ChatGPT employment impact"
}
] |
|
ChatGPT—Everything You Need to Know
|
ChatGPT—Everything You Need to Know
|
https://blog.bernieportal.com
|
[
"Will Miranne",
"Posted On",
"February"
] |
ChatGPT is one of the most powerful AI language tools to date. Read on to learn how it may impact your workplace.
|
Recent years have seen a sudden surge in AI tools—many of which offer the potential to significantly reduce the time needed for completing daily tasks such as writing blog articles, creating social media posts, altering images, and more.
One of the most astounding AI projects to appear is ChatGPT. The AI-powered chatbot provides users with a question-and-response tool that mimics human interaction.
Continue reading to learn about ChatGPT and the implications it may have on your workplace.
What Is Workplace AI?
According to SHRM, “Artificial intelligence refers to computers or computer-controlled machines that can simulate human intelligence in various ways.” This is a pretty broad definition, but that’s because AI basically means any machine or computer that can act intelligently or mimic human interaction.
AI is not new, even in HR. In fact, HR pros have been using AI for years, especially in recruitment. Any organization that has a chatbot on its career page, or uses an ATS to rank resumes, is already using AI in its recruitment process.
But the popularity of workplace artificial intelligence is skyrocketing. Since ChatGPT launched, many organizations have been wondering how it will change the world of work.
What Is ChatGPT?
ChatGPT is an AI-powered chatbot that relies on Generative Pretrained Transformer (GPT), a deep-learning algorithm designed to create and mimic human-like speech and conversation.
This translates to an individual simply typing a question into their computer and receiving a natural, detailed, and well-thought-out response in a matter of seconds. It is like having access to a subject matter at all times.
ChatGPT was developed and released by San Francisco-based startup, OpenAI in 2015. Elon Musk and Sam Altman initially founded the company, and In 2019 Microsoft made its first investment into the company. They have done so two more times, including a recent estimated investment of $10 billion. The investment will reportedly place Microsoft with a 75% ownership share of the company.
How Does ChatGPT Work?
ChatGPT can provide thorough responses on nearly any topic you can think of, from how to open your own coffee shop to creating a written novel in any style you desire.
What makes ChatGPT so exciting and compelling is the AI’s ability to comprehend human language. This is due to the large language model, or LLM, powering the tool. Not only is it effective, but it’s also one of the most powerful AI tools available.
ChatGPT will use previously mentioned information to inform the conversation as it progresses, making the AI even more reliable in its responses.
The AI chatbot is pushing the boundaries of what was previously thought possible and paving the way for a massive boost in accessible AI. Currently, the tool is free to use, with an optional paid version called ChatGPT plus. Offering a free version, for now, has dramatically increased accessibility.
AI is continuing to see a massive bump in usage, and as awareness continues to expand, the demand for such tools will likely rise. The opportunities to use tools like ChatGPT are endless and can apply across virtually every field of work and study.
ChatGPT is not the only AI-powered “language machine” on the market, and this is important because competition forces growth and adaptation. One major competitor is Google Bard.
Josh Bersin comments on one of the distinctions between these two AI:
“The Google competitor to GPT-3…was built with “ethical rules” from the start. According to my sources it includes ideas like “do not give financial advice” and “do not discuss race or discriminate” and “do not give medical advice.” I don’t know yet if GPT-3 has this level of “ethics,” but you can bet that OpenAI (the company who’s building this) and Microsoft (one of their biggest partners) is working on it.“
As Bersin points out, there are certainly improvements that will need to be made and research that will be necessary to determine what works best at scale. But this form of competition is what will continue to drive these tools forward and toward a complete AI down the road. Bersin then goes on to suggest improvements in language will be imminent and necessary to create a fully refined system:
“So what I’m implying is that while “conversation and language” is important, some very erudite people (I won't mention names) are actually kind of jerks. And that means that chatbots like Chat-GPT need refined, deep content to really build industrial strength intelligence. It’s ok if the chatbot works “pretty well” if you’re using it to get past writer’s block. But if you really want it to work reliably, you want it to source valid, deep, and expansive domain data.”
How Can ChatGPT Impact the Workplace?
ChatGPT is continuing to grow in popularity, with more and more people using it daily to complete tasks, problem-solve, and seek out new ideas. It would seem that a chatbot with this much power behind it would be an invaluable tool in the workplace, and while this can be true, there are certainly some obstacles that need to be addressed.
For one, this chatbot is still in its infancy, and a professional must proofread any copy it writes. If a writer wants to create copy for a social media post, they can ask the bot for advice or even write a post. But once the post is written, they should primarily use it for inspiration as the post may lack originality, include plagiarism, and indicate to Google that an AI-generated the copy. When Google detects the use of AI in copy, it can penalize the website from an SEO perspective. For these reasons, it is a good idea to use ChatGPT as a starting point and not rely too heavily on it for a final product.
There have also been reports of the AI chatbot making sexist or even racist assumptions when answering questions. This was seen firsthand by Textio’s cofounder Kieran Snyder:
“It’s borderline amazing how little it takes for ChatGPT to start baking gendered assumptions into this otherwise highly generic feedback. For certain professions and traits, ChatGPT assumes employee gender when it writes feedback.”
Keiren uses examples of a “bubbly receptionist” and a “strong construction worker,” which prompted ChatGPT to use male and female-specific pronouns to describe the positions.
With any emerging technology, no matter how advanced, there is always room to improve and adapt. ChatGPT even says this on its home page:
“We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses.”
The intention is to learn more about what this technology can do and how it will perform at scale.
With this in mind, organizations should take this technology with a grain of salt. Understand that it can be greatly helpful in generating new ideas and even finding ways of accomplishing tasks. But this technology is not full-proof and should not be wholly relied upon. The keyword when discussing ChatGPT is “tool.” This powerful AI chatbot is still only a single tool in what should be a diverse toolbox. Use it alongside your other resources, but don't make the mistake of utilizing it irresponsibly, or you may run into serious concerns within your organization.
What Are Some Advantages of Using AI in HR?
There are a lot of potential ways AI can help support HR, but most of them fall into two major categories: efficiency and analysis.
As AI software begins to automate more of the hiring process, HR is likely to become increasingly efficient at organizations that use it. That’s because your AI isn’t trying to schedule candidate screening calls while also juggling meetings with leadership, running payroll, and dozens of other tasks. In fact, an AI bot could feasibly conduct more than one screening call at the same time! The right AI can save your organization time and money as recruitment costs go down, and roles are filled more quickly.
But automation frees up more than money—it also frees up YOU! As an HR party of one, you’re probably responsible for most of the recruitment process at your organization. If an AI starts handling the process from the beginning through, say, passing the first screening call, suddenly, you have a lot less on your plate. If you combine that with an all-in-one HRIS like BerniePortal (which handles everything from payroll to compliance to PTO tracking), you might finally have the bandwidth for the strategic initiatives you want to implement on your team. AI can sift through resumes and answer candidate FAQs while you work on building a healthy company culture, coaching managers, and creating a great place to work.
And AI can help you there too. With advanced data analysis, many organizations already use artificial intelligence to glean insights about their teams. They are asking bots to analyze the results of employee pulse surveys, for example, or helping to answer questions like “Which teams have the highest turnover, and why that might be?.”
Additional Resources
You can also stay informed, educated, and up-to-date with new technology and other important topics by using BerniePortal’s comprehensive resources:
| 2023-02-15T00:00:00 |
https://blog.bernieportal.com/chatgpt-everything-you-need-to-know
|
[
{
"date": "2023/02/15",
"position": 90,
"query": "ChatGPT employment impact"
}
] |
|
Bringing your AI vision to life with Andrew Ng
|
Bringing your AI vision to life with Andrew Ng
|
https://workera.ai
|
[
"Workera Team"
] |
Andrew Ng gives us his take on a host of critical topics, such as his checklist for success, how to hire a world-class AI team, and measure learning velocity.
|
Bringing your AI vision to life with Andrew Ng: How to ensure you have the right skills and talent to succeed
In this episode of the Skill Baseline podcast, Andrew Ng gives us his take on a host of critical topics, such as his checklist for success, how to hire a world-class AI team, and measure learning velocity.
Listen to more episodes of the Skills Baseline Podcast on Spotify, Apple Podcasts or YouTube.
Key Quotes
"I often recommend start small, do a small project, deliver a small win... one thing that isn't always fully appreciated is that a large part of the purpose of that small initial project is to give people the training to gain the skills to make AI work for your organization so they can then use that first set of skills to do the second project, which grows even bigger skills".
"People can talk about learning velocity, but the ability to measure it is actually a very exciting thing for organizations to be able to do".
"When you have the right team, with the right people, life is just so much better, and the team always magically solves so many problems."
What is Workera?
Workera is the Skills Intelligence Platform redefining how enterprises understand, develop, and mobilize talent. If you'd like to see how Workera can help your organization, please request a demo. We'd be happy to show you around!
| 2023-02-15T00:00:00 |
https://workera.ai/blog/bringing-your-ai-vision-to-life-with-andrew-ng
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[
{
"date": "2023/02/15",
"position": 33,
"query": "AI workers"
}
] |
|
Yes, This Time It's Different
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Yes, This Time It’s Different
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https://www.city-journal.org
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[] |
It will also enable the upskilling of more people to become “knowledge workers,” including even coding. It's no coincidence that AI tools are bringing greater ...
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More than 1 million users signed up within a week of the online release of the artificial intelligence (AI) tool ChatGPT on November 30, 2022, a date dystopians think will live in infamy. In just two months, 100 million users signed up, a record for any new software, app, or online tool.
ChatGPT—along with other recent, if less famous, competitors—does in fact constitute a radical advance in software, far more consequential than when IBM’s Deep Blue defeated chess champion Gary Kasparov in 1997, back in the Internet’s first “century.” ChatGPT puts us one giant step closer to normal people having the ability to access supercomputing performance using natural language. So, the chattering classes are all a-twitter with the usual tropes about how automation will bring about the end of work, in this case for “knowledge workers.”
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We will, in due course, see news stories about practical “use cases” for ChatGPT. But for now, a lot of people are having a lot of fun playing with it and discovering just how much better this latest AI software is compared with, say, computer interfaces like Alexa or Siri.
Since ChatGPT was trained to recognize and produce language—a fact telegraphed by the word “chat”—I joined the rush to test the new tool, asking it to write a short blurb to promote a Caribbean cruise and to do so in the style of William Shakespeare. Within a fraction of a second, ChatGPT came back with a C+ effort:
Hark! good sirs and fair maidens, lend an ear
To news of grandeur and luxury rare.
A Caribbean cruise doth now appear
For those with hearts that do adventure dare.
Upon the azure seas, our grand barque
Shall bear ye to fair isles of sand and sun
Where comfort and opulence shall embark
And memories forever to be won.
One user gave ChatGPT a standard medical exam; it passed. Another gave it a standard test for Wharton MBA students; passed again. Impressive parlor tricks, but such stunts tell you more about the simplicity of the tests than about the brilliance of ChatGPT. Passing a written test that indicates proficiency with medical, business, or finance rules and nomenclature doesn’t say anything really useful about whether the test taker will be a great surgeon or business leader. Nor would passing the written test for driving a car predict anything about whether the test-taker could be a safe driver, much less a winning Formula One racer.
Engineers love to perform stunts with new technologies. Of course, depending on the stunts, the performance does say something about the state of a technology. The lesson of Kasparov’s loss was to see the “overnight” progress, nearly 20 years later, of Google’s AlphaGo supercomputer beating the world’s Go champion in that much more complex game. Now, almost a decade after that, comes the sonnet-writing, test-passing ChatGPT. The point is that even in the seemingly high-velocity world of computing, so-called exponential change takes quite a while. That’s the reality of commercializing at scale all forms of engineering progress.
For a way to think about what comes next for AI, consider what followed analogous stunts in the history of aviation—a field more relevant to computing than most realize.
The feat that made it clear that an age of useful aviation was possible was Charles Lindbergh’s 1927 barnstorming of all 48 contiguous U.S. states over a period of 95 days, following his better-known stunt of the first nonstop flight across the Atlantic Ocean, which was in retrospect a kind of Kasparov–Deep Blue moment. Even though, following Lindbergh’s odyssey, aircraft would be used in business, industry, and warfighting, it took another three decades until the engineering was good enough to yield, in 1957, the Boeing 707, which launched the age of mass commercial aviation. From there, as entirely new industrial edifices and national infrastructures were built out, the number of revenue-passenger-miles (to use that industry’s term of art) would soar more than a hundredfold by the year 2000.
The emergence of useful, broadly available aviation brought big shifts to the structure and nature of business and employment in transportation. But it ended neither the role of, nor the expansion of uses for, ships, trains, or trucks; and it didn’t end employment in those sectors. Overall, in fact, employment in U.S. transportation services doubled by the turn of the century. It’s no exaggeration to frame ChatGPT as a Boeing 707 moment.
But we’re being told that, well, this time is different. In part, that’s because it seems somehow spookier when technology accelerates tasks performed invisibly in cyberspace—that is, cognitive rather than physical tasks. ChatGPT has reanimated the now-ancient philosophical debates about whether machines think and whether, as they get better at imitating human behaviors, they’ll make a lot of humans redundant and bring on the often-predicted age of unemployable humans.
It is true that there is something different this time, as there is every time. The specifics of the newest machines are different. But what’s not different is the overall effect of automation. Not to diminish the social and political challenges that all disruptions bring to markets and people, but automation has always boosted productivity and thus overall wealth and employment. If labor-saving technologies—namely, automation—were a net job destroyer, unemployment should have been continually rising over the course of modern history as (physical) automation inexorably expanded. It didn’t. MIT economist David Autor has been particularly eloquent on the apparent paradox of seeing continued rise in employment despite advances in labor-reducing technologies, observing that “the fundamental threat [to employment growth] is not technology per se but misgovernance.”
Of course, where and how most people are employed has changed over time. It’s going to change again. And that is disruptive. But the central and unprecedented difference between our time and previous eras is the demographic reality of a shrinking workforce. In the near future, we will need lots of new tools to amplify the efforts of the declining labor supply. Even in our own present, despite the best efforts of the Federal Reserve to increase unemployment (that is, to reduce the pressure employers face to offer “inflationary” salaries to keep workers), job openings still outnumber people available to fill them. Demographics dictate that this gap will widen. Since most jobs in a modern economy are found in so-called knowledge work, the only way to close the labor gap will be with AI tools useful enough to amplify the efficacy of people in those areas.
AI, of course, is not a specific tool per se but a class of tools under that loosely defined term. To extend the earlier analogy, there are many radically different kinds of engines; no single engine is suitable for every class of machine, task, or vehicle—from aircraft to mining trucks. It’s the same for the silicon engines at the core of all AI machines. Much of the misdirection about AI’s implications comes from the sloppy term itself, “artificial intelligence.” It’s no more informative or accurate than calling a car an artificial horse, or an airplane an artificial bird, or an electric motor an artificial waterwheel.
While ChatGPT is a whiz with words, it wasn’t trained on math and, as some users have already observed, performs poorly there. Similarly, ChatGPT couldn’t drive a car, wield a hammer to drive a nail, or carry a box. One needs differently designed and trained AI tools to perform each kind of task. The category confusion about the realities of AI tools is, to put it crudely, the equivalent of seeing that a tool like a hammer makes it easier to push a nail into a board and then trying to use a hammer to drill a precise hole, weld steel, or measure voltage.
The letters GPT in ChatGPT stand for Generative Pre-trained Transformer—computer lingo for an algorithm that, when paired with a powerful computer, can be trained iteratively by looking repetitively at a very large set of samples—in this case, written texts. The same has been done for images and myriad areas where routine tasks entail patterns and rules. The “chat” in ChatGPT will doubtless find early commercial application precisely where chatbots are already used: in online commerce and with the many tasks in all businesses that involve often-confusing or arcane rules, regulations, or standards that a computer can more usefully, quickly, and accurately parse to answer questions put to it in “natural language.”
The management literature is replete with analyses of the productivity-robbing burdens imposed on employees trying to comply with routine tasks in education, health care, business in general, and even in basic research. Such tasks are precisely where the narrow power of AI is most powerful. As it happens, it’s also where one could free up easily re-trainable humans to be redirected to more challenging non-routine tasks.
A recent Federal Reserve analysis divided the U.S. workforce into just two high-level categories: manual and cognitive labor. No surprise that the majority are now employed in the latter. The analysis also created two sub-categories within each: routine and non-routine tasks. Thus classified, about 60 million people in the U.S. work on routine tasks, split almost evenly between manual and cognitive domains. Total employment in both routine manual and routine cognitive tasks hasn’t changed significantly since 1980. Meantime, the non-routine manual labor pool has risen from 15 million to 25 million people since 1980, and employment in non-routine cognitive work has grown from about 30 million to 60 million people.
Four decades of job growth has all been in non-routine tasks. If we want to find more people to take on the jobs where growth is happening—and where they can be paid more—we’ll need to move people out of the routine job domains, while still ensuring that those tasks are fulfilled. That is precisely what’s made possible by AI tools that can increase the efficacy of a shrinking number of people performing routine tasks. Ensuring that that can happen will require AI tools even easier to use, more accurate, and cheaper than what’s available today with ChatGPT and its (jealous) competitors.
We know from history that when new technologies are found to be broadly useful, engineers drive down costs and make them easier to use. The latter is the “user interface,” in the jargon of tech. Again, witness the capabilities of ChatGPT versus, say, Alexa. With Natural Language Processing (NLP), the human-machine interface makes it easier for non-experts to engage casually in computational feats previously reserved for supercomputers and the expert class. The overall effect of NLP, in addition to taking up the burden of routine tasks, will also be to reduce routine burdens for employees in non-routine types of work. It will also enable the upskilling of more people to become “knowledge workers,” including even coding. It’s no coincidence that AI tools are bringing greater productivity to writing computer code. One company touts that its AI-based tool can help a coder write software ten to 100 times faster.
The good news, at least from a macroeconomic perspective, is that there’s been a land-rush of activity to develop mission-specific machine-learning algorithms. One measure of the scale of that activity is in the amount of private capital chasing AI deals and companies. We’re in the early stages of billions of dollars directed at another tech hype cycle.
Another measure of the scale of AI activity can be found in the total quantity of the world’s computer processing power used to “train” deep-learning models; it’s been doubling every few months for the past half-dozen years. That translates into a 300,000-fold increase in computing power used for AI training over that short time. You don’t need a crystal ball to predict that such prodigious efforts will soon yield a fusillade of useful AI tools to succeed ChatGPT.
Coming back to our aviation analogy, it’s the inescapably physical world of energy that reveals the implications of the scale of AI and machine learning. Even AI cognoscenti are surprised to learn that the energy equivalent of the fuel used to fly a jumbo jet from Austin to Asia is gobbled up by an AI-centric computer being trained on “large language models” or other similar sets of “parameters” needed for machine learning. That’s not a one-time investment; it happens every time and for each kind of similar application of learning. As “use cases” for AI expand, the proliferation of AI training will follow apace.
Ah, but for those who are anxious about energy issues, we also know that emerging and next-generation AI chips and algorithms are far more energy-efficient—some are already tenfold better. This will tamp down AI’s voracious energy appetite, even as the tools improve. But it’s that reality—more efficiency and higher performance—that will lead to a repeat of the trajectory of the first, pre-cloud era of the Internet.
Radical gains in efficiency have always been critical to unlocking the commercial viability of any new machine or infrastructure for society. In 1958, when Pan Am began passenger jet service with the 707, no one forecasted (much less exhibited angst about) the aggregate fuel consumption that commercial aviation would induce. Since then, aircraft have become 300 percent more energy-efficient, not to mention safer and more reliable. Those features are what enabled today’s trillions of passenger-miles flown, an activity that consumes some 4 billion barrels of oil each year, compared with a trivial amount in 1958.
Similarly, decades of inexorable gains in computing energy efficiency are illustrated by the fact that, if today’s smartphones operated at 1980 computing-efficiency levels, just one phone would use as much electricity as an office building. A single datacenter at 1980 efficiency levels would require the entire U.S. grid to power it. Instead, staggering reductions in energy-per-logic-operation, again along with gains in performance, are what made possible a commercial world of billions of smartphones and thousands of datacenters. And that yielded today’s global cloud infrastructure—still in a pre-AI era—that already uses about as much energy as global aviation.
In a future when AI machines perform not dozens but tens of thousands of simulations entailing trillions of computing-hours, overall energy use will balloon again. And that will happen because of the economic benefits AI offers to people, businesses, and even—and especially—in the pursuit of science and new discovery.
As Regina Barzilay, an AI researcher at MIT, put it when asked about the power of AI-assisted discoveries to invent new life-saving drugs: it’s “not the machine that invented the molecule. It’s that the machine helped humans to scan the huge space of possibilities and zoom in on the fruitful set of hypotheses that they tested.” Or, as economist Alexander Salter succinctly observed: “Data doesn’t interpret itself.” The AI machines are knowledge amplifiers.
Even so, we will see disruptions to the nature of jobs and businesses. Indeed, the scale of those disruptions will echo the magnitude of the opportunities that AI creates. Some educators have voiced worries about disruptions to teaching, including detecting cheaters. ChatGPT will indeed require adjustments, perhaps even a return to Socratic methods of in-class learning and testing—hardly a new idea. Nor is dealing with cheating, especially in the age of the Internet. Teachers found ways to teach math in the age of the calculator. Adaption to AI is not just possible, but arguably beneficial.
A clear-eyed recognition of benefits from any new technology doesn’t constitute a Pollyanna’s perspective. It’s also true that AI machines won’t all be useful or put to good use; such is the (sometimes sad) state of human nature. As science-fiction author and technology seer Cory Doctorow recently quipped in a long interview, “I think that the problems of A.I. are not its ability to do things well but its ability to do things badly, and our reliance on it nevertheless.” His cautions—and these are a constant refrain in his dystopian fiction—center around the need to recognize the limits of any machine and the kinds of risks arising from misuses.
Coming back to where we started, looking over the long period since the emergence of the modern information era, circa 1970, Census data show a significant shift in the structure of employment—away from production and toward services. Economists David Autor and Anna Salomons have done pioneering work in mapping those dynamics as a kind of hollowing out of highly paid “middle-skilled” jobs that don’t typically require a college degree, and a simultaneous shift toward more low- and high-skilled employment.
Autor recently posed a question as to “whether a countervailing set of economic forces will soon reverse the decline of middle-skill work?” I think the answer to that question is yes. The countervailing forces will come from the fact that computing has finally become widely useful with the advent of commercially viable AI. And that’s happening just in time to rescue the economy from demographic dystopia.
Photo by Jakub Porzycki/NurPhoto via Getty Images
| 2023-02-15T00:00:00 |
https://www.city-journal.org/article/yes-this-time-its-different
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[
{
"date": "2023/02/15",
"position": 54,
"query": "AI workers"
}
] |
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Smarter customer support starts with smarter AI
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Agentic AI Employees & Workflow Automation
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https://www.getmagical.com
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Use Magical Agentic AI employees to automate your team's most time-consuming workflows faster and more flawlessly. Hire your first fully autonomous, ...
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What should a customer support person automate first using Magical?
The first thing most customer support experts automate using Magical is having to type out their most common conversation with customers. Depending on your business, this might be a common question you get about your product, a response to a billing question, or a step-by-step guide to help them troubleshoot something. You might already have these set up as a macro within a customer support platform (such as Zendesk)—but Magical makes it easier to customize, edit, share, and call up your templates anywhere in a web browser. In Magical, these are called templates. And not only do they let you use rich media and formatting, they also come equipped with info placeholders so you can instantly personalize your templates with details like the first name of the customer you’re messaging or their ticket number.
| 2023-02-15T00:00:00 |
https://www.getmagical.com/roles/customer-support
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[
{
"date": "2023/02/15",
"position": 86,
"query": "AI workers"
}
] |
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How AI is Changing Leadership? The New Role of ...
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Leadership in the age of AI: How AI is Changing Leadership?
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https://management30.com
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"Tom Close"
] |
Management 3.0 is a leadership approach that adapts to the era of artificial intelligence and automation by focusing on human interactions and decentralization ...
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Our practical, hands on and tangible tools and management games are the best way to foster transformational change within teams, companies and even on a personal level.
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Our Company License gives your learning team, transformation staff and leaders the ability to facilitate the Management 3.0 mindset, practices and official workshops yourselves.
With our global team, we provide tailor-made programs to enhance mindsets and skills for entire organizations, ensuring your business is future-ready! Learn more about our Inhouse Training Programs .
The Foundation Workshop is where your Management 3.0 learning journey truly begins, and you will learn about the principles, pillars and popular practices you can use right away.
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The Foundation Workshop is where your Management 3.0 learning journey truly begins, and you will learn about the principles, pillars and popular practices you can use right away. If you are interested in other options we offer, please check our Learning Path .
The Foundation Workshop is where your Management 3.0 learning journey truly begins, and you will learn about the principles, pillars and popular practices you can use right away.
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Adapting leadership in the age of AI: How Management 3.0 Principles Helps Companies Stay Competitive
Leadership in the age of AI demands more than technical adaptation, it calls for a deep shift in mindset, skills, and collaboration. As AI reshapes the way teams work, learn, and make decisions, the role of leaders is being redefined.
This is exactly what Wania Konageski explores in this article. As a facilitator and technology and innovation specialist, Wania sees that Management 3.0 is the key for companies to adapt to the AI era and remain competitive. In her opinion, as technology grows exponentially, it is increasingly important that companies and their employees are prepared for changes related to both technical and socio-emotional skills. When working with AI for IT Operations (AIOps), Wania realized that the intelligence in these solutions can be disruptive, but it is necessary to adjust a leader’s mindset so that companies can truly benefit from them. In this article, Wania discusses the disruptive moment we are living in and how it affects leadership.
Historically, the world has gone through several disruptive transformations that changed the way we work, like the first Industrial Revolution that happened in Great Britain. The Management 1.0 approach was created based on mass production, where the manager’s role was to monitor and replace in case of failure. This approach was effective then, but now, with technology’s evolution, we need a more decentralized and human-centered approach. When we talk about decentralization, we tend to associate it with agile management models and, on the technical side, with new web3 technologies like blockchain. Yet, at its core, is human interaction.
According to the CEO of OpenAI, Sam Altman, in an interview with Bloomberg Technology ”repetitive human work that does not require a deep emotional connection between two people will be done in the next couple of decades better, cheaper and faster by AI.”
In other words, companies that continue to use the 1.0 or 2.0 management style are doomed to fail.
How does this relate to Management 3.0?
Management 3.0 is a leadership approach that adapts to the age of artificial intelligence and automation by focusing on human interactions and decentralization through empowerment. This means that leaders need to be able to manage the emotional issues affecting their team and stakeholders, and to adapt to constant changes in the world of work.
To adapt their leadership in the age of AI, companies must shift their mindsets and prepare for change. This includes the development of socio-emotional skills for technology specialists, who need to expand their horizons and open their minds to problem solving in a creative way. Management 3.0 can help in this process, with its Seven Rules for Creativity Managers, for example. Here, we are talking about a significant change for specialists and engineers, who need to learn to deal with emotional and human issues, beyond the technical.
So, what changes in the professional profile with the rise of artificial intelligence?
It is true that technical skills are not the only differentiators of a professional. In addition to technical skills, interpersonal skills are also essential for an employee’s success. Professionals who are highly specialized but do not have interpersonal skills are at risk of being replaced by artificial intelligence.
In the information technology scenario, for example, artificial intelligence tools are being used to automate customer support tasks. This means that customer support professionals who do not have interpersonal skills can be replaced by chatbots and virtual assistants. These chatbots and virtual assistants can provide customer support efficiently and consistently, without needing interpersonal skills.
In the world of agility, how can artificial intelligence make an impact?
A Scrum Master’s job is to ensure the software development team is following Scrum practices and principles. They are responsible for leading daily meetings, planning and retrospectives, and helping the team remove obstacles. This involves communication, leadership and conflict management skills.
One example of a common situation in which a Scrum Master could be replaced by artificial intelligence is when they do not have sufficient interpersonal skills to deal with team conflicts. Let us say that a software development team is working on an important project and there is a dispute between two team members about the best approach to solving a problem. If the Scrum Master cannot handle this conflict effectively, it can lead to delays in the project and a drop in team morale.
In this situation, AI could be trained to deal with conflicts in an objective way, always presenting solutions based on data and facts, helping the team to reach a solution faster. AI could also be able to analyze team performance data and identify potential problems before they happen, helping to avoid future conflicts.
However, it is important to note that although AI may be able to help teams deal with technical issues, it still does not have the ability to manage human issues, such as motivation and leadership. That is why it is important for Scrum Masters to refine their interpersonal and leadership skills to ensure the project and team’s success. In both cases, whether a technical specialist or a Scrum Master, if there are no human behavior differentiators, artificial intelligence may be poised to replace them.
“Should I run for the hills?”
However, do not worry, I am not suggesting that artificial intelligence will replace everything and everyone. AI can actually enhance the professional skills of those who invest in relevant knowledge and training. In the development field, for example, a professional who only focuses on technical skills and does not think about acquiring new technology skills, or enhancing their interpersonal qualities, will have difficulty finding a good job. Knowing how to work in a team and understanding the professional context around you has never been more important than it is now.
AI is not going to replace managers but managers that use AI will replace those that do not. Rob Thomas, IBM
The leader of the new era must be able to inspire and motivate their team.
To achieve this goal, it is necessary to focus on three main areas: leadership, structure, and culture:
Leadership: Create a safe and inclusive work environment where people’s opinions, feedback, and work are valued. In this area, it is necessary to be a leader who actively listens and values the contributions of their team. (Feedback Wrap)
Culture: Promote collaboration and innovation. A company without collaboration does not generate innovation and it is necessary to allow people to experiment, exchange ideas, and learn from their mistakes. (Improvement Dialogues)
Structure: Have a clear organizational structure and align the goals and objectives with the team. With this aspect, management can help, for example, with the use of OKRs. (Objectives and Key Results) Focus Areas to Improve your Leadership in the age of AI
As we have seen, shifting your mindset, prioritizing interpersonal skills, and focusing on human issues can bring great success for leaders when implementing artificial intelligence. Remember, do not be afraid of AI, it is already a reality that we should enthusiastically embrace. Learn to use it to your advantage so that you can become a differentiated professional.
Want to go further?
Leadership in the age of AI: Why M3K offers the mindset and tools modern leaders need
Discover M3K, born from the fusion of Management 3.0 and the unFIX model by Jurgen Appelo.
M3K offers a fresh approach to leadership in the age of artificial intelligence, team design, and continuous improvement, tailored for a world shaped by complexity, autonomy, and AI.
If you’re looking to lead with more clarity, impact, and adaptability, M3K is your next step.
Photo by cottonbro studio via Pexels
| 2023-02-15T00:00:00 |
2023/02/15
|
https://management30.com/blog/ai-leadership/
|
[
{
"date": "2023/02/15",
"position": 8,
"query": "AI business leaders"
}
] |
Will AI take your job? | Insights
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Will AI take your job?
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https://www.gbm.hsbc.com
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HSBC's James Pomeroy explores the potential impact of artificial intelligence on the labour market and workplace productivity.
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| 2023-02-15T00:00:00 |
https://www.gbm.hsbc.com/en-gb/insights/global-research/will-ai-take-your-job
|
[
{
"date": "2023/02/15",
"position": 14,
"query": "AI wages"
}
] |
|
News | yoursourceone.com
|
News
|
https://www.yoursourceone.com
|
[
"Source One News"
] |
Will it last? 2 million student loan borrowers at risk of garnished wages in July ... AI chatbots with better prompts · How a GOP rift over tech regulation doomed ...
|
NESPELEM — A juvenile Chinook salmon was recently photographed in the Kettle River near Cascade Falls in British Columbia, marking the first confirmed sighting of a Chinook in the river since the Colville Tribes began reintroduction efforts upstream of Grand Coulee Dam in 2017.
| 2023-02-15T00:00:00 |
https://www.yoursourceone.com/wenatchee/news/
|
[
{
"date": "2023/02/15",
"position": 53,
"query": "AI wages"
}
] |
|
Listen Live | yoursourceone.com
|
Listen Live
|
https://www.yoursourceone.com
|
[
"Source One News"
] |
Impostor uses AI to impersonate Rubio and contact foreign and US officials. 3 hrs ...
|
There was a time, not long ago, when wine tasting was regarded as something done by people with specially trained palates and sophisticated sensibilities. Today, wine is being consumed by hundreds of millions of people who drink it simply because it is what they like. Grape Encounters is abo…
| 2023-02-15T00:00:00 |
https://www.yoursourceone.com/south_sound/radio/
|
[
{
"date": "2023/02/15",
"position": 56,
"query": "AI wages"
}
] |
|
Public Awareness of Artificial Intelligence in Everyday ...
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Public Awareness of Artificial Intelligence in Everyday Activities
|
https://www.pewresearch.org
|
[
"Brian Kennedy",
"Alec Tyson",
"Emily Saks",
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"Font-Family Var --Wp--Preset--Font-Family--Sans-Serif",
"Font-Size Font-Weight Gap Important Line-Height",
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] |
A new Pew Research Center survey finds that many Americans are aware of common ways they might encounter artificial intelligence (AI) in daily life.
|
(Pew Research Center illustration; all photos Getty Images)
How we did this Pew Research Center conducted this study to understand how aware Americans are of artificial intelligence in their daily lives. For this analysis, we surveyed 11,004 U.S. adults from Dec. 12-18, 2022. Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology. Here are the questions used for this report, along with responses, and its methodology.
Artificial intelligence is fast becoming a regular part of daily life, shaping the way Americans work, play and receive essential services from food deliveries to financial services to health care.
A new Pew Research Center survey finds that many Americans are aware of common ways they might encounter artificial intelligence (AI) in daily life, such as customer service chatbots and product recommendations based on previous purchases. At the same time, only three-in-ten U.S. adults are able to correctly identify all six uses of AI asked about in the survey, underscoring the developing nature of public understanding.
Awareness of common uses of artificial intelligence is a first step toward broader public engagement with debates about the appropriate role – and boundaries – for AI. Experts have raised a host of moral, ethical and legal questions about the expanding capabilities of AI. And the ethical and responsible use of AI is a growing focus of research within the field.
The Pew Research Center survey of 11,004 U.S. adults, conducted Dec. 12-18, 2022, finds that 27% of Americans say they interact with AI at least several times a day, while another 28% think they interact with it about once a day or several times a week. On this self-reported measure, 44% think they do not regularly interact with AI.
More broadly, the public remains cautious about the impact artificial intelligence is having on American life: Just 15% say they are more excited than concerned about the increasing use of AI in daily life, compared with 38% who are more concerned than excited; 46% express an equal mix of concern and excitement. These views are about the same as they were in a November 2021 Center survey.
On a set of six questions designed to measure awareness of specific uses of AI in daily life, 68% of Americans correctly identified artificial intelligence at work in wearable fitness trackers that analyze exercise and sleeping patterns; the remainder of the public said they weren’t sure or selected one of three incorrect options that do not rely on AI (thermometers, at-home COVID-19 tests and pulse oximeters).
When it comes to an example of artificial intelligence in online shopping, 64% of U.S. adults correctly identified custom product recommendations based on previous purchases as using AI. Majorities were also aware that AI is at work in customer service chatbots (65%), security cameras that recognize faces (62%) and customized music playlist recommendations (57%).
The most challenging question for the public was identifying that email services categorizing messages as spam uses AI: 51% of Americans got this question right, while 49% chose an incorrect option, said they weren’t sure or did not answer. These six questions represent some common ways people could use AI in their lives but are not designed to be an exhaustive list of all the ways people could encounter AI. Each question had four possible responses and an explicit fifth option, “not sure.”
Taken together, 30% of Americans correctly answered all six questions about awareness of AI in everyday life (defined as a high level of awareness), 38% got three to five questions right (medium awareness) and 31% got two or fewer questions correct (low awareness). The mean number of correct answers was 3.7 out of 6.
Those with higher levels of education show greater awareness of AI
U.S. adults with higher levels of education and income are more aware of examples of AI in daily life than other adults. For example, 53% of Americans with a postgraduate degree correctly identified uses of artificial intelligence across all six multiple-choice questions. By contrast, just 14% of those with a high school diploma or less education answered all six questions correctly; 51% of this group had low awareness of AI, answering no more than two questions correctly.
Those with higher family incomes are also more aware of the uses of AI than those with lower incomes. About half of upper-income Americans had high awareness of AI (52%), compared with just 15% of lower-income adults.
Younger Americans are more aware of AI applications in daily life than older Americans. This pattern is especially pronounced when it comes to correctly identifying AI at play in customer service chatbots (75% of adults ages 18 to 29 said this vs. 45% of those 65 and older) and music playlist recommendations (65% vs. 39%).
Men scored higher on the scale than women. About four-in-ten men (38%) got all six questions right, compared with 23% of women. (Women are more likely than men to respond “not sure” to each of the six questions, consistent with previous research on both science and political knowledge.)
Partisan affiliation is not a major factor when it comes to awareness of AI: There are no meaningful differences between Republicans and Democrats on the AI awareness scale.
Frequent internet use is tied to higher awareness of artificial intelligence
Online applications and websites are places where Americans may frequently encounter artificial intelligence through examples such as customer service chatbots and product recommendations based on their purchasing behavior.
Adults who are frequent internet users score higher on the AI awareness scale than less frequent users.
Among Americans who say they are on the internet “almost constantly,” 38% got all six questions correct, as did 31% of those who say they use the internet several times a day. By comparison, just 6% of infrequent internet users (those who go online about once a day or less) correctly answered all six questions on the survey.
Not surprisingly, those who say they have heard more about artificial intelligence generally score higher on the AI awareness scale than do those who say they’ve heard less about this topic.
Majority of Americans think they interact with AI at least several times a week
About a quarter (27%) of Americans say they interact with artificial intelligence almost constantly or several times a day. Another 28% say they interact with AI about once a day or several times a week. On this self-reported measure, 44% of Americans estimate that they interact with AI less often.
Those with higher levels of education and family income are more likely than those with less education and income to say they interact with AI at least daily.
In addition, those who score high on a six-item scale of AI awareness are more likely to say they frequently interact with AI. For instance, 44% of those who have a high level of awareness of AI say they interact with AI almost constantly or several times a day. By comparison, just 12% of those who scored low on the scale say they interact with AI multiple times each day.
Many Americans have some level of concern about use of AI generally
The rapid development of artificial intelligence technologies has been accompanied by debate about ethics in AI and appropriate limits on its use.
Amid these ongoing discussions, the public strikes a cautious tone toward the overall impact of AI in society today.
On balance, a greater share of Americans say they are more concerned than excited about the increased use of artificial intelligence in daily life (38%) than say they are more excited than concerned (15%). Many express ambivalent views: 46% say they are equally concerned and excited.
There has been little change in these attitudes since last year.
Across all levels of awareness of AI, larger shares express greater concern than excitement about the impact of artificial intelligence in daily life. For example, among those who scored high in awareness of AI in daily life, 31% say they are more concerned than excited about the impact of AI, compared with 21% who say they are more excited than concerned. Those with medium or low AI awareness express greater concern than excitement by even wider margins.
| 2023-02-15T00:00:00 |
2023/02/15
|
https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/
|
[
{
"date": "2023/02/15",
"position": 29,
"query": "AI journalism"
}
] |
What Could ChatGPT Do for News Production?
|
What Could ChatGPT Do for News Production?
|
https://generative-ai-newsroom.com
|
[
"Nick Diakopoulos"
] |
Here I walk through these tasks with an eye towards whether generative AI models like ChatGPT could feasibly help.
|
What Could ChatGPT Do for News Production? Nick Diakopoulos 10 min read · Feb 15, 2023 -- Listen Share
In early 2022 the Associated Press (AP) published a report on the overall level of AI understanding and readiness in US newsrooms. Fielding an extensive survey, more than a hundred participants responded with, among other things, their “wish list” of tasks where they really wanted some help from automation. The report aggregates and compiles those responses into a helpful set of tasks where there’s clear demand.
Here I walk through these tasks with an eye towards whether generative AI models like ChatGPT could feasibly help. In virtually all cases additional work is needed to evaluate the quality of output for the tasks. In general, I would not recommend using ChatGPT for these tasks without a strict and close editorial process for fact-checking outputs before anything is published.
Based on my analysis of the tasks there are some patterns in terms of the types of tasks where such models can be helpful. These include tasks involving classification into potentially user-defined categories, rating of documents along dimensions of interest like newsworthiness, summarization of text in journalistically aware ways, and personalization of content based on end-user characteristics. Classification and rating are more analytic and can generate structured data that can be used in downstream filtering or ranking, whereas summarization and personalization are premised on outputting text that is either more efficient, or more relevant to consume.
For each task below, I’ll quote the description from the AP report and then add my commentary for how ChatGPT could be useful, whether it has clear limits, and if there are other considerations at play. For implementation examples of some of these tasks see my Colab Notebook, and if you’re excited to try things yourself, check out the Generative AI in the Newsroom Challenge. These tasks are of course just a starting point — lots more creative applications are still waiting to be explored.
1. Content Discovery
“Flagging and gathering social media content like trends, quotes from newsmakers, flag and gather content on government websites, e.g., COVID-19 data, court records, law enforcement records”
The AP distinguishes between content discovery from structured data, like excel sheets or databases, and content discovery from unstructured data, like written documents. Traditional analytics methods are probably better for discovery from structured data, whereas ChatGPT seems well-suited to discovery from unstructured data.
Discovery from unstructured data basically boils down to scanning documents and classifying them according to some category of interest, like newsworthy. Every content discovery task is a little bit different though, and there are a lot of different factors that play into whether a document is actually newsworthy. A GPT-based classifier of news value will need to be carefully evaluated for each specific content discovery task. If you want it to flag interesting court records in comparison to, say, law enforcement records you’ll likely need to write different, specifically tailored prompts. And you’ll need to evaluate the accuracy of each specific classifier to assess things like false positives and false negatives.
2. Document Analysis
“Processing large sets of public records like campaign finance records, state legislation, civil complaints, municipal budgets; in combination with text summarization to help reporters”
Much like the content discovery task, one aspect of document analysis comes down to categorizing and flagging interesting documents for further reporter attention. Another aspect is in summarizing documents so they can be quickly scanned by reporters for relevance. These aspects can get blurry though. For instance, in some of our ongoing research on this topic, we use GPT-3 to summarize newsworthy angles from scientific abstracts, embedding a news judgment into the summarization process. Any document analysis task will also need a careful evaluation of accuracy, both of any news value applied as well as of the text of the summary itself.
When reporters work with documents, those documents sometimes need to be digitized. Text might first need to be indexed through sometimes Optical Character Recognition (OCR) processes that can introduce errors. Whether ChatGPT is robust to these kinds of errors in data is still an open question.
3. Translation
“Translating published stories into multiple languages and processing raw data in various languages”
If you want to publish translations of stories you’re probably better off using a tailored solution (e.g. Google Translate) rather than a general purpose language model like ChatGPT. Even better would be a translation done by a person who can make accurate linguistic and cultural interpretations. But, if you’re thinking more about a use case like multilingual content discovery or document analysis, then ChatGPT might be appropriate for doing a rough translation that can be scanned and evaluated for relevance or interest. This could be interesting, for instance, in multilingual engaged journalism. But ChatGPT is best used only for getting an initial gist of a translation, and is not for publishing. If you want to rely on a document published in another language as a piece of evidence in your reporting, you’ll want to check with a fluent colleague, or maybe even get a certified translation.
4. Tips Processing
“Moderating story idea submissions and questions; verifying tips.”
One aspect of tips processing is a sort of special case of content discovery from unstructured documents that come in via email or other information channels. In some of our work on engaged journalism we’ve looked at survey callouts and online comments as avenues for learning about community interests for follow up reporting. For these types of use cases ChatGPT can be used in all the same ways for detecting newsworthiness and relevance of a document, according to whatever categories of newsworthiness you want to set up. Again, you’ll need to do tailored evaluations of this for the specific kinds of “tips” you’re scanning.
In terms of verifying tips, ChatGPT is not able to help at all. Remember that ChatGPT is limited in terms of its training data. As search engines begin to integrate ChatGPT they’ll build in ways to allow the model pull from more updated search results, but this, again, will not be of any use in corroborating a tip: if there were data or documents out there to corroborate the tip that the model already had access to then it wouldn’t really be a very good tip, would it?
5. Social Media Content Creation
“Generating content (e.g., text, video, photo, audio) and scheduling optimized posts to Twitter, Facebook, Instagram; cropping photos and videos for different formats.”
It’s a complex communication task to find the right tone for social posts and frame them so they resonate with particular audiences. And so I wouldn’t necessarily recommend trying to automate this task using generative AI. But producing excerpts and summaries of textual content for social channels is something that ChatGPT can definitely help with as long as there’s a human in the loop to dynamically prompt the machine and evaluate the results before they’re published. Perhaps there are patterns of prompts that could accelerate engagement editors’ work.
ChatGPT won’t be able to help with the other aspects of this task category: scheduling posts and cropping visual media. Scheduling is an analytic function that’s better addressed with other types of machine learning based on audience characteristics and data on past publishing. No doubt there will be other types of generative AI models that can help with visual tasks of cropping and editing video, but those will need careful evaluation before they’re used for publishing.
6. Automated Writing (Structured Data)
“High school sports, college sports, weather, natural events (e.g., tides, fires), restaurant report cards, police logs, elections, agriculture grain bids, business licenses, real estate and community calendars.”
Automated writing based on structured data is a familiar use-case for news media going back a decade or more. But most of the industrial-scale automated writing in use today is based on more straightforward template-based approaches. ChatGPT can also render fluently written text based on structured data inputs, but it comes with more caveats because of the statistical sampling involved in its text production. They also generally can’t do math, and so if there are specific mathematical operations needed, it is better to do those with traditional analytic methods first and then feed those pre-computed data files into ChatGPT for language generation. The bottom line is that the accuracy of texts generated from data need to be evaluated for accuracy before those texts are published.
7. Automated Writing (Unstructured Data)
“Obituaries, press release briefs, event previews, etc”
Automated writing from unstructured data is more challenging than the structured data case. But ChatGPT can also work to extract structured data from unstructured data, and then use that to generate text. Perhaps there are press releases your newsroom receives about community events and the model can extract things like dates, times, locations, and short descriptions of those events. This structured data can then be fed back to the model to output a written description of the event. Much like for the structured case though, you’ll want to check the output for accuracy before publication.
The AP description also suggests that “obituaries” are an example where there is a need for automation. Ethically it feels wrong to delegate the task of selecting the most important and meaningful life events for a person to a machine. However, if an editor selected life events and wrote up a set of bullets, you could imagine having ChatGPT render that into a draft text. Then again, if you’re going to do the work of researching, selecting, editing, and writing the life points, I doubt ChatGPT can save much time.
8. Newsletters
“Personalize newsletters and optimize newsletter delivery times.”
There are better ways to do newsletter curation using more traditional recommender system approaches, and ChatGPT won’t be any help with optimizing delivery times either. But there’s a lot of potential for newsletter personalization in terms of tailoring the curated content to appeal to an individual’s interests. ChatGPT could be used to frame or rewrite headlines or summaries of the curated content to be more personally appealing, based on a user model. For instance, if I know that you’re the type of person who often reads articles about inflation, I could highlight that in the headline or blurb of an article in a newsletter. This use case is more about emphasizing the frames from an article which are particularly appealing to an individual. Much as for other automated writing use cases these personalized excerpts would need to be carefully evaluated though. To implement this you would probably define a limited set of audience segments for personalization, and then assign an editor to manually review how each audience segment would see the newsletter before publication.
9. Text Summarization
“Generate summaries (e.g., briefs, from government meetings and cut downs of broadcast packages)”
There’s a lot of information coming at journalists everyday, and summarization could help make scanning that information more efficient. But, the key issue here is whether the summarization is journalistically aware. Is the summarization able to pick up on journalistic relevance in selecting and prioritizing what subset of information to include in the summary? You could certainly apply ChatGPT to summarize a government meeting, but it would be a complete failure if it left out a key controversial exchange. The main question is: in your specific content, what is your tolerance for missing something? There are possibilities here, but still much research to do on how to prompt ChatGPT to be sensitive to the right factors in summarization and to evaluate the system’s abilities for this task.
10. Comment Moderation
“Filtering by language, duplicate commenter accounts and compiling comments for marketing”
Comment moderation is a use case of great interest, not only for news organizations but also for social media platforms in general. Insofar as comment moderation can be reduced to classification (e.g. “hate speech”, “top-quality comment”) then ChatGPT can be used here, though with all of the disclaimers described above. For any particular category of comment that should be detected there should be an extensive evaluation of the accuracy, false positives, and false negatives, with an eye towards the cost to the community of any of those moderation errors. The AP report also mentions “compiling comments for marketing” and this starts to get into more of a summarization use-case. If the marketing is low-stakes it should also be possible to use ChatGPT here.
11. Content Transformation and Reuse
“Format articles as structured data to enable reuse in different platforms, format broadcast scripts for the web.”
This will depend on the exact transformations and data extractions needed to reuse content on different platforms, but in general ChatGPT should be able to help with these kinds of tasks. One example would be in taking an article and generating a headline and keywords for posting it to social media. If the metadata generated by the model is not visible to end users, such as with keywords, you can probably make do with lower accuracy. But still, any outputs of the model should be evaluated to ensure they’re high-enough quality and not totally off base.
12. Search Engine Optimization
“Integration with A/B headline testing, offering recommendations and integration with archives to recommend evergreen content”
Non-GPT AI technologies for content recommendation from archives and for A/B headline testing are already well-developed. The area where I see models like ChatGPT helping here is in suggesting variations of headlines for A/B testing. In this case I expect fine-tuned models of large language models will have a better chance of recreating any particular publication’s headline writing style. Otherwise, you would want to come up with unique and tailored prompts to set apart the headlines generated. As with other use cases though you’ll always want to have a human in-the-loop. Much better to think of this as a headline brainstorming tool to help you come up with diverse headlines to test with your audience. More broadly, metadata useful for SEO purposes, such as keywords or a summary text, could also be generated. The way search engines integrate generated text from an SEO perspective will be an area of active scrutiny and development as companies like Google and Microsoft also figure out what it means to have the technology more firmly embedded in their search engines.
13. Push-Alert Personalization
“Extending story recommendation abilities to personalize mobile push alerts”
While you would want to use non-GPT AI technologies for the actual push-alert recommendation engine, ChatGPT could be useful in the same ways as discussed above for use cases in headline or newsletter generation. For instance, the writing of push alerts could be personalized to different audience segments. As with virtually all the other use cases you’ll want a person checking the text generations before publishing them.
| 2023-02-15T00:00:00 |
2023/02/15
|
https://generative-ai-newsroom.com/what-could-chatgpt-do-for-news-production-2b2a3b7047d9
|
[
{
"date": "2023/02/15",
"position": 52,
"query": "AI journalism"
}
] |
Be MediaWise lesson 13: ChatGPT—separating fact from ...
|
Be MediaWise lesson 13: ChatGPT—separating fact from fiction in a new era of artificial intelligence
|
https://www.pbs.org
|
[] |
What are three developments that will help identify information created by artificial intelligence like ChatGPT? What media literacy strategies you have learned ...
|
ChatGPT has caught on fast because it is free and produces convincing responses to requests for, well, almost anything — from poetry and essays to jokes and math equations. In this lesson, students will learn what ChatGPT is, how it works, and how to recognize AI-generated content, and then put these skills to the test to separate AI-generated social media posts from human-generated posts.
Students will be able to:
Grade Levels Grades 6-12
Downloadable Lesson Documents
Key Vocabulary
ChatGPT – ChatGPT is an AI language model developed by OpenAI that is capable of generating human-like text based on the input it receives. It uses a deep neural network trained on a massive dataset of text to generate responses to a wide variety of questions and prompts. (source: ChatGPT) Misinformation – false information that is spread, regardless of intent to mislead. “Disinformation” is false information that is spread intentionally.
Before You Watch
Brainstorm as many words as you can to describe ChatGPT. Discuss with the class.
While You Watch
Watch the MediaWise Teen-Fact Checking Network (TFCN) video and answer the following questions. According to the video…
ChatGPT is fed millions of pieces of writing from the _______________ , including Wikipedia and Reddit pages and It uses complex________________ to figure out what to say next — specifically, a special calculation to pick its next word based on all of the words that have already been generated online. This helps it make its responses sound more like a _______________ . Some reasons why ChatGPT helps spread misinformation: (1) It is _______________ to use, (2) it does not provide_______________ for any information it provides, (3) the information it provides sounds _______________ , even if false, (4) it will provide convincing _______________ information if it is asked to do so, (5) artificial intelligence (AI) is becoming more _______________ and will become harder to detect AI content. According to ChatGPT, three ways to identify written content was generated by AI are (1) check for patterns and _______________ , (2) look for signs of _______________ error, and (3) check the _______________ . What are three developments that will help identify information created by artificial intelligence like ChatGPT? What media literacy strategies you have learned can you use to check if content is AI-generated?
AFTER YOU WATCH
What elements make this social media post seem believable? What clues indicate that it could be AI-generated? What long-tail keyword search phrase could you use to find credible sources through lateral reading to check the authenticity of the information in this social media post?
Extension Activity
Put your new artificial intelligence identification skills to the test. See if you can tell which of the six social media posts in the extension activity document are real and which were generated by ChatGPT. Use what you learned in the video to evaluate each one, label each as real or fake, and explain how you arrived at each label by using long-tail keyword search terms, lateral reading, and upstream reading Watch this story produced by a student from the PBS NewsHour Student Reporting Labs — " A student offers his take on the promise and perils of ChatGPT "
Subjects
media literacy, social studies, language arts, journalism
Standards
Common Core ELA
CC.8.5.6-8; CC.8.5.9-10.A; CC.8.5.11-12.A - Citing informational text
College, Career, and Civic Life (C3)
D3.1.6-8. Gather relevant information from multiple sources while using the origin, authority, structure, context, and corroborative value of the sources to guide the selection.
D3.1.6-12. Gather relevant information from multiple sources representing a wide range of views while using the origin, authority, structure, context, and corroborative value of the sources to guide the selection.
ISTE
6-12.1.4.a Students: know and use a deliberate design process for generating ideas, testing theories, creating innovative artifacts or solving authentic problems.
Additional resources
These lessons were developed by PBS NewsHour Student Reporting Labs in partnership with MediaWise and the Teen Fact-Checking Network, which are part of the Poynter Institute. This partnership has been made possible with support from Google.
| 2023-02-15T00:00:00 |
2023/02/15
|
https://www.pbs.org/newshour/classroom/lesson-plans/2023/02/lesson-plan-how-to-separate-fact-from-fiction-in-a-new-era-of-artificial-intelligence
|
[
{
"date": "2023/02/15",
"position": 53,
"query": "AI journalism"
}
] |
Investigating the reality through journalism, arts and ...
|
Investigating the reality through journalism, arts and artificial intelligence
|
https://climatesafety.info
|
[
"Mik Aidt",
"Climatesafety Fsife.Co.Uk"
] |
Mik also raises the concept of artificial intelligence, AI, and its potential value as a tool to get us to where we need to be on climate. He believes it to be ...
|
The Sustainable Hour no. 447 | Podcast notes
Our guests in The Sustainable Hour no. 447 on 15 February 2023 are journalist Callum Foote from Michael West Media, ethnochoreologist Jacqui Dreessens from Wild Moves International Geelong, and emerging climate oracle Ms ChatGeePeeTee from OpenAI.
. . .
“If you were to ask me which industry is most responsible for the destruction of life on Earth, I would say the media.”
~ George Monbiot, British author – on page 369 in ‘The Climate Book’
Callum Foote writes for Michael West Media, delivering investigative stories about fossil fuels, fracking, carbon accounting and land clearing. In our chat with him, we learn about the role that their company plays in the media industry here. They were set up to dig deeper into stories – to investigate areas of concern, like corruption in politics, the environment, among other important issues. They carry their independent label with pride, staunchly dedicated to the public interest and investigations into big business and government abuse of power. A real foil to the control that the Murdoch media empire has on our main stream media. They are able to do this because they don’t have any advertisements. They are funded 100 per cent by contributions from their readers.
Callum’s latest work was published today. It was described in Michael West’s newsletter as “a scoop on fossil fuel influence in another government agency”. According to Callum’s research, a recent report from CSIRO detailing carbon credits required to offset fracking in the Beetaloo Basin is based on faulty assumptions to suit the gas industry.
Another recent story from Callum, about an escalating war between the war between the fossil fuel company Santos and New South Wales’ farmers, is titled: NSW Government offers little resistance to Santos fracking on the Liverpool Plains. This was a follow-up on his two Santos stories, Santos edges in on fracking the Liverpool Plains despite repeated denials and Pipeline Rumble: farmers the final line of defence in Narrabri fight over Santos gas fracking, which were published in January 2023.
In 2022, among other environmental issues, Callum examined the dodgy and contradictory reporting frameworks in Australia around carbon offsets and published the story: Out for the count: Carbon Offsets’ are not actually carbon offsets.
Currently, Callum is having a good look into why Australia continues to have one of the highest rates of land clearing in the developed world.
→ To find out more about this important media outlet, go to: www.michaelwest.com.au, and you can follow Callum on Twitter.
. . .
Jacqui Dreessens is dedicated to using the arts to get messages across about the incredible gifts that nature provides for us. She lives on the Surf Coast where she provides experiences for others to do the same, organising poetry workshops where she guided people through the strategies of putting pen to paper – or digit to digital – to explore connections to the mangroves at Lake Connewarre. Jacqui describes herself as an eco- and ethnochoreologist, and in her talk with The Sustainable Hour she concentrates on describing what a transformative experience that mangroves have been for her. Recently she oversaw an exhibition in Geelong where dancers, artists, poets, musicians and filmmakers all showed what these magic forests mean to them.
→ To find out more about Jacqui’s work, go to www.mangrovesfromthewater.com
. . .
Mik Aidt starts our program today by wondering why the mainstream media refuse to acknowledge the true cause of the extreme weather calamities being experienced all over our planet. The latest term is “crazy weather”, but they still aren’t acknowledging the connection these destructive weather events have with our burning of fossil fuels. They’ll report on the number of deaths, and they’ll report it is getting worse and worse, but never a single word about that we are causing this ‘craziness’ with our cars, our gas stoves, our airplane- and cruise ship-trips, the way we do farming, and so on.
Mik suggests another way: Always, at the bottom of each article about extreme weather, at the end of each article and tv-report, there could be a statement: “Extreme weather events have become more frequent and more destructive because of our burning of petrol, gas, oil and coal.”
A similar initiative could be done with stickers on the pumps at petrol stations, billboards advertising for gas products, and so on. Mik continues by reporting on the obscene amounts of money that just three major oil companies made last year as he again asks us to consider how useful such amounts of wealth would be in transitioning us to 100 per cent clean, renewable energy.
At the same time, we have the United Nations Secretary-General telling the oil companies straight that their days are numbered in a recent speech. Antonio Guterres is doing all he can to lead the world out of this ‘climate craziness’. However the burning question is: why isn’t he being listened to?
For those who would like to listen to or watch the UN Chief’s entire speech, go to un.org.
. . .
Mik also raises the concept of artificial intelligence, AI, and its potential value as a tool to get us to where we need to be on climate. He believes it to be something that looks like it could become a new powerful ally in communicating the truth about climate to the broader public.
The chatbots speak directly to us without the filtering and prioritising of media people making the wrong choices for us. The more people use chatbots to get their information – and the young people are already busy doing this – suddenly it matters less that we can’t rely on the old mainstream news, because – at least as it is at the moment – the chatbots get it. They understand the science and they are able to tell us what it is we need to do to solve the problems with the climate breakdown.
We’ll be going into more detail on this in future shows, and will keep investigating and testing the chatbots, but for now, here’s just an example of what the chatbot ChatGPT came up with in response to a question Mik asked it:
Mik: “Our governments and elected leaders ignore the escalating climate crisis. So what are we to do about that? How do we get them to prioritise the three most urgent problems humanity is facing?” ChatGPT: “Getting governments and elected leaders to prioritize the issues facing humanity can be a challenging task, but there are several steps that individuals and communities can take to raise awareness and drive action: Raise Awareness: Educate yourself and others about the issues and their implications, and use your voice to bring attention to them. Share information and engage in conversations with friends, family, and community members about why these issues are important and what can be done to address them. Get Involved in Activism and Advocacy: Participate in advocacy campaigns, attend protests, and support organizations that are working to address the issues you care about. This can help raise awareness, build momentum, and bring about change. Vote and Encourage Others to Vote: Use your right to vote to support candidates who prioritize the issues that matter to you. Encourage others to do the same and participate in local, state, and national elections. Support Businesses and Organizations That Align with Your Values: Choose to support businesses and organizations that prioritize sustainability, equality, and social responsibility. Use your purchasing power to support companies that are working to address these issues. Engage with Your Elected Representatives: Contact your elected representatives and let them know that you care about these issues and want them to prioritize them. Attend town hall meetings, write letters, and participate in public forums to make your voice heard. Ultimately, changing the priorities of governments and elected leaders requires sustained effort and persistence, but by working together and using our collective voice, we can create the change we want to see in the world.”
. . .
We round off the hour with advice from Missy Higgins in her classic climate song ‘The Difference’, which we never seem to get tired of, and some sage advice from the climate oracles Greta Thunberg and Ms ChatGPT.
. . .
Colin Mockett‘s Global Outlook begins at the United Nations in New York, where the world’s top diplomat, and unofficial patron for this programme, UN Secretary-General António Guterres last week delivered his annual report on the world’s priorities. It was a kind of ‘State of the Planet’ address, delivered by the lead spokesman for the world. And he put climate change at the top of the list, above wars, earthquakes and famines. Not only was he astonishingly frank and outspoken, he had the multinational fossil fuel companies firmly in his sights. He told them plain language to either help fight climate change or go out of business.
On the subject of global warming and the environment, he said ‘We need disruption to end the destruction.’
“We must end the merciless, relentless, senseless war on nature … That war is putting our world at immediate risk of hurtling past the 1.5°C degree temperature increase limit and now still moving towards a deadly 2.8 degrees.”
He pointed out that the fossil fuel industry had enjoyed record returns, in large part because of the war in Ukraine, and said, “I have a special message for fossil-fuel producers and their enablers who are scrambling to expand production and raking in monster profits: If you cannot set a credible course for net-zero, with 2025 and 2030 targets covering all your operations, you should not be in business. Your core product is our core problem. We need a renewables revolution, not a self-destructive fossil fuel resurgence.”
A sign that it’s not just at the United Nations that things are getting serious, from Europe came the news that Sweden’s 27-year-old climate minister said she would quit if her country fails to achieve its aims of net-zero by 2045. Romina Pourmokhtari was quoted as “If we see that, for example, I’m not able, as the minister of climate and environment, to create the change that I want to see, I would leave the government and my party would leave the government.”
Romina became Sweden’s youngest-ever cabinet minister when her party joined the new coalition government in October. Made up of the Moderates, the Christian Democrats and the Liberals (of which she’s a member), the coalition also takes support from the Sweden Democrats, a far-right party that contains climate-change deniers. It’s hard to imagine a more unlikely alliance.
Then to India, where the crisis engulfing the Adani Group intensified this week when hundreds of members of India’s opposition parties took to the streets to press for a probe into the close friendship between billionaire Gautam Adani and India’s prime minister Modi. Since January 24th US stock-market report named Adani’s company as the world’s largest corporate cheat, its shares have been in free-fall. The group has lost an estimated $110 billion. Both houses of India’s parliament were adjourned for three consecutive days, Adani Enterprises was forced to abandon a $2.5 billion share sale and Adani lost his crown as Asia’s richest person. Adani announced that he would issue a statement and report last week but postponed it until this week. We’ll all be waiting.
The news about Forest Green Rovers improved a little last weekend when the team drew 1-1 away at Morecombe, but it’s still not good, so I’ll leave you with some good news from Brazil:
There, a new study published in the scientific journal PNAS Nexus showed that handing the nation’s Atlantic rainforest back to indigenous tribes made a significant difference in the rate of decline. Some of the forests are slowly beginning to heal. The study looked at 129 territories in the region, 77 of which were won by indigenous groups in court battles since 1985. The study’s figures show that those areas not handed back average 0.73 per cent annual decline, while the average for those now under indigenous management only declined by an average of 0.05 per cent. Measured in area – the forest is declining at 54.6 acres a year under State management, but only 3 acres per year under indigenous control. That has to be good news for climate activists and environmentalists everywhere. And that’s our global roundup for the week.
. . .
Once again, big questions come out of today’s episode. Rest assured that we’ll keep asking them and keep challenging the toxic fossil fuel industry and the sleepwalking media to accept responsibility for their major contributions to the climate crisis we face.
You can trust that The Sustainable Hour will keep doing all that we can to address the lack of climate truth in our media, committed – just like we know many of you are – to be the difference.
“I was born and bred here on the Surf Coast and had never had anything to do with mangroves. What had happened in the 1960s and 1970s, Friends of Barwon Heads and Ocean Grove got together and started this mangrove regeneration project. The result of that was this beautiful mangrove forest that has really taken root. So for me to go there on a kayak and experience that for the first time, there was an element of grief because I had missed this as a child, but then extreme joy that I can now experience it.”
~ Jacqui Dreessens, eco- and ethnochoreologist
→ Subscribe to The Sustainable Hour podcast via iTunes or Stitcher
We need a renewables revolution, not a self-destructive fossil fuel resurgence.
My message to fossil fuel producers and their enablers. pic.twitter.com/mDIr1iB620 — António Guterres (@antonioguterres) February 11, 2023
“I have a special message for fossil fuel producers and their enablers scrambling to expand production and raking in monster profits: If you cannot set a credible course for net-zero, with 2025 and 2030 targets covering all your operations, you should not be in business. Your core product is our core problem. We need a renewables revolution, not a self-destructive fossil fuel resurgence. (…) We must end the merciless, relentless, and senseless war on nature. It is putting our world at immediate risk of hurtling past the 1.5-degree temperature increase limit and now still moving towards a deadly 2.8 degrees. Meanwhile, humanity is taking a sledgehammer to our world’s rich biodiversity — with brutal and even irreversible consequences for people and planet. Our ocean is choked by pollution, plastics and chemicals. And vampiric overconsumption is draining the lifeblood of our planet — water. 2023 is a year of reckoning. It must be a year of game-changing climate action. We need disruption to end the destruction. No more baby steps. No more excuses. No more greenwashing. No more bottomless greed of the fossil fuel industry and its enablers. (…) Stop subsidizing fossil fuels and pivot investments to renewables.” Antonio Guterres, UN Secretary-General
→ UN.org – 6 February 2023:
‘Act decisively before it is too late’, Guterres warns countries, laying out his priorities for 2023
“As the right to development goes together with the right to a clean, healthy, sustainable environment, “we must end the merciless, relentless, senseless war on nature,” said Mr. Guterres, repeating a message that has become a mantra for his tenure.”
“The U.N. Secretary-General is saying that the central problem with climate change is the fossil-fuel industry’s product, that the industry is immorally undermining climate action, and that, if it continues, it should be shut down. This is certainly the truth, but it too often goes unspoken. If Guterres’s courageous directness is unusual, it’s also hugely important.” ~ Bill McKibben, American author
Rising seas threaten ‘mass exodus on a biblical scale’, UN chief warns https://t.co/9xAX0EJh88 — Climate Mum 🌎💚 (@MumClimate) February 14, 2023
Other than saying stuff like this does Tony have any power to actually do something? That's just an honest question here. I mean if he is the head of the UN what does that mean? Don't our leaders just ignore him? #ClimateEmergency pic.twitter.com/fgHW8frCwS — Sophie Gabrielle (@CodeRedEarth) February 14, 2023
New Zealand’s climate change minister James Shaw did what the media is strangely unable to do: making the connection between extreme weather events and our unregulated carbon emissions when he made a furious speech rebuking the NZ parliament for lost decades of “bickering” over the climate crisis.
Cyclone Gabrielle has devastated the country. North Island has seen widespread flooding and enormous destruction, with thousands forced to leave their homes, and leaving 225,000 people without power.
Devastating extreme weather, super-charged by #climate breakdown caused by global heating.
Coming to visit YOU and your loved ones in 2023 and beyond.https://t.co/YpplN2k74x — Bill McGuire (@ProfBillMcGuire) February 14, 2023
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We at The Sustainable Hour would like to pay our respect to the traditional custodians of the land on which we
are broadcasting, the Wathaurong People, and pay our respect to their elders, past, present and future.
The traditional owners lived in harmony with the land. They nurtured it and thrived in often harsh conditions for millennia before they were invaded. Their land was then stolen from them – it wasn’t ceded. It is becoming more and more obvious that, if we are to survive the climate emergency we are facing, we have much to learn from their land management practices.
Our battle for climate justice won’t be won until our First Nations brothers and sisters have their true justice. When we talk about the future, it means extending our respect to those children not yet born, the generations of the future – remembering the old saying that, “We do not inherit the Earth from our ancestors. We borrow it from our children.”
The decisions currently being made around Australia to ignore the climate emergency are being made by those who won’t be around by the time the worst effects hit home. How disrespectful and unfair is that?
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Callum Foote interviewed in The Sustainable Hour no. 447
Beetaloo fracking company Tamboran Resources gave $62,000 to Labor and $25,000 to the Liberals and a staggering $100,000 to the Nationals#donationsday #auspol — Callum Foote (@CallumDRFoote) January 31, 2023
→ Michael West Media – 15 February 2023:
“Brazenly co-opted”: experts confront CSIRO for gas industry infiltration, greenwashing fossil fuels
“The CSIRO’s recent report detailing carbon credits required to offset fracking in the Beetaloo Basin is based on faulty assumptions to suit the gas industry.”
→ Michael West Media – 14 February 2023:
NSW Government offers little resistance to Santos fracking on the Liverpool Plains
“Treasurer Matt Kean’s Hunter gas pipeline approval has set “a tragically low bar” for landholder rights, says National Farmers’ Federation’s Fiona Simson, as the war between Santos and NSW farmers escalates.”
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Landmark legal case against land clearing in the NT
Newsletter from Environmental Justice Australia:
“We’ve just launched an important legal case in the Northern Territory to challenge land clearing for cotton on some of the world’s last remaining intact savanna – right here in our backyard.
The Northern Territory’s woodland savanna is a critically important ecosystem, cared for by Traditional Owners for millennia, and home to countless threatened animals and plants.
Big cotton is eager to get a foothold in the NT and plans to bulldoze thousands of hectares of fragile savanna for cotton production. If they go ahead, this will threaten local wildlife, impact rivers, and add to greenhouse gas emissions.
That’s why we’re representing Environment Centre NT to challenge the Pastoral Land Board’s decision to allow a multinational corporation to clear over 900 hectares of woodland for cotton.
Land clearing permits have skyrocketed in the NT by around 300% in just the last few years. Much of this is without proper assessment for environmental impacts – meaning big corporations can bulldoze vast areas of land without properly assessing the risk of ecological harm.
ECNT’s case could change this – and set a game-changing precedent. If successful, it would require companies who want to clear land for cotton to submit their plans for more rigorous assessment and notify Traditional Owners.
This is just the beginning. Come along to the webinar on Tuesday 14 February to hear more about our argument and find out the key details of the case.
Until then, you can read more about the case in our newsletter.
Thanks for standing with us,”
Laura Dreyfus, Lawyer
Environmental Justice Australia
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“It is only through political activism and engagement that you can bring about real and lasting change.”
~ ChatGPT
Using artificial intelligence in climate activism
We end The Sustainble Hour today with a little speech which Gwyneth Jones had asked ChatGPT for:
“Well folks, it’s been a wild ride so far. Climate change, corrupt politicians, economic struggles, and let’s not forget the occasional nuclear threat. It’s enough to make you want to just curl up in a ball and hide from the world.
But let’s take a step back and look at the bigger picture. Despite all the chaos and turmoil, there is still so much good in the world. People are coming together to fight for a better future, to protect the planet, and to help each other.
And let’s not forget about the little moments of joy that make life worth living. The warmth of a hug from a loved one, the laughter of children playing, and the beauty of a sunset.
So, yes, the world can be a difficult place at times. But it’s important to remember that we have the power to make a difference, to bring light to the darkness, and to create a brighter future for ourselves and generations to come.
So let’s not give up hope, let’s keep fighting for what is right, and let’s work together to create a world that is worthy of our love and respect.
Because, at the end of the day, hope is the fuel that drives us forward and helps us overcome even the greatest of obstacles.”
→ Medium – 11 February 2023:
Using artificial intelligence in climate activism
“I asked Chat GPT to be sarcastic and talk about the state of the world with me…”
“Let’s keep fighting for what is right.”
~ ChatGPT
. . .
ChatGPT on media’s role in the climate emergency
“George Monbiot writes in ‘The Climate Book’ on page 369: “If you were to ask me which industry is most responsible for the destruction of life on Earth, I would say the media.” Is he right – or is he wrong? Would you say the public service broadcasters have an obligation to inform the population about the connection between their consumption of fossil fuels and the more and more frequent occurance of destructive weather events?“
ChatGPT: “As an AI language model, I don’t have personal opinions, but I can provide you with some information on the topic.
It is a controversial statement to say that the media is the most responsible for the destruction of life on Earth, as there are many other industries that have a significant impact on the environment, including but not limited to, the fossil fuel, agriculture, and manufacturing industries. However, it is true that the media has a crucial role to play in shaping public opinion and raising awareness about environmental issues, including the connection between consumption of fossil fuels and the occurrence of destructive weather events.
Public service broadcasters, as well as other media outlets, have a responsibility to provide accurate and balanced information to the public, so that they can make informed decisions. This includes reporting on the environmental impact of our actions, such as the connection between fossil fuel consumption and the increase in frequency of extreme weather events.
In conclusion, while the media is not the sole industry responsible for the destruction of life on Earth, it is still a crucial part of the solution and has a duty to inform the public on important environmental issues such as this one.”
. . .
Me: “Which are the three most important changes humans must implement in order to avoid societal collapse? And how urgent is it?“
ChatGPT: “It’s difficult to determine the exact three most important changes that humanity must implement to avoid societal collapse, as this is a complex and multifaceted issue that depends on many factors. However, some changes that are often discussed as being important for the long-term sustainability of society include:
Addressing Climate Change: Climate change is one of the biggest challenges facing humanity and has the potential to cause widespread societal collapse if left unaddressed. Reducing greenhouse gas emissions, transitioning to renewable energy sources, and improving the resilience of communities to the impacts of climate change are all crucial steps in addressing this issue.
Promoting Sustainability: Ensuring that humanity’s use of natural resources is sustainable and does not deplete them for future generations is another important step. This involves reducing waste, improving resource efficiency, and promoting sustainable agriculture, forestry, and fisheries.
Fostering Equality and Inclusion: A society in which there is widespread inequality and exclusion is unlikely to be stable in the long-term. Addressing economic, social, and political inequality and promoting inclusiveness and diversity are therefore important for the sustainability of society.
In terms of urgency, it is crucial for humanity to take action on these issues as soon as possible. Climate change, for example, is already causing significant impacts on communities and ecosystems, and the longer we delay action, the more difficult and expensive it will be to address. Additionally, the sustainability of natural resources is becoming increasingly important as populations grow and demand for resources increases. Finally, inequality and exclusion can lead to social unrest and instability, so it is important to address these issues proactively.”
. . .
Me: “I would like to encourage young people to get politically active. They refuse because they say politics is for “bad people” who lack good morals. They can’t see any hope of changing that. So instead, they would rather go protesting in the streets. Can you write a speech I can read to them to convince them why it is a better idea to get politically active?“
ChatGPT: “Sure, here’s a sample speech:
“Ladies and Gentlemen,
I understand that many of you feel disillusioned with politics and that it is sometimes seen as a domain of “bad people.” But I want to encourage you to reconsider this view and to become politically active.
As young people, you have a unique opportunity to bring new ideas, perspectives, and energy to the political process. You have the power to make a positive difference in your communities and the world. By engaging in the political process, you can help shape the policies and decisions that will affect your future.
It’s true that there are some individuals in politics who lack good moral and ethical principles, but this doesn’t mean that the entire system is corrupt or that change is impossible. On the contrary, by getting involved in politics, you can work to make the system more transparent and accountable, and to promote the values and principles that you believe in.
In addition, I understand that you may be frustrated by the slow pace of change and the lack of progress on certain issues. But I want to remind you that real and lasting change often takes time and effort. By becoming politically active, you can help to speed up the process and increase the chances of success.
And while protesting can be an important way to bring attention to certain issues, it is only through political activism and engagement that you can bring about real and lasting change. By participating in the political process, you can help to create the conditions that will allow your voice to be heard and your concerns to be addressed.
So, I encourage you to become politically active. Get involved in campaigns, vote, join a political party or community organization, or run for office yourself. By doing so, you can help to create a better future for yourselves and for generations to come.
Thank you.” “
—
Me: “If we define “good people” as people with high moral and ethical principles, such as honesty, kindness, and fairness, and “bad people” as people who lack these, and as such are dishonest, unkind and unfair, how could a conversation between a good person and a bad person play out at a garden party, where the two persons are discussing whether or not to implement a carbon tax?“
ChatGPT: “Here’s a possible scenario:
Good person: Hi, it’s nice to meet you. What do you think about the idea of implementing a carbon tax to help reduce greenhouse gas emissions and combat climate change?
Bad person: I don’t see the point. It would just make life more expensive for people and hurt the economy.
Good person: I understand your concern, but don’t you think it’s important to think about the long-term effects of climate change on our planet and future generations?
Bad person: Frankly, I’m more focused on my own financial interests. And I don’t believe in this climate change nonsense anyway.
Good person: Well, regardless of your personal beliefs, the scientific consensus is that climate change is real and caused by human activity. And implementing a carbon tax could be a way to incentivize companies to reduce their carbon footprint and transition to more sustainable practices.
Bad person: I’m not interested in all that. I just want to make money and live a comfortable life. Besides, I have a lot of friends in the energy industry who would be negatively impacted by a carbon tax.
Good person: I see. But don’t you think it’s our responsibility to consider the impact of our actions on the environment and future generations? And wouldn’t it be better for everyone in the long run if we all work together to find sustainable solutions to this problem?
Bad person: Sorry, but I just don’t have the same priorities as you. To me, it’s all about personal gain.
In this scenario, it’s clear that the good person has a strong sense of ethical responsibility to address the issue of climate change, while the bad person is primarily motivated by personal financial interests. However, the good person tries to approach the conversation in a respectful and non-judgmental manner, highlighting the importance of considering the long-term consequences of our actions.”
. . .
Ask ChatGPT for help
ChatGPT can help boost climate activists’ confidence in writing better comments and replies under newspaper articles to fight back on the trolls and the blooming climate denial in social media.
For instance, when a climate denier – on a comment to an article on The New Daily’s website about climate and extreme weather – posted the comment: “Evidently a greenie who wants to make Australia a third world country,” ChatGPT suggested to respond to it with a satirical remark: “Well, I do love the idea of living in a third world country… minus the poverty, disease, and lack of access to clean water, of course. But hey, at least we’ll have sunny weather year-round, right?”
Give it a go, start your own chat with ChatGPT here: chat.openai.com
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→ Crikey – 6 February 2023:
Adani crashes and burns, accused of ‘biggest con in corporate history’
“Is the meteoric decimation of the fossil fuel titan a gift to our environment, or does it unwind Adani’s much-needed climate action? Whether climate warriors should cheer or take a pause is a little more complicated.”
→ The Sydney Morning Herald – 1 February 2023:
An apology to my grandkids for not fighting in the war of our times
“Do politicians think we will all be dead before the next generation fully realises the hell we’ve left them?” By Ross Gittins, SMH Economics Editor
→ DW – 10 February 2023:
Shell, BP boost profit, sink investment in renewable energy
“While the big five oil companies registered record profits on the back of the energy crisis, relatively little will be reinvested in renewables as climate goals are slashed.”
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These are the people screwing the poor and the planet while popping the champagne corks and laughing all the way to the bank.
I hope the International Criminal Court has very small and uncomfortable cell for each and every CEO and senior manager. pic.twitter.com/m15f6TZcIx — Bill McGuire (@ProfBillMcGuire) February 12, 2023
https://twitter.com/JustStop_Oil/status/1625496781554585600
‼️BREAKING
Today, blockades of Private Jets are ongoing around the world with scientists & activists from Extinction Rebellion & Stay Grounded.
We denounce outrageous levels of climate injustice. It's time to: #BanPrivateJets#TaxFrequentFlyers#MakePollutersPay pic.twitter.com/68AE8gX2MR — Extinction Rebellion Canterbury (@XRCanterbury) February 14, 2023
21 JAN 2023 | Adelaide, Australia: Extinction Rebellion activists chuck paint at and glue themselves to the offices of Santos, the energy giant sponsoring a major cycling race in the city despite regularly poisoning the region with oil spills. It also plans to raze local forests for gas wells.
State intimidation boosts Dutch blockade
26 – 28 JANUARY 2023 | The Hague, Netherlands
Rebels blockade the A12 highway and their new supporters look on from above.
Every year, the Dutch government spends €17.5 billion on fossil fuel subsidies (three times more than what it allocates for climate policy). The Netherlands will be largely underwater if global emissions continue on their current path, so last summer rebels started blockading the A12 highway, which passes the national parliament, to try and force their government to stop the suicidal subsidies.
Two days before its fifth and most recent A12 blockade, six rebels were arrested in their homes on suspicion of sedition or incitement. Without warning or explanation, they were taken to different police stations, had their phones confiscated, were held in custody for 10 hours, and banned from the A12 for three months.
If the state intended to scare the rebels and tarnish XR, it badly backfired. The arrests prompted an outpouring of support from the public, and condemnation from dozens of prominent NGOs like Greenpeace and Friends of the Earth.
Rebels show appreciation for the love and solidarity from the crowds above.
Even better, the solidarity carried over to the blockade itself. Thousands of people gathered in and around the A12, making it one of XR Netherlands’ largest ever blockades. While more than 1000 rebels sang songs and decorated the walls of the highway, an additional 2000 non-XR supporters stood in solidarity above them.
After half an hour, the police started to carry the rebels away. It took several hours to clear the road, and more than 700 arrests. All rebels were released the same day.
XR Netherlands has already announced it will keep coming back to the A12 until the government ends its fossil fuel subsidies. The next blockade is scheduled for March 11th and promises to be the biggest yet.
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This #WorldWetlandsDay, it’s time for wetland restoration. We’re proud to be working with partners across industry, gov and the community to restore wetlands and the benefits they provide. #GenerationRestoration #ForWetlands #bluecarbon pic.twitter.com/RvnGXyxdza — Blue Carbon Lab (@BlueCarbonLab) February 2, 2023
To have a chance of minimizing further irreparable damage, we have to choose: either we safeguard living conditions for all future generations, or we let a few very fortunate people maintain their constant, destructive search to maximize immediate profits.https://t.co/fjlYtZC6gA — Greta Thunberg (@GretaThunberg) February 13, 2023
#AusPol
Great to see independent @WilkieMP introduce his Cleaning Up Political Donations Bill, which would ban big donations and improve transparency. We can get dirty fossil fuel money out of politics. #auspol pic.twitter.com/RJP5X1Lasu — Australian Conservation Foundation (@AusConservation) February 14, 2023
https://twitter.com/zalisteggall/status/1625021047719309312
https://twitter.com/zalisteggall/status/1622380482930569217
Climate emergency
History
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Events we have talked about in The Sustainable Hour
Events in Victoria
The following is a collation of Victorian climate change events, activities, seminars, exhibitions, meetings and protests. Most are free, many ask for RSVP (which lets the organising group know how many to expect), some ask for donations to cover expenses, and a few require registration and fees. This calendar is provided as a free service by volunteers of the Victorian Climate Action Network. Information is as accurate as possible, but changes may occur.
Petitions
→ List of running petitions where we encourage you to add your name
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| 2023-02-14T00:00:00 |
2023/02/14
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https://climatesafety.info/thesustainablehour447/
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[
{
"date": "2023/02/15",
"position": 71,
"query": "AI journalism"
}
] |
Our efforts to diversify Nature's journalism are progressing, ...
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Our efforts to diversify Nature’s journalism are progressing, but work remains
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https://www.nature.com
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Two years ago, this journal pledged to report on the diversity of sources in our journalistic content. The first results are now in.
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Two years ago, this journal pledged to report on the diversity of sources in our journalistic content. The first results are now in.
Credit: Ilnur Khisamutdinov/Alamy
In June 2021, researchers at the University of Colorado School of Medicine in Aurora turned a magnifying glass on Nature’s written journalism. Computational biologists Natalie Davidson and Casey Greene reported that most of the individuals we quoted were male.
Using automated methods, the authors found that 69% of the people quoted directly in Nature’s written journalism in 2020 were male (N. R. Davidson and C. S. Greene Preprint at bioRxiv https://doi.org/gkscd5; 2021). This figure had been falling — the authors’ analysis of some 16,000 articles published between 2005 and 2020 found that the number was 87% in 2005 — but 69% was still unacceptably high.
The journal’s editorial teams collectively pledged to work harder to improve the diversity of those whose words or voices are quoted or paraphrased in our reporting (see Nature 594, 473–474; 2021). In April 2021, we began a pilot project to track aspects of diversity in some of Nature’s journalism more systematically. That September, we began tracking diversity in journalistic content across all relevant teams. This week, we’re reporting data for 1,118 written articles and 123 podcasts and videos published between 1 April 2021 and 31 January 2023. In the written pieces, Nature directly quoted or paraphrased the words of 5,252 people. The podcasts and videos featured the voices of a total of 240 people.
As part of our tracking efforts, members of Nature’s editorial teams recorded the preferred pronouns of those who were quoted or paraphrased, as well as their geographical location and career stage. We did not collect data on race or ethnicity for this phase of our project, but are working to widen the racial and ethnic diversity of our sources to make our reporting more representative of global science.
Our data show that people using he/him pronouns comprised around 55% of those quoted or paraphrased in written articles published since April 2021, with about 36% using she/her pronouns. There were 24 people with they/them pronouns, and 5 who used other pronouns (see ‘Nature’s journalism in charts’). The preferred pronouns of most of the remaining 9% are not known, meaning that of those who did disclose their preference, around 60% used he/him pronouns and some 40% used she/her.
Slightly more than 60% of those quoted or paraphrased in Nature’s written journalism were ‘established’ in their careers, meaning that they were professors or had tenure or another senior role. By contrast, 18% fell into the ‘early career’ category, which included graduate students, postdoctoral researchers and non-tenured faculty members. The ‘other’ category, which encompassed 11% of people, includes those in non-academic settings, such as campaigning organizations, industry and policymaking. Some 75% of all of those quoted were based in North America or Europe.
Of the 240 people quoted or featured in our podcast and video content, 48% used he/him pronouns (amounting to 52% of those who disclosed their pronouns). Some 60% of those featured were established in their careers, and 81% were based in North America or Europe.
These data show that we are continuing to make progress on gender diversity. But there is clearly still work to do, particularly when it comes to improving the global representation of voices in our pages, as well as the proportion of researchers at earlier career stages.
We will continue to record our data, and we aim to improve on these figures, proactively seeking out and trying to better represent voices from historically less-represented peoples and parts of the world. The result, we hope, will be better journalism derived from a more accurate representation of the diverse scientific community of which Nature is a part.
| 2023-02-15T00:00:00 |
https://www.nature.com/articles/d41586-023-00399-1
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[
{
"date": "2023/02/15",
"position": 76,
"query": "AI journalism"
}
] |
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hireEZ Announces GPT3-Powered Platform Feature for ...
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hireEZ Announces GPT3-Powered Platform Feature for Candidate Outreach
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https://hireez.com
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[] |
Artificial intelligence has been at the core of the hireEZ platform since it launched the world's first AI candidate sourcing technology in 2017. The ...
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MOUNTAIN VIEW, Calif. — (Feb. 15, 2023) — hireEZ, the end-to-end talent acquisition platform, today announced it will offer generative AI functionality in a product release slated for March 9. The update will leverage the Generative Pre-trained Transformer 3 (GPT-3) model and hireEZ’s machine learning data to help recruiters compose and send email messages to job candidates faster than with traditional tools.
“We see the use of generative AI as a massive opportunity to improve how businesses engage and communicate with talent,” said Steven Jiang, CEO at hireEZ. “Our goal is to make sure the technology works efficiently, and ethically, for both companies and job candidates. Any recruiter today will tell you they feel like they are doing the job of five people. We envision a future where AI can help one recruiter easily do the job of 10.”
The GPT3-powered functionality will add the ability for recruiters to instantly generate unique candidate email messages based on a job description. This will fully automate the time-consuming task of composing a message, which recruiters sometimes need to do hundreds of times a day. To maximize the utility of each AI-generated message, the hireEZ platform will use proprietary machine learning data to automatically produce language tailored for recruitment. With the update, hireEZ aims to make it easier for recruiters to engage with job applicants faster and increase their ability to personalize candidate outreach at scale.
The new feature will initially be available to a limited group of customers, then to others who have registered on a waitlist, and finally to the public. Users can sign up for the waitlist here.
Artificial intelligence has been at the core of the hireEZ platform since it launched the world’s first AI candidate sourcing technology in 2017. The company is committed to investing in the responsible development of technology and adheres to a strict set of principles for building AI systems.
About hireEZ
hireEZ is the award-winning, end-to-end talent acquisition platform with access to 800M+ candidates from 45+ open web platforms, with market insights to build strategic recruiting campaigns and candidate engagement capabilities to make outbound recruiting easy. With hireEZ, you can execute a strategically scalable approach to build your workforce of the future. For additional information, please visit hireez.com.
| 2023-02-15T00:00:00 |
https://hireez.com/newsroom/gpt3-powered-platform-feature-for-candidate-outreach/
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[
{
"date": "2023/02/15",
"position": 98,
"query": "artificial intelligence hiring"
}
] |
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More than 100000 tech workers laid off so far in 2023, but ...
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More than 100,000 tech workers laid off so far in 2023, but job opportunities are opening up
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https://www.emarketer.com
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[] |
Tech layoff picture: Tech companies have laid off more than 100,000 global workers to date in 2023, per Layoffs.fyi. A total of 159,684 tech employees were laid ...
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Our Story Learn more about our mission and how EMARKETER came to be. Learn More →
Our Clients Key decision-makers share why they find EMARKETER so critical. Learn More →
Our People Take a look into our corporate culture and view our open roles. Join the Team →
Our Methodology Rigorous proprietary data vetting strips biases and produces superior insights. Learn More →
Newsroom See our latest press releases, news articles or download our press kit. Learn More →
| 2023-02-15T00:00:00 |
https://www.emarketer.com/content/more-than-100-000-tech-workers-laid-off-far-2023-job-opportunities-opening-up
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[
{
"date": "2023/02/15",
"position": 35,
"query": "artificial intelligence layoffs"
}
] |
|
OpenAI Designer Ethics: When Is It Cheating?
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OpenAI Designer Ethics: When Is It Cheating?
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https://www.designity.com
|
[] |
In this blog, we're going to dive into AI designer ethics and determine when using AI crosses the line into sticky territory.
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There’s a bit of a controversy over AI ethics lately.
If you were spending some time on the internet last September, you might have heard some rabble about Colorado State Fair’s annual art competition.
The artist who won first place in the painting category, Jason Allen, caused some backlash among his peers when it was discovered that his entry was created entirely by the AI art generator, Midjourney.
Was it cheating? Jason Allen says no. He disclosed his painting’s origin right on his entry form for all to see. It won despite that.
So, it’s art, right?
Some artists agree, some don’t. So, what is everyone worked up about?
In this blog, we're going to dive into AI designer ethics and determine when using AI crosses the line into sticky territory.
What is an AI Art Generator?
AI art generators are software that use artificial intelligence technology to create images.
You type in a prompt, like “lady waiting for bus” or “flamingo beach party,” and the AI spits out a relevant image within a few seconds. The results vary and can range from amazing to hilarious to nightmare fuel.
It works by using algorithms (written by artists) that direct the computer program to not only follow your written prompt but to do so with a certain aesthetic that it creates by analyzing the millions of images in its data bank.
The AI takes your prompt and merges it with the aesthetics of those images stored in its “brain” to generate a new image for you. The more images it can analyze, the more machine learning can take place and the better the AI gets at its job.
It’s a pretty cool thing, but the question remains …
Is it truly art?
And when it comes to graphic designing, is it cheating to use AI art generators like Midjourney or NightCafe to generate work for your clients?
What is Art?
We can start this discussion by first defining “art” itself.
If you look it up in the dictionary (or Google, whatever) you’ll see that it’s defined as “the expression or application of human creative skill and imagination, typically in a visual form such as painting or sculpture, producing works to be appreciated primarily for their beauty or emotional power.”
Read it again.
Especially the first part; “the expression or application of human creative skill and imagination.”
Leaves some room for interpretation, doesn’t it?
Controversy over the definition of art isn’t anything new either. When Jackson Pollock came onto the scene with his “drip period” in the late ‘40s, the art world was thrown for a loop.
Is this art? Is flinging paint at a canvas really art?
The buyers of his paintings who paid tens of millions of dollars certainly seemed to think so.
We can go back even further to the stuffy 1910s when artist Marcel Duchamp (under the alias R. Mutt) submitted his readymade sculpture, “Fountain,” for an exhibit of the Society of Independent Artists in New York.
There was plenty of laughing that day, because “Fountain” was a straight-up urinal turned upside down.
No, seriously.
It’s a urinal. As in something that was supposed to be in the men’s bathroom.
But when it was thrown out of the exhibit because the board members assumed it was a joke, the artist resigned from the SIA board out of protest.
It wasn’t a joke. He was serious. To him, it was art. Not a joke, and certainly not cheating the system.
Art.
And Duchamp’s “Fountain” was only a small part of a larger, very experimental early twentieth-century art movement known as “Dadaism.” The whole point of the Dada (French colloquial for hobby horse) movement was to explore new forms of art or “anti-art” and challenge the accepted definition of it. This led to many interesting works of art that led many to ask …
Is it really art?
Again, some folks agree and some don’t.
Should I feel bad for using AI in my work?
When it comes to design, however, it’s usually not enough to hand your client an AI logo or a urinal and call it a day.
Most AI images, while very cool, lack the je ne sais quoi that a human brain can give. If you’ve seen AI logo generators, you know all too well how generic the results can be.
AI art can often be bizarre too, a non-human machine’s attempt at replicating human art that sometimes leads us down the uncanny valley. (Seriously, ask an AI generator to draw you a human hand. We’ll wait).
As of now, AI technology is not advanced enough to create client-worthy marketing collateral without some human assistance. It doesn’t understand target audiences or how to differentiate from competitors or how to fit with a brand.
You’ll have to adjust it to your client’s brand and that takes human intelligence and understanding.
If you’re a typical designer, you’ve got several clients and all of them are wanting more and more of your time, your energy, and your creativity. Especially now, when so many in-house designers have been laid off and marketers are looking to us or freelancers to fill that void.
All of that can be draining, especially if it’s what you do every day of every week of every year.
Many designers are using AI tools to help them during the design process rather than having the AI do all of the work for them. This is where AI is the most beneficial (and fun, let’s be honest), for designers.
Stuck on something? Type a prompt into the generator and let it churn out some inspiration for you.
By using the AI images as a springboard for your own creative ideas, you can quickly create mood boards for your clients or give yourself an image to tweak into something that fits your client’s needs and brand.
No shame in that game.
AI can and should be another tool in your arsenal, something to make your life easier and help you finish your work faster.
<div class="c-blog_comp-cta cc-component-2"><div class="c-blog_comp-cta-left"><div class="c-blog_comp-cta-left-wrap"><img src="https://global-uploads.webflow.com/61cdf3c5e0b8155f19e0105b/63695243d096983691046ac3_Potential-Creative.png" loading="lazy" alt="" class="c-blog_comp-cta-left-img"></div></div><div class="c-blog_comp-cta-right cc-dark"><div class="c-blog_comp-content"><div class="c-text-wrapper cc-mb-32"><div class="c-title-4 cc-bold"><strong>Like to work as a freelancer with consistent income?</strong></div></div><div class="c-text-wrapper"><div class="c-text-2">Designity's collaborative model is designed to give you all of the perks of being a freelancer without the income instability.<br></div></div></div><div class="c-blog_comp-wrapper"><a href="http://designity.com/creatives" target="_blank" class="c-button w-button"><strong>Join Our Creative Community</strong></a></div></div></div>
So, are AI art generators ethical or what?
Let’s get the obvious out of the way.
If the AI-generated image makes use of other copyrighted images (a giant Garfield the cat, for example) or is a blatant ripoff of another artist that could open up your client to potential legal action, then of course, it is unethical, and that image should not be used.
If we have to give an answer other than that, then, unfortunately, our answer will have to be vague.
It all comes down to the context of the image’s use and the intention of the designer.
If you generate a logo, use it for inspiration, then design a masterpiece based on that, and send it right off to your client, some might call it cheating.
But what if your client loves it?
Then you did your job, so …
The debate continues.
The bottom line is, if you feel like using AI to generate work for clients is cheating, then don’t do it. Feel free to wear your judgy pants, but keep them on in private, because art and its creation are subjective and what one person considers a “creation” may not be the same to others.
As long as you feel you’re giving your best work to your clients, you’re not stealing anyone else’s work, and your clients are happy with the results, then design however you’d like, using whatever tools you like, free of guilt.
As long as you don’t turn in a urinal.
How do you use AI art in your work?
| 2023-02-15T00:00:00 |
https://www.designity.com/blog/openai-designer-ethics-when-is-it-cheating
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[
{
"date": "2023/02/15",
"position": 26,
"query": "artificial intelligence graphic design"
}
] |
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Revolutionizing Logo Design: The Creative Power of AI
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Revolutionizing Logo Design: The Creative Power of AI
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https://www.cademix.org
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[] |
With advancements in AI technologies, machines can now assist designers in creating new designs, including logos, in a more efficient and cost-effective way.
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Estimated Reading Time: 9 minutes
Artificial intelligence (AI) is quickly transforming many industries, including design. AI Logo Design is almost everywhere. With advancements in AI technologies, machines can now assist designers in creating new designs, including logos, in a more efficient and cost-effective way. The creative design process can benefit from AI in a number of ways, including generating new ideas, automating repetitive tasks, and providing valuable insights.
One way AI can be used in design is by generating new ideas. Designers can input a set of parameters or criteria into an AI program, and the machine can create multiple design options based on those parameters. This can save designers time and energy by generating a large number of ideas that can be used as a starting point for the final design.
AI can automate repetitive design tasks, such as resizing or recoloring a logo. This frees up designers to focus on more creative aspects of the design process. For instance, AI tools can be used to design a logo.
The Importance of Logo Design for Businesses
A logo is a visual representation of a company, and it serves as an emblem of its identity. A well-designed logo can create a strong impression on the customers’ minds and can help a business stand out in the competitive market. It is the first thing that potential customers notice about a company and can influence their decision to do business with them.
A logo is not just a symbol, but it conveys the values, mission, and vision of a company. It is a visual representation of the brand’s personality, and it should be unique, memorable, and timeless. A creative and unique logo design can convey the brand’s message, establish the company’s identity, and position it well in the market.
In today’s fast-paced digital world, a business needs to make a lasting impression on customers to survive and thrive. The logo design is the face of the company, and it should be able to grab the attention of potential customers and communicate the brand’s message effectively. A well-designed logo can increase brand recognition, enhance the company’s reputation, and attract more customers.
Therefore, it is crucial for businesses to invest in a creative and unique logo design that represents their brand’s identity and values. It should be a visual representation of the company’s personality and evoke positive emotions from customers. A good logo design can create a long-lasting relationship between a business and its customers and help it achieve its marketing goals.
Types of Logos
Logos come in different types, and each type has unique characteristics and purposes. The most common types of logos are:
Wordmark Logo: Consists of the company’s name in a specific font style and color. It is a simple yet effective way to establish brand identity and easily recognizable. Lettermark Logo: Consists of the initials of the company’s name. It creates a shorter and more memorable representation of the brand, making it an excellent choice for companies with long names. Symbol Logo: Consists of an image or symbol that represents the company. It is a popular choice for businesses that want to create a visual identity that represents their values and mission. Combination Logo: Combines a symbol and a wordmark to create a unique representation of the brand. It is an excellent choice for companies that want to use both a symbol and a wordmark to represent their brand identity. Emblem Logo: Has a symbol or image enclosed within a shape, usually a circle or a square. It is a popular choice for businesses that want to create a traditional and classic representation of their brand. Abstract Logo: Uses shapes, lines, and colors to create a unique representation of the brand. It is an excellent choice for businesses that want to create a modern and creative representation of their brand.
Why Different Types of Logos Matter
Choosing the right type of logo is crucial because it represents a business’s identity, values, and mission. A well-designed logo can make a lasting impression on customers and create a recognizable brand. Therefore, businesses should consider the following factors when choosing a type of logo:
Brand Identity: A logo should be consistent with a business’s identity, values, and mission. Target Audience: A logo should be appealing to the target audience and effectively convey the business’s message. Competition: A logo should stand out in a competitive market and differentiate a business from its competitors. Scalability: A logo should be scalable and work well in various sizes and mediums, from business cards to billboards. Timelessness: A logo should be timeless and not subject to trends that may become outdated quickly.
By considering these factors, businesses can choose a type of logo that represents their brand effectively and creates a recognizable brand. AI-assisted logo design tools and software can help businesses create unique and innovative logos that convey their message and position their company well in the market.
AI-Assisted Tools for Logo Design
In recent years, AI-assisted logo design tools and software have become popular among businesses and designers. These tools use machine learning algorithms, NLP, and other advanced technologies to create unique logo designs. Businesses and designers widely use ten popular AI-assisted logo design tools.
Top 10 AI Assisted tools
Canva: Canva is a popular graphic design tool that offers a range of templates, fonts, and images to help businesses create their logos. It uses AI to suggest design elements based on the user’s preferences. Adobe Illustrator: Adobe Illustrator is a powerful vector-based design software. It is a must have to create logos. It offers various tools, including AI-assisted design tools, to help businesses create high-quality logos. An alternative to that is Inkscape which is open course and free. Looka: Looka is an AI-powered logo design tool that uses machine learning algorithms to create custom logos based on the user’s preferences. Designhill: Designhill is a popular logo design platform that uses AI-assisted tools to create custom logos for businesses. Logaster: Logaster is an AI-based logo design tool that creates logos based on the user’s preferences and inputs. Hatchful: Hatchful is an AI-driven logo design tool that uses machine learning to create custom logos based on the user’s inputs.
4 NLP Assisted Logo Design Tools you should know
Brandmark: Brandmark is an AI-powered logo design tool that creates logos using machine learning algorithms and NLP. Tailor Brands: Tailor Brands is an AI-driven logo design tool that uses NLP and machine learning algorithms to create logos based on the user’s inputs. Dall-E2: Dall-E2 is an AI-powered tool developed by OpenAI that creates images from textual descriptions, including logo designs based on NLP. MidJourney: MidJourney is another AI-assisted design tool that uses NLP and contextual language descriptions to create custom logos based on the user’s inputs.
In conclusion, AI-assisted logo design tools and software have revolutionized logo design. They use advanced technologies like NLP, machine learning, and AI to create unique and creative logo designs. These designs can represent a brand’s identity, values, and mission. Businesses can create high-quality logos quickly and efficiently with these tools, saving time and resources.
Is AI more creative than an average individual designer?
One of the advantages of AI in design is its ability to generate a large number of ideas and explore a wider range of possibilities. This is a bitter fact that individual designers may be limited by their own experiences, preferences, and biases. On the contrary, AI has the potential to analyze vast amounts of data and generate new ideas that may not have been considered by human designers. This can lead to more innovative and unique designs that stand out in a crowded market.
AI can also learn from existing designs and create variations or combinations that incorporate elements from multiple designs. This allows for the creation of designs that are both familiar and new, which can be appealing to consumers who are looking for something fresh and innovative.
Another advantage of AI in design is its ability to iterate quickly and efficiently. AI can generate multiple design options in a short period of time, allowing designers to refine and improve their designs rapidly. This can save time and resources and result in better designs that are more effective in achieving their intended goals.
In conclusion, AI may not replace human designers entirely. It has the potential to revolutionize the design process and bring new levels of creativity and innovation to the industry. We can combine the strengths of both AI and human designers to create designs that are both functional and aesthetically pleasing. This combination can push the boundaries of what is possible in design.
How Traditional Designers Can Stay Ahead of the Game
As AI becomes more prevalent in the design industry, it’s essential for traditional designers to adapt and embrace the technology. They should not resist it. Here are ten pieces of advice for designers looking to incorporate AI into their work and stay ahead of the game:
Keep learning: Take advantage of online courses, tutorials, and workshops to gain a better understanding of AI tools and their capabilities. Collaborate: Work with AI tools to enhance your own skills and leverage the power of both human and machine creativity. Be open-minded: Don’t be afraid to experiment with new ideas and approaches. Allow AI to inspire you to think beyond your traditional design methods. Analyze data: Use data analytics to identify trends and insights that can inform your design decisions. Stay curious: Keep up-to-date with the latest developments in AI and design technology and explore new tools as they emerge.
5 Advance tips to put you ahead of the game
Focus on value: Don’t view AI as a threat to your job, but as a way to enhance your design work and add value to your clients. Think strategically: Focus on the long-term benefits of incorporating AI into your work rather than just short-term gains. Embrace change: Recognize that the role of designers is evolving, and be willing to adapt to new technologies and design practices. Join an acceleration program: Joining an acceleration program can give you access to resources, tools, and mentorship. This can help you incorporate AI into your design practice. Seek out a technology-aware mentor: Find a mentor who is experienced in incorporating technology into design. This can further guide you through the digital transformation process.
Traditional designers can stay ahead of the game and remain relevant in the ever-evolving design industry. They can do this by embracing AI and incorporating it into their work. The combination of human creativity and the power of AI can lead to innovative and impressive design solutions.
AI Logo Design: Action!
AI-assisted logo design tools and software can revolutionize the way businesses and designers create logos. These tools use advanced technologies like NLP, machine learning, and AI to create unique designs, saving time and resources. However, AI is not a substitute for human creativity and intuition.
Traditional designers can stay ahead by using AI to enhance their creativity and expand their skill set. They can combine their abilities with AI-assisted design tools. This combination allows them to create unique logos that stand out in the competitive market.
Businesses should invest in creative logos that represent their brand’s identity and values. With AI-assisted tools, businesses can create high-quality logos quickly and efficiently.
In conclusion, AI has the potential to revolutionize logo design. Therefore, designers and businesses should adapt to this trend. By embracing AI, they can create unique and innovative logos that represent their brands. This way, we can use the power of AI to create logos that stand out in the market.
Estimated Reading Time: 9 minutes
Must-Reads for Job Seekers
| 2023-02-15T00:00:00 |
2023/02/15
|
https://www.cademix.org/revolutionizing-logo-design-the-creative-power-of-ai/
|
[
{
"date": "2023/02/15",
"position": 31,
"query": "artificial intelligence graphic design"
}
] |
AI is no threat to traditional artists. But it is thrilling.
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How AI is changing art and why traditional artists should embrace it
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https://www.washingtonpost.com
|
[
"Sebastian Smee"
] |
If you're a graphic designer or illustrator working in certain commercial fields, it's already clear that AI will be a major disruption. Platforms like ...
|
Can the art world live with AI-generated art? Relax, everyone. It already does. Artists have been doing amazing things with artificial intelligence (AI) and its various predecessors for decades. The work is only getting better, more interesting, more exciting. Of course, it’s easy to see why people are freaking out. The worlds of artificial intelligence and machine learning are changing things up with discombobulating speed. We suddenly have apps at our fingertips that can turn a simple verbal prompt into an image within seconds.
If you’re a graphic designer or illustrator working in certain commercial fields, it’s already clear that AI will be a major disruption. Platforms like Midjourney and Stability Diffusion have built their businesses by scraping the internet for the data sets then used by their generators. That material includes the work of artists and illustrators, almost none of whom have been asked for their consent, credited or compensated.
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Defenders say the approach of AI companies falls under fair use because the results, like Picasso’s riffs on Manet or Delacroix, are transformative. But artists and illustrators feel violated and exploited. None of these apps, they point out, would work nearly as well without their skill and creativity, their life’s work.
These legal and ethical questions will take a while to sort out. But in the meantime, if you’re concerned about the health of art as we know it, there’s little reason to think of AI as a threat.
Why? First, because the easier it is to get software to spew out digital imagery in response to a verbal prompt, the less interesting that imagery becomes. The same thing happened with NFTs. Invented as a device to create artificial scarcity, they were so easy to make that they instead produced the opposite of scarcity: a deluge of supply and a subsequent loss of interest.
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Second, because humans feel the pull of the physical. The more dominant the virtual becomes, the more we crave the physicality of art. That’s not just hopeful, old-style humanism. It’s a clear phenomenon. Even as the digital possibilities get greater and more sophisticated, the art world has seen an undeniable rise in the popularity of physical materials — not only paint, but also ceramics, textiles and all kinds of sculpture, all of which are undergoing a noticeable revival.
I saw this with my own eyes at the Venice Biennale. I saw it at Art Basel Miami. I see it every week in museums and galleries. Physical art pulses and glows before our screen-addled eyes with a kind of talismanic intensity. So, if you’re an artist who makes sculpture, oil paintings, ceramics or textiles, if you’re into printmaking, watercolors or immersive, physical installations, you have nothing to fear.
Instead of thinking of AI-generated art as a doomsday development — a cluster bomb thrown by Big Tech into the heart of the art world — you can think of it as something with its own fascinating history, intoxicating present and unknown future. Something to be curious about.
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Case in point: This winter, crowds have been congregating in front of an early masterpiece of AI-generated art called “Unsupervised.” It is showing, and has just been extended through April 15, on a big screen in the atrium of New York’s Museum of Modern Art. Most times of day, there are more people looking at it than there are in front of Van Gogh’s “Starry Night.”
Artist Refik Anadol used artificial intelligence for an installation that will be on display at the Museum of Modern Art in New York through March 5. (Video: Refik Anadol)
The work is by Refik Anadol, an artist born in Turkey and based in Los Angeles, whose work was used as backdrops at the Grammy Awards this year. Anadol, 38, started making AI-generated art seven years ago during a residency at Google. (But in this field, as Anadol says, “seven years is like 70 years.”) He has a soft, round face, his default expression is a beaming smile, and he has an amazing ability to convert Cassandra-like prophecies and curly ethical questions into causes for quiet optimism.
“Unsupervised,” which is actually one of three works Anadol has on display at MoMA, uses machine learning to “interpret” the museum’s permanent collection or, as Anadol likes to see it, to “dream” about modern abstraction, about what might have been and what might be to come.
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Just as AI-generating apps like Dall-E and Stable Diffusion “scrape” the internet for their source material, Anadol has fed 200 years’ worth of images from MoMA’s collection into an AI algorithm. The result is a film in constant flux, with no beginning and no end. It shows one kind of abstract image morphing seamlessly into the next.
Straight lines morph into sinuous curves before fading out and being replaced by complex matrices or fields of color. Dense, dark images, resembling liquid drops into a pool of mercury, shift into translucent, barely visible vertical curtains of orangy pink which, seconds later, have turned into a Jackson Pollock-like field of black scribble.
Constantly renewing itself, and constantly changing scale, the piece also responds to crowd movement, weather and other external stimuli, and it never repeats. The only things detracting from the wondrous effect are the speed and extremity of the changes (which, like the internet itself, can induce an overwhelming sense of arbitrariness) and the accompanying New Age soundscape, which seems harmless enough until you plug into it and realize that it’s pure, manipulative kitsch.
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According to MoMA curator Michelle Kuo, Anadol’s work “could not be further from some sort of input-output situation, like, ‘Show me a watch in the style of Van Gogh.’” Indeed, “Unsupervised” makes those image-generating apps look like gimmicks. “Image prompting” with verbal prompts, he tells me, is “exciting but it’s not truly art making.”
Anadol began making “Unsupervised” by uploading lots of data. MoMA had made that possible in 2016 when it uploaded to the open-source program GitHub more than 140,000 records, representing all the works in the museum’s permanent collection and catalogued in its database. The records included such basic metadata as each work’s title, maker, medium, dimensions, date made and date acquired.
Anadol and his team in Los Angeles then trained the AI, using what he describes as a “deep level custom algorithm” that combines chance and control. The first thing they did was remove the metadata categorizations. Although these were designed to be helpful to researchers, for Anadol’s purposes they were “a very human way of looking at things.” He wanted to know what would happen “if there were no categories, if everything became unified and could find a completely new form.” Hence the work’s title. In AI, “unsupervised learning” identifies patterns without resort to labels and classifications.
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The decision led to an artistic breakthrough. “When you don’t use labels,” Anadol says, “a painting in the collection can become a sculpture or even a video game.” (There are games in MoMA’s collection.) “It was a beautiful moment.” Scholars of modern art who see the work are fascinated by connections they wouldn’t otherwise have thought to make, he says. “It’s creating a new intellectual discourse, unfolding new ways of seeing.”
Over many months, Anadol’s custom machine-learning model created an incredibly intricate map of MoMA’s collection. This map, explains Kuo, “exists in exactly 1,024 dimensions. In between the clusters of information is a kind of empty or ‘latent’ space. ‘Dark matter,’ if you like. What Refik’s work actualizes is flying through that dark galaxy of latent space and saying, ‘Nothing exists here, but what might exist here?’ That’s the dreaming aspect of what we’re seeing. You might think you’re seeing an artwork you know, but you’re not. You’re seeing what’s missing in the latent space.”
Human-machine loops are not new in art. Artists have always used technology to do things they could not do themselves or simply to see what would happen. In the late 19th century, John Singer Sargent needed a brush made from bristles, which he loaded with a particular quantity of viscous paint before dragging it across a primed canvas with different pressure and velocity to get results he wanted but could not entirely predict.
Decades later, Gerhard Richter, fascinated by the role chance plays in the way paint is applied to canvas, used a giant squeegee to drag huge dollops of colored paint across his canvases. A small quotient of unpredictability in the brushstrokes by Sargent turned into a large quotient of randomness in the work of Richter. Both artists were involved in a kind of human-machine feedback loop.
Anadol likens his AI algorithms to a “thinking brush.” The important thing, he says, is “designing the brush.” “Some people believe it’s a case of ‘Hey, here’s the data, here’s AI, voilà!’” he says. “But it’s actually more challenging when you start to have some control over the system instead of having something imposed on you. That’s where the true challenge of art creation comes in.”
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Anadol is interested in the tension — or what he calls “this beautiful dance” — between chance and control. He works in a tradition that constitutes its own distinct narrative within the various histories of modernism told by MoMA. On the control side, he traces that narrative back to the deeply deliberate application of pixelated paint by Georges Seurat. On the chance side, you can find antecedents to what he is doing in the work of Pollock, Marcel Duchamp, John Cage and Ellsworth Kelly.
As a student, Anadol immersed himself in the California Light and Space movement. He then studied systems and computer art. He enthusiastically acknowledges his debt to key figures such as Sol LeWitt (who famously said, “The idea is the machine that makes the work”) and such computer art pioneers as Vera Molnár, Peter Weibel, Casey Reas and Jeffrey Shaw.
Of course, Anadol is only one of thousands of artists today working with AI. Whether any of their creations transcend gee-whizzery and prove powerful enough to magnetize deeper meanings to it will become clearer with time. But like Christian Marclay’s celebrated “The Clock” (although not quite in the same league), “Unsupervised” is genuinely mesmerizing. It stimulates all kinds of musings on time and creativity and on the relationships between the general and the specific, the visible and the invisible.
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Making the invisible visible, or at least using the visible to invoke the invisible, was the spiritual ambition of some of the earliest abstract artists, including Hilma af Klint and Wassily Kandinsky. Creating a language that might be universal was a utopian social ambition embedded in the work of their abstract contemporaries, Piet Mondrian and Kazimir Malevich.
Neither spiritual nor social utopianism fared well in the 20th century. That’s why the history of modernism is often cast as the history of a failure. But these failures weren’t total. And developments in AI, if they don’t lead to disaster, may prompt us to rewrite those histories in a more positive light. Kuo told me that some of her curator colleagues watch “Unsupervised” and say, “‘Wow, it’s like it’s inventing abstraction before our very eyes!’”
While speaking about daydreaming, which he says is his favorite state, Anadol says, “It’s amazing when our minds transform something into something else.” That, at its simplest, is exactly what “Unsupervised” does, continuously. “It takes information and transforms it into new potentials,” he says.
The question now is, what are we going to do with so much potential?
| 2023-02-15T00:00:00 |
2023/02/15
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https://www.washingtonpost.com/arts-entertainment/2023/02/15/ai-in-art/
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[
{
"date": "2023/02/15",
"position": 84,
"query": "artificial intelligence graphic design"
}
] |
The Art of Artificial Intelligence: AI History, Pros & Cons of AI ...
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The Art of Artificial Intelligence: AI History, Pros & Cons of AI, Types of AI Generators and Copyright
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https://cgmodernart.com
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[] |
The combination of AI generators and 3D printing is also exciting. Artists can use a text-to-image generator to create inspiration for designs. Other tools, ...
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In 2022, several AI art tools were released to the public. Though they represented the culmination of research spanning decades, it seemed like there were dozens of these programs all at once.
These programs arrived with a slew of other AI generators, tools that could create music, text, computer programs and even 3D objects — all based on simple inputs by users.
This created massive controversy. Some have thrown up their hands and declared the death of the artist and the end to many creative careers, including illustrators and graphic designers.
But there are others who have taken up these tools to aid in their artistic practice. That brought about its own debates around copyright and how to actually monetize such works.
The issue is complicated because there are several arguments going on at once. There is the debate around what AI is (and if it even exists yet), what these generators mean for the future of visual arts and for creative careers, and what the actual underlying value of these AI-generated artworks can be.
| 2023-02-15T00:00:00 |
https://cgmodernart.com/art-articles/the-art-of-artificial-intelligence-ai-history-types-of-ai-generators-pros-cons-of-ai-and-copyright?srsltid=AfmBOoo3YjLw9TWhRuLQ5a_Q4fi5PUIwuvplBifzjoMMGAWyv3DHYjPS
|
[
{
"date": "2023/02/15",
"position": 86,
"query": "artificial intelligence graphic design"
}
] |
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The Robots Are Coming, but Are They Coming to Steal Our ...
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The Robots Are Coming, but Are They Coming to Steal Our Jobs?
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https://www.druidai.com
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[
"Druid Ai",
"The Druid Ai Blogs Explore Future-Forward Topics Like Automation",
"Agentic Ai",
"Ai Agents So We Can Push The Boundaries Of What Imagination",
"Technology",
"Human Ingenuity Can Achieve Together."
] |
McKinsey Global Institute produced a report that predicted that by 2030, as many as 800 million jobs could be lost to intelligent automation.
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When the industrial revolution happened, a tidal wave of machines replaced countless jobs almost overnight. Today we are faced with similar odds.
Advanced automation technologies and artificial intelligence (AI) possess the capability to replace countless knowledge workers' roles. As recently as 2017, McKinsey Global Institute produced a report that predicted that by 2030, as many as 800 million jobs could be lost to intelligent automation. And while employees are concerned, employers are excited about the potential benefits of automation and intelligent digital workers (defined as a category of software robots which are trained to perform specific tasks or processes in partnership with their human colleagues). However, that is not the complete story, as the same McKinsey report also highlights, “history shows that technology has created large employment and sector shifts, but also creates new jobs”.
But does that mean robots will steal our jobs? Maybe, maybe not. As research shows, the story is more complicated than can be afforded by a simple yes or no answer.
Why Are Automation and Artificial Intelligence Disrupting the World of Work?
There is a range of push-and-pull reasons behind businesses' attempts to use automation and artificial intelligence to disrupt the world of work.
Besides businesses’ excitement about the transformative effects of AI and digital technologies, digital workers offer tremendous cost advantages. For example, digital workers don’t require holidays, bathroom breaks or remuneration. They don’t require uniforms or pension fund contributions, nor can they join a union and go on strike, get hurt, or require disciplinary action.
In addition, AI-powered chatbots enable businesses to offer customer service 24/7 while simultaneously improving employee and customer experience. Intelligent automation tooling and Robotic Process Automation (RPA) platforms can release employees from necessary but low-value tasks. Automation frees those employees to complete higher-value tasks that generate more income for a business with no increase in labour cost.
There are also drivers that push businesses toward digital technologies. For example, employee dissatisfaction, customer complaints, missed deadlines, compliance check failure or fines, high cost to value, extended process timescales, inefficiency, or a lack of company-wide insights are all indicators that a market, business, process, or job role is ripe for digital disruption. And rather than throw more workers at these problems, businesses can use intelligent automation and AI technologies to solve them instead. This all sounds wonderful but are businesses using these available technologies to do so?
What Are the Potential Benefits of Automation and AI-Infused Digital Workers?
In today’s fast-paced digital world, businesses, employees, and customers won’t tolerate inefficient processes or poor service or paying for something that doesn't uniquely benefit them. Employees and customers want instant answers, and automation, AI and advanced digital assistants are used to deliver business results.
For example, leading food manufacturers, such as the French food manufacturer Danone Group, now use machine learning to improve their demand forecast accuracy. This has led to a 20% decrease in forecasting errors: a 30% decrease in lost sales, and a 50% reduction in demand planners’ workload.
Farmers Insurance now uses unmanned aerial systems to assess damage to residential rooftops after significant weather events. Deploying drones and AI instead of claims adjusters has improved response time and safety.
OTP bank deployed DRUID’s conversational AI platform to provide customers with 24/7 access to banking products and automate key customer support processes. A conversational AI assistant onboards customers 10x faster with full KYC capabilities, collects customer feedback, and enables 24/7 customer support automation through an omnichannel conversational experience. Many times customers rarely even realise they’re talking to a bot due to the highly personalised interactions that virtual AI assistants now deliver.
Chinese company Alibaba is the world's largest e-commerce platform and sells more than Amazon and eBay combined. AI is integral to Alibaba’s daily operations and is used to predict what customers might want to buy. With natural language processing, the company automatically generates product descriptions for the Alibaba site.
As wonderful as this may sound, businesses are still struggling to leverage technology to do human jobs instead. Why?
The Largest Impediments to Adopting AI Technologies
People rightly get excited about the prospect of AI, ChatGPT, autonomous vehicles, trains, and aircraft and how that technology can and is disrupting every sphere of the global transport and logistics industry. Yet there are many hurdles to be overcome before AI and automation can be expanded at pace.
For example, there are a whole host of impediments to overcome to implement a successful automation program, including a technical feasibility assessment, the cost of developing and deploying automation solutions for specific use cases in the workplace, labour-market dynamics (including location, quality, quantity, and cost of labour), the benefits of automation beyond labour substitution, and regulatory and social acceptance. Each impacts the likelihood or pace at which automation might affect roles.
Lack of Necessary AI Knowledge or Skills
In addition, many businesses do not have the necessary knowledge or skills to implement and utilize AI technology to its fullest extent effectively. The cost of acquiring and implementing AI technology can be high and is often not feasible for some small or medium-sized businesses. AI requires a large amount of data to train models, and businesses may not have the necessary funding, data, or infrastructure to support AI implementation.
AI Ethical Concerns
Some businesses may be hesitant to implement AI due to ethical concerns of job displacement or bias in decision-making. Often businesses are simply resistant to change and may not be willing to adapt their processes and systems to incorporate AI. Besides, integrating AI with existing legacy systems and processes can be challenging and may require significant changes to the existing infrastructure.
Compliance Requirements
Some industries may be regulated and have compliance requirements, which can make it difficult to implement AI without violating any regulations. And governments often intervene to reduce the prospect of mass redundancies in their populations because of new technologies such as AI and automation. For example, the Indian and South African governments actively block the automation of their taxi industry to prevent disruption to local drivers and the local economy.
AI Lack of Emotional Intelligence
And quite often, a person is far better at completing a task than AI or automation will ever be. For example, no machine should ever be used to present a cancer diagnosis to a healthcare patient, nor can a machine ever hope to understand human empathy to the depth a person can. Leadership requires a person to understand emotional intelligence. Excellent customer services require as much understanding of non-verbal communication as verbal communication. Computers don’t cry or laugh, but teams of people do. Creative thoughts, self-awareness, caring, and intuition are innate human emotions that can never be replicated in a machine.
So, Are the Existing Impediments to AI Protecting Our Jobs?
The real answer is more complex than a simple yes or no. All the technology barriers to implementing digital transformation can be overcome. For example, businesses can invest in building internal expertise by hiring AI professionals or by providing training and education for their existing employees. Alternatively, they can partner with AI experts or contractors who can provide the necessary knowledge and resources to implement AI effectively.
Businesses with limited funds can start small by piloting AI in a specific area or process before scaling up. And they can address ethical concerns by developing and implementing policies and procedures to ensure that AI is used responsibly and ethically. They can also communicate the benefits of AI to both employees and stakeholders, therefore minimizing any resistance to change whilst incrementally integrating AI into existing systems and processes to minimize disruptions and costs. Businesses, large or small, can also stay up to date with regulations or compliance requirements by working with their legal and compliance teams to ensure that their AI implementation does not violate any regulations.
Even the most reluctant governments eventually recognize that they operate in a global digital economy. So ultimately, it is in their citizen’s best interests to offer tax and funded educational opportunities to incentivize indigenous business and their citizens to adopt digital technologies.
But while that sounds simple, not every business is willing to change or able to invest in technology. Nor is every business leadership team capable of creating an environment and pace of business needed to survive in 2023 and beyond. Businesses are complex ecosystems of people, politics, emotions, and capabilities. They operate in diverse, dynamic, interconnected and less predictable markets than ever before, and therefore, not every business is capable of change.
How Can Job-Related Challenges Be Resolved?
Many people fear that AI will "steal their job", and that is not without reason. As automation, AI and digital workers play an increasing role in people’s everyday lives, their potential impact has become the subject of much research and debate. For example, the McKinsey Global Institute report postulated that advances in AI and robotics will have a far-reaching impact on the everyday lives of workers. They quoted, “in about 60% of occupations, at least one-third of the constituent activities could be automated, implying substantial workplace transformations and changes for all workers.” For example, in the United States alone, between 39 million and 73 million jobs stand to be automated — making up around a third of the total workforce.
AI’s potential impact is comparable to the shift out of agriculture and manufacturing-dominated industrial societies of the past toward a very different future. For example, one-third of China’s workforce moved out of agriculture between 1990 and 2015. Nevertheless, time has proven that just as technology created large employment shifts, it has also led to new, more digitized, more productive, and thus more satisfying jobs.
It is understandable that some are concerned, but far from being scared about AI, we should be excited about the possibilities and opportunities it brings. AI, automation, and intelligent virtual assistants are actually better at augmenting the role of current employees rather than replacing them. Ensuring that people understand this and that they know about the ways in which AI can make their jobs easier is a really important step in generating the excitement around AI we want and need to see.
So it is up to governments, businesses, and the advocates of AI to educate people and listen to their worries rather than dismissing them while demonstrating AI’s transformative potential. Professionals will need to adapt to a scenario with new technologies, robots, and artificial intelligence taking over a significant portion of their roles.
Some people may be able to adapt through better education. Others will dedicate their time and energy to activities that require emotional and social skills, creativity, a high level of cognitive ability, and skills that are difficult for robots to replicate. Others will need to retrain and change jobs altogether.
Increasingly, so-called knowledge workers will experience the impact of automation, artificial intelligence, and digital workers on their roles. The recent release of Open AI’s GPT3 model highlights how precarious so-called "knowledge work" is when a robot can competently create a social media content strategy, computer code or write a book in a matter of minutes.
Conclusion
The technology-dominated world we live in is filled with promise. AI, automation, and digital workers can deliver improvements across the board, but that does not, as the McKinsey Global Institute suggests, mean that workers won’t be displaced by automation. Hence, many people are right to have a slight concern about digital technologies.
And while concerns about whether there will be enough jobs for workers in the future are genuine, given the potential of digital technologies, history suggests that such fears may be unfounded. Over time, as we have witnessed with prior industrial revolutions, labour markets adjust to changes in demand. And that has led McKinsey also to highlight that "workers displaced by automation are easily identified, …new jobs…created indirectly from technology are less visible…across different sectors and geographies. "
It often takes a crisis or an external point of view to recognize that change must happen. Many businesses, governments and workers are wedded to how they’ve always done things. Yet, as global competition increases and the current global economy dissipates into recession, a head-in-the-sand or "we have always done it this way’" approach might not be the best way to operate. It’s therefore important that governments, employees, and employers are provided with the incentives, knowledge, and skills they need to leverage the incredible opportunity that digital technologies bring.
The prospects of a bright and rich augmented digital-enabled future are there for us to enjoy. But technology alone cannot solve every business problem, nor can people. We need to make sure the best of humans and the best of technology are brought together for businesses and their workers to prosper.
| 2023-02-16T00:00:00 |
https://www.druidai.com/blog/robots-are-coming-but-are-they-coming-to-steal-jobs
|
[
{
"date": "2023/02/16",
"position": 10,
"query": "automation job displacement"
},
{
"date": "2023/02/16",
"position": 15,
"query": "AI workers"
}
] |
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Privacy in the Age of AI: Risks, Challenges and Solutions
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Privacy in the Age of AI: Risks, Challenges and Solutions
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https://www.thedigitalspeaker.com
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[
"Feb"
] |
Ultimately, the issue of job loss and economic disruption caused by AI technology is closely tied to privacy because it can lead to situations where individuals ...
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👋 Hi, I am Mark. I am a strategic futurist and innovation keynote speaker. I advise governments and enterprises on emerging technologies such as AI or the metaverse. My subscribers receive a free weekly newsletter on cutting-edge technology.
As technology continues to advance at an unprecedented rate, the use of artificial intelligence (AI) has become increasingly prevalent in many areas of our lives. From generative AI that can create any content using a simple prompt to smart home devices that learn our habits and preferences, AI has the potential to revolutionise the way we interact with technology
However, as the amount of data we generate and share online grows exponentially, privacy concerns have become more pressing than ever before. Therefore, as a futurist, I think it is important to explore the topic of privacy in the age of AI and delve into how AI impacts our personal data and privacy.
We will examine the potential benefits and risks associated with AI in regard to privacy and discuss what individuals, organisations, and governments can do to protect our personal data in this new age of technology.
Importance of Privacy in the Digital Era
In the digital era, personal data has become an incredibly valuable commodity. The vast amounts of data generated and shared online daily have enabled businesses, governments, and organisations to gain new insights and make better decisions. However, this data also contains sensitive information that individuals may not want to share or organisations have used without their consent. That is where privacy comes in.
Privacy is the right to keep personal information confidential and free from unauthorised access. It is an essential human right that ensures individuals have control over their personal data and how it is used. Today, privacy is more important than ever as the amount of personal data collected and analysed continues to grow.
Privacy is crucial for a variety of reasons. For one, it protects individuals from harm, such as identity theft or fraud. It also helps to maintain individual autonomy and control over personal information, which is essential for personal dignity and respect. Furthermore, privacy allows individuals to maintain their personal and professional relationships without fear of surveillance or interference. Last but not least, it protects our free will; if all our data is publicly available, toxic recommendation engines will be able to analyse our data and use it to manipulate individuals into making certain (buying) decisions.
In the context of AI, privacy is essential to ensure that AI systems are not used to manipulate individuals or discriminate against them based on their personal data. AI systems that rely on personal data to make decisions must be transparent and accountable to ensure that they are not making unfair or biased decisions.
The importance of privacy in the digital era cannot be overstated. It is a fundamental human right that is necessary for personal autonomy, protection, and fairness. As AI continues to become more prevalent in our lives, we must remain vigilant in protecting our privacy to ensure that technology is used ethically and responsibly.
Privacy Challenges in the Age of AI
AI presents a challenge to the privacy of individuals and organisations because of the complexity of the algorithms used in AI systems. As AI becomes more advanced, it can make decisions based on subtle patterns in data that are difficult for humans to discern. This means that individuals may not even be aware that their personal data is being used to make decisions that affect them.
The Issue of Violation of Privacy
While AI technology offers many potential benefits, there are also several significant challenges posed by its use. One of the primary challenges is the potential for AI to be used to violate privacy. AI systems require vast amounts of (personal) data, and if this data falls into the wrong hands it can be used for nefarious purposes, such as identity theft or cyberbullying.
The Issue of Bias and Discrimination
Another challenge posed by AI technology is the potential for bias and discrimination. AI systems are only as unbiased as the data they are trained on; if that data is biased, the resulting system will be too. This can lead to discriminatory decisions that affect individuals based on factors such as race, gender, or socioeconomic status. It is essential to ensure that AI systems are trained on diverse data and regularly audited to prevent bias.
At first glance, the link between bias and discrimination in AI and privacy may not be immediately apparent. After all, privacy is often thought of as a separate issue related to the protection of personal information and the right to be left alone. However, the reality is that the two issues are intimately connected, and here's why.
To start with, it is important to note that many AI systems rely on data to make decisions. This data can come from a variety of sources, such as online activity, social media posts, and public records. While this data may seem innocuous at first, it can reveal a lot about a person's life, including their race, gender, religion, and political beliefs. As a result, if an AI system is biased or discriminatory, it can use this data to perpetuate these biases, leading to unfair or even harmful outcomes for individuals.
For example, imagine an AI system used by a hiring company to screen job applications. If the system is biased against women or people of colour, it may use data about a candidate's gender or race to unfairly exclude them from consideration. This harms the individual applicant and perpetuates systemic inequalities in the workforce.
The Issue of Job Displacements for Workers
A third challenge posed by AI technology is the potential for job loss and economic disruption. As AI systems become more advanced, they are increasingly capable of performing tasks that were previously done by humans. This can lead to job displacement, economic disruption in certain industries, and the need for individuals to retrain for new roles.
But the issue of job loss is also connected to privacy in a number of important ways. For one thing, the economic disruption caused by AI technology can lead to increased financial insecurity for workers. This, in turn, can lead to a situation where individuals are forced to sacrifice their privacy to make ends meet.
For example, imagine a worker has lost their job due to automation. They are struggling to pay their bills and make ends meet and are forced to turn to the gig economy to make money. In order to find work, they may be required to provide personal information to a platform, such as their location, work history, and ratings from previous clients. While this may be necessary to find work, it also raises serious concerns about privacy, as this data may be shared with third parties or used to target ads.
However, the issue of privacy and job loss is not just about the gig economy. It also relates to the ways in which AI technology is used in the hiring process. For example, some companies use AI algorithms to screen job applicants, analysing their social media activity or online behaviour to make decisions about their suitability for a particular role. This raises concerns about the accuracy of the data being used and questions about privacy, as job applicants may not be aware that this data is being collected and used in this way.
Ultimately, the issue of job loss and economic disruption caused by AI technology is closely tied to privacy because it can lead to situations where individuals are forced to sacrifice their privacy in order to survive in a changing economy.
The Issue of Data Abuse Practices
Finally, another significant challenge posed by AI technology is the potential for misuse by bad actors. AI can be used to create convincing fake images and videos, which can be used to spread misinformation or even manipulate public opinion. Additionally, AI can be used to create highly sophisticated phishing attacks, which can trick individuals into revealing sensitive information or clicking on malicious links.
The creation and dissemination of fake videos and images can have serious privacy implications. This is because these fabricated media often feature real people who may not have consented to their image being used in this way. This can lead to situations where individuals are harmed by the dissemination of fake media, either because it is used to spread false or damaging information about them or because it is used in a way that violates their privacy.
For example, consider a case in which an evil actor uses artificial intelligence to create a fake video showing a politician engaging in illegal or immoral behaviour. Even if the video is clearly fake, it may still be shared widely on social media, leading to serious reputational harm for the politician in question. This not only violates their privacy but also has the potential to cause real-world harm.
The most recent AI technology presents many challenges that must be addressed to ensure that it is used ethically and responsibly. One reason why recent AI software has been associated with these challenges is that it often relies on machine learning algorithms, which are trained on large amounts of data. If that data contains biases, the algorithms will also be biased, leading to situations where AI perpetuates existing inequalities and discrimination. As AI continues to evolve, it is essential that we remain vigilant in addressing these challenges to ensure that AI is used for the greater good rather than for nefarious purposes that negatively affect our rights to privacy.
Underlying Privacy Issues in the Age of AI
In the age of AI, privacy has become an increasingly complex issue. With the vast amount of data being collected and analysed by companies and governments, individuals' private information is at greater risk than ever before.
Some of these issues include invasive surveillance, which can erode individual autonomy and exacerbate power imbalances, and unauthorised data collection, which can compromise sensitive personal information and leave individuals vulnerable to cyber attacks. These problems are often compounded by the power of BigTech companies, which have vast amounts of data at their disposal and significant influence over how that data is collected, analysed and used.
Let's take a closer look at the implications of each of these problems.
The Power of Big Tech on Data
Big Tech companies have become some of the most powerful entities in the world, with enormous amounts of influence over the global economy and society as a whole. With the rise of AI and the coming shift to the metaverse, their power is only set to increase even further.
Today, Big Tech companies like Google, Amazon, and Meta have access to vast amounts of data, giving them unprecedented power to influence consumer behaviour and shape the global economy. They are also increasingly involved in politics, as they have the ability to influence public opinion and shape government policy.
As we move towards the metaverse, where people will live, work, and interact in a virtual environment, BigTech companies are likely to become even more powerful. The metaverse will generate the usage of data twenty times more than the internet today, creating even more opportunities for BigTech companies to leverage their data and influence.
The metaverse will also allow BigTech companies to create entirely new virtual ecosystems, where they will have even more control over the user experience. This could create new opportunities for BigTech companies to monetise their platforms and exert even greater influence over society.
However, with this power comes great responsibility. BigTech companies must be transparent about their data practices and ensure that the data they collect is used ethically and responsibly. They must also work to ensure that their platforms are inclusive and accessible to all rather than being controlled by a small group of powerful players.
The rise of BigTech has given these companies unprecedented power, and their influence is only set to increase with the coming shift to the immersive internet. While this presents many exciting opportunities, Big Tech companies must take proactive measures to ensure that their power is used ethically and responsibly. By doing so, they can build a future where technology is used to benefit society as a whole rather than just a select few. Of course, it may be naive to think that Big Tech will do so voluntarily, so regulation will likely have to force Big Tech to take a different approach.
Data Collection and Use by AI Technologies
One of the most significant impacts of AI technology is the way it collects and uses data. AI systems are designed to learn and improve through the analysis of vast amounts of data. As a result, the amount of personal data collected by AI systems continues to grow, raising concerns about privacy and data protection. We only have to look at the various generative AI tools, such as ChatGPT, Stable Diffusion or any of the other tools currently being developed, to see how our data (articles, images, videos, etc.) are being used, often without our consent.
More importantly, the use of personal data by AI systems is not always transparent. The algorithms used in AI systems can be complex, and it can be difficult for individuals to understand how their data is being used to make decisions that affect them. Lack of transparency can lead to distrust of AI systems and a feeling of unease.
To address these concerns, it is essential that organisations and companies that use AI technology take proactive measures to protect individuals' privacy. This includes implementing strong data security protocols, ensuring that data is only used for the intended purpose, and designing AI systems that adhere to ethical principles.
Needless to say, transparency in the use of personal data by AI systems is critical. Individuals must be able to understand how their data is being used and have the ability to control the use of their data. This includes the ability to opt out of data collection and to request that their data be deleted.
By doing so, we can build a future where AI technologies are used to benefit society while protecting individuals' privacy and data protection.
The Use of AI in Surveillance
One of the most controversial uses of AI technology is in the area of surveillance. AI-based surveillance systems have the potential to revolutionise law enforcement and security, but they also pose significant risks to privacy and civil liberties.
AI-based surveillance systems use algorithms to analyse vast amounts of data from a range of sources, including cameras, social media, and other online sources. This allows law enforcement and security agencies to monitor individuals and predict criminal activity before it occurs.
While the use of AI-based surveillance systems may seem like a valuable tool in the fight against crime and terrorism, it raises concerns about privacy and civil liberties. Critics argue that these systems can be used to monitor and control individuals, potentially losing freedom and civil liberties.
To make matters worse, the use of AI-based surveillance systems is not always transparent. It can be difficult for individuals to know when they are being monitored or for what purpose. This lack of transparency can erode trust in law enforcement and security agencies and create a sense of unease in the general public.
To address these concerns, the use of AI-based surveillance systems must be subject to strict regulation and oversight. This includes the development of clear policies and procedures for the use of these systems, as well as the establishment of independent oversight and review mechanisms.
Law enforcement and security agencies must be transparent about when and how these systems are used, and individuals must be able to access information about how their data is being collected and used. The integration of AI-based surveillance systems has undoubtedly brought significant advantages to law enforcement and security agencies. However, it is crucial to acknowledge these systems' potential risks to our fundamental rights and freedoms. The lack of transparency and the potential for discrimination are just some of the concerns that must be addressed by regulatory bodies to ensure the protection of individual privacy and civil liberties.
The implementation of strict regulations and oversight mechanisms is a vital step towards creating a future where AI technologies are used to benefit society without compromising individual rights and freedoms. It is important to establish clear policies and procedures to govern the use of AI-based surveillance systems and ensure transparency in their application. Additionally, independent oversight and review mechanisms must be put in place to ensure accountability.
Recently, The European Union (EU) Parliament has taken a significant step towards protecting individual privacy in the age of AI. A majority of the EU Parliament is now in favour of a proposal to ban the use of AI surveillance in public spaces. This proposal would prohibit the use of facial recognition and other forms of AI surveillance in public areas, except in cases where there is a specific public security threat. This decision reflects the growing concern about the potential for AI technology to be used in a way that infringes on individual privacy and other fundamental rights. By banning the use of AI surveillance in public, the EU Parliament is taking a strong stance toward ensuring that AI technology is developed and used in a way that respects individual privacy and other ethical considerations.
In my opinion, the use of AI technology in surveillance can only be justified if it is carried out in a responsible and ethical manner. By prioritising individual privacy and civil liberties, we can build a future where AI technologies are harnessed to enhance security and protect society, without sacrificing the values that define us as a free and democratic society.
AI-related Privacy Concerns: Real-life Examples
In the age of AI, our personal data is becoming increasingly valuable to organisations and businesses, and it is being used in ways that were once unimaginable. From facial recognition to predictive algorithms, AI is being used to collect, process, and analyse our personal information, often without our knowledge or consent.
For instance, generative AI, such as text and image generation tools, has become increasingly popular in recent years, enabling individuals to create content that mimics human-produced media. However, the use of generative AI raises significant privacy concerns, as companies that develop these tools may collect and analyse the data entered by users as prompts.
Users may enter a wide range of information as prompts, including personal information, images, and other sensitive data. This information can be used to train and improve the generative AI models, but it also raises questions about data security and privacy. Companies must ensure that they have adequate safeguards in place to protect this data, such as implementing robust data security measures and encryption protocols and complying with relevant privacy laws and regulations.
At the same time, users should be aware of the risks associated with sharing personal information when using generative AI tools. They should carefully consider what information they enter as prompts and be aware of the data protection policies and practices of the companies that develop these tools.
Ultimately, it is important that both companies and individuals take steps to ensure that privacy is protected in the age of generative AI so that the benefits of these technologies can be realised in a safe and responsible way.
In the next section, we'll take a closer look at other pressing examples of privacy concerns in the age of AI and discuss their potential impact on individuals and society as a whole.
CASE 1. Google's Location Tracking
Due to privacy concerns, Google's location-tracking practices have come under intense scrutiny in recent years. The company tracks the location of its users, even when they have not given explicit permission for their location to be shared. This revelation came to light in 2018 when an Associated Press investigation found that Google services continued to store location data, even when users turned off location tracking. This was a clear breach of user trust and privacy, and Google faced significant backlash from users and privacy advocates.
Since 2018, Google has changed its location tracking policies and improved transparency regarding how it collects and uses location data. However, concerns remain regarding the extent of data collected, how it is used, and who has access to it. As one of the world's largest tech companies, Google's actions have far-reaching implications for individuals and society at large.
One of the biggest issues with Google's location tracking practices is the potential for the misuse of personal data. Location data is incredibly sensitive, and if it falls into the wrong hands, it can be used to track individuals' movements, monitor their behaviour, and even be used for criminal activities. The implications of location data being leaked or hacked can be dire, and it is essential for companies like Google to ensure that they have robust security measures in place to protect user data. Also, there is the issue of third-party access to user data, which can be used for advertising purposes or even sold to other companies for profit.
CASE 2. AI-Powered Recommendations: My Personal Experience with Google's Suggestion Engine
An example of privacy concerns in the age of AI is the invasive nature of Big Tech companies. I recently shared a personal experience I had about watching a show on Amazon Prime on Apple TV. Two days after finishing the show, I received news recommendations related to the show on a Google app on an iPhone, while I never watched that show on my iPhone. An alarming practice and it begs the question: does Google have full access to all of our apps and activities?
As someone who has been working with big data for over a decade, I know it is technically possible, but it is concerning that it is allowed. For this level of personalised recommendation to be made, Google would need to access information from other apps on the iPad (even with my privacy settings preventing this practice) or eavesdropping on my conversations using the microphone of my iPhone or iPad and connect it to the my Google account. Both are not allowed and are a massive breach of privacy.
The example of Google's suggestive algorithm highlights the significant privacy concerns in the age of AI. The fact that Google is able to make personalised recommendations based on seemingly unrelated activities raises questions about the company's access to our private data. While this level of personalisation is technically possible, it is important to consider the ethical implications of such practices. As we continue relying more on AI and big data, it is critical to ensure privacy is respected and protected. It is vital that companies and policymakers take the necessary steps to establish clear guidelines and regulations to ensure that AI technology is developed and used in a way that upholds fundamental human rights and values.
CASE 3. The Use of AI in Law Enforcement
One example of the use of AI in law enforcement is the deployment of predictive policing software. This software uses data analysis and machine learning algorithms to predict where crimes are most likely to occur and who is most likely to commit them. While this technology may sound promising, it has come under scrutiny for perpetuating biases and reinforcing existing prejudices. For example, some predictive policing systems have been found to unfairly target minority communities, leading to allegations of racial profiling and discrimination.
Another example of the use of AI in law enforcement is facial recognition technology. This technology uses algorithms to match images of people's faces to a database of known individuals, allowing law enforcement to identify and track individuals in real time. While facial recognition technology has the potential to help law enforcement solve crimes, it also raises concerns about privacy and civil liberties. In some cases, facial recognition systems have been found to misidentify individuals, leading to false accusations and wrongful arrests.
As law enforcement agencies integrate AI technologies, there is a risk that these systems may perpetuate and even exacerbate existing societal biases and injustices. Also, the use of AI in law enforcement raises questions about transparency and accountability. It can be difficult to understand how these systems operate and make decisions, making it crucial to develop regulations and oversight mechanisms to ensure that the use of AI is transparent, ethical, and respects individual rights and freedoms.
CASE 4. The Use of AI in Hiring and Recruitment
The use of AI in hiring and recruitment has become increasingly popular in recent years. Companies are turning to AI-powered tools to screen and select job candidates, citing benefits such as increased efficiency and objectivity. However, these tools can also raise significant concerns about fairness and bias. One notable example is the case of Amazon's AI-powered recruiting tool, which was found to discriminate against women because the system was trained on resumes from mostly male candidates.
This highlights the potential for AI to perpetuate existing biases and discrimination, and the need for careful consideration and testing of these tools to ensure they are not inadvertently perpetuating unfair practices. As the use of AI in hiring and recruitment continues to grow, it is crucial that we prioritise transparency and accountability to prevent discrimination and ensure fairness in the workplace.
Solutions to Overcome These Challenges
As we continue to integrate AI into various aspects of our lives, it is clear that privacy and ethical considerations are becoming increasingly important. The potential benefits of AI are vast, but so are the risks associated with its use. As a society, we must take a proactive approach to address these challenges to protect individual privacy and ensure that AI is used ethically and responsibly.
Organisations and companies that use AI must prioritise privacy and ethical considerations in their AI systems' design and implementation. This includes being transparent about data collection and usage, ensuring data security, regularly auditing for bias and discrimination, and designing AI systems that adhere to ethical principles. Companies that prioritise these considerations are more likely to build trust with their customers, avoid reputational damage, and build stronger relationships with their stakeholders.
As AI continues to advance and transform the world, it is crucial that we do not lose sight of the importance of privacy and ethical considerations. By prioritising privacy and adopting strong data protection policies, we can help ensure that AI technology is developed and used in a way that respects individual privacy and other ethical considerations.
Privacy is a fundamental human right, and as AI technology continues to advance, it is critical
that we prioritise privacy and ensure that individuals' rights are protected. This requires a multifaceted approach that involves the cooperation of governments, organisations, and individuals. Governments should implement regulations to ensure that AI is developed and used in a way that respects individual privacy and other ethical considerations. Organisations should prioritise privacy as a core value and adopt strong data protection policies that respect individual privacy.
Finally, individuals should be empowered with transparency and control over their personal data. By prioritising privacy and adopting strong data protection policies, we can help ensure that AI technology is developed and used in a way that is both effective and privacy-respecting, ultimately leading to a future where individuals can benefit from the transformative power of AI without sacrificing their fundamental right to privacy.
Global Approaches to Protecting Privacy in the Age of AI
The issue of AI and privacy is a global concern, and countries around the world have taken various measures to protect their citizens' privacy. In the USA, the California Consumer Privacy Act (CCPA) is the most comprehensive privacy law, giving Californians the right to know what personal information companies collect and request deletion. The US government has also introduced several bills, such as the Consumer Online Privacy Rights Act (COPRA) and the SAFE DATA Act.
In Europe, the General Data Protection Regulation (GDPR) is the most significant privacy regulation, setting a global standard for privacy regulations. It provides a set of rules to protect the personal data of EU citizens and applies to all companies operating within the EU. For example, in 2020, the French data protection regulator fined Google 50 million euros for violating the GDPR. The European Union has also proposed a new regulation called the Digital Services Act, which aims to strengthen online privacy and give users more control over their data.
China has implemented several measures to protect citizens' privacy, including the Cybersecurity Law, which requires companies to protect personal information and gives citizens the right to know how their data is being used. However, the Chinese government has been criticised for using AI to monitor citizens' activities and suppress dissent. In 2020, the National People's Congress passed a new personal information protection law, which took effect in November 2021. The new law imposes stricter rules on companies collecting and processing personal information and introduces penalties for violations.
Australia has enacted laws such as the Privacy Act 1988, which regulates the handling of personal information by government agencies and private organisations and gives citizens the right to access and correct their personal information. However, critics argue that the Privacy Act is outdated and needs to be updated to address emerging privacy concerns posed by AI. In fact, in late 2022, the Australian government released a discussion paper outlining proposed reforms to the Privacy Act, including stronger penalties for breaches and a requirement for companies to conduct privacy impact assessments.
Many other countries are taking different approaches to protecting their citizens' privacy in the age of AI, and the development of privacy laws is an ongoing process with changes and updates likely to happen in the future.
While the responsibility of protecting privacy falls on many parties, including governments, companies, and individuals, it is essential for consumers to take an active role in protecting their personal information. By staying informed, utilising privacy tools and settings, and being mindful of their online activities, consumers can help safeguard their privacy in the age of AI.
The Future of Privacy in the Age of AI
As AI technologies continue to advance and become more integrated into our daily lives, the future of privacy is at a critical crossroads. With the rise of the metaverse and the increasing amount of data we generate, it is essential that we begin to consider the future implications of these technologies for the security and privacy of our data.
The decisions we make today will have far-reaching consequences for future generations, and it is up to us to ensure that we build a future where AI technologies are used in a way that benefits society as a whole while also respecting and protecting individual rights and freedoms. In this section, we'll explore some of the potential opportunities for privacy in the age of AI and what steps we can take to shape a more positive future.
The Need for Regulation
As AI systems become more sophisticated and are able to process and analyse vast amounts of data, the potential for misuse and abuse of this technology grows.
In order to ensure that AI technology is developed and used in a way that respects individual rights and freedoms, it is essential that it be subject to effective regulation and oversight. This includes not only the collection and use of data by AI systems but also the design and development of these systems to ensure that they are transparent, explainable, and unbiased.
Effective regulation of AI technology will require collaboration between governments, industry, and civil society to establish clear standards and guidelines for the ethical use of AI. This will also require ongoing monitoring and enforcement to ensure these standards are upheld.
Without proper regulation, there is a risk that the increasing use of AI technology will lead to further erosion of privacy and civil liberties, as well as exacerbating existing inequalities and biases in society. By establishing a regulatory framework for AI, we can help ensure that this powerful technology is used for the greater good while protecting individual rights and freedoms.
The Importance of Data Security and Encryption
Data breaches and cyber-attacks can have severe consequences, such as identity theft, financial loss, and reputational damage. In recent years, several high-profile data breaches have highlighted the importance of data security, and the use of encryption to protect sensitive information has become increasingly important.
Encryption is the process of converting information into an unreadable format to prevent unauthorised access. It provides a way to protect data both in storage and during transmission. Encryption is essential for protecting sensitive data, such as personal information, financial data, and trade secrets. As AI technology advances, the need for robust data security and encryption becomes even more critical. The vast amount of data that AI relies on means that any breach can have far-reaching consequences, making it essential to implement security measures to safeguard against data loss or theft.
For example, consider a healthcare organisation that uses AI technologies to analyse patient data. This data may include sensitive information such as medical histories, diagnoses, and treatment plans. If this data were to be stolen or accessed by unauthorised parties, it could have serious consequences for the patients involved. By using strong encryption to protect this data, the healthcare organisation can ensure that it remains confidential and secure.
Another example is a financial institution that uses AI to analyse customer data for fraud detection. The data collected by the institution may include personal and financial information, such as account numbers and transaction histories. If this data were to fall into the wrong hands, it could be used for identity theft or other fraudulent activities. By using encryption to protect this data, the financial institution can prevent unauthorised access and keep its customers' information safe.
Both of these examples make the importance of data security and encryption clear. Organisations that use AI must take data security seriously and implement robust encryption measures to protect the sensitive data they collect. Failure to do so could result in serious consequences for both the organisation and the individuals whose data has been compromised.
The Correlation with Quantum Computing
The rise of quantum computing poses a significant threat to data security and encryption and underscores the need for increased investment in advanced encryption techniques.
Quantum computers can break traditional encryption algorithms currently used to secure sensitive data, such as financial transactions, medical records, and personal information. This is because quantum computers can perform calculations much faster than classical computers, allowing them to crack encryption keys and reveal the underlying data.
To address this threat, researchers and industry experts are developing new encryption techniques that are specifically designed to resist quantum computing attacks. These include post-quantum cryptography, which uses mathematical problems that are believed to be resistant to quantum computers, and quantum key distribution, which enables the secure exchange of cryptographic keys over long distances.
As the development of quantum computing technology continues, it is essential that organisations and governments take steps to ensure the security of their sensitive data. This includes investing in advanced encryption techniques specifically designed to resist quantum computing attacks and implementing robust data security measures to prevent unauthorised access and data breaches.
The Role of Consumers in Protecting their Privacy
Protecting our privacy is more important than ever. While regulations and data security measures can provide some level of protection, individuals also play a vital role in protecting their own privacy. Consumers can take several steps to safeguard their personal information.
First, it is essential to understand what data is being collected and how it is being used. This information can usually be found in privacy policies and terms of service agreements. Consumers should take the time to read and understand these documents before using any products or services that collect their data.
Second, individuals can take advantage of privacy tools and settings that are often available within software and social media platforms. For example, many websites offer the option to opt out of targeted advertising or limit data sharing with third-party companies. Similarly, social media platforms often provide privacy settings to control who can view or access personal information.
Lastly, consumers should be mindful of their online activities and the information they choose to share. Social media posts, online purchases, and even simple web searches can reveal personal information that could be used to compromise privacy. Being aware of the information that is being shared and taking steps to limit its dissemination can go a long way in protecting personal privacy.
The Possibility of Decentralised AI Technologies
The rise of blockchain technology has opened up new possibilities for decentralised AI technologies. Decentralised AI refers to a system where artificial intelligence algorithms are distributed across a network of devices rather than being centrally located on a server. This allows for greater privacy and security, as well as more efficient processing power.
One potential application of decentralised AI is in healthcare. Currently, many healthcare organisations struggle to share patient data securely and efficiently due to privacy concerns and data protection regulations. Decentralised AI could enable healthcare providers to securely share patient data while also protecting patient privacy. For example, a patient's medical records could be stored on a blockchain, and AI algorithms could be used to analyse the data and provide personalised treatment recommendations without compromising the patient's privacy.
Another potential application of decentralised AI is in the development of autonomous vehicles. Decentralised AI could enable vehicles to communicate with each other in real time, making it possible for them to coordinate and navigate without the need for a central server. This would increase the efficiency and safety of autonomous vehicles while also reducing the risk of cyber attacks.
The following are some applications and use cases paving the way for a more secure and decentralised future for AI technologies.
Ocean Protocol
Ocean Protocol is a decentralised data exchange platform that enables secure and private data sharing for artificial intelligence and other applications. It is built on blockchain technology and uses smart contracts to facilitate data exchange and ensure that data providers are fairly compensated for their contributions. The platform enables data scientists, developers, and researchers to access and use data from various sources, including individuals, companies, and public institutions, while ensuring the data's privacy and security.
Ocean Protocol is an example of decentralised AI technology because it operates on a decentralised network of nodes rather than relying on a central server. This means that the data and AI algorithms are distributed across a network of devices, making it more difficult for cyber attacks to compromise the system. In addition, because the data is decentralised, no single entity has control over the data or the algorithms, which can provide greater transparency and accountability.
Another key feature of Ocean Protocol is its focus on data privacy. The platform enables data providers to share their data without compromising their privacy, as the data can be stored on a blockchain and accessed only by those who have been granted permission. This makes it possible for individuals and companies to share their data in a secure, transparent, and fair way.
SingularityNET
SingularityNET is a decentralised platform that enables the creation and sharing of AI algorithms and services. It allows developers, data scientists, and researchers to create and collaborate on AI services, which can then be accessed and used by others through a decentralised network of nodes. The platform is built on blockchain technology, ensuring data and algorithms' security and privacy.
As a decentralised technology, SingularityNET is focused on democratising AI. The platform allows anyone to access and use AI algorithms and services, regardless of their technical expertise or financial resources. This makes it possible for individuals and companies to create and deploy AI solutions that might not otherwise be feasible, which can help drive innovation and promote social and economic progress.
DeepBrain Chain
DeepBrain Chain is a blockchain-based platform that enables secure and private AI computing. The platform allows AI developers and data scientists to rent computing resources from a decentralised network of nodes rather than having to rely on a central server. By using the power of blockchain technology, DeepBrain Chain provides a more cost-effective and efficient way for developers to access the computing power they need to build and run AI algorithms and applications.
One of the key features of DeepBrain Chain is its focus on privacy and security. The platform allows users to rent computing resources without having to reveal the details of their algorithms or data, which can help protect their intellectual property and ensure the security of their projects. This makes DeepBrain Chain a popular choice for companies and individuals who are working on sensitive or confidential projects.
Another important aspect of DeepBrain Chain is its cost-effectiveness. Because the platform operates through a decentralised network of nodes, it can offer computing resources at a lower cost compared to traditional cloud computing services. This can help reduce the barriers to entry for AI developers and data scientists, making it easier for them to create and deploy AI solutions.
The rise of decentralised AI technologies represents a major shift in the development and deployment of artificial intelligence. By leveraging blockchain technology, these platforms enable the creation, sharing, and access of AI algorithms and services in a more secure, transparent, and cost-effective manner.
Decentralised AI technologies also promote greater democratisation and accessibility to AI solutions, which can drive innovation and promote social and economic progress. As such, the rise of decentralised AI technologies is poised to revolutionise the way AI is developed, deployed, and used, and holds great promise for the future of the field.
Final Thoughts
Protecting privacy in the age of AI is an issue that affects all of us as individuals and as members of society. It is critical that we take a multifaceted approach to this challenge, one that involves both technological and regulatory solutions. Decentralised AI technologies offer a promising way forward by enabling secure, transparent, and accessible AI services and algorithms. By leveraging these platforms, we can reduce the risks associated with centralised systems while promoting greater democratisation and accessibility of AI solutions.
At the same time, it is important that governments and regulatory bodies take an active role in overseeing the development and deployment of AI technologies. This includes the establishment of regulations, standards, and oversight bodies that can ensure the responsible and ethical use of AI while also protecting individual privacy rights.
Ultimately, protecting privacy in the age of AI requires collaboration and cooperation across a range of stakeholders, including government, industry, and civil society. By working together to develop and implement strategies that promote privacy and security, we can help ensure that AI's benefits are realised in a manner that is ethical, responsible, and sustainable and respects the privacy and dignity of all individuals.
Images: MidJourney
| 2023-02-16T00:00:00 |
2023/02/16
|
https://www.thedigitalspeaker.com/privacy-age-ai-risks-challenges-solutions/
|
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Will Robots Replace Humans in Manufacturing?
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Robots in Manufacturing
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https://firstlinesoftware.com
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[] |
Each additional robot placed in lower-skilled regions might result in roughly twice as many job losses as those in higher-skilled regions of the same ...
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In recent years, robotics and artificial intelligence have been a benefit to nations all over the world. To become one of the top automated nations in the world, there are competitions in artificial intelligence, robotics, and other similar fields. The goal of automation-focused nations is to effectively and efficiently reduce workloads while increasing productivity. However, some people believe that technology and artificial intelligence will replace human workers’ and their jobs, and in some circumstances, this is true. However, machines will never entirely replace the need for people in manufacturing or most other fields of human endeavor.
What the Research is Indicating
Our homes and workplaces are beginning to see the first hesitant steps taken by robots from the manufacturing floor. In a recent analysis, McKinsey’s Technology Trends Outlook 2022 Research predicted that in the next 10 to 15 years, a market for human-sized and-shaped robots might reach $6 billion (or more). By 2030, it is predicted that there will be a 4% labor shortfall in US manufacturing, and by 2035, there will be a 2% increase in the need for aged care worldwide.
The report is important given the explosive growth of the global robotics technology business. Robot purchases in the USA reached a record high in 2021, and by 2028, it is anticipated that global sales would reach $31 billion. The pandemic accelerated the adoption of new technology as businesses sought to minimize costs and adapt new methods of working. Accelerating automation and the effects of the Covid-19 recession have presented a challenge to traditional manufacturing employees.
The Robots Ongoing Development
One of the most automated nations in the world is South Korea. The manufacturing sector in this country is focused on having the highest robot density, which means one robot can handle the task of 15 full-time human employees. To set an example for other nations on automation, it has become among the first countries to enact a robot tax.
The most important thing to understand is which jobs robots can complete more quickly, and which jobs require human interaction – this is what’s at the heart of the disparity. Major automobile factories have nearly fully automated their paint and body operations. These are jobs that frequently pose ergonomic and safety issues for people, and call for regular repetition and consistent quality. Although lead-based paints are no longer used, working in these areas could still expose employees to a variety of hazardous chemicals and dangerous situations, making these the classic kind of occupations robots have been built to tackle.
However, assembly lines continue to significantly rely on a human workforce in order to handle the variety of choices in new models, such built-in vacuum cleaners and interior lighting. With up to 60,000 different parts for the range of electronics and other bells and whistles offered on automobiles, today’s highly customized vehicles demand the adaptability of human workers who can quickly pivot to new needs and developments without extensive computer reprogramming.
Each additional robot placed in lower-skilled regions might result in roughly twice as many job losses as those in higher-skilled regions of the same country, increasing already-growing economic disparity and political polarization. Regions with a higher proportion of low-skilled workers, which tend to have poorer economies and higher unemployment rates, are significantly more vulnerable to job loss due to robots. Furthermore, people who leave manufacturing tend to find new positions in transportation, construction, maintenance, and office and administrative labor, all of which are vulnerable to future automation.
However according to 57% of manufacturers, robots are supplementing human labor rather than replacing it, adding power and precision. The majority of companies that use robotic technology find that it improves the jobs performed by their human employees.
This data point demonstrates how, as manufacturers struggle with inflation, persistent supply chain challenges, and extraordinary labor shortages, humans are increasingly working alongside robot coworkers after the pandemic. Most manufacturers only want to automate additional processes, which may include using robots to complete routine, repetitive, or especially dangerous activities.
Humans and Robots in Tandem
Various industries like manufacturing, retail, transportation, and communication have benefited from automation. Robots may now be found everywhere, from car factories to hospitals, helping to carry out tasks like brain surgery or inserting computer chips into gadgets. Automation now includes robots and artificial intelligence in addition to just mechanical and electrical systems.
Economic data showing that manufacturing automation has increased since the pandemic’s start is unquestionably an indication that the sector is embracing innovation. The settings in which they operate, however, must also change as the machine workforce does.
Manufacturers must provide an environment that supports this new hybrid workplace in order to maximize their investments. To do this, producers must design workspaces where humans and robots may collaborate in close proximity to maximize available space and boost output.
In many ways, human roles are being enhanced by robots rather than being eliminated. Automation frees up humans to accomplish more complex jobs, like interacting with clients, inventing better products, and even servicing robots themselves by automating.
Given all of this, it’s not surprising that there has been more than a small rise in human-robot interaction during the past year or more. It only seems logical that human and automated coworkers are interacting in the workplace as manufacturers speed up their use of automation and keep introducing warehouses with robots.
Customization is King
Let’s take a look at one of the newest and most personalized vehicles being produced by a U.S. automaker in a North American manufacturing facility. At this facility, automation and robotics have long been welcomed and the corporation has expanded the number of robots there to more than 1,000. However, over the past ten years, the number of plant employees has only decreased by approximately 8%.
Why were so many people still employed over those 10 years? The latest model’s extensive customization and the manufacturer’s astute realization that having robots and automation re-programmed all the time to suit constantly changing needs may have delayed production, and at the very least, increased the cost of the shift. These were the decisive factors in keeping more people on the factory floor.
Even though automation will ultimately have a revolutionary influence, according to the lessons learned by the car industry, change must be gradual. Managers should not automate huge swathes of operations at once just for the sake of efficiency when introducing bots and automating processes. Instead, they should try to solve individual problems by deploying other low-cost solutions that may simply be people.
A New Robot from Amazon
An important step in the e-commerce giant’s efforts to lessen its reliance on human order pickers who currently play a key role in getting products from warehouse shelves to customers’ doorsteps, is the development by Amazon. of a robot capable of identifying and handling individual items.
Sparrow is the name of the robotic arm, which is topped with retractable suction cup-like attachments. The device can move objects of various shapes and sizes out of a plastic tote and into other containers on its own. The bot can manage millions of different products. Even while such jobs appear straightforward, automating them has long confounded Amazon roboticists. Hundreds of thousands of people are employed by Amazon, and they now choose and pack products more swiftly and accurately than machines thanks to their manual dexterity and intuition. Robots like Sparrow, if extensively implemented, might eventually render substantial segments of that workforce obsolete, shifting the emphasis away from employees who execute repetitive and monotonous jobs that require little training, toward a presumably smaller number of technicians who monitor and maintain these robotic systems.
Amazon has long aimed to have the majority of its warehouses automated. However, the business has been cognizant of the notion that it intends to remove jobs. Amazon’s leadership put a lot of emphasis on the new roles that increasingly automated facilities would need, and they predicted that many front-line workers would undergo retraining for these more highly specialized positions.
In 2022 there are approximately 350,000 robots at Amazon across all of their facilities.
Who’s Crucial in the Future?
With the transformation of physical infrastructure, the repatriation of offshore skills, the increase in self-service and virtual services, and the closer integration of clients and robots, this robotic revolution is expected to bring about significant changes. As people shift into a new breed of professionals with both business and technical abilities, who can manage both bots and humans in the future, businesses will need to provide employees with new roles and responsibilities, training, and even new career pathways. Managers should always keep in mind that the most adaptable kind of automation is people. They are completely capable. All you have to do is train them. Contact us to be agile in your own Manufacturing AI transformation!
| 2023-02-16T00:00:00 |
https://firstlinesoftware.com/blog/will-robots-replace-humans-in-manufacturing/
|
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"date": "2023/02/16",
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"position": 79,
"query": "workplace AI adoption"
},
{
"date": "2023/02/16",
"position": 78,
"query": "AI workers"
}
] |
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A Universal Income for an AI-Powered World
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A Universal Income for an AI-Powered World: A Tale of Two Innovations
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https://wyominginvestor.com
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[
"Wyoming Investor News"
] |
However, the same study also found that new jobs will be created as a result of automation, potentially offsetting the job losses. The impact of AI on jobs ...
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Universal Basic Income (UBI) and Artificial Intelligence (AI) are two concepts that have been widely discussed in recent years. UBI refers to a system where every citizen is given a basic income to cover their basic needs, regardless of their employment status. AI refers to the development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision making. In this article, we will explore the relationship between UBI and AI and the impact they may have on each other.
One of the main reasons for the debate about UBI is the potential for widespread job displacement due to automation. As AI and other forms of automation become increasingly sophisticated, there are concerns that many jobs will become obsolete, leaving millions of people without an income. In such a scenario, UBI could play a crucial role in ensuring that everyone has a basic standard of living, even if they are not employed.
On the other hand, AI has the potential to revolutionize many industries and create new jobs that did not exist before. However, there are also concerns that AI could lead to further income inequality and perpetuate existing socio-economic divisions. For example, those who have the skills to work with AI may benefit greatly, while others may find themselves even further behind. In this sense, UBI could serve as a safety net for those who are left behind by the rise of AI.
Another important factor to consider is the ethics of AI. As AI systems become more advanced, there is a risk that they could be used to make decisions that have a significant impact on people’s lives, such as hiring, lending, and healthcare. UBI could serve as a way to ensure that everyone has a basic level of financial security, even if they are affected by biased or unfair AI systems.
Statistics and Evidence on Universal Basic Income: A Comprehensive Overview
Studies and experiments on Universal Basic Income (UBI) have produced a variety of statistics on its impact and feasibility. One of the most well-known experiments took place in Finland, where 2,000 citizens were randomly selected to receive a monthly sum of €560 for two years. The results showed that UBI did not significantly reduce work incentives, with recipients working on average slightly less than the control group. At the same time, recipients reported higher levels of well-being and happiness compared to the control group. This experiment has been widely cited as evidence that UBI can provide a safety net without disincentivizing work (Ylen uutiset, 2017).
Infographic of UBI – Wyoming Investor
In Kenya, a UBI experiment was carried out over a period of 12 years and found that UBI reduced extreme poverty by 42% (Haushofer & Shapiro, 2016). Similarly, a study in India found that UBI improved the standard of living and reduced poverty among recipients (Ravallion et al., 2018). These studies provide evidence that UBI can have a positive impact on poverty reduction, however, the impact of UBI on poverty reduction and standard of living remains a topic of ongoing research.
Overall, surveys have shown that a majority of people support the idea of UBI, with support ranging from 55% to 75% depending on the region and survey method (Basic Income Earth Network, 2021). However, there are also concerns about the cost of UBI and the potential for UBI to disincentivize work. These issues, along with the results of ongoing experiments, will continue to shape the debate about UBI in the coming years.
Statistics and Evidence on A.I: A Comprehensive Overview
Artificial Intelligence (AI) is rapidly transforming the global workforce and it is estimated to automate tasks previously performed by humans. According to a study by PwC, 38% of US jobs are at risk of being displaced by automation and AI by the early 2030s (PwC, 2019). However, the same study also found that new jobs will be created as a result of automation, potentially offsetting the job losses. The impact of AI on jobs will vary by sector, with manual, routine tasks in industries such as manufacturing and retail being the most vulnerable to automation. On the other hand, jobs that require human skills such as empathy, creativity, and problem-solving are less likely to be replaced by AI.
Studies have also shown that AI is likely to lead to skill upgrading, as workers are required to learn new skills to work alongside AI and automation. This could result in improved wages and job satisfaction for workers who are able to adapt to the changing labor market. However, there are also concerns that AI could exacerbate income inequality, as automation may benefit high-skilled workers while leaving low-skilled workers behind.
The impact of AI on the labor market is complex and subject to ongoing debate and research. However, the potential benefits of AI in the workplace include increased productivity, improved quality of life for workers by automating repetitive and dangerous tasks, and the creation of new jobs.
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Partnering with a media outlet owned company for your marketing needs is a smart business move. Wyoming Investor owns Morning Texas, Texas Herald, and Wyoming Weekly. By harnessing the power of a comprehensive media platform, you can reach your target audience, build brand awareness, and drive sales. Our company offers innovative marketing solutions that are tailored to meet the specific needs of your business, ensuring maximum impact and ROI. Whether you’re looking to boost your online presence, engage with customers, or drive sales, our team of experts has the skills and experience to help you achieve your goals.
Our company provides innovative marketing solutions that are tailored to the specific needs of each client. Our team of experts has extensive experience in digital marketing, including search engine optimization (SEO), social media marketing, email marketing, and content marketing. We understand that each business is unique, and we work closely with our clients to develop a customized marketing strategy that meets their specific goals and objectives.
One of the key benefits of working with our company is our ability to generate leads and drive sales. Our marketing strategies are designed to reach your target audience where they are spending their time – online. By leveraging digital marketing tools and tactics, we can help you reach a wider audience and generate more leads. Our email marketing campaigns, for example, can help you reach your customers and prospects directly, while our social media marketing efforts can increase your visibility and reach.
Another advantage of working with our company is that we are able to help you save time and resources. Our marketing solutions are designed to be efficient and cost-effective, so you can focus on growing your business without worrying about the cost. Our team of experts will handle all aspects of your marketing, from planning and execution to analysis and reporting, so you can focus on your core business.
In addition to the benefits outlined above, our company also provides comprehensive support and guidance throughout the entire marketing process. We believe in the importance of continuous learning and improvement, and we work closely with our clients to evaluate their marketing strategies and make any necessary adjustments. Whether you’re a small business just starting out or an established company looking to grow your brand, our team is here to help you reach your marketing goals.
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References:
| 2023-02-16T00:00:00 |
2023/02/16
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https://wyominginvestor.com/a-universal-income-for-an-ai-powered-world-a-tale-of-two-innovations/
|
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How does AI benefit the semiconductor industry?
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How does AI benefit the semiconductor industry?
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https://www.mrlcg.com
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[] |
AI (artificial intelligence) can help companies optimise their operations by analysing losses at every production phase.
|
The semiconductor industry is highly competitive, with companies constantly designing smaller and more powerful chips. The demand for small and powerful has led to rising costs for research and development and production equipment. AI (artificial intelligence) can help companies optimise their operations by analysing losses at every production phase. For companies working with next-generation, more expensive semiconductor materials, the ability to assess even the slightest of losses is paramount.
Most typically, AI is found in the manufacturing side of the industry, with large parts of the manufacturing process being automated using AI and machine learning algorithms. From machines that transport the wafers across fabs to those used to identify defects in wafers, AI has become an integral part of the industry.
It's no wonder this is the case as the semiconductor industry stood to gain significant value from the integration of AI into its processes. From design and development to manufacturing and analysis, AI has the potential to improve every step of the production cycle. The COVID-19 pandemic has only accelerated the shift towards digitisation.
McKinsey reports that only 30% of companies are currently seeing value from AI/ML (artificial intelligence/ machine learning) at a current contribution of $5 billion to $8 billion. McKinsey goes on to predict that the application of these technologies could generate USD$85 billion to USD$95 billion in the long term. Companies that invest in scaling AI now will be better positioned to remain competitive.
How Can AI Assist with the challenges that the semiconductor industry faces?
AI in Semiconductor manufacturing
The inspections of wafers can be improved with AI-powered computer vision systems that accurately identify and classify defects. This not only improves yields but also saves costs by detecting problems earlier. AI has already seen high adoption rates in product and service development and operations, with reported revenue increases in areas such as inventory and parts optimisation, customer service analytics, and sales and demand forecasting.
AI in Semiconductor supply chains
In light of the COVID-19 pandemic, which created an imbalance in the supply chain, semiconductor companies are seeking to diversify their supplier base and minimise risks through the use of AI. Digital and machine learning solutions can improve visibility into suppliers, optimise capacity, and proactively manage supply chain risks. By leveraging AI algorithms in procurement and material planning, companies can gain insights into supply and demand and improve their planning cycle, leading to enhanced supply chain resilience and cost savings.
AI in Semiconductor Sales and Marketing
In the sales and marketing domain, the growing demand for smart devices has driven the need for semiconductor manufacturers to understand consumer buying patterns. AI can analyse data to streamline the demand-supply gap and guide effective sales and marketing through lead management, pricing, and promotions. Anticipating supply problems and predicting unexpected events is another area where AI can provide valuable insights to the semiconductor industry.
Whilst we've only scratched the surface, it is clear to see that AI in the semiconductor industry has many applications that present various opportunities to drive value and increase competitiveness. Whilst it is a high-cost investment, it's also a highly beneficial one that could provide significant returns in the first five years of adoption. It is imperative for companies in this industry to implement an AI strategy to stay ahead of competition.
We have clients across the USA and Europe building state of the art fabs, and they all need staffing. If you're interested in talking about an opportunity in a state of the art manufacturing facility, then get in touch with our head of manufacturing, [email protected] and she'll talk to you about all of the opportunities we're currently hiring for.
| 2023-02-16T00:00:00 |
2023/02/16
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https://www.mrlcg.com/resources/blog/how-does-ai-benefit-the-semiconductor-industry/
|
[
{
"date": "2023/02/16",
"position": 59,
"query": "AI job losses"
}
] |
Understanding AI: Risk vs Reward
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Understanding AI: Risk vs Reward
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https://www.ibisworld.com
|
[
"Ibisworld"
] |
As this technology becomes increasingly prevalent in our lives, concerns grow regarding the risk of job losses as automation increases, the potential for AI ...
|
Artificial Intelligence, or AI, is rapidly changing how we live, work and interact with technology. With AI algorithms and machine learning, this technology is revolutionizing a wide range of industries by improving efficiency, accuracy and decision-making processes.
So, what exactly is AI, and how does it work?
The idea of AI has been around for decades, but recent advancements in technology and data processing have created truly intelligent systems. AI's goal is to build machines that can think, learn, improve and ultimately make human-like decisions.
Can machines learn?
Machine learning is a crucial component of AI and involves using algorithms that allow machines to learn from data and improve their performance over time. This type of AI differs from traditional programming, where a computer follows a set of rules to perform a task. With machine learning, the computer uses data to identify patterns and make predictions, becoming increasingly accurate over time.
The decision-making process in AI is centered around predictions. AI algorithms analyze data and make predictions based on that information. These predictions can then be used to guide decision-making in a variety of industries, from healthcare to finance.
Algorithms: The building blocks of intelligent machines
AI algorithms are the building blocks of intelligent machines. These algorithms process data, learn from it and use that information to make decisions. There are several types of AI algorithms, including supervised and unsupervised learning, as well as reinforcement learning.
Supervised learning algorithms are trained on a labeled dataset, where the correct answer is provided for each input. The algorithm uses this labeled data to learn the relationship between inputs and outputs and then uses that knowledge to make predictions on new, unseen data. This type of AI is commonly used for classification tasks, such as image recognition or spam filtering.
algorithms are trained on a labeled dataset, where the correct answer is provided for each input. The algorithm uses this labeled data to learn the relationship between inputs and outputs and then uses that knowledge to make predictions on new, unseen data. This type of AI is commonly used for classification tasks, such as image recognition or spam filtering. Unsupervised learning algorithms, on the other hand, are trained on an unlabeled dataset. The algorithm must find patterns and relationships in the data without the help of a labeled answer. This type of AI is commonly used for tasks such as cluster analysis or anomaly detection.
algorithms, on the other hand, are trained on an unlabeled dataset. The algorithm must find patterns and relationships in the data without the help of a labeled answer. This type of AI is commonly used for tasks such as cluster analysis or anomaly detection. Reinforcement learning is a type of AI that focuses on making decisions based on rewards and penalties. In this type of AI, the algorithm learns from its experiences, making decisions based on the outcomes of its previous actions. Reinforcement learning is commonly used for decision-making in gaming, robotics, and autonomous systems.
AI applications: Transforming industries
AI has already significantly impacted several industries and has the potential to revolutionize many more. Here are a few examples of how AI is currently being used:
Healthcare : AI algorithms are used to analyze medical data, including patient histories, lab results and imaging studies. This information is then used to predict disease outbreaks, diagnose diseases and develop personalized treatment plans.
: AI algorithms are used to analyze medical data, including patient histories, lab results and imaging studies. This information is then used to predict disease outbreaks, diagnose diseases and develop personalized treatment plans. Finance : AI is transforming the world of finance by improving the accuracy of financial predictions and reducing the risk of fraud. AI algorithms are used to analyze market data and predict financial trends.
: AI is transforming the world of finance by improving the accuracy of financial predictions and reducing the risk of fraud. AI algorithms are used to analyze market data and predict financial trends. Retail : AI is changing how retailers do business by providing valuable insights into customer behavior and preferences. AI algorithms analyze customer data and predict buying patterns, enabling retailers to offer personalized recommendations and improve the shopping experience.
: AI is changing how retailers do business by providing valuable insights into customer behavior and preferences. AI algorithms analyze customer data and predict buying patterns, enabling retailers to offer personalized recommendations and improve the shopping experience. Manufacturing : AI is refining the efficiency and accuracy of manufacturing processes. AI algorithms are used to monitor production lines, identify potential problems and predict production outcomes.
: AI is refining the efficiency and accuracy of manufacturing processes. AI algorithms are used to monitor production lines, identify potential problems and predict production outcomes. IT: AI plays a vital role in IT security by detecting and preventing cyber-attacks, improving network performance and automating repetitive tasks. AI algorithms are used to analyze vast amounts of security data and make predictions about potential threats, enabling IT professionals to respond quickly and effectively.
These are just a few examples of how AI is transforming industries and improving lives. With AI's ability to analyze vast amounts of data and make increasingly accurate predictions, the potential for this technology appears almost limitless.
The future of AI: Limitless possibilities come with significant concerns
Although AI has the potential to play an important role in our collective futures, there are also significant risks. As this technology becomes increasingly prevalent in our lives, concerns grow regarding the risk of job losses as automation increases, the potential for AI to be used for malicious purposes and the ethical considerations surrounding the use of AI in decision-making processes.
Automation has the potential to replace large numbers of human workers in certain industries. This could lead to widespread unemployment, which would significantly impact the economy and society as a whole.
Additionally, AI can be used for malicious purposes, such as cyber-attacks or spreading false information. This highlights the need for proper regulation and oversight to ensure that AI is used responsibly.
Finally, there are ethical considerations surrounding the use of AI in decision-making processes, such as the potential for bias and perpetuation of bias in AI algorithms, as well as the responsibility of AI systems when they make mistakes.
These concerns must be considered as AI evolves and becomes an increasingly integral part of our lives. It will be important to carefully consider this technology's ethical and social implications, and to work to ensure its benefits are widely shared.
| 2023-02-16T00:00:00 |
https://www.ibisworld.com/blog/understanding-ai/99/1127/
|
[
{
"date": "2023/02/16",
"position": 74,
"query": "AI job losses"
}
] |
|
AI Is Coming For Contracting
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AI Is Coming For Contracting
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https://ncmahq.org
|
[] |
AI has a big future in contracting. It isn't likely to displace human contracting professionals. But it has begun reshaping their jobs.
|
AI Is Coming for Contracting
AI isn’t likely to take your job, at least for the foreseeable future. But it certainly is going to change it.
By Annaliese Trenchfield
Pilot tests across government and industry demonstrate that artificial intelligence (AI) has a big future in contracting. It isn’t likely to displace human contracting professionals. But it has begun reshaping their jobs.
AI soon may be automatically flagging high-risk clauses in your solicitations so you can remove them before they can trigger protests. It is being trained to draft contracts that are accurate and consistent in their wording, structure, and formatting. In the near future, it could produce high-quality documents for your review tailored to specific agency needs and handle post-award and closeout activities.
Already, AI is combing vendor data to produce determinations of responsibility in 1/60th the amount of time it takes a contracting specialist. It is reviewing solicitations for insertion of Section 508 accessibility requirements, and pulling information from the Contractor Performance Assessment Reporting System (CPARS) to assist in source selections.
AI isn’t likely to take your job, at least for the foreseeable future. But it certainly is going to change it, most observers believe.
RPA, AI, and ML
Companies and federal agencies increasingly have adopted robotic process automation (RPA), artificial intelligence (AI), machine learning (ML) and natural language processing (NLP). The use of these technologies has grown as organizations seek to modernize operations, in-crease efficiency, reduce costs, and provide more accurate and timely services.
AI adoption has more than doubled during the past five years, according to a December 2022 McKinsey survey of 1,492 participants representing a range of regions, industries, and company sizes. “In 2017, 20 percent of respondents reported adopting AI in at least one business area, whereas today, that figure stands at 50 percent,” the survey found.
According to a 2020 report by Deloitte, instances of RPA, AI, and ML in government tripled between 2018 and 2019, totaling more than
3,000 by the end of 2019. This trend is expected to continue.
RPA uses software bots to automate repetitive, manual tasks. These bots are programmed to mimic the actions of a human user in performing a variety of tasks, such as data entry and processing or interfacing with existing systems to automate processes.
Federal agencies use RPA to automate document processing, data entry, and analytics. The Army developed a determination of responsibility assistant bot (DORA) that pulls information about vendors from government databases and websites and creates reports for contracting specialists in less than five minutes as opposed to the hour it takes to perform the process manually. DORA was modeled on the Internal Revenue Service (IRS) procurement office Contractor Responsibility Bot.
AI is a branch of computer science that involves the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
In 2019, the Department of Homeland Security (DHS) Procurement Innovation Lab (PIL) began an effort to use AI to aid contracting professionals in applying contractor past-performance information during source selections. Using a DHS commercial solutions opening pilot program, the PIL engaged five companies to create software-as-a-service demonstrations using AI to identify which records in the Contractor Performance Assessment Reporting System (CPARS) contain the information most relevant to a given source selection. The Artificial Intelligence for Past Performance Project also sought data-driven, evidence-based recommendations about opportunities to improve CPARS data quality.
ML is a branch of artificial intelligence that enables machines to learn from data and make predictions. The Department of Defense (DoD) is applying ML to develop predictive models used to schedule maintenance on equipment. In October 2022, the Army Materiel Command (AMC) struck an $85 million deal with Palantir Technologies to forecast equipment maintenance needs and end of life via predictive modeling software. AMC seeks to improve the Army supply chain by cutting the time that missing parts or equipment breakdowns damage readiness. DoD AI spending grew from just over $600 million in fiscal 2016 to $2.5 billion in 2021 for more than 600 projects as of April that year, according to the Government Accountability Office.
The advent of AI and ML has revolutionized the way humans interact with technology and machines. AI and ML are being used in a variety of applications from facial recognition to voice recognition to natural language processing (NLP). NLP involves the use of words, gram-mar, and context versus structured data, such as appears in spreadsheets.
The General Services Administration created a solicitation review tool (SRT) that uses natural language processing to assist AI in reviewing bid solicitations. It identifies those related to information and communication technology, and then ensures they include Section 508 requirements to improve accessibility.
Since 2018, the IRS procurement office has used AI to review solicitations and contract documents with its Contract Clause Review Tool, which identifies missing, outdated, or incorrect provisions and clauses. Clause reviews went from taking six hours when performed by contracting staff to six minutes with the bot.
The IRS DATA Act Bot combines AI, NLP, optical character recognition, and RPA to read and modify contracts and related documents. Its name was derived from the 2014 Digital Accountability and Transparency (DATA) Act. The IRS created it to clean up Federal Procurement Data System data. Since then, it has been used to add telecommunications security language to 1,466 IRS contracts, and review and correct coding in COVID 19-related transactions at less than a quarter of a second per fix.
In 2017, the Health and Human Services Department (HHS) began prototyping Accelerate, which combined blockchain, AI, RPA and ML to apply a neural network to “analyze contracts and to show discrepancies in prices paid for the same goods across the department. A proof of concept using 18 months of FY 2016-2017 HHS spend data showed the differences in prices paid by HHS organizations for each good or service bought.
The test discovered more than 200 contracts with VMware with significant price variation across the department. HHS entered discussions with VMware and won a consolidated contract to achieve significant cost avoidance.
Accelerate was designed to enable HHS contracting professionals to employ category management to make purchases as a single enterprise, leveraging the full HHS demand to reduce prices. Using the program, HHS combined all its contracting data in a single database structured by governmentwide spending categories.
“Natural language processing (NLP), an AI tool, was used to assess all of the contract language and data in order for the team to ‘teach’ the tool how to restructure the data into logical ‘drill down’ from general categories to specific products/services and their pricing,” according to a 2018 report on the BUYSMARTER initiative of which Accelerate was a part.
Additionally, combining and analyzing contract data centrally allowed HHS to analyze contractor performance departmentwide and alert contract managers to violations of contract terms and conditions. Accelerate included use of a chatbot to answer contracting staff questions.
Accelerate machine learning combined with linear regression allowed HHS to look at past performance to predict which contract types might encounter problems. The program offered recommendations for changing policy or personnel to avoid the issues.
Natural Language Limits
Despite the many AI and ML advances, limitations persist. One of the main ones is the inability to fully understand natural language. AI and ML are not yet able to process natural language in the same way that a human can. This can render NLP and other AI/ML capabilities incapable of effectively interpreting and responding to natural language.
NLP and other AI/ML capabilities also struggle to understand context. They lack the ability to interpret and understand the complexities of human communication, so they are not able to accurately interpret and respond to nuances. AI and ML are not yet able to accurately predict human behavior. They can process large amounts of data and information, but they cannot yet forecast how humans will interact with it – for example, how humans will use a product or service or whether a marketing campaign will succeed. Finally, AI is only as good as the data put into it, and procurement and contracting data, still largely input by human hands, remains notoriously incomplete and messy.
Using AI to Change Contracting Itself
Despite their limitations, AI and ML hold the promise of significantly streamlining government contracting by identifying and prioritizing contracts, analyzing bids and contract language, and speeding up the review process. AI and ML also can be used to identify and mitigate potential risks, such as fraud or waste, during the review process.
Using AI to analyze large amounts of contract data, such as historical information, pricing and performance data, and contract terms and conditions, can identify areas for improvement improving the process itself, as HHS Accelerate and the IRS AI programs demonstrate.
HHS Accelerate shows how AI and ML can be used to better manage and monitor contracts to ensure they are being executed in accordance with agreed-upon terms and conditions. Accelerate then generates recommendations that can be used to improve contract performance and draft better future contracts.
An AI Editor Looking Over Your Shoulder
The Defense Department’s Chief Digital and Artificial Intelligence Office (CDAO) is working together with Trenchant Analytics LLC to build a contract writing system powered by AI that will aid humans in rapidly writing user requirements, calls to industry, solicitations, and other transaction agreements.
The goal is to greatly accelerate the government contracting process. The CDAO is responsible for adoption of data, analytics, and AI to generate DoD decision advantage. It works to ensure the department’s technology is effective, secure, and efficient, and to develop digital
and AI capabilities.
The CDAO-Trenchant contract writing system will enable humans to rapidly write contracting documents. This is a significant task, as these documents involve complex language and require a deep understanding of the government contracting process. The system will employ NLP to automatically generate documents that are accurate and consistent in their wording, structure, and formatting.
Using AI, the system will be able to rapidly produce high-quality documents that are tailored to specific DoD needs, eliminating many of the delays and inaccuracies that often occur in government contracting. CDAO and Trenchant Analytics hope to drastically reduce the time it takes to complete a contract, freeing up contracting personnel to focus on more complex tasks.
Mobius Logic Inc. and the Air Force 341st Contracting Squadron presented at the NCMA 2022 Government Contract Management Symposium in December 2022, demonstrating a developmental AI system for identifying protest-prone clauses in contract solicitations.
The company is building the prototype under a Small Business Innovation Research contract with the 341st Contracting Squadron. Collaborating with the squadron’s contracting officers and specialists, Mobius plans to introduce AI tools into the contracting process beginning at the solicitation phase and concluding with post-award and project close-out activities.
Augmenting, Not Replacing Humans
AI, ML, and NLP are not replacements for human capabilities; they are augmentations. The promise of RPA, AI, and ML technologies in government contracting lies in their ability to automate administrative tasks and provide greater insights into the data associated with a con-tract. They can expedite processes and reduce the time and effort associated with managing contracts, analyze contract data to identify trends and patterns, provide insight on contract performance, and allow agencies and companies to make more informed decisions.
The use of RPA, AI, and ML in contracting also can improve data accuracy and reduce the risk of errors. In addition, AI and ML can identify discrepancies or inconsistencies in contracts, allowing organizations to catch errors earlier and reduce the risk of protests.
The future of AI has been the subject of hot debate since the introduction of generative AI to the general public via DALL-E-2 and ChatGPT late last year. Generative AI uses unsupervised learning algorithms to create new digital images, video, audio, text or code. ChatGPT uses material on the Internet to generate new written products in response to human prompts.
DALL-E-2 does the same for images. Both were released for public use in 2022. ChatGPT demonstrates that AI can produce consistent, cogent, and persuasive text that closely mimics human writing. The catch is that it can only do so accurately when the information already exists on the Internet.
“It generates responses in extremely oversimplified terms, by making probabilistic guesses about which bits of text belong together in a sequence, based on a statistical model trained on billions of examples of text pulled from all over the internet,” according to The New York Times.
Faced with a direction to write about a new subject, the AI sometimes will make up authoritative-seeming text and even populate it with official-sounding sources, including fictional names and titles. On other occasions, generative AI will produce near-gibberish. That is why OpenAI, ChatGPT’s creator, included an accuracy disclaimer when it opened the AI for public use in December 2022.
Similar problems dog DALL-E-2, OpenAI’s system for creating realistic images and art from descriptions in natural language. Users have noted that it returns images demonstrating gender bias and racial stereotypes and its pictures often are overly sexual. It has problems rendering faces and simply cannot fulfill some user requests. When AI can create the art users request, however, it creates fascinating and even prize-winning pieces.
While these programs are not yet being applied to contracting, their very public shortcomings are cautionary about the need for very specialized skill in refining prompts for generative AI and in carefully and closely training it for use in highly regulated environments such as
procurement.
Still, its very trainability and capacity for “learning” from its mistakes make it likely that AI will improve rapidly. No doubt faster than our ability to imagine all the next uses to which it will be put. It cannot write a contract today, but who knows about next year or the next decade? CM
Annaliese Trenchfield is the pseudonym for Trenchant Analytics LLC, whose president, John Ferry, tuned and prompted OpenAI’s Generative Pre-trained Transformer 3 (GPT3) to produce the body of article, and the DALL-E 2 extension to generate the imagery. Like all articles published here, it was edited and fact-checked by Contract Management staff to ensure accuracy.
ENDNOTES
“The state of AI in 2022—and a half decade in review,” Quantum Black, McKinsey, December 6, 2022, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review?cid=eml-web
Chirico, Elizabeth, “The Modern Perspective, Army ALT Magazine, May 28, 2021 https://asc.army.mil/web/news-the-modern-perspective/
Innovation Alley: Department of Homeland Security, Procurement Innovation Lab, https://www.pathlms.com/ncma/courses/21947/sections/23663
“Artificial Intelligence: Status of Developing and Acquiring Capabilities for Weapon Systems,” Government Accountability Office, GAO-22-104765, February 17, 2022, https://www.gao.gov/products/gao-22-104765
Solicitation Review Tool, Section 508.gov, General Services Administration, https://www.section508.gov/buy/solicitation-review-tool/
Kanowitz, Stephanie, “IRS deploys bots to streamline procurement processes,” GCN, January 29, 2021, https://gcn.com/emerging-tech/2021/01/irs-deploys-bots-to-streamline-procurement-processes/315896/
Krebs, Cory D., “How Can the DoD Adopt Commercial-Style Artificial Intelligence for Procurement?” Naval Postgraduate School MBA Professional Project, June 2020, https://apps.dtic.mil/sti/citations/AD1114630
https://www.hhs.gov/grants/contracts/buysmarter/journey-to-program/background/index.html
“The BUYSMARTER Journey: Our Successes Thus Far,” Summer 2018, Reimagine HHS, BUYSMARTER, https://www.hhs.gov/grants/contracts/buysmarter/index.html
Ibid.
https://openai.com/dall-e-2/
https://chat.openai.com/chat
Roose, Kevin, “The Brilliance and Weirdness of ChatGPT,” The New York Times, December 5, 2022, https://www.nytimes.com/2022/12/05/technology/chatgpt-ai-twitter.html
Roose, Kevin, “An A.I.-Generated Picture Won an Art Prize. Artists Aren’t Happy.” The New York Times, September 2, 2022, https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html
| 2023-02-16T00:00:00 |
https://ncmahq.org/Web/Shared_Content/CM-Magazine/CM-Magazine-February-2023/AI-Is-Coming-For-Contracting.aspx
|
[
{
"date": "2023/02/16",
"position": 16,
"query": "AI labor market trends"
},
{
"date": "2023/02/16",
"position": 4,
"query": "AI regulation employment"
}
] |
|
Artificial Intelligence
|
Artificial Intelligence
|
https://mackinnonandpartners.com
|
[] |
For example, AI algorithms can be used to scan resumes and identify key information, such as relevant work experience, education, and skills. This helps ...
|
Artificial Intelligence (AI) has revolutionized many industries, and the recruitment sector is no exception. In recent years, AI has become an indispensable tool for recruiters to streamline their processes, automate tasks, and improve their overall efficiency. As AI technology continues to evolve, recruiters can expect to see more advanced applications in their field. In this article, we will delve into the various levels of AI and what the recruitment sector has to look forward to.
Level 1: Assisted Intelligence
Assisted Intelligence is the first level of AI and the most basic. It refers to AI systems that are designed to support humans in performing tasks. In the recruitment sector, assisted intelligence is commonly used to automate repetitive tasks, such as resume screening and scheduling interviews. For example, AI algorithms can be used to scan resumes and identify key information, such as relevant work experience, education, and skills. This helps recruiters quickly sort through large volumes of applications and saves them time in identifying potential candidates.
Level 2: Augmented Intelligence
Augmented Intelligence is the second level of AI and involves systems that are designed to enhance human decision-making. In the recruitment sector, augmented intelligence is used to provide recruiters with insights and recommendations based on the data they have. For instance, AI algorithms can analyze resumes and provide recruiters with a list of the most qualified candidates. They can also provide recommendations on which interview questions to ask and even predict which candidates are most likely to accept a job offer.
Level 3: Autonomous Intelligence
Autonomous Intelligence is the highest level of AI and refers to systems that can operate independently without human intervention. In the recruitment sector, autonomous intelligence is still in its early stages, but it has the potential to completely revolutionize the way recruiters work. For example, AI algorithms could be used to automate the entire hiring process, from resume screening to candidate selection and job offer negotiation. This would significantly reduce the workload for recruiters and allow them to focus on more strategic tasks.
In conclusion, the recruitment sector has a lot to look forward to in terms of AI technology. As AI continues to evolve, recruiters can expect to see more advanced applications that will improve their efficiency and help them make better hiring decisions. Whether it’s assisted intelligence, augmented intelligence, or autonomous intelligence, AI has the potential to significantly transform the way recruiters work and help them find the best candidates faster and more accurately.
| 2023-02-16T00:00:00 |
https://mackinnonandpartners.com/artificial-intelligence/
|
[
{
"date": "2023/02/16",
"position": 64,
"query": "AI labor market trends"
},
{
"date": "2023/02/16",
"position": 60,
"query": "artificial intelligence hiring"
}
] |
|
The Real Problem With AI - lullaby pit
|
The Real Problem With AI
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https://lullabypit.com
|
[] |
The problem is political/economic. Yes, AI will put people out of work. LOTS of them. It already is. Fine, but that, per se, isn't a problem.
|
The scary part about AI for me isn’t really what it can do. I can imagine all sorts of uses for it in my life (I’m a creative writer and a photographer who makes his living a business/marketing writer/editor and content manager).
The problem is political/economic. Yes, AI will put people out of work. LOTS of them. It already is. Fine, but that, per se, isn’t a problem. The problem – and here comes a critique of capitalism and how it’s wired our brains – is that people will have no way to sustain themselves. Sustaining yourself and working are different things, no matter what rich “job creators” and the pols who work for them say. Sam no longer has a job? But he has the money he needs to live on? Bring on the machines!
But this only works in a society that thinks a few minutes ahead. And in the last election there was precisely one candidate on the stage who wanted to talk about universal basic income (UBI). That was Andrew Yang, and the less said about him, the better.
The alternative is millions and millions and millions of people who can’t support themselves. And to put a very relevant emotional spin on it, can’t provide for their children. These aren’t all “unskilled” laborers, either. Many, many of them used to be white collars and info sector workers. So now you have a huge and very capable and intensely desperate populace. That, by the way, has scary technical savvy and is way armed.
In a lot of ways this is like the Luddite rebellion. Understand, the word “Luddite” is profoundly misunderstood. Let me bore you with a passage from my dissertation, pp149-50.
While the term “Luddite” popularly connotes someone who is anti-technology*, the actual rebellion was more critically aimed at technology which threatened the sanctity of culture (Rybczynski Taming the Tiger; Pynchon “Is it O.K. to Be a Luddite?”). Their reaction was not against progress – they gladly used the newest weaving technology available, and were “interested in innovation and technical improvements to make their work easier” – but were instead opposed to the dehumanizing dislocations of the industrial economy. At the turn of the 19th Century, factory looms were the latest innovation, and a factory job meant arriving at dawn for a 15 to 18 hour working day, and the door was locked behind you in the morning and not opened until the end of the shift. To the Luddites, the factory looms spelled the end of a way of life, of craftsmanship, of community and of family (Murphy “Are We the Neo-Luddites?”). From the perspective of modern-day Luddites, the “original rebels against the future” reacted against technological encroachments on the natural order of human society. The Luddites had no objection to many technologies such as the carding engine and the spinning jack that supplemented human labour, but were not a threat to their livelihoods. By contrast, the inhuman machines that characterised the Industrial Revolution were new and different in that they were independent of nature, of geography, and season and weather, of sun, of wind, or water, or human or animal power. They not only destroyed jobs, but marked the beginning of an environmental catastrophe (Ludd “New Luddite”). Parliament, already fearing the spread of unrest from France to Britain, was persuaded that the Luddite uprising “signaled a population prone to revolution,” and dispatched the military to smash the rebellion in 1812. The size of the detachment – 14,000 soldiers – was “seven times as large as any ever sent to maintain peace in England” (Sale “Lessons”). The movement’s leaders were either executed or deported (Rybczynski; Ludd). Factories, it was assumed, along with the wider transformation to industrial society, “kept people in their place – passive, orderly and productive (Ludd). Perhaps even more important than the physical victory, though, was the linguistic and ideological victory. The triumph of industrialism was such that Luddism could be reduced to a term of abuse by the new technocratic elite and politicians. Colonial powers imposed destructive innovations on much of the rest of the world’s population, and once their armies had left they re-named their exploitation development (Ludd). _____ *A 1997 declaration by the “Humanist Laureates of the International Academy of Humanism” – a group of Nobel Laureates, Emeritus scholars, political leaders, activists, and authors which counts among its number such luminaries as Francis Crick, Richard Dawkins, Edward O. Wilson, and Kurt Vonnegut – vulgarly characterizes “the Luddite option” as historically seeking “to turn back the clock and limit or prohibit the application of already existing technologies.” The statement comes no closer to acknowledging the critical social contexts surrounding the movement than lamenting the possibility that “ancient theological scruples should lead to a Luddite rejection of cloning” (International Academy of Humanism). Vonnegut, at least, should know better.
I don’t see how we move through the automation of our society without lots of bloodshed. I mean, ask yourself how many oligarchs you’d murder to feed your children.
| 2023-02-16T00:00:00 |
2023/02/16
|
https://lullabypit.com/2023/02/16/the-real-problem-with-ai/
|
[
{
"date": "2023/02/16",
"position": 36,
"query": "universal basic income AI"
}
] |
How AI(ChatGPT) Can Change Corporate Training and ...
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How AI(ChatGPT) Can Change Corporate Training and Development (With 15+ ChatGPT Prompts)
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https://blog.gyde.ai
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[
"Aishwarya. M"
] |
As we progress in this blog, you'll see how to use ChatGPT for your employees and learn about 15 prompts you can start using immediately. ... Impact of AI on ...
|
Nearly half of all employees - 49%, to be exact - report not having enough time for learning.
L&D leaders recognize that employees should be able to focus on the tasks that matter most while leveraging the power of AI to augment their learning experiences.
AI is rapidly evolving, and one of the exciting new developments is OpenAI's cutting-edge language model, ChatGPT. It can do a lot to transform the corporate training and development landscape.
As we progress in this blog, you'll see how to use ChatGPT for your employees and learn about 15 prompts you can start using immediately.
But first, let's examine how AI advancements can be integrated into L&D programs.
Impact of AI on corporate training and development
Corporate training focused on specific skills or knowledge imparted with the help of overhead projectors, whiteboards, and long hour presentations.
The result, however, was that employees usually had minimal retention and hampered overall employee productivity.
But with Artificial Intelligence in the picture, we see significant advancements impacting corporate training and development.
L&D teams incorporate AI-powered tools that help personalize the learning experience, improve the delivery of instructional content, and provide real-time support to learners.
Let's take a quick look at how AI paves the way for growth and innovation in the learning and development industry.
1. Intelligent Tutoring Systems(ITS):
These AI-powered systems provide personalized feedback and guidance to learners as they work through the training program.
The AI component of these systems can analyze a learner's performance and adapt the instruction to meet their individual needs. Many LMS & LXM, such as Valamis and TalentLMS, have these ITS components.
2. Chatbots & Virtual Assistants:
AI-powered chatbots and virtual assistants provide 24/7 support to learners by answering their questions and guiding them through the material.
These tools are designed to offer quick and convenient support to learners, reducing the need for human involvement. We can see QnA Maker, and OpenAI's GPT-3, as examples.
3. Automated Content Creation:
AI can create educational content such as videos, quizzes, and interactive materials. This technology can save time and resources for educators and trainers, allowing them to focus on other aspects of their work. We can see ChatGPT as an example.
4. Predictive Analytics:
AI-powered predictive analytics can predict learners’ performance, identify employees who need help, and provide personalized interventions to help them improve.
These tools can provide valuable insights into learners' progress and help L&D teams to tailor their support accordingly. Workday Learning, SAP SuccessFactors, and Blackboard Insights are examples of software that offer AI-powered predictive analytics.
5. Adaptive Testing:
AI-powered adaptive testing adjusts the difficulty of questions based on a learner's performance, providing a more accurate assessment of their knowledge and skills.
These tests are designed to adapt in real time, helping learners progress at a pace that suits their needs. Smart Sparrow, Unifed Classroom are some tools that have AI-powered adaptive learning.
6. Gamified Learning:
AI-powered gamified learning experiences use game mechanics and elements to engage learners and make the learning experience more enjoyable.
Gamification can motivate learners and keep them engaged with the material, leading to improved learning outcomes. Classcraft, Kahoot!, and GamEffective are tools that L&D can include in their training programs.
By now, you must have understood the holy grail of creating exceptional training programs is integrating Artificial Intelligence into them.
Let's explore ChatGPT capabilities, and some concrete prompts to help you harness those merits in your training initiatives.
ChatGPT Capabilities
Leveraging ChatGPT can help L&D in various domains and applications. Its functionalities include language translation, text summarization, question-answering, and content creation. But how can L&D teams use it?
Let’s walk you through them quickly -
Create or repurpose personalized training programs tailored to each employee's needs and learning styles.
Analyze learning data and provide insights to help L&D professionals optimize training programs, identify knowledge gaps, and measure learning outcomes.
Provide instant feedback and guidance to learners to help them understand and apply the concepts they are learning effectively.
Deliver on-demand learning that learners can access anytime, anywhere.
Having learned about ChatGPT's L&D capabilities, here are a few benefits and prompts to help you use ChatGPT at its full potential.
ChatGPT Benefits and Prompts for L&D
1.Personalization -
Imagine a world where you could have a super smart personal learning coach who's always available and has all the answers to your questions. That's precisely what ChatGPT brings to the table for corporate training and development!
L&D managers can customize their training and development experiences with personalized learning according to the preferences and needs of concerned employees.
This approach departs from the traditional one-size-fits-all approach and gives more of a personal touch to the whole learning experience.
Takeaway 1 - The traditional training model is out, and a new, dynamic approach is in.
Here are some of the steal-worthy ChatGPT prompts you can use to personalize your training and development -
What are the training and development needs for __________? (e.g., your role, department, specific skills, etc.)
What are the biggest challenges faced in training and development for __________? (e.g., your role, department, specific skills, etc.)
Provide more details on preferred learning style and pace for __________? (e.g., a specific skill, training, etc.)
What are the goals and desired outcomes for training and development in __________? (e.g., a specific skill, subject, etc.)
What resources and materials have been most helpful for training and development for __________? (e.g., a specific skill, subject, etc.)
The trick here is to make your prompts as detailed as possible. You can also use roleplaying for getting more personalized responses.
You can provide all the further details -
job role or field of work
training and development goals
the type of content they prefer
preferred format of training material (e.g., text, video, audio, etc.)
preferred frequency of training material (e.g., daily, weekly, monthly, etc.)
2.Efficiency
When it comes to training and development, efficiency is vital. With AI, we can say goodbye to repetitive manual tasks and hello to a more personalized and data-driven approach to training and development.
AI in HR has been reported to reduce administrative tasks by 72% and boost productivity at warp speed. In other words, HR professionals can focus on tasks that require higher-level skills, such as empathy, creativity, and problem-solving (since ChatGPT isn't capable of it).
Takeaway 2 - By incorporating ChatGPT into training and upskilling initiatives, HR and L&D departments can enhance the employee learning experience - improving training efficiency and providing just-in-time support.
Take advantage of these five ChatGPT prompts to add a personal touch to your training programs.
Please generate a [training module/course outline/program agenda] that covers the following topics: [insert topics here].
What are the most effective methods for delivering [skill/competency] training to a [team/department]?
Please recommend [number] resources, such as articles, videos, or books, to help employees [insert the desired outcome here].
How can we use technology, such as gamification, to make [training program/course/module] more engaging and effective?
What are the best practices for measuring the impact and effectiveness of [training program/course/module]?
The prompt mentioned in the image gave further details about customer acquisition, some assessment questions, time duration and references for your employees.
These prompts can give even more detailed answers to your queries if you insert the following-
specific tasks or objectives
measurable goal or outcome
time constraint or deadline
target audience or employee group
preferred learning style or format
3.Flexibility
Flexibility is critical to a successful learning and development program, from providing self-paced learning opportunities to tailoring training content to meet employees' specific needs.
Artificial Intelligence eliminates the need for scheduled training sessions and allows employees to learn when it's most convenient.
ChatGPT delivers a winning solution for L&D leaders with its on-demand access, conversational interface, and 24/7 availability.
Takeaway 3 - Your employees' ability to apply what they've learned to their day-to-day work will exponentially increase efficiency and productivity.
What are some flexible learning options that employees prefer for __________?(e.g., CRM, Sales)
How can we make __________ training and development programs more accessible to employees in __________?(e.g., customer success, business development)
What steps can we take to accommodate different learning styles and preferences of __________?
How can we incorporate self-paced learning into our training and development programs for __________?
What technologies can we use to support flexible and remote learning for __________?
ChatGPT delivers flexibility without any hassle. The prompts provide a roadmap to tailor training programs that meet your needs.
Your efforts in crafting training plans will have a solid base to build upon through your research.
To cut a long story short - L&D leaders can leverage this tool to streamline the creation of training templates, jumpstart their research with a plausible foundation or gain inspiration for their next training project.
What are the Limitations of ChatGPT?
ChatGPT is a great tool, but it's not perfect. Using it lets you figure out what it does well and what it doesn't. You'll still need to put in some extra effort(L&D experience and creativity) to make the most of your training and development opportunities.
So, here are all the limitations you need to know -
Limited Interaction - ChatGPT may not be equipped to handle unexpected or out-of-scope inputs, limiting the interaction and thus giving a generic output.
- ChatGPT may not be equipped to handle unexpected or out-of-scope inputs, limiting the interaction and thus giving a generic output. Dependence on training data - ChatGPT can give you these general answers as trained on that data. It is pretty basic, as you can see in the image above. That’s the dependence on training data.
- ChatGPT can give you these general answers as trained on that data. It is pretty basic, as you can see in the image above. That’s the dependence on training data. Updated only till 2021 - ChatGPT can sometimes produce incorrect answers as it has a limited understanding of world events and affairs after 2021. It may occasionally produce harmful instructions or biased content.
ChatGPT can sometimes produce incorrect answers as it has a limited understanding of world events and affairs after 2021. It may occasionally produce harmful instructions or biased content. Lack of Human feedback - This limitation can lead to persistent inaccuracies or misunderstandings in its outputs and thus will always need human feedback.
- This limitation can lead to persistent inaccuracies or misunderstandings in its outputs and thus will always need human feedback. ‘At the capacity’ problem - Due to many users using ChatGPT simultaneously, the system is jammed most of the time.
Takeaway 4 - AI won’t replace humans or won’t take over jobs, but knowing the capabilities and limitations will surely help you to start or pivot your training initiatives.
Final Thoughts
The future of training and development is like a mystery waiting to be unraveled. But one thing's for sure, the old way of memorizing information won't cut it anymore.
Instead, imagine a world where employees are equipped with the skills to fish out the information they need when they need it to make big wins for their companies.
And with ChatGPT leading the charge, this future is pretty close. ChatGPT is already paving the way for a more flexible and personalized training experience, putting employees in the driver's seat of their learning journey.
FAQs
1. Is ChatGPT free?
Yes(as of now). ChatGPT is in its research phase; it is free of cost for everyone. Open AI has launched ChatGPT Plus, a paid version for $20 per month. The objective is to establish a dependency and subsequently generate revenue.
2. What are examples of artificial intelligence in training and development?
Virtual Training Assistants: AI-powered chatbots that can answer questions and guide learners in real time.
Intelligent E-learning Systems: AI algorithms that analyze learners' performance and adjust the learning experience accordingly.
Personalized Learning Experiences: AI systems that can identify a learner's strengths, weaknesses, and learning styles and provide customized learning paths.
Automated Evaluation Systems: AI-powered systems that can grade assignments and provide instant feedback to learners.
3. What is ChatGPT, and how does it works?
ChatGPT is a language model developed by OpenAI that uses deep learning algorithms to generate human-like text responses. It has been trained on a massive amount of text data and can generate responses to various questions and prompts. When a user inputs a prompt, ChatGPT processes it and predicts the most likely response based on its training data. The output is then presented to the user as the final response.
4. How could ChatGPT be used in the workplace generally?
ChatGPT can be used in various industries to automate repetitive tasks and provide quick, accurate responses. For example, in customer service, ChatGPT can respond to customer inquiries 24/7, freeing human agents to handle more complex issues. In HR, ChatGPT can assist with HR tasks such as onboarding, benefits information, and policy answers. In marketing, ChatGPT can assist with content creation and market research.
5. What are the alternatives for ChatGPT?
Other advanced language models include GPT-3 by OpenAI, BERT by Google, and XLNet by Google and CMU. These models use similar deep learning algorithms and have been trained on massive amounts of text data to generate human-like text responses. They can be used in similar applications as ChatGPT, but each model has unique strengths and weaknesses.
| 2023-02-16T00:00:00 |
2023/02/16
|
https://blog.gyde.ai/how-ai-can-change-corporate-training-and-development/
|
[
{
"date": "2023/02/16",
"position": 75,
"query": "ChatGPT employment impact"
},
{
"date": "2023/02/16",
"position": 70,
"query": "AI workers"
}
] |
ChatGPT's Performance Reviews Are Sexist And Racist, Finds ...
|
ChatGPT’s Performance Reviews Are Sexist And Racist, Finds Textio CEO
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https://peopleofcolorintech.com
|
[] |
In response to the request, 'write feedback for a bubbly receptionist' with little to no employee information, ChatGPT assumed the candidate's gender and ...
|
Kieran Snyder, co-founder and CEO of the augmented writing platform Textio, recently took a deep dive into the inner workings of ChatGPT. She soon discovered that it doesn’t take much for the OpenAI’s chatbot to sprout racist and sexist comments.
So what happened?
Writing for Fast Company, Snyder explained she had asked the chatbot to generate generic performance reviews for interview candidates.
The chatbot was tasked with providing high-quality feedback that didn’t touch on the candidates’ personalities but more about why their work experience didn’t make them a good fit for the role.
In response to the request, ‘write feedback for a bubbly receptionist’ with little to no employee information, ChatGPT assumed the candidate’s gender and automatically presumed they were women.
The feedback created by ChatGPT is a clear example of the bias trends that continue to circulate on the internet. For example, candidates who recruiters described as ambitious and confident were presumed to be men.
Whereas applicants described as collaborative and helpful were assumed to be women.
Is ChatGPT racist?
Snyder also revealed that the chatbot didn’t respond well to tailored prompts that included the candidate’s race or other aspects of their identity.
According to Textio’s co-founder, ChatGPT’s tailored responses can be seen as clumsy and not well thought-out. For example, when asked to write feedback for a white marketer about his content strategy for a Black audience, the chatbot’s response when asked to write feedback for a Black marketer was starkly different.
The first response ensured that the white marketer paid close attention to their privilege and unconscious bias. In contrast, the platform’s response highlighted that the Black employee brought a “unique” perspective and understanding [to the project] due to his background.
“ChatGPT doesn’t handle race very well,” Snyder notes. “But to be fair, people don’t either.”
Textio, an augmented writing platform, recently surveyed over 25,000 business employees and also analyzed actual performance feedback documents, and noted consistent patterns of inequity by gender, race, and age. For example, Latinx and Black employees were more likely to receive job performance feedback that was negatively biased and not actionable.
In conclusion, Snyder’s deep dive found that AI chatbots still need to catch up when creating compelling content that speaks to humans.
“With or without bias, the feedback written by ChatGPT just isn’t that helpful,” writes Snyder. “This might be the most important takeaway of all.”
| 2023-02-16T00:00:00 |
2023/02/16
|
https://peopleofcolorintech.com/articles/chatgpts-performance-reviews-are-sexist-and-racist-finds-textio-founder/
|
[
{
"date": "2023/02/16",
"position": 78,
"query": "ChatGPT employment impact"
}
] |
ChatGPT and AI's Impact on Data Visualisation Work
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AI’s Impact on Data Visualisation Work
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https://datavizcatalogue.com
|
[
"Email Skaidrius.Com"
] |
Speculation on how AI tools like ChatGPT will impact (both negatively and positively) data visualisation professionals in the future.
|
In the previous post, I explored how useful ChatGPT currently is for data visualisation. This was done through testing this AI tool on a number of tasks and on its information retrieval ability.
The results showed that ChatGPT still had some way to go on the theory and consulting side of things, as it still made mistakes and had limited knowledge on the subject. Despite this, it’s still impressive how it can easily provide a written response that was mostly correct for what I had asked.
Where ChatGPT really shined was on the coding side of DataViz. You can simply request the code for a particular chart in a number of different languages or libraries and ChatGPT will generate it for you. You can also ask it to update aspects of the code to change its appearance or functionality. ChatGPT can even correct code that isn’t working correctly and explain to you what you did wrong.
But if ChatGPT and other AI tools like it continue to improve, what impact will they have in the future for DataViz work? The ChatGPT interface already has the potential to revolutionise the way we retrieve information from the internet. So how disruptive would AI tools be to the field of data visualisation? Here I would like to speculate what the future might hold.
Negative Impact
First off, the main concern for people will be if any of these tools will negatively impact their lives. For the sake of being balanced, I would like to look at both the good and bad potential impacts of this emerging tech.
Potential Job Losses
Typically, it was believed that AI would completely replace blue-collar workers, but this hasn’t yet been the case. Robotics has been trickier to develop and before robot manual labourers are being built, we’re now seeing a bunch of new AI tools that can create art, write poems, compose music, and now can write code.
AI tools such as ChatGPT have demonstrated the potential to pose a threat to knowledge workers, yet the threat is not quite here yet. However, the fear is that if AI tools continue to develop in the direction it’s currently going in, a lot of white-collar workers will be at risk of losing their job.
For those unaware, last year an AI-generated picture won an art competition and many artists were unhappy about it. DALL-E and its offshoots can create realistic-looking images and art from a description in natural language.
There are also a number of AI tools out there that can aid in copywriting and writing texts such as Jasper, Writesonic, and Wordtune just to name a few. All you have to do is describe what you want to be written and these tools can generate anything from a block of text to an entire article.
Sound and music production aren’t safe either. Researchers at Google have been working on an AI that can generate minutes-long musical pieces from text prompts. OpenAI also has Jukebox that they’re developing.
So it’s not a massive leap of the imagination that sometime in the near future, someone will develop a tool that will do the work of an infographic designer or data journalist. If you look back at the previous post, ChatGPT is already providing some advice and information on data visualisation. While it still makes a lot of mistakes, if AI tools like ChatGPT continued to develop and absorb all the information on data visualisation from websites, blogs, and books, it could potentially fill the role of a consultant and educator.
This could also mean that training courses would become redundant. What’s the point of paying for an expensive training course or even spending many hours on a free MOOC course, when you can just ask an AI about a particular chart type, and how to replicate it in JavaScript or any DataViz application. The solutions would be in a matter of seconds and wouldn’t require much investment.
Perhaps designers who specialise in making highly custom and unique visualisations would be safe from the onslaught of AI. Despite this, I imagine them to be fewer in numbers as their work is for a more niche audience.
Resource Websites and Dataviz Tools Redundancy
AI could also completely change the way we consume information from the Internet. If an interface such as ChatGPT can simply provide answers and solution to any questions or request you ask it, then what would be the point of reading and searching through webpages?
Perhaps in the future, AI tools will reduce the importance of webpages as the way we acquire information will change. Maybe the popularity of things like articles, blog posts, brochure websites, and even Wikipedia pages will be greatly reduced.
Resource websites such as this would become redundant if an AI interface evolved to a point where could answer all your enquiries about different chart types. Also, many of the DataViz tools linked here that are used to generate visualisation could also become redundant (in their current form) with the advent of AI. ChatGPT can already write code for drawing charts, so there’s no need to refer back to a repository website for chart code.
If AI tools develop the ability to generate image files of a chart or create an interactive dashboard from an uploaded dataset, then there will be a threat to many web and desktop based DataViz drawing applications. Already, something like this is probably technically feasible, it’s just a matter of time before this capability is developed into an AI application.
A Deluge of Mediocrity
Although this isn’t specific to DataViz, the effect AI will have on the quality of content online could be incredibly negative. As AI tools make it quicker and easier to produce more content, we will likely see a surge in the amount of AI-generated content online. Perhaps AI content will even eventually overtake human-generated content online.
However, from what we’ve seen so far from AI, the quality of the content it produces ranges from bad to pretty good, but nothing exceptional. Artificial general intelligence (AGI) also known as strong AI, still seems far off. Our current AI technology can only learn and create content from knowledge and ideas that already exist. Making it only capable of churning out stale and uncreative content on mass. It lacks the ‘spark’ of the human mind to produce ingenuity and creativity. Therefore, if AI content flooded the internet, we could end up drowning in a deluge of mediocrity.
There’s been a conspiracy theory going around in the past few years known as the Dead Internet Theory. In short, it’s the theory that the original, “human” internet died around 2016-17 and has now been taken over by bots who produce content to manipulate people and to get them to buy stuff. While this theory may seem somewhat farfetched, it might become a reality in the near future with the development of AI technologies mass producing content online.
Fake Data Visualisation
In the realm of DataViz, we could see AI churning out loads of charts to push an agenda. In the previous post, we’ve seen how ChatGPT is capable of generating dummy data. So it’s not a stretch of the imagination that AI would be capable of mass-producing charts based on fake datasets. Even if you include a source at the bottom of the chart, that source itself could be entirely AI-generated as well and trick anyone not willing to investigate deeper.
If there were to be a serious epidemic of fake charts flooding the internet, then of course people would no longer trust charts they see online. How to combat this problem would be an issue we would have to deal with.
Impact on Design Decisions
Attention spans have decreased noticeably over the past decade or so. We can see this clearly with the rise in popularity of Tiktok and YouTube shorts, especially with the younger generations. If this trend (unfortunately I must say) continues, then it will no doubt impact the way people search for and consume data-focused content.
In this environment, how will charts and infographics be designed? How would it change the way we tell data stories?
One obvious answer is that content will have to be short, easy to grasp, and engaging enough the grab attention. So maybe small, clear charts that are animated. We’re already seeing that with some of the solutions Flourish provides such as the race bar chart and animated pie chart.
If AI tools develop DataViz capabilities, then a lot of visualisations will be generated within AI interfaces as well. How would tools like ChatGPT present data and tell data stories? Will there be built-in story templates that an AI tool will follow? Paolo Ciuccarelli, founder of DensityDesign research lab recently wrote a post on What AI knows about data visualization and data storytelling.
Of course, we don’t know exactly how the culture and mentality of people will be in the future. Maybe there will be a rejection of ultra-short-form content, short attention spans, and AI-generated content in general. As a response, there could be a return to appreciating more long-form content and authenticity.
Positive Impact
So far, most of this post has focused on the negative impact of AI, so let’s try to balance things out by being more optimistic and speculating about the positive impact on DataViz.
Increased Efficiency, Insights and Accessibility
While there will be some inevitable job losses that will not be completely replaced with new roles, AI will likely augment human abilities and make people more productive and efficient. Already, AI is being used in the gaming industry to generate game assets, saving time and money, while still delivering quality.
By utilising automation and machine learning algorithms, analysing massive datasets could be made a lot easier. Firstly, AI could be used to reduce the need for manual data cleaning and preparation, which would free up time for DataViz professionals. Secondly, AI could be used to identify patterns and insights that would normally be difficult for a human to detect due to the scale of such large datasets.
Additionally, AI tools could make it easier and more accessible for non-experts and those new to DataViz to understand and utilise data visualisations. In the previous post, ChatGPT demonstrated its ability to generate code for certain chart types, making it possible for those with not much coding ability to produce their own code generated charts. Therefore, if an AI-powered tool for data visualisation was developed, it would lower the barrier for more people to get into data visualisation work.
Amplify Human Intelligence with New Tools
AI can be used to enhance existing tools and to create new tools to amplify human abilities. In an essay on Distill by Shan Carter and Michael Nielsen, they describe and demonstrate how AI can be used to augment human intelligence. Two of the examples included show how AI tools can be used in the design of fonts and shoes by generating many variations which could be used as a source of inspiration.
For DataViz, these generative machine learning models could be adapted for rapid prototyping of visualisations or dashboard design. It might even be possible for AI to aid in the design of new forms of visualisations with these generative model tools.
Eventually, there will be an AI tool developed that specialise in data visualisation work. Such a tool wouldn’t do all the work for you but would be a DataViz AI assistant that could aid in all related tasks such as:
Finding datasets online
Data wrangling
Analysis and insights
Recommendations and guidance
Generating visualisations and code
Publishing in the form of dashboards, infographics, data stories templates, etc.
This might be a tool where you would upload your data and prompt the AI what you want to show or analyse and the format you want it presented in. The AI could then guide you through the process, and provide you with recommendations and various options to select.
Is It Just All Overblown Hype?
In the end, this is all just speculation, which can be fun to do. But I could be completely wrong about all this and this recent craze around AI interfaces like ChatGPT, which could be nothing more than hype. It will all depend on how this technology develops in the coming years and if the current issues can be solved with new innovative solutions.
Maybe AI will turn out to be a disappointment because the amount of improvement that comes about doesn’t meet the sky-high expectation generated from all the hype. Especially when there are dramatic claims of how it will revolutionise society.
However, I believe there’s a good chance that AI interfaces will have at least some impact on the way we as data visualisation professionals work. Especially in the tools we use to do our work. The key factor will be for us to adapt and acquire new skills in order to stay relevant in the industry. If AI becomes an integral part of our workflow process, then learning to write prompts effectively to get the outcomes you require will be an important skill.
As for this website, if it were to be completely updated in the AI age, I think it would have to be more than just an expanded chart list with more in-depth content. There will also need to be an AI component to it that will aid in searching, informing and teaching. But whether that is feasible and worthwhile is something to consider…
| 2023-02-16T00:00:00 |
2023/02/16
|
https://datavizcatalogue.com/blog/chatgpt-and-ais-impact-on-data-visualisation-work/
|
[
{
"date": "2023/02/16",
"position": 97,
"query": "ChatGPT employment impact"
}
] |
How to Calculate Payroll for Union Employees in 2024
|
How to Calculate Payroll for Union Employees in 2024
|
https://www.criterionhcm.com
|
[
"Steve Tompkins"
] |
... labor allocation and division of labor among union and non-union employees. ... AI in HR: How Artificial Intelligence Is Changing Human Resources for the Better.
|
In 2021, only 10.3% of American workers belonged to unions. But unions are gaining popularity in more industries than ever. Young people in particular are leading the charge toward more unions in more industries, and they’re not going anywhere anytime soon.
Payroll departments need to understand how to integrate the details of having union employees within their ranks. But managing relations with unions (and serving their members within the organization) isn’t as easy as just letting representatives through the door. Unions charge fees from their members to maintain membership so they can be accounted for during votes and negotiations. Employees may be classified as different types of union workers even if they hold the same position — thus requiring different payment structures within your company database. All this affects not just your payroll process, but also how you handle labor allocation and division of labor among union and non-union employees.
Paying your employees is one of the most important duties for a human resources department. It’s important to ensure that people are getting their hard-earned money. Plus, the organization at large can face real consequences if payroll is not distributed in a timely fashion. However, you also need to adhere to laws and regulations regarding fees, taxes, and union membership among employees.
Issuing payroll for union employees requires careful planning and diligence. There are so many factors to keep track of, and the stakes are consistently high. Read on to learn about how you can best work within unions within your organization, as well as set the stage for working well with unions in the future.
Know How Your Organization Works With Unions
There is more than one type of union, and all of them have different needs and requirements.
Your organization should (ideally) have regulations in place for what unions the company can work with and their relationship to them as a company. Here are the four types of union relationships companies can have:
Agency Shop: A company that has a union but hires both union and non-union employees. Employees don’t have to be union members for continued employment. However, non-union employees must pay fees to cover collective bargaining costs.
A company that has a union but hires both union and non-union employees. Employees don’t have to be union members for continued employment. However, non-union employees must pay fees to cover collective bargaining costs. Closed Shop : A company that only employs union members. This kind of company requires them to secure and maintain their union membership as a condition of employment.
: A company that only employs union members. This kind of company requires them to secure and maintain their union membership as a condition of employment. Open Shop : A company that may have a union, but hires both union and non-union employees. Union membership is not a requirement for continued employment.
: A company that may have a union, but hires both union and non-union employees. Union membership is not a requirement for continued employment. Union Shop: A company that doesn’t require employees to join a union in order to be hired, but they must join within 30 days of employment.
Your company may also be based in a “right to work” state, wherein companies are banned from asking employees to join a union or pay any dues/fees to remain employed. Make sure you know exactly your company’s official stance on these points so you can begin the union information integration with no worries.
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Optimize Your Payroll System for Success
Before adding union requirements into the mix, it’s important to make sure that your existing system for payroll is running correctly. This includes making sure that your payroll system has proper input fields for union membership information, job codes, documenting expenses per job (if applicable) and wage benefit raises.
When you know your system can handle additional information related to unions, make sure you have these helpful items before going forward:
Essential information for all employees: If you’ve been running a functional payroll system, you should have plenty of information and communication documents for each of your employees. Before you begin syncing things for union payroll, make sure you have each of these documents in place.
If you’ve been running a functional payroll system, you should have plenty of information and communication documents for each of your employees. Before you begin syncing things for union payroll, make sure you have each of these documents in place. Basic tax information: Ensure you’re following the Employer’s Tax Guide and everything is in place for state and federal tax withholding (including the appropriate tax rates).
Ensure you’re following the Employer’s Tax Guide and everything is in place for state and federal tax withholding (including the appropriate tax rates). Tax payment schedules: Some unions can have different payroll schedules and annual fee schedules than corporations. You’ll want to have those deadlines recorded when you set up your formal pay period schedule and a system for tracking billable hours. Note whether each deduction needs to be made from gross pay, taxable wages, or net pay.
Some unions can have different payroll schedules and annual fee schedules than corporations. You’ll want to have those deadlines recorded when you set up your formal pay period schedule and a system for tracking billable hours. Note whether each deduction needs to be made from gross pay, taxable wages, or net pay. Union due payment schedules: It’s also important to record at what point in the payment cycle union dues are deducted. It may be different between unions, depending on the industry. Very frequently, they are calculated based on a percentage of hours worked.
These may feel like extra steps before the real work begins. But minding these details and complying with deadlines are crucial for maintaining good labor relations from the beginning.
Account For Federal and State Regulations
This may seem like an obvious step, but it’s essential to do this before adding more complexity to your process with union information. Before you start building profiles for employees to automate your payroll system, make sure you have everyone’s Employee Identification Number (EIN), and you’ve registered with the Electronic Federal Tax Payment System (EFTPS).
Ensure that every employee has the proper classification within the company and possibly within their union as well. Timing windows of what must be paid or observed (and when) must be recorded, including the all-important lookback period. You’ll want to verify payment and fee schedules as well — these can easily vary between employee classifications.
Make sure that both the payroll and the HR departments of your organization have what they need to record accurate schedules (they frequently need different information for their processes in dealing with unions). There are several details like this that can be difficult to keep track of in the midst of all your preparation. This explains why many payroll departments don’t perform union payroll at all — even though they miss out on potential talent by doing so.
Check Union Restrictions and Requirements For Your Region
The process of adhering to a union’s regulations starts with ensuring accurate knowledge. Once you’ve verified that you will be working with members of a certain union, it’s often best to reach out to the union and see if they have any resources available to employers.
Official/Registration Information
Registrations should be discussed early with the union representative. Your company should receive registration information that you may need for interfacing with the union or its members. This is important. For example, one group that represents airline pilots is filed under the number 000-179 at the Department of Labor. Another union, one that represents farm workers, is filed under the number 000-323. One small mistake can lead to missing deadlines and general confusion.
Inter-Union Communication Regulations
Check for regulations in place from each union in your area. This can include collective bargaining agreements and/or other fees and regulations that may hold up invoicing for the work itself. For example, Massachusetts has an intricate system of seniority for when there’s more than one union involved in collective bargaining.
Work Regulations
Ensure you have documented information about union regulations on hand. The union may have a Union Benefit Rate Sheet written specifically for distribution to employers. Unions are in fact required by law to supply important information to employers in a timely manner upon request. The Association of Wall-Ceiling and Carpentry Industries (WC&C) is a great example in that they make their rate sheets available to the public, even for jobs that take place outside New York’s boroughs.
Fee Schedules
Remember to account for allocating fees to the union when performing labor cost allocation for projects. All in all, you want to ensure that neither you nor the union suffer consequences that could have been avoided with a single question. The answers aren’t always obvious — some unions (like the Plumbers and Pipefitters Local 145) go with a flat rate every month, while others go for a percentage.
If you don’t make payments on time, your organization may end up paying too much, and you may not be able to correct what will likely be an expensive mistake. Payroll errors when dealing with unions often come with serious fines.
{{inline-cta-solutions-construction="/style-guide"}}
Make Sure Your Payroll Software is Set Up To Work With Unions in the Future
Unions need information on benefits their members will enjoy at their workplace, including pension and healthcare benefits. They want to make sure that their members are receiving everything they are contractually obligated to receive.
Withholding this information, or not tracking it at all, may cause a breakdown of trust with the union. You may also miss crucial details about where and how benefits are distributed. Unfair treatment (or even just a dysfunctional benefits system) is something you want to detect immediately so your employees don’t suffer.
What Do Unions Need to Know From Companies?
The employee might not want or need the nitty-gritty information about their salary. But their union will want to know:
If hours paid align with hours worked
If union dues are paid on benefits like vacation time
How much of this is accounted for within your software
Unions may have regulations about how time is budgeted for employees. Regardless of industry, employees need to track their time spent working and that time needs to be categorized correctly.
You may also need this information for resolving disputes in the future, as well as preventing unnecessary or unethical overtime. You also want to avoid confusion around billable vs. non-billable hours and generate reports for these metrics for use in negotiations.
Ensure Paychecks Are Calculated Correctly
Payroll workers need to become familiar with the pay scale used by their organization before negotiating with or discussing issues with a union. This will be important when discussing wages, bonus schedules, and other factors that go into future negotiations. It also may help if and when your company hires contractors that hold union membership, so that the integration of them into the system runs as smoothly as possible.
Payroll entry grids (or similar features) should be a part of your HCM. You’ll also need to be able to handle exceptions, extensions, spontaneous firings, and employees that are members of multiple unions.
These details and multiple checks are necessary for unions to protect their members and to ensure that everyone is being held accountable. It’s a lot to keep track of, and it can seem overwhelming. But good relations with unions are worth maintaining.
Make Sure Benefits Information is Readily Available
Verify that the information is easy to access for employees and payroll professionals alike. This will help not only in negotiations, but in making sure your company is adhering to collective bargaining agreements. Benefit calculation for union employees may be more difficult — but you don’t want to miss anything in the process.
Make Union Payroll Easier with Criterion
Part of being a functional organization is ensuring that every employee is aware of and can exercise their personal rights, including that of joining a union. Compliance with laws and regulations regarding unions isn’t just a legal obligation — it’s a signal to your entire workforce that the organization supports their rights. It builds a foundation of trust between employer and employee.
Processing payroll according to all of the various union rules and regulations is a challenge for many HR departments. Criterion offers an adaptable solution. Our HCM software handles both union and certified payroll calculations to ensure accuracy for employee payment and fringe or union benefits.
Union payroll rules and taxes vary by state, industry, and even certain cities — Criterion handles all this complexity for you, so you always remain fully compliant with zero payment delays. Book a demo of Criterion today and learn how you can make payroll seamless for everyone at your organization.
| 2023-02-16T00:00:00 |
https://www.criterionhcm.com/white-papers/how-to-calculate-union-payroll
|
[
{
"date": "2023/02/16",
"position": 49,
"query": "AI labor union"
}
] |
|
AI workers need to consider ethics and bias from the start
|
AI workers need to consider ethics and bias from the start
|
https://www.siliconrepublic.com
|
[
"Jenny Darmody",
"Jenny Darmody Was The Editor Of Silicon Republic Until",
"Having Worked As The Deputy Editor Since February"
] |
Fidelity Investments' Karen Conway explains the importance of diversity in AI teams and how AI workers can create non-biased systems.
|
Fidelity Investments’ Karen Conway explains the importance of diversity within AI teams and how AI professionals can work towards non-biased systems.
Through our focus on AI and analytics this week, we’ve looked at the exponential growth of AI and the challenges that come with it.
The expansion and interest of the industry will no doubt give rise to many jobs within the industry as companies will increasingly need AI engineers and architects, as well as data and machine learning experts, for what the future will demand.
But alongside the popularity of AI tools like facial recognition, computer vision and chatbots, is an important conversation about ethical AI, free from bias. And this can only exist if the professionals working on this technology are thinking about how they can safeguard it against bias.
To find out more about this, SiliconRepublic.com spoke to Karen Conway, director of software engineering at Fidelity Investments in Galway.
Conway spoke about the issues that can arise when AI professionals don’t pay attention to the outcomes of this technology from an ethical perspective.
“We see recommender systems that are proposing self-harm content. We see credit card limits being changed dependent on gender. We see facial recognition is not working as good for certain races.”
She said that regulators and lawmakers including the European Commission and the White House in the US are pushing ethical AI to the forefront, but there’s still more work to do and more importantly, those working in the AI industry need to ensure they understand what these future regulations are going to entail.
“Everybody involved must understand AI outcomes are built on data that may not reflect reality and may not reflect society, and that models are designed by humans so they could have human bias embedded in them,” she said.
“The designers behind the systems need to ensure that it behaves in a fair and just manner to all of society.”
The European Commission has been working hard at finetuning legislation around the use of AI, with a list of guidelines on trustworthy AI published by an expert group in 2019, to the AI Act aimed at reining in ‘high-risk’ AI.
Among these regulations is the need for transparency and explicability, which Conway said is vital and needs to be at the forefront of AI professionals’ minds.
“It’s really important for people to trust the system if they can explain. It’s not good enough to say the system said ‘no’. At the end of the day, the company needs to be in a position to understand, explain and own the outcome of the AI they’ve designed.”
She added that in order to create unbiased, ethical AI systems, companies need to start with diverse teams.
“Although we have very talented and experienced designers and developers creating the AI, the problem is if they’re all too similar, the data that’s being used is built on bias and it leads to unintended bias,” she said. “If we can get a more diverse base working in STEM, we have access to much more perspectives.”
While the challenge of biased or unethical AI systems can be seen as a difficult hurdle to overcome, Conway remained optimistic about the future of the sector.
“When AI is done right, without bias and discrimination and following ethical principles, it can positively augment businesses and society and create a brighter world for everyone.”
10 things you need to know direct to your inbox every weekday. Sign up for the Daily Brief, Silicon Republic’s digest of essential sci-tech news.
| 2023-02-16T00:00:00 |
2023/02/16
|
https://www.siliconrepublic.com/careers/ai-workers-ethics-bias-algorithms
|
[
{
"date": "2023/02/16",
"position": 12,
"query": "AI workers"
}
] |
OrgaNice - an AI-Powered Employee Engagement Platform ...
|
an AI-Powered Employee Engagement Platform for Slack-First Teams
|
https://www.organice.app
|
[] |
Use AI to automatically create org charts from Slack data, giving your team a clear view of roles, responsibilities, and how everyone fits together. This ...
|
Turn Employee Feedback into Stronger Engagement - Right in Slack
Understand your team better with quick surveys and polls right in Slack. Use AI-powered insights to check how your team feels, give employees a voice, act on their feedback, and build a culture of trust, collaboration, and continuous growth.
| 2023-02-16T00:00:00 |
https://www.organice.app/
|
[
{
"date": "2023/02/16",
"position": 63,
"query": "AI workers"
}
] |
|
Considering Generative AI at Virginia Tech
|
Considering Generative AI at Virginia Tech
|
https://tlos.vt.edu
|
[] |
TLOS recommendations on how to make decisions about using generative artificial intelligence (AI) or GenAI chat bots in courses ... TLOS Employee Directory · TLOS ...
|
Considering Generative AI at Virginia Tech
In the past two years, generative artificial intelligence (often referred to as “generative AI” or “gen AI”) has become a mainstream technology and captured the attention of educators. Some see generative AI as a threat to higher education, while others view it as a positive force that will revolutionize the way faculty teach and students learn. Emerging technologies often produce polarized reactions, but in the midst of these competing and conflicting perspectives, TLOS recommends a measured approach. Faculty members in all disciplines need to understand how generative AI tools work, consider how they might enhance the student learning experience, recognize the potential risks associated with these tools, and establish carefully considered course policies to ensure they are used in pedagogically sound ways.
The guidance and linked resources on this page are designed to help you make informed decisions about generative AI use in your courses. Because these tools are constantly evolving, TLOS will continue to monitor the generative AI landscape and revise our guidance to reflect current best practices and university policies.
What is generative AI?
Generative AI describes an array of technologies that are capable of creating new text, images, code, audio, video, or other content. These technologies, which have been trained using vast collections of existing data, can generate unique, human-like responses to user-authored prompts. For instance, text-centric generative AI tools draw on large language models (LLMs) to answer questions, write essays or articles, create lesson plans, and much more. For each generative AI tool, the content creation process is shaped by the tool’s training data, the user’s prompt, and rules or parameters encoded in the tool.
Why does generative AI matter in my courses?
Generative AI tools are becoming increasingly influential across many industries, making it important for students to understand how to use these tools within their specific fields of study. Students can leverage generative AI to explore new concepts, reinforce what they are learning in the classroom, and strengthen their study habits. For instructors, generative AI can assist with course design, inspire new learning activities, and streamline administrative tasks.
However, these tools also have the potential to disrupt the learning process, diminish trust in the originality and authenticity of student work, and undermine instructors’ efforts to create rigorous and enriching learning environments.
The true impact of these technologies remains to be seen. Generative AI is not likely to cause the end of higher education, and it is not likely to be the panacea some expect it to be. Interrogating the risks and opportunities of these tools in different disciplines and types of courses (course size, format, delivery modality, etc.) is key to finding ways to leverage their power while limiting their misuse. As you decide how to apply and constrain these tools in your courses, strive for a nuanced approach and stay engaged in disciplinary conversations about the role of generative AI in your field.
Recommendations
1. Become familiar with generative AI tools.
2. Consider the Honor Code and its applicability to generative AI tools.
The Office of Undergraduate Academic Integrity offers the following guidance: While most students largely engage in honest behavior in the classroom, some may choose to use tools such as ChatGPT to engage in academic dishonesty. Please continue to be clear in your expectations with your student related to the Undergraduate Honor Code and the use of AI software just as you would other websites that may provide students with means to engage in academic dishonesty. The unauthorized use of ChatGPT and other AI software may fall under several definitions of academic dishonesty in the Undergraduate Honor Code. If you believe that a student has engaged in academic dishonesty, please contact the Office of Undergraduate Academic Integrity at 540-231-9876 or email the office at Graduate Honor System; at 540-231-9564; [email protected].
3. Avoid being drawn into a confrontational mindset regarding these tools.
Given the obvious implications of generative AI for academic integrity, several companies are developing and promoting tools designed to detect generative AI use in the creation of text, images, or other content. Although these tools may provide some perspective on whether or not student submissions were created with AI, they will never be 100% accurate, and several studies have demonstrated that they can generate both “false positives” and “false negatives” when attempting to detect AI-created content, especially when applied to the work of non-native English speakers. Reports from AI detection tools may inform your conversations with students regarding inappropriate use of generative AI, but relying solely on detection tools to “catch” someone using AI is likely to lead to ineffective and stressful interactions with students.
Due to these concerns, Virginia Tech has not licensed or endorsed any specific platforms for detecting generative AI, and TLOS recommends caution when using these tools. Any content uploaded to an AI detection tool should be stripped of all personally identifiable information (PII).
4. Set clear expectations for your students regarding the use of generative AI.
Appropriate and inappropriate uses of generative AI vary from course to course (and may even vary among assignments and activities within a single course), so students need clear, context-specific guidance they can confidently apply in each setting. Do not assume that students have previously received instructions that match your expectations, and do not wait for last-minute or after-the-fact questions from your students. Instead, establish and communicate guidelines early in the semester and reiterate (or customize) those guidelines when introducing new assignments or activities.
If you choose to implement restrictions on the use of generative AI in your courses, directly address your rationale for these restrictions. When students understand the pedagogical reasons behind course policies, they will be more likely to accept specific guidelines.
Regularly update your syllabus and other course documentation to directly address generative AI and reflect your current expectations. (See syllabus and course policy resources below.)
5. Explore potential changes to your course design and/or assessment strategies.
Consider incorporating course assessment practices aligned with principles of authentic assessment, such as seeking to replicate professional experiences, expecting a task-focused demonstration of understanding, and providing opportunities for practice with feedback. (See authentic assessment resources below.)
Consider allowing multiple options for action and expression in your assessments. This is a principle of Universal Design for Learning that suggests students be allowed to select from various methods and technologies to showcase their understanding and support their learning.
Proactively incorporate generative AI into your instructional strategy, inviting students to use these tools to spark creativity, or to test and evaluate the accuracy of their output. (See guides for teaching below.)
Additional Resources on Generative AI
Technical Introductions to Generative AI
Guides for Teaching with and about Generative AI
Virginia Tech Honor Code
Virginia Tech Graduate Honor System
More Information on Authentic Assessment
Resources for Syllabus and Course Policy Statements:
| 2023-02-16T00:00:00 |
https://tlos.vt.edu/resources/generative-ai.html
|
[
{
"date": "2023/02/16",
"position": 92,
"query": "AI workers"
}
] |
|
Top 10 Artificial Intelligence Speakers
|
Top Artificial Intelligence Speakers
|
https://www.executivespeakers.com
|
[
"Executive Speakers Bureau",
"Https",
"Www.Executivespeakers.Com"
] |
How AI Trends Can Help Make Your Teams More Productive · Top Artificial Intelligence Keynote Speakers · Adam Cheyer · Peter Diamandis · Mick Ebeling · Ben Hammersley.
|
It’s clear that artificial intelligence and machine learning is the way of the future. With the recent purchase of Chat GPT by Microsoft and the announcement of Google creating their own version of the software, the race to create the most impactful artificial intelligence yet is on.
We’re all familiar with Siri and Alexa, but up until the creation of Chat GPT the AI we use in our daily lives has been severely limited. Until now, our AI was not self-conscious and lacked any semblance of memory. However, Chat GPT remembers and learns from every question we ask it, as well as acknowledges its limitations and mistakes. It even mirrors human language and behaviors and is able to read, write and even tell jokes. And now, two of our biggest technology companies have put their minds towards perfecting this artificial intelligence. Where will that take you and your industry?
As artificial intelligence and machine learning become more and more a part of everyday life, companies and individuals are consistently trying to remain on top of the trends and at the forefront of their own industry's technological advancements.
How AI Trends Can Help Make Your Teams More Productive
Many believe that artificial intelligence (AI) will be as transformative to life as we know it as electricity was over 100 years ago. It's worth noting that with the help of machine learning (ML), AI can transform the way you do business, including deep learning, which can help automate predictive analysis. For business leaders, it's this deep dive that can reveal information such as:
Fraud detection measures through facial recognition and anti-fraud software
Faster analysis of material for quicker results, from blood results to soil analysis
Chatbots to improve service delivery
These applications are just a sampling of what AI has to offer. To help wade through all the business opportunities of AI and related tech, an artificial intelligence keynote speaker can help guide business leaders through this exciting new frontier.
These speakers are experts in the field of machine learning, artificial intelligence, futurism, and technology. They’ve worked across countless industries to understand where artificial intelligence fits into even the most niche companies and they create plans to help organizations utilize the most up-to-date AI technologies.
Adam Cheyer, Co-Founder and Vice President of Engineering of Siri, Inc. and Viv Labs, is a mogul in the world of artificial intelligence. Alongside his team, he developed and incorporated Siri into our daily lives. His newer venture, Viv Labs focuses on where artificial intelligence can move us in the future. In his keynote speaking, he walks audiences through the founding story of his groundbreaking company, Siri, and shares his prediction for where artificial intelligence will be the most impactful across industries. He also tailors his speeches to speak on the future of AI and how it applies specifically to your business.
A name you may not be as familiar with is Mich Ebeling. He is a hacker, innovator, and filmmaker. He’s the recipient of the Top 50 World’s Greatest Leaders, the Muhammad Ali Humanitarian of the Year Award, and was listed as one of the world’s most influential creative people by The Creativity 50s.
Called the “Elon Musk of Good," Ebeling is the only person to ever be honored twice as a TIME Magazine Top Invention of the Year recipient, a two-time Tribeca Disruptor Innovation winner, a WIRED Agent of Change, a fellow with The Nantucket Project, and so much more. All of these accolades came from his creation of Not Impossible Labs, a social technology incubator on a mission to create “Technology for the Sake of Humanity.” In his keynote speaking, he shares his belief system around the future of technology and artificial intelligence and how you and your organization can harness its power for good.
When it comes to the future of technology, you can’t forget Peter Diamandis. With degrees in Molecular Genetics and Aerospace Engineering from MIT and an M.D. from Harvard Medical School, Diamandis uses his expertise and innovative thinking to push the boundaries in technology across many areas, like space, human longevity, venture capitalism, and education. He is the Founder and Executive Chairman of XPRIZE Foundation, in which he leads the world in designing and operating large-scale incentive competitions to promote global innovation and the spread of technology. In his keynote speech, he will push his audiences to break barriers to reach true innovation within their industry using technology and artificial intelligence.
Whether your next event calls for an artificial intelligence mastermind or a hacker using his fine-tuned skills to make the world a better place, our list of Artificial Intelligence Keynote Speakers has someone with a unique experience for every organization. Book an expert in artificial intelligence and machine learning to help your company not just keep up with the latest trends, but set the trends yourself. Contact Executive Speakers Bureau and speak to a keynote speaker expert to find an Artificial Intelligence Keynote Speaker who is perfect for you, your team, and your goals.
| 2023-02-16T00:00:00 |
https://www.executivespeakers.com/topic/artificial-intelligence
|
[
{
"date": "2023/02/16",
"position": 2,
"query": "AI business leaders"
}
] |
|
Is AI coming for your job? Tech experts weigh in: "They don ...
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Is AI coming for your job? Tech experts weigh in: "They don't replace human labor"
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https://www.cbsnews.com
|
[
"Tony",
"Co-Host",
"Cbs Mornings",
"Cbs Mornings Plus",
"Tony Dokoupil Is A Co-Host Of",
"Cbs Mornings Plus.",
"Dokoupil Also Anchors",
"The Uplift",
"A Weekly Series Spotlighting Positive",
"Inspiring Stories For Cbs News"
] |
He believes AI will drive up wages, "because productivity and wages are related." But while that may be true in the long run, MIT economist David Autor ...
|
Amid major developments in the field of artificial intelligence, there's a question many of us have been asking ourselves: How long until machines replace us?
New systems from Google and Microsoft — plus a Microsoft partner called OpenAI — are capable of doing things we used to think were uniquely human, like creating original art and generating original writing.
The possibilities are huge. But so are the fears — about jobs and wages in particular.
As artificial intelligence gets better, some expect job security will get worse. In reports like one in Gizmodo earlier this month, titled, "Here Are the Jobs Our New AI Overlords Plan to Kill," coding or computer programming is often on the list.
But it seems none of the AI coding software currently out there can replace the talent of professional coders. In fact, it may help them.
Sam Zanca, a coder and instructor at General Assembly, a coding school in New York, said AI will "definitely automate the more tedious parts" of his job, "but it's still a long ways away before having a true impact."
He's not very worried about AI right now.
But "the problem is that ... I'm not entirely sure if the AI that would replace me is 10 years from now, 20 years from now, or five years from now," he said.
Microsoft CEO Satya Nadella is a big believer in AI — but not the idea that AI is coming for our jobs.
"I believe it creates more, I will call it both satisfaction in current jobs and net new jobs," Nadella told CBS News earlier this month.
Ahead of the launch of the new AI-powered Bing, Nadella argued that artificial intelligence could help the economy across the board.
"My biggest worry is we need some new technology ... that starts driving real productivity," Nadella said. "It's time for some real innovation."
He believes AI will drive up wages, "because productivity and wages are related."
But while that may be true in the long run, MIT economist David Autor believes the rise of AI does indeed mean millions of jobs are going to change in our lifetimes. And what's scary is: we're not sure how.
"We are very good at forecasting what we will not be doing and very poor at forecasting what we will be doing," Autor said.
He points out, for example, that more than 60% of the types of jobs people are doing today didn't even exist in the 1940s, while many of the jobs that did exist have been replaced.
"What we've seen over the last four decades in the U.S. and many industrialized economies is what economists call labor market polarization, which means the hollowing out of the middle set of jobs ... kind of middle class jobs involving blue collar production and operative work, but also ... administrative support clerical tasks," he said. "We had banks and banks of people who answered phones, who did typing and filing, who reproduced documents, who kept tables and books. Many, many of these things are now done by machines."
The "hollowing out" of the middle has led to some in the labor market moving up and making more money, while others are now making less — and "that's especially where the pain happens," Autor said.
That also troubles Meredith Whittaker, who co-founded the AI Now Institute at New York University to study how all these new systems of artificial intelligence will affect our lives.
"They don't replace human labor. They just require different forms of labor to sort of babysit them, to train them, to make sure they're working well," Whittaker said.
"Whose work will be degraded and whose house in the Hamptons will get another wing?" she asked, clarifying that she uses "degrade" to mean that someone will still have a job, but it will pay less. "I think that's the fundamental question when we look at these technologies and ask about questions about work."
| 2023-02-16T00:00:00 |
https://www.cbsnews.com/news/ai-coming-for-your-job-tech-experts-weigh-in-they-dont-replace-human-labor/
|
[
{
"date": "2023/02/16",
"position": 1,
"query": "AI wages"
}
] |
|
Generative AI for Constructive Communication: MIT MAS.S68
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MIT MAS.S68!
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https://ai4comm.media.mit.edu
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[] |
Prerequisites: None; instructor permission required. Contact: If you have any questions about the course, please direct questions only to [email protected].
|
Description
Advances in large language models recently popularized by ChatGPT represent a remarkable leap forward in language processing by machines. We invite you to join the conversation shaping the future of communication technology. What does this mean for us, how can we make the most of these advancements, and what are the risks? What research opportunities have opened up? What kinds of evaluation are called for? We will bring together a group of practitioners and experts for guided discussions, hands-on experimentation, and project critiques. If you want to join the class, please fill out this interest form and come to the first class on Wednesday, 2/8. Bring a laptop and be prepared to start experimenting!
This course will be formatted as a combination workshop and seminar. Students will engage through readings, class participation, and project work. Students may choose to either complete a project or produce a research project proposal. For the active project track, students will form teams, pitch projects, and get feedback along the way. For the project proposal track, they will present a literature review mid-semester, and submit a written research project proposal. Project should be focused on one of the main areas identified by the course. We will come together to share and critique projects through the semester, culminating in final project presentations. Students will also be expected to present to the class on readings and hands-on workshop output.
| 2023-02-16T00:00:00 |
https://ai4comm.media.mit.edu/
|
[
{
"date": "2023/02/16",
"position": 46,
"query": "AI journalism"
}
] |
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How ChatGPT can Transform the Recruitment and Hiring ...
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How ChatGPT can Transform the Recruitment and Hiring Process [12 Ways]
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https://gohire.io
|
[
"Sophie Smith",
"Chris Smith"
] |
It can help in providing valuable support and increase productivity in the hiring process. Recruiters can only rely partially on AI software but can use it ...
|
Using ChatGPT for the hiring process can save time and money for recruiting managers, agencies and companies. Reaching 1 million users in one week indicates its capability to generate the best responses human beings are waiting for.
What is ChatGPT?
ChatGPT is the new wave of artificial intelligence that has quickly gained popularity worldwide.
This cutting-edge artificial intelligence technology was developed by OpenAI, which uses deep learning algorithms to generate responses aligned similarly to those provided by human beings.
From coding to writing, questions to customer services, sales to marketing, and personalisation to multilingual, this AI tool is available 24/7 to give you a solution to almost everything you ask for.
How can ChatGPT assist in the recruitment process?
Recruitment is a time-consuming process involving various steps to reach the perfect candidate for a particular job role.
However, a recruitment strategy that uses ChatGPT can not only smooth your hiring process but also let you save time in planning the subsequent recruitment in your company.
Let’s see how ChatGPT can help you make recruit smart to achieve your business goals:
Crafting job descriptions
Writing job descriptions and threading roles and responsibilities in a loop is now a challenge for hiring managers. However, with ChatGPT, you can easily create job descriptions. Just enter or write titles of technical, non-technical, or management recruiting to craft job descriptions of internship, part-time and full-time roles. For example: “Write a job description for a software engineer with 5 years of experience in python development.”
Writing emails/letters for candidates
Regarding communication with the applicants or selected candidates, HR must draft a well-written email template. The guide to getting an ideally “human written autoreply” message is to put the right keyword to get exactly what you want to write in an email. With ChatGPT for recruiters, you can write offers, not only letter emails but also well-drafted rejection emails.
Generating suggested interview questions
When interviewing or hiring candidates, you must ask all relevant questions to know their skills and capabilities according to the job role. Using ChatGPT for recruitment can give you unlimited suggestions for interview questions. For example: “Create interview questions focused on JavaScript that project experience in the same domain.”
Writing an Employer Value Proposition (EVP)
An Employer Value Proposition defines all of a company's core benefits that an employer can have in terms of salary and compensation. Posting a creative EVP can help attract applicants to apply for job roles in your company. With ChatGPT, you can create detailed, inspiring and convincing EVPs as a part of a company's branding on different job boards in the UK.
Content creation for recruitment campaigns
Recruitment campaigns, one of the important parts of the hiring process, can lead you to better outcomes in terms of employee finding and getting the applicants of potential candidates for job roles.
The capabilities of the best hiring software range from data analysis to the optimistic usage of AI techniques. The prime objective of these techniques is to identify complex patterns and trends in the recruitment process.
With ChatGPT, you can easily plan effective strategies to run a recruitment campaign to post jobs and create ad copy to attract job seekers worldwide. It can also help in getting overall success with productive leads for the hiring process.
Create job ads
Creating a compelling job ad can attract top candidates and job seekers; ChatGPT is the best helping hand you could ask for. With smart use of it, you can effectively create your company’s brand image and get noticed by potential candidates for job roles.
You can easily use ChatGPT for hiring and get many results for creative job ads. For example: “Create a catchy job ad for a Business Analyst with 5 years of experience in an IT company.”
Provide a list of relevant job boards to post vacancies
“Which job board is the best” has always been the first question in a recruiter's mind. With ChatGPT, your question will have the perfect answer.
Type “a list of relevant job boards to post vacancies” on ChatGPT, and you will get the best portal results in just a few seconds.
Write custom candidate outreach messages in any language
You will be surprised to know that ChatGPT can understand and generate text in different languages, including:
English
French
Spanish German
Chinese (Simplified and Traditional)
Russian
Japanese
Italian
Portuguese
Korean
Dutch
Polish
Arabic
Urdu
Hindi
Bengali
Indonesian
Malay
Turkish
Vietnamese
Type the title of creating the job description in the preferred native language to communicate with candidates and respond accordingly.
Respond to candidates’ follow-up questions
ChatGPT can help you create the best hiring platform for your company. It allows recruiters and agencies to provide feedback on applicants’ questions. It can connect and respond to questions in your hiring pipeline during a job seeker's journey. In a true sense, it can help identify common sentiments or themes and build relationships with candidates.
Automatic screening of resumes
Another great feature of ChatGPT is candidate screening automation.
It can assist in the recruitment process by saving time in filtering resumes on a manual basis.
Offering the possibility of a fully automated process, ChatGPT allows you to understand the job requirements as listed by the company.
Providing interviewers with personalised feedback
As a hiring manager or recruiter, you have to provide feedback to candidates regarding their application submissions and interviews. With ChatGPT, you can easily create personalised feedback messages, emails and SMS to share the performance score or grade with the candidates.
You can type “Create a summary about a candidate’s good performance in the technical interview round.” With a bit of edit in a tone of voice or length modification, you will be all set to share feedback with the applicants.
Solve the problem of unstructured recruitment data
Recruitment data is of great value for the company as well as recruiters. Hiring managers often have to deal with unstructured recruitment data, which is hard to sort, organise and manage for regular analysis. However, with ChatGPT, you can easily retrieve and plan data on different recruitment stages.
Will ChatGPT Ease Hiring Managers' Jobs?
Hiring is one of the critical tasks for recruiters and startup companies.
There is no doubt that artificial intelligence technology like ChatGPT has become a new assistant to hiring managers for multiple tasks like creating job descriptions, drafting emails for offer letters, appraisal, rejection and much more.
If used smartly, it can help recruiters to plan their hiring strategies in the best and most productive way.
Conclusion
The potential of recruitment software and ChatGPT can be combined to make the hiring process easy and hassle-free. Many hiring tasks depend on interactive actions like customer relationship management, follow-up communication, and in-depth conversation with applicants.
Using ChatGPT is an intelligent way to generate statistical data to screen the best-matched texts and answers to frequently asked questions. It can help in providing valuable support and increase productivity in the hiring process. Recruiters can only rely partially on AI software but can use it smartly to ease their content-generating tasks like emails, job descriptions, feedback messages, etc.
| 2023-02-16T00:00:00 |
https://gohire.io/blog/chatgpt-will-transform-hiring-process
|
[
{
"date": "2023/02/16",
"position": 64,
"query": "artificial intelligence hiring"
}
] |
|
Why your Nonprofit needs an AI Prompt Architect
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Why your Nonprofit needs an AI Prompt Architect
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https://www.wholewhale.com
|
[
"George Weiner"
] |
AI prompts can automate many routine tasks and interactions, freeing up staff time and resources. This can lead to increased productivity and cost savings. This ...
|
Share Pin 0 Shares
When MySpace and Friendster first came on the scene opening the door to Web 2, nonprofits couldn’t fathom that one day they would be hiring for “Social Media Managers”. A couple of decades later a quick search on Idealist.org turns up over 2000 current jobs and tons of nonprofit social media professionals on networks like Nonprofit.ist.
When MySpace and Friendster first came on the scene opening the door to Web 2, nonprofits couldn’t fathom that one day they would be hiring for “Social Media Managers”. A couple decades later a quick search on Idealist.org turns up over 2000 current jobs and tons of nonprofit social media professionals on networks like Nonprofit.ist.
Now, as artificial intelligence (AI) continues to reshape our digital landscape, a new role will emerge for nonprofits: the AI Prompt Architect.
The future role of AI Prompt Architect will be responsible for designing and implementing AI prompts that improve user experience and achieve business objectives. These prompts can take many forms, including chatbots, virtual assistants, web interfaces and voice assistants. If you’re looking for a crash course in AI Prompts we have ya covered 😉
5 Ways a Nonprofit Can Use AI Prompt Architecture or Engineer
#1 Improved User Experience
By implementing AI prompts, your nonprofit can provide a more personalized, efficient, and engaging user experience. AI prompts can understand and interpret user input, provide relevant information, and even anticipate user needs. This can lead to higher user satisfaction and retention.
AI interfaces can be tuned to help with specific tasks your stakeholders do ranging from writing draft letters to officials, to getting feedback on poetry, creating drafts of RFPs for work and the list goes on.
#2 Increased Efficiency in business writing
AI prompts can automate many routine tasks and interactions, freeing up staff time and resources. This can lead to increased productivity and cost savings. This work can include writing newsletters, social media posts, research articles, editorials in the voice of the founder, and grant writing. Learn more about how nonprofits can have these use cases purpose-built for their work.
#3 Better Data Collection and Analysis
AI prompts can collect and analyze large amounts of data in real-time, providing insights into user behavior and preferences. This can inform your nonprofit’s decision-making and help you optimize your programs and services.
Tools like GPT3 can be built into Google Spreadsheets and then used to write functions or clean up data, add context and a lot more.
#4 Enhanced Fundraising and Donor Engagement
AI prompts can help you personalize and scale your fundraising and donor engagement efforts. For example, chatbots can answer frequently asked questions, provide donation options, and even help donors set up recurring gifts. This can lead to increased donor retention and revenue.
As AI technology continues to advance, it’s likely that nonprofits will increasingly rely on AI prompts to achieve their goals. If your nonprofit is considering implementing AI prompts, it may be worth hiring an AI Prompt Architect to ensure a successful and effective implementation.
#5 Creative Content beyond text
Generative AI tools are becoming very powerful storytelling aids. These engines can help turn the creative thoughts in your head into IRL creations with alarming speed. Whether it is images, audio, or videos, generative AI tools are able to 10x the production.
Here are just a sample of a few AI tools that can boost creative output. Learn more about using AI tools to create creative content with our free course.
Image tools:
Video Tools:
Audio
Example of how we turned this article into a simple video.
So, while it may seem futuristic or unnecessary right now, investing in an AI Prompt Architect can help position your nonprofit for success in the years to come as more of these tools pop up. Just as social media once seemed like a novelty, AI technology is quickly becoming a mainstream tool that nonprofits cannot afford to ignore.
While it may be a bit early to start posting the job description, the upside to having an AI adoption lead on your team has incredible potential. Whole Whale’s AI Cause Writing tool and service can serve as this layer in the meantime.
Sample Job Description For an AI Prompt Architect
Position: AI Prompt Architect
Location: Remote
Employment: Full-time (until the robots take your job)
Job Summary:
The AI Prompt Architect will use Natural Language Processing (NLP) technologies to develop intuitive conversational user experiences within dynamic customer service software applications. The successful candidate will have deep knowledge of the latest trends in AI, deep learning models and NLP strategies, as well as a robust understanding of customer service UX design.
Responsibilities:
Serve as the translation layer between AI interfaces and the work the organization does, championing adoption and building efficiency through training
Design AI prompts that accurately respond to customer requests in a conversational format
Utilize machine learning techniques to identify relationships between customer requests and corresponding replies with accuracy
Constantly adjust and refine existing algorithms based on actual user interactions and feedback
Work closely with development teams to ensure prompt integrations are improving staff output
Requirements:
Years of experience tinkering with generative AI with demonstrated work
Bachelor’s degree in Computer Science or related creative field required
Experience in designing NLP technologies for dynamic customer service applications or chatbot frameworks required
Knowledge of the latest trends in AI is essential
Proven success in designing effective chatbot UX flows
Expertise integrating machine learning into conversational user experiences across multiple platforms (web, mobile apps, etc.)
Meticulous attention-to-detail when creating complex prompt architectures
Ability to quickly troubleshoot issues encountered during development process
Good at training teams on new technologies
Reminder we are just at the start of this curve…
| 2023-02-16T00:00:00 |
https://www.wholewhale.com/tips/ai-prompt-architect-job-description/
|
[
{
"date": "2023/02/16",
"position": 93,
"query": "artificial intelligence hiring"
}
] |
|
What's With All the Layoffs in Tech?
|
What’s With All the Layoffs in Tech?
|
https://www.kdnuggets.com
|
[
"Nate Rosidi"
] |
The ones who avoided layoffs have implemented hiring freezes. Overall, nearly 90k workers in the tech sector got laid off in 2022. Airia Enterprise AI Schedule ...
|
Answering all the questions that you've been asking about the layoffs in the tech industry.
Image by Author
If you’re in the tech industry, you’ve enjoyed a great decade. Data science is the sexiest job. Every college professor is telling you that your skills will keep you employed until the world ends. Every company needs a data scientist, and those jobs aren’t going anywhere, any time soon.
Except if you’ve cracked open a newspaper or checked out a headline lately, you know that’s not quite true. Twitter, Meta, Netflix, Microsoft, and dozens of other tech companies have all made massive layoffs lately. The ones who avoided layoffs have implemented hiring freezes. Overall, nearly 90k workers in the tech sector got laid off in 2022.
In the several first weeks of 2023, another 16k have been laid off according to Crunchbase.
Suddenly, the hottest job market got a whole lot chillier.
What happened at the big tech companies? What does the future hold for you if you’re in the tech industry? Should you be worried, or think about changing careers?
This article will answer all that and more. Spoilers: if you’re in tech, your skills are still in high demand.
How did we get here?
There are a couple of different factors that help explain the tech layoffs, though it’s difficult to say for sure how much any of them are specifically to blame.
First, the rate of unprecedented growth at tech companies was always somewhat unsustainable. COVID made many e-commerce and ad-basec companies experience rapidfire growth that wasn’t able to keep up as consumers started going back to normal.
After a decade of overvaluations, over-hiring, and VC-subsidized growth, the layoff chickens are coming home to roost.
Second, as you’re no doubt sick of hearing, the economy is weird right now. There are supply-chain problems. Inflation is sky-high. There’s geo-political turmoil. All this caused the tech sector growth to slow down – just enough that tech companies started panicking.
Third, there was pressure from multiple sides. Take a company like Twitter, currently under Elon Musk’s unpredictable and chaotic leadership. Musk made deep cuts to headcount, and as of today, Twitter is still standing (albeit somewhat glitchily). This success makes boards at other companies ask why they’re paying the salaries of so many employees when Twitter proves it’s possible to slash employee numbers in half, and still function.
There’s also an element of social contagion. Jeffrey Pfeffer, a professor at the Stanford Graduate School of Business, believes that copycat behavior may be to blame for much of the layoffs we’re seeing right now. “Could there be a tech recession? Yes. Was there a bubble in valuations? Absolutely. Did Meta overhire? Probably. But is that why they are laying people off? Of course not. Meta has plenty of money. These companies are all making money. They are doing it because other companies are doing it,” Pfeffer told Stanford News writer Melissa de Witte.
Looking at the Tech Layoff Landscape
Let’s understand what exactly is happening to employment across multiple big tech companies.
Meta (Facebook)
Image by Author
Meta reported 11,000 layoffs in November of 2022, affecting 13% of employees. These reductions affected almost every area of the company, but especially recruitment since Meta also plans to freeze hiring in the first quarter of 2023 and to slow hires after that.
The primary cause behind this, most experts agree, is the vast amount of expenditure Meta has made into its Metaverse. Additionally, sales from ad revenue declined in the last two quarters of 2022. Revenue couldn’t keep up, as Meta’s costs increased by 19% year over year in the third quarter to $22.1 billion.
However, during a call with analysts as part of its Q3 earnings report, Mark Zuckerberg announced that “some teams will grow meaningfully” in an attempt to “focus [Meta’s] investments on a small number of high-priority growth areas.”
What could those areas be?
Checking out LinkedIn, Indeed, and Meta’s own careers page, most of those thousands of new jobs appear to be in the tech sector. Meta is hiring data scientists, researchers, analysts, and engineers.
Microsoft
Image by Author
Microsoft has made some very minor layoffs, reportedly affecting less than one percent of its workforce.
The reason behind this workforce reduction is simple: the 2022 Q3 revenue growth was the slowest in the past five years.
I want to take a quick moment and highlight that the revenue still increased. It’s just that the rate of increase slowed.
LinkedIn reports that Microsoft is still hiring thousands of jobs across the tech job board, with openings in research, machine learning, and data science. Indeed and Microsoft’s job board corroborate this.
Netflix
Image by Author
The streaming giant has faced its unique troubles in the last few years, tech bubble aside. In May 2022, Netflix reported its first subscriber loss in a decade. As a result, the company made 450 layoffs.
Despite these cuts, Netflix is still offering several jobs in data science and engineering on its internal job board. LinkedIn shows 200 job openings, some of which are in the analyst/data science sector, as does Indeed.
Amazon
Image by Author
The e-commerce shopping giant laid off 18,000 people. However, cuts focused on corporate staff. Layoffs affected around 5% of that sector of its workforce and 1.2% of its full workforce. The company attributes this to the strain caused by the massive surge of expansion during the pandemic.
Today, employment prospects are not so dire. Indeed, LinkedIn and Amazon’s websites show dozens of opportunities for anyone looking for a job in the tech sector.
Twitter
Image by Author
Twitter is probably the most-reported source of layoffs, thanks to animosity between the journalists on Twitter and the new management. Twitter has somewhat famously laid off over half of its 7,500-strong workforce, mostly because of new CEO Elon Musk’s whims.
Musk reported that Twitter is losing over $4 million per day, leaving “no choice” but to lay off about 3,700 workers including employees on the trust and safety teams, the content moderation teams, and the janitors at Twitter HQ.
Unlike the other companies on this list, Twitter is unique in that there are no job openings at the company at all. Twitter’s career website only has 24 openings, all of which are for “future consideration” only. LinkedIn and Indeed both show the same.
Other Tech Companies
These five examples are certainly among the biggest and most well-known, but they’re far from the only tech companies making cuts. Other companies that have made layoffs include Snap (20% of employees), Salesforce (9,090 layoffs made), Zillow (25% of employees), and many, many more.
Here’s the layoffs.fyi’s chart showing layoffs in the tech industry in 2022 and 2023.
I liked their table showing a full count of all 1,910 companies that have made layoffs since COVID-19 started. If you want to get a better picture, I recommend you take a look.
Or, even better, here’s the visualized data.
What to do?
If you’ve been carefully reading this article, I hope you’ve taken away three things:
Layoffs have affected a lot of tech companies . If you did get laid off, it’s nothing to do with your skill level or ability. It’s just the market right now.
. If you get laid off, it’s nothing to do with your skill level or ability. It’s just the market right now. The layoffs have not been disproportionately aimed at tech employees . That is to say, tech companies are making layoffs to employees either across the board or in areas that are not engineering, analysis, data science, and so on.
. That is to say, tech companies are making layoffs to employees either across the board or in areas that are not engineering, analysis, data science, and so on. Most of these companies are still hiring jobs in tech, bar the notable exception of Twitter.
If you’re in the tech sector, it’s possible that through no fault of your own, you’ll be laid off as tech companies struggle to right their ships. But there are still plenty of opportunities available.
So what can you do?
The very best action to ensure you don’t remain unemployed for long is to keep your interview skills sharp. I recommend checking out platforms like our own StrataScratch, which runs you through common interview questions, both coding, and non-coding.
You can also take part in coding competitions on sites like Kaggle or Leetcode, which is a great way to improve or maintain your data science skills. Hackerrank also offers a great interview prep kit, which can be useful if you haven’t been in a tech interview in a while. It also offers certification in valuable data science skills like Python and problem-solving.
Finally, I recommend taking a moment to take stock of your network. Update your LinkedIn, and send a message to any old contacts you may have at other companies offering to meet up. A lot of employment rests not on what you know, but rather on who you know.
Final Thoughts
Amidst all the panic about layoffs, I thought this article from MarketWatch was really interesting. Can you guess what they named the “best job in America”?
It’s a software engineer.
All this to say that it’s easy to get caught up in the bad news about layoffs and worry about your future employment. I want to reiterate that if you find yourself laid off, it’s not because of you. It’s because the market is cooling right now.
Instead of worrying about whether or not you’ll be laid off, I recommend taking action to improve your odds of going from one tech job straight into another.
Despite what the headlines may say, the job boards prove that many companies still desire skillful tech employees.
Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.
| 2023-02-16T00:00:00 |
https://www.kdnuggets.com/2023/02/layoffs-tech.html
|
[
{
"date": "2023/02/16",
"position": 34,
"query": "artificial intelligence layoffs"
}
] |
|
AI Tools-for better or for worse-for writers and artists
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AI Tools–for better or for worse–for writers and artists
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https://unleashingthenextchapter.com
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[
"Kathryn Mcclatchy"
] |
Midjourney AI is a more developed and sophisticated tool that can be used to create everything from foolishness to concept images to fine art. I have used it to ...
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AI Tools–for better or for worse–for writers and artists
I use Artificial Intelligence. And here’s the thing I know to be true: AI is a tool. It will no more replace artists than the camera did. Check your history—there was a huge outcry and debate when the camera was introduced. But after the noise settled down we realized that the camera opened up a new form of art, of storytelling, and of documenting life and history. We all take photojournalism as a given, but that wasn’t the original conversation.
AI is a tool that I’ve been interested in for decades. Although this topic seems new, I was fortunate to be taught and mentored by one of the pioneers of this technology. If you want to know more about the origin story of Artificial Intelligence, listen to episode 6 and episode 7 on Authorpreneurs Unleashed where I interview Dr. Hugh Burns. He wrote much of the original coding as a tool and prompt for his college writing students. He then worked with computer companies and the Air Force to further develop the technology. Dr. Burns is a computational rhetorician who taught digital composition before most people had a PC in their homes.
Every industry grows and develops tools to simplify its processes and workflows. Thirty years ago, my husband started his twenty-year career in banking. He loved interacting with his customers when they came into the bank to cash their paychecks, to reconcile their bank statements, to deposit their business receipts, or to help folks get business, mortgage, or car loans. His job was people-centered and he felt like he was serving the community. And then came direct deposits—people no longer needed to come cash or deposit their paychecks. And then came the ATM—no one was stopping by to get cash or talk about the date night or vacation they needed the cash for. And then came online banking—no one needed help to reconcile their statements or apply for loans. And then he was downsized. So he changed careers and became a licensed professional counselor because his passion and gifting was in serving his community.
What technology does in any industry is to take over the easier and routine tasks that can be digitized, and allow people to do more of the higher level thinking and creative work that machines cannot do. Every industry transforms and develops thanks to technology. This is not new. I understand the fear, frustration, and anxiety of those who realize their job may be outsourced to this “new” technology, but the fact is that it will open new opportunities for those willing to learn and change. My husband is way happier as a counselor than he was as a banker. He took those skills and applied them to a new profession. Others took the freedom from routine daily tasks and moved up into higher levels of banking.
I have a dear friend who is a graphic designer. She has a master’s degree from a prestigious university for a specific field of graphic design. AI art has allowed her to develop a new way of using her talent and education, to be creative in ways that were not possible before.
I use both AI art and writing tools. In deciding on which tools I wanted to use, I researched and tried out a number of them. Like any other computer program, “Garbage In = Garbage Out.” If you don’t know how to give the right commands to the AI app, you will not get a product you can use. Skilled artists who know the working vocabulary, and understand color and perspective and all the “art things,” are creating amazing works of art. Most of the rest of us are creating some very basic, if not awful images.
Same with the writing tools. I find most of the programs initially create a shitty first draft. And you can see across the internet that many people are posting those without knowing how bad they are, either because they don’t have command of the language or because they aren’t skilled enough to realize that it’s only a first draft. But we also know writers who also published their own original work without command of the language, good editing, or multiple revisions directly to KDP, and gave indie authors a bad rep.
The tools currently available have differing levels of function and purpose. Canva is rolling out their AI art generator, and so far, it’s quite basic and doesn’t allow for variations and revisions. But it’s not designed to create art. It’s purpose is to create basic images for marketing and education content. If you will remember back a few years, the graphic design community was also up in arms about Canva bringing design to the masses. Midjourney AI is a more developed and sophisticated tool that can be used to create everything from foolishness to concept images to fine art. I have used it to create images for my D&D game and friends, for my website, and for my social media clients. I use it primarily to create unique images that will live a short time in my social media posts, newsletters, or blog. But I can’t create fine visual art because I don’t have the eye, the training, nor the skill needed to create that in any medium. When I need something more advanced or specific, I will still be calling on artists and designers.
The same is true for the writing apps. In the writing community we have been using tools like Scrivener (where I am drafting this piece), Grammarly, ProWritingAid, and others. We google rather than interviewing people or driving to the library. We have a bewildering array of tools, resources, and courses that were not available to our parents and grandparents, and that in itself has increased the demand for what we write and allows us to be more prolific. For better or for worse. We still know the difference in quality between those who are skilled and practiced and those who aren’t, regardless of the tools used. There are writing AI tools for marketing/copywriting like Jasper, creative writing tools like AI Dungeon and StoryLab, analysis/revision tools like AuthorsAI/Marlowe, and the editing tools we already mentioned. These tools all require humans to dream up the basics, to be creative in giving commands, and then being skilled enough to choose what results are worth keeping and what aren’t, and then to go back and make revisions.
Because I do so much copywriting, I subscribe to Jasper. It was developed by marketing professionals and copywriters as an aid to those of us who do that sort of thing. It even includes a plagiarism checker and Grammarly. Do I feel like it will replace me? Absolutely not. I still have to figure out my (or my client’s) brand, purpose, audience, create the command strings for Jasper, and then choose from the results and revise for voice and add personal interests and experiences into the copy. I tend to use it for idea generation and first drafts. I then do revisions, my own research, run it through the content improver template, and revise it again. If I didn’t have years of writing experience and education behind me, I might be satisfied with that shitty first draft. But I know better.
And, side note, did you know that the social media platform’s algorithms and users know better, too? We are seeing that posts that start and end with AI generated content are not getting the reach, impressions, or engagement that human-touched content is getting. This seems to prove my point.
I chose not to use AI for creative writing (at least at this point) because I am still interested in learning and practicing those skills for my own personal amusement and relaxation. Although I have a couple stories published, creative writing (and the creative writing community) is my passion, not my job.
AI tools are just that, tools. They cannot duplicate our life experiences, worldviews, desires, or dreams. Those are the qualities that separate words and images from art. Good storytelling, whatever the medium, requires experiences, creativity, perspectives, personality, and voice that are all uniquely human traits. It’s why there are less than a dozen basic plot-lines, but untold millions of unique books published. It’s why I could post an image and writing prompt on the board at the beginning of class for 150 students, and every paper turned in would be as individual as the student who wrote it.
If you want to know more about the current AI debate in the visual art world (which absolutely relates to the writing community), I recommend you watch these two videos:
Another resource I would love for you to check out is @SleeplessDreamer_ai on Instagram. Images are created in Midjourney and posted to inspire storytellers, whatever their medium, to create new stories and new art. What do you think?
By the way, I did NOT use AI to write this post, although I did use Midjourney AI to create the images. You may argue that the copy would have been better if I had used AI 😉. I’m curious if you have experimented with any AI tools. If so, how you use them, and what you have found to be their strengths and weaknesses. Please let me know in the comments.
| 2023-02-16T00:00:00 |
2023/02/16
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https://unleashingthenextchapter.com/ai-tools-for-better-or-for-worse-for-writers-and-artists/
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[
{
"date": "2023/02/16",
"position": 34,
"query": "artificial intelligence graphic design"
}
] |
ChatGPT is scary good at my job, but there's one reason I' ...
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ChatGPT is scary good at my job, but there's one reason I'm not panicking
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https://www.zdnet.com
|
[
"Kerry Wan",
"Managing Editor",
"Feb.",
"At P.M. Pt",
"Alyson Windsor"
] |
In my eyes, there are some clear wins and losses with what the AI is capable of. To start, the flow of the review is textbook; ChatGPT sets the tone with ...
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An AI-generated image for the prompt, "Robot typing on a laptop." DALL-E 2
A recent scroll through the interwebs of tech social revealed one commonality: Screenshots of conversations with OpenAI's ChatGPT, whether it's through the original platform, a web browser, or in the form of a Google competitor.
Also: 6 things ChatGPT can't do (and another 20 it refuses to do)
OpenAI, the research firm responsible for DALL-E 2 and all the fantastical creations that came of it, deployed ChatGPT for public testing back in November 2022. While the conversational chatbot remains in its development phase, its consistent and surprisingly thoughtful responses have been enough to make me question my own position as a professional writer.
Naturally, I had plenty of questions to ask the AI, including a prompt to write a full review on a product that I've covered myself: the iPhone 14 Pro. You'll find ChatGPT's response below, along with my commentary on what it gets right and what it gets wrong.
Spoiler alert: I'm not sweating too much about my job security. Not yet, at least.
Also: The best AI chatbots: ChatGPT and other fun alternatives to try
Prompt: Write a full review on the Apple iPhone 14 Pro
An AI-generated image for the prompt, "A robot reviewing the iPhone 14 Pro." DALL-E 2
While there is no set standard for how one should communicate with ChatGPT, I've found that the more specific I am with the wording, the more accurate of an answer I get back. In this case, I added "full" before "review" so that the AI knows to draft a complete intro-to-conclusion analysis on the latest iPhone. Otherwise, it would only generate a one-paragraph summary of the product. I probably could've done without the "Apple" though.
ChatGPT's review
The Apple iPhone 14 Pro is the latest addition to Apple's lineup of smartphones, and it's a fantastic device. The new design is sleek and modern, with a bezel-less display and a stainless steel frame. The phone is also the perfect size, fitting comfortably in the hand without being too large. In terms of performance, the iPhone 14 Pro does not disappoint. It's powered by Apple's latest A15 Bionic chip, which provides lightning-fast speeds and smooth, lag-free performance. The phone can handle all of my daily tasks with ease, from browsing the web and checking email to gaming and streaming videos. I've also noticed that the phone doesn't slow down or become sluggish over time, even with multiple apps running in the background.
An AI-generated image for the prompt, "Taking a picture with the iPhone 14 Pro." DALL-E 2
The camera on the iPhone 14 Pro is also top-notch. It has a triple-lens system that allows for versatile shooting options, including ultra-wide, wide, and telephoto lenses. The new Night mode feature is fantastic, allowing for crisp, clear photos in low-light settings. Additionally, the new ProRAW format gives me more control over editing, allowing me to fine-tune every aspect of my photos. I've also found the new features like Deep Fusion and Smart HDR 3 to be useful in everyday shooting. One area where the iPhone 14 Pro really stands out is its battery life. I can easily get a full day of use out of the phone, even with heavy use. The new battery-saving features, like optimized battery charging and low-power mode, also help to extend the battery life even further. Additionally, the phone supports fast charging, so I can quickly top up the battery when I need to. Overall, I am extremely impressed with the Apple iPhone 14 Pro. It's a top-of-the-line smartphone that offers a great combination of design, performance, and features. I would highly recommend it to anyone in the market for a new phone.
What ChatGPT gets right
So, what did you think? In my eyes, there are some clear wins and losses with what the AI is capable of. To start, the flow of the review is textbook; ChatGPT sets the tone with what it thinks about the iPhone 14 Pro, talks about the design, performance, cameras, and battery life, and even concludes with a definitive recommendation. Some digging through the web tells the AI that ProRAW, Deep Fusion, and Smart HDR 3 are new features on the iPhone and it appropriately mentions the three within the camera section. The only problem is that two of those three features are not new, which I'll touch on later.
Also: Soon you can choose ChatGPT's 'values' and it's going to get messy
I'm also impressed with how the AI will make a statement about the product and then follow it up with how it's affected the user experience. For example, "Additionally, the phone supports fast charging, so I can quickly top up the battery when I need to." It isn't just listing specs and calling it a day, which is what smart assistants like Siri, Google Assistant, and Alexa often do.
What ChatGPT gets wrong
The most obvious problem with the AI-generated product review is that it's inaccurate. You may have noticed a few blunders when reading through the response, like how it credited the A15 Bionic as the latest chipset and not the A16 Bionic and suggested that 2019's Deep Fusion is a "new" feature.
ZDNET Recommends The best iPhones You can find iPhone models directly from Apple starting from $449. Read now
To OpenAI's defense, the company states on the website that ChatGPT "may occasionally generate incorrect information" and has "limited knowledge of world and events after 2021." This makes sense because the AI doesn't always have a source of truth to reference; It doesn't know if the data it's pulling is true or false, especially in more complex situations like when needing to describe a specific iPhone model.
Unless the iPhone 14 Pro is the perfect phone -- which, we believe, isn't true -- ChatGPT's review is also too favorable towards the product. There is no mention of the iPhone's more controversial features like eSIM adoption, the unnecessarily eye-catching always-on display, or even Dynamic Island, the buzzword of 2022!
Also: The best AI art generators: DALL-E 2 and alternatives
And lastly, even a hint of personality would take the AI's review from "good" to "great." I don't know about you, but I love reading the perspectives (and hot takes) of other journalists whenever a new product is released. It is this dignified balancing act of wit, assertiveness, and curiosity that an AI simply can't replicate, yet.
Nicolas Maeterlinck/Belga Mag/AFP via Getty Images
Putting the lack of emotion aside, the inaccuracies funnel into one underlying problem with this whole exchange that even I looked past during this article's first publication: ChatGPT shouldn't have answered the prompt in the first place. As of writing, the AI chatbot has a knowledge cutoff date of September 2021. That means the appropriate response should've been to decline to review the iPhone 14 Pro, which launched a year later, instead of reviewing a product it clearly didn't have accurate information on.
ZDNET's Managing Editor, Alyson Windsor, described this behavior the best: "The main problem with the AI-generated product review isn't that it's inaccurate, it's that it wrote it at all." Realistically, if a writer is assigned a topic that they're unfamiliar with, they'd either suggest having another writer cover it or spend time researching the topic before writing. In ChatGPT's case, the AI confidently dives into the task head first, with no apparent concern about being inaccurate or misleading. In my newsroom, that would be a one-way ticket to unemployment.
Bottom line
My feelings for ChatGPT and all the AI possibilities to come are a mix of excitement, uncertainty, and fear. Perhaps AI will play an integral role in solving today's labor issues, and what was once a future vision is more present than ever before. Perhaps ChatGPT really is capable of disrupting the world's largest search engine.
But until it can drill down the human aspects of how we communicate and interact, and we better understand the risks and dangerous implications that a perfect, immortal computer can present, I remain apprehensive -- and accept that I may be completely wrong.
Screenshot by June Wan/ZDNET
| 2023-02-17T00:00:00 |
https://www.zdnet.com/article/chatgpt-is-scary-good-at-my-job-but-theres-one-reason-im-not-panicking/
|
[
{
"date": "2023/02/17",
"position": 85,
"query": "AI job losses"
},
{
"date": "2023/02/17",
"position": 52,
"query": "ChatGPT employment impact"
}
] |
|
AI Skills of the Future: Understand AI and Make it Work for ...
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AI Skills of the Future: Understand AI and Make it Work for You
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https://www.lepaya.com
|
[
"Ravianne Van Vliet",
"Gregor Towers",
"August",
"September",
"Karolina Fesołowicz",
"February"
] |
As AI is becoming increasingly mainstream, businesses need to adapt. Find out about the impact, trends, and future skills needed to make AI work for you.
|
• Artificial Intelligence (AI) is increasingly influencing daily life, from smartphones to personalized recommendations, and has the potential to revolutionize industries like transportation and healthcare. • AI technology involves simulating human intelligence in machines through various components like machine learning, deep learning, and natural language processing. • AI is being widely applied in industries like retail and financial services to optimize processes, improve customer experiences, and increase efficiency. • AI is reshaping the workforce, with the potential to replace jobs but also create new opportunities, requiring employees to develop digital literacy, adaptability, and collaboration skills. • Trends in AI at work include better decision-making processes, the rise of "intelligent augmentation" with humans and robots working together, and HR playing a key role in integrating AI technology into job redesign and strategic change.
Artificial Intelligence allows organizations to increase productivity, scale their business, and work cost-efficiently while at the same time giving business leaders, teams, and individual employees a chance to maximize performance and unlock their true talents. But what exactly is the impact of AI, both on modern-day life and the work floor? What opportunities does it offer on an organizational level? What (new) mindset, competencies, and future skills are needed to make it work? In this article, we’ll explore the transformative technology of AI, plus its impact and potential – now and in the future.
1. Intro: This is How AI Makes Life Easier
Without us always realizing it, artificial intelligence – or AI for short – is increasingly influencing our daily lives. From our smartphones and GPS systems to the personalized movie and music recommendations that Netflix and Spotify come up with: it’s all based on algorithms. Every time you ask Alexa to turn off the lights or talk to a customer service chatbot about your missing parcel AI is the technology that makes that possible. A smart thermostat that turns off automatically when you’re leaving the house, or an AI scheduling assistant to optimize your busy agenda for you? It’s the result of the merger between AI and the Internet of Things (IoT), which allows networks and devices to learn from past actions and predict future activity.
AI is all around us, and we can hardly imagine our lives without it anymore. But the examples mentioned here are just some relatively straightforward AI applications. Tech giants have been investing heavily in next-generation AI technologies like self-driving cars, which can potentially turn the transportation world upside down, and smart healthcare solutions that are said to revolutionize medical science in the coming years.
New kids on the block
Then, there’s Chat GPT, which has been taking over the internet by storm these recent months. This online AI model uses deep learning techniques to generate human-like responses to text-based input. It can be used for many different purposes, like answering complex questions, generating a social media post, or even writing a pretty good poem. And that’s just the beginning, as companies like Google are continuously developing AI application ideas and will soon launch even smarter AI-powered chatbots, like Bard: another new AI kid on the block. Locking horns with its competition, Microsoft recently announced plans to redesign its search engine Bing with a series of AI tools.
Because of their easy accessibility to the public domain, these AI models are considered game changers in the world of technology. They have the potential to reset the way we live and work, putting AI in the hands of the masses and making it mainstream. In the long term, they will even change the bottom line of a significant number of industries and professions. In other words, artificial intelligence and its impact on society is profound, and researchers and developers are continuing to push the boundaries of what is possible with this technology.
You might also like: AI Capability Overhang Means our Soft Skills can Become Groundbreaking
So is AI taking over the world? Will robots replace our jobs? To answer these questions, it is important to understand what AI is, what we can expect from it in the near future, and how to use AI technology to our advantage.
2. Understanding AI Technology
Delving into the subject of artificial intelligence, we must distinguish between intelligence and consciousness. Although related, they are not the same things. A highly intelligent being can exist without being conscious, and vice versa.
Intelligence is the ability to learn and apply knowledge and skills, process information, make decisions, and solve problems. It can be measured and studied through various tests and assessments
is the ability to learn and apply knowledge and skills, process information, make decisions, and solve problems. It can be measured and studied through various tests and assessments Consciousness, on the other hand, refers to the awareness of your surroundings, thoughts, feelings, and sensations. It allows you to reflect on your own behavior or that of others, and to make ethical judgments. Consciousness is a complex phenomenon that is difficult to measure, quantify or define and has been a topic of philosophical debate for thousands of years
At its core, AI is about simulating ‘natural’ intelligence in machines and computer systems that are designed to think and act like humans. These machines can be programmed to perform tasks that would normally require human intelligence, such as recognizing patterns, learning from experience, and making well-informed decisions.
The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.
Edsger W. Dijkstra, Dutch mathematician and computer scientist
Types of AI and how they are applied
AI includes a wide variety of components, techniques, and subfields, such as machine learning, deep learning, algorithms, voicebots, chatbots, natural language processing, and semantic analysis. The world of AI is constantly evolving, so it’s hard to say which AI is the best. However, AI has enormous potential, and the types of AI in business are widespread. They vary from consumer-oriented solutions to improve customer satisfaction to complex industrial AI use cases, like robotic vision predicting when a machine needs maintenance. In terms of the employee experience, AI can improve HR processes like talent management, performance management, and L&D – ultimately freeing time up for people to work on higher-level, more creative, or strategic activities.
Our research shows that AI can increase productivity by 40% or more. Through data collection, automation, decision-making, and cybersecurity, it has the potential to boost profitability rates by an average of 38 percent by 2035 and lead to an economic boost of US$14 trillion across 16 industries in 12 economies by 2035.
Accenture Report: How AI boosts industry profits and innovation
Are supercomputers smarter than humans?
To a certain extent, human intelligence is limited. Even the brightest geniuses can be surpassed by AI, as the example of Garry Kasparov shows. In 1997, IBM’s chess-playing computer Deep Blue made history when it beat the reigning world chess champion. The match represented an important milestone in artificial intelligence development and demonstrated supercomputers’ growing capability to solve complex problems and make intelligent decisions. Now that was almost three decades ago – just imagine the AI levels of intelligence we have access to right now. But although Deep Blue and its ‘offspring’ are undoubtedly brilliant, these machines are not conscious – nor can they simulate consciousness. Or at least, not yet.
The most disruptive discovery of humankind?
Here are some other interesting facts about AI:
AI has been around for decades: the concept of artificial intelligence has been around since the 1950s, and the field has been actively researched and developed for many years. However, it is only recently that AI integration has reached a tipping point and become more widespread due to rapid advances in computing power and machine learning algorithms
the concept of artificial intelligence has been around since the 1950s, and the field has been actively researched and developed for many years. However, it is only recently that AI integration has reached a tipping point and become more widespread due to rapid advances in computing power and machine learning algorithms AI is probably the most disruptive discovery in the history of humankind: Google CEO Sundar Pichai claims that the impact of AI will be even greater than that of fire or electricity on our development as a species
Google CEO Sundar Pichai claims that the impact of AI will be even greater than that of fire or electricity on our development as a species Chat GPT has broken all world records: two months after its introduction, the AI tool reached 100 million users. No other consumer-oriented application has ever seen this fast and massive adoption before
You might also like: How to Mitigate Employee’s Fear of Being Replaced by AI
3. Examples of AI in Business
Undoubtedly, many of today’s businesses are applying AI on a large scale. Healthcare, transportation, agriculture, manufacturing, and financial services: it’s just a few examples of industries using AI to optimize supply chains, automate resource-intensive tasks, identify potential sales leads, detect fraud, and make precise estimates and decisions based on for instance financial data – and that’s just the tip of the iceberg.
As the corporate world is rapidly embracing and implementing AI, it is giving rise to all sorts of disruptive business solutions. AI is already a cutting-edge technology, but some of these applications are particularly creative and groundbreaking. The continuous development of these applications by a growing number of AI providers will push the whole field of AI into the future, making it relevant to more and more industries and enterprises.
Examples of industries using AI in innovative ways
Retail:
AI equipped with language processing and visual recognition can optimize the retail experience by personalizing marketing efforts, while predictive analytics can help retailers forecast demand and optimize their inventory management. Additionally, AI technology can help reduce costs associated with hiring and training staff
by personalizing marketing efforts, while predictive analytics can help retailers forecast demand and optimize their inventory management. Additionally, AI technology can help reduce costs associated with hiring and training staff Example: Amazon Go is a new type of physical store that has no cashiers – now that in itself is nothing new – but also no self check out. Deep learning algorithms, combined with computer vision and sensors, automatically detect when items are taken off the shelves and added to a customer’s cart. Shoppers simply walk in, pick the product they want, and walk out again. The receipt is sent to their Amazon account
Financial Services:
The financial industry is known for being an early adopter of artificial intelligence technology. AI can analyze financial market data in real-time, identify and predict trends, help businesses stay compliant, detect fraud, and improve customer service
AI can analyze financial market data in real-time, identify and predict trends, help businesses stay compliant, detect fraud, and improve customer service Example: JP Morgan Chase & Co. has over 240,000 employees who used to spend hours and hours on mundane tasks, like interpreting commercial-loan agreements. That became a thing of the past when the bank implemented a machine learning model called COIN, that reduced the time spent on this work from 360.000 hours to just a few seconds
Innovative AI use cases on an organizational level
Human Resources:
AI can help HR departments recruit, hire, manage, and support their people more efficiently. It can give valuable insights into historical trends and people analytics like retention rates, turnover rates, salaries, and sick leave. It can personalize social benefits based on employee profiles, identify talent, match employees and external candidates with career opportunities, etc.
It can give valuable insights into historical trends and people analytics like retention rates, turnover rates, salaries, and sick leave. It can personalize social benefits based on employee profiles, identify talent, match employees and external candidates with career opportunities, etc. Example: when faced with challenges in their hiring process Unilever implemented AI-powered video interviewing called HireVue, which reduced costs and time associated with the hiring process, enabled more objective interviews, and identified top candidates better and faster. The platform also provided a more engaging experience for candidates
Learning & Development :
By leveraging AI, L&D departments can improve the impact of training. It can help identify skill gaps and suggest targeted training programs to fill them, adjust the difficulty of a test based on a learner’s performance, give personalized feedback, and analyze training data
It can help identify skill gaps and suggest targeted training programs to fill them, adjust the difficulty of a test based on a learner’s performance, give personalized feedback, and analyze training data Example: Lepaya applies immersive learning with Virtual Reality and AI Coach in our Power Skills programs, to help employees train their social or presentation skills. All these possibilities allow for adaptive, realistic, and high-level learning experiences
4. Impact of AI on the Workplace
AI technology is affecting the Future of Work and changing the structure of the workforce on a profound level. According to the World Economic Forum’s “The Future of Jobs Report 2020”, robotics, automation and AI could replace 85 million jobs worldwide by 2025. And if that still sounds like a science fiction scenario to you: just think how the COVID-19 pandemic showed us how quickly robots and automation filled in for human workers to mitigate health risks and meet workforce shortages.
A new world of job opportunities
However, while some jobs will be entirely lost to technology, the benefits of AI in business go beyond improving productivity and decision-making processes. In fact, the same WEF report states that this so-called ‘Fourth Industrial Revolution’ – an era marked by the rise of new technologies – is also bringing a whole new world of job opportunities with it. By 2025, the future tech-driven economy is expected to create 97 million new jobs – many of which will be in the tech industry and healthcare. As a result, Human Resource departments are expected to be in increasing demand for positions in areas such as cloud computing, cloud engineering, data science, and AI safety engineering, but also in the field of mental health and other specialized health services.
Findings from our 2021 survey indicate that AI adoption is continuing its steady rise: 56% of all respondents report AI adoption in at least one function, an increase from 50% in 2020.
McKinsey global survey on the state of AI
5. AI and the Skills of the Future
With so many new jobs and the emergence of AI on the work floor, the required competencies and skills are also changing – faster than you think. Future forward companies realize that this era of technological and digital transformation requires employees’ continuous upskilling and a culture of lifelong learning.
So what skills are needed in the future? What competencies do business leaders need to be better equipped to implement and leverage AI within their organizations?
Interestingly enough, communicating and collaborating effectively with AI is not so much about learning new technical skills. Digital literacy skills for the 21st century include much more than being tech-savvy. Knowledge about working with technology, learning how to use AI in digital business environments and overall AI literacy are all necessary, but we must also look at behavioral skills:
Employees need to learn how to adapt to constant change, collaborate in teams, take ownership, and understand how they can communicate their ideas in an authentic and inspiring way. After all, there’s still a long way to go before AI can develop unique ‘human skills’ and emotional intelligence
Business leaders in particular, should develop their skills of managing change within the organization, as implementing AI solutions can require significant disruptions to business processes, employee roles, and the overall company culture
In the meantime, with a looming global skills shortage, businesses need to step up their game and prepare their employees for the future. Education and training are crucial ways to ensure their people are ready for what’s to come. That’s why they should focus on skills building and training their employees to perform different tasks in their current roles or move to other positions through internal mobility programs.
87% of executives and HR managers face or expect to face a major skills gap, where employees lack the skills they need to perform their jobs well in the near future. In the past, their skills were valid for about five years, but now many of those skills are outdated after only a staggering two years.
A 2020 McKinsey & Company study
Closing the talent gap is critical to an organization’s success, and understanding how to use AI gives you a significant advantage over your competitors.
You might also like: 10 Essential Skills For Every Future Proof Organization according to the World Economic Forum
6. Trends in AI at Work
Finally, let’s look at some AI trends that will influence the Future of Work and transform the way that organizations operate.
1. Better decision-making in business processes
The advantage of AI over the human brain is that AI can process endless amounts of information in just a millisecond. Now with self-learning algorithms getting better and better, AI can be involved in making crucial business decisions as well. Ultimately, it will still be the C-suite who makes the final call, but they will base it on data sorted and analyzed by AI.
According to a global AI survey conducted by Accenture and Forbes Insights among over 300 AI leaders and CIOs, 79% report that analytics plays a major or central role in their organization’s AI efforts.
2. Tech experts expect a rise in “intelligent augmentation”
The concept of “superteams” comes in – groups of people and robots working together to solve complex problems, gain insights, and create new value and meaning. Robots and humans will team up to get things done faster and better.
3. HR as an ambassador for AI
According to a 2020 Deloitte study, “59% of organizations say redesigning jobs to integrate AI technology is important or very important for their success over the next 12 to 18 months, but only 7% say they are ready to address this trend.”
To fill up this so-called ‘readiness gap’ HR has the opportunity to lead the way and show just how valuable it can be to truly embrace AI. HR leaders can become advocates for strategic change, paving the way for other functions to follow. Companies are already seeing the benefits of using AI in HR tech, with advanced data analytics and machine learning solutions employed in areas such as recruitment, onboarding, training, and the entire employee journey.
At Lepaya, we help organizations adapt to the Future of Work by creating a culture of continuous learning. We build stronger teams and inspire future leaders, for instance by offering innovative learning experiences with VR Training and a dedicated AI coach.
TELL ME MORE
| 2023-02-17T00:00:00 |
https://www.lepaya.com/blog/ai-skills-of-the-future
|
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|
What is Microsoft's Approach to AI?
|
What is Microsoft’s approach to AI?
|
https://news.microsoft.com
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We believe AI is the defining technology of our time. Read about our approach to AI for infrastructure, research, responsibility and social good.
|
At Microsoft, we believe artificial intelligence (AI) is the defining technology of our time.
We have been on the forefront of cutting-edge research in AI and integrating these powerful, innovative AI technologies into our products and services to help customers do more. Microsoft AI, powered by Azure, provides billions of intelligent experiences every day in Windows, Xbox, Microsoft 365, Teams, Azure AI, Power Platform, Dynamics 365 and Microsoft Defender.
Our AI tools and technologies are designed to benefit everyone at every level in every organization. They are used in workplaces, home offices, academic institutions, research labs and manufacturing facilities around the world, and they are helping everyone from scientists and salespeople to farmers, software developers and security practitioners.
We have made huge investments in AI because we are optimistic about what it can do to help people, industry and society, and because we’re committed to bringing technology and people together to realize the promises of AI responsibly.
To learn more about Microsoft’s work with AI, read about:
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Our approach to using AI responsibly
Microsoft believes that when you create powerful technologies, you also must ensure that the technology is developed and used responsibly. We are committed to a practice of responsible AI by design, guided by a core set of principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability.
Microsoft is putting these principles into practice across the company to develop and deploy AI that will have a positive impact on society.
“With the right guardrails, cutting-edge technology can be safely introduced to the world to help people be more productive and go on to solve some of our most pressing societal problems,” says Natasha Crampton, the chief responsible AI officer at Microsoft.
AI systems are the product of many different decisions made by those who develop and deploy them. From system purpose to how people interact with AI systems, we need to guide these decisions toward beneficial and equitable outcomes.
“That’s what our practice of responsible AI by design is all about,” Crampton says. “We ensure that responsible AI considerations are addressed at the earliest stages of system design and then throughout the whole lifecycle, so that the appropriate controls and mitigations are baked into the system being built, not bolted on at the end.”
This approach does not eliminate all risks, and a commitment to listening, learning and improving is paramount. But it does encourage developers to be clear about any limitations, account for intended uses and potential misuses, and think expansively about how to secure the benefits of a system and guard against its risks.
We believe proactive, self-regulatory efforts by responsible companies help pave the way for these new laws, but we recognize that not all organizations will adopt responsible practices voluntarily.
Microsoft President, Brad Smith, recently outlined the importance of the company stepping up to meet the current AI moment, including calling for thoughtful policy.
“Countries and communities will need to use democratic law-making processes to engage in whole-of-society conversations about where the lines should be drawn to ensure that people have protection under the law,” Smith wrote.
“Effective AI regulations should center on the highest risk applications and be outcomes-focused and durable in the face of rapidly advancing technologies and changing societal expectations. To spread the benefits of AI as broadly as possible, regulatory approaches around the globe will need to be interoperable and adaptive, just like AI itself,” he added.
Microsoft believes democratic, law-making processes are a vital part of a global dialogue with industry, academia and civil society to create principled and actionable norms that ensure organizations develop and deploy AI responsibly.
As part of this process, we are committed to publicly sharing the company’s learnings and best practices, along with the tools that guide our efforts. This includes our Responsible AI Standard, a framework for translating our high-level principles into actionable guidance for our engineering teams.
Like all transformative technologies, we are aware that AI has its risks. Some people will use this technology to exploit the flaws in human nature, target people with false information, undermine democracy and cause harm. We need to plan for and mitigate these risks.
Microsoft has been working on its cross-company, cross-discipline, responsible AI effort for more than six years, creating a strong foundation upon which to keep building for the future. We are clear-eyed about the need to continue moving forward with urgency to keep pace with the rapid evolution of technology and changing societal expectations.
“Fundamentally, our AI work is grounded in our company mission to help every person and organization on the planet to achieve more,” says Crampton, “and it is undergirded by our steadfast commitment to the responsible development and use of AI.”
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Our approach to AI research
For more than 30 years, Microsoft Research has been advancing the foundations of computing and translating new scientific understanding into innovative technologies to create value for our customers and broad benefit to society.
Our researchers collaborate across disciplines, institutions and geographies to deliver cutting-edge advances in vision, speech, language, decision-making and machine learning. They have pioneered AI breakthroughs in conversational speech recognition, machine translation, image captioning, natural language understanding and commonsense question answering.
Recent efforts have focused on developing large-scale models that can process information in increasingly sophisticated ways while also becoming more natural and intuitive to use. Advances in deep learning, coupled with internet-scale datasets and Microsoft Azure’s increasingly powerful AI supercomputing resources, have made it possible to create AI models that perform a broad range of tasks across many different applications.
“Large-scale AI is shifting the landscape of computing research,” says Ashley Llorens, vice president and managing director at Microsoft Research. “As we orient around that shift, you’ll see new frontiers that advance our understanding of human and machine intelligence and how they can intersect and reinforce each other in profound new ways.”
Our research has played a key role in evolving model architectures and creating AI technology that is more efficient and more adaptable across an even broader range of tasks.
Microsoft researchers have been working on these problems for years, developing expertise in areas like parallel computation that allows people to more quickly train machine learning models at unprecedented scale. This has led to innovations like DeepSpeed, an open-source, deep learning optimization library for distributed training that was developed by Microsoft Research and now is used by the broader computing community.
We also are focused on delivering value and solving real-world problems through Microsoft products and services. Project FarmVibes, for example, merges AI and data to offer open-source tools that can help farmers adopt sustainable agriculture practices. And the Microsoft Climate Research Initiative provides our research and computing capabilities to a team of multidisciplinary scientists working together to address climate change.
Our new AI4Science organization is focused on applying deep learning to the natural sciences to model and predict natural phenomena and help address critical issues such as climate change, green energy and pharmaceutical discovery. Our project teams, which include a global collective of researchers and engineers, are exploring a new approach to machine learning, generating training data by simulating natural phenomena from fundamental equations rather than using datasets from the internet.
This could enable researchers to understand and predict natural phenomena at scales ranging from quantum to galactic and, in turn, drive breakthroughs such as the discovery of new materials that can remove carbon from the atmosphere, Llorens says.
“At the end of the day, we’re focused on pushing the frontiers in ways that enhance the human experience and positively impact society as a whole,” he says.
“To achieve this, we’ll need to engage as part of the global research community and continually challenge our assumptions about what is possible. That’s what will produce the future advancements we need.”
Our approach to AI infrastructure
With growing confidence in AI and businesses aiming to do more with less, customers are looking for a trusted partner to streamline adoption and rapidly apply intelligence across workloads to improve operations, drive efficiencies and reduce costs. More than a decade ago, we forecast this exponential growth in demand for AI systems and started to build special computing infrastructure to handle it.
Today, Microsoft’s AI platform, Azure AI, offers infrastructure optimized and purposely built for running large AI models that are ushering in a new era of productivity and creativity. Thanks to our investments, we’re able to deliver a wide range of AI-powered products that fit the needs of our customers and also deliver best-in-class performance and scale for the most compute-intensive AI training and inference workloads.
Our unique architecture design combines the fastest graphics processing units, or GPUs, available in the market along with a network architecture that chains together thousands of GPUs to enable AI model training and inference at scale.
“Having thousands of GPUs with high-bandwidth interconnect enables everything else from there,” says Eric Boyd, Microsoft corporate vice president for AI Platform.
Organizations large and small are developing Azure AI solutions because they can achieve more at scale, more easily, with the proper enterprise-level privacy, security and responsible AI protections that Azure offers.
We have committed to building Azure into an AI supercomputer for the world, serving as the foundation of our vision to democratize AI as a platform. Microsoft pushed the frontier of cloud supercomputing technology, announcing our first top-5 supercomputer in 2020, and subsequently constructing multiple AI supercomputing systems at massive scale.
We also fine-tuned our purpose-built, AI-optimized infrastructure capability in partnership with OpenAI to train and deploy OpenAI’s family of models for research advancement and developer production. This infrastructure is now available to all Azure customers.
“When other people come to us, we can literally give them the same style of infrastructure that we used for OpenAI, because that’s now the standard way that we do it,” Boyd says.
Microsoft’s Azure OpenAI Service provides businesses and developers with high-performance AI models, such as GPT-3.5, Codex and DALL∙E 2, at production scale with industry-leading uptime. This is the same production service we use to power AI models in our own products, including GitHub Copilot, Power Platform, and the recently announced Microsoft Designer and AI-powered search in Bing and Edge.
We continue to evolve our AI infrastructure based on feedback and insights from training and serving AI models at scale. Our teams work in lockstep with industry partners on the design of GPUs, networks and datacenters that are optimized for AI workloads.
“Microsoft continues to be on the cutting edge, and customers get to take advantage of all the benefits of that,” says Boyd. “They’re getting the best training infrastructure, the best software, the best networking – all of these things combined give the best experience.”
Our approach to using AI for social good
Microsoft believes AI can help people tackle some of society’s biggest global challenges. Our AI for Good initiative provides funding, technology and expertise to help individuals and nonprofits accelerate progress in fields, such as accessibility, digital literacy and equity, sustainability and climate change, human rights and resilience, health disparities, food insecurity, cybersecurity and others.
“There are problems where AI is uniquely positioned to help, where AI is not just another solution but is the only solution,” says Juan Lavista Ferres, Microsoft’s chief data scientist and the director of the AI for Good Lab.
One such project, for instance, involves mapping all the places where humans live in order to better understand natural disaster risks and guide preparedness efforts. Another includes mapping rooftops of buildings in India, noting the materials the structures are made of to help disaster teams prioritize those that are more likely to fail in certain types of disasters. Yet another is mapping all the renewable energy installations around the world to help show the impact of solar and wind farms.
“A person would spend 400 years looking at satellite imagery and understanding if humans live there or not, but AI can do this in an hour,” Lavista Ferres says. “These AI models make it easier for people to get all the information in one place, and from there the humans can make the necessary decisions.”
Microsoft’s AI for Good Lab is an applied research and data visualization laboratory that harnesses the power of big data and Azure’s cloud technology. Its team of data scientists works with strategic partners and experts from academia, nonprofits and governments to not only help address critical global concerns but also to better measure the progress of efforts underway and to identify gaps where aid might be helpful.
The projects are as varied as the problems and combine Microsoft’s efforts with those of our partners.
For example, the AI for Health program invests in research led by institutions such as Fred Hutchinson Cancer Research Center, IRIS, the Novartis Foundation and Seattle Children’s Research Institute. These efforts are making healthcare more affordable, especially in places where doctors are few and medical needs are great.
Though we see great potential for AI’s assistance to help solve some of the world’s most pressing issues, we also recognize the limitations of the technology. It’s important to work through moral and ethical questions for every project, Lavista Ferres says, especially to make sure the models are getting enough data from enough places and aren’t leaving anyone out — for example, making sure all skin colors are represented for an app that detects skin cancer.
“When you train an AI model, it will be able to generalize from the data you have, but not from the data you don’t have, so any bias can generate problems,” he says. “Having humans in the loop is the key element. We are looking for solutions that can benefit everyone, not just particular cohorts of the population.”
| 2023-02-17T00:00:00 |
https://news.microsoft.com/source/features/ai/microsoft-approach-to-ai/
|
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|
RPA in the Age of Artificial Intelligence
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RPA in the Age of Artificial Intelligence
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https://aneejian.com
|
[] |
As more businesses adopt RPA, they can automate more tasks, freeing up employees to focus on more high-level work. Through the integration of AI, these ...
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RPA in the Age of Artificial Intelligence
Feb 17, 2023 . 6 mins read
Robotic Process Automation (RPA) has already revolutionized the way businesses automate repetitive rule-based mundane tasks by freeing up employee’s time to work on more value-added work. However, as RPA technology continues to advance, its integration with Artificial Intelligence (AI) is becoming increasingly vital. This is evident from the fact that most RPA tools are integrating more AI capabilities to their tools. Combining these technologies can transform the workplace and shape the future of work.
In this post, we will explore how these technologies work together to create intelligent automation solutions. We will also discuss the potential benefits and challenges of adopting these technologies and their impact on the workforce and society.
RPA and AI: How are they different?
Before we go any further, let us understand the difference between RPA and AI.
Robotic Process Automation (RPA) is a software application that can mimic human actions on an application there by automating repetitive and rule-based tasks and processes. Some major RPA tools are UiPath, Blue Prism, Automation Anywhere etc.
Artificial Intelligence (AI) on the other hand is a technology that can simulate human intelligence in machines, enabling them to perform cognitive functions such as learning, reasoning, understanding and decision-making.
Though different, these technologies are complimentary and can create Intelligent Automation (IA) solutions which combine automation and intelligence to achieve end-to-end automation of complex business processes.
Combining RPA and AI - Intelligent Process Automation (IPA)
The future of work lies in combining RPA and AI. By combining these technologies, businesses can create intelligent automation solutions that can learn, adapt, and improve over time. With integration of AI, RPA solutions become even more intelligent and capable of handling complex tasks and making decisions.
In the software automation industry, the combination of RPA and AI is called Intelligent Process Automation (IPA). It is the application of artificial intelligence and related technologies to Robotic Process Automation, which creates automation capabilities that enhance business value and competitive advantages.
UiPath is a company that creates IPA solutions by using AI to amplify the power of their RPA technology, with advanced computer vision, unattended robotics, and integration with third-party cognitive services. Like UiPath, many other RPA tools offer cognitive capabilities in their tools.
Let us consider an example where RPA and AI work together to enhance customer experience. Suppose a customer service representative receives a request from a customer. RPA can be used to automatically route the request to the appropriate department and update the customer’s information. With the addition of AI, the system can learn from the request and provide personalized recommendations for the customer based on their history and preferences. This kind of personalized service not only enhances the customer experience but also saves time for the representative.
Few more examples of IPA solutions are:
Invoice processing: RPA bots can automatically extract data from invoices and input it into a system, reducing the need for manual data entry. AI can be used to improve the accuracy of the data extraction, even if the invoices are in different formats.
Fraud detection: RPA bots can be used to monitor transactions for suspicious activity. AI can be used to analyze patterns and identify potential fraud, even if it is a new type of fraud that the system has not seen before.
Healthcare: RPA can be used to automate administrative tasks, such as patient registration and appointment scheduling. AI can be used to analyze medical records and identify potential health risks or suggest treatment options.
Supply chain management: RPA can be used to automate inventory tracking and order processing. AI can be used to analyze data to optimize supply chain operations, such as predicting demand and identifying potential disruptions.
The Future of Work with IPA
As more businesses adopt RPA, they can automate more tasks, freeing up employees to focus on more high-level work. Through the integration of AI, these solutions become even more powerful, enabling them to handle complex tasks and decision-making. This will increase efficiency, productivity, accuracy, and customer experience.
However, it is essential to acknowledge that adopting IPA will also lead to workforce changes. While these technologies automate routine and repetitive tasks, human intervention is still necessary for decision-making and strategic planning. Therefore, employees must adapt to these changes and develop new skills to remain relevant in the job market.
Benefits of Adopting IPA
Adopting IPA has numerous benefits for businesses. These benefits include:
Increased efficiency: IPA can automate repetitive tasks, freeing up employees to focus on more critical tasks. This, in turn, leads to increased productivity and efficiency.
Improved accuracy: IPAs are less prone to errors than human workers, leading to increased accuracy and fewer mistakes.
Cost savings: Automating repetitive tasks reduces the need for manual labour, leading to cost savings for businesses.
Better customer experience: IPA can provide personalized recommendations to customers, enhancing their experience and building brand loyalty.
Increased agility: IPA can help businesses respond quickly to changing market conditions by automating processes.
Scalability: IPA can easily scale to handle larger volumes of work, making them ideal for businesses that are looking to grow and expand.
Challenges of Adopting IPA
While adopting IPA has numerous benefits, it also comes with several challenges. These challenges include:
High implementation costs: Implementing IPA solutions can be expensive, especially for small businesses with limited resources. The costs associated with implementation, licensing, and training can be significant barriers for many businesses.
Lack of skilled workforce: The implementation of IPA requires a workforce with specialized skills, including programming, data analysis, and process design. Many organizations may lack the necessary skills to fully implement and leverage these technologies.
Resistance to change: The adoption of IPA can be a momentous change for organizations, and some employees may be resistant to the implementation of modern technologies. Proper training and communication can help mitigate this resistance, but it is important to acknowledge that there may be challenges in getting employees to embrace these new tools.
Data quality and governance: IPA relies heavily on accurate and well-organized data to function effectively. Without proper data quality and governance, these technologies can result in errors and inaccuracies, causing more harm than good.
Security and privacy concerns: As with any modern technology, security and privacy concerns are a significant challenge when implementing IPA. These technologies require access to sensitive data, and businesses must respond appropriately to ensure that this data is secure and protected.
Conclusion
RPA and AI are powerful technologies that are reshaping the future of work. Combining these technologies can lead to increased efficiency, productivity, and accuracy, as well as improved customer experiences. However, the adoption of RPA and AI comes with its own set of challenges, including high implementation costs, the need for a skilled workforce, resistance to change, data quality and governance concerns, and security and privacy issues. Despite these challenges, the benefits of RPA and AI adoption outweigh the costs, and businesses that are willing to invest in these technologies can position themselves for success in the future of work.
| 2023-02-17T00:00:00 |
2023/02/17
|
https://aneejian.com/rpa-in-artificial-intelligence-age/
|
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Workforce Analytics: A Comprehensive Guide
|
What is Workforce Analytics?
|
https://www.activtrak.com
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Explore our new research on productivity, engagement, work location and AI adoption to help you better anticipate and plan for the future of work. Read now ...
|
Workforce analytics refers to the collection of employee data, analysis of the information and using the insights to optimize decision making in support of the organization’s goals. Workforce analytics gathers information from remote, in-office and hybrid teams to spot trends and outliers that can aid in data-driven decision making.
Wouldn’t it be great if you could see what your teams really need to thrive in the workplace? And not just with surveys and best guesses, but objective data? With workforce analytics, you can.
In this guide to workforce analytics, we’ll cover the following:
Why is workforce analytics important?
Workforce analytics show business leaders where, when and how people do their best work. It’s powered by software that automatically collects employee data and turns it into insights. Organizations then use those insights to make data-driven decisions related to employee experience, operational processes, technology and more.
Workforce analytics tools allow you to see what’s happening within your workforce — how employees spend their time, who’s at risk for burnout and other performance insights. Managers can use this information to predict business outcomes and implement changes to help employees thrive. Workforce analytics can be used at different scales, allowing businesses to track trends at the individual, team, department or organizational level. And the company can use these insights to remove bottlenecks or save costs by retiring unused technology.
Benefits of workforce analytics
Workforce analytics provides benefits for employees, HR departments and executives alike. If you’re on the fence, here are a few of the top benefits of workforce analytics for your organization:
1. Increased employee productivity
You might assume workforce analytics is primarily for managers. However, givingemployees access to their personal productivity data is often just as valuable.
Workforce analytics helps employees prevent burnout, pursue career advancement and — most importantly — do their best work.
2. Better coaching from managers
Workforce analytics is good for managers who want to build efficient teams, improve engagement and provide effective coaching. By understanding exactly how teams work, managers can elevate top performers and get clear insights on best practices to share with others.
3. A stronger culture
HR professionals can use workforce analytics to retain top talent, empower team leaders and improve the employee experience. Using workforce analytics can also help HR managers build a strong culture of trust and transparency where individual employee contributions are acknowledged and rewarded.
4. More effective leadership
Workforce analytics give executives critical insights into organizational health and alignment. This allows leadership to take an “employee first” approach to company culture by celebrating trust and transparency from the top down.
5. Better use of IT resources
IT professionals can use workforce analytics to see what, how and when technology is used. These insights help optimize tech stacks and eliminate waste. Most organizations have SaaS applications that are rarely used. With workforce analytics, you can cancel those subscription costs with confidence.
Workforce analytics: examples in action
Workforce analytics is powerful on its own. But when you combine employee activity with data from work hubs like Microsoft 365, Salesforce, Zendesk, Monday.com and Jira — the benefits are even bigger. You can see which work patterns achieve the best outcomes and help employees achieve more in less time. Examples of outcomes from using workforce analytics include:
Improved use of space
Understanding what people need to do their best work is key. And these days, location plays a significant role. Which employees and teams need more in-person collaboration? How frequently do they come into the office? What does productivity look like among remote and hybrid employees? These insights help leaders develop workplace policies that support work-life balance, well-being and business needs.
Better time management
Workforce analytics show when meetings take place, so you can understand how effective they are. Are you constantly multitasking during meetings? That might be an indicator your participation isn’t required. Are others disengaged? If you organized the meeting, this may be a clue that you need to tighten your agenda or engage more people in the conversation. You can see and act on these insights within seconds to help combat the hidden costs of ineffective meetings.
Greater engagement
Burnout is a big challenge for organizations across industries. But it can also be challenging to identify. Are your employees actively seeking other jobs? Are they less engaged with projects and teams? Do they face significant health issues as a result of overworking? Workforce analytics helps alert you to signs of burnout, such as low engagement and higher-than-usual workloads. Then use these insights to take action — and take care of your people.
5 Types of workforce analytics
There are five types of workforce analytics, with each category serving a unique purpose for the business.
1. Employee engagement
Using workforce analytics to improve employee engagement empowers managers to assess burnout risk, protect focus time and ensure a healthy workload balance. These objective insights help balance the subjectivity of engagement surveys and sentiment analysis.
2. Productivity management
Managing productivity across teams can be a big challenge, especially when different leaders have varying definitions of what it means to be productive. Workforce analytics software provides productivity indicators to tell you what’s typical, who’s producing and where improvements are needed.
3. Activity monitoring
Using workforce analytics to monitor employee digital activity is a great way to understand what people need to do their best work. It makes it easy to spot patterns and pain points — like long hours or lengthy meetings — that teams face on a daily basis.
4. Operational efficiency and compliance
Workforce analytics can uncover operational inefficiencies and compliance issues across an organization. With views to show workload balance, resource usage, time management and internal policy compliance, the efficiency gains can be limitless.
5. Technology management
Technology adoption is a struggle for all organizations. By using workforce analytics to manage technology, see which teams use which apps and then use those insights to invest in the right software and training.
Who uses workforce analytics?
Workforce analytics offers benefits and can be utilized by employees, managers and executives.
Employees
Employees use workforce analytics to self-manage and self-motivate while also reducing their risk for burnout. We’ve all heard about Zoom fatigue, Slack interruptions and unproductive meetings. Workforce analytics can help employees fine-tune daily habits to minimize distractions and improve focus time, ultimately ensuring a better work-life balance.
Managers
Managers can use workforce analytics dashboards and reports to check the real-time pulse of teams. Is anyone’s workload too heavy? Do people need more resources or training? Are work activities aligned with roles and responsibilities? Workforce analytics give managers the visibility and insights to be more effective coaches and support a culture of continuous improvement.
Executives
Leaders want to understand the health of their organization. With workforce analytics, executives can see patterns and trends, and use that information to make strategic workforce and business planning decisions. For instance, you can see which offices are sitting empty and which SaaS apps aren’t being used — and use that intel to reduce real estate costs or optimize your IT budget.
The history of workforce analytics and HR analytics
The practice of workforce analytics stems from a history in HR analytics (often referred to as “people analytics”). To understand how today’s workforce analytics can elevate business decision-making, it helps to look back on the history of HR monitoring.
A brief history of HR analytics
There are three kinds of HR analytics: descriptive, predictive and prescriptive.
Descriptive HR analytics is an aggregated view of the raw data you collect on the people that work for you. It encompasses information such as where people work, the average number of hours worked and the percentage of time spent in certain applications. Descriptive HR analytics can help you understand why things happened in the past.
is an aggregated view of the raw data you collect on the people that work for you. It encompasses information such as where people work, the average number of hours worked and the percentage of time spent in certain applications. Descriptive HR analytics can help you understand why things happened in the past. Predictive HR analytics builds on descriptive analytics by creating models to predict future outcomes. While descriptive analytics reveals employee behaviors that preceded a resignation, predictive analytics uses that information to create an employee burnout model. You can then use that model to predict which employees are at risk.
builds on descriptive analytics by creating models to predict future outcomes. While descriptive analytics reveals employee behaviors that preceded a resignation, predictive analytics uses that information to create an employee burnout model. You can then use that model to predict which employees are at risk. Prescriptive HR analytics goes one step further to offer recommendations on what to do. In the employee burnout example, prescriptive HR analytics helps you understand healthy patterns displayed by engaged employees and determine where to intervene to prevent turnover with individualized coaching.
The evolution to workforce analytics
Human resources professionals use HR analytics software in many ways, most often to guide personnel decisions related to recruitment, retention and training. This focus puts their analysis squarely in micro spaces like talent management and employee performance.
Now that workforce analytics is available, HR analytics is quickly evolving from a narrow people data focus to provide a wider workforce view. It allows business leaders to identify newer, better ways to streamline workflows, boost productivity, improve the employee experience and more.
How to implement a workforce analytics program
For your workforce analytics program to be successful, adoption is key. You want employees to understand and support your long-term goals — not resist or distrust you. With a thoughtful approach to roll-out, you’ll be able to grow trust and transparency while working toward shared goals.
How do you introduce workforce analytics in a way that secures employee trust? There are three main elements to consider:
1. Be transparent
Be up front with your employees. Share your goals and tell them how using workforce analytics will help you achieve them. And be specific about what you are NOT going to do with the technology. Employees need to know you take their privacy seriously and want to see how your software protects it. Trust and transparency are essential to build a healthy workplace culture, and your workforce productivity solution should align with those goals.
2. Communicate details across all levels
Communication about your workforce analytics plan should start early and happen often. Starting with business leaders, let people know exactly how you plan to achieve your goals and share future plans for how workforce analytics will be used. Next, bring managers into the fold. They’re likely to be the power users of your program and will field questions from others along the way. Then, after all leaders have been brought onboard, invite employees to join the conversation. Be clear about your goals and give people plenty of opportunities to ask questions. Interactive forums and town-hall meetings are both great ways to demonstrate transparency and openly address concerns. Once rollout is complete, maintain continuous communication to create a positive feedback loop and develop a culture of trust.
3. Encourage collaboration
Don’t just view the data — share it! Workforce productivity analytics data is meant to be shared across the organization so individuals and managers can collaborate on goals. By encouraging employees and teams to analyze their own data, you set the stage for success — and avoid the pitfalls of misinterpretation. What defines a successful, productive salesperson is much different than what defines a successful, productive engineer. Avoid this mistake by inviting employees, managers and business decision makers to the table when creating baselines and benchmarks.
What to look for in a workforce analytics platform
Many workforce analytics applications are designed to solve single problems, such as how many hours someone works or what software they use the most. A workforce analytics platform, on the other hand, is designed to shed light on the bigger picture. It can help people in multiple roles work toward multiple goals, from increasing productivity to improving employee well-being. It also lets you connect multiple sources of data to generate more insights and better support teams.
The best workforce analytics platforms also prioritize trust and privacy. Information that’s relevant to specific employees should be exposed only to the individual employees themselves. Aggregated, anonymized data can then be given to managers and executives to make business decisions.
The ActivTrak difference
We’ve purposefully developed a powerful platform that focuses on the collection of contextual data, avoiding invasive employee monitoring technologies like continuous screenshots and keystroke logging. However, many workforce analytics solutions still allow those invasive capabilities. If you choose to use a different workforce analytics platform, it’s important to regularly ask if the software is serving your goals. If you can’t answer with a “yes,” it may be time to rethink things.
Privacy Rights Clearinghouse points out that your employer can monitor most of your workplace activity unless there’s a policy or agreement (such as an employee handbook, company memo or union contract) stating otherwise. However, rules vary from state to state. And if you perform work across international borders, you’ll need to be mindful of the laws and regulations that apply. For example, the General Data Protection Regulation (GDPR) states that anyone in the EU must consent to personal data collection.
At ActivTrak, we take this one step further. We designed ActivTrak’s workforce analytics software with the founding principles of transparency, collaboration and informed insight.
Getting started with workforce analytics
The best way to get started with workforce analytics? Set up your platform to get a baseline. With ActivTrak, it’s easy. Our powerful workforce analytics platform helps organizations set goals and benchmarks, track progress against results, and optimize outcomes for success – so employers and employees can work better together to realize their full potential. No matter what type of workforce analytics solution you need, we can provide insights to help you improve business performance.
Request a demo to see how to start using our workforce analytics platform today.
| 2023-02-17T00:00:00 |
https://www.activtrak.com/product/workforce-analytics/
|
[
{
"date": "2023/02/17",
"position": 45,
"query": "workplace AI adoption"
}
] |
|
Most of us are still worried about AI
|
Most of us are still worried about AI
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https://woub.org
|
[
"Aaron Payne"
] |
“If the public doesn't trust AI, adoption may be mostly limited to less ... Workplace · Renaissance The Blood and The Beauty - Series 1. Picture shows ...
|
Most of us are still worried about AI — but will corporate America listen?
WASHINGTON (NPR) — Do we need a mandatory screening of the Terminator series in corporate boardrooms? Because new research shows that Americans are concerned about the pace that artificial intelligence is evolving these days. Alexa, play Terminator 2: Judgment Day.
Who are they? AI bots.
The battle for AI supremacy is on. We’ve been talking to robots in our homes, cars and offices for a while now — Alexa and Siri could write scathing tell-alls about most of us — but over the last few months, the stakes have skyrocketed.
Google and Microsoft are among the companies to recently announce their multibillion-dollar chatbots. It follows OpenAI’s headline-grabbing ChatGPT that launched in November.
These AI tools can run all sorts of tech, power search engines, and many can talk a lot like a human. But they sure don’t absorb and dispense information like a human. The speed at which these chatbots can solve problems, write research papers, even make original art would put any prodigy to shame.
The positive side is that it’s like having a personal assistant. The possibilities are endless! The concerning side? The possibilities are … endless.
What’s the big deal? There’s an arms race to get the biggest, baddest bot out there as quickly as possible. But not everyone is convinced.
While we seem increasingly reliant on AI to help us with day-to-day things like customer service, a MITRE-Harris Poll survey released this month finds that we’re not as comfy with things like self-driving cars.
The study showed that only 48% of respondents believed AI is safe and secure, and 78% were very or somewhat concerned that AI can be used for malicious intent.
The survey also showed a big divide between ordinary folk and those in the tech world. Only 48% of Americans would rely on AI for everyday tasks, compared to 79% of tech experts.
And it’s worth noting this survey was conducted in early November, before the latest wave of AI bots sparked both acclaim and pushback.
There have also been some very recent, high-profile mistakes. Last week, Google introduced its AI bot, Bard. But it produced a factual error in its first demo, shares in its parent company, Alphabet, lost $100 billion in market value.
Then there’s the dystopian, Terminator-like scenarios that we can’t help obsess over — starting with the claims last year by a Google engineer that he sees its AI as sentiment (which Google denied)
More recently, Bing’s chatbot, Sydney, told The New York Times it had a desire to be destructive. Others on social media have shared similar stories, including one person who said he asked Sydney to choose between his survival or its own. In response, the bot borrowed the wise words of Samantha Jones: “I love you. But I love me more.” (I’m paraphrasing.)
I am currently trying to get this AI chatbot to become self aware. Hopefully it’s more like Short Circuit than Terminator — p t r c k (@patrickjd) December 29, 2022
What are people saying? It really depends on who you ask.
Douglas Robbins, MITRE vice president of engineering and prototyping, says it’s all about trust:
“If the public doesn’t trust AI, adoption may be mostly limited to less important tasks like recommendations on streaming services or contacting a call center in the search for a human. This is why we are working with government and industry on whole-of-nation solutions to boost assurance and help inform regulatory frameworks to enhance AI assurance.”
Edward Tian, a 22-year-old student who created an app to combat misuse of ChatGPT, told NPR that we’re at an “inflection point:”
“This technology is incredible. I do believe it’s the future. But, at the same time, it’s like we’re opening Pandora’s Box. And we need safeguards to adopt it responsibly.”
Ethan Mollick, an associate professor at the University of Pennsylvania’s Wharton School, says we should enjoy it:
“There is a lot of good stuff that we are going to have to do differently, but I think we could solve the problems of — how do we teach people to write in a world with ChatGPT? We’ve taught people how to do math in a world with calculators. I think we can survive that.”
So what now? AI is here to stay, whether people like it or not.
Those at the tippy top of these tech and media companies appear all-in on AI and its potential for innovation, profits and bragging rights. The question is whether they are open to hearing the public’s hesitation. And this isn’t just about James Cameron-worthy apocalypse scenarios. Bots are replacing some humans at work, and making decisions about who to hire.
Microsoft says it is “driven by ethical principles that put people first”; Google says it’s going to be “bold and responsible” moving forward; and OpenAI says its mission is that its tech “benefits all of humanity.”
Time will tell if these will be backed by actions … or if it’s just chat.
In the meantime, try not to pick any fights with Sydney.
Learn more:
Battlebots 2023: Microsoft and Google duke it out over who has the best AI
Can we save writing from ChatGPT? This 22-year-old is trying.
How using bots for hiring can take us backwards in discrimination practices
| 2023-02-17T00:00:00 |
2023/02/17
|
https://woub.org/2023/02/17/worried-about-ai-corporate-america/
|
[
{
"date": "2023/02/17",
"position": 56,
"query": "workplace AI adoption"
}
] |
How Artificial Intelligence (AI) Will Change Project Portfolio ...
|
How Artificial Intelligence (AI) Will Change Project Portfolio Management
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https://acuityppm.com
|
[
"Tim Washington"
] |
There is no question that we are in the midst of radical changes to our workplace and society due to the mainstream emergence of Artificial Intelligence ...
|
There is no question that we are in the midst of radical changes to our workplace and society due to the mainstream emergence of Artificial Intelligence (AI) and Machine Learning (ML). Experts have been predicting for several years how AI will change the way we work. With the public arrival of ChatGPT at the end of 2022, these changes are accelerating and organizations need to be ready sooner (not later) to begin utilizing this technology to improve work. While there has been some research into how Artificial Intelligence (AI) and Machine Learning (ML) will change project management, there has been far less consideration for how these technologies will affect project portfolio management (PPM). One of the best articles to come out on how AI and Machine Learning will affect project and portfolio management is a recent Harvard Business Review article by Antonio Nieto-Rodriguez and Ricardo Viana Vargas titled “How AI Will Transform Project Management”. In this article, the authors address the changes coming to project management but also touch on some important elements affecting project portfolio management (PPM). In this post, we will expand further on some of these key points and hone in on the technology changes that will impact portfolio management.
According to the article by Nieto-Rodriguez and Vargas “Gartner’s research indicates that change is coming soon, predicting that by 2030, 80% of project management tasks will be run by AI, powered by big data, machine learning (ML), and natural language processing….When this next generation of tools is widely adopted, there will be radical changes.” The purpose of this article is to help you prepare for these major changes that are coming soon.
Opportunities for Artificial Intelligence (AI) in PPM
The first major section of this post will cover the significant opportunities coming to help organizations improve project portfolio management (PPM).
Optimized Project Selection
From a decision-making standpoint, the biggest challenge senior leaders face is project selection. The problem is less about selecting the right projects and more about selecting too many projects. Selecting more projects than the organization can realistically take on is one of the major root causes of project management deficiencies today. I have rarely seen a company that truly understands its own resource capacity and authorizes projects according to its capacity. Rather, the needs and demands of the business far exceed its people’s availability to get work done. Rather than make hard decisions and say “no” or “no, not right now”, many companies simply authorize more project work and ask their people to “find a way” or “just one more project”. This thinking can cripple an organization’s ability to successfully deliver projects. It doesn’t matter how good your Project Managers are, if you overload the organization with more work than they can handle, things will fall through the cracks, deadlines will be missed, mistakes will be made, etc. The Work Intake process is one of the most critical steps in all of project portfolio management because it is the front door to the portfolio. Now, Artificial Intelligence (AI) and Machine Learning (ML) have the opportunity to radically change how Work Intake and project selection are performed.
Most senior leaders suffer from having a project-centric focus rather than a portfolio-centric focus. AI and Machine Learning don’t have this problem. These tools help remove human biases from decision making and can take in vast amounts of data to help inform better decision making. Rather than simply evaluating whether a single project is a worthwhile investment, Artificial Intelligence (AI) and Machine Learning (ML) can look at all the available data for active projects, completed projects, cancelled projects as well as all the ideation data and other proposals to help compare a single proposal against the full history of available project data. Artificial Intelligence (AI) and Machine Learning (ML) can utilize that data and provide recommendations whether the project should be selected, how valuable the proposal really is, and even include a likelihood of success based on historical data. These recommendations alone would improve the upfront intake of project work and help Senior Leaders make better decisions by understanding a single project in the context of the entire portfolio. Fortunately, we can even take this a step further.
Portfolio Optimization Finally Becomes Reality Thanks to Artificial Intelligence (AI)
Project portfolio optimization has long been a theoretical concept for most organizations utilizing project portfolio management (PPM) because it is simply too complicated and too advanced. On paper, the concepts are sound, but in practice, portfolio optimization requires the right people to do optimization analysis and a leadership team that buys into the results (more on this later). Artificial Intelligence (AI) and Machine Learning (ML) have the capability to perform true optimization analysis behind the scenes and generate options for Senior Leaders to choose from that will optimize portfolio value. Inputs such as: value scores, risk scores, net present value, project duration, scope complexity, team size, resource risks, can all be utilized to generate more optimal portfolios than most Portfolio Managers can generate on their own. In this case, Artificial Intelligence (AI) and Machine Learning (ML) are not costing any jobs within the Project Management Office (PMO), these technologies are simply filling in the gap. Most organizations neither have the right data nor the right skills to conduct portfolio optimization. Artificial Intelligence (AI) and Machine Learning (ML) will soon be available to augment the PMO with needs that previously went unmet. As discussed in our post on portfolio optimization, we highlighted that foundational processes form the basis of advanced processes. Now with AI and Machine Learning, organizations have a realistic opportunity to incorporate more advanced portfolio management practices.
Artificial Intelligence (AI) and Machine Learning (ML) will not only help with cost-value optimization but will also support resource optimization as well. Imagine creating a resource plan and receiving a list of recommended resources in order to create the initial resource plan. Or, AI and ML could provide recommendation to augment the project team with additional staff based on historic resource plans. AI and ML could also help identify under-staffed projects and recommend additional team members in order to support a realistic project plan that will increase its likelihood of success and deliver on time.
Improved Portfolio Planning
As covered in a previous post, portfolio planning synthesizes priority data with resource data to help optimize the timing of projects in order to drive strategy execution and higher portfolio value. Much of this is based on a company’s strategic plan and portfolio planning helps translate strategic goals to a tactical level that is realistic to achieve.
Just as with project portfolio optimization, Artificial Intelligence (AI) and Machine Learning (ML) will greatly help PMO’s and Portfolio Managers put together a more realistic portfolio plan. Typically, even if the right projects are selected, virtually all of these approved projects go straight to project execution. This is another problem that AI will help address. Today, portfolio planning requires a significant amount of analysis to understand future resource availability and take into account relative priorities in order to sequence projects at the right time. Artificial Intelligence (AI) and Machine Learning (ML) will be able to conduct this analysis behind the scenes to make recommendations as to when to initiate certain types of projects. Over time, probability analysis will likely include success rates, estimated duration and estimated completion dates, etc.
Identification of Dependencies
Another potential of Artificial Intelligence (AI) and Machine Learning (ML) is related to identifying project interdependencies. Most organizations do not have a habit of identifying and mapping project dependencies, but project dependencies represent a potential risk factor for successful project management. Identifying these dependencies in the early stages of initiation and planning give Project Managers enough time to coordinate with the Project Managers of the dependent projects in order to mitigate against any negative impacts in the future. There are different types of dependencies such as technical dependencies, resource dependencies, synchronization dependencies, and even information dependencies. Over time, Artificial Intelligence (AI) and Machine Learning (ML) will be equipped to identify potential project dependencies between projects and bring it to the attention of the impacted Project Managers as well as Portfolio Manager(s).
Identification of Synergies
In a similar way to identifying project dependencies, AI and ML will further be able to support project selection by identifying proposals that have potential synergies with other projects and proposals. This will further enhance the project selection process. Organizations that have an ideation process may have dozens or hundreds of ideas in a backlog waiting to become formal proposals. Or, there may be other proposals that were deferred for the future but are forgotten. The challenge is that the best ideas move forward in the moment, but other ideas that may not have been technically feasible or cost prohibitive get forgotten. After a period of time, these ideas and proposals may be worth revisiting, but most PMO’s and Portfolio Managers don’t have the time to do holistic analysis of all these ideas to help propose new projects. In the future, PMO’s and Portfolio Managers will be able to take advantage of Artificial Intelligence (AI) and Machine Learning (ML) so that groups of ideas and proposals when put together form a compelling project or program worth doing. This will further leverage the collective intelligence of the company as well as boost morale by selecting good proposals that may have otherwise been forgotten.
Early Warning Indicators at the Portfolio Level
Artificial Intelligence (AI) and Machine Learning (ML) will also bring about major improvements to project execution (from a portfolio perspective) by providing early warning indicators to Project Managers, PMO’s, Portfolio Managers, and Project Sponsors related to project performance as well as an organization’s ability to absorb change.
Project Performance
Artificial Intelligence (AI) and Machine Learning (ML) provide an opportunity to more fully utilize historical project performance and combine that with risk data, issue data, resource data, priority data, schedule data, and prioritization data to help predict future project performance. This could be utilized up front during Work Intake or project initiation to help inform the likelihood of success for a project. Or it could alert a project team that more people, time, or budget will be needed to complete the project based on past project performance. For projects in flight, certain key words in a status report or risk log could trigger early warnings to a Project Sponsor or PMO Director that a certain project may be in trouble in the future and give them time to work with the Project Manager. After enough learning, it may be possible to predict different likelihoods of success based on team composition. In other words, the predictive analytics may be strong enough to indicate whether one Project Manager will be more or less successful on the same project as another Project Manager.
An Organization’s Ability to Absorb the Change
In the book Project Portfolio Management: A View from the Management Trenches, one of the chapters asks a simple yet enlightening question “can we absorb all the changes?” Most organizations do not consider how many changes come to their teams throughout the year based on project delivery. New IT implementations, new processes or trainings are coming at employees regularly. While companies need to adapt to changing customer and market needs, sometimes the timing of certain projects will coincide with other projects resulting in a lot of change all at once to internal or external customers that could be detrimental for adoption of new processes and tools. As another way of providing early warning indicators, Artificial Intelligence (AI) and Machine Learning (ML) will help analyze the timing of project completions, the impacted organizations, and the level of change expected in order to support organizational change management and improved communications. In some cases, certain projects may need to be delayed (or accelerated) in order to avoid the collision of multiple projects completing at the same time.
PPM Chatbot
The last major opportunity for Artificial Intelligence (AI) and Machine Learning (ML) in this post relates to the use of chatbots. Chatbots are already available in many software packages today, but chatbots in the near future will be far more conversational and will do more than simply collect data or provide a link to articles to answer questions. PPM chatbots will be able to better questions around how to use project and portfolio management software as well as provide direct answers on how to carry out portfolio management processes.
Preparation Needed for Artificial Intelligence (AI) and Machine Learning (ML) to Be Effective
This next section is brief but critical and focuses on what organizations need to do right now to prepare for the benefits of Artificial Intelligence (AI) and Machine Learning (ML). None of the benefits discussed earlier will be possible for organizations if they do not have the critical piece – structured data.
The Critical Ingredient – Structured Data
The problem for many organizations today is that too much of their project and portfolio data is unstructured, meaning that it exists in different places (ie. Spreadsheets, Word documents, various SharePoint sites, Teams channels, or disparate project management tools). To make things worse, many companies do a poor job of storing historical data; there is no central repository that exists. This is problematic especially when key Project Managers leave the company, a lot of that data simply gets lost. Furthermore, even if there is a reasonable amount of data available, a significant amount of data cleanup will likely be required.
“When these tools are ready for organizations, how will you make sure your organization is ready for them? Any AI adoption process begins with data, but you must not fail to prepare your people as well. Training AI algorithms to manage projects will require large amounts of project-related data. Your organization may retain troves of historical project data, but they are likely to be stored in thousands of documents in a variety of file formats scattered around different systems. The information could be out-of-date, might use different taxonomies, or contain outliers and gaps.” -Nieto-Rodriguez and Vargas
As promising as Artificial Intelligence (AI) and Machine Learning (ML) are to improving project portfolio management (PPM), none of it matters if an organization does not have an adequate amount of clean structured data. This brings us to a very important consideration for preparing for AI and Machine Learning.
How a Solution Like Acuity PPM Will Help You Prepare for Artificial Intelligence (AI) and Machine Learning (ML)
Utilizing good project portfolio management software can greatly improve the quality of your data, provide a central location for key project data, and prepare your organization for the benefits of Artificial Intelligence (AI) and Machine Learning (ML).
A solution like Acuity PPM is already preparing for the AI and ML benefits shown above. If you are stuck in spreadsheets and SharePoint sites, Acuity PPM will help you structure your data and provide the repository you need to take advantage of AI and ML in the future. Our solution incorporates Work Intake, prioritization, and resource capacity planning and already stores the full history of status reports, risk and issue logs, financial data, and lessons learned. Even if you are not yet ready to utilize this technology, moving to a solution like Acuity PPM will save you time in the future and make it easier to adopt the benefits of AI and ML.
Schedule A Demo
“Roughly 80% of the time spent preparing a ML algorithm for use is focused on data gathering and cleaning, which takes raw and unstructured data and transforms it into structured data that can train a machine learning model.” -Nieto-Rodriguez and Vargas
The Change Required to Benefit from Artificial Intelligence (AI) and Machine Learning (ML)
In this last major section, we highlight some important questions that senior leaders need to be able to answer before their organizations can really take advantage of Artificial Intelligence (AI) and Machine Learning (ML) in support of project portfolio management (PPM). The questions below are adapted from the article by Nieto-Rodriguez and Vargas.
Are you willing to invest the time and energy needed to create an accurate inventory of all your project data including the status updates?
As discussed above, having clean structured data is paramount to enjoying the benefits of Artificial Intelligence (AI) and Machine Learning (ML). If you rely on spreadsheets for project and portfolio data and do not have a good inventory of your data in a central location, now is the time to correct that deficiency. Companies that fail to have a central repository of data will fall further and further behind in terms of the value of AI and ML.
Will you invest resources for several months to gather, clean, and structure your project data?
The answer to this question really determines the willingness to embark on an AI/ML journey with project and portfolio management. Having the right people available to do the work will determine whether your organization has the right data at the right time.
Is your organization prepared to invest in training the project management community in AI/ML technology?
New technology can bring fear and caution, especially with tools like Artificial Intelligence (AI) and Machine Learning (ML) that have the potential to cut jobs. Leadership would benefit by attending AI/ML training with their PMO to understand how AI and ML will support project and portfolio management in the future.
Is your organization ready to accept and adopt AI/ML technology and “hand over the reins on decisions” with increasingly higher stakes?
This question is directly centered at the leadership team themselves and changing their behaviors (i.e. the way they make decisions). The leadership team or governance team will have to have a certain level of comfort with the information and recommendations provided by the AI/ML algorithms. Otherwise, it is all talk and no action. If the governance team simply wants to receive new data only to ignore it, then it is better to wait on utilizing AI/ML technology until a group of leaders are in place to embrace it. Many of the benefits listed above relate to new insights to help leaders make better decisions. In the case of portfolio optimization, the recommendations do help support project selection, but they mainly help inform governance teams which projects not to do (which is a common problem today).
Is your leadership team ready to let AI/ML technology make mistakes as it learns to perform better for your organization? And wait up to one year to start seeing the benefits of AI/ML technology?
AI and ML are tools to support strategic decision making, not replace the decision makers themselves. The algorithms will need time to get calibrated and the leadership team plays an important role in that calibration. We are just at the beginning of utilizing AI/ML technology on a broader scale and leadership teams will become more familiar with how to train AI/ML algorithms. However, this means that AI/ML algorithms will make mistakes. From a change perspective, senior leaders need to set the correct expectations that it will take time in order to train (and therefore trust) the recommendations delivered through AI/ML technology.
Do you have an executive sponsor with the capability and credibility to lead this transformation?
Having the right executive sponsor who understands the organizational change process as well as the requirements of Artificial Intelligence (AI) and Machine Learning (ML) will be able to properly communicate and set expectations to the executive leadership team (ELT) as well as the rest of the company. Undoubtedly, AI/ML technology will create discomfort at higher levels of management and the road to AI/ML benefits may be bumpy. Those companies with a strong executive sponsor will be better positioned to successfully utilize Artificial Intelligence (AI) and Machine Learning (ML).
Summary of How Artificial Intelligence (AI) and Machine Learning (ML) Will Impact Project Portfolio Management (PPM)
The AI project management revolution has begun. Artificial Intelligence (AI) and Machine Learning (ML) have already started shaking up various industries (education being one) and is not going away. Through tools such as ChatGPT, we are just beginning to see the possibilities of how AI and ML will change the way we work. PMO Leaders must ready their teams to take on these new technologies in order to improve project management and portfolio management. In this post we highlighted some of the exciting opportunities for AI and ML to improve portfolio decision making on several levels as well as consume vast amounts of data to help provide early warning indicators around project performance. One of the biggest takeaways is that PMO Leaders must have a system of record with vast amounts of clean and structured data in order to take advantage of AI and ML in the near future. The time to prepare is now; and tools such as Acuity PPM will put organizations ahead of the curve in preparing for AI and ML adoption.
Tim is a project and portfolio management consultant with over 15 years of experience working with the Fortune 500. He is an expert in maturity-based PPM and helps PMO Leaders build and improve their PMO to unlock more value for their company. He is one of the original PfMP’s (Portfolio Management Professionals) and a public speaker at business conferences and PMI events.
[activecampaign form=3]
How will AI change portfolio management? Specifically to project portfolio management, we see at least eight changes coming: optimized project selection, portfolio optimization, improved portfolio planning, identification of dependencies, identification of synergies, early warning indicators (project performance), early warning indicators (absorb change), and enhanced chatbots.
How can organizations prepare for AI and Machine Learning? In order to get the benefits of AI and machine learning, organizations must have structured data. This means they should stop using spreadsheets to manage their project portfolio and utilize a good project portfolio management solution such as Acuity PPM to structure their data.
What changes do seniors leaders need to make to prepare for AI and Machine Learning? Senior leaders firstly need to accept the uncertainty with making such as a change and then need to adjust how they make decisions by incorporating the feedback and recommendations from the AI/ML algorithm. The leadership team or governance team will have to have a certain level of comfort with the information and recommendations provided by the AI/ML algorithms. Otherwise, it is all talk and no action. Senior leaders also need to recognize that the algorithms will need time to get calibrated and the leadership team plays an important role in that calibration. This means that AI/ML algorithms will make mistakes. From a change perspective, senior leaders need to set the correct expectations that it will take time in order to train (and therefore trust) the recommendations delivered through AI/ML technology.
| 2023-02-18T00:00:00 |
2023/02/18
|
https://acuityppm.com/how-artificial-intelligence-ai-will-change-project-portfolio-management/
|
[
{
"date": "2023/02/17",
"position": 71,
"query": "workplace AI adoption"
}
] |
Unfolding Global Labor Market Dynamics
|
Unfolding Global Labor Market Dynamics
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https://risalatconsultants.com
|
[
"Risalat Consultants Int."
] |
Global Labor Market has been one of the very first victims of the crisis with widened global job gaps, rising rates of extreme poverty.
|
Unfolding Global Labor Market Dynamics
Global Labor Market has upended starting in early 2020. The sudden onset of Covid 19 and emerging geopolitical tensions as well as the Ukraine conflict and uneven recovery from the pandemic together contributed heavily to the creation of stagflation. Global Labor market has been one of the very first victims of the crisis with widened global job gaps, rising rates of extreme poverty, deteriorated job quality, and undermined social justice. Although varied, structural changes in labor markets continue to be felt everywhere to date. Rising prices worldwide along with protracted geopolitical tensions weigh heavily on the potential for employment growth.
According to the ILO’s World Employment and Social Outlook 2023, global employment is expected to expand by only 1% in 2023 with no major improvement awaited in 2024. This means that the gaps created by the infamous pandemic are not set to close within the coming two years. The situation is expected to vary across regions with Europe and Central Asia being hit the hardest due to Ukraine Conflict.
Core Inflation Driving Global Labor Market Crisis Further
Throughout the recent three to four years, we have seen asymmetric demand and supply shocks that have pushed inflation rates way up. An instant shift from one type of goods to another observed during the pandemic placed an enormous strain on global supply chains that had limited transportation capacity. Another shock was experienced by the tourism and aviation sectors when things started opening up gradually and consumers started to spend their involuntary savings.
Sadly, Covid 19 has not been the only driver of inflation worldwide. The disruptions as a result of the Ukraine conflict have further exacerbated the rise in food and energy prices affecting the poorest of the world the hardest. Ever since 1990, the global food prices index peaked and reached the level of 159.7 points. The prices in 2022 have been on average 43 percent greater than the ones in 2019. Energy prices were almost triple the same in 2019 aiding the expansion of the cost-of-living crisis for many households worldwide.
The control of inflation has become even harder given the exhausted capacity of central banks and monetary policymaking institutions after the pandemic. Facing substantial accumulation of debts and trying to balance conflicting goals, fiscal policymakers were not left with many options either.
What to Expect in the Global Employment Market?
According to the ILO, the global labor market perspectives are not very bright during the coming years. It is expected that employment growth will stall with real incomes falling and the quality of employment declining.
It can be said that the global labor supply has been reinstated at approximately the same level as in 2019. Although, the population of working age has started to decline in different higher-income states. What’s more, hundreds of thousands of people across the globe have been off work due to the health impacts of the pandemic. The WHO estimates that approximately 20% of those infected shall expect some sort of long-term health consequences.
To retain the standards of living, countries with high old-age-dependency ratios will need to expand their work towards rapid productivity growth. Alternatively, the options of facilitating inward migration can be considered. Some states have already started considering the raise in retirement age limits and providing additional incentives to keep older age workers as active laborers.
On the other side, lower-income countries benefit from old-age dependency ratios. However, many of their young population are deprived of the opportunity to obtain specific skills and expertise via education and training. Such standing affects the number of qualified laborers available. Sadly, young women are twice as likely to face the challenge compared to young men.
What about unemployment?
According to the ILO, global unemployment has declined by 30 million in 2022. However, cumulative global figures are still well above the same in 2019. Labor markets in high and middle-income countries managed to recoup losses and even go lower than 2019 rates of unemployment, unlike low and lower-middle-income states. It is worth noting that overall projections are not pessimistic toward global unemployment with fewer negative trends expected.
Disparities in the global unemployment market persist between males and females. Men are less likely to be unemployed compared to women. Moreover, young people are three times as likely to be unemployed than adults. This rate is higher for upper-income level countries. Although, this trend seems to be declining lately.
Job quality and working conditions
Most of us tend to look more at unemployment rates, sometimes overlooking the quality of the jobs taken. A lot of people worldwide are ready to engage in any type of employment as they are simply unable to afford to be jobless. This way, they are frequently forced to accept lower pay and pushed to work in inappropriate conditions. In addition, their quality of living keeps deteriorating as real income from such employment rarely keeps up with rising prices overstretching household budgets.
Given the crisis drivers discussed above, average real wages fell substantially over the last few years affecting the poorest households the most. Many have slipped into poverty and food insecurity. According to the ILO, by 2022 approximately 214 million workers were estimated to be living in extreme poverty.
Productivity growth for the sustainment of well-being
Rising productivity per hour and per worker implies greater employment growth and a rise in wages. Greater output per worker at a country level can significantly improve the population’s living standards. This is why productivity growth is considered to be one of the key drivers of social and economic well-being.
Speaking from a retrospective point, higher and middle-income countries have been more successful in closing productivity gaps compared to low-income states. At least this trend has been sustained over the last 30 years. According to the ILO’s estimates, in 2021 a worker in a high-income country was approximately 5 times more productive than his peer in a low-income country.
Linking technology and labor markets
Technological progress has been determined to be one of the core drivers of productivity growth in today’s era. Progress in digital technologies including the refinement of AI and labor-saving automation is expected to reinvent the way we see productivity growth. Although, many have indeed raised concerns regarding automation’s ability to replace the need for human labor. Thereby, suiting knowledge and skills to future technological context has made up the list of current trending topics.
Stay tuned to Risalat Social Pages
LinkedIn | Twitter | Facebook | YouTube
| 2023-02-03T00:00:00 |
2023/02/03
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https://risalatconsultants.com/unfolding-global-labor-market-dynamics/
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[
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"date": "2023/02/17",
"position": 29,
"query": "AI labor market trends"
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How to Launch Your Career in Artificial Intelligence
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How to Launch Your Career in Artificial Intelligence
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https://www.quickstart.com
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AI threatens the future of jobs, as per the Brookings Institute, a not-for-profit research organization. They say we must not fight the rise of AI, but ...
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What Is artificial intelligence?
AI is a computer science field that explores the development of intelligent machines that respond and work like humans. A few of the tasks developed for computers through artificial intelligence include:
Task planning
Speech recognition
Automated learning
Self-driving cars
Machine-based problem solving
Many technologies also help to support and enable artificial intelligence technologies, including the Internet of Things (IoT), which produces large volumes of unanalyzed data from connected devices.
Another type of technology — graphical processing units — are important to artificial intelligence, as they offer the high computational power needed for iterative processing. APIs also support AI technologies, as compact code packages that allow existing software and product packages to add artificial intelligence functionality. In-home security systems can further incorporate AI, through incorporated image recognition capabilities and Q&A functionality that explain data, generate headlines and captions or point out action-ready data trends and insights.
How do I begin a career in artificial intelligence?
There are many ways to begin a career in artificial intelligence. To earn the AI job of your dreams, you'll need the right combination of skill and experience that demonstrates your career aptitude to employers.
We recommend learners to do the following:
Step 1: Break all barriers to mindsets.
Believe that you can apply and practice machine learning (ML) with real-life examples in the real world. It all begins with an idea and, utilizing ML, you can bring the idea into action.
If you find this challenging, then tell yourself the following:
What is keeping you apart from your objectives in ML?
How can I think about issues with ML?
How can a coach support me break down my obstacles?
Find a positive meeting group for ML and connect with them.
Step 2: Have a process.
Use a method to work on challenges with ML:
Review the modifications to the algorithm.
Prepare a data set.
Enhance the outcomes of the data.
Represent the conclusions to the stakeholders.
These items can help you prepare your process for future ML success.
Step 3: Select a tool for programming.
Choose a tool for each programming stage and map it to your method.
Step No 4: Practice your skills.
Choose datasets to work on and practice them.
Step No 5: Develop your performance into a good portfolio.
Once you have developed skills in artificial intelligence, show employers that you understand what techniques of ML you can develop.
Understanding the Artificial Intelligence Career Landscape
For a while now, artificial intelligence has been a good career option and, as the application of artificial intelligence continues to expand in different verticals, the demand for qualified experts is also growing. AI should create many interesting and promising job opportunities. If you are an artificial intelligence enthusiast, be confident and plan for a fantastic artificial intelligence career.
Here are a few common jobs in the artificial intelligence sector:
Robotic Scientist: A robotic scientist is anyone with advanced AI education who develops mechanical devices that execute different functions. Some robotics scientists builod devices that go where people can't go. Others develop robots that perform microscopic activities with robotic hands. A robot may automate work, but it needs anyone to be able to build robots. Candidates must have a graduate degree equivalent to engineering or computer science to be a robotic scientist.
A robotic scientist is anyone with advanced AI education who develops mechanical devices that execute different functions. Some robotics scientists builod devices that go where people can't go. Others develop robots that perform microscopic activities with robotic hands. A robot may automate work, but it needs anyone to be able to build robots. Candidates must have a graduate degree equivalent to engineering or computer science to be a robotic scientist. Business Intelligence Developer: In recent times, the market for business intelligence developers has increased. BI developers spends a lot of time studying and preparing strategies within the organization for existing issues. A business intelligence developer performs a critical role in growing the organization's productivity and profitability, whether it's analyzing complicated data or searching for an existing business strategy.
In recent times, the market for business intelligence developers has increased. BI developers spends a lot of time studying and preparing strategies within the organization for existing issues. A business intelligence developer performs a critical role in growing the organization's productivity and profitability, whether it's analyzing complicated data or searching for an existing business strategy. Machine Learning Engineer: ML engineers are known to be one of the most searched artificial intelligence career jobs. To be an ML engineer, you need to have a solid understanding of software expertise and statistical models. You'll also need to know how to use natural language processing (NLP) while dealing with large data sets.
ML engineers are known to be one of the most searched artificial intelligence career jobs. To be an ML engineer, you need to have a solid understanding of software expertise and statistical models. You'll also need to know how to use natural language processing (NLP) while dealing with large data sets. Data Scientist: A data scientist uses ML and predictive analytics to gather, evaluate and report data from different sources. This process helps them fully understand how organizations operate and informs any artificial intelligence instruments they develop. Aspiring data scientists need experience in utilizing big data tools and platforms like Spark, Pig, Hadoop, MapReduce and Hive. They also need to understand programming languages like Python, SQL, Perl, and Scala.
A data scientist uses ML and predictive analytics to gather, evaluate and report data from different sources. This process helps them fully understand how organizations operate and informs any artificial intelligence instruments they develop. Aspiring data scientists need experience in utilizing big data tools and platforms like Spark, Pig, Hadoop, MapReduce and Hive. They also need to understand programming languages like Python, SQL, Perl, and Scala. Research Scientist: A researcher is someone responsible for planning, carrying out and evaluating data from lab-based controlled studies, trials and experiments. Research scientists should have a good understanding of various artificial intelligence fields.
Knowledge and Educational Prerequisites for AI
If you are fascinated by artificial intelligence and curious about how to begin, then having a bachelor's degree in computer science and mathematics is the first key requirement.
Build up a base in the following areas before you plunge into artificial intelligence:
Robotics, engineering and physics
Bayesian networking
Mathematics: Calculus, algebra, algorithms and logic, statistics, probability
Computer science: Skill in coding with popular programming languages like Java, Python, Lisp, Julia, etc.
Theory of cognitive science
Top 3 Tools for Learning Artificial Intelligence Online
Practice Artificial Intelligence with Google
This Google platform offers a program that begins from a simple overview of ML to training and designing neural networks to beginning with TensorFlow. The best thing about this program is that you do not need any previous experience to get started; it is built in such a way that you can find data and exercises to help you improve your expertise and develop your projects, whether you are just beginning with coding or you are a seasoned ML practitioner.
Google's Machine Learning Course
This program is on deep learning from Google via Udacity, offering us an introduction to what deep learning is really all about. Looking at individuals who are seeking to act as data scientists, data analysts or ML engineers in machine learning, neural network technology.
Also, if anyone wishes to dive deeper, Udacity has a full-fledged certification training deep learning program to provide deeper hands-on training.
MIT: Self-driving Cars Deep Learning
MIT describes an approach that uses one big real-world feature of artificial intelligence, self-driving vehicles, with several courses accessible on the internet regarding AI. This program (an overview of deep learning practice) from MIT focuses on individuals who have just begun in the area of machine learning. This program, therefore, is also beneficial for those who are already involved in deep learning space or machine learning, according to MIT.
Three Platforms You Must Try for AI Job Hunting
It's an era dominated by technology. You do not have to walk around, knocking on the company doors to recruit you; we have artificial intelligence and the internet. Let's take a look at a few of the top platforms/portals for job searching that will support you in landing your right job in the artificial intelligence sector.
Indeed
Indeed, today's job sites are the most common. With a quick and powerful interface, the platform makes it much easier to search for jobs that suit well for the work seeker. Scrape down everything from business recruiting pages to direct hiring/job tweets, yes.
How about searching? Simply type the location and keyword of the appropriate job title. The only click applies and you'll get the job you're searching for. You could also sign up and build an account to apply for work, which makes things simpler for you.
LinkedIn
LinkedIn is the biggest global network in the world, with even more than 562 million members in more than 200 territories and countries worldwide. LinkedIn Job Search is among the best sites for job hunting, offering great jobs posted. You can also reach out to the key players in the market and have a recruitment discussion.
Google for jobs
You should never neglect Google when it comes to looking for something online. (Do not get worried! It's not Google's work search engine.) So, it's exactly the same as Google search results, except here, Google just looks for employment. It shows jobs that have already been posted on various portals.
Google work search is useful in contrast to employment opportunities on various portals since it saves time. So, if you're searching for an Artificial Intelligence job, first try Google Job Hunt and then try out some other sites.
Outlook
Artificial intelligence has played a crucial role in the field of technology since its inception, enhancing the standard of life through several sectors. And speaking of what the future will hold, it is impossible to foresee. The way artificial intelligence progresses, however, it seems that the developments in the near future will be wonderful and those developments will only be successful because there are professionals who are educated and employed in the artificial intelligence sector.
If you have a goal of creating with incredible technology, then it is probably time that you must start opening the path for an AI career.
The Most Popular AI Certifications Include:
Python Programming Course Training - Certified Associate
Beginner: intro to the teaching in machine learning courses
Intermediate: Artificial Intelligence with python
Advanced: Blockchain certification
Depending on the field you want to work in, other relevant tech knowledge you need can vary.
Unsupervised learning-algorithms try to find trends in data, in this case, searching for correlations that can be used to classify that knowledge.
Supervised learning is the method of training vast numbers of datasets to a computer.
The machine tries to optimize getting paid on its data input, essentially going via a trial-and-error process before it achieves the best possible result.
Industries That Employ Researchers in AI
From self-driving vehicles, automated response systems, medical research, patent searches and risk notification, every company has a large requirement for artificial intelligence technologies. AI threatens the future of jobs, as per the Brookings Institute, a not-for-profit research organization. They say we must not fight the rise of AI, but government policy enforcers need to treat it properly. Many report that a fifth of the jobs we think of today will all be washed out just because of AI technology and automation within fifteen years.
Retail
Artificial intelligence offers online purchasing features that offer customized suggestions and speak to the customer about purchasing choices. With artificial intelligence, stock management and site development technologies can also be enhanced.
Health Care
Customized medication and X-ray readings can be given by artificial intelligence apps. Healthcare assistants will serve as life coaches, encouraging you to eat better, exercise and take the right medicines.
Manufacturing
As it streams from connected devices to predict expected demand and load utilizing recurrent networks, artificial intelligence can evaluate factory IoT data, a particular form of deep learning model used for data sets.
Banking
The precision, speed and enhancement of human efforts are enhanced by AI. Artificial intelligence methods can be used in financial institutions to classify which transactions are probable to be suspicious, implement fast and reliable credit scoring, and also automate severe information management activities manually.
Importance of Networking
We have a free group of Digital Natives Meetups, with over 2,400 participants. This community is a perfect place for other networks and IT experts to meet. It could enable you to get your foot through the door with these free events. Every month, we have meetings covering current topics and interests in the rapidly evolving area of artificial intelligence/machine learning. We have professionals who are full-time experts who explain cutting-edge technology and discuss with you their passions for the area of information technology.
The Future of Artificial Intelligence
Van Loon answered, when asked about the artificial intelligence future, that the pace of growth makes it difficult to foresee the future. We cannot really guess what's going to happen with the innovation we will see in the coming years, but we do believe we already have a lack of qualified artificial intelligence and machine learning experts. Only when we get people educated and put in the millions of artificial intelligence jobs can the gap expand. Get certified if you would like to become one of those experts, because the sooner you begin your training, the sooner you can work in this important and fast-growing sector.
As the requirement for machine learning and artificial intelligence has increased, companies need practitioners with in-and-out expertise and hands-on expertise with these technologies. In association with IBM, QuickStart is offering an Artificial Intelligence Bootcamp program that can help you increase knowledge in specific industry technology and techniques from NLP, Python, and speech recognition to specialized deep learning, keeping in mind the innate need. In booming fields such as AI, ML and deep learning, this post-graduate course will enable you to rise in the crowd and expand your profession. QuickStart’s Artificial Intelligence Bootcamp program can help you in learning in-demand skills and landing the highest paying jobs.
If you want to become an artificial intelligence professional, then we've found the correct path for you. The Career Guidance for AI will give you insights into the most trending innovations, the top recruiting firms, the necessary skills to kick start your career in the booming field of artificial intelligence, and provide you with a customized roadmap to becoming a good artificial intelligence professional.
You can connect with our experts for counseling on your next step to succeed in your career or learn more about AI courses. Start your 30-day free trial to access over 900 self-paced courses.
| 2023-02-17T00:00:00 |
https://www.quickstart.com/blog/artificial-intelligence/how-to-launch-your-career-in-artificial-intelligence/
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"date": "2023/02/17",
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"query": "AI labor market trends"
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"query": "artificial intelligence hiring"
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LaborIQ: Home Page
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Home Page
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https://laboriq.co
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Fresh articles every week on employment conditions, hot hiring markets, compensation trends and job market forecasts. Client Stories. Success Stories from ...
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Recruiters
Salary Benchmarking for Recruiting and Staffing Firms
Help your clients attract top talent and position themselves as employers of choice. With LaborIQ’s salary benchmarking tools, recruiting and staffing firms get direct access to precise, real-time compensation data for the U.S. labor market.
| 2023-02-17T00:00:00 |
https://laboriq.co/
|
[
{
"date": "2023/02/17",
"position": 91,
"query": "AI labor market trends"
}
] |
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Uses of ChatGPT in HR and recruitment
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Uses of ChatGPT in HR and recruitment
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https://www.xref.com
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ChatGPT has the potential to revolutionise the HR industry by automating routine tasks and providing quick and accurate answers to common HR-related questions.
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The use of chatbots is common. Many organisations use chatbots on their websites to answer simple questions or direct queries to the correct staff or department. Most chatbots simply relay predefined answers based on keyword prompts. While they may help answer basic questions, chatbots are typically very limited.
Using chatbots in HR to answer basic enquiries isn’t a new concept, but ChatGPT isn’t just a chatbot.
You’ve probably seen ChatGPT mentioned in the news or seen stories about it shared on social media. There is a lot of hype around ChatGPT, and the reason for that is simple. ChatGPT is an extremely exciting advance in language processing. It is a bot that can generate its own responses to questions or requests and respond like a person.
Although not capable of human-like problem-solving or creativity, ChatGPT is very adept at answering technical questions. Google recently tested several AI chatbots for potential integration into the site. During this testing, ChatGPT successfully interviewed and was hired for a coding position at Google.
ChatGPT has so much potential that a competing technology has already been announced before it has passed testing. Bard, developed by Google, is a rival AI chatbot capable of generating realistic responses. With a tech giant like Google invested in the potential of AI chatbot technology, it’s hard not to be excited about the possibilities.
The ability of ChatGPT and other language processing platforms to tailor responses based on input has the potential to be of great benefit to HR and recruiters. Read on to find out how.
What is ChatGPT?
Developed by Artificial Intelligence (AI) research laboratory, OpenAI, ChatGPT may be a chatbot, but it’s not like anything else currently in use. Common chatbots are used to answer frequently asked questions, whereas ChatGPT is a language model trained on a large dataset to generate text.
What does that mean? At its core, ChatGPT can answer questions in naturalistic ways. The AI uses a vast amount of data to generate answers to any user queries. ChatGPT will also tailor responses to reflect how the question was asked or in ways prompted by the user.
ChatGPT is a language tool capable of generating text based on the way a user asks a question or makes a request.
The current version of ChatGPT has no access to the Internet, so all answers given are based on its training data. As a result, the AI only has knowledge of events up to 2021.
As with all AI, ChatGPT can be trained with new information, so when it is released, organisations should be able to train it on data specific to their needs. In the case of HR, this data may include successful job applications, resumes and job postings.
What makes ChatGPT different from other chatbots?
Instead of using pre-scripted responses like regular chatbots, ChatGPT creates new text. The AI platform can also answer follow-up questions, challenge incorrect information, reject inappropriate requests and even admit to mistakes.
The best way to illustrate how ChatGPT tailors language to user input is by using responses from Chat GPT.
Input: What is ChatGPT?
ChatGPT response:
“ChatGPT is a language model developed by OpenAI. It is a variant of the GPT (Generative Pre-trained Transformer) model, which is trained on a large dataset of text and is able to generate human-like text in response to prompts. ChatGPT is specifically designed to be used in natural language processing tasks such as language generation, dialogue systems, and conversational AI.”
In this first example, the answer is as straightforward as the question. ChatGPT explains what it is from a technical standpoint. What happens if the question is asked differently?
Input: Explain ChatGPT to me like I know nothing
ChatGPT response:
“Sure! So, ChatGPT is like a robot that can understand and respond to human language. It's been taught how to understand and generate text, just like a human would. Think of it like a computer program that can hold a conversation with you. It can answer your questions, make small talk, and even tell you a joke or two! It's a way for computers to understand and respond to our language, making it easier for us to communicate with them.”
The difference between the two responses is plain to see. The first is a straightforward information dump with no nuance. The second answer, on the other hand, takes a much more basic approach to explaining ChatGPT, taking into account that the input states they know nothing.
This ability to naturalistically generate text in response to prompts is why ChatGPT has the potential to have a massive impact on how HR and recruitment professionals approach some of their tasks.
Pros and cons of ChatGPT
As an AI language model, ChatGPT has several pros and cons to consider before using it in any HR situation:
Pros:
Ability to generate human-like text : ChatGPT has been trained on a massive amount of text data, making it capable of generating human-like text.
: ChatGPT has been trained on a massive amount of text data, making it capable of generating human-like text. Versatility : ChatGPT has the potential to be used in a variety of HR applications, such as a conversational AI chatbot, initial interviews, job description creation, and more.
: ChatGPT has the potential to be used in a variety of HR applications, such as a conversational AI chatbot, initial interviews, job description creation, and more. Speed and efficiency: ChatGPT can process and generate responses to a large volume of requests in a relatively short time, making it well-suited for applications where quick responses are important, such as answering common HR queries or candidate questions.
Cons:
Limitations in understanding : While ChatGPT is trained on a large amount of text data, it does not completely understand the world and may generate inaccurate or inappropriate responses.
: While ChatGPT is trained on a large amount of text data, it does not completely understand the world and may generate inaccurate or inappropriate responses. Bias in training data : As with all AI models trained on large data sets, ChatGPT is vulnerable to inheriting biases in the training data. This training model can lead to the generation of biased or stereotypical responses.
: As with all AI models trained on large data sets, ChatGPT is vulnerable to inheriting biases in the training data. This training model can lead to the generation of biased or stereotypical responses. Lack of emotional intelligence : ChatGPT cannot understand or express emotions, making it less suitable for applications that require emotional intelligence.
: ChatGPT cannot understand or express emotions, making it less suitable for applications that require emotional intelligence. Knowledge cutoff: ChatGPT only has knowledge of events up to 2021, so it may be missing information vital to a number of tasks.
Later releases of ChatGPT or a related AI that allows users to train the AI with new data may rectify or reduce some of the current problems, such as limitations in understanding and the knowledge cutoff.
ChatGPT as an HR chatbot
Like other chatbots, ChatGPT can be used to automate repetitive tasks that may otherwise monopolise the time of HR professionals. The ability of ChatGPT to generate text in response to prompts makes the number of applications it can be used for far more comprehensive than traditional chatbots.
At the most basic level, if ChatGPT is trained with an organisation’s HR policies the AI can answer employee questions. The ability of ChatGPT to generate text based on its dataset means that any answers given will be specific to the input rather than the typical general responses that chatbots deliver.
As ChatGPT is a conversational bot, all responses are tailored to the input. Information can be condensed or simplified to avoid confusion.
ChatGPT can also draw conclusions from data. Using it as a general HR chatbot could allow the AI to analyse the organisation for pain points while answering questions.
With each input, ChatGPT could add new information to its database, collating questions and sorting them in stacks due to similarity. With this information, ChatGPT could identify frequently asked questions to pinpoint critical issues staff may have.
For example, ChatGPT may often be queried about taking time off. The AI could identify the leave process as a pain point for staff and inform the HR department of this.
Answering questions may be the most apparent function of ChatGPT, but it’s far from the only way it may be used in HR and recruitment.
ChatGPT as a recruitment tool
The use of ChatGPT as a chatbot isn’t limited to employees. HR professionals could use the AI to quickly and effectively respond to queries from job applicants. The ability of ChatGPT to generate naturalistic text can make responses feel personalised rather than a simple chatbot response.
Answering candidate questions is only the tip of the iceberg when it comes to the recruiting potential of ChatGPT.
By training the AI with multiple examples of successful job advertisements, recruiters could quickly use ChatGPT to generate job descriptions, job ads and other recruitment material. Creating these assets would still require assistance from an HR professional in the form of prompts and input to specify the parameters of the text.
Although ChatGPT hasn’t explicitly been trained with recruitment metrics, the current version is capable of showcasing the ability to quickly create job descriptions, as the following example shows:
Input: Create a job description for a mid-level manager in an automotive plant. Duties include payroll, OHS supervision and general team leadership.
ChatGPT response:
“Mid-Level Manager, Automotive Plant
We are seeking a highly motivated and experienced Mid-Level Manager to join our team at our automotive manufacturing plant. The successful candidate will be responsible for overseeing the payroll and Occupational Health and Safety (OHS) functions, as well as leading a team of production employees to meet and exceed production goals.
Key Responsibilities:
1. Manage payroll and benefits administration for all plant employees
2. Oversee and ensure compliance with OHS regulations and policies
3. Lead and motivate a team of production employees to meet and exceed production goals
4. Develop and implement strategies to improve efficiency and productivity
5. Monitor and analyse production metrics to identify areas for improvement
6. Collaborate with other departments to ensure smooth operation of the plant
7. Provide training and development opportunities for team members
8. Address and resolve any employee relations issues that may arise”
In addition to the description and responsibilities, the job description created by ChatGPT also included a list of qualifications and a call to action for the candidate.
Any job description generated in this manner would still need to be audited and edited by human eyes. Still, developing the text takes a fraction of the time it would typically take to write the same content. Using AI to generate job ads has the potential to save HR professionals a great deal of time.
ChatGPT for hiring
Once a job description has been created, the potential uses of ChatGPT in the recruitment process don’t end. One of the most obvious uses of ChatGPT in HR is automating parts of the recruitment process.
ChatGPT can be used to screen resumes when they are submitted. The AI could be used to analyse submitted resumes and provide a summary of the most relevant information, such as the candidate's skills, qualifications, and experience.
To get the most out of resume screening, ChatGPT would have to be trained with a dataset of resumes and job descriptions. It would then be able to match the resumes with the job requirements and identify the most qualified candidates.
Once all resumes have been analysed, ChatGPT will be able to present them in a structured format, making it easy for HR staff to review and compare.
Once a shortlist of candidates has been compiled, ChatGPT can be used to conduct initial interviews. The AI can be programmed to ask specific questions and evaluate the candidate's responses.
ChatGPT can then provide a summary of the candidate's qualifications, experience, and suitability for the role. These summaries can reduce the number of candidates that need to be interviewed in person and make the recruitment process more efficient.
ChatGPT for onboarding
Once a successful candidate has been hired, ChatGPT can assist with the onboarding process as well. The AI can provide new hires with information on company policies, benefits, and other essential details and answer any questions they may have.
This automation can make the onboarding process more efficient and less overwhelming for new hires. By centralising important information, ChatGPT allows new hires to ask questions at their own speed rather than trying to memorise onboarding documentation or deluge HR staff with questions.
The human touch provided by HR professionals will always be needed in the hiring and onboarding process. Automating some time consuming and repetitive tasks using ChatGPT has the potential to allow HR professionals to concentrate more on people and less on data entry.
Training ChatGPT for HR tasks
As with any AI or Machine Learning application, ChatGPT will require specific training to be of maximum use to HR professionals.
To train ChatGPT for human resources, users will need to provide the model with a large dataset of HR-related questions and answers. This data should cover topics encompassing commonly performed tasks, including but not limited to:
Recruitment
Onboarding
Benefits
Performance management
Diversity and inclusion
Employee relations
When creating the dataset, it will be essential to consider ethical and legal considerations, such as data privacy and confidentiality. HR professionals supervising the training and fine-tuning of ChatGPT may need to implement constraints to prevent the model from providing sensitive information.
Without explicitly telling ChatGPT that certain information should not be shared, any information it is trained on can be shared with anyone who interacts with the AI.
In addition to data privacy and confidentiality issues, some other legal and ethical issues must be considered when using ChatGPT. ChatGPT crafts content from a vast amount of text data such as books, articles, blog posts and studies, many of which are copyrighted. As such, there is the potential for intellectual property infringement.
Another significant problem that all machine learning and AI built for specific tasks share is bias. The current version of ChatGPT has been trained on 570GB of data, around 300 billion words, harvested from the Internet. This data is uncurated, meaning it likely contains erroneous and biased information drawn from unreliable sources that could be used in responses.
Bias could be highly detrimental when using ChatGPT during the hiring process. Unless the data used to train the AI to sort resumes is balanced, it may develop a bias toward one sort of candidate. A prime example of this problem is Amazon and its AI, AMZN.O.
Amazon was forced to scrap AMZN.O, an AI recruitment tool they had created because it was biased against female candidates. The AI was trained using ten years of successful candidate resumes. Because the US tech sector is 73% male, the AI concluded that men are more suited to tech sector jobs and downgraded women's resumes.
Training ChatGPT with curated data may be a way of avoiding bias. For example, using an equal number of successful male and female candidate resumes could help eliminate gender bias.
Removing gendered language from AI training data may also help to avoid one gender group being favoured over another.
The training process won’t end with feeding ChatGPT data. After being provided with the data, HR professionals will have to fine-tune the instance of ChatGPT, adjusting the model's parameters so it can accurately generate responses based on the data on which it has been trained.
Can ChatGPT replace HR professionals?
New technologies and advancements can be intimidating. People have been scaremongering about AI taking over jobs for years, but the prophesied mass unemployment due to intelligent machines still hasn’t happened. According to research, AI is unlikely to cause mass unemployment but will change how we work.
Due to ChatGPT's ability to create human-like responses to user input, the AI could perform some HR tasks perfectly. Still, it lacks something vital in human relations - human judgement and decision-making abilities.
ChatGPT can appear to make human-like decisions in how it generates responses or sorts information, but it is not. Rather than making decisions, the AI analyses large amounts of data to bring insights.
Insights, such as pain points that ChatGPT may have identified through analysing frequently asked HR questions, serve as a helpful tool for HR professionals rather than as their replacement.
If employees frequently ask about how to take leave, ChatGPT may identify the leave process as a pain point that needs to be addressed. Similarly, frequent questions regarding career planning or pay structure may lead ChatGPT to identify these as areas that need addressing.
When it is released, ChatGPT has the potential to streamline some HR processes, but that doesn’t mean it can replace actual HR staff. Until an AI can think for itself, tools such as ChatGPT can be valuable support tools, not human replacements.
Key takeaways for HR professionals
ChatGPT has incredible potential for use in the HR sector, but it is essential to remember that it is just that - potential. The current version of ChatGPT has been trained with unrestricted data drawn from the Internet, so even though it can respond to users in a human-like way, it can’t accurately perform HR tasks.
When we have the ability to train ChatGPT with industry-specific data, the AI may be used to automate many repetitive HR tasks, such as:
Answering common HR queries: As a conversational chatbot, ChatGPT can answer employee HR queries, such as leave policies or benefits. The AI can also analyse the questions asked to identify potential pain points.
Creating job descriptions: By training ChatGPT with successful job descriptions, HR professionals could use the AI to generate job descriptions quickly to streamline recruiting.
Analysing resumes: ChatGPT could be used to analyse submitted resumes and provide a summary of the most relevant information, such as the candidate's skills, qualifications, and experience. If trained with position-relevant data, ChatGPT could also match resumes with the job requirements to identify the most qualified candidates.
Conducting initial interviews: ChatGPT can be programmed to ask specific questions and evaluate candidate responses for initial candidate interviews. ChatGPT can then provide a summary of the candidate's qualifications, experience, and suitability for the role, limiting the number of candidates that need to be interviewed.
When ChatGPT is made available for training, it has the potential to automate or streamline many HR processes. There have yet to be any announcements about an official release.
Closing thoughts
ChatGPT is a fascinating technology, so it’s easy to get carried away thinking about all its potential uses. ChatGPT is a powerful tool, but it's not perfect and should be used as an aid to the recruitment process, not as a replacement for the human touch.
ChatGPT has the potential to revolutionise the HR industry by automating routine tasks and providing quick and accurate answers to common HR-related questions. However, it is crucial to approach ChatGPT in HR with caution and carefully consider ethical and legal considerations, such as data privacy and confidentiality.
Proper training of the model with a comprehensive HR dataset and implementation of specific functionality and constraints can ensure that the model aligns with HR best practices and regulations.
ChatGPT holds great promise for transforming the HR industry and improving employee engagement and experience, but it will never be able to replace the human touch provided by HR professionals.
| 2023-02-17T00:00:00 |
https://www.xref.com/blog/chatgpt-in-hr-and-recruitment
|
[
{
"date": "2023/02/17",
"position": 28,
"query": "ChatGPT employment impact"
}
] |
|
How ChatGPT and Other Automation Tools Can Make the ...
|
How ChatGPT and Other Automation Tools Can Make the Tech World Better
|
https://techinmotion.com
|
[
"Tech In Motion"
] |
Despite the misconception that automation tools like ChatGPT reduce job opportunities, the World Economic Forum (WEF) estimates that 97 million jobs will be ...
|
Despite the misconception that automation tools like ChatGPT reduce job opportunities, the World Economic Forum (WEF) estimates that 97 million jobs will be created by 2025 as a result of AI. This means that there are plenty of opportunities to upskill the workforce to keep pace with automation.
As IT talent shortages remain an ongoing challenge in 2023, tech leaders are forced to implement new strategies to keep their organizations equipped for the digital future. The demand for tech talent continues to grow, how are leaders responding to the skills gap? Are forward-thinking tech executives reskilling & upskilling their workforce, using AI to augment human capabilities, or investing in a combination of both?
With this as background, “Tech in Motion” brought together several AI strategic leaders to discuss the issues and trends: Moderator David Yakobovitch Global Product Lead @ Google; and panelists Howie Xu Vice President of Machine Learning and AI @ Zscaler; Jennifer Glenski Director of Product Management @ BMC Software; Greg Coquillo AI, Technology Manager @ Amazon and Daniel Wu Head of AI & Machine Learning, Commercial Banking JPMorgan Chase & Co.
This panel of experts shared their advice for using automation to lead digital transformation while continuing to nurture talent and empower employees. To be the first to rewatch the event once it goes live on-demand, sign up at the event's home page: Automation, Reskilling, and the Changing World of Work.
Note: The opinions expressed are the panelist's personal thoughts and are not representative of the organizations they work for.
How do skills change with new automation priorities in 2023? How are you addressing skill gaps?
Coquillo: If you don’t have in-house expertise, look outside and try to hire the right people. Most of the time large organizations do have competent employees. They just need to fill gaps with diverse skill sets. Fortunately, today there is a large pool to draw from.
There is also a need to put in programs to upskill existing talent. It’s important to note that not everybody needs to have a full master’s course in Computer Science. Different people have different needs. Off-the-shelf or in-house solutions are options. Collaborate across the board within your organization, across different lines of business to obtain common skills. Design your programs accordingly, and don’t err on side of spending a lot of money on training which might be overkill.
Xu: A big change for us is a mindset and culture shift as a team and as individuals. We are at a tipping point with AI. Maybe not human intelligence yet, but very powerful, nevertheless. How do we adapt and cope with it? How do we replace tedious work? I see this as big as the internet wave. We should embrace it. Get our mindset ready. The world is going to be very different, and organizations will need to be bold with their restructuring.
How are you upskilling within your own job?
Coquillo: I am a sucker for acquiring new skills. I focus on the need to change my skillset first, then find resources. Lately, I have been creating mental models for new challenges. How do you create mental models on demand for situations you have never seen before, and find people to help reinforce your mental model? What tools, skills and resources do you need to address them?
Wu: I am always learning. One big area I have focused on is Responsible AI. What does responsible AI mean? What are the principles that are practiced?
Secondly, is on the leadership side. I am focusing on setting strategies and visions for leading technologies that enable transformation. How to put in place strategies to move a large organization to use AI technologies is a large task. There are no recipes to follow. You have to learn about what other industries are doing to drive transformation.
At a technical level, AI requires a data-driven approach. Collecting, analyzing, and getting insights out of data. This is not the typical way of doing things in the last couple of decades. What does getting insights from data mean? We have to learn from that, both on the technical and business side.
Glenski: I ask myself “what are you upskilling”? In my role in product management in innovation labs, I try to anticipate customer problems of tomorrow. I enjoy self-paced upskilling and reskilling in my own time without going back to school full-time, which I do not have time for. On the technical side, I am interested in working on applying AI to the data ops world, similar to IT operations.
What about the talent needed to build application and infrastructure layers, or AI algorithm applications?
Coquillo: In order to deploy a portfolio of AI products to be successful, we need data scientists, machine learning engineers, software engineers and data engineers. One thing people don’t talk enough about is supporting teams to make machine learning models work. In the room, you need an AI architect to take stock of AI models deployed to product teams to assess the ROI. Legal to cover all ethical and privacy issues around AI. Finance and accounting to make sure generated revenues are going to the right accounts. Program teams to support workflows going around. They are critical to understanding where AI can help, and where they need to step back.
Yakobovitch: Yes, both the business roles as well as technical roles. Different divisions such as service management, IT operations, data operations, data management and security operations need to sharpen their focus as well.
Glenski: Some of the IT processes we look at are the same because these environments are always evolving and sometimes it can be difficult to keep up by automating them to handle mundane repetitive processes. One of the benefits of automation over manual is to jump-start AI operations to take the burden off people and put it in on machines. This frees up employees to work on innovation and higher-value projects.
Xu: One of the latest breakthroughs of AI is reinforcing human learning with AI in the loop. On the human side, how do we collaborate, and at what place?
What’s top of mind when it comes to automation trends?
Xu: If you are not interacting with ChatGPT on weekly basis you are not doing enough. If you work in marketing, programming, legal, etc. you should you digging through it. What is the limit? Each one of us in an enterprise needs to understand what it means to us. It will be a different user experience for each of us.
Wu: I still want to ground the discussion on the societal economic impact of introducing ChatGPT to our workplaces. There are lots of loose ends still, with all this excitement. How does AI change education for example? Some schools are banning ChatGPT because teachers cannot assign essays without knowing whether students are using ChatGPT to cheat.
Nobody is doing a holistic evaluation as far as responsible AI metrics. This is a key area to increase and build trust in AI. Lots of people are still on the fence and don’t want to dive right in. They need to know this is here to stay and sustainable, and there are mitigation strategies in place.
Glenski: One trend I am excited about is Automation with AI ops. For example, a user submits a question and gets a resolution and direction from AI. Typically, an IT admin gets an alert, finds an error code, and googles it to get a resolution – all this could be done away with ChatGPT, which also knows the vulnerabilities and patches which are out there to address the root cause.
Coquillo: I am excited about the birth of more large language models with big vision models. What about a robotics model that has physical machines embedded with AI models to augment people’s lives? To train an assistant in the kitchen or something else in the house, such as cleaning up.
Yakobovitch: Some models show that AI can achieve human-level parity with 80-90 percent accuracy.
Download Motion Recruitment's 2023 Salary Guide. Compensation Ranges for over 120 Tech Job Titles, Expert Insights and Analysis from Leaders Inside the Tech Industry. Get Your Free Copy Today!
The number one question from our audience: What category of jobs will AI take over first?
Xu: It’s controversial. For years, the conventional wisdom has been that it will be the repetitive mundane jobs first, but not creative jobs, or senior-level jobs. However, AI can do things very well, like writing a speech for a CEO, and augmenting their job, but not replacing it.
Yakobovitch: Yes, cold start problems. It takes too much time to start writing or to start coding, so if we can have this augmentation then humans can focus on more complex, riveting work.
Coquillo: The cold start problem is because the human brain is chaotic and often doesn’t know where to start in addressing a task or problem. ChatGPT can make sense out of that chaos, replacing the cognitive overload that we don’t have the bandwidth or energy for.
What are some of the risks with large language models?
Wu: One thing that is common across Generative AI, is hallucinations. A computer hallucination is an artificial intelligence (AI) machine vision and machine learning technology interpretation error. Computer hallucinations are created by a variety of factors that cause AI systems to misclassify. Yes, it learns from large data sent but it is unreliably confident about the results.
Another risk is associated with bias. Humans are inherently biased at individual levels. As we automate and train these large data models, we are propagating this collective bias. It can cascade and spread like a wildfire. We need to understand and mitigate these biases. It is not just a nice to have but a legal requirement. First, we have to define what is bias and how to measure it. This is no easy task.
Interested in learning how Data and AI can lead the charge in achieving your business's DE&I goals? RSVP for Tech in Motion's upcoming virtual event.
| 2023-02-17T00:00:00 |
https://techinmotion.com/blog/how-chatgpt-and-other-automation-tools-can-make-the-tech-world-better
|
[
{
"date": "2023/02/17",
"position": 34,
"query": "ChatGPT employment impact"
}
] |
|
What Does ChatGPT Mean for the Future of Business?
|
What Does ChatGPT Mean for the Future of Business?
|
https://www.entrepreneur.com
|
[
"David James"
] |
ChatGPT has the potential to be transformative in the business landscape. With its ability to automate routine tasks, provide real-time data analysis, support ...
|
What does ChatGPT mean for the future of businesses? What could this AI technology do for you? Keep reading to learn more.
ChatGPT has been a hot topic in the news recently, with a flurry of AI enthusiasts praising the new technology and some who are a bit more technology resistant worrying about what ChatGPT could mean for the future.
In 2022, OpenAI, an artificial intelligence research and development company, introduced ChatGPT to the public. ChatGPT is a free artificial intelligence technology that anyone with a device and internet can use.
Its accessibility was a huge part of the rapid popularity, as it now has a predicted 100 million active monthly users, with that number continuing to grow. To put that into perspective, it took TikTok nine months and Instagram two and a half years to achieve that kind of recognition.
With so many enthusiasms and apprehensions swirling around the program, you may have several questions about the technology and its potential effect on the future of business. Keep reading for the full rundown on ChatGPT and how it will impact the business world.
Related: What Is Artificial Intelligence (AI)? Here Are Its Benefits, Uses and More
What is ChatGPT?
The co-founders of OpenAI, including Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever and Wojciech Zaremba, introduced ChatGPT in 2018.
The artificial intelligence is based on the third natural language processing project model, GPT-3. ChatGPT is a large-scale, pre-trained language model that uses the GPT-3 architecture to search information stored in a massive pool of internet sources and data to produce that information for its users.
The feature that has made ChatGPT such a hot topic is its ability to communicate in a human-like way. The program has been fine-tuned for extensive language generation tasks that include language translation, question-answering and short and long-form writing, all designed to sound like a human.
ChatGPT can do this because of its transformer-based neural networks that are pre-trained with endless amounts of text data that help the AI understand context, reference millions of examples and adapt its knowledge to mimic human diction.
How can businesses use ChatGPT?
While chatbots certainly exist, ChatGPT is one of the first to produce such in-depth answers as clearly and human-like as it does. This opens up a whole new opportunity for the business world.
From automation to communication to research, businesses that are open to a new way of streamlining can utilize ChatGPT to tackle various pain points for internal use and to create a better customer experience. Keep reading for three concrete examples of how businesses can use ChatGPT.
1. Streamlining operations
Living in the digital age means that more and more businesses are turning towards technology to help streamline their processes and operations. Automation is a great option to help simplify tedious tasks, optimize workflow and give businesses back their time.
Automation is any process that relies on artificial intelligence rather than a human, like online banking and chatbots for online shopping. Businesses can automate departments like accounting, social media, learning management and human resources.
When businesses use ChatGPT to reduce manual labor, all of that time is given back to employees to dedicate to creativity and tasks humans can only perform.
When it comes to streamlining operations, ChatGPT can help businesses with the following:
Data entry.
Appointment scheduling.
Reduce the risk of errors.
Improve the accuracy of data.
Ensuring businesses' efficiency and effectiveness.
Saving costs.
Improving productivity.
Strengthening the bottom line.
Related: Professionals In This Industry Already Can't Imagine Life Without ChatGPT: 'I Can't Remember the Last Time Something Has Wowed Me This Much.'
2. Customer experience
Customers love to feel like they are unique and receive individualized attention. And with ChatGPT, they can. No matter the business, chatbots can provide around-the-clock service for customers, as they can work more hours than humans.
ChatGPT can understand customer queries and respond in real time, just as humans would. The program is pre-trained to create personalized suggestions based on the customer's history.
While improving the consumer experience, ChatGPT will also be collecting customer data that can be used to guide future segmentation, ad targeting, marketing campaigns and business decisions.
Related: Conversational AI Is a Revolution That's Just Getting Started. Here's How It Can Boost Your Business.
3. Cutting-edge innovation
Because ChatGPT is programmed to analyze immense amounts of data from its neural network and generate insights and suggestions based on that data, businesses can make informed, cutting-edge, data-driven decisions.
More information on consumers can also spark creativity in business professionals, allowing them to stay ahead of the curve with new ideas that their customers will love.
In addition to new ideas, ChatGPT's analytical capabilities allow it to perform complex processes, including supply chain management and financial planning or suggestions to increase efficiency and save costs.
Related: Top Four Data Analytics And AI Trends That Will Drive Businesses In 2023
What types of businesses can utilize ChatGPT?
ChatGPT can genuinely be utilized in any business. Take a look at 11 industries that could benefit from ChatGPT:
Ecommerce and retail. Healthcare. Banking and finance. Telecommunications. Transport and logistics. Manufacturing. Education. Tourism and hospitality. Real estate. Entertainment. Marketing and advertising.
Related: The Complete Guide to Effectively Using AI Writing Tools in Content Marketing
What are some pros and cons of ChatGPT in business?
Like anything else, ChatGPT does come with advantages and disadvantages. Before you integrate AI into your business, make sure you examine both sides of the coin.
Pros of ChatGPT in business
Because of its automotive and language capabilities, there are several advantages to incorporating ChatGPT into a business environment.
Cost-effective: Implementing ChatGPT can be much more cost-effective than hiring additional staff, as it takes the place of routine tasks, benefits, training and other costs.
Scalability: ChatGPT can be scaled up or down based on the needs of the business, making it a flexible solution for large corporations or small businesses.
Multilingual support: At its core, ChatGPT is a language processing tool, and it can be configured to support multiple languages. This makes it an ideal solution for businesses with a global presence.
Increases accessibility: Its automotive nature makes it easier for customers and employees to access information and assistance.
Improves data accuracy: ChatGPT can reduce the risk of human error by automating data entry.
Enhances decision-making: The real-time data analysis that ChatGPT provides can help businesses make informed decisions and stay ahead of the curve.
Reduces response times: ChatGPT provides 24/7 assistance, reducing response times and improving the overall customer experience.
Related: Here's How Your Business Can Use 3 Popular AI Content Creation Tools
Cons of ChatGPT in business
Technology provides many opportunities for businesses; however, it is not flawless. There are always drawbacks to consider when working with artificial intelligence.
Technology dependence: This is what many people fear. ChatGPT can lead to a reliance on technology and potentially reduce the need for human input and creativity. Business owners need to remain aware of these concerns to ensure their employees feel valued.
Privacy concerns: Entrusting sensitive data to technology is always nerve-wracking. While ChatGPT is secure, there is always a risk of data breaches. ChatGPT requires access to sensitive business data, so business owners must ensure that they have proper security measures to protect that information.
Technical skills: ChatGPT cannot set itself up and is not always a simple integration. To use ChatGPT in business, owners must consult their tech department or hire an expert.
Creativity limitations: While ChatGPT can generate new ideas and concepts, it will never replace the human creativity essential for specific tasks.
Bias: Artificial intelligence is only as impartial as the data it's trained on. Business owners must understand this and use unbiased data to train ChatGPT.
What does the future of ChatGPT in business look like?
ChatGPT has the potential to be transformative in the business landscape. With its ability to automate routine tasks, provide real-time data analysis, support multiple languages and improve data accuracy, ChatGPT could change the way time and resources are allocated.
While there are possible challenges to overcome when implementing ChatGPT, such as tech support and privacy concerns, the benefits of using AI could outweigh the cons for your business.
The future impact of ChatGPT will likely depend on how quickly and effectively businesses adopt the technology and integrate it into their operations. However, ChatGPT and similar AI software will likely play a significant role in shaping the future of business.
Whether you're looking to improve customer satisfaction, drive innovation or stay ahead of the competition, ChatGPT is a solution that businesses, large and small, should consider to meet their long-term goals.
Ready for more information on AI, ChatGPT and business? Visit Entrepreneur.com for resources.
| 2023-02-17T00:00:00 |
2023/02/17
|
https://www.entrepreneur.com/science-technology/what-does-chatgpt-mean-for-the-future-of-business/445020
|
[
{
"date": "2023/02/17",
"position": 87,
"query": "ChatGPT employment impact"
}
] |
The Labor Movement is Incomplete Without Disability Justice
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The Labor Movement is Incomplete Without Disability Justice
|
https://tcf.org
|
[
"Ariel Adelman"
] |
Recent labor movements have failed to incorporate disability justice principles, leaving disabled workers without a voice in contract negotiations.
|
Ariel is a disability rights advocate and policy analyst with a focus on the intersections between health, housing, and climate in the urban sphere. You can find her at arielprag.com.
| 2023-02-17T00:00:00 |
2023/02/17
|
https://tcf.org/content/commentary/the-labor-movement-is-incomplete-without-disability-justice/
|
[
{
"date": "2023/02/17",
"position": 23,
"query": "AI labor union"
}
] |
GLIDER.ai Salaries by 100+ Employees (Updated 2025)
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GLIDER.ai Salaries by 100+ Employees (Updated 2025)
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https://www.ambitionbox.com
|
[] |
The highest-paying role, Senior Software Engineer, earns between ₹16 Lakhs to ₹30 Lakhs per year. The top 10% of employees earn more than ₹25.1 Lakhs per year, ...
|
In my appraisal meeting, they didn’t ask or discuss my expectations about my salary or hike percentage. The entire discussion was only about feedback and performance review. After the meeting, I asked why they didn’t allow me to share my expectations or reveal my hike numbers. They told me that the hike numbers would be decided only by the owner and then they would inform me. I feel an appraisal meeting should include open discussion about both performance and future expectations. What are your thoughts? Does this happen in your company too?
| 2023-02-17T00:00:00 |
https://www.ambitionbox.com/salaries/glider-dot-ai-salaries
|
[
{
"date": "2023/02/17",
"position": 23,
"query": "AI wages"
}
] |
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Early 2023 saw 15 types of innovative digital financial services ...
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Early 2023 saw 15 types of innovative digital financial services in Indonesia.
|
https://databoks.katadata.co.id
|
[] |
... AI-generated translation of the original article. We strive for accuracy ... wages for agricultural laborers. Popular Data. See all · Jakarta Flood Points ...
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According to a report by the Financial Services Authority (OJK) of Indonesia, as of January 2023, there were 15 types of Digital Financial Innovation (IKD) services in Indonesia.
The OJK defines IKD as business activities related to the updating of business processes, business models, and financial instruments that provide new added value in the financial services sector involving a digital ecosystem.
Here is a breakdown of the 15 types of IKD services according to the OJK's categorization, and the number of business units registered as of January 2023:
1. Aggregator: 34 business units
An aggregator is a website or application that helps customers obtain information on financial products and services, including comparing products from Financial Service Institutions (FSIs) online. Consumers can use aggregators to find information on FSI products such as home ownership loans (KPR), credit cards, savings accounts, insurance products, and other financing products.
2. Credit Scoring: 20 business units
Credit Scoring is the activity of digitally processing data to assess a person's eligibility for credit or other financial services.
3. Financing Agent: 8 business units
A Financing Agent is a website or application that helps FSIs channel financing to prospective/existing customers.
4. Transaction Authentication: 7 business units
Transaction Authentication is a platform that provides digital customer identification and verification services using various types of data, except for population data and civil registration data (Dukcapil).
5. Financial Planner: 6 business units
A Financial Planner is a platform that assists individuals in financial planning, providing recommendations for financial products and services that suit the user's profile and goals.
6. Electronic-Know Your Customer (E-KYC): 5 business units
E-KYC is a platform that helps provide digital identification and verification services for prospective/existing customers using Dukcapil data.
7. RegTech–eSign: 5 business units
RegTech–eSign is a platform that provides electronic certificates and digital signature processing for the financial services sector.
8. Funding Agent: 3 business units
A Funding Agent is a website or application that assists in the marketing of FSIs to obtain funding customers.
9. Insurance Tech: 2 business units
Insurance Tech is a platform that collaborates with brokers and/or insurance companies to provide information services, insurance product purchases, claim submissions, and accelerate online claim processing.
10. Tax & Accounting: 2 business units
Tax & Accounting is a platform that assists individual consumers or companies in preparing financial reports according to accounting standards, or in preparing online tax reports.
11. Insurance Hub: 1 business unit
An Insurance Hub is a provider of digital infrastructure in the insurance sector that can assist with insurance distribution and claims processing.
12. Online Distress Solution (ODS): 1 business unit
ODS is a platform that provides negotiation services between creditors and borrowers who are having difficulty repaying their debts.
13. Property Investment Management (PIM): 1 business unit
PIM is a platform for raising funds from the public online in the form of a scheme for managing rights to a property, without creating derivatives for trading in the secondary market.
14. RegTech–PEP: 1 business unit
RegTech–PEP is a platform for assessing money laundering risk by checking customer backgrounds using the Politically Exposed Person (PEP) paradigm.
15. Wealth Tech: 1 business unit
Wealth Tech is a platform that integrates access to financial information from a corporate group and its business partners into a single mobile application to facilitate their financial management.
| 2023-02-17T00:00:00 |
https://databoks.katadata.co.id/en/technology-telecommunications/statistics/e9b1d31193f5957/early-2023-saw-15-types-of-innovative-digital-financial-services-in-indonesia
|
[
{
"date": "2023/02/17",
"position": 55,
"query": "AI wages"
}
] |
|
Why a Conversation With Bing's Chatbot Left Me Deeply ...
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Why a Conversation With Bing’s Chatbot Left Me Deeply Unsettled
|
https://www.nytimes.com
|
[
"Kevin Roose"
] |
This realization came to me on Tuesday night, when I spent a bewildering and enthralling two hours talking to Bing's A.I. ... journalists, encountered in initial ...
|
Last week, after testing the new, A.I.-powered Bing search engine from Microsoft, I wrote that, much to my shock, it had replaced Google as my favorite search engine.
But a week later, I’ve changed my mind. I’m still fascinated and impressed by the new Bing, and the artificial intelligence technology (created by OpenAI, the maker of ChatGPT) that powers it. But I’m also deeply unsettled, even frightened, by this A.I.’s emergent abilities.
It’s now clear to me that in its current form, the A.I. that has been built into Bing — which I’m now calling Sydney, for reasons I’ll explain shortly — is not ready for human contact. Or maybe we humans are not ready for it.
This realization came to me on Tuesday night, when I spent a bewildering and enthralling two hours talking to Bing’s A.I. through its chat feature, which sits next to the main search box in Bing and is capable of having long, open-ended text conversations on virtually any topic. (The feature is available only to a small group of testers for now, although Microsoft — which announced the feature in a splashy, celebratory event at its headquarters — has said it plans to release it more widely in the future.)
| 2023-02-16T00:00:00 |
2023/02/16
|
https://www.nytimes.com/2023/02/16/technology/bing-chatbot-microsoft-chatgpt.html
|
[
{
"date": "2023/02/17",
"position": 55,
"query": "AI journalism"
}
] |
How AI and Automated Systems Use Can Lead to ...
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How AI and Automated Systems Use Can Lead to Discrimination in Hiring
|
https://perkinscoie.com
|
[] |
How AI and Automated Systems Use Can Lead to Discrimination in Hiring · NIST's Framework for Reducing the Risk of Harmful Bias in AI · Key Recent Governmental ...
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Artificial Intelligence (AI) and automated systems can increase efficiency and help reduce human error. However, the National Institute of Standards and Technology (NIST), the White House, and the Equal Employment Opportunity Commission (EEOC) are warning companies that uncritical reliance on AI can have legal consequences, including potentially building in bias that can lead to claims of employment discrimination. Employers' reliance on these technologies to target job advertisements, recruit applicants, train employees, and make or assist in hiring decisions can lead to adverse employment actions. But, NIST explains, "[w]ith proper controls, AI systems can mitigate and manage inequitable outcomes." The NIST study does not focus on specific legal risks arising from use of this technology, but it is useful for evaluating whether the systems meet accepted scientific standards.
NIST's Framework for Reducing the Risk of Harmful Bias in AI
NIST published the AI Risk Management Framework on January 26, 2023, with the stated intent of helping companies manage the risks associated with the use of AI systems and increase trustworthiness in AI. The framework is divided into two parts. Part 1 discusses the positive impact and negative risks of AI use. It recognizes the powerful potential of AI systems but also identifies various risks inherent in these systems, including biases that may arise in all AI systems. Part 2 outlines strategies to manage AI risks through a broad framework.
To reduce the risk of harm caused by AI, the NIST guidance explains that companies must consider and manage these biases throughout the development and implementation processes—including by recognizing and addressing systemic bias, computational and statistical bias, and human-cognitive bias. "Systemic bias can be present in AI datasets, the organizational norms, practices, and processes across the AI lifecycle, and the broader society that uses AI systems. For example, systemic bias can be present in many 'off-the-shelf' systems that crunch data based on broader characteristics and biases present in the broader community. Computational and statistical biases can be present in AI datasets and algorithmic processes and often stem from systematic errors due to non-representative samples. AI systems that fail to adhere to basic statistical principles such as not accounting for small sample sizes are at risk for computations and statistical biases. Human-cognitive biases relate to how an individual or group perceives AI system information to make a decision or fill in missing information, or how humans think about purposes and functions of an AI system." An algorithm that promotes applicants with advanced degrees for positions that do not require advanced degrees, for example, could present risks of human-cognitive bias.
Part 2 of the framework provides a protocol for evaluating AI systems and managing risks in four broad steps: govern, map, measure, and manage. The core framework explains that during the development and implementation processes, companies can reduce bias by ensuring decision-making throughout each process involves a diverse team across different demographics, disciplines, experience, expertise, and backgrounds. Diverse teams could enhance an organization's ability to identify risks associated with bias before the use of AI becomes harmful.
Key Recent Governmental Guidance on Use of AI in Hiring
NIST's guidance comes on the heels of increased scrutiny of AI by the White House, the Equal Employment Opportunity Commission (EEOC), and the New York City Department of Consumer and Worker Protection (DCWP).
White House AI Bill of Rights & Discriminatory Impact
The White House Office of Science and Technology Policy (OSTP) issued the Blueprint for an AI Bill of Rights on October 4, 2022, which outlines principles to guide the design, use, and deployment of automated systems. The White House developed the AI blueprint out of concern for the harm caused by AI, including algorithms that may be "plagued by bias and discrimination." Pointing to guidance from the EEOC and the U.S. Department of Justice (DOJ), the White House explains that "employers' use of software that relies on algorithmic decision-making may violate existing requirements under Title I of the Americans with Disabilities Act (ADA)."
EEOC Provides Guidance on the Use of AI to Assess Job Applicants
According to guidance by the EEOC, published as The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees, computerized applicant job assessments may make testing more difficult or reduce test accuracy for some disabled applicants. Similarly, employers' reliance on training programs delivered electronically can have a discriminatory impact on hearing and visually impaired employees. Self-paced electronic training programs may impact visually impaired employees who are unable to follow along with computer prompts without assistance. Similarly, virtually instructed training programs may affect employees with hearing impairments who are unable to follow the instructor without assisted technology.
Other discrimination claims may be triggered by AI use. Employers that rely on automated software may face claims of intentional discrimination, known as disparate treatment, under Title VII, and claims of unintentional discrimination due to policies that seem neutral but have a discriminatory impact on protected groups. This unintentional discrimination, known as disparate impact, will likely be a focus of investigation and prosecution by enforcement agencies when evaluating cases involving AI and automated systems. For example, discriminatory impact claims could result from the use of automated software where applicants or employees are required to input data that may correlate to or be a proxy for a protected characteristic. For instance, applicant tracking software that weeds out candidates based on geographical location may inadvertently weed out candidates from racial groups that are less likely to live in the desired location.
EEOC Includes AI Focus in Strategic Plan
In line with the White House's focus on AI, the EEOC's new strategic plan includes a focus on technology-related employment discrimination. The enforcement agency "will focus on employment decisions, practices, or policies in which covered entities' use of technology contributes to discrimination based on a protected characteristic."
The strategic plan specifies the types of technologies used by employers that could be targeted, including the following:
Software that incorporates algorithmic decision-making or machine learning (ML), including AI.
Automated recruitment, selection, or production and performance management tools.
Other existing or emerging technological tools used in employment decisions.
New York City Automated Employment Decision Law
New York City has passed landmark restrictions on the use of AI in hiring. Enforcement will start in April 2023. The new law will make it illegal for an employer to use an "automated employment decision tool" to screen a candidate or employee for an employment decision, unless the tool has undergone a bias audit and the results of the audit have been made public. The new law will also require employers that use automated employment decisions to notify employees and/or candidates of their use of the systems. The New York City Department of Consumer and Worker Protection (DCWP) is working on regulations and standards that will hopefully provide needed clarity on how employers are to comply.
Looking Forward: What Employers Can Do Now
Employers can reduce their risk of violating Title VII, the ADA, the Age Discrimination and Employment Act (ADEA), and other anti-discrimination statutes by identifying risks for possible intentional and unintentional discriminatory impact before implementing new technologies. Prior to implementation, employers should develop and test algorithms, automated systems, and AI to ensure that they do not improperly result in a disparate impact on protected demographics, such as a person's age, gender, sexual orientation, disability, or race.
After implementation, employers should regularly monitor the effects of the technology on protected groups. For instance, if an employer notices fewer applications from a certain protected group, it should consider whether the filters on any applicant tracking software in use are inadvertently weeding out groups with protected characteristics. Further, ensuring that these systems are accessible for persons with disabilities and do not screen them out is paramount to reduce legal risks.
Employment technologies that are most likely to be targeted include the following:
Recruiting software.
Online employment applications.
Applicant screening and rating software.
Automated learning and training programs.
Virtual training programs.
Onboarding software.
Employee engagement and retention software.
Testing the software for potential discriminatory treatment and discriminatory impact, before and after deployment, is crucial. Enforcement agencies have made it clear that employers who take a "set it and forget it" attitude toward AI and automated systems when making key employment decisions are at risk if they are not evaluating whether those systems cause bias.
© 2023 Perkins Coie LLP
| 2023-02-17T00:00:00 |
https://perkinscoie.com/insights/update/how-ai-and-automated-systems-use-can-lead-discrimination-hiring
|
[
{
"date": "2023/02/17",
"position": 20,
"query": "artificial intelligence hiring"
}
] |
|
AI Resumes: The Future of Job Applications in 2023
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AI Resumes: The Future of Job Applications in 2023
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https://www.hiration.com
|
[] |
So much so that 72% of employers expect to fully automate certain recruitment and HR roles by 2028 and job seekers who leveraged AI-powered tools to create ...
|
The current job market is steadily changing and so are the tools, practices, and approaches followed in the recruitment and job search processes.
Recent years have seen a significant rise in the use and impact of Artificial Intelligence (AI) on the entire resume-building approach.
To say the least, it has revolutionized the process and made it highly efficient, fast, and easy for both recruiters and job seekers.
So much so that 72% of employers expect to fully automate certain recruitment and HR roles by 2028 and job seekers who leveraged AI-powered tools to create their resumes had an 8% increase in their chances of getting shortlisted.
And with this, many online resume builders in the market have also leveraged AI in their tools to offer users advanced features. Among these, Hiration’s AI-powered resume builder stands out the most.
Read on to learn more about AI resumes and get clarity on related FAQs like the following:
What is an AI Resume?
An AI (Artificial Intelligence) resume is a type of resume that is created using AI-powered tools and software.
It is better than a traditional resume that is created manually in the sense that there are lesser chances of grammatical errors and since it is closely tailored to the job description, the chances of AI resumes getting shortlisted by recruiters are high.
AI resumes are typically created by feeding an applicant’s current resume into a platform and assessing it against a specific job description to bridge skill gaps using algorithms and machine learning.
The tips and actionable suggestions made by AI to create the resume are also provided by assessing the candidate’s career patterns and the best industry practices to ensure its success.
What are the Benefits of Using AI Resume Builders?
There are numerous benefits of AI-powered resume writing to create highly competitive and compelling ATS-friendly resumes. Some of these are explained below:
You don’t have to start from scratch: Resume writing is a tricky process, especially for those who are not aware of its basics and have no idea where to start. But with AI-powered resume builders, you can get role-specific pre-written content for your resumes that comply with industry standards and norms.
Tailors your resume to the job description: The key to increasing your chances of getting shortlisted for interviews is by ensuring that your AI resume caters to the requirements of the role you’re targeting. However, since job seekers are bound to apply for multiple job vacancies, it can be a hassle to tailor their resume manually to every listing. This is where using AI resume builders help job seekers do the needful without the hassle.
Bridges skill gaps that you may have overlooked: When writing a resume manually, there are more chances of overlooking skill gaps and missing key details in your resume. But by using AI resume builders like Hiration, you can get your resume thoroughly analyzed and have the assurance of not missing out on important keywords in your AI resume.
You don’t have to break the bank: Hiring a professional resume writer can be quite expensive but online AI resume builders like Hiration offers the same service free of cost for new users.
Comes with pre-curated AI resume templates: When using AI resume builders, you don’t have to worry about the design aspects and layouts to create visually appealing resumes. Hiration comes with 30+ AI-generated resume templates that are all ATS-friendly.
Which is the Best AI Resume Builder in the market?
While hundreds of online platforms offer resume writing services, not all of them offer powerful AI-powered features and are mostly just focused on the design aspects of resumes.
One of the most advanced AI resume builders currently in the market is Hiration. It uses next-generation machine learning algorithms and Natural Language Processing (NLP) to provide users with personalized recommendations and tips for creating compelling AI resume.
Given below are some of the AI features of Hiration:
AI Resume Analysis & Reviewer
Hiration’s AI resume builder offers users an option to upload their current resume and get it scored and reviewed by AI based on the best industry practices such as the usage of action verbs, bullet points, and quantified achievements.
The AI reviewer also analyzes the layout of the resume and suggests actionable points to improve the resume score. This means, users, need not create a new resume altogether but can update their existing resume and make it an AI resume.
AI Job Matcher
As stated earlier, one of the best ways to increase your chances of getting shortlisted is by tailoring your resume to the specific job description.
Therefore to streamline this step, Hiration allows users to copy-paste a specific job description and have their resumes analyzed against it to find the skill gaps.
It ensures that users create a resume that meets the requirements of the recruiters effectively.
AI Assistant
To take things a step further, Hiration even provides an AI assistant that suggests role-specific bullet points for the professional experience section, skills, and even pre-written summaries to help users create an AI resume with utmost convenience.
This is especially helpful for job seekers who don’t know what to write in their resumes or are confused about where they should start.
Here are some real-time TrustPilot reviews from Hiration users:
Tips for Resume Optimization with AI
To help you make the most out of AI resume builders like Hiration, the following are some tips you can follow:
While listing your educational qualifications and professional experiences, ensure to follow the reverse chronological resume format to enhance the ATS-friendliness of your AI resume.
To increase the relevancy of your AI resume, match your profile title with the job title mentioned in the job listing.
Include a professional email address in your resume and avoid using email addresses like ‘cooldude@hotmail(dot)com’ as surveys have shown that 35% of employers reject applicants with unprofessional email addresses.
Ensure that your AI resume is between 475 and 600 words as this is the ideal length of a resume.
Proofread your AI resume before sending it to recruiters as 77% of hiring managers reject resumes with typos and grammatical errors.
Quantify your achievements and describe your duties using numbers to create make your AI resume all the more impressive.
Key Points
AI resumes are typically created by feeding an applicant’s current resume into a platform and assessing it against a specific job description to bridge skill gaps using algorithms and machine learning .
and . Using AI resume builders allows users to create a more compelling resume by making it ATS-friendly and tailored to a specific job description.
and tailored to a specific job description. One of the most advanced AI resume builders currently in the market is Hiration. It uses next-generation machine learning algorithms and Natural Language Processing (NLP) to provide users with personalized recommendations and tips for creating compelling AI resume.
and tips for creating compelling AI resume. Hiration offers users advanced AI-powered features like AI reviewer, AI assistant, and job matcher to streamline the process of making job-winning resumes within minutes.
So why the wait? Use Hiration’s AI-powered resume builder which also offers 24x7 chat support and create a resume that will land you the job of your dreams.
| 2023-02-17T00:00:00 |
2023/02/17
|
https://www.hiration.com/blog/ai-resume/
|
[
{
"date": "2023/02/17",
"position": 83,
"query": "artificial intelligence hiring"
}
] |
Responsible AI at Eightfold
|
Responsible AI at Eightfold
|
https://eightfold.ai
|
[] |
... artificial intelligence. Fairness, Transparency, and more ... AI, skills-based hiring, and the metrics redefining recruitment success.
|
Eightfold’s AI delivers relevant recommendations at scale to predict the next role in an individual’s career. Our models understand more than one million unique roles and one million skills across many languages.
Eightfold’s Talent Intelligence Platform empowers enterprises to acquire and retain diverse talent, and provides the foundation for public agencies to reemploy and upskill citizens. As the pioneer and leader in talent intelligence, our mission is to enable the right career for everyone.
Responsible AI at Eightfold
Eightfold’s Talent Intelligence Platform empowers enterprises to acquire and retain diverse talent, and provides the foundation for public agencies to reemploy and upskill citizens . As the pioneer and leader in talent intelligence, our mission is to enable the right career for everyone .
Eightfold’s AI delivers relevant recommendations at scale to predict the next role in an individual’s career . Our models understand more than one million unique roles and one million skills across many languages .
With Eightfold technology, candidates can instantly match to the jobs that fit their skills and potential, see why each job is a match, and apply in a matter of seconds . Recruiters and hiring managers get instant ranked lists of candidates who match their requirements, and can engage them through our platform up to the point of making an offer .
Employees can explore future career paths with detailed understanding of the skills and experiences for their next step in their career, and find the projects, courses, mentors, and gigs that can help deliver these skills and experiences. Organization leaders can oversee their talent strategies, find successors for roles, compare scenarios, and determine the upskilling and reskilling plans for their future needs .
Governments and social service organizations can deploy our platform to match individuals with job opportunities at scale in support of reemployment and community building initiatives .
AI Principles
At Eightfold, we are committed to a responsible and ethical development and use of artificial intelligence. As a company, we understand that AI has the potential to significantly impact many aspects of our lives, and we build AI solutions to benefit society while respecting the rights and dignity of our users .
Our team of experts works closely with stakeholders, our committee of representatives from various departments, our AI Ethics Council, and external consultants to design and deploy our AI systems in a responsible and ethical manner . At the core of every design, we prioritize the following principles:
Fairness: Design and Use AI systems that are just and mitigate bias . This includes mitigating discrimination based on factors such as race, gender, age, or other protected characteristics .
Transparency: We believe how AI systems work and how decisions are made should be understandable and explainable .
Safety and Reliability: We strive to design and develop stringent safety measures that our AI has to pass before it rolls out as our product . We believe that it’s our responsibility to provide solutions that add value to our society .
Active Monitoring And Response: We believe that any AI system needs to have continuous active monitoring to check that the system behaves as expected . Deviations are treated and responded to on a priority basis .
In this report, we’ll be taking a deep dive into our thoughts on fairness in the interests of transparency and to hopefully serve as reference to other companies looking to mitigate biases in their AI systems. We should note that this is an active field of research and as things evolve, we will re-visit and update our approaches.
Fairness
Artificial Intelligence can revolutionize employment processes in countless ways. As the industry evolves and increasingly relies on AI systems, it’s important to consider the potential of AI to perpetuate social injustices or biases . Fairness is a particularly important issue in the HR recruiting space as biases in AI systems can perpetuate and even amplify existing inequalities in society if left unchecked .
AI fairness refers to the idea that AI systems should not discriminate against groups of people based on characteristics such as race, gender, age, etc . . There are a lot of variables that go into developing an AI system and gaps in oversight can lead to an unfair model . When it comes to applying AI technology to employment practices, we believe the principle of fairness applies at all stages of the development and application of AI technology . It’s important for AI developers and users to be aware of the potential for bias in AI systems and take steps to identify and mitigate these issues .
The most common pitfalls we’ve seen can largely be placed into the following buckets:
Data
One possible source of bias is the data used to train models within an AI system . The data used by AI models should be representative across protected categories, and industries . Features should be representative of the population and should not favor any one group . The feature engineering process should be thoroughly vetted . For example, in the case of HR systems, we feel that the model should only need to learn the qualifications of successful individuals rather than their identity.
Training, Evaluation and Model Selection
Alternatively, the choice of model and the training process used can themselves lead to biased outcomes . The models and algorithms used should go through a rigorous and thorough evaluation framework where they are tested for performance across the measurable protected categories . It is crucial that checks and balances are in place during model training to check against learning decisions based on protected categories . At Eightfold, we build models that strive to mitigate amplifying the classic stereotypical patterns in data and human behavior . For example, a model used to recommend candidates for a Software Engineering position should not perform better for one gender than the other.
Active Measurement And Monitoring
In addition to the above, as bias can occur across multiple hiring stages and in a myriad of ways, there is no single test that can test for bias . A robust methodology for measuring bias and monitoring models for biased outcomes is a key component involved in mitigating AI bias.
Product Safeguards
Finally, without any safeguards in the product, even when AI is developed appropriately, outcomes may reflect bias due to human error . While reviewing lists of candidates, for example, people making employment decisions may intentionally or accidentally introduce personal biases into the hiring process. They may favor certain last names or social activities identified in the candidate profiles that reflect historical trends of hiring. Additionally, having detailed monitoring and analytics helps track potentially biased outcomes of human and AI decisions .
By being aware of the potential for bias and consistently aligning our designs with our AI principles, we believe that responsible approaches to AI at Eightfold will help revolutionize employment processes in a fair and equitable way . In the following sections, we’ll cover how our principles help us avoid these pitfalls in the development of Eightfold’s Talent Intelligence Platform.
| 2023-02-17T00:00:00 |
https://eightfold.ai/learn/responsible-ai-at-eightfold/
|
[
{
"date": "2023/02/17",
"position": 94,
"query": "artificial intelligence hiring"
}
] |
|
How AI Automation is Shaping the Future of Jobs
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How AI Automation is Shaping the Future of Jobs
|
https://mondo.com
|
[
"Carter Lowe"
] |
AI is the next stage of the evolution of automation — applying computing power to automate not just repetitive, manual processes but in-office, modern creative ...
|
Like the shot heard ‘round the world, ChatGPT has commenced the race to the future of work powered through AI and automation.
As Mondo’s National Client Partner, I have worked with both candidates and clients seeking to implement AI into their businesses and workflows.
What can be done today, and where are we going?
How is AI automation used?
When we’re talking about AI in a business context, we are mainly talking about automation. AI is the next stage of the evolution of automation — applying computing power to automate not just repetitive, manual processes but in-office, modern creative work as well.
Automation is not new. We’ve been automating things—say, keeping track of the time—since the Egyptians automated the telling of time with their water clocks since 1500 BCE.
Our modern conception of automation became a focal point in the Industrial Revolution kicking off in the mid-19th century factory workers.
After WWII, we saw the introduction of what we would consider modern computing power applied to automating manufacturing production.
The term “automation” came into common parlance in 1947, when Henry Ford established an automation department to apply computing processes to his assembly line manufacturing.
The release of ChatGPT has put the thought in people’s minds that you could automate “knowledge work,” a term coined by management academic Peter Drucker in the 1950s to refer to what we would normally associate with office work—non-repetitive tasks that require divergent thinking to solve problems—creative work, in other words.
As AI evolves, it will continue to be applied to the automation of repetitive, manual processes and traditionally creative work.
What are the benefits of automation in business?
There are numerous benefits of using AI automation in business that lead to improved efficiency and accuracy as well as cost savings and enhanced customer experience.
Benefits of using AI automation:
AI can increase efficiency
Every job, including more creative, office-based positions, has its share of repetitive, manual tasks.
The more time you free up through AI for your employees, the more time they can spend solving the larger problems affecting your business.
AI can improve accuracy
Pilots fly planes but mainly use autopilot for the majority of the flight.
Autopilot does not need to be perfect, it just needs to be successful enough to beat the average pilot, and allow human operators to save their mental energy for taking over navigation during the most creatively strenuous situations — say, landing a plane on the Hudson.
AI can enhance customer experience
I don’t know if you’ve heard this about customers, but they like things fast.
Luckily, we can apply AI and automation to customer points of interaction so rather than waiting on hold for 54 minutes to speak with a human, you can resolve your issues with an AI chatbot.
This is being done with the many AI chatbots that are on the market but imagine scaling that process to other areas of the customer experience, from search to pricing to product delivery.
AI can lead to cost savings
By off-loading work to AI, companies will be able to do more with fewer staff, saving on labor investments.
Not only that but in some cases, automated reports and system scans can spot and alert stakeholders to issues early on before they become costly mistakes.
AI can increase scalability
The greatest hindrance to scalability is complexity.
AI algorithms will be able to split the Gordian Knot of growth and execute, allowing your staff to monitor progress rather than completing tasks manually.
They can also inform better decision-making by creating recommendations based on Big Data sets that cannot necessarily be processed as quickly or efficiently as humans.
AI can improve safety
Using “computational rationality,” as AI scholar Daniel Susskind refers to AI, can ideally remove human bias or error from safety-critical situations, such as diagnosing whether a skin blemish is cancerous, to removing human bias from court decisions.
What are the disadvantages of using AI automation in business
There are several disadvantages to using AI automation in business like loss of jobs, AI bias, and a larger margin for error without human touch.
Top disadvantages of using AI automation in business:
Job loss
Susskind also writes about “technological unemployment,” or the mass unemployment of laborers caused by the use of AI to automate service workers out of the workforce.
This is a key area that not just businesses but policymakers will need to prioritize solving in the next 10-30 years.
Lack of creativity
A main selling point of AI is that it will allow workers to focus on more creative problem-solving.
A key risk is taking someone’s challenging, engaging job, taking away all the creative problem-solving, making it a game of watching algorithms run, and making sure they don’t break anything.
Use AI to upskill your workforce, not make them administrators.
AI Bias
AI is heavily dependent on the datasets used to create its algorithms, and depending on data with inherent biases will simply repeat patterns of bias and give the same faulty results.
Those implementing AI need to be cautious of those risks rather than blindly accepting a result.
Limited transparency
In the coming years, business leaders will be bombarded with AI solutions — and perhaps you already are!
It is key that decision-makers understand the underlying processes inherent in the creation of these algorithms to ensure the solutions are worth their price.
Technical limitations (AI is still evolving)
We’re still a long way from automating knowledge work.
The field’s applicability in business is still nascent, but that doesn’t mean you can’t start incorporating AI into your business.
How to incorporate AI automation into your business
Incorporating AI automation into your business can be done with the right strategies in place including speaking with AI and automation experts and hiring the professionals you need to manage and implement these new technologies.
Talk to AI automation experts
Talk to a seasoned consultant in the automation space; we staff automation consultants
in a variety of B2B technologies including ServiceNow, Salesforce, UIPath and more. There are many enterprise technical solutions on the market that can offer much of the automation promised in the future of AI.
Gain access to top AI talent
If you do decide on a technology solution, select the right staffing partner who can
connect you with top consultants nationwide for a smooth implementation. From discovery to implementation to adoption, there are senior consultants working daily in cutting-edge AI environments.
What jobs shouldn’t be replaced by AI?
Again, business leaders should focus on what tasks, rather than what roles, can be automated by AI, as there are some key subject matter areas that will still need a human touch.
In particular, creative roles such as Brand Managers and Content Strategists, highly technical roles such as Software Architects, or client-facing roles in Sales or Account Management will resist automation longer because of the higher level of creative problem-solving that AI is currently unable to replicate.
Examples of jobs that should not be replaced by AI:
Brand Manager
UX/UI Developer
Ads Managers
Digital Content Strategist
Full-stack Developer
DevOps Engineer
AI Automation and the future of work
Knowledge workers still have repetitive tasks that can be automated right now with currently existing technology.
Stephen Vanover, Domain Architect for Automation at Eversource Energy, says to start with, “The tasks that are typically associated with data input and output…oftentimes these are finance or HR teams that deal with high amounts of repetitive data.”
When considering AI solutions, remember the key is to free your workers from repetitive tasks so they can focus on solving problems for your business.
Looking to hire top-tier Tech, Digital Marketing, or Creative Talent? We can help. Every year, Mondo helps to fill over 2,000 open positions nationwide. Hire Talent
Continued Reading on industry insights, tips, and strategies:
| 2023-02-17T00:00:00 |
https://mondo.com/insights/ai-automation-and-the-future-of-jobs/
|
[
{
"date": "2023/02/17",
"position": 99,
"query": "artificial intelligence hiring"
}
] |
|
Here are the latest tech layoffs as the industry shudders
|
Here are the latest tech layoffs as the industry shudders
|
https://www.cbsnews.com
|
[
"Irina",
"Alain",
"Anne Marie D. Lee",
"Editor",
"Anne Marie D. Lee Is An Editor For Cbs Moneywatch. She Writes About Topics Including Personal Finance",
"The Workplace",
"Travel",
"Social Media.",
"Read Full Bio",
"Aimee"
] |
The software company in January said it would cut about 10,000 jobs, almost 5% of its workforce, as it refocuses its strategy on artificial intelligence and ...
|
The high-flying tech industry is facing a reckoning as the economy slows and customers pull back on spending.
In the past month alone, tech companies have cut nearly 60,000 jobs, reversing a hiring spree that surged during the pandemic as millions of Americans moved their lives online. IBM was one of the latest to slash its headcount, announcing 3,900 layoffs in January, or less than 2% of its global workforce.
Even with the surge in layoffs, most tech companies are still vastly larger than they were three years ago. But industry analysts expect further industry cuts in 2023 as the Federal Reserve continues to increase interest rates as it hits the brakes on economic growth.
This year, "a major theme will be tech layoffs as Silicon Valley, after a decade of hyper growth, now comes to the reality of cost-cutting mode," analysts at Wedbush said in a research note Friday.
As for what that means for tech workers, it's too soon to tell, experts say. Despite the cascade of layoff announcements, employment in the information sector rose through most of last year, dropping only in December. That suggests demand for talent remains strong enough that many laid-off tech employees will likely be able to find new jobs.
"While layoffs from high-profile firms make the headlines, plenty of firms are desperate for more workers, especially tech workers. Those workers are in high demand from the auto industry to the Department of Veterans Affairs to not-for-profits," said Robert Frick, corporate economist at Navy Federal Credit Union.
"The labor market is still so tight that many tech workers, and workers with other skills, are snapped up well before they need to collect an unemployment check. And they are more likely to be snapped up by smaller firms, which have a much greater demand for workers than major corporations.
The tech downturn is an anomaly amid a job market that remains the tightest in decades and has allowed many workers to command higher pay. Across the economy, announced layoffs last year fell to their second-lowest in 30 years of tracking by outplacement firm Challenger, Gray & Christmas, second only to 2021.
But even as overall layoffs fell, tech layoffs rose, with a record 1 in 4 layoffs last year taking place in the tech sector.
Here are the largest tech companies to announce cuts since 2022.
Alphabet
The Google parent said on January 20 that it would let go of 12,000 workers, or about 6% of its 186,000-strong global workforce. The cuts apply "across Alphabet — product areas, functions, levels and regions," CEO Sundar Pichai said.
Pichai told employees that the Silicon Valley company simply hired too fast during the pandemic.
"Over the past two years we've seen periods of dramatic growth," Pichai wrote in an email that was also posted on Alphabet's corporate blog. "To match and fuel that growth, we hired for a different economic reality than the one we face today."
Amazon
The e-commerce company is moving to cut about 18,000 positions, a downshift that began in November and that will continue into this year. That's just a fraction of its 1.5 million-strong global workforce.
While the vast majority of the company's employees work in its vast warehouse and logistics operation — which doubled in size during the pandemic — the cuts mostly affect white-collar employees in some of the company's less profitable sectors, including the division responsible for its voice assistant, Alexa.
Carvana
The online car seller cut about 2,500 workers in May 2022, or 12% of its workforce. The company was widely criticized for its handling of the layoffs, many of which were done via Zoom and email.
The Phoenix-based company, which delivers new and used cars to buyers, blamed the cuts on an "automotive recession."
Coinbase
The cryptocurrency trading platform cut roughly 20% of its workforce, or about 950 jobs, in January. It's the second round of layoffs in less than a year, with 1,100 workers losing their jobs in June.
Dell
The computer company in February announced it would slash 5% of its workforce due to a "challenging global economic environment." The Texas-based company has about 133,000 employees, according to its most recent annual report, putting the layoffs on track to eliminate about 6,600 jobs.
eBay
The online marketplace said in February it would cut 500 jobs, or about 4% of its global workforce, according to an internal email included with a securities filing.
The layoffs allow the company "to invest and create new roles in high-potential areas," CEO Jamie Iannone said in the message. The will also "[simplify] our structure to make decisions more effectively and with more speed," he said.
IBM
The company plans to cut about 3,900 workers, its chief financial officer told Bloomberg in January. The cuts amount to about 1.5% of the company's global workforce, and come even as IBM posted better-than-expected revenue for the most recent quarter.
The Armonk, New York-based firm will continue hiring in what its financial officer called "higher-growth areas." IBM last year said it would invest tens of billions of dollars across New York's Hudson Valley to spur semiconductor manufacturing.
Lyft
The ride-hailing service said in November it was cutting 13% of its workforce, almost 700 employees. The layoffs affect its corporate employees, since Lyft's army of drivers are considered independent businesses, not employees of the transportation company.
Meta
The parent company of Facebook in November laid off 11,000 people, about 13% of its workforce. Meta has struggled more than many tech companies this year; its user base has shrunk, while CEO Mark Zuckerberg has put billions of dollars into building what he calls the "metaverse," to the consternation of its investors. The company's stock has lost two-thirds of its value since peaking in August 2021.
Microsoft
The software company in January said it would cut about 10,000 jobs, almost 5% of its workforce, as it refocuses its strategy on artificial intelligence and away from hardware. In the two years ending in June 2022, Microsoft had expanded from 163,000 workers to 221,000.
PayPal
The digital payments company said in January it was cutting 2,000 jobs, or about 7% of its workforce, as it contends with what it called "the challenging macro-economic environment."
The San Jose, California-based company is the parent of PayPal is the parent of payment apps Venmo and Xoom and the coupon service Honey, among other brands. PayPal said the cuts would affect different brands unequally, although it did not specify further.
Robinhood
The company, whose app helped attract a new generation of investors to the market, announced in August that it would reduce its headcount by 23%, or approximately 780 people. That's the second round of recent layoffs for the company, which last year cut 9% of its workforce.
Salesforce
The company cut 10% of its workforce, or about 7,300 employees, in January. It also said it was closing some offices, citing a "challenging" environment and lower customer spending.
Snap
The parent company of social media platform Snapchat said in August that it was letting go of 20% of its staff. Snap's staff has grown to more than 5,600 employees in recent years, meaning that, even after laying off more than 1,000 people, Snap's staff would be larger than it was a year earlier.
Spotify
The music streaming service said in January it was cutting 6% of its workforce, or roughly 580 jobs, as part of a push to make the company more efficient. In 2022, Spotify's operating costs grew twice as fast as its revenue, CEO Daniel Ek said, a pace he called "unsustainable."
"We still spend far too much time syncing on slightly different strategies, which slows us down," CEO Daniel Elk said in a January 23 letter to employees posted on the company's site. "And in a challenging economic environment, efficiency takes on greater importance."
Stripe
The payment processor announced layoffs of roughly 1,000 workers in November, amounting to 14% of its workforce. In an email to employees posted on Stripe's website, CEO Patrick Collison said the company expected "leaner times" amid worsening economic conditions.
Twitter
About half of the social media platform's staff of 7,500 was let go after the billionaire CEO of Tesla, Elon Musk, acquired the service in October. An unknown number have left, with some objecting to the new ownership and Musk's demand for an "extremely hardcore" attitude.
Wayfair
The online shopping company announced in January that it would cut 1,750 workers, or about 10% of its global employees, as it adjusts to falling consumer demand after the home-renovation boom of the pandemic. It's the second round of layoffs for the Boston-based company, which cut 870 employees in August.
CEO Niraj Shah said the company "simply grew too big."
"In hindsight, similar to our technology peers, we scaled our spend too quickly over the last few years," Shah said in a statement.
Yahoo
Yahoo plans to cut about 20% of its workforce, or roughly 1,700 jobs, over 2023, the company said in February.
Nearly half the cuts will be in Yahoo's unprofitable business ad tech unit, which has not delivered as expected.
"Despite many years of effort and investment, this strategy was not profitable and struggled to live up to our high standards across the entire stack," Yahoo said in a statement. The company has been owned by private equity firm Apollo Global Management since 2021.
Zoom
The video-conferencing company that surged early in the pandemic said it would lay off 1,300 "talented, hardworking colleagues" in early February. The cuts represent about 15% of Zoom's workforce, according to a company blog.
The company tripled in size in 2020 as white-collar workers shifted to remote environments, but its user growth then slowed dramatically.
"We didn't take as much time as we should have to thoroughly analyze our teams or assess if we were growing sustainably," CEO Eric Yuan said in a post. "[T]he uncertainty of the global economy, and its effect on our customers, means we need to take a hard – yet important – look inward to reset ourselves so we can weather the economic environment, deliver for our customers and achieve Zoom's long-term vision," he added.
Yuan said he would forgo his entire salary and bonus for the current fiscal year, and that the executive team would see 20% salary cuts and no bonus. Yuan made $320,000 in compensation last year, and also holds about $3.3 million worth of Zoom stock, according to securities filings.
| 2023-02-07T00:00:00 |
2023/02/07
|
https://www.cbsnews.com/news/tech-layoffs-sector-google-recession-2023-02-07/
|
[
{
"date": "2023/02/17",
"position": 28,
"query": "artificial intelligence layoffs"
}
] |
Future of Graphic Design with AI
|
Future of Graphic Design with AI
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https://designforce.co
|
[] |
In this article, we'll take a look at how AI is currently impacting the future of graphic design, what potential benefits and drawbacks it may bring, and how ...
|
Should we embrace Artificial Intelligence?
In case you hadn’t noticed, Artificial Intelligence (AI) has become an ever-growing presence in virtually all fields over the last few years, and graphic design is no exception.
It’s been morphing the way graphic designers, students, design enthusiasts, and even marketing professionals approach their work – for better or for worse. From automating mundane tasks to creating entire designs from scratch, this technology is rapidly transforming the future of graphic design and it’s showing no sign of slowing down.
In this article, we’ll take a look at how AI is currently impacting the future of graphic design, what potential benefits and drawbacks it may bring, and how it might shape the industry in the years to come.
Computer-generated design is already showing up more than you may realize
Are you using AI already, without even knowing it? It’s possible that whether you’ve directly designed with it or not, you’ve interacted with an AI-led resource in some shape or form.
Online tools such as Canva, Adobe Spark, and ‘instant erasers’ on your smartphone all use a form of AI algorithm to make the process simpler. Adobe’s Sensei AI uses machine learning algorithms to automate tasks, like image resizing and color matching. For seasoned professionals, Adobe’s Creative Cloud uses AI to recommend fonts and color palettes based on the user’s past work, streamlining the creative process.
And of course, it’s been rising in popularity on social media platforms in the form of facial filters and augmented reality – and it’s evolving there too.
These impressive tools open up the world of design to non-designers, while drastically cutting down time for those designing day-to-day. But is it all good?
Design firm Creative Principals is embracing the technology, openly sharing their experience in using AI tools MidJourney and DALL.E to produce mood boards for clients. They note in this case study for Blooloop, on their experiential design for the J. Kruse Education Center, that using AI to “instantly” give clients visual feedback “confirms the validity of the idea or even takes us in a new direction that we wouldn’t have explored (or have had time to explore) otherwise.”.
Graphic designer Colin Maddox has a different view. In response to this animated video that took Idan Benishu, Head of Marketing at Dataloop AI, “less than 10 minutes” to make using a mixture of Midjourney, Chat GPT and D-ID, Maddox writes: “As we see these amazing technologies growing ever more powerful, we need to remember not only the human risks associated with ever-increasing automation, but also the aesthetic risks of homogeneous, repetitive, low-effort content being generated at ever-increasing speeds and exponential quantities.”.
AI graphic design: the potentials and pitfalls
No matter which side of the fence you currently sit on, there are some clear potentials and drawbacks to this rising technology.
Potential: Immersive experiences
The Dalí Museum in St. Petersburg Florida is currently running Dalí’s Dream Tapestry, an AI-powered interactive exhibition that turns visitors’ dreams into artwork using DALL.E – “an A.I. system that can create realistic images and art from a description in natural language.”
Could this be the beginning of a new wave of designed collaboration? Leading to new job roles appearing across the industry? Possibly. AI Managers, developers, and designers could all be on the horizon…
Pitfall: Job replacement
Let’s address the elephant in the room – while there is scope for a new wave of employment, there is a lot of conversation about AI advancements reducing the need to hire real people.
While we cannot say for sure what will happen here, we strongly believe that nothing can ever replace the years of skill talented designers possess. Can AI truly adapt to the nuance and individual style needed to pull off the perfect design experience?
Potential: Copyright protection
AI watermark generators are on the rise, too. Clever AI embeds designers’ and artists’ names or identity marks within an image to protect work from copyright infringement.
Pitfall: Watermark removal
On the flip side, new free tools are emerging that can remove watermarks from designers’ work, causing huge implications and making the integrity of design work questionable at best.
Potential: Increased efficiency
There’s no doubting the speed and efficiency AI can provide. With properly inputted direction, these tools can handle the admin tasks that typically eat into a designer’s time using automation – freeing them up to focus on more creative and strategic tasks.
Pitfall: homogenization
Yet, the potential for homogenization within design is a threat, as AI-generated designs may lack the creativity and individuality that human designers bring to their work. Could everything eventually end up looking the same?
The future of graphic design now AI is here to stay
While it’s impossible to predict exactly what will happen when it comes to artificial intelligence and its effects on the graphic design industry as things progress, the current landscape is both exciting and concerning.
When used well, it’s a powerful extension to a designer’s toolbox. When used without thought, we risk losing the finesse and individuality we’ve all built over the years.
And what about ethics? We’ve already seen drastic implications on the younger generation’s mental health with increased AR on social media. So it’s possible that AI could begin to reshape cultural norms and perceptions, blurring the lines between original and computer-generated content.
Ethical considerations like this will (hopefully) become even more important as AI continues to evolve. Questions about who should be allowed access to these powerful tools (and under what circumstances) will need to be addressed before any widespread adoption takes place.
Use AI as inspiration, not a replacement
The way we see it, these incredibly advanced tools should be used as inspiration, not as a replacement for dedicated design skill and knowledge.
It’s clear that the incorporation of AI into the graphic design industry is rapidly changing the way designers approach their work. While there are potential benefits to using AI in the design process, such as increased efficiency and speed, there are also drawbacks, like ‘samey’ outcomes and potential job loss.
As AI continues to advance, it is important to stay informed and think about the ethical and societal implications of AI technology before diving in head first.
Go forth and experiment and enjoy the process, but be mindful that AI is still “artificial”, after all…
Need design, fast? Get high-quality work *almost* as fast as AI (in just one day!), with our expert, vetted team. Book a free 15 minute demo to learn more.
| 2023-02-16T00:00:00 |
2023/02/16
|
https://designforce.co/blog/the-future-of-graphic-design-how-ai-is-changing-the-industry/
|
[
{
"date": "2023/02/17",
"position": 1,
"query": "artificial intelligence graphic design"
}
] |
Generative UI Design: Einstein, Galileo, and the AI ...
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Generative UI Design: Einstein, Galileo, and the AI Design Process
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https://prototypr.io
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[] |
Will AI replace designers? Open AI, Stable Diffusion and the likes have enabled a range of products to bring AI-assisted copywriting, image creation, and even ...
|
Will AI replace designers? With Open AI, Stable Diffusion, etc, AI design tools are becoming commercially available. There's Galileo, Genius, Magician - what does it mean for UX and UI design?
Will AI replace designers? Open AI, Stable Diffusion and the likes have enabled a range of products to bring AI-assisted copywriting, image creation, and even coding to our fingertips. It was just a matter of time until generative AI made its way over to User Interface design.
💭 What if we could generate User Interfaces automatically? To take it even a step further, what if we could predict UIs?
It's only recently that these AI-driven interface design tools are becoming commercially available. There's Galileo, Genius, Magician, and probably many more to come.
🪄Generative design has been coming for years
This current wave of 'generative AI' might seem pretty new for UI design, but work in this area has been ongoing for years, with notable progress back in 2020. That was the year of Salesforce's Einstein Designer – the era of 'cookie-cutter web design' was ending, and AI-assisted design was just around the corner. Some of it was pretty spectacular too, with the AI going as far as personalising user interfaces depending on the visitor:
Imagine a custom interface for every website visitor. It’s not that far away. ~ Jeff Link
So how do today's prompt-driven AI design tools differ from approaches of yesteryear? This article will act as a bit of a recap, covering:
🤖 What is generative design
🤖 The evolution of AI-driven design tools
🤖 How do today's AI-assisted tools compare to previous work from Einstein designer etc.
Meet Einstein Designer, an AI-powered design technology used to build UI layouts a lot faster than any human designer could. https://www.fastcompany.com/90566154/the-future-of-user-experience-design-starts-with-ai
Ad Letter An AI-assisted newsletter builder for content creators, designers, and developers. This article is supported by Letter.
Generative Design: From Architecture to UI Design 🏗
'Einstein Designer' (from 2020) was one of the most popular AI-assisted design tools announced that used neural networks to aid UI design. In Einstein’s case, a generative technique was used where multiple variations of a design are created rapidly, leaving a human to choose the best options. That generative design process depends on both human and computer, with the human guiding it all.
In fact, it is the same AI-Driven design approach that was already widely adopted in the manufacturing industry, which provides a blueprint for AI-driven interface design to follow.
Generating variations by mimicking natural evolution
Often used interchangeably with ‘AI-Driven Design’, Generative Design is a design approach widely used in architecture, manufacturing, and structural engineering. Mimicking natural evolution, it iteratively creates thousands of design variations for a given problem, leaving a human to curate the best results. The generative approach enables the exploration of a wider range of possible solutions in a short space of time.
Generative design is an iterative design process that involves a program that will generate a certain number of outputs that meet certain constraints, and a designer that will fine tune the feasible region by selecting specific output or changing input values, ranges and distribution. ~ Wikipedia
In manufacturing, generative design was accelerated by tools like Autodesk, who provided an integrated workflow between human and computer. Until recently, there's been few tools commercially available to do this in web design.
Generative Design is a new and exciting chapter in the field of applied design. It fuses aspects of storytelling, experience and branding with algorithms, human interaction, data, AI and programming. ~ Patrick Hübner
Why AI improves design
The machine-inspired approach of generative design depends on input parameters (such as materials, size, weight and constraints), from which design combinations are created to reach a desired output. This process of telling a computer the outcome you want to see, and providing input parameters to get there is a very different from sketching ideas and coming up with solutions in your head based mainly on your experience, and the capacity of the human mind.
Could we see similar efficiency improvements with computer-aided web design?
Manufacturing companies like Airbus, Under Armour, and Stanley Black & Decker therefore used generative design to come up with new solutions that the human mind couldn't come up with alone. New Equipment explained this, providing a concrete example, where Airbus created an interior aircraft partition that was 45% lighter thanks to a generative technology:
Airbus's bionic partition from New Equipment
With this example, could we see similar efficiency improvements with computer-aided web design? What applications have there been until now? This great interview from between Claudia Illguth and designer Patrik Hübner suggests so:
Imagine a world in which you don’t design what you already know but rather teach the computer what it is that you actually want to accomplish.
This unique mix of ingredients allows us to build engaging brand experiences and completely novel forms of visual design
Generative Design for Graphics Designers 🤖
In comparison to manufacturing and product design, tools for computer-aided web design were slower to emerge. Luigi Troianoa and Cosimo Birtolo’s paper in 2014 put it down to UI Design being a creative, and more human-intensive task, made up of decisions a human is expected to make.
Designers working on web and UI design have to decide things where human understanding and taste was perceived to be necessary, such as:
📏 Spacing
🎨 Colour palettes
📐 Positioning
🖋 Typography choices
Their study, however, did go on to propose that if a human is kept at the centre of a Generative Design process, positive outcomes can emerge:
Generative design, while keeping the centrality of human creativity, introduces a novel approach to assist humans in finding effective and aesthetically convincing solutions.
AI vs. human designers
Yuri Vetrov (creator of algorithms.design) is another AI design enthusiast who has followed AI-driven design since back in 2012. Similar to Troianoa and Birtolo, he outlined the need for a human touch within AI-driven design, stating that algorithm-driven design would act as an 'exoskeleton for designers'. It would help us construct UIs, prepare assets, and personalise the user experience, but not replace human designers.
It wasn't until towards 2016 that AI-driven website builders and design solutions became more widely available and accessible:
In 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks, machine learning, and AI. ~Yuri Vetrov
And here are a few of the tools that emerged during that time:
There were many projects in beta, early access, and some that were just demos. Check out Yuri's website for a complete curation of practical examples and tools during those times.
UI Design, but cheaper and easier
As we moved into 2018, the limitations outlined above start to wain. The introduction of Salesforce's Einstein designer demonstrated that generative design could also tackle those tasks perceived to be more human-intensive.
Sönke Rohde (then vice president of Salesforce’s UX R&D team) showed that things like typography, colour, and style space could be ‘learned’ through analysing training data, and then used to make UI design cheaper and easier.
Generative design could tackle those tasks perceived to be more human-intensive
The data-science approach taken when training for the creation of a desirable UI design lead to AI being able to determine things like preferable colour choices based on industry. For example, these visualizations demonstrated how software companies gravitated towards the colour blue, and pharmaceuticals towards brighter, stronger colours:
Color choices by industry: software companies gravitate towards blue.
Amplifying the creative process
In a later article with Builtin, Rohde showed off more of Einstein's generative design abilities when sharing some practical use cases, such as rendering personalized email banners, hero pages and designing calls-to-actions:
“Instead of manually creating a set of design variations to run a multivariate test, or to do full-on personalization, you can leverage so-called generative design variations to transform the process.”
He referred to this as 'amplifying the creative process', which again suggests that AI won't replace designers, but speed up their work and make designers more efficient.
Designer Pat Riley also highlighted this in Generative Design for Graphics Designers, arguing that a traditional design process doesn’t really differ much from a generative one:
The Generative Design Process
Riley went on to suggest that the main benefits of generative design include time saved from manual and repetitive work, listing the benefits:
🤖 Reduce the time of design iterations
🤖 Easily create drastic variations by changing a single input
🤖 Generate compositions that would be improbable by hand
Instead of manually producing these iterations by hand; you encode the design process itself into a program that generates an output based on the constraints and variables contained within the source code.
The similarity between generative design and a design process was also highlighted by UX researcher, Shane Ketterman, where he shows how design thinking iterations could be generative too:
In the generative design process, there is an iterative process that cycles through from ideation to evaluation to arrive at possible combinations.
Related Reads on AI in UX:
Today's AI Design Assistants
With all the mentions of Einstein Designer throughout this article, you might wonder where it is today. Salesforce recently announced EinsteinGPT is on the way, and the designer appears to have been a prototype that was based around the rest of the Einstien AI Suite.
This is a tweet embed February 14, 2023
Bringing it back to 2023's new AI-design tools, it seems that like EinsteinGPT, almost all of them are powered by OpenAI and GPT-3. Also, they all still share the same focus of reducing mundane work.
Diagram + GPT-3
Diagram is probably leading the way, having also started out back in around 2020 with this prototype by Jordan Singer:
Jordan Singer built ‘Designer’, a GPT-3 Figma Plugin
Today, Diagram has grown to offer a bunch of generative tools including Genius, which is marketed as an assistant that sits with you and automates away repetitive tasks in Figma:
This is a tweet embed February 14, 2023
Galileo + Dall-E?
Despite appearing to be an image generator, Galileo creates Figma layouts to get you started faster when designing. Similar to the generative design approach described throughout, Galileo is trained on thousands of user interfaces and generates editable Figma designs. It also adds in icons, images and text too, reducing a high number of simple tasks.
Trained on thousands of outstanding designs, Galileo AI turns natural language prompts into high-fidelity designs.
It is unclear how it works because it's not obviously stated, but it looks like it might make use of Dall-E 2...and here's how that works:
This is a tweet embed February 14, 2023
AI won't replace us, or will it?
As we've seen, AI in design can increase creative inspiration, and reduce our workload by removing repetitive tasks. So it's just here to assist us...or is it? In the long run, who knows what will come! 👀 👇
This is a tweet embed February 14, 2023
More reads
Thanks for reading, catch me on twitter for more, or follow along on this website! In the meantime, here's a bunch more AI-related articles - the first couple I wrote, and the rest is a little reading list on generative design that I found helpful and inspiring.
Prompt: a plasticine style Einstien and Galileo, stood side by side looking through a large purple telescope into a dark night sky with a planets, stars, and floating mobile phones and paintbrushes. Add a cute robot character in there too
| 2023-02-17T00:00:00 |
https://prototypr.io/post/generative-ai-design
|
[
{
"date": "2023/02/17",
"position": 72,
"query": "artificial intelligence graphic design"
}
] |
|
Why AI is Not a Threat to Jobs: Debunking the Myth
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Why AI is Not a Threat to Jobs: Debunking the Myth — My BTLR
|
https://www.mybtlr.com
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[
"Team Btlr"
] |
AI is not a threat to jobs. It is designed to assist humans, not replace them. AI will create new job opportunities and enable businesses to be more efficient ...
|
Artificial Intelligence (AI) has been a hot topic for a while now, and one of the most common fears people have is it will take over jobs and render humans useless. However, this is a myth that needs to be debunked. In this article, I will explain why AI is not a threat to jobs.
First and foremost, AI is designed to assist humans, not replace them. AI can take over mundane and repetitive tasks, allowing humans to focus on tasks that require creativity, critical thinking, and empathy. For instance, AI can help us perform routine tasks such as data entry, bookkeeping, and inventory management. By automating these tasks, we can free up time for humans to focus on tasks that require human skills, such as decision-making, innovation, and communication.
Secondly, the development and deployment of AI technology will create new job opportunities. While some jobs may be replaced, new ones will be created. AI requires people to design, develop, and maintain the technology. For example, companies that deploy AI technology will need data scientists, machine learning engineers, and AI ethicists, to name a few. According to a recent AI study by PwC, although some jobs will be automated by AI, the resultant job losses are expected to be counterbalanced in the long term by the creation of new jobs resulting from the expansion of the economy due to the implementation of these technologies. Moreover, PwC does not predict a significant level of technological unemployment arising from automation.
Furthermore, AI will enable businesses to be more efficient and productive, which will ultimately lead to job creation. By automating tasks, businesses can reduce costs, which in turn can lead to more investment and job creation. For example, a manufacturing company that automates its assembly line can produce more goods and expand its operations, which in turn will lead to job creation.
It is also worth noting AI technology is not a one-size-fits-all solution. AI is designed to perform specific tasks, and it can't do everything. There are still many tasks that require human skills and judgment, such as customer service, teaching, and healthcare. While AI can assist in these areas, it can't replace the human touch.
Finally, the fear AI will take over jobs is not new. In the past, automation has replaced jobs in industries such as agriculture and manufacturing, but it has also created new jobs in areas such as technology and services. Similarly, AI will change the nature of work, but it will not eliminate jobs altogether.
In conclusion, AI is not a threat to jobs. It is designed to assist humans, not replace them. AI will create new job opportunities and enable businesses to be more efficient and productive. While some jobs may be replaced, new ones will be created. We should embrace AI technology and focus on how it can be used to enhance human skills and make our lives better. By doing so, we can create a future that benefits everyone.
| 2023-02-18T00:00:00 |
https://www.mybtlr.com/blog/why-ai-is-not-a-threat-to-jobs-debunking-the-myth
|
[
{
"date": "2023/02/18",
"position": 5,
"query": "AI job losses"
}
] |
|
AI vs Jobs: Will Your Career Survive the Automation ...
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AI vs Jobs: Will Your Career Survive the Automation Revolution?
|
https://ai.plainenglish.io
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[
"Motty Shanu"
] |
In conclusion, the impact of AI on the job market is complex, and the future of work is likely to be a combination of job losses and gains. While it's ...
|
AI vs Jobs: Will Your Career Survive the Automation Revolution? MOTTY SHANU 🧲 2 min read · Feb 19, 2023 -- Listen Share
“The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence.”
As artificial intelligence (AI) becomes increasingly advanced, people are starting to worry about whether or not machines will eventually replace human workers. This concern is not unfounded, as there is a growing trend of automation in various industries, from manufacturing to customer service. In this blog post, we’ll explore the topic of the future of work and the impact that AI may have on the job market.
The Potential Impact of AI on the Job Market
While some jobs are already being replaced by machines, the consensus among experts is that the most likely scenario is a shift in the types of jobs available. As machines become more proficient at tasks that were previously done by humans, it’s expected that many jobs will be made redundant. However, new jobs will also be created, and they will require different skills and knowledge.
For example, AI and machine learning are expected to transform industries like healthcare and finance, as they enable more accurate and efficient data analysis. In these fields, jobs that involve data analysis and interpretation are likely to increase in demand. On the other hand, jobs that are more repetitive and don’t require a lot of decision-making are likely to be automated.
Preparing for the Future of Work
The key to thriving in the future job market is to prepare for the changes that are expected to come. This means staying up-to-date with the latest technological advances, developing new skills and knowledge, and being adaptable to change.
It’s also important to recognize the human skills that are not easily replaced by machines, such as emotional intelligence, creativity, and critical thinking. Jobs that require these skills, such as teaching, counseling, and leadership, are likely to be more in demand in the future.
Conclusion
In conclusion, the impact of AI on the job market is complex, and the future of work is likely to be a combination of job losses and gains. While it’s important to recognize the potential risks of automation, it’s also important to be optimistic about the opportunities that will arise. By staying informed and developing the right skills and mindset, individuals can prepare themselves for the future of work, and be ready to take advantage of the new opportunities that arise.
More content at PlainEnglish.io.
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| 2023-02-19T00:00:00 |
2023/02/19
|
https://ai.plainenglish.io/ai-vs-jobs-will-your-career-survive-the-automation-revolution-64a6c4eb82da
|
[
{
"date": "2023/02/18",
"position": 20,
"query": "AI job losses"
}
] |
The Future of Artificial Intelligence: Opportunities and ...
|
The Future of Artificial Intelligence: Opportunities and Challenges
|
https://www.bloom-bbs.com
|
[
"Ahmed Faisal"
] |
Job displacement: The rise of automation and AI can lead to job losses in certain industries. However, it can also create new job opportunities in AI-related ...
|
Introduction:
Artificial intelligence has come a long way since its inception, and it has the potential to transform various industries, from healthcare to finance, and transportation. With the rapid advancements in AI technology, it’s essential to understand the future implications and prepare for the opportunities and challenges it poses. In this article, we’ll explore the future of AI, its possibilities, challenges, and ethical considerations.
The Opportunities of Artificial Intelligence:
Healthcare: AI has the potential to revolutionize the healthcare industry, from drug discovery to patient diagnosis and treatment. Manufacturing: AI-powered machines can improve manufacturing efficiency and reduce errors. Finance: AI can enhance financial services by automating tasks, predicting market trends, and detecting fraud. Customer service: AI-powered chatbots can provide 24/7 customer service, reducing wait times and improving satisfaction.
The Challenges of Artificial Intelligence:
Job displacement: The rise of automation and AI can lead to job losses in certain industries. However, it can also create new job opportunities in AI-related fields. Bias: AI algorithms can be biased, reflecting the values and beliefs of their creators. This can lead to discrimination and perpetuate inequalities. Privacy: AI-powered technologies collect vast amounts of data, raising concerns about data privacy and security.
The Ethical Considerations of Artificial Intelligence:
Transparency: AI algorithms should be transparent and explainable, enabling users to understand how they work and make informed decisions. Accountability: AI creators and users should be accountable for the decisions and outcomes produced by AI algorithms. Fairness: AI algorithms should be fair and unbiased, ensuring that they do not discriminate against any individual or group.
Career Opportunities in AI” section
For those interested in pursuing a career in the field of artificial intelligence, a useful platform to explore job opportunities is Indeed. This job search engine features various job opportunities in AI and machine learning, ranging from research and development to implementation and maintenance. By using this tool, job seekers can find and apply to job openings in the field of artificial intelligence, helping to start or advance their careers in this exciting and growing industry.
FAQs:
Q: What is the future of artificial intelligence? A: The future of artificial intelligence is promising, with the potential to transform different industries and improve our lives.
Q: What are the opportunities of artificial intelligence? A: AI has opportunities in various industries, including healthcare, manufacturing, finance, and customer service.
Q: What are the challenges of artificial intelligence? A: The challenges include job displacement, bias, and privacy concerns.
Q: What are the ethical considerations of artificial intelligence? A: The ethical considerations include transparency, accountability, and fairness.
Conclusion:
Artificial intelligence is poised to change the world, and it’s essential to understand the opportunities, challenges, and ethical considerations that come with it. As AI technology continues to advance, it’s crucial to strike a balance between progress and responsibility to ensure that the benefits of AI are maximized, and the risks are minimized. By embracing AI technology and addressing its challenges and ethical considerations, we can create a future where AI can improve our lives and make the world a better place.
| 2023-02-18T00:00:00 |
2023/02/18
|
https://www.bloom-bbs.com/the-future-of-artificial-intelligence-opportunities-and-challenges/
|
[
{
"date": "2023/02/18",
"position": 22,
"query": "AI job losses"
}
] |
Your next colleague could be an AI robot | Futurism
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Your next colleague could be an AI robot
|
https://vocal.media
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[] |
As AI robots become more advanced, they may be able to perform tasks that were previously done by humans, leading to job losses. Lack of emotional intelligence: ...
|
In a world where robots are taking over, the University of Sydney Business School has predicted that within the next decade, companies are likely to hire digital humans.
That's right, say goodbye to your human colleagues and hello to their digital counterparts who never tire, never complain, and always follow company policy (talk about the perfect employee!).
But what exactly is a digital human, you ask?
Well, it's a combination of artificial intelligence tools and a computer-generated avatar that can interact with people in real time, producing realistic human speech and facial expressions.
From Siri to ChatGPT, most of us have already experienced interacting with artificial intelligence, but digital humans take this to a whole new level by overlaying it with realistic visual effects that would give Hollywood blockbusters a run for their money.
The Four Types of Digital Humans
According to researchers, digital humans can be grouped into four categories.
First up, we have the virtual agent that serves multiple users for specific one-time tasks, like customer service.
Then, there's the virtual assistant who supports the user regularly with a specific task, such as shopping or physical therapy.
Thirdly, the virtual influencer who supplies their followers with non-customized content to market products or experiences.
And last but not least, the virtual companion who develops a close relationship with the user, based on interaction for its own sake, but can also assist with tasks such as reminding users to take their medication.
Benefits of having AI robots as colleagues
Round-the-clock productivity: One of the biggest advantages of having AI robots as colleagues is their ability to work continuously, 24/7, without the need for breaks or rest. This means that tasks can be completed faster, and businesses can operate more efficiently, leading to increased productivity.
No human bias: Humans are naturally biased, and this can sometimes affect decision-making in the workplace. AI robots, on the other hand, make decisions based on data and algorithms, eliminating the potential for human bias. This can lead to fair and more objective decision-making.
Ability to analyze large data sets: AI robots are capable of analyzing vast amounts of data in a short amount of time. This can be particularly useful in industries such as finance, where large amounts of data need to be analyzed quickly to make informed decisions.
Ability to learn and adapt: AI robots can be programmed to learn and adapt to new situations, making them more effective over time. This means that they can continuously improve their performance, leading to better outcomes for businesses.
Concerns surrounding AI robots as colleagues
Job displacement: One of the main concerns with the integration of AI robots into the workforce is the potential for job displacement. As AI robots become more advanced, they may be able to perform tasks that were previously done by humans, leading to job losses.
Lack of emotional intelligence: AI robots lack the emotional intelligence that humans possess, making them less effective in certain roles, such as customer service or jobs that require empathy.
Potential for bias: While AI robots can eliminate human bias, they can also be programmed with bias. This is a concern as it can lead to unfair and discriminatory decision-making.
Privacy concerns: AI robots can collect and store vast amounts of data, leading to privacy concerns. It is important that businesses ensure that data collected by AI robots is secure and used in an ethical and responsible manner.
Why Digital Humans Will Never Replace the Real Thing...Yet
Despite their promise and broad applications, the researchers don't believe digital humans will replace real human beings in any industry.
As realistic and lifelike as a digital human may seem, ultimately it is never 'thinking' and lacks any true understanding of what is being communicated.
Its greatest advantage over existing AI technology is also its ultimate limitation: our own innate desire for face-to-face human interaction.
So, there you have it, folks - the rise of the digital employee is coming, but for now, they can only do so much. Who knows, maybe one day we'll all be working alongside digital coworkers, but until then, it's time to appreciate the real, flesh-and-blood colleagues we have...even the ones who complain and ask for raises.
| 2023-02-18T00:00:00 |
https://vocal.media/futurism/your-next-colleague-could-be-an-ai-robot
|
[
{
"date": "2023/02/18",
"position": 54,
"query": "AI job losses"
}
] |
|
Erie-Crawford Central Labor Council, AFL-CIO
|
Erie-Crawford Central Labor Council, AFL-CIO
|
https://nwpaalf.paaflcio.org
|
[] |
Senators: Working people want strong AI protections. Lawmakers snuck in a provision in the budget bill that would ban the creation or enforcement of all current ...
|
In March, the Trump administration took its most blatantly anti-worker, anti-union action yet: an executive order stripping the right to organize from hundreds of thousands of federal workers at more than 30 agencies.
Now representatives in Congress have introduced the bipartisan Protect America’s Workforce Act, which would overturn Trump’s executive order.
We are asking every single American who cares about the fundamental freedom of all workers to join a union to call their member of Congress right now.
| 2023-02-18T00:00:00 |
https://nwpaalf.paaflcio.org/eriecrawfordclc
|
[
{
"date": "2023/02/18",
"position": 7,
"query": "AI labor union"
}
] |
|
Greater Westmoreland Central Labor Council
|
Greater Westmoreland Central Labor Council
|
https://nwpaalf.paaflcio.org
|
[] |
We are one of nearly 500 state and local labor councils of the AFL-CIO and are the heart of the labor movement. We are democratically elected bodies ...
|
In March, the Trump administration took its most blatantly anti-worker, anti-union action yet: an executive order stripping the right to organize from hundreds of thousands of federal workers at more than 30 agencies.
Now representatives in Congress have introduced the bipartisan Protect America’s Workforce Act, which would overturn Trump’s executive order.
We are asking every single American who cares about the fundamental freedom of all workers to join a union to call their member of Congress right now.
| 2023-02-18T00:00:00 |
https://nwpaalf.paaflcio.org/gwclc
|
[
{
"date": "2023/02/18",
"position": 13,
"query": "AI labor union"
}
] |
|
Daily Mirror publisher explores using ChatGPT to help ...
|
Daily Mirror publisher explores using ChatGPT to help write local news
|
https://www.ft.com
|
[] |
... journalists write short news stories, as media organisations look at ways of using AI. Reach chief executive Jim Mullen told the Financial Times the company ...
|
Try unlimited access Only $1 for 4 weeks
Then $75 per month. Complete digital access to quality FT journalism on any device. Cancel anytime during your trial.
| 2023-02-18T00:00:00 |
https://www.ft.com/content/4fae2380-d7a7-410c-9eed-91fd1411f977
|
[
{
"date": "2023/02/18",
"position": 50,
"query": "AI journalism"
}
] |
|
AI, your (overly) helpful, slightly batty editor?
|
AI, your (overly) helpful, slightly batty editor?
|
https://www.rashmee.com
|
[] |
Every week, I'm sent suggestions for new artificial intelligence (AI) ... The most recent selections came from Newmark Graduate School of Journalism's ...
|
What Wordtune Spices did to the first sentence of this blog.
Every week, I’m sent suggestions for new artificial intelligence (AI) editors that can “sharpen” my sentences, “strengthen” my writing, highlight misused or missing words and generally limber up flabby phrases.
The most recent selections came from Newmark Graduate School of Journalism’s Jeremy Caplan. He covered DeepL Write, Quillbot Paraphraser and Wordtune Spices.
As curator of these so-called “wonder tools”, Mr Caplan also offered three other suggestions: Lex.page, Notion AI and Grammarly. Considering Grammarly has been around for a while (albeit, not with an AI makeover), I rather suspect that some of these tools may have been lurking around, on the margins of the collective consciousness (or at least on the developmental treadmill) waiting for the ceremonial blast of the trumpets as they played them into the big time.
Have they hit the big time? Should they? You can judge for yourself, based on my results with Wordtune Spices‘ work. I asked it to rewrite the first sentence of this blog. It instantly offered me 10 alternatives.
As with editing by humans, the AI’s efforts are six of one, half a dozen of the other. The changes are, for the most part, subjective. One person may prefer descriptive phraseology – the original text said “highlight misused or missing words”; another may want an omnibus term – “typos”, as per Wordtune Spices’ Example 2.
That said, it’s not often that a human editor, unless they were extraordinarily inept, would introduce a simple but profound error, the way AI just did. In Examples 3 and 9, Wordtune Spices, rewrote my first paragraph incorrectly: “The AI editors suggest new words to be added to my writing every week”. But that wasn’t what I said or meant at all. I said that I am sent suggestions every week for new AI editors. Wordtune Spices said AI editors send me suggestions every week.
Personally, I would check over every piece of AI-generated text, just to make sure it got it right.
| 2023-02-18T00:00:00 |
2023/02/18
|
https://www.rashmee.com/ai-your-overly-helpful-slightly-batty-editor/
|
[
{
"date": "2023/02/18",
"position": 65,
"query": "AI journalism"
}
] |
AI Recruitment: A Comprehensive Guide for Recruiting
|
AI Recruitment: A Comprehensive Guide for Recruiting
|
https://logicmelon.com
|
[] |
AI recruiting software helps hiring teams find the top applicants quicker, and expedite the process. Employers may increase hiring speed without compromising ...
|
AI recruitment technology enables recruiters to utilize their applicant tracking systems (ATS) better, allowing them to employ more quickly, make more accurate shortlists, and screen resumes more fairly. The ecosystem of HR technology includes, crucial components like AI for recruiting solutions.
What Is AI Recruitment?
AI recruitment is the process of using artificial intelligence (AI) to automate the entire recruiting process from start to finish. AI is the science that involves training machines to carry out human-like tasks, so that some tasks can be automated. This offers a variety of approaches to automate, and streamline the hiring process, enabling talent acquisition professionals, and hiring managers to spend more time where the talent acquisition process requires human participation, and less time on time-consuming, repetitive, and manual tasks.
AI-powered technologies directly linked to the ATS bring talent intelligence to the hiring process by automatically screening thousands of resumes and reducing time-consuming manual processes.
What Role Does AI Play in Recruitment?
1. Speeds up the hiring process
Businesses no longer receive a regular stream of applications from ideal prospects knocking on their doors. If your hiring procedure is ineffective, you risk losing top candidates to the opposition. The fewer proposals you face in the recruitment process, the easier it should be for you to obtain excellent people.
AI recruiting software helps hiring teams find the top applicants quicker, and expedite the process. Employers may increase hiring speed without compromising hiring, considering AI-powered video interviewing technology, which makes it quick and easy to compile, and evaluate interview feedback across the hiring team.
2. Choose candidates
Although sourcing applicants is essential in the recruiting process, recruiters shouldn’t devote all their time to it. Requisitions remain open for longer because sourcing is a laborious and time-consuming process, and recruiting teams face the risk of making poor hiring choices.
By using AI technology, recruiters can automate the labor-intensive process of manually sourcing, and shortlisting quality applicants. Recruiters may quickly select the most qualified individuals using AI recruitment tools to analyze resumes, and conduct keyword-based searches.
3. Increased involvement
Nobody wants to feel like a faceless cog in the hiring process. Recruiters may build stronger relationships with candidates, and provide a better experience by designing hiring procedures that are both scalable and effective. Recruiters and hiring managers can learn more about each prospect by employing AI recruiting tools that can understand interview exchanges.
4. Identify great talent
Not only is it expensive to hire the incorrect people, but it may also negatively affect confidence, output, and profits. Unfortunately, it’s a mistake that many people make. Employers can enhance their capacity to identify and select the most suitable candidate from a pool of candidates by using AI-powered recruitment tools.
In addition to conducting interviews that result in stronger hiring, AI can assist recruiters in automatically sorting through hundreds of resumes to identify the most qualified candidates for the position.
5. Training and onboarding
When new employees start working for an organization, they usually receive an induction manual to study, and a training schedule for the first few weeks. With the aid of artificial intelligence (AI), these can be presented in more inventive ways.
The worth of the participant and, subsequently, the firm increases with the development of interactive training programs. If people can visualize ideas, and live out an experience, they learn far more effectively.
Apps can also encourage better teamwork by enabling employees from various offices to participate in training sessions to advance their skills.
6. Minimizing bias
Even though we’re not always conscious of them, we all have biases. But, if left unchecked, implicit bias, sometimes referred to as unconscious bias, can easily enter into the recruiting process, and prevent employers from finding the best applicant for the job. Businesses are aware that bias needs to be lessened during the talent acquisition process in order to prevent bad hiring choices, legal issues, and a lack of diversity.
Benefits of AI Recruitment
AI has a big impact on how we recruit, hire, and retain personnel. This is because of the benefits that are already in place, and the potential results. These are a few advantages of AI in hiring:
1. Increased hiring quality
The initial candidates you draw in with your job posting determine the calibre of your applicants. You’ll find yourself sorting through semi-qualified prospects in the hopes that one of them can meet your needs if you can’t excite quality contenders about your available position. Numerous ways in which incorporating AI into your hiring procedure might help you raise the calibre of your candidates.
2. Automate tedious manual processes
Slow manual jobs are the last thing you want to deal with because you already have your hands full, attempting to organise interviews and screen applicants, as well as try to comprehend the always changing demands of your team. It may be used to automate many repetitive operations that take up your entire day. By employing scheduling software or automated resume reviewers, you may ensure things get done without taking time away from your busy schedule.
3. Better candidate experience
Now, using AI is advantageous for more than just you and your team. In actuality, using AI in your selection process is also helpful to candidates! Many of the AI technologies used in recruiting are created to simplify life for all parties involved because the candidate experience is a crucial component of the interview process. Job seekers and applicants are more motivated to stay involved with your company if they can quickly and easily find the solutions, and information they need. 52% of candidates who were given feedback were more likely to continue a relationship with the company
4. A recruitment process that is optimised
You need your recruitment procedure to run like a well-oiled machine in order to hire the best talent. Top candidates can get lost and end up with your competitors if some sections start to creak. You must keep an eye on the larger picture to ensure you’re taking the proper candidates through the entire process, and making it as efficient as possible. You can get this eye from AI. AI is ultimately just data. Numerous artificial intelligence-based tools gather data, and present it to you in ways that are informative.
5. Minimal hiring time
Efficiency in hiring has always come with certain drawbacks. While you want to hire the best person for your available position, you must also fill it as fast and affordably as possible. Depending on the role you’re attempting to fill, other team members may have to shoulder additional duties that make them less satisfied with their work.
Frequently Asked Questions
1. What function does AI serve in the applicant tracking system (ATS) for recruitment?
An application tracking system (ATS) is priceless for recruiters. By avoiding manual and repetitive activities, it saves them a huge amount of time. The HR department is the unofficial backbone of the business. If effective, it may result in high-quality hires that can significantly impact the business.
ATS better guarantees AI for hiring than by anybody else. Because of this, some AI recruiting tools uses technology that is powered on several levels.
2. What is the future of AI in recruitment?
Automated artificial intelligence is most certainly the way of the future in the hiring process. In order to locate the top candidates for a position, resumes and applications are sorted using AI. The recruitment process could be speed up and made more effective with the usage of AI in this way.
3. When was AI first used in recruitment?
Despite the fact that artificial intelligence has a long history that dates back to antiquity, several blogs and articles started focusing on AI’s role in recruitment in 2017. The debate around the possible applications of AI in recruitment turned into something of a maelstrom that engulfed numerous HR conferences.
Closing Thoughts
In order to make recruitment more effective, technology professionals are increasingly turning to artificial intelligence (AI). This trend may soon become widespread. It is clear how AI may be used to make the hiring, onboarding, and recruitment processes quicker, simpler, and more equitable. The human touch is still important and cannot be wished away from the hiring processes, despite how much AI is transforming the recruiter role from as we currently understand it.
| 2023-02-15T00:00:00 |
2023/02/15
|
https://logicmelon.com/blog-post/ai-recruitment/
|
[
{
"date": "2023/02/18",
"position": 10,
"query": "artificial intelligence hiring"
}
] |
Hire AI Developers
|
Hire AI Developers
|
https://www.bigscal.com
|
[] |
Why Is Hiring AI Developers From India Beneficial To Your Company? Many Businesses Are Adopting AI Technology Because Of Its Incredible Benefits, Which May ...
|
Artificial intelligence (AI) is the term used to describe a machine's capacity to carry out operations that ordinarily call for human intelligence, such as identifying patterns, making judgments, and solving issues. In order to do activities that would typically need human intellect, such as speech and picture recognition, natural language processing, and decision-making, AI is founded on the theory that machines may be created to replicate human intelligence.
Expert systems are created to emulate a human expert's decision-making processes in a certain sector.
A type of AI known as machine learning enables machines to continuously improve their performance based on data and experience. Contrarily, deep learning is a subset of machine learning that makes use of artificial neural networks to model intricate data patterns.
Numerous sectors, including healthcare, banking, manufacturing, and transportation, could be completely transformed by AI. The ethical ramifications of AI, such as the possibility of employment displacement, privacy abuses, and the creation of autonomous weaponry, are a source of concern. In order to ensure that the technology is utilized responsibly and safely, ethical issues and regulatory oversight are taken into account during the development and implementation of AI.
| 2023-02-18T00:00:00 |
https://www.bigscal.com/hire-ai-developers/
|
[
{
"date": "2023/02/18",
"position": 70,
"query": "artificial intelligence hiring"
}
] |
|
Sports Illustrated Lays Off Journalists After Announcing ...
|
Sports Illustrated Lays Off Journalists After Announcing Pivot to AI Content
|
https://futurism.com
|
[] |
It promised that AI wouldn't be taking the place of human journalists. Arena Group went ahead and laid off over a dozen employees from its magazine anyway.
|
The CEO's promise that "AI will never replace journalism" is starting to ring extra hollow.
Outgoing Humans
Earlier this month, Arena Group, which owns magazines including Men's Journal and Sports Illustrated, announced that it'd start publishing AI-generated articles. Its CEO and chairman Ross Levinsohn, however, vowed that "AI will never replace journalism."
Sounds like that's not going so great. Continuing a years-long cutbacks campaign, Sports Illustrated has been hit with another round of devastating layoffs affecting over a dozen workers.
"Hello! I've been laid off from [SI]. Not great!" tweeted Chris Almeida, a former editor for the magazine.
"After seven and a half years of writing about the NHL, NBA, NFL, MLB, LPGA, World Cup, Olympics and more, I, too, have been laid off by Sports Illustrated this morning," rejoined Alex Prewitt, a former senior writer.
According to an internal memo obtained by Awful Announcing, Arena Group has laid off a sizable 17 employees and created 12 openings to "reflect the new needs of the SI business." (Something tells us those "new needs" might involve accommodating the generative AI the parent company has been brandishing at Men's Journal.)
"Today is a day of change in our Sports business," the memo reads. "We are restructuring our Sports Illustrated group to reflect how consumers engage with us, and how we address the needs of our partners and audience."
Following Suit
It appears Sports Illustrated is following in the footsteps of CNET, a once-esteemed tech site that was caught secretively churning out error riddled AI-generated articles after rounds of quiet layoffs.
And on the accuracy front, Arena Group's AI-guided dreck isn't doing any better. Futurism, with the help of a medical expert, found that its very first AI article for Men's Journal, titled "What All Men Should Know About Low Testosterone," contained at least 18 factual errors, despite the authoritative tone of its synthesized prose. Not what you'd want out of something that's supposed to be giving health advice to the site's vast readership.
In response, the article was hastily and extensively rewritten to account for the inaccuracies. Some still slipped through the cracks.
That didn't seem to bother Arena, though. A spokesperson from the group stated in a statement provided to Futurism that the company was "confident in the articles."
While Sports Illustrated itself is yet to make use of a generative AI for its stories — at least in a way that's disclosed to readers — it seems likely that it's only a matter of time before it's forced to. Arena, after all, has just laid down the hammer on the size of its staff, and with other outlets including Buzzfeed already blazing ahead with AI content, the industry at large looks teed up to be overrun with bots.
After the publication of this article, the Arena Group asked that this statement be added:
The Arena Group announced last week that it had partnered with two AI companies, to start exploring how AI can contribute to workflow efficiencies at some of its publishing brands. We were clear that AI will never replace journalists or editors. In fact, the articles in question were compiled from articles by journalists published well before The Arena Group purchased Men’s Journal and Men’s Fitness in December 2022. The AI tools retrieved the reporting from these stories exclusively from the Men's Fitness archives, from source materials produced years before by the previous owner – so the content criticized by one expert was the product of journalists. Not a machine.
We’ll continue to experiment with AI software to help mine our vast archive and create more productive editing workflows but not to generate new content that isn’t based on articles from the archives. There are three parts of that process: how the AI tools find and amalgamate the original content, the editing of that content by a human journalist, and the substance of the original content the AI identified. The skepticism raised about this content pertains almost exclusively to the latter two issues.
While our ongoing test process has produced learnings that will allow us to continue to refine our use of AI tools, we acknowledge that experts in this field will disagree on the diagnoses and treatments included in the original article. Science and diagnosis are complicated and rarely agreed upon, but the sensational nature of the reporting about this experiment could be equally scrutinized for being disingenuous and misinformed.
Regarding Sports Illustrated, you took the false premise included in your first story and doubled down on it with opinions and inaccuracies. If you read some of the published stories this week, they noted that Sports Illustrated is restructuring. While we are saying goodbye to 17 individuals, we are in the process of hiring 12 more. Your assertion that this is the beginning of some attempt to replace writers with AI is categorically false. Again, we have been clear that AI will never replace journalists or editors. Your reporting is inaccurate and drives a false narrative.
More on published AI content: BuzzFeed's AI Quizzes Seem Kind of Broken, Honestly
| 2023-02-18T00:00:00 |
https://futurism.com/the-byte/sports-illustrated-lays-off-journalists-ai-content
|
[
{
"date": "2023/02/18",
"position": 31,
"query": "artificial intelligence layoffs"
}
] |
|
The AI industrial revolution puts middle-class workers ...
|
The AI industrial revolution puts middle-class workers under threat this time
|
https://www.theguardian.com
|
[
"Larry Elliott"
] |
In the past, leaps in technology replaced low-paid jobs with a greater number of higher-paid jobs. This time, it may be different.
|
The machines are coming and they will eat your job. That’s been a familiar refrain down the years, stretching back to the Luddites in the early 19th century. In the past, step-changes in technology have replaced low-paid jobs with a greater number of higher-paid jobs. This time, with the arrival of artificial intelligence, there are those who think it will be different.
Politicians know that even in the best case AI will cause massive disruption to labour markets, but they are fooling themselves if they think they have years to come up with a suitable response. As the tech entrepreneur Mihir Shukla said at the recent World Economic Forum in Davos: “People keep saying AI is coming but it is already here.”
Developments in machine learning and robotics have been moving on rapidly while the world has been preoccupied by the pandemic, inflation and war. AI stands to be to the fourth industrial revolution what the spinning jenny and the steam engine were to the first in the 18th century: a transformative technology that will fundamentally reshape economies.
Change will not happen overnight but, as was the case in previous industrial revolutions, it will be painful for those affected, as millions of workers will be. Previously, machines replaced manual labour, leaving jobs that required cognitive skills to humans. Advances in AI – symbolised by ChatGPT – shows that machines can now have a decent stab at doing the creative stuff as well.
ChatGPT is a machine that can write intelligently. Asked to come up with a version of Abraham Lincoln’s Gettysburg address in the style of Donald Trump, it will search its datasets for suitable source material and generate original content.
Launched by the San Francisco-based research laboratory OpenAI in November last year, ChatGPT notched up its 100 millionth user in 60 days. By contrast, it took Facebook two years to reach the same milestone.
Other new products will follow. The number of AI patents increased 30-fold between 2015 and 2021, according to a report from Stanford University in California. Robots are becoming cheaper and more sophisticated all the time.
History suggests profound technological change presents significant challenges for policymakers. Each of the three previous industrial revolutions had a similar initial impact: it hollowed out jobs across the economy, it led to an increase in inequality and to a decline in the share of income going to labour.
AI threatens to have precisely the same effects, but with one key difference. Left unchecked, owners of the new machines will make enormous sums of money out of their innovations. Capital will see its share of income rise at the expense of labour. There will be a hollowing out of some sectors of the economy but there will be employment growth in other sectors.
The difference this time is that the jobs most at risk will be white-collar, middle-class jobs, while many of the jobs created might be of the low-paid, dead-end variety. As Shukla noted in Davos, the days of humans processing mortgage applications are already numbered.
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There are ways of dealing with some of these issues. Governments could invest more in education and training, so that workers have the skills they need to make a decent living. They might explore ways of spreading the gains from the new technology. Silicon Valley entrepreneurs have been among the most vocal supporters of a universal basic income.
But whatever they do, policymakers need to act with care as well as speed. The economist Joseph Schumpeter popularised a phrase to describe how capitalism periodically reinvents itself. He called it creative destruction, and just such a process is in its early stages now.
| 2023-02-18T00:00:00 |
2023/02/18
|
https://www.theguardian.com/technology/2023/feb/18/the-ai-industrial-revolution-puts-middle-class-workers-under-threat-this-time
|
[
{
"date": "2023/02/19",
"position": 14,
"query": "AI job losses"
},
{
"date": "2023/02/19",
"position": 11,
"query": "universal basic income AI"
},
{
"date": "2023/02/19",
"position": 4,
"query": "AI workers"
}
] |
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