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Top 6 Most In-Demand Tech Skills for 2024
|
Top 6 Most In-Demand Tech Skills for 2024 | FDM Group
|
https://www.fdmgroup.com
|
[
"Paul Brown"
] |
Key essential skills for those who want to work in Machine Learning and Artificial Intelligence include domain knowledge, programming languages, and data ...
|
Career Advice Top 6 Most In-Demand Tech Skills for 2024 Paul Brown
The technology industry is booming and there are plenty of exciting opportunities out there for tech professionals in the UK, offering high earning potential, job satisfaction, and security. And since it’s very much a candidate’s market in the tech industry, job seekers have the opportunity to find a role that truly ticks all their boxes. In fact, according to surveys, over half of tech professionals feel comfortable enough in their skills and current market prospects to change jobs!
This puts tech candidates in a very powerful position, as a record number of employers are now on the lookout for the top tech talent to fill their skills gaps. We reveal the top six most in-demand tech skills for 2024 and a few tips on how to get started in the technology industry – even if you have no experience at all.
Top 6 tech skills employers are looking for in 2024
1. Machine Learning (ML) and Artificial Intelligence (AI)
In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have become a huge part of our daily lives – whether that’s talking to a human-like chatbot online or asking Alexa to play our favourite song. In 2023, the AI market was valued at around $100 billion, however, is set to grow at least twentyfold by 2030. It’s not surprising that businesses now look to adopt these technologies, which means ML and AI are in-demand tech skills for 2024. And it looks like candidates are already jumping on board. Recruitment insights reveal that the number of professionals on LinkedIn with AI skills has increased by 68% this year.
Key essential skills for those who want to work in Machine Learning and Artificial Intelligence include domain knowledge, programming languages, and data analysis. Showing no signs of slowing down, updating your skill set and learning more about ML and AI could be a smart move for 2023! Learn more about the fastest-growing AI roles.
2. Data handing and storytelling
World leader in AI computing, NVIDIA, predicts that many companies will begin rolling out generative AI across operations to improve productivity, creativity, and innovation. However, deep learning algorithms that fuel this technology rely on copious amounts of data for training, and management of this data will prove challenging. Experts predict that cloud, data storage, and analytics partnerships will be pivotal to overcome this issue. As such, data scientists, analysts, engineers, and specialists in data storage and management will be in high demand to ensure the efficient handling and utilisation of vast datasets. In fact, the number of professionals with ‘data science’ listed as a skill on LinkedIn rose by 54% in 2023.
However, data roles will encompass more than just handling and interpretation. In 2023, according to Forbes, data communication and storytelling were two key skills to have, as an increasing number of jobs require teams to work with data and this year is no different, especially with the increased focus on AI. This includes data collection, preparation, visualisation, analysis, and storytelling. Essentially, the focus will be on transforming data into digestible and useful insights to be shared and, most importantly, understood across teams and external parties – be that stakeholders or customers!
This could include additional skills such as producing data reports, designing creative visualisations using tools like Tableau or PowerBI, or delivering engaging presentations.
2. Cybersecurity
Cyber attacks and data breaches are on the rise and businesses are beginning to feel the financial impact of this. Especially with many employees now working remotely, using their own internet networks and electronic devices, there is an even greater threat posed to businesses and their data. As a result, many are falling victim to hefty fines, suffering from reputational damage and losing customer trust.
In order to address the issue, businesses are on the lookout for talented cybersecurity experts to join their teams, however, not necessarily just in their IT departments. With the increase in cyber threats becoming a serious matter, businesses will expect each and every employee to be well-versed in cybersecurity risks and best practices, whether that’s in the finance, sales or marketing team. Additionally, AI in cybersecurity will also play a huge role in the coming year.
That being said, cybersecurity specialists will still be in high demand to work on larger, more complex projects, such as handling sensitive data, dealing with cloud migrations, and protecting a company’s IT infrastructure. In fact, it is estimated that an incredible 10 million jobs were needed in cybersecurity in 2023 alone and the UK government has launched the £2.6 billion National Cyber Strategy, aimed at increasing the number and diversity of skilled individuals in cybersecurity. So, now’s the time to upskill yourself!
Are you interested in Risk, Regulation & Compliance (RRC)? Kickstart your RRC career today.
4. User Experience (UX) Design
With the acceleration of digital transformation, technology has become a part of our day-to-day lives, no matter who we are and what we do. As such, it is essential that tech is accessible to all and designed with the user in mind.
UX Designers are responsible for making sure technology is user-friendly – whether that’s reconsidering font size and style to improve a mobile banking app, categorising article themes to help users find what they are looking for on a blog, or adding a favourites function on an e-commerce shop. Without UX Designers, technology would be difficult to use and people would be less likely to buy it. In fact, every $1 invested in UX design brings $100 in return on average, so it’s a no-brainer for many businesses.
Some of the skills required for a UX Designer include conducting market and audience research, creating user personas, designing wireframes and carrying out testing. Although coding knowledge is not essential to the role, a solid understanding of programming languages could give you a competitive edge in the job market!
5. Cloud Computing
The global Cloud Computing marker is expected to grow at a CAGR of 17.8% between 2023 to 2032, taking it to a projected value of $42,495.2 billion. And with 82% of businesses considering cloud migration as an essential step in their digital transformation, cloud computing will play a vital role in modernisation and growth for organisations across the world. However, to facilitate this expansion, businesses will need to hire the very finest cloud professionals, who are comfortable with programming languages, such as SQL and Linux, database management, AI and ML and cloud technologies, like AWS, Google, Oracle, and Microsoft.
Does this sound like the right fit for you? FDM has teamed up with AWS to offer a full-time skills development programme to help you on your way to becoming a successful Cloud Computing Engineer – no matter your academic or professional background.
6. Blockchain
Blockchain is gaining popularity due to its secure and transparent nature, recording transactions across a network and creating an immutable chain. Its cryptographic features ensure data integrity, making it ideal for applications like cryptocurrency, smart contracts, and transparent supply chains, driving widespread adoption across industries. In 2021, the global blockchain market was valued at 45.58 billion and is expected to reach a mammoth value of $41,235.71 billion by 2030. This represents an annual compound growth rate of 82.8%!
Companies are increasingly integrating blockchain solutions, creating a demand for skilled professionals. As blockchain applications diversify beyond cryptocurrencies to areas like finance, healthcare, and supply chain, individuals with blockchain skills are positioned for exciting and varied career opportunities. As such, now is an opportune time to train in blockchain technology, whether that’s to become a blockchain developer, UX designers, or solutions architect. There has already been a 14% increase in the number of professionals with ‘Cloud Computing’ listed as a skill this year, so now’s the time to get involved.
For more information about how to get started in a tech career, get in touch or learn more about how you can get started in a job in technology with no experience.
| 2022-12-13T00:00:00 |
2022/12/13
|
https://www.fdmgroup.com/news-insights/most-in-demand-tech-skills/
|
[
{
"date": "2022/12/13",
"position": 79,
"query": "machine learning job market"
}
] |
Responsible AI by design: Building a framework of trust
|
Responsible AI by design: Building a framework of trust
|
https://enterprisersproject.com
|
[
"Christina Mongan",
"December"
] |
The opportunities and the market for artificial intelligence are growing rapidly, and organizations are increasingly relying on it to improve productivity ...
|
The opportunities and the market for artificial intelligence are growing rapidly, and organizations are increasingly relying on it to improve productivity and profitability. However, AI deployment is not without risks – including customer privacy, bias, and security concerns. As AI becomes more embedded in decision-making, the potential to amplify both the positive and negative impacts of decisions at scale escalates.
To fully realize the transformative potential of AI, we must responsibly harness the technology and establish a framework of trust. Resources and guidance can help organizations avoid potentially harmful situations and look optimistically at how this disruptive technology can benefit humanity.
It’s essential to develop a comprehensive responsible AI framework that includes practices, tools, governance, responsibilities, and more. This framework should enable responsible AI by design, resulting in increased transparency and trust across the AI lifecycle.
Learning from the not-so-distant past
AI has seen its share of failures and poor implementations. Several public cases of intentional and unintentional overreaching have raised concerns.
Let’s start with a cautionary tale: European authorities have fined a facial recognition firm nearly €50 million, and several additional lawsuits are pending in the U.S. because the company has been accused of selling access to billions of facial photos, many culled from social media without the knowledge or consent of the individuals.
This controversial business model is a clear example of the misuse of data to train AI and demonstrates some potential negative consequences of AI: ethical and financial risks, breach of privacy and trust, erosion of brand value, steep fines, and legal trouble.
[ Related read: 3 steps to prioritize responsible AI. ]
Often, disparities and poor outcomes of AI decisions only come to light through retrospective analysis, audits, and public feedback – long after the damage is done. Responsible AI requires a design-first approach that considers stakeholders, transparency, privacy, and trust before implementation, throughout the lifecycle, and across business roles.
Barriers to responsible AI
Responsible AI practices have not kept pace with AI adoption for various reasons. Some firms put responsible AI on hold because of legislative uncertainty and complexity, thus delaying value realization on business opportunities. Other challenges include concerns about AI’s potential for unintended consequences, lack of consensus on defining and implementing responsible AI practices, and over-reliance on tools and technology.
Responsible AI requires a design-first approach that considers stakeholders, transparency, privacy, and trust.
To overcome these challenges, it’s important to understand that technology alone is insufficient to keep up with the rapidly evolving AI space. Tools, bias detection, privacy protection, and regulatory compliance can lure organizations into a false sense of confidence and security. Overly defined accountability and incentives for responsible AI practices may look good on paper but are often ineffective. Bringing multiple perspectives and a diversity of opinions to technology requires a disciplined, pragmatic approach.
To adopt a responsible AI strategy, some key concepts must be kept in mind, starting with setting a strong foundation.
Putting responsible AI into practice
When creating AI-powered systems, organizations must set ground rules. Establishing a dedicated internal review body and putting safeguards in place as part of the organizational AI strategy and program help protect against intentional or unintentional misuse of the technology.
Trust is critical when it comes to implementing responsible AI by design. Organizations can instill trust as evidenced through principles, practices, and outcomes the company delivers in the market. Consistently and continuously scale Al with trust and transparency and operationalize Al throughout and beyond your organization.
The following guiding principles can help you establish a framework and strategy for responsible AI by design:
Human oversight and governance
Fairness, inclusiveness, and prevention of harm
Transparency and explainability
Reliability, safety, security, and respect for privacy
Trust starts with protecting data to ensure it is accurate, timely, and secure. Maintaining the right level of privacy and access to an organization’s data is foundational to any strategy and governance. Having the right safety nets in place to ensure checks and balances enables the freedom for innovation. The outcomes of these AI efforts can then be realized internally and externally through products and services, driving digital engagement, cost reduction, and new market opportunities.
Here are some actionable ways to translate these principles into responsible AI by design:
Establish a review board that represents cross-functional disciplines across the organization.
Create a governance structure focused on security, reliability, and safety.
Cultivate a culture of trust by employing guiding principles of fairness and inclusivity.
Identify and engage with stakeholders early and often in the process to identify and mitigate potential harm while driving business value and addressing customers’ needs.
Build a diverse work culture and promote constructive discussions to help mitigate bias.
Ensure design and decision-making processes are documented to the point where they can be reverse-engineered if an incident occurs to determine the root cause.
Prioritize and embed responsible AI into the process through fairness tests and explainable features that can be easily understood by internal teams and customers alike.
Develop AI observability practices and create a rigorous development process that values visibility across development and review teams.
Infuse an ethical lens into how teams adopt AI to ensure equitable and responsible outcomes.
Create a community ecosystem of support by collaborating with the AI community about the future of responsible AI.
Following these principles can help you champion responsible AI leadership and guide your teams to create equitable and responsible outcomes while using AI.
[ Also read AI ethics: 5 key pillars. ]
Engaging stakeholders
When engaging with stakeholders, ask targeted questions that seek perspective on any potential global implications of the proposed system. For example, applying AI systems in medical applications might be inappropriate or even harmful. The systems could reveal private medical details without a patient’s consent or mistakes in patent diagnosis and treatment options.
Such challenges cannot be mitigated through a purely technological lens. The policy approach to the application and deployment of AI, and the model itself, determine these outcomes. Therefore, it is imperative to engage a diverse group of stakeholders and seek information about any potential implications.
Often, practitioners engage with stakeholders to gauge the efficacy of user interactions or determine a system's usability. View stakeholder feedback as an opportunity to learn and mitigate the unintended consequences of technology as much as to learn and develop for the intended outcomes. Establishing a review board that reflects diverse perspectives for ongoing project review can help build a culture of responsible AI.
With AI regulations on the horizon, organizations will be better prepared if program guardrails and processes are already in place. By establishing clear processes, communicating the capabilities and purpose of AI systems, and explaining decisions to those directly and indirectly affected, companies can fully realize the potential of AI.
Skip to bottom of list More on artificial intelligence
Calls to action
As AI technologies and regulation continue to evolve, there are several ways companies can employ responsible AI by design:
Partner with a trusted service provider on your AI strategy and program.
Create an ethical foundation for AI practices by establishing guiding responsible AI principles and an AI governance system.
Ensure that your organization’s teams follow responsible AI practices throughout the AI lifecycle, including assessment, development, deployment, and monitoring, to ensure checks and balances.
Champion responsible AI by participating in global consortia and communities.
Establish a review board to consult on the broader impacts of AI systems.
Ensure that AI outcomes are clearly explained by the input data and are understandable to the broader team and consumers.
Conduct regular audits of deployed AI systems and their decisions by searching for evidence of disparate treatment, inequitable outcomes, and other unintended consequences. If you find an issue, retrain the system and remediate it when possible.
Even outside your organization, you can champion responsible AI as a private citizen in the following ways:
Seek brands that value trust, transparency, fairness, and responsibility in their systems.
Look for partners committed to responsible AI and demonstrate it in their products.
Advocate for AI governance and accountability and encourage lawmakers to enact regulations regarding the use of AI.
Engage in professional development and/or organizations that help define standards for the ethical use of technology.
[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]
| 2022-12-13T00:00:00 |
https://enterprisersproject.com/article/2022/12/responsible-ai-design-building-framework-trust
|
[
{
"date": "2022/12/13",
"position": 56,
"query": "workplace AI adoption"
}
] |
|
Is Our Digital Future At Risk Because Of The Gen Z Skills ...
|
Is Our Digital Future At Risk Because Of The Gen Z Skills Gap?
|
https://bernardmarr.com
|
[
"Bernard Marr"
] |
... adoption of game-changing technologies like artificial intelligence (AI). A ... Workplace Revolution In 25 Years - Livestream With Salesforce. Bernard ...
|
According to a recent survey by Gartner, 64% of IT professionals think the skills shortage is the biggest barrier to adoption of game-changing technologies like artificial intelligence ( AI ). A separate survey of banking, insurance and telecoms professionals published by SunTec found that the difficulty in recruiting skilled staff is the biggest obstacle to achieving business goals in 2022.
One optimistic way to look at this, perhaps, is that education is simply lagging behind industry demand – and when the next generation of technology workers graduate, they will be equipped with the skills needed to get the job done. Unfortunately, findings of another survey suggest that this might not be the case.
Baby boomers, Gen Xers and often even Millennials have become used to thinking of Gen Z as the first truly “digital native” generation. They were born when the internet was available to everyone and don’t remember a time when it wasn’t normal to carry a smartphone wherever they go and document their lives on TikTok and Instagram. Unfortunately, it turns out that this form of digital native might not translate to being able to work with the tools and technologies that are expected to shape the 21st century.
Research published recently by Intel found a surprising lack of understanding around some of the most important technology trends, which are widely forecast to drive business success over the next ten years. In particular, it found deficiencies in the understanding of AI, cybersecurity and quantum computing.
Although I personally feel we may be waiting a little while longer until quantum computing truly makes an everyday impact on business and society, I have no doubt that the other two technologies are critical for driving growth and creating opportunities today. Being unable to exploit them due to lack of workforce skills is certain to put any industry or economy at a disadvantage.
The report focuses on the UK but will be equally relevant to other developed countries – and it seems likely the problem will only be more pronounced in developing countries. While 45% of the 1,000 18 to 21-year-olds surveyed were interested in taking up a career in technology, 55% of them admitted they don’t understand or have no idea of what AI is.
Additionally, it highlighted a mismatch between the perception of generation Z when it comes to the impact that new technology trends will have on the future of work, and forecasts of what the situation is likely to actually be. For example, the World Economic Forum predicts that nine out of ten jobs will require digital skills in the future, while the representatives of generation Z who took part in the survey pegged the figure at just over six out of ten.
According to Trish Blomfield, Intel’s general manager for the UK, this shows that the younger working generation (which will make up 27% of the workforce by 2025) has not been educated on the important role that technology will have on their careers.
She told me “Four out of five jobs already ask for digital skills and that demand is only increasing. We’ve had accelerated digital transformation throughout the covid pandemic, and already a quarter of employers are saying they face skills gaps … it’s a limiter to business success and it’s a limiter to people’s life opportunities. It’s also a limiter on different country’s economies.”
This is important work that highlights a real danger. To be clear, I don’t think that everyone needs to be able to write with computer code (let alone program a quantum computer).
But it is important that as many people as possible finish their education with an understanding of how technology is being used to transform just about every industry and job, and how it’s likely to be used going forward. This encompasses ideas such as augmented working, where we will increasingly work alongside smart machines and AI-enabled tools that will help us work more efficiently and effectively.
Crucially, I feel, this report doesn’t simply highlight the need to empower today’s learners with “hard” tech skills. The rise of low-code/no-code solutions and platforms mean that augmenting one’s ability to work via technology isn’t limited to engineers and programmers. Soft skills, including communication, creativity, critical thinking and design skills, are just as important when it comes to leveraging technology to its full potential.
Blomfield says, “We’ve got into, I think, these stereotyped beliefs about STEM (science, technology, engineering and math) and future jobs … so our respondents thought studying traditional STEM subjects was likely to help them find a job, more so than studying creative arts … but [that leads to] this binary thinking … that if you are a student that’s talented in the humanities you might feel excluded from STEM.
“Conversely, if you’re a STEM student you’re going to miss out on these softer skills, critical thinking, communication skills – these are really necessary even in a highly digital market.”
This research resonated with me, as it touches on something I have always thought is important. Creative, human skills – which won’t be replicated by machines any time in the near future than hard technical skills such as mathematics or programming – are really more likely to be the skills that will define us as successful humans in the near future. This is illustrated by the fact that 16 of the 20 skills I picked out as important enough to include in my recent book, Future Skills, are “soft skills”.
“STEM to STEAM (science, technology, engineering, arts, math) should be the long-term vision for digital skills in education,” Blomfield tells me.
This will potentially also have an impact when it comes to encouraging more females to look to technology as a future career.
“We found in our research that females, even in generation Z, self-score themselves as better at creative subjects than [STEM subjects], and that narrows the pathway, they feel there’s a narrower pathway into being an engineer or a scientist or a mathematician.”
Identifying the problem is always the first step towards effecting a solution. It’s clear that studies like this one play an important role in communicating the importance of reassessing the way we are teaching and promoting technology in education. Once again, it highlights the importance of promoting accessibility and diversity when it comes to STEM learning in schools, and broadcasting the fact that technology is for everyone, not just those with an affinity for learning about math or computer science.
You can click here to see my conversation with Trish Blomfield, Intel’s head of country for the UK, where we dive deeper into the importance of education in overcoming the tech skills crisis.
| 2022-12-13T00:00:00 |
2022/12/13
|
https://bernardmarr.com/is-our-digital-future-at-risk-because-of-the-gen-z-skills-gap/
|
[
{
"date": "2022/12/13",
"position": 73,
"query": "workplace AI adoption"
}
] |
Text-to-image AI - Benefits for Business
|
Benefits for Business
|
https://addepto.com
|
[
"Edwin Lisowski"
] |
But ultimately, AI-powered image-generating models will pave the way for further AI-human collaboration in the workplace ... AI adoption in compliance is ...
|
Images are the most frequently used form of visual content in marketing campaigns [1], with 32% of marketers spending 2 to 5 hours a week creating visual content [2]. The advent of text-to-image AI tools like Dall-E 2 has made the process even easier, allowing businesses to reap long-lasting benefits in increased conversion, sales, and leads, without over-exerting their marketing teams.
Read on as we evaluate how businesses can leverage text-to-image AI technologies. But first, let’s look at text-to-image generation and how it works.
Text-to-image AI in marketing
Marketing is frequently based on graphic design. However, sometimes it’s difficult to find a relevant stock photo or to organize a photo shoot. In such a situation, AI comes to the rescue with the solution known as text-to-image. How can it improve the lives of marketers?
Text-to-image AI allows you to generate images from scratch based on a text description. Essentially, you only need to input text describing an image you’re thinking of, and the AI will generate a surprisingly accurate picture matching your description.
This technology has been around for years, but recent developments in AI have allowed researchers to develop strong machine-learning models that can use large datasets to create high-quality models.
Typically, text-to-image generation tools like Dall-E 2[3] work by inputting natural language processing (NLP)[4] with text descriptions of an image. The AI tools then use machine-learning algorithms to convert the descriptions into images.
Leveraging text-to-image generators in business
Businesses are rapidly adopting AI-powered text-to-image tools for their ability to create custom assets on the fly. With tools like Dall-E 2, your marketing team can create flashy and attractive visuals to attract customers and show off your products. Here are a few ways in which businesses can leverage text-to-image tools to boost customer engagement and conversion.
Managing creative content
Experts predict that by 2030, 99% of online content will be AI-generated [5]. Organic content remains one of the best long-term strategies for growing your customer base on social media. Creating and posting content organically enables businesses to gain long-lasting benefits in leads, increased traffic, and sales.
On the downside, creating and maintaining your social media presence with visuals takes a lot of work. Businesses need a dedicated team of marketers and content creators working together with other departments to create content that resonates with the company’s customer base.
However, by using AI-powered text-to-image generators, businesses can significantly reduce the human resources needed to create and run an effective social media campaign. Text-to-image generators can also help you create high-quality content quickly and consistently. The result is a more effective campaign and cost-saving benefits.
Read more about AI in Digital Marketing
Creating graphics seamlessly
The popularity of text-to-image generators doesn’t come from their ability to mirror the world accurately but from their ability to create wonderfully styled images. Most companies rely on stock images to boost content engagement. While this strategy may be effective to some degree, it lacks originality, and in most cases, it doesn’t fully convey the message in relation to the brand’s uniqueness.
Text-to-image generators, on the other hand, open up new opportunities for creativity with unique, playful, and well-structured images. This doesn’t mean that technology will replace human designers. On the contrary, AI image generators will pave the way for hybrid roles by incorporating designers’ creativity with AI capabilities.
The result is a seamless graphics creation process that cuts design time by half and creates opportunities for unique images that resonate with the target audience.
Generating social media content
Visual content performs up to 4.4 times better than text-based content in any social media campaign [6]. Text-to-image generators allow marketers to create visually appealing images and graphics in just a few clicks.
Additionally, businesses don’t run the risk of running into any copyright issues since all content is generated by a third party who owns the right to the software and all its processes.
One of the biggest trends in social media marketing today is memes. Memes offer a great way to engage with your customers on social media. Relatable memes help increase brand awareness, which, in turn, boosts engagement and conversion.
Text-to-image generators have numerous advantages over traditional methods of creating memes and other graphics. You can make more complex memes and other graphics with an AI-powered image generator without contracting a professional. And, since the process is automatic, you can create a lot more images than you would manually.
It might be interesting for you: How Artificial Intelligence Is Transforming Influencer Marketing Industry
Issues with using text-to-image AI generators
Despite having the potential to revolutionize image generation in marketing, AI image generator tools face a few drawbacks, especially in the first months of implementation. Here are two of the most common challenges facing the implementation of the technology:
Shallow data pool
Image generator models need vast amounts of image data to turn basic text into an image. Essentially, these models rely on training data to learn how to process requests. Since most companies don’t have their own data sets, they rely on the web – and that’s where the problem lies.
Sourcing training data from the web is quite problematic since some images aren’t always appropriate and require a tighter filtration process to curate relevant, comprehensive data sets.
Bias in output
Image generation tools offer remarkably creative and innovative design opportunities. Unfortunately, these models often replicate prevailing stereotypes and social biases.
For instance, Google recently acknowledged Imagen’s tendency to portray various professions to reflect Western gender stereotypes and an overall bias towards producing images of people with lighter skin tones [7].
The output is, therefore, sexist, racist, and toxic in a way. These unwanted results can be traced back to incomplete data sets that are not comprehensive and representative, resulting in bias. On the bright side, the company is currently trying to resolve the issue.
The bottom line
Text-to-image generation is the future of graphic design, particularly in marketing. By using vast amounts of training data, these models can create unique images for company logos, marketing campaigns, and other purposes.
Companies can then use these models to cut their marketing budget and produce images that resonate better with their target audience. There, however, is the issue of bias in output images resulting from poor training data. But ultimately, AI-powered image-generating models will pave the way for further AI-human collaboration in the workplace, particularly in marketing. See more about AI consulting services.
References
[1] Socialmediaexaminer.com. Social Media Marketing Industry Report. URL: https://www.socialmediaexaminer.com/social-media-marketing-industry-report-2022/. Accessed December 6, 2022
[2] Venngage.com. Visual Content Marketing Statistics. URL: https://venngage.com/blog/visual-content-marketing-statistics/. Accessed December 6, 2022
[3]Openai.com. Dall-E-2. URL: https://openai.com/dall-e-2/. Accessed December 6, 2022
[4] Ibm.com. Natural Language Processing. URL: https://www.ibm.com/cloud/learn/natural-language-processing. Accessed December 6, 2022
[5] Futurism.com. Internet generation. URL: https://futurism.com/the-byte/ai-internet-generation. Accessed December 6, 2022
[6] Tapsnap.net. 5 Stats Explaining the Importance of Visual Content. URL: https://blog.tapsnap.net/5-stats-explaining-the-importance-of-visual-content-on-social-media. Accessed December 6, 2022
[7] Imagen.research. URL: https://imagen.research.google. Accessed December 6, 2022
| 2022-12-13T00:00:00 |
2022/12/13
|
https://addepto.com/blog/how-businesses-can-take-advantage-of-text-to-image-ai/
|
[
{
"date": "2022/12/13",
"position": 81,
"query": "workplace AI adoption"
},
{
"date": "2022/12/13",
"position": 58,
"query": "AI graphic design"
}
] |
Ethics View: Cloudflare in Russia - Seven Pillars Institute
|
Ethics View: Cloudflare in Russia
|
https://www.sevenpillarsinstitute-org.sevenpillarsconsulting.com
|
[] |
Universal Basic Income Series · Impact Investing Series · Cryptocurrency Series ... AI Job Displacements: UBI to the Rescue? August 13, 2024. By Chuyuan Sun ...
|
By Minhaj Miah
Source: The Cloudflare Blog
Amidst the 2022 Russian invasion of Ukraine, over a thousand companies committed to withdraw from the country. According to a Yale School of Management database as of the 4th of July 2022, 305 have completed this withdrawal, while 243 continue to operate in Russia as normal despite their promises [1]. There appears to be a financial incentive to this move, with CELI Research Insights finding equity markets to be rewarding companies for leaving Russia, while punishing those who remained, ignoring the asset write-downs and lost revenue [2].
In spite of the markets valuing the reputational damage of remaining in Russia to be significant, some companies remain. One such company is Cloudflare, which continues to have a presence in Russia.
What’s Cloudflare?
Cloudflare is a California-based, American content delivery network and DDoS (Distributed Denial of Service) mitigation company, acting as a reverse proxy between a website’s visitor and the Cloudflare customer’s hosting provider.
Cloudflare’s Purpose
Cloudfare reports to be on a mission to help build a better internet, increasing speeds alongside the security of websites.
As part of its mission to help aid speed of access, it has data centers in 270 cities globally across the world [3]. Furthermore, it provides essential services in security through protecting sites from malicious activity such as “DDoS attacks, malicious bots, and other nefarious intrusions.” [4] Alongside this, it provides a free DNS service to users which, can be used on any device to protect data from being analyzed.
Cloudflare’s ‘Project Galileo’ commits to providing free security to “important, yet vulnerable targets” [5]. This alludes to websites which are often political in nature, such as voices of political dissent, as well as humanitarian organizations. Often such websites are under attack from a much more well-resourced entity, such as their respective national governments. Operating on a limited budget, these voices of dissent are thus generously aided by Cloudflare’s free services.
Given its strong mission-statement and commitment to the protection of the internet, for both users and websites themselves, Cloudflare appears to be a company committed to maintaining a high ethical standard. Its involvement in ‘Project Galileo’ especially implies this inference. Its direct protection of political opponents of national governments, for example, evidence commitment to the protection of freedom of speech. The Sarawak Report, for example, and its exposé of corruption and cronyism in Malaysia, involving the Prime Minister and several other senior ministers, was protected by Project Galileo. The entire exposé was hosted online and came under attack during the 2011 elections in Malaysia.
Presence in Russia
Regarding its presence in Russia, Cloudflare continues to provide services in general. These services are the content delivery, alongside DDoS mitigation. Essentially, the company serves to protect websites and ensures their presence on the internet remains unchallenged. Cloudflare does not have any offices or employees in Russia and claims to pay no taxes or fees to the Russian Government [6].
Considering Cloudflare’s commitment to supporting those attacked, the decision of the company to remain active in Russia may initially appear surprising. This was certainly the stance taken by Ukraine Digital Minister Mykhailo Fedorov. He made the claims that Cloudflare was “continuing to enable Moscow”, and it ought to pull out of Russia [7]. The Minister pointed out Cloudflare was protecting Russian websites. Cloudflare’s response implied this protection to be a positive; fearing a further entrenchment of “the interests of the Russian government to control the internet in Russia.” [8]
The company is not, however, totally indifferent to sanctions. Rather, it has committed to complying with the new sanctions as announced; closing off access to its network and systems in Russia, alongside terminating customers tied to sanctions [9]. “This includes clients related to Russian influence campaigns and the Russian-affiliated Donetsk and Luhansk governments”.
Protection of Russian Internet Access
The underlying disagreement between the Ukrainian digital minister and Cloudflare appears to be around the morality of protecting Russian websites. While Mykhailo Fedorov posits this to be negative and a protection of Russian government interests, Cloudflare takes the opposite stance.
Context is important in determining which stance is most appropriate. Over the past 5 years the Russian Government had been tightening its control over the Internet in Russia, from legislation allowing the government to monitor and block internet activity, to the establishment of an exclusively Russian DNS [10]. Through this, Russian access to a free internet has been significantly under risk, with the government gaining the ability to disconnect citizens from the global internet. As yet, this ability has not yet been exercised. Since the Russian invasion of Ukraine, however, government agencies switched to Russian hosting services, reducing reliance on non-Russian providers. Further, infringing on free Internet access, officials have targeted social media sites as well as Russian language news outlets based outside the country.
The effect of this repression on the public is seen in the popularity of apps aimed at bypassing blocks of these sites. ‘1.1.1.1’ is in fact Cloudflare’s own WARP, protecting internet traffic with encryption. Further, Cloudflare’s data for Russian user DNS requests show them to be using this service to access news sources from outside of Russia. It can thus be concluded that restricting Russian access of the internet, withdrawing Cloudflare’s services, further isolate the Russian people [11]. They would be limited in access to impartial reporting.
In sum, Cloudflare’s approach seems decided by a firm belief in the good of the internet. In a 2020 letter from Cloudflare’s founders, they describe the Internet as ‘a force for good worth fighting to defend [12].’ This may be reflected in their commitment to maintaining Russian access.
Nature of DDoS Attacks
The threat of attacks on media is a decidedly real one. These attacks more typically global and focus on gaming/gambling sites, and banking and financial services. It is atypical for news sites to face these attacks [13].
In Russia and Ukraine, however, this is not the case. In Ukraine, Broadcast Media companies were the most targeted. The top 5 industries were all related to the circulation of news and media. In Russia, Online Media was the third most targeted industry, topped by Banking and Financial Services.
Given the real threat of cyber-attacks on the access to news, Cloudflare’s work appears imperative, and its status in Russia appears justified. The attacks on Banking and Financial services further present another element of the story, and another justification for the presence of Cloudflare in the country.
It appears Cloudflare’s security role also is critical to a country’s financial service provision. This is a service which impacts the day-to-day lives of numerous ordinary Russian citizens. With an inflation rate of above 15%, and the rapid rise in cost of living, aggravated by sanctions, Cloudflare thus plays an important part in helping the general public [14].
The Stance of Other Companies
Cloudflare is not the sole tech company to remain in Russia, nor is it the largest. Apple and Google also continue to operate and support their app stores in Russia. These three companies based in California do so with an apparent commitment to providing Russia’s citizens access to free and fair news.
This move is supported by activists and officials alike, a US State Department spokesperson commenting on Cloudflare’s continued presence in Russia as “critical to maintain the flow of information to the people of Russia” [15].
Justifications for Calls to Withdraw
Calls for companies to withdraw from Russia can be placed in two categories. They may be called to withdraw out of a regard for the direct impact of their services and goods on the war. A company dealing arms, for example, will be directly helping facilitate the war, as would a company aiding Russian supply-lines. Many may also justify their calls for the withdrawal of companies by way of their indirect impact on the Russian war. Most commonly, indirect impacts concern taxes and fees to the Russian government. Thus, these companies would play a part in funding the war effort potentially, while their withdrawal and thus the withdrawal of these taxes would be seen as beneficial to the anti-war movement.
Direct Impact
Numerous companies have ceased operations in Russia in response to the war, due to a sense of corporate social responsibility. This appears obviously applicable to many companies, such as those helping to facilitate the war, perhaps through provision of financing or arms. The merits of such an exit from Russia are, however, less clear for companies whose goods produced do not directly have an impact on the conflict. It’s difficult to state definitively that Cloudflare’s services provide any direct benefit to the belligerents.
In their provision of protection to anti-war sites, and neutral reporting, the service that Cloudflare provides in Russia appears contrary to the war-effort. Rather than facilitating the war, its services appear to hinder it, by strengthening the opposition. Using the criteria of the direct impact of a companies’ goods and services, Cloudflare seems justified in remaining in Russia.
It must, however, be noted Cloudflare does not exclusively support anti-war sites. Rather, it also protects sites such as TopWar.ru, a Russian-biased pro-war news site. It is important to not overstate this role, however. Russia has been in the process of switching state-owned agencies to its own native infrastructure providers. This means that, given the absence of Cloudflare, the Russian websites would still be protected and accessible, while anti-war news sites would not.
Indirect Impact
For companies whose goods and services do not have a direct impact on the war, such as Cloudflare, the argument is made they should still withdraw due to the indirect benefit they provide to the State and therefore the war. This could be through their payment of taxes, or to the general positive impact they have on the economy and thus government revenues and spending, of which a great portion goes towards its military. This argument is well-grounded, with war tax resistance present through history. Henry David Thoreau in his 1849 essay Civil Disobedience endorsed this method of protest as a means for “instigating governmental change” [16]. Theoretically tax payment is therefore a sound criterion by which the merits of a company’s status in Russia can be measured.
Cloudflare’s indirect benefit to the government budget is questionable. It makes clear in a recent blog post it has “minimal sales and commercial activity in Russia”, lacking a corporate entity there, and stress it is “not paying taxes or fees to the Russian government.” [17] Given the criteria of the indirect impact companies have through taxes, it appears again Cloudflare is justified to remain operating in Russia.
An Inconsistent Standard?
The basis for the call to withdraw from Russia is opposition to the war. This already assumes the war to be a moral evil. This proposition may over-simplify a complex issue [18]. When questioning the ethics of Cloudflare’s decision, one must consider the role companies are expected to play in taking moral positions. Their stances are especially relevant when placed in context: it is generally not normal for corporations to take a moral position regarding wars as they have with Ukraine. Typically, corporations exhibit neutrality because they are profit-motivated actors. Companies’ approach in Ukraine does seem to suggest ulterior motives for the withdrawal from Russia. Perhaps the bonus from positive public perception outweighs the profits from continuing to operate.
Cloudflare, however, appears to take a more neutral stance. While it remains operating in Russia, it condemns the war. Cloudflare’s response is one which prioritizes freedoms. The freedom of Russians to access a free internet, where they can be informed by unbiased news. The company condemns the war verbally, its actions support the triumph of truth.
In sum, Cloudflare appears justified in its continued presence in Russia. Its mission is to help build a better internet, and the company commits itself to protecting the vulnerable. In this regard, Cloudflare appears to have been acting in accordance with the company’s code of ethics. Its role in Russia can be seen by the popularity of its own WARP in Russia. The service it provides has been of utmost importance in ensuring Russians are able to access free internet and news. Underlying the continued provision of services is a belief the best ally to a fair outcome is a body of well-informed Russians, who may go on to influence the actions of the Russian Government.
Bibliography
The Editorial Board. “Companies should follow through on pledges to leave Russia“ The Financial Times. 4 Jul. 2022 https://www.ft.com/content/cc1c12a5-29fd-4d58-8e5f-6258fb15e11e
CELI Research Insights. “Chief Executive Leadership Institute Research Insights: “It Pays to Leave Russia” Yale. 17 May 2022 https://som.yale.edu/story/2022/chief-executive-leadership-institute-research-insights-it-pays-leave-russia
Cloudflare “What is Cloudflare” Cloudflare blog. https://www.cloudflare.com/en-gb/learning/what-is-cloudflare/
Cloudflare “Project Galileo” Cloudflare blog https://www.cloudflare.com/en-gb/galileo/
Prince, Matthew. “Steps we’ve taken around Cloudflare’s services in Ukraine, Belarus, and Russia“ Cloudflare blog. 7 March 2022 https://blog.cloudflare.com/steps-taken-around-cloudflares-services-in-ukraine-belarus-and-russia/
Jack, Victor and Anderlini, Jamil “Ukraine accuses SAP and Cloudflare of enabling Russia’s war effort” Politico. 23 May 2022 https://www.politico.eu/article/sap-cloudflare-enable-russia-war-effort-ukraine-digital-minister-fedorov/
Prince, Matthew. “What Cloudflare is doing to keep the Open Internet flowing into Russia and keep attacks from getting out“ Cloudflare blog. 2 April 2022 https://blog.cloudflare.com/what-cloudflare-is-doing-to-keep-the-open-internet-flowing-into-russia-and-keep-attacks-from-getting-out/
Signitaries in “Letter to U.S. government: Do not disrupt internet access in Russia or Belarus” AccessNow 18 Mar. 2022 https://www.accessnow.org/letter-us-government-internet-access-russia-belarus-ukraine/
Prince, Matthew and Zatlyn, Michelle “A letter from Cloudflare’s founders“ Cloudflare blog. 27 Sept. 2022 https://blog.cloudflare.com/a-letter-from-cloudflares-founders-2020/
Beshaireh, Bashar. “Cloudflare reports Q2 2022 DDoS attack trends” Zawya. 5 Aug. 2022 https://www.zawya.com/en/press-release/research-and-studies/cloudflare-reports-q2-2022-ddos-attack-trends-x3erqlfs
Cawthorne, Andrew. “Russian consumer prices dip again as cenbank rate meeting looms” Reuters. 20 Jul. 2022 https://www.reuters.com/markets/europe/russian-consumer-prices-dip-again-cenbank-rate-meeting-looms-2022-07-20/
Menn, Joseph. “Apple and Google app stores remain available in Russia. Activists and officials say that’s a good thing.” The Washington Post. 16 Mar. 2022 https://www.washingtonpost.com/technology/2022/03/16/apple-google-cloudflare-russia/
Conscience Online. “War Tax Resistance” https://conscienceonline.org.uk/about/war-tax-resistance/
Mearsheimer, John. “John Mearsheimer on why the West is principally responsible for the Ukrainian crisis” The Economist. 19 Mar. 2022 https://www.economist.com/by-invitation/2022/03/11/john-mearsheimer-on-why-the-west-is-principally-responsible-for-the-ukrainian-crisis
Endnotes
[1] The Editorial Board. “Companies should follow through on pledges to leave Russia“ The Financial Times. 4 Jul. 2022 https://www.ft.com/content/cc1c12a5-29fd-4d58-8e5f-6258fb15e11e
[2] CELI Research Insights. “Chief Executive Leadership Institute Research Insights: “It Pays to Leave Russia” Yale. 17 May 2022 https://som.yale.edu/story/2022/chief-executive-leadership-institute-research-insights-it-pays-leave-russia
[3] Cloudflare “What is Cloudflare” Cloudflare blog. https://www.cloudflare.com/en-gb/learning/what-is-cloudflare/
[4] Ibid.
[5] Cloudflare “Project Galileo” Cloudflare blog https://www.cloudflare.com/en-gb/galileo/
[6] Prince, Matthew. “Steps we’ve taken around Cloudflare’s services in Ukraine, Belarus, and Russia“ Cloudflare blog. 7 March 2022 https://blog.cloudflare.com/steps-taken-around-cloudflares-services-in-ukraine-belarus-and-russia/
[7] Jack, Victor and Anderlini, Jamil “Ukraine accuses SAP and Cloudflare of enabling Russia’s war effort” Politico. 23 May 2022 https://www.politico.eu/article/sap-cloudflare-enable-russia-war-effort-ukraine-digital-minister-fedorov/
[8] Ibid.
[9] Prince, Matthew. “Steps we’ve taken around Cloudflare’s services in Ukraine, Belarus, and Russia“ Cloudflare blog. 7 March 2022 https://blog.cloudflare.com/steps-taken-around-cloudflares-services-in-ukraine-belarus-and-russia/
[10] Prince, Matthew. “What Cloudflare is doing to keep the Open Internet flowing into Russia and keep attacks from getting out“ Cloudflare blog. 2 April 2022 https://blog.cloudflare.com/what-cloudflare-is-doing-to-keep-the-open-internet-flowing-into-russia-and-keep-attacks-from-getting-out/
[11] Signitaries in “Letter to U.S. government: Do not disrupt internet access in Russia or Belarus” AccessNow 18 Mar. 2022 https://www.accessnow.org/letter-us-government-internet-access-russia-belarus-ukraine/
[12] Prince, Matthew and Zatlyn, Michelle “A letter from Cloudflare’s founders“ Cloudflare blog. 27 Sept. 2022 https://blog.cloudflare.com/a-letter-from-cloudflares-founders-2020/
[13] Beshaireh, Bashar. “Cloudflare reports Q2 2022 DDoS attack trends” Zawya. 5 Aug. 2022 https://www.zawya.com/en/press-release/research-and-studies/cloudflare-reports-q2-2022-ddos-attack-trends-x3erqlfs
[14]Cawthorne, Andrew. “Russian consumer prices dip again as cenbank rate meeting looms” Reuters. 20 Jul. 2022 https://www.reuters.com/markets/europe/russian-consumer-prices-dip-again-cenbank-rate-meeting-looms-2022-07-20/
[15] Menn, Joseph. “Apple and Google app stores remain available in Russia. Activists and officials say that’s a good thing.” The Washington Post. 16 Mar. 2022 https://www.washingtonpost.com/technology/2022/03/16/apple-google-cloudflare-russia/
[16] Conscience Online. “War Tax Resistance” https://conscienceonline.org.uk/about/war-tax-resistance/
[17] Prince, Matthew. “What Cloudflare is doing to keep the Open Internet flowing into Russia and keep attacks from getting out“ Cloudflare blog. 2 April 2022 https://blog.cloudflare.com/what-cloudflare-is-doing-to-keep-the-open-internet-flowing-into-russia-and-keep-attacks-from-getting-out/
[18] Mearsheimer, John. “John Mearsheimer on why the West is principally responsible for the Ukrainian crisis” The Economist. 19 Mar. 2022 https://www.economist.com/by-invitation/2022/03/11/john-mearsheimer-on-why-the-west-is-principally-responsible-for-the-ukrainian-crisis
| 2022-12-13T00:00:00 |
2022/12/13
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https://www.sevenpillarsinstitute-org.sevenpillarsconsulting.com/ethics-view-cloudflare-in-russia/
|
[
{
"date": "2022/12/13",
"position": 64,
"query": "universal basic income AI"
}
] |
Human Capital Solutions - NFP
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Human Capital Solutions
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https://www.nfp.com
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[] |
Coworkers around a laptop. Upskilling and Employee Development in the AI Era. Modern employee development must go beyond basic training to foster a culture of ...
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NFP Human Capital Solutions — optimizing the employee experience for today’s complex workforce.
Your people are the most critical factor in your organization’s overall success. Every stage of the employee journey and their interactions with your brand are shaped by the human capital solutions you have in place.
Your HR team influences nearly every meaningful interaction an employee has with your organization. HR strategies are integral to fostering workplace culture, employee engagement and productivity. Gone are the days when job security and a competitive salary alone equated to retaining and attracting talent. Today’s employees want an elevated experience beyond just compensation. They want to feel valued holistically, have the opportunity to learn and grow and utilize their talent to bring meaning and purpose to their work.
NFP Human Capital Solutions was designed with all of this in mind. Covering the entire employee lifecycle from hire to retire, our team of best-in-class experts delivers a full suite of comprehensive solutions that drive HR and business performance while creating a positive employee experience. Whether it’s securing talent, managing operations, selecting technology or developing a total rewards strategy, our team can help.
We work with leaders to strategize, plan and implement cost-effective, people-first programs that add long-term value and improve individual and organizational performance with exemplary support. Our mission is to empower your organization with hands-on guidance and specialized expertise, aligning HR with your strategic goals to drive business success.
Your HR team’s capacity to align with your organization’s overall strategy while providing an experience that’s attractive to your talent will determine how your workforce thrives and your business succeeds. NFP Human Capital Solutions has the expertise, insight and capabilities to meet the challenges that extend beyond attracting, retaining and engaging employees.
| 2022-12-13T00:00:00 |
https://www.nfp.com/benefits/human-capital-solutions/
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[
{
"date": "2022/12/13",
"position": 87,
"query": "universal basic income AI"
}
] |
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Artificial Intelligence and the threat to tech jobs under ...
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Artificial Intelligence and the threat to tech jobs under capitalism
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https://www.socialistparty.ie
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[
"Socialist Party"
] |
As AI systems become more advanced and are able to perform a wider range of tasks, however, it is possible that some jobs may be automated and replaced by AI.
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By Dan O’Rourke
In recent months, AI (Artificial Intelligence) programmes such as MidJourney and ChatGPT have gained significant attention and many observers have raised serious questions about the future of work in this digital age. No question, that technology has the potential to greatly impact workers, the economy and society as a whole.
Some workers in technology, art and design have talked about how AI could lighten their workload, while others have raised concerns about the potential for job loss.
There’s no denying the technology is being overhyped to a certain degree. In a recent tweet the founder of AI company Open AI, acknowledges this, stating that ChatGPT shouldn’t be used for serious applications.
However, there’s also no denying that AI is used for very serious purposes elsewhere. BlackRock, a multi-trillion dollar investment firm, uses its AI system to make decisions on astronomical amounts of money. Facial recognition systems are being used globally, particularly in China, to oppress dissent. In warfare, AI is being hailed as the “third revolution in weaponry”, and is used particularly by the Israeli army
A new industrial revolution?
For better or worse, many see the rise of artificial intelligence as a new industrial revolution.
Digital automation has the potential improve working conditions, but in a capitalist system, it will be used to increase profits at the expense of workers – lower wages, worse conditions and fewer jobs.
During the industrial revolution – the age of mechanical automation – artisans and craftworkers were forced into factories where they lost their skills and tools and became appendages of the machine. Will today’s technology workers follow a similar path, losing their often well-paid jobs to the machines?
The AI seems to think so. When asked “How will AI affect technology workers, and the wider economy?” the AI chatbot ChatGPT responded:
“AI has the potential to greatly impact both technology workers and the wider economy. In the short term, the development and implementation of AI is likely to create new jobs and opportunities in the technology sector. As AI systems become more advanced and are able to perform a wider range of tasks, however, it is possible that some jobs may be automated and replaced by AI. This could potentially lead to job loss and disruption in the economy, at least in the short term.”
This short answer isn’t quite satisfactory, so for the moment at least, it is up to humans to respond.
Lessons from early workers’ struggles
The industrial revolution disrupted the lives of highly skilled artisans who initially benefited from the new technologies. Small steam engines, bandsaws, and powerlooms allowed them to increase their output. But these new tools soon became their masters.
Automation gradually reduced the start-to-finish role of artisans, until workers were left with a single repetitive task dictated by the speed of the machine.
The horrific working conditions and loss of status radicalised the artisans and craftworkers, leading to the rise of militant socialists and communists, such as the Chartist Movement. The working class began to organise and fight back. The reforms they fought for, many of which we enjoy today, inspired the work of Marx and Engels, and many other revolutionaries who followed them.
Similar to preindustrial production, software development is highly skilled and generalised. There is some minimal division of labour, particularly between design and development, but the modern technology worker bears striking resemblance to the artisan who would take a design and construct the product from start to finish.
Even without AI, driven by capitalists’ requirement to lower labour costs, tech jobs are likely to become increasingly less technical, require fewer years of training, and become more and more divided into separate tasks. Designers will become “prompt generators”, typing in suggestions for the AI to create new designs. Developers will be relegated to bug-checkers for code written by AI.
New technology and capitalism’s boom bust cycle
As with all new advances in technology and production methods, an unprecedented economic boom soon followed the original industrial revolution. Industrialists were able to undercut their craft-based competition and mass produce goods. Products which previously took hours of expensive skilled labour to create were churned out at the speed of a machine gun.
However, as new competitors entered the market with more efficient machines and production processes, they undercut the prices of the incumbents. Factories shut as they were no longer profitable, and the new working class was cast out into destitution. Wars erupted as economies floundered and the new technology was put to use in the massacring of millions.
This boom-bust cycle of recession and war has repeated itself ad-nauseam since the dawn of capitalism, and there is no reason to believe that this new method of digital automation will follow a more benevolent path than its mechanical cousin. If anything, the increased efficiency brought about by AI is likely to have a magnifying effect.
The way forward
If the trajectory of social media is anything to go by, the latest AI tools will devolve into a nightmare as their investors decide the time has come to extract profit. One of the original investors of Open AI, the owners of Dall-E and ChatGPT, is Elon Musk. As his expensive foray into social media starts to bite, he, and others, will want their pound of flesh from their investment.
It will be incumbent upon technology workers, unions and the wider socialist and workers’ movements to collaborate and follow the example of the workers who built the powerful labour movements that came out of the industrial revolution.
This time we must overthrow this system once and for all, and use both mechanical and digital technology for the benefit of all people and the planet. Under capitalism, technology and machinery is not, and has never been, about improving our lives. To do so would go against the brutal logic of profiteerring that this system is built.
These resources must be seized from the hands of the super-rich and big business and placed into democratic public ownership. On this basis, we can use them to build a democratic, socialist and humane society.
| 2022-12-13T00:00:00 |
2022/12/13
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https://www.socialistparty.ie/2022/12/artificial-intelligence-and-the-threat-to-tech-jobs-under-capitalism/
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[
{
"date": "2022/12/13",
"position": 7,
"query": "AI economic disruption"
}
] |
How Walmart enhances its inventory, supply chain through AI
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How Walmart enhances its inventory, supply chain through AI
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https://www.ciodive.com
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[
"Roberto Torres"
] |
Beyond daily operations and expected spikes in consumer traffic, AI is also there to quickly simulate new scenarios and offer plans of action in the event of a ...
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Listen to the article 4 min This audio is auto-generated. Please let us know if you have feedback
Technology is critical to Walmart’s ability to put items promptly in the hands of its customers — and anticipate what merchandise shoppers need or want even before they do.
"There are a few things that you need to get right, and that's why AI and ML works very well for us," said Srini Venkatesan, EVP, U.S. Omni Tech at Walmart Global Tech. "We need to decide what are those catalogs that the customer wants."
AI shapes that catalog of items by analyzing a host of inputs, including customer trends, shopping trends, seasonality and in-demand items. Once third-party sellers add items into the company's Marketplace offering, AI and ML support logistics.
"What we've seen is the adoption of Walmart Fulfillment Services has seen substantial growth compared to the sellers fulfilling themselves," said Venkatesan.
Walmart uses AI to enhance daily supply chain workflows, helping anticipate cycles in demand, especially amid peak or unexpected events in customer traffic. But the solutions have required a multiyear push toward data collection and curation, the creation of flexible algorithms and a global, not piecemeal, approach to technology.
These capabilities did not come together overnight. Venkatesan says there are three core pieces to building AI and ML components that are accurate but also effective:
A foundation of data, which Walmart has been collecting and curating
Taking an ensemble approach – not every problem has the same solution, and AI and ML models should be able to adapt to different problems.
End-to-end thinking: AI and ML must not focus on smaller events but be able to optimize processes at the global level.
Using AI to forecast demand supports the company through sizable sales spikes. The bulk of the shopper volume on Black Friday, the largest shopping event of the year, takes place online.
Nearly 200 million people shopped online and in-person during the break between Thanksgiving and Cyber Monday, according to data from the National Retail Federation. The onslaught of shoppers marked an increase of more than 17 million people year-over-year, the trade group found.
AI and ML based predictions help the company balance its network, placing inventory in the right location and at the right time as shoppers pack their physical or digital shopping carts.
"The technology is the same," said Venkatesan. "You have to actually simulate exactly what the scenario will be for a Black Friday. And the best output you will see is if nobody notices that Black Friday happened and just moves on."
But AI also helps the company overcome pressure tests no one can see coming.
Preparing for the unforeseen
Consider AI's potential as a planning tool. Fed the right data sets, this technology can anticipate stress points, allowing businesses the flexibility to react before any negative consequences arise.
Three-quarters of retailers say AI is essential to supply chain operations and management, according to IDC's Industry AI Path report published last year. A similar proportion of leaders said AI was key to marketing, operations and merchandising.
Beyond daily operations and expected spikes in consumer traffic, AI is also there to quickly simulate new scenarios and offer plans of action in the event of a natural catastrophe.
When Hurricane Ian hit Southwest Florida in the fall, the inclement weather damaged a Walmart distribution center, eventually staying offline for about seven days, according to Venkatesan.
"Not only was a node offline, we also had a lot more demand because people shop a lot more after a weather event," he said. AI allowed Walmart to reroute shipments and ensure the demand is met.
| 2022-12-13T00:00:00 |
2022/12/13
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https://www.ciodive.com/news/walmart-AI-ML-retail/638582/
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[
{
"date": "2022/12/13",
"position": 49,
"query": "AI economic disruption"
}
] |
Jobs at Visier Solutions Inc
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Jobs at Visier Solutions Inc
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https://job-boards.greenhouse.io
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[] |
34 jobs. Alliances. Partnerships. Job. Partner Development Manager ... Generative AI. Job. Software Developer Intern (June - December 2025). Singapore ...
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A career without compromise
Visier is where mission-driven employees find like-minded people. Our team members are connected to each other, as well as to purpose, united by the impact our company has on people and organizations.
| 2022-12-13T00:00:00 |
https://job-boards.greenhouse.io/visiersolutionsinc/jobs/4363216006?gh_src=537001af6us
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[
{
"date": "2022/12/13",
"position": 18,
"query": "generative AI jobs"
}
] |
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Search our Job Opportunities at Boeing
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Search our Job Opportunities at Boeing
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https://jobs.boeing.com
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[] |
Lead Generative AI Product Evangelist – Digital Aviation Solutions Bengaluru, India 07/07/2025. Save for Later; Lead AI Developer – Digital Aviation Solutions ...
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Data Science and Analytics Careers at Boeing
Data doesn’t work in silos — it needs to breathe and interact with other information in order to tell the whole story, not just parts of it. At Boeing, we’re working across our enterprise to let data and information drive our collective decision-making. Join our Data Science and Analytics team today.
| 2022-12-13T00:00:00 |
https://jobs.boeing.com/business/custom_fields.remotetype/hybrid/185-18469/5
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[
{
"date": "2022/12/13",
"position": 21,
"query": "generative AI jobs"
}
] |
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ChatGPT, generative AI, and the future of technical interviews
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ChatGPT, generative AI, and the future of technical interviews
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https://karat.com
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[
"Gordie Hanrahan"
] |
... generative Artificial Intelligence (AI) will challenge this entire system. ... Top Software Engineers Choosing Remote Jobs. According to technical interview ...
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While many companies use automated coding tests as the first step of their software engineering interview, recent developments in generative Artificial Intelligence (AI) will challenge this entire system. On November 30, 2022, OpenAI released a research version of their new AI-powered chatbot, ChatGPT. This chatbot has the ability not only to answer users’ questions but to write code in response to text prompts including a detailed explanation of how the code works, and test cases showing the output.
Automated coding tests are seen as a cost-effective filtering mechanism to find the right candidates to bring on-site in order to build world-class engineering teams. With these automated tests, dishonesty has always been a key concern for companies. As candidates have access to the internet while taking the test, they can always search for similar questions, making it hard to evaluate a candidate’s true technical abilities. In practice, engineers use existing solutions on sites like Stack Overflow to see how others have solved similar problems and build an ideal solution to the challenge they are facing. With the lack of content in an automated coding test (companies only see the candidate’s final answers and not how they got there), it becomes difficult to distinguish between candidates who use other code to create an inspiring solution and candidates who use other code without really understanding it.
Automated test providers have historically managed this problem in two ways. First, they develop large banks of questions that continually rotate, reducing the chance that a candidate can find the same question online. And second, they use plagiarism detection tools to see how closely a candidate’s answers are to those found online and those submitted by previous candidates. This strategy can sometimes work because originality is a strong indicator that the engineer did use the required skills to develop the solution.
Taking a common question that someone might encounter in an automated coding test, ChatGPT easily creates a solution, explanation, and test output.
While it would have previously taken a developer time first to devise a solution and then write the code, ChatGPT completes the entire process in seconds.
With these advances in AI, candidates trying to solve a coding test aren’t limited to searching for previous answers on Stack Overflow. Instead, they can ask the chatbot for the answer directly, and have the AI customize the code to whatever type of solution they are looking for in whatever language they want to program. While people are still exploring ChatGPT, so far it has also been able to develop solutions to more difficult common coding challenges such as the Coin Change Problem or Dining Philosophers Problem.
The ability of AI to turn a word problem into an algorithmic coding solution makes it incredibly difficult to moderate offline coding challenges. You cannot be sure whether the solution was written by the candidate, or by a machine. Moreover, because there can be many different solutions to the same problem created by AI, it will become increasingly hard to check for similarities to existing code. In theory, AI could create original solutions to coding problems in the same way a person could.
With AI, automated coding tests become less valuable not just because of the difficulty in assessing technical abilities, but because AI is likely to change the way software engineering operates. There is a very real future where these tools enable developers to do things a lot faster than they do today–where developers use AI to write and debug code so they can spend more time tackling the most complex problems.
Karat recognizes how AI is already changing the future of software engineering. Assessing developers’ skills in a natural environment–one that includes referencing Stack Overflow, Google searches, or even generative AI tools–allows hiring managers to get a true picture of a candidate’s abilities. This will allow employers to build the engineering teams of a future that will thrive in a world where AI becomes a commonplace tool in helping engineers write and debug code.
As AI continues to improve, we see a future with fewer automated coding tests that need a candidate to reach a known solution, and more interviews with a person that test how a candidate approaches, explains, and solves problems that have many possible answers (perhaps even using AI to help write the code). This future includes Interview Engineers (professional software developers who facilitate live technical assessments), subject matter experts that are continually developing new interview content and formats, and a coding studio that allows candidates to work alongside the engineer conducting the interview. The future of interviewing will help both candidates and employers take advantage of the changes AI presents for technical recruiting. By assessing developers’ skills in a natural environment and discussing problems rather than simply solving them, Karat empowers companies to better recruit and hire world-class technical talent.
Finally, as we explore the abilities of ChatGPT and future iterations of AI, we wanted to know, How would ChatGPT perform in an actual Karat Interview? To do this, we had a Karat Interview Engineer run a technical interview with ChatGPT as the candidate, typing the questions into the interface rather than asking them out loud.
The result? It wasn’t great. ChatGPT was able to solve an introductory question quickly. But technical interviews aren’t just about producing code. They are an assessment of problem-solving abilities and decision-making, and this is where ChatGPT fell short. The bot struggled to adapt the code to account for more complex scenarios in follow-up questions and it wasn’t able to respond to probing questions about its decisions.
Continue reading our full recap of the interview with more details and take a deeper look at what happens when an AI bot sits down with a Karat Interview Engineer in real life.
| 2022-12-13T00:00:00 |
2022/12/13
|
https://karat.com/chatgpt-generative-ai-and-the-future-of-technical-interviews/
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[
{
"date": "2022/12/13",
"position": 67,
"query": "generative AI jobs"
}
] |
Top Generative AI and AI Art News from 2022
|
Top Generative AI and AI Art News from 2022
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https://opendatascience.com
|
[
"Odsc Team"
] |
This time, a fake episode of the popular podcast, Joe Rogan Experience, went live with co-founder and former CEO of Apple, Steve Jobs. One could listen to the ...
|
The world of art has been rocked by advancements in artificial intelligence. The advancement has manifested so quickly, that as the...
The world of art has been rocked by advancements in artificial intelligence. The advancement has manifested so quickly, that as the year comes to a close, the art community is worried. Many feel that thanks to AI’s ability to learn from human artists and mimic their style, soon the need for artists will shrink even more. Not only that, but public use of AI-powered tools such as DALL-E has brought this technology to the forefront of public conversation. It’s been an interesting year for generative AI.
So, here are our top stories in generative AI involving art, creativity, and generative AI news.
New AI-Powered App Lensa is Causing a Stir Within the Art Community
Let’s begin with Lensa, an app powered by AI that is taking the internet by storm. If you’re a user of just about any social media platform, it’s likely that you’ve seen someone use Lensa to create a variety of AI-generated photos. The way the app works is that users upload their own images and the program morphs the photos into stylized art. Even though this sounds harmless, many within the art world aren’t too pleased.
That’s because the program and others like it utilize training data from existing photos and pieces of art. In essence, taking images created by humans enhances its own ability. For many within the art community, it feels like plagiarism. On Twitter, the debate is raging on Lensa, and other programs, benefit or harm to art in general. Either way, AI is likely here to stay. The question is, how it will affect the art community in the long term?
Microsoft Introduces Designer Which Allows You to Use Text-to-Image Tech in-App
Even though Meta and Google, among others, are pushing out text-to-image AI programs, Microsoft wasn’t going to be left behind. But instead of just releasing their own kind of text-to-image image generator, Microsoft is looking to make their program accessible and easy to use for the general public. Which, unlike Google and Meta, would be a first.
Called Microsft Designer, it is powered by DALL-E 2 and will be integrated into the overall Microsft services family, allowing users to generate images on their browser Edge. In short, the purpose would be a one-stop shop where users can build up decks, cards, and social media posts in one area using the family of Microsoft tools, including Designer.
AI Used to Create a Fake Podcast with Joe Rogan and Steve Jobs
Back in October, deep fake technology achieved another milestone. This time, a fake episode of the popular podcast, Joe Rogan Experience, went live with co-founder and former CEO of Apple, Steve Jobs. One could listen to the podcast and it’s difficult to tell that it’s a deepfake at first listen.
The interview goes for about 19 mins, and during that time you can almost forget that Jobs had passed away over ten years ago. Deepfake technology has been a concern for policymakers and law enforcement due to its potential to create fake evidence against individuals.
Meta Introduces New Text-to-Video AI Generator, Make-A-Video
Throughout 2022, Google, DALL-E, ROBOMOJO, DeepDream, and others have stolen headlines with magnificent photos made using text data. Meta wasn’t going to be left in the dust, and during the early Fall it introduced its own generator, but unlike the others, it would be a text-to-video program called Make-A-Video.
Similar to its text-to-photo cousins, Make-A-Video uses text data to create short videos. Following Meta’s Responsible AI Framework, Make-A-Video can also create text-to-photo. Right now, the Meta team has much to release, but it’s clear that there is a lot of research and development behind the program, which can potentially provide creators with a new avenue to create original content.
James Earl Jones Retires Iconic Darth Vader Voice With the Help of AI
Disney has been a pioneer in using AI-powered tools in its films over the last few years. From de-aging actors with ML tools to creating an original scene with actors who’ve died decades prior, it’s no surprise that Disney is going all in with AI. So it was no surprise that the company would be at the forefront of AI-generated voice tracks. In this case, it would be the iconic voice of James Earl Jones that brought to life one of the greatest villains in cinema history, Darth Vader.
The legendary actor announced that he officially retired the voice of the sith lord, but gave Disney permission to use existing voice data files to keep the character alive. So Disney partnered with the Ukrainian tech company, Respeecher, to use archival recordings of Jones to create new dialogue as needed for future Star Wars projects.
Popular Sites are Already Getting Tired of AI-Generated Art
As mentioned earlier, the art community has a growing uneasy relationship with AI-powered art-generating tools such as MidJourney and DALL-E 2. It’s become such an issue this year that popular sites dedicated to art, such as Newgrounds and Inkblot, have made moves to either outright ban generative AI art or curb their use.
It’s clear that AI is causing a stir within the art community and many within it are not sure how to handle these tools. Other websites such as ArtStation and others have yet to weigh in on the matter, but it’s likely that as we enter 2023, the disruptions from AI-powered art-generating tools will cause more pushback from the art community.
Everyone, Meet Loab – The Internet’s First Terrifying AI Cryptid
The internet has been filled with mysteries for as long as it has been used and more so as usage has exploded over the past thirty years. So it’s no surprise that the first AI cryptid came to light back in September. In a lengthy Twitter thread, an AI art creator discussed how they stumbled on a recurring and creepy image of a female humanoid that is now dubbed Loab.
Through the thread, they explained how the image came to be and how it continued to regenerate over and over again, leaving them wondering how it was possible and others calling it the first AI cryptid.
AI-Generated Art Wins Contest and Stirs Controversy Online
Now to an event that is elevating the worry of the art community, where an AI-generated piece of art took the prize at the Colorado state fair earlier this year. Using MidJourney, video game designer and artist Jason Allen created the piece and even mentioned that he expected some pushback from the win. What makes this story more interesting, is the category it won was digital arts/digitally manipulated photography, so it could be seen as well within the bounds of the category. The art piece competed against 18 other entries for a $300 prize.
DALL-E Has Taken the Most Terrifying Selfies Ever
Finally, let’s talk about DALL-E and the selfies that took left the internet in awe. Back in late July, Open AI’s DALL-E was given a simple text description – the last selfie that was taken by a human on Earth. What was produced was quite haunting, to say the least as the theme created by the series of images all connected in a morbid display of humanity on its last legs.
As the photos go on, they didn’t just depict Earth as just dead, or on fire, but a series of odd and disastrous events that seemed to have made the landscape unrecognizable as the humanoids taking the photos. The photos became a hit on TikTok where Robotoverlords made an eighteen-second video that showed each selfie.
Wrapping up
There you have it, the top generative AI and AI art stories of 2022. What do you think? Are the fears of the art community valid? What do you think of all of these text-to-image generators and other programs that take text data and turn them into images and videos? Finally, now that AI seems to have entered the mainstream, how do you think it will affect the popular imagination and interest in artificial intelligence?
If you’re looking to make the news in the future (hopefully in a non-controversial way!) then learn more about what’s big in data science at ODSC East 2023 this May 9th-11th, currently 70% off!
| 2022-12-13T00:00:00 |
2022/12/13
|
https://opendatascience.com/top-generative-ai-and-ai-art-news-from-2022/
|
[
{
"date": "2022/12/13",
"position": 77,
"query": "generative AI jobs"
}
] |
Artificial intelligence (AI) in healthcare
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Artificial intelligence (AI) in healthcare
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https://www.statista.com
|
[] |
This report presents a range of statistics and facts about artificial intelligence (AI) in healthcare. The role of AI in healthcare will become increasingly ...
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This report presents a range of statistics and facts about artificial intelligence (AI) in healthcare. The role of AI in healthcare will become increasingly important as the technology has the potential to save health workers a lot of time which can be refocused on patients. Included within this report are chapters focused on the situations in North America and Europe, attitudes of both clinicians and patients towards AI use in healthcare, and a final chapter looking at the potential future of AI in healthcare.
| 2022-12-13T00:00:00 |
https://www.statista.com/study/115827/artificial-intelligence-ai-in-healthcare/
|
[
{
"date": "2022/12/13",
"position": 44,
"query": "AI healthcare"
}
] |
|
Digital Health
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Digital Health
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https://www.ces.tech
|
[] |
... healthcare. CES 2025: The Future of Care. Digital Health. Health AI Startups: Innovating the Future of Healthcare. Artificial Intelligence Digital Health ...
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Technology has the potential to transform healthcare as we know it — and at CES, that potential becomes progress. Only the world’s most powerful tech event draws companies from across the globe who are innovating to improve health equity, lower costs, bring essential care to underserved communities and give patients more control. Join them, and help define the future of health.
| 2022-12-13T00:00:00 |
https://www.ces.tech/topics/digital-health/
|
[
{
"date": "2022/12/13",
"position": 47,
"query": "AI healthcare"
}
] |
|
Graphic Design Trends That Will Be Everywhere in 2023
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Graphic Design Trends That Will Be Everywhere in 2023
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https://www.digitalinformationworld.com
|
[
"Web Desk"
] |
With the progression of artificial intelligence (AI), especially AI art, as well as the Metaverse, designers in 2023 are inspired to push their own ...
|
Exploration of other worlds
Reviving counter- and sub-cultures
New textures adding depth to design
A blurring of boundaries
Finding delight in details
For anyone ending the year seeking creative rejuvenation, look no further than the graphic design trends set to dominate visual branding over the next 12 months.We tapped into the insights of the global community of professional freelance creators working on 99designs by Vista to gather their predictions around what we should expect to see in 2023.Looking at these trends holistically, it’s clear how design has been impacted by the unmet expectations held for 2022. Designers and consumers had high hopes for a year filled with adventure and freedom, but for many these were overshadowed by unwelcome and challenging global events.For some, design creates the opportunity to counteract negativity by exploring light-hearted styles threaded through with escapism. Others favor capturing today’s challenging global climate with a grittier realism. Suffice it to say that businesses looking to refresh their brand in a trend forward way in 2023 have an eclectic set of styles and options to choose from.Unsurprisingly, when life continues to feel like a rollercoaster ride, we are seeing creatives turn to ethereal elements to find peace, comfort and refuge. Infusing the trend of mysticism in branding through the use of zodiac symbols and astrological imagery like moons and stars is enabling designers to create a sense of serenity. Delicate lines and muted colors often complement this style, creating designs that are gentle and calm.Similarly pleasant energy is found through a fresh approach to surrealism in 2023. Surrealism, which seeks to capture the magic of the unconscious and unconventional, has been popular for a long time, but in the year ahead expect to see it coupled with 80s airbrush techniques. Characterized by intense saturated colors, floating objects and a soft glow, this design trend feels dream-like and approachable all at the same time.On the darker end of this otherworldly exploration is the trend of experimental escapism, strongly influenced by recent technological advancements. With the progression of artificial intelligence (AI), especially AI art, as well as the Metaverse, designers in 2023 are inspired to push their own boundaries of creativity, leading us into unknown worlds of mesmerizing and unimaginable landscapes.Designers, like much of the world, are fed up with a myriad of systemic global issues and in 2023 they are channeling past movements to communicate their opposition. In many ways, it’s no surprise that punk revival is a trend coming back into the mainstream spotlight. This anti-establishment style breaks the rules of traditional design, making use of chaotic collages, hand-lettering, newspaper typefaces and distorted imagery. While messy and jumbled, its energy is honest and forthright – and something that many can relate to.Less rebellious but still here to make a statement is the trend of acid graphics. Defined by bright colors, warped typography and chrome textures, this aesthetic has ties to 90s goth subculture that steered away from conventional design. With digital tools offering design templates that are easy to use and follow all design norms, it makes sense that graphic designers are working to prove that branding, if done right, doesn’t need to follow any rules.Designers are constantly exploring and experimenting with fresh shapes and textures to keep things interesting. In 2023, we anticipate more and more designs hitting the scene that digitally capture the effect of the risograph, a type of print technology invented in 1986 with a distinctive aesthetic. The style is about highly saturated colors, fluid abstract shapes and grainy textures that give depth to simple visuals.Similarly dynamic, abstract gradients are predicted to play a significant role in the year ahead. While gradients are nothing new, they are taking new forms in abstract and oblong shapes: with frothy smooth curves, this style feels fluid and soothing. At a time when things often feel uncertain, it’s nice to have a trend that feels reliable and familiar.Design has never been about staying in a single lane, and in 2023, this is truer than ever. Through 90s space psychedelia, designers are all about pairing the future and the past. Blending 90s retro styles like Memphis design and sci-fi elements like androids and spaceships with neon vibrant colors, this aesthetic suggests a sense of optimism for the future by putting a nostalgic spin on the high-tech world of tomorrow.On top of a time warp, designers are also playing with the dichotomy between physical and digital spaces. This mixed dimension trend is all about adding doses of cheery color and cartoonish illustrations to real-life photography. Rather than taking away from the real life imagery, the blending of these styles actually emphasizes the power and impact that imagination and art can have in the world.Nature’s influence always shines through via one graphic design trend and this year it’s in folk botanical. This style is about displaying natural patterns in a less polished way, with hand-made textures, doodles and unexpected color combinations. This approach works well in making digital work feel more natural and perfectly imperfect.Alongside this nature-focused trend, we’ll see all sorts of stories being told through complex compositions. Consisting of multiple illustrations in one scene, this aesthetic is like walking into a room and creating a snapshot of everything happening at one moment in time. Despite having several elements, this style tends to feel quite minimal - engaging but not overwhelming.In 2023, many designers are also turning to minimal line art to create vintage cartoon style illustrations that make a fun and light-hearted impression. Thanks to the simplicity of the line drawings, designers tend to complete the look with splashes of bright colors and playful bubble fonts.There’s no denying that 2023 is going to be filled with an eclectic mix of graphic design styles, mirroring the issues and attitudes in the world around us. What we are really seeing is the evolution of whatever reality we want to be a part of. Whether that’s a trip to the 90s or to the depths of outer space, creatives are here to make it happen.Written by Caitlin Collins, Head of Brand Marketing & Comms at 99designs by VistaRead next: Customers Want More Advanced Imagery On Online Products As Many Online Shoppers Are Not Satisfied With the Images of Products on Websites
| 2022-12-13T00:00:00 |
https://www.digitalinformationworld.com/2022/12/graphic-design-trends-that-will-be.html
|
[
{
"date": "2022/12/13",
"position": 53,
"query": "AI graphic design"
}
] |
|
15 Biggest AI Companies In The World
|
15 Biggest AI Companies In The World
|
https://finance.yahoo.com
|
[
"Usman Kabir",
"Tue",
"Dec",
"Min Read"
] |
The biggest AI companies in the world like Microsoft Corporation (NASDAQ:MSFT), Alphabet Inc. (NASDAQ:GOOG), and Apple Inc. (NASDAQ:AAPL), is the AI-enabled ...
|
SAP SE (NYSE:SAP) operates as an enterprise application software company worldwide. On December 7, the firm announced that UST, a leading digital transformation solutions company, in collaboration with SAP, had signed a partnership with Intel, an American multinational corporation and technology company, and a top AI firm, to assess and enable the digital transformation journey for small and medium-sized enterprises in northern Malaysia. On July 21, SAP SE reported that it had acquired Askdata, a search-driven analytics startup. The company said that this acquisition will help organizations to make better-informed decisions by leveraging AI-driven natural language searches.
These were picked from a careful assessment of the artificial intelligence (AI) industry. The details of each AI company are mentioned alongside a discussion around top firms in the sector in order to provide readers with some context for their investment decisions. Hedge fund sentiment is also included for further clarity on the money being poured into these firms and the confidence that elite investors have in their success.
Another important market trend in the sector, evidenced by the focus of the biggest AI companies in the world like Microsoft Corporation (NASDAQ: MSFT ), Alphabet Inc. (NASDAQ: GOOG ), and Apple Inc. (NASDAQ: AAPL ), is the AI-enabled industrial robotics development. AI tools are being used on social media, for big data analytics, the Internet of things, and cloud computing. The integration of these digital technologies into corporate operations, more commonly referred to as digital transformation, is on the rise as well.
One of the most important parts of this growth is the chip industry. According to a report by the Business Research Company, the artificial intelligence chip market size is expected to grow from $10 billion in 2021 to $15 billion in 2022, expanding at a compound annual growth rate of more than 42%. Despite supply issues due to rising demand and the global geopolitical crisis, the AI Chip market is expected to grow to $49 billion in 2026. The use of AI in fields like food and beverage development is growing too.
Artificial intelligence tools have been rapidly evolving across the world to personalize experiences for individuals, helping businesses with digital adoption, and lowering overall cyber security risks for financial institutions. Globally, AI has been integrated into corporate processes to improve business operations as well. As 5G deployment increases, the real-world uses of AI in automation, cloud, and large databases, are likely to drive the demand for the new technology even further, a prime example being the popularity of smart homes and smart cities.
In this article, we discuss the 15 biggest artificial intelligence (AI) companies in the world. If you want to read about some more AI companies, go directly to 5 Biggest AI Companies In The World .
繼續閱讀
The users of SAP will be able to search, interact and collaborate on live data to maximize business insights. Askdata’s IP will become part of the SAP Business technology Platform and it will contribute to a next-generation lightweight analytics experience for SAP Analytics Cloud solution customers and line-of-business applications. On December 2, Stifel analyst Brad Reback maintained a Buy rating on SAP SE (NYSE:SAP) stock and raised the price target to EUR 135 from EUR 130.
Among the hedge funds being tracked by Insider Monkey, Washington-based firm Fisher Asset Management is a leading shareholder in SAP SE (NYSE:SAP) with 6.8 million shares worth more than $549.6 million.
Just like Microsoft Corporation (NASDAQ:MSFT), Alphabet Inc. (NASDAQ:GOOG), and Apple Inc. (NASDAQ:AAPL), SAP SE (NYSE:SAP) is one of the biggest AI companies in the world.
In its Q3 2022 investor letter, Polen Capital, an asset management firm, highlighted a few stocks and SAP SE (NYSE:SAP) was one of them. Here is what the fund said:
“SAP SE (NYSE:SAP) is Europe’s largest software company and the global leader in enterprise resource planning (ERP) software. ERP is a software category that is particularly critical to business functions, and, therefore, has high retention rates even in times of economic stress. For the past several years, SAP has been going through several transitions, including moving to cloud-based SaaS (Software as a service) solutions and an initiative to better integrate its various software solutions. In recent quarters, we have seen increasing evidence that both transitions are being successfully executed, and the result should be a faster-growing, more consistent, higher margin, and more advantaged business. As investment costs from these transition programs wane, and as the benefits of higher growth continue, we expect that earnings will grow at a double-digit rate from next year (2023) onwards. In light of this, we believe the valuation is very attractive for long-term investors.”
14. Rockwell Automation, Inc. (NYSE:ROK)
Market Capitalization: $30 Billion
Rockwell Automation, Inc. (NYSE:ROK) provides industrial automation and digital transformation solutions in North America, Europe, the Middle East, Africa, the Asia Pacific, and Latin America. On November 10, ZEDEDA, a leader in edge orchestration, revealed a key supply agreement with Rockwell Automation to provide industrial automation with distributed edge management and orchestration capabilities. This automation will be provided by edge AI and edge computing. Rockwell Automation offers AI products related to industrial automation and information. For example, the FactoryTalk Analytics LogixAI module of the firm provides embedded analytics that empowers customers to apply machine learning concepts without needing expertise in data science.
Applying analytics within the controller application is a new fundamental approach to achieving process improvements. Rockwell Automation is among a group of global innovators exploring how the United Kingdom’s second-largest water company, Severn Trent, can use artificial intelligence and other advanced technologies to improve the region’s environmental well-being. Rockwell also offers specialized products in cyber security, machine learning and other emerging technologies.
On November 17, Baird analyst Richard Eastman maintained an Outperform rating on Rockwell Automation Inc. (NYSE:ROK) stock and raised the price target to $265 from $250, noting that tighter integration of hardware, software and services and scalable platforms accepting of vertical-specific applications is driving broader penetration for the firm.
At the end of the third quarter of 2022, 35 hedge funds in the database of Insider Monkey held stakes worth $768.8 million in Rockwell Automation, Inc. (NYSE:ROK), compared to 28 in the preceding quarter worth $399.5 million.
In its Q1 2021 investor letter, Harding Loevner, an asset management firm, highlighted a few stocks and Rockwell Automation, Inc. (NYSE:ROK) was one of them. Here is what the fund said:
“Rockwell Automation, Inc. (NYSE:ROK), a Milwaukee-based industrial automation company, struggled early in the year with component shortages that prevented it from clearing its overflowing order books and rising input costs. Uncertainty arising from the COVID-19 lockdowns in China and the war in Ukraine prompted management to warn in May of lower profits for the rest of the year. Behind the frustrations, however, were indications that the business outlook remained strong, as order volume continued to grow following price increases at the end of 2021. In the recent quarter, the company reported stronger results despite ongoing supply chain issues that hindered its ability to fulfil orders. To mitigate the risk of supply disruptions, Rockwell has been accumulating inventory and redesigning certain products. We believe Rockwell will see its revenues expand as its resilience improves and supply shortages recede.”
Market Capitalization: $40 Billion
Baidu, Inc. (NASDAQ:BIDU) offers internet search services in China. It operates through Baidu Core and iQIYI segments. On November 29, Baidu revealed its plans in celebration of Apollo Day to build the world’s largest autonomous ride-hailing service area in 2023. This autonomous ride-hailing service area has Baidu’s AI big model autonomous driving maps enabled. Baidu App enables users to access their search, feed, content and other services through mobile devices. Baidu App offers twin-engine search and feed functions that leverage the AI-powered algorithms and deep user insight functions.
Baidu is a leading AI company with a strong internet foundation. Baidu Brain is another core artificial intelligence innovation from the company that features advanced technology for recognizing and processing speech, images and words as well as building user profiles based on big data analysis. On December 6, Capital analyst Rob Sanderson maintained a Buy rating on Baidu, Inc. (NASDAQ:BIDU) stock and lowered the price target to $167 from $185, noting that another y/y decline in advertising revenue is anticipated in the fourth quarter before a return to growth in the first quarter and a 5% growth rate for 2023.
Among the hedge funds being tracked by Insider Monkey, Chicago-based investment firm Ariel Investment is a leading shareholder in Baidu, Inc. (NASDAQ:BIDU) with 2.6 million shares worth more than $305.6 million.
In its Q3 2022 investor letter, Ariel Investment, an asset management firm, highlighted a few stocks and Baidu, Inc. (NASDAQ:BIDU) was one of them. Here is what the fund said:
“China’s internet search and online community leader Baidu, Inc. (NASDAQ:BIDU) also weighed on relative results in the quarter. Continuing macro headwinds driven by China’s Zero-Covid policy are resulting in sluggish advertising trends. Looking ahead, the company remains committed to sustainable, profitable growth as management works to improve overall operational efficiency, executing its strategic commitments around technological innovation and the transition to a green economy. We also continue to be enthusiastic about Baidu’s longer-term opportunity for revenue growth and margin expansion across internet search, cloud, autonomous driving, artificial intelligence and online video.”
12. Micron Technology, Inc. (NASDAQ:MU)
Market Capitalization: $60 Billion
Micron Technology, Inc. (NASDAQ:MU) designs, manufactures and sells memory and storage products worldwide. On December 5, Micron Technology announced that its low-power double data rate 5X (LPDDR5X) mobile memory is continuing to gain market traction with the validation of its mobile memory for Qualcomm Technologies, an American multinational corporation, the latest mobile platform for flagship smartphones. Micron’s mobile memory and storage solutions are built to fuel the next wave of 5G and AI-enabled smartphone experiences. Micron has created the world's fastest discrete graphics memory solution: GDDR6X. Launched with NVIDIA on the GeForce 3090 and GeForce 3080 GPUs, GDDR6X takes graphics to new levels of gaming realism and unleashes high-performance AI inference.
Micron has been chasing maximum bandwidth memory to support gaming and professional visualization for a long time. It also includes how Micron has achieved staggering speeds with GDDR6X, the future of AI training with HBM2E, and other high-bandwidth products. On December 7, Fargo analyst Aaron Rakers maintained an Overweight rating on Micron Technology, Inc. (NASDAQ:MU) stock and lowered the price target to $70 from $75, noting that it would not be surprising to see shares trading lower ahead of the upcoming first-quarter earnings release.
At the end of the third quarter of 2022, 74 hedge funds in the database of Insider Monkey held stakes worth $2.5 billion in Micron Technology, Inc. (NASDAQ:MU), compared to 69 in the preceding quarter worth $2.2 billion.
In its Q2 2022 investor letter, Meridian Funds, an asset management firm, highlighted a few stocks and Micron Technology, Inc. (NASDAQ:MU) was one of them. Here is what the fund said:
“Micron Technology, Inc. (NASDAQ:MU) is a leader in DRAM and NAND memory production. We invested in the stock in the third quarter of 2019 during a cyclical downturn in the memory industry. Our rationale was that, while the memory industry is cyclical, we believed there are strong secular drivers in place that will lead to higher peaks and long-term growth. Our secular thesis is based on our conviction that the quest for ever-increasing compute speeds will increasingly rely on memory to solve bottlenecks and that increased memory content in nearly everything from mobile phones to automobiles will drive demand. Micron’s stock traded lower during the quarter due to macroeconomic concerns that led to lower earnings expectations. We increased our stake in the company, as we believe our secular thesis remains intact. We wanted to take advantage of what we view as temporary cyclical concerns that caused the stock to trade at less than 10x reasonable trough earnings per share (EPS) estimates and less than 7x recent peak EPS.”
11. General Electric Company (NYSE:GE)
Market Capitalization: $90 Billion
General Electric Company (NYSE:GE) operates as a high-tech industrial company in Europe, China, Asia, the Americas, the Middle East, and Africa. It operates through four segments: Power, Renewable Energy, Aviation, and Healthcare segments. On November 27, GE Healthcare, a subsidiary of General Electric, introduced SIGNA Experience. SIGNA is a new platform of four synergistic transformative technologies that leverage the power of Artificial Intelligence and deep learning in MRI scanning. On September 5, General Electric announced the launch of its first Made in India Optima IGS 320, an AI-powered Cath lab, to advance cardiac care in India. Optima IGS 320 was built at Wipro GE Healthcare’s new factory launched under the production-linked incentive scheme in Bengaluru. The Cath lab leverages the GE proprietary AutoRight technology.
AutoRight is the first neural network-based interventional image chain. AutoRight features Artificial Intelligence that automatically optimizes image and dose parameters in real-time, which enables clinicians to focus their attention and expertise on patients. On December 5, Deutsche Bank analyst Nicole Deblase maintained a Buy rating on General Electric Company (NYSE:GE) stock and raised the price target to $94 from $89, noting that the industrial economy can show unprecedented resilience, as the supply chain is only just beginning to heal, backlogs are extended, and order growth generally remains quite healthy.
Among the hedge funds being tracked by Insider Monkey, New York-based investment firm Pzena Investment Group is a leading shareholder in General Electric Company (NYSE:GE) with 13.8 million shares worth more than $852 million.
In its Q2 2022 investor letter, Longleaf Partners, an asset management firm, highlighted a few stocks and General Electric Company (NYSE:GE) was one of them. Here is what the fund said:
“General Electric Company (NYSE:GE) – Aviation, Healthcare and Power conglomerate GE was punished in the quarter amid top-down economic fears for this collection of seemingly cyclical businesses. However, the market is not giving the company credit for the material improvements CEO Larry Culp has made in his tenure. The balance sheet today is stronger than it has been in a very long time, and each of the three primary business segments has strong paths to increasing earnings, regardless of the economic environment. Healthcare has historically not been a cyclical business. While Aviation typically has some economic sensitivity, the business still has a strong COVID rebound tailwind that should continue even in an uncertain environment. Power is a less cyclical business, and GE maintains a steady business servicing approximately one-third of the world’s electricity. GE is another example of strong insider buying indicating management’s confidence in the business, while the company also began buying back discounted shares. GE is still on track to break the company into three separate businesses, and we believe this will help the market properly weigh the value of each core segment.”
10. Intel Corporation (NASDAQ:INTC)
Market Capitalization: $118 Billion
Intel Corporation (NASDAQ:INTC) engages in the design, manufacture, and sale of computer products and technologies worldwide. On November 18, Intel AI announced that it has introduced FakeCatcher, a new real-time Deepfake Detector, that analyzes the Blood Flow in video pixels to return results in milliseconds with an accuracy of 96%. Intel offers an unparalleled AI development and deployment ecosystem combined with a heterogeneous portfolio of AI-optimized hardware. Intel's goal is to make it as seamless as possible for every developer, data scientist, researcher, and data engineer to accelerate their AI journey from the edge to the cloud.
Intel has acquired multiple companies across various AI disciplines. Some of the AI-focused companies that Intel has acquired in recent years include DeepMind in 2014 for $500 million, Habana Labs in 2019 for $2 billion, and Granulate in 2022 for $650 million. Intel developed oneDNN to help companies optimize computer hardware such as CPUs and streamline the development of deep learning applications. On October 31, Citi analyst Christopher Danely maintained a Neutral rating on Intel Corporation (NASDAQ:INTC) stock and lowered the price target to $27 from $30, noting that the company announced a cost reduction plan, which should help but doesn’t help the core problems of manufacturing and wasting money on growth markets that will never pan out.
At the end of the third quarter of 2022, 69 hedge funds in the database of Insider Monkey held stakes worth $1.9 billion in Intel Corporation (NASDAQ:INTC), compared to 65 in the preceding quarter worth $2.5 billion.
In its Q3 2022 investor letter, ClearBridge Investments, an asset management firm, highlighted a few stocks and Intel Corporation (NASDAQ:INTC) was one of them. Here is what the fund said:
“Also on the detractor side, Intel Corporation (NASDAQ:INTC) delivered a disappointing revenue miss and lowered full-year revenue and earnings guidance as COVID-19-driven demand for PCs abated (where Intel enjoys half its sales) and a delay in its flagship Sapphire Rapids CPU hurt its data center business. Despite these issues, we still believe Intel is an economically sensitive turnaround story with substantial upside.”
9. Advanced Micro Devices, Inc. (NASDAQ:AMD)
Market Capitalization: $113 Billion
Advanced Micro Devices, Inc. (NASDAQ: AMD) operates as a semiconductor company worldwide. On December 2, Advanced Micro Devices announced the expansion of its partnership with Viettel High Tech, a member of Viettel group, to exploit a 5G network field to serve the latter’s more than 130 million mobile customers globally. Viettel is using AMD Xilinx Zynq UltraScale+ MPSoC devices. AMD works extensively with the AI open community to promote and extend machine & deep learning capabilities and optimizations to help to broaden the range of workloads.
AMD’s ROCm open software platform is constantly evolving to meet the needs of the machine learning community. With the latest release of the company’s ROCm 5.0, developers are equipped with turn-key AI framework containers on AMD Infinity Hub, improved tools, and streamlined installation, and can expect to experience reduced kernel launch times and faster application performance. On November 14, UBS analyst Timothy Arcuri upgraded Advanced Micro Devices, Inc. (NASDAQ:AMD) to Buy from Neutral with a price target of $95, up from $75, noting that the PC segment has been early in digesting inventory and been dragging down growth, but overall CPU shipments are annualizing to about 230MM units in fourth quarter, which is far below even the most bearish market forecasts for PCs in 2023.
At the end of the third quarter of 2022, 89 hedge funds in the database of Insider Monkey held stakes worth $4.99 billion in Advanced Micro Devices, Inc. (NASDAQ: AMD), compared to 87 in the previous quarter worth $4.8 billion.
In its Q2 2022 investor letter, Baron Funds, an asset management firm, highlighted a few stocks and Advanced Micro Devices, Inc. (NASDAQ: AMD) was one of them. Here is what the fund said:
“Advanced Micro Devices, Inc. (NASDAQ:AMD) is a global fabless semiconductor company focusing on high-performance computing technology, software, and products. AMD designs leading high-performance central and graphics processing units (known as CPUs and GPUs) and integrates them with hardware and software to build differentiated solutions for customers.
8. International Business Machines Corporation (NYSE:IBM)
Market Capitalization: $134 Billion
International Business Machines Corporation (NYSE:IBM) provides integrated solutions and services worldwide. On November 14, - Infosys BPM, the business process management arm of Infosys, and IBM launched the Center of AI and Automation. This announcement underscores two years of strong collaboration between Infosys BPM and IBM. On December 9, IBM revealed a new suite of environmental intelligence software that uses AI to enable organizations to prepare for and respond to weather and climate risks. The IBM Environmental Intelligence Suite helps companies streamline and automate the management of environmental risks and operationalize underlying processes like carbon accounting.
The four main goals of this suite are monitoring for disruptive environmental conditions like severe weather, wildfires, flooding and air quality while sending alerts; predicting the potential impacts of climate change and weather across the business through climate risk analytics; gaining insights into potential operational disruptions and prioritize mitigation and response efforts; to measure and report on environmental initiatives and operationalize carbon accounting. On October 17, analyst David Grossman maintained a Buy rating on International Business Machines Corporation (NYSE:IBM) stock and lowered the price target to $140 from $150, noting that the company is defensive and likely impacted less than most large-cap tech, but not immune to slowing GDP growth.
Among the hedge funds being tracked by Insider Monkey, Boston-based investment firm Arrowstreet Capital is a leading shareholder in International Business Machines Corporation (NYSE:IBM) with 4.3 million shares worth more than $515.8 million.
In its Q4 2021 investor letter, St. James Investment Company, an asset management firm, highlighted a few stocks and International Business Machines Corporation (NYSE:IBM) was one of them. Here is what the fund said:
“IBM was not the first company to build computers. The distinction belongs to Sperry-Rand’s subsidiary UNIVAC, which introduced the first commercially successful computers in the early 1950s. In this era, IBM did possess the largest research and development department in the business machines industry and quickly caught up, introducing cost-competitive computers a few years after UNIVAC. By the late 1950s, IBM held the dominant market share in computers. IBM also touted a vastly superior sales organization, which used a sales tactic called “paper machines” (the equivalent of today’s “vaporware”). If a competitor’s product was selling well in a market segment that IBM had yet to penetrate, the company would announce a competing product and start taking orders for the “paper machine” long before it was available.
7. Cisco Systems, Inc. (NASDAQ:CSCO)
Market Capitalization: $202 Billion
Cisco Systems, Inc. (NASDAQ:CSCO) designs, manufactures and sells Internet Protocol-based networking and other products related to the communications and information technology industry in the Americas, Europe, the Middle East, Africa, the Asia Pacific, Japan, and China. On October 6, Cisco Systems announced that the office of the UAE Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications had signed a memorandum of understanding with the firm for a collaboration framework expanding Cisco's Country Digital Acceleration programme in the UAE. Cisco is closely partnered with Tealbook, a vendor that uses natural language processing to help procurement teams find the right suppliers.
On November 18, Deutsche Bank analyst Matthew Niknam maintained a Hold rating on Cisco Systems, Inc. (NASDAQ:CSCO) stock and lowered the price target to $52 from $54, noting that the upside from the fiscal first quarter beat is tempered by a more cautious view on a tougher macro backdrop in upcoming periods.
Among the hedge funds being tracked by Insider Monkey, New York-based firm Two Sigma Advisors is a leading shareholder in Cisco Systems, Inc. (NASDAQ:CSCO) with 9.7 million shares worth more than $388 million.
In its Q1 2022 investor letter, Carillon Towers Advisers, an asset management firm, highlighted a few stocks and Cisco Systems, Inc. (NASDAQ:CSCO) was one of them. Here is what the fund said:
“Cisco Systems (NASDAQ:CSCO) traded lower as investors weighed how supply chain concerns would impact sales growth. The company has been upgrading its switching and routing offerings, which should lead to strong demand as on-site locations upgrade infrastructure.”
6. Oracle Corporation (NYSE:ORCL)
Market Capitalization: $219 Billion
Oracle Corporation (NYSE:ORCL) offers products and services that address enterprise information technology environments worldwide. On December 5, Twelve Labs Inc, the developer of a search platform that enables users to find moments of interest quickly in a video, partnered with Oracle Corp. to build an AI-powered model for the former. As part of the partnership, the startup will use infrastructure resources from Oracle’s cloud platform to support its AI development efforts. On October 18, NVIDIA and Oracle revealed an expanded, multi-year partnership at Oracle CloudWorld to help customers speed up the adoption of Artificial Intelligence. This partnership is another example of recent Big Tech moves to offer increasingly sophisticated AI and machine learning-powered cloud services. The collaboration will bring Nvidia’s full accelerated computing stack from GPUs to systems to software to Oracle Cloud Infrastructure OCI.
OCI will add tens of thousands more NVIDIA GPUs, including the A100 and upcoming H100, to its capacity. Oracle noted that, along with OCI’s AI cloud infrastructure of bare metal, cluster networking, and storage, enterprises will get a broad, accessible portfolio of options for AI training and deep learning. On December 5, Barclays analyst Raimo Lenschow maintained an Equal Weight rating on Oracle Corporation (NYSE:ORCL) stock and raised the price target to $81 from $70, noting that the company’s exposure to back-office workflows and its smaller but hyper-growth OCI business make sales less susceptible to growing macro pressure than other software names.
Among the hedge funds being tracked by Insider Monkey, New York-based investment firm First Eagle Investment Management is a leading shareholder of Oracle Corporation (NYSE:ORCL) with 25.9 million shares worth more than $1.6 billion.
In addition to Microsoft Corporation (NASDAQ:MSFT), Alphabet Inc. (NASDAQ:GOOG), and Apple Inc. (NASDAQ:AAPL), Oracle Corporation (NYSE:ORCL) is one of the biggest AI companies in the world.
In its Q2 2022 investor letter, Baron Funds, an asset management firm, highlighted a few stocks and Oracle Corporation (NYSE:ORCL) was one of them. Here is what the fund said:
“The sell-off in the enterprise software sector, combined with the complexity related to the acquisition of Cerner, provided an opportunity for us to re-establish a position in Oracle (NASDAQ:ORCL). Oracle is one of the world’s largest and most profitable software companies-generating more than $42 billion in revenue and 40% operating margins. We have always admired the stability of Oracle’s business and the strength of its customer relationships. Now, the company’s organic growth is beginning to accelerate. Specifically, total revenue grew 7% in the fiscal year 2022 and 10% in the fourth fiscal quarter. In addition, management believes that Cerner’s growth and margins can be higher under Oracle’s ownership than they could on a standalone basis. Finally, we commend Oracle’s repurchase of roughly half its share base over the past decade, which has nearly doubled each remaining share’s interest in the business. Trading for only 12x calendar 2023 earnings ex-cash, we believe Oracle’s risk/reward is attractive.”
Click to continue reading and see 5 Biggest AI Companies In The World.
Suggested Articles:
Disclosure. None. 15 Biggest AI Companies In The World is originally published on Insider Monkey.
| 2022-12-13T00:00:00 |
https://finance.yahoo.com/news/15-biggest-ai-companies-world-140230064.html
|
[
{
"date": "2022/12/13",
"position": 2,
"query": "artificial intelligence employers"
}
] |
|
Executive Order 13859 - Maintaining American Leadership ...
|
Maintaining American Leadership in Artificial Intelligence
|
https://www.accessagility.com
|
[
"Blog Team",
"Fa Icon",
"Calendar",
"Dec",
"Pm"
] |
Executive Order 13859 seeks to ensure that the United States remains a leader in AI technology by promoting research and development.
|
On February 11, 2019, Executive Order 13859 was signed, which aims to ensure that the United States remains at the forefront of artificial intelligence (AI) development. This order recognizes the potential of AI to revolutionize many aspects of society, including healthcare, transportation, and manufacturing, and seeks to establish a framework for continued American leadership in the field.
The executive order establishes the American AI Initiative, a national strategy for AI research and development. This initiative calls for increased investment in AI research and development, as well as greater coordination among federal agencies to ensure that the United States remains a global leader in AI technology.
One of the key goals of the American AI Initiative is to prioritize the development of AI technologies that will benefit American workers and the economy. This includes investments in AI research that will lead to the creation of high-paying jobs and the development of AI technologies that will help American businesses compete in the global market.
The executive order also calls for the promotion of AI research and development in academia and the private sector. This includes increased funding for AI research at universities and other research institutions, as well as the creation of partnerships between the government and private companies to advance AI technology.
In addition, the executive order calls for the creation of an interagency committee on AI to coordinate the efforts of federal agencies and ensure that the United States remains at the forefront of AI development. This committee will be tasked with identifying challenges and opportunities in AI research and development, as well as developing recommendations for the federal government to support the continued growth of the AI industry.
Below are twenty ways the federal government could implement AI for digital transformation and citizen services.
Personalized health and wellness services using AI-powered tools for early disease detection and prevention Enhanced public safety and security through the use of AI for predictive policing and threat analysis Improved transportation services through the implementation of AI for traffic management and autonomous vehicles Increased efficiency and accuracy in government operations through the use of AI for data analysis and decision-making Enhanced customer service through the use of AI-powered chatbots and virtual assistants Improved disaster response and recovery efforts through the use of AI for predicting and mitigating potential risks Streamlined tax collection and fraud detection through the use of AI for data analysis and predictive modeling Enhanced national defense through the use of AI for surveillance, threat detection, and military strategy Improved border security through the use of AI for facial recognition and threat detection Enhanced education and training opportunities through the use of AI for personalized learning and skill development Improved public services through the use of AI for predictive maintenance and infrastructure management Enhanced environmental protection through the use of AI for monitoring and analyzing environmental data Improved food safety and quality through the use of AI for detecting and preventing foodborne illnesses Enhanced financial services through the use of AI for fraud detection and risk assessment Improved energy conservation and management through the use of AI for optimizing energy use and predicting demand Enhanced national security through the use of AI for intelligence gathering and analysis Improved public health through the use of AI for disease surveillance and epidemic forecasting Enhanced public transportation through the use of AI for route optimization and demand prediction Improved air quality through the use of AI for monitoring and analyzing air quality data Enhanced social services through the use of AI for predicting and preventing potential cases of abuse and neglect.
Overall, Executive Order 13859 seeks to ensure that the United States remains a leader in AI technology by promoting research and development, supporting American workers and businesses, and coordinating efforts among federal agencies. By taking these steps, the United States can continue to be a global leader in AI and reap the many benefits that this technology has to offer.
Link to EO 13859 here
| 2022-12-13T00:00:00 |
https://www.accessagility.com/blog/executive-order-13859-maintaining-american-leadership-in-artificial-intelligence
|
[
{
"date": "2022/12/13",
"position": 20,
"query": "artificial intelligence business leaders"
}
] |
|
The AL/ML Marketing Deployment Matrix
|
The AL/ML Marketing Deployment Matrix
|
https://broadhurst.digital
|
[
"Martin Broadhurst"
] |
But for those companies that have the means to dedicate resources to AI/ML development within the organisation, they will only need a few successful projects to ...
|
This article is a summarised version of the journal article How businesses of any size can use AI in a digital marketing strategy, published in Applied Marketing Analytics Volume 8 Number 2 by Henry Stewart Publications.
When it comes to implementing AI/ML tools into their business, companies will vary according to a number of factors, including the company’s resources and data. To help give business leaders a sense of how best to utilise AI/ML within their marketing function, they can use the AI/ML marketing deployment matrix.
AI/ML marketing deployment matrix
We are at the very beginning of an AI revolution. In markets around the world, AI technology is disrupting the old way of doing things. Businesses must now adopt or risk being left behind.
The use cases for artificial intelligence in marketing are vast and in the coming years we will see some incredible case studies from the leaders in the field who are developing their own models. For most marketing professionals, this will continue to be a world away from their day-to-day reality. Yet AI will continue to be built into the tools that digital marketing professionals use every day; from Google Analytics to Mailchimp, AI will increasingly be ‘under the hood’ of the marketing technology that businesses, large and small, deploy.
In order to understand how best to utilise AI/ML within their marketing function, business leaders can use the AI/ML marketing deployment matrix. This is a simple framework that aims to steer marketing leaders towards the most appropriate AI/ML use cases and solutions for their organisation.
The AI/ML marketing deployment matrix is a two by two matrix. On the vertical axis is the amount of resources (human, financial) a business has. The lower half represents businesses with smaller teams and budgets and the upper half a larger teams and budgets. The horizontal axis is the amount and the quality of data a business has. The left hand side represents a small amount of data, the right hand side a large amount of data. Good quality is that which is well-structured and readily accessible (eg e-commerce transaction data, or user engagement data within an app).
Low resource, low data businesses
The quadrant in the lower left hand side represents businesses with a small amount of resource and a small amount of data. In this quadrant we find many solopreneurs and micro-businesses. These businesses have small amounts of data (little or no CRM), few resources and a lot of the marketing effort focuses on increasing reach through social media, advertising, or SEO. The management of the marketing will often fall at the feet of the business owner/manager.
A recent survey of 1,000 SMBs found small businesses consider social media their most successful digital marketing tool in 2022. Yet social media also presents challenges for small owner-managed businesses as social media network algorithms reward fresh content and demand engagement. As a result, the average half-life duration for Facebook posts is just 60 minutes. For time-poor business owners, creating new content for their social media marketing channels can often take longer than the lifespan of the post.
Given the demands on micro-business owners who wear many hats and find themselves responsible for marketing, finance, sales, service and more, AI that can improve the efficiency of any tasks should be embraced with open arms. For these resource-poor and data-lite businesses, the best use of AI comes from using AI to perform repetitive tasks, such as producing content or providing simple analysis of marketing campaign performance through free tools such as Google Analytics.
In the last twelve months, many small businesses have been able to benefit from the release of many new AI large-language models (LLMs), such as OpenAI’s GPT-3 and AI21’s Jurassic model. These LLMs can be used to manage repetitive tasks or to assist with content creation. For example, GPT-3 powered AI writing tools can greatly reduce the amount of time spent writing marketing copy or producing social media content. For a small business owner looking to have an active digital marketing presence while managing the rest of their business, AI can remove writer’s blocks and help to ensure they continue to reach and engage with audiences on social media.
Low resource, high data businesses
In the upper left hand quadrant, we find organisations with more data but still lacking in resources. These will often be small businesses with business models that produce high levels of data, such as e-commerce or SaaS technology start-ups. In this quadrant, businesses have more data to work with, but may not have the resource to make use of it effectively.
For businesses in this quadrant, the best use of AI is to analyse the company’s first-party data to improve marketing processes, such as identifying opportunities for customer segmentation or automating tasks using customer data.
Take one of the most expensive functions for a growing business: customer service. Businesses, particularly those who have customers using their services around the clock (such as e-commerce or SaaS companies), are increasingly expected to offer round-the-clock customer care but doing so is resource heavy and expensive. This is where using AI to deliver personalised customer service is extremely effective.
Modern customers are prepared to help themselves and are increasingly less reliant on customer support interactions. In ‘The Zendesk Customer Experience Trends Report 2020’ by Zendesk, 69 per cent of consumers first try to resolve their issue on their own.
Companies can support customers to do this by using AI chatbots that can be accessed by customers through the channels that they find most convenient, such as WhatsApp, Facebook Messenger, or website live chat. When connected to sources of customer data, such as e-commerce platforms (eg Shopify, WooCommerce) or CRM (eg HubSpot, Salesforce), AI Chatbots can provide first-line customer support capable of resolving simple customer service issues, such as handling return requests, or redirecting customers to a knowledge base article that answers their question.
High resourced, low data businesses
The lower right hand quadrant represents businesses with more resources but not so much data, or a poor management of any data that they have. Companies that fall into this bracket may look like ‘traditional’ businesses — manufacturing business, professional services — businesses that have sales and marketing resources, but they lack a quality data management process. Where there is a CRM in place, it may be poorly maintained or siloed from other products due to a lack of integrations.
In such circumstances, whilst the business may have a sales and marketing team that can execute, they lack sufficient first-party data, so AI cannot be used to automate customer service requests or provide insights into customer segmentation strategies. Instead, companies can use AI powered products that make use of third-party or publicly available data to inform their strategy. This could be in the form of SEO strategy, buyer persona development, or market research.
In these instances, companies should look to off-the-shelf solutions that are well-trained and already delivering value for customers. AI tools operating in the Natural Language Processing (NLP) space such as Marketmuse, use Natural Language Programming AI to assess online content which can help businesses understand what content is missing from their website, or what topics they should be writing about to rank higher in search engines.
High resource, high data businesses
The final quadrant, in the upper right hand side, is where we find companies with both a lot of resource and data. These businesses have made a significant investment in collecting and managing their data and as a result have a lot of data to work with. Enterprise organisations will typically fall into this category.
In this quadrant, businesses can use AI for a variety of tasks, including customer segmentation, predictive analytics and identifying opportunities for marketing and sales efficiencies. Not only can these organisations make use of all of the previously mentioned technologies and use cases, but these organisations have the resources to train and develop their own models.
Businesses in this category have vast amounts of data and the resources to create AI/ML models uniquely designed to solve the challenges of their business. While they can use off-the-shelf AI software and will be using ready-made solutions for standard marketing optimisation (eg email marketing send-time optimisation), they will see the biggest impact when they design their own AI/ML programs for their organisations.
It is in this quadrant that we will see the highest volume of AI/ML projects failing. Creating new ML models is difficult; common challenges such as navigating ethical concerns and managing biases, as well as the myriad other challenges associated with deploying new technologies at scale, will continue to exist. But for those companies that have the means to dedicate resources to AI/ML development within the organisation, they will only need a few successful projects to realise significant gains. This is why Google, Microsoft, Amazon, NVidia, Meta and Apple are investing so heavily into AI/ML technology.
Limitations of the AI/ML marketing deployment matrix
The quadrants as described will not neatly contain all businesses; for example, there will be micro-businesses with immense data science talent and access to vast troves of data who do not appear to slot neatly into the matrix.
This simple matrix is not intended to describe the world perfectly. In fact, it is not supposed to describe the world at all. Instead, it is intended to be an inspirational tool for marketing professionals who are interested in AI/ML but don’t know where to start. It should inspire marketing professionals to ask questions about their organisation and to understand how best to see some early successes through the deployment of AI/ML products or services.
| 2022-12-13T00:00:00 |
https://broadhurst.digital/blog/the-ai-ml-marketing-deployment-matrix
|
[
{
"date": "2022/12/13",
"position": 44,
"query": "artificial intelligence business leaders"
}
] |
|
Introduction to AI - eCornell - Cornell University
|
Introduction to AI
|
https://ecornell.cornell.edu
|
[] |
Artificial intelligence (AI) is a key area of innovation for business and everyday life, and understanding the possible applications of this technology as ...
|
Artificial intelligence (AI) is a key area of innovation for business and everyday life, and understanding the possible applications of this technology as well as the downstream impacts of these solutions is key to creating a foundation for your work involving AI.
In this course, you will be introduced to AI and examine some of the trends, applications, benefits, and risks associated with AI. You will also explore the origins, evolution, and limitations of AI. You will be challenged to provide examples of how AI is being used in your work environment, in your personal life, and in society at large, noting potential impacts and risks of AI across these areas. You will also unpack some of the social and ethical implications surrounding the use of AI to help solve everyday problems. Finally, you will discover how to examine potential AI applications and identify the critical drivers supporting an AI solution. By the end of this course, you will be set up with the foundational knowledge you need to better understand AI applications and their impact on the world today.
| 2022-12-13T00:00:00 |
2022/12/13
|
https://ecornell.cornell.edu/courses/financial-management/introduction-to-ai/
|
[
{
"date": "2022/12/13",
"position": 91,
"query": "artificial intelligence business leaders"
}
] |
IT Salary Overview: Breakdown by Role and Technology
|
IT Salary Overview: Breakdown by Role and Technology
|
https://qubit-labs.com
|
[
"Oksana Zabolotna",
"Updated Apr",
"Hbd At Qubit Labs",
"Hbd",
"Head Of Partnerships At Qubit Labs"
] |
... salary of around $36,600-$49,200. IT Salary Analytics for Artificial Intelligence Developers. Artificial Intelligence (AI) is one of the highest-paying IT jobs.
|
What will happen with the tech market in 2025? We make predictions in this IT salary guide by comprehensively analyzing IT wages for various roles, technologies, and locations.
The average software engineer salary in the USA is $135,000, but our research shows that in Eastern Europe, the rates are ~ 70% lower. Qubit Labs has prepared a detailed developers’ average salary guide for tech companies, revealing how salaries may change in 2025, what IT technologies you should invest in, and why now is the best time to hire software engineers.
IT Salary Guide Prediction for 2025
Based on our experience from previous years, we suspect 2025 will be a year of salary increases. IT wages rose in 2024, and this trend will remain in 2025. What are the highest-paid jobs? Where is the most cost-effective location for hiring developers? What are future trends? Our tech salary guide comprehensively overviews the most popular technologies in the most significant tech hubs.
IT Salary Guide: Prediction for Front-End Developers in 2025
Front-end developers are always in high demand because they are responsible for many processes, such as developing new user-facing features, determining the structure and design of web pages, building reusable codes, optimizing page loading times, and creating web pages using various markup languages.
Let us show you the annual front-end developer rates worldwide based on your experience level:
Country Junior ▾ Middle Senior Switzerland $84,000 $115,200 $138,000 USA $72,000 $114,000 $162,000 Australia $72,000 $114,000 $136,800 Canada $92,500 $105,000 $151,167 Singapore $65,500 $94,000 $141,500 Denmark $62,400 $93,600 $141,600 Netherlands $43,500 $48,000 $57,000 Norway $58,000 $65,000 $75,000 Denmark $49,600 $55,000 $63,000 Finland $57,600 $90,000 $132,000 Germany $58,500 $84,000 $126,000 Netherlands $58,000 $80,000 $98,000 Japan $54,670 $78,100 $117,150 Israel $54,000 $78,000 $110,400 Norway $58,000 $75,000 $96,000 Sweden $53,000 $73,000 $92,000 Spain $46,500 $66,000 $98,500 UK $54,000 $65,000 $87,000 Portugal $24,600 $48,000 $83,400 Poland $22,800 $47,400 $84,600 Romania $22,800 $43,800 $76,800 Brazil $22,200 $40,200 $69,000 Moldova $18,000 $39,000 $64,800 South Africa $7,291 $38,222 $48,150 Azerbaijan $17,700 $37,800 $72,000 Ukraine $15,900 $37,200 $69,000 Mexico $21,000 $37,200 $67,200 Georgia $15,900 $37,200 $66,000 Kazakhstan $16,800 $37,200 $68,400 Philippines $11,288 $14,363 $22,000 India $6,522 $14,018 $26,490
* Salary data is based on Qubit Labs’ research, indeed.com, talent.com, salaryexpert.com, and jobicy.com
Switzerland, the USA, Australia, and Canada are the highest-paying countries for front-end software developers. Western Europe offers competitive rates ranging from $65,000 to $93,600. Most Eastern European countries offer attractive wages that start at $37,200 for mid-level specialists. The same level of salary is in Mexico ($37,200), Brazil ($40,200), and South Africa ($38,222). Asia remains the most cost-effective destination, with a salary of $6,522-$11,288 for junior specialists and $22,000-$26,490 for the most experienced professionals.
IT Salary for Back-End Developers
Back-end developers are always needed as tech specialists as they are responsible for work in the background of software. They can link whatever happens on the client side with the databases by exchanging information smoothly and effectively. The main job of back-end developers is to write code and work with APIs, server logic, integration, and other processes to make your product work. Here, you can see the IT salary survey for back-end developers:
Country Junior ▾ Middle Senior USA $84,000 $120,000 $156,000 Singapore $78,500 $112,500 $169,000 Canada $100,000 $110,000 $149,978 Switzerland $79,200 $108,000 $144,000 Australia $78,000 $102,000 $138,000 Denmark $54,000 $86,400 $114,000 UK $65,000 $86,000 $118,000 Finland $50,400 $84,000 $98,400 Germany $60,000 $84,000 $132,000 Netherlands $55,000 $79,000 $112,000 Norway $56,000 $79,000 $103,000 Israel $48,000 $76,800 $114,000 Sweden $54,000 $76,000 $98,000 Japan $51,940 $74,200 $111,300 Spain $48,000 $66,000 $108,000 Poland $21,000 $45,000 $81,000 Portugal $22,600 $42,000 $70,000 Romania $20,400 $40,200 $66,000 Brazil $19,800 $37,200 $59,400 Mexico $18,600 $35,400 $57,000 Georgia $15,600 $34,800 $60,000 Kazakhstan $15,600 $34,200 $58,200 Ukraine $13,800 $33,600 $61,800 Azerbaijan $14,400 $32,400 $57,000 Moldova $12,000 $27,000 $51,000 Philippines $18,025 $25,750 $38,625 South Africa $19,591 $22,049 $55,152 India $7,000 $11,796 $25,708
* Salary data is based on Qubit Labs’ research, indeed.com, talent.com, and jobicy.com
The US IT salary survey shows that back-end developers earn the highest salary, $120,000 annually. Singapore and Canada offer $112,500 and $110,000, respectively.
In Western Europe, back-end developers earn between $76,000 and $108,000, depending on the country. Eastern Europe and Asia offer the most competitive rates. Moldova offers the lowest salary of $27,000 in Eastern Europe, and India is the cheapest country in Asia, with an annual salary of $11,796.
Information Technology Salary Guide for Python Developers
One programming language that meets all their demands is Python. This general-purpose language has been around for decades and is still gaining strong momentum for its simplicity, platform independence, user-friendliness, GUI support, and quick development time. Let’s look at the IT salary survey for Python technology worldwide.
Country Junior ▾ Middle Senior Canada $100,000 $146,119 $156,613 Switzerland $102,000 $120,000 $150,000 USA $84,000 $117,600 $160,800 Singapore $72,500 $103,500 $155,000 Australia $69,600 $102,000 $132,000 Germany $64,800 $92,500 $138,500 Japan $60,130 $85,900 $128,850 Denmark $57,600 $84,000 $108,000 Israel $57,600 $84,000 $102,000 Finland $54,000 $78,000 $98,400 UK $48,000 $75,000 $97,000 Norway $54,000 $75,000 $106,000 Netherlands $58,000 $74,000 $88,000 Spain $50,500 $72,500 $108,000 Sweden $51,000 $72,000 $88,000 Romania $24,000 $49,200 $84,000 Portugal $26,400 $48,000 $84,000 Poland $24,600 $46,200 $84,000 Ukraine $19,200 $42,600 $79,200 South Africa $12,267 $40,867 $49,594 Brazil $22,200 $39,600 $69,000 Georgia $16,200 $37,800 $72,000 Mexico $19,800 $37,200 $67,200 Kazakhstan $16,800 $37,200 $68,400 Azerbaijan $17,400 $36,000 $69,000 Moldova $15,600 $35,400 $66,000 Philippines $12,553 $14,992 $19,884 India $7,000 $12,853 $24,539
* Salary data is based on Qubit Labs’ research, indeed.com, talent.com, glassdoor.com, and jobicy.com
Canada is a top country and the highest-paying place for Python software engineers. The country offers salaries ranging from $100,000 to $156,613 annually. The US IT salary survey also shows high rates of $117,600 yearly. In Western Europe, the average salary for IT ranges from $72,000 to $120,000 for mid-level developers.
The lowest rates are in Eastern European countries and the Caucasus Region, with annual salaries from $10,000 to $20,000 based on experience level.
Tech Salary Info for Java Developers
Java is a popular technology used by 30.3% of developers worldwide. Besides, according to the report, 50% of businesses that use or deploy Java-based applications use Java to code AI functionality. Java is fast, versatile, and powerful, making it a top choice across various industries. Let’s analyze the average IT technician salary worldwide.
Country Junior ▾ Middle Senior Switzerland $102,000 $126,000 $150,000 Canada $114,685 $123,238 $152,601 USA $70,000 $110,400 $156,000 Australia $78,000 $108,000 $138,000 Singapore $69,000 $98,500 $148,000 Denmark $62,400 $96,000 $138,000 Finland $60,000 $93,600 $132,000 Germany $62,000 $88,500 $135,000 Israel $57,600 $86,400 $126,000 Netherlands $56,000 $85,000 $105,000 UK $57,000 $84,000 $96,000 Norway $56,000 $77,000 $98,000 Sweden $52,000 $73,000 $92,000 Spain $48,500 $69,000 $103,500 Poland $23,400 $48,000 $73,200 Portugal $21,600 $46,800 $72,000 Romania $19,200 $43,800 $69,000 Brazil $15,000 $43,200 $67,200 Kazakhstan $17,400 $42,000 $70,800 Mexico $16,200 $37,200 $63,000 Ukraine $14,400 $36,000 $63,000 Azerbaijan $13,800 $36,000 $63,600 Japan $20,679 $35,623 $50,550 Georgia $13,200 $31,800 $58,800 Moldova $12,600 $31,200 $55,200 Philippines $19,915 $28,450 $42,675 India $4,659 $6,944 $11,547
* Salary data is based on Qubit Labs’ research, indeed.com, talent.com, payscale.com, salaryexpert.com, and jobicy.com
Switzerland has the highest Java developer salary in the world — mid-level specialists earn $126,000 in this country. Canada, the USA, Australia, and Singapore also offer competitive rates that range from $98,500 in Singapore to $123,238 in Canada. In Western Europe, Switzerland is the highest-paying country, and the salary range in other nations is from $73,000 in Sweden to $96,000 in Denmark.
Eastern European and Caucasian countries are cost-effective yet provide the highest quality of services. The average IT specialist salary in the region is $31,200 in Moldova and $48,000 in Poland. Latin America has almost the same rates – $37,200 in Mexico and $43,200 in Brazil. Asia remains the most cost-efficient spot, with the average IT jobs salary of $6,944 in India and $28,450 in the Philippines.
IT Salary for PHP Developers
PHP has been an integral part of the web development community. The open-source language, the foundation for WordPress, Facebook, Drupal, and many other popular applications and websites, has completed its 31st anniversary in 2025. However, according to the Tiobe index, PHP is ranked 13th, making it a popular choice among global developers. How much do IT people make? The average salary for PHP specialists is as follows.
Based on these facts, we do not suspect a salary rise for specialists of this technology.
Country Junior ▾ Middle Senior USA $67,200 $105,600 $144,000 Canada $87,500 $103,873 $175,500 Switzerland $74,400 $96,000 $120,000 Australia $54,000 $93,600 $114,000 Singapore $72,000 $90,000 $144,000 Denmark $52,800 $84,000 $98,400 Germany $54,000 $80,000 $120,000 Norway $56,000 $78,000 $97,000 Israel $48,000 $73,200 $98,400 Finland $50,400 $72,000 $96,000 Netherlands $52,000 $72,000 $90,000 Sweden $51,000 $72,000 $91,000 UK $49,000 $68,000 $87,000 Spain $43,500 $66,000 $96,000 Japan $46,448 $53,083 $66,360 Portugal $22,800 $45,600 $78,600 Poland $22,800 $43,200 $75,000 Romania $22,800 $42,600 $69,600 Brazil $20,400 $37,800 $64,800 Kazakhstan $16,800 $37,200 $65,400 Mexico $19,800 $36,600 $63,600 Georgia $13,800 $34,800 $60,600 Ukraine $15,000 $34,200 $58,800 Azerbaijan $13,800 $34,200 $64,200 Moldova $12,600 $32,400 $63,000 South Africa $16,670 $28,859 $39,633 Philippines $7,323 $10,985 $14,230 India $4,090 $5,842 $13,241
* Salary data is based on Qubit Labs’ research, talent.com, indeed.com, morganmckinley.com, and jobicy.com
As for income, the US IT salary survey shows one of the highest rates, along with Australia, Canada, and Switzerland. Middle PHP specialists earn from $93,600 to $105,600 annually. Middle PHP developers in Western Europe receive approximately $68,000-$96,000. Senior specialists have an average IT engineer salary of $87,000-$98,400.
Eastern Europe and the Caucasus Region offer the cheapest rates. Mid-level PHP developers make between $32,400 and $43,200. India and the Philippines have the most reasonable rates, offering middle software engineers $5,842-$10,985 and $4,090-$7,323 to juniors.
Average IT Salary for Node.js Developers
Node.js is a popular open-source runtime environment based on JavaScript. Its popularity will grow as more companies rely on it to create programs efficiently. Node.js allows developers to write code and perform server-side scripting using JavaScript.
Country Junior ▾ Middle Senior Australia $130,000 $140,000 $151,500 USA $104,121 $122,742 $150,000 Canada $100,289 $115,000 $155,000 Switzerland $90,524 $110,326 $121,643 Singapore $31,357 $94,308 $131,700 Sweden $57,470 $82,100 $123,150 Finland $56,070 $80,100 $120,150 UK $58,087 $77,450 $103,267 Denmark $57,918 $76,020 $101,359 Netherlands $64,769 $75,556 $77,941 Israel $49,070 $70,100 $105,150 Norway $48,915 $66,567 $85,576 Spain $45,920 $65,600 $98,400 Germany $48,617 $62,117 $70,220 Poland $27,600 $49,200 $84,600 Romania $25,800 $46,200 $81,000 Japan $21,283 $44,377 $69,652 Portugal $22,800 $43,800 $81,000 Brazil $22,800 $40,800 $69,000 Georgia $15,600 $38,400 $67,800 SouthAfrica $7,291 $38,222 $48,150 Ukraine $16,800 $37,800 $67,800 Kazakhstan $16,800 $37,200 $68,400 Mexico $21,000 $37,200 $67,200 Azerbaijan $17,400 $36,000 $67,800 Moldova $15,600 $36,000 $61,800 Philippines $11,288 $14,363 $22,000 India $8,177 $14,018 $26,868
* Salary data is based on Qubit Labs’ research, talent.com, swissdevjobs.ch, talentup.io, nodeflair.com, germantechjobs.de, tech-careers.dk, spotsalary.com, devitjobs.nl, worldsalaries.com, and jobicy.com
The IT salary guide shows Australia has the highest-paying salary among other countries, with a $140,000 annual salary. The US IT salary survey shows high rates as well. Node.js developers earn $122,742 on average.
In Western Europe, the average annual income for Node.js developers is around $62,117-$110,326, depending on experience. Mid-level specialists earn from $36,000 to $49,200 in Eastern Europe and the Caucasus Region, depending on the country. An IT salary survey in South Africa offers a similar income of $38,222 yearly. India and the Philippines remain the most cost-effective destinations with the most attractive salaries for IT professionals.
IT Salary Survey for .Net Developers
NET developers use a variety of programming languages, including C#, VB.NET, and F#.
Country Junior ▾ Middle Senior Canada $95,000 $136,500 $154,599 Switzerland $90,000 $114,000 $144,000 USA $69,600 $110,400 $162,000 Denmark $69,600 $98,400 $120,000 Australia $69,600 $91,200 $114,000 Singapore $72,000 $90,000 $144,000 Norway $63,000 $84,000 $104,000 Netherlands $60,000 $82,000 $101,000 Germany $54,000 $81,600 $120,000 Finland $55,200 $80,400 $98,400 Sweden $56,000 $78,000 $94,000 UK $58,000 $78,000 $92,000 Israel $48,000 $72,000 $96,000 Spain $43,500 $69,600 $96,000 Portugal $24,000 $46,800 $78,600 Poland $24,000 $45,000 $75,000 Romania $24,000 $43,800 $69,600 Brazil $20,400 $39,000 $64,800 Kazakhstan $16,800 $37,200 $65,400 Mexico $21,000 $36,600 $64,200 Japan $16,340 $35,470 $56,417 Ukraine $15,600 $35,400 $60,000 Georgia $13,800 $34,800 $60,600 Azerbaijan $15,000 $34,800 $64,200 Moldova $15,000 $33,000 $64,200 SouthAfrica $21,397 $32,385 $44,251 Philippines $4,803 $12,630 $17,337 India $4,663 $5,887 $12,240
* Salary data is based on Qubit Labs’ research, talent.com, glassdoor.com, indeed.com, worldsalaries.com, and jobicy.com
The highest IT job salary for a .NET specialist is in Canada— $136,500 —whereas Switzerland and the USA offer competitive rates — $114,000 and $110,400, respectively. In Western Europe, the average salary in IT ranges from $78,000 in the UK to $98,400 in Denmark. Eastern Europe offers superior quality at attractive rates. Moldova has the lowest IT jobs salaries — $15,000 for juniors, $33,000 for middle specialists, and $64,200 for senior roles.
The IT tech salary range in Latin America is $36,600-$39,000. Asia remains a top location for hiring developers due to its top IT salary jobs and affordable wages — $5,887 in India and $12,630 in the Philippines.
IT Salary Survey for Mobile Developers
Mobile app development keeps on showing massive progress every year. Such technologies in app development constantly improve the features and performance at a faster speed. We suspect a constant pay rise for mobile developers in 2025.
But, now let’s take a look at the potential rates of Android developers in our IT salary guide based on level of experience:
Country Junior ▾ Middle Senior Canada $105,000 $121,987 $154,087 USA $69,600 $117,600 $144,000 Singapore $82,000 $117,500 $176,000 Switzerland $74,400 $108,000 $126,000 Australia $72,000 $98,400 $114,000 Norway $62,000 $88,000 $115,000 Denmark $57,600 $86,400 $114,000 Netherlands $62,000 $85,000 $108,000 Germany $63,000 $84,000 $126,000 Finland $50,400 $84,000 $102,000 Israel $57,600 $84,000 $110,400 Spain $57,500 $82,000 $122,500 Sweden $58,000 $77,000 $105,000 UK $53,000 $72,000 $86,000 Japan $39,831 $46,470 $59,751 Portugal $21,000 $42,600 $69,000 Brazil $24,000 $42,600 $63,000 Poland $20,400 $42,000 $69,000 Romania $19,800 $40,200 $67,200 Mexico $18,000 $36,600 $56,400 Georgia $15,600 $36,000 $59,400 Kazakhstan $15,600 $33,000 $57,000 Azerbaijan $14,400 $33,000 $57,000 Ukraine $14,400 $31,200 $61,800 South Africa $16,424 $28,677 $44,151 Moldova $12,000 $28,200 $51,000 Philippines $10,422 $11,568 $15,634 India $5,248 $9,331 $23,329
* Salary data is based on Qubit Labs’ research, talent.com, morganmckinley.com, and glassdoor.com
Canada has the highest IT job income, at $121,987. The USA and Singapore offer $117,600 and $117,500, respectively. The Australian IT salary survey shows the highest rate among the other countries, with an average salary of $98,400 yearly. In Western Europe, mobile app specialists can earn from $72,000 in the UK to $108,000 in Switzerland.
An IT salary survey in Eastern Europe and the Caucasus Region shows quite affordable rates compared to other countries. Mobile app developers there receive around $31,200 to $42,000 yearly. In Latin America, the average salary of an IT is $36,600-$42,600. Asia is a cost-effective region with many IT jobs and salary of $9,331-$11,568.
IT Technology Salary Guide for Game Developers
Game development has shown considerable progress and interest worldwide. Building these games is an arduous process that requires unique game engines. Two popular game engines are Unity and Unreal Engine.
This software is designed primarily to ease the game development process. Based on our experience, we predict a salary increase for game developers specializing in Unity and Unreal engines. Our IT salary guide shows the yearly income of Unity developers.
Country Junior ▾ Middle Senior Canada $110,786 $140,000 $154,862 USA $74,400 $106,800 $138,000 Switzerland $90,000 $105,600 $126,000 Australia $72,000 $93,600 $117,600 Denmark $72,000 $91,200 $110,400 Singapore $72,000 $90,000 $144,000 Finland $64,800 $86,400 $102,000 Japan $60,130 $85,900 $128,850 Norway $58,000 $76,000 $91,000 Germany $54,000 $72,000 $102,000 Israel $50,400 $72,000 $108,000 UK $48,000 $69,000 $85,000 Netherlands $56,000 $68,000 $85,000 Spain $48,000 $66,000 $108,000 Sweden $49,000 $60,000 $78,000 Portugal $23,400 $40,800 $60,000 Poland $19,200 $37,200 $62,400 South Africa $24,710 $35,300 $52,950 Ukraine $13,800 $33,600 $57,000 Romania $16,200 $33,000 $56,400 Philippines $20,860 $29,800 $44,700 Mexico $16,200 $28,800 $44,400 Georgia $13,200 $27,600 $48,000 Brazil $14,400 $25,800 $40,800 Azerbaijan $12,000 $24,600 $44,400 Kazakhstan $11,400 $24,000 $43,200 Moldova $10,200 $24,000 $46,800 India $5,245 $9,326 $17,484
* Salary data is based on Qubit Labs’ research, talent.com, and jobicy.com
As you can see, Canada, the USA, and Switzerland are leading countries regarding high IT wages. Mid-level game developers earn from $105,600 to $140,000 on average. In Western Europe, specialists receive around $60,000-$91,200 annually. India has the lowest rates among other Asian countries, with an average IT income of $9,326 for mid-level developers.
Tech Salary Survey for Blockchain Developers
Blockchain developers create apps using blockchain technology. They also design the infrastructure, establish security standards, write software, and more. They must know the blockchain channel’s architecture, operation, cryptography, web development, and more. The organizational hierarchy gives blockchain developers a high position, and they are paid very well for their competence.
Let’s analyze the C++ developer salary, one of the core languages for blockchain development.
Country Junior ▾ Middle Senior USA $76,800 $120,000 $162,000 Switzerland $90,000 $114,000 $144,000 Canada $77,945 $111,350 $167,025 Denmark $72,000 $102,000 $144,000 Japan $68,320 $97,600 $146,400 Australia $74,400 $96,000 $117,600 Finland $66,000 $96,000 $120,000 Singapore $62,000 $89,000 $134,000 Israel $62,000 $86,400 $114,000 Norway $59,000 $86,000 $104,000 Sweden $56,000 $83,000 $99,000 Germany $54,000 $80,000 $120,000 UK $60,000 $78,000 $92,000 Netherlands $56,000 $77,000 $92,000 Spain $43,500 $69,600 $96,000 Brazil $21,000 $45,000 $62,400 Portugal $18,000 $42,000 $65,000 Mexico $19,800 $42,000 $60,600 Poland $18,000 $41,400 $69,600 South Africa $28,070 $40,100 $60,150 Romania $17,400 $37,200 $64,200 Ukraine $12,600 $34,200 $60,000 Philippines $23,695 $33,850 $50,775 Georgia $12,600 $30,000 $54,000 Kazakhstan $15,600 $29,400 $47,400 Azerbaijan $12,000 $28,800 $52,800 Moldova $11,400 $28,800 $52,200 India $7,058 $14,862 $18,198
* Salary data is based on Qubit Labs’ research, talent.com, indeed.com, glassdoor.com, careervira.com, and jobicy.com
In 2025, the highest IT professional salary is in the USA — $120,000 for middle professionals and $162,000 for seniors. Switzerland, Canada, and Denmark also have high IT tech salaries.
In Western Europe, blockchain specialists have a starting salary of $56,000-$72,000, whereas in Eastern Europe, the IT major salary is $28,800-$41,400 for middle specialists, making it an attractive destination. In Asia, India has the lowest IT pay rate of $14,862.
IT Salary Analytics for Cloud Engineers
Nowadays, cloud computing is the gold standard for a career in technology. As technology progresses and all tech operations transition from on-site to “on-cloud,” there is an ever-increasing demand for cloud engineers and architects. Based on these facts, we predict an even higher demand for skillful Cloud engineers, hence salary increases.
Here is a breakdown of an IT degree salary for Golang professionals.
Country Junior ▾ Middle Senior Canada $126,750 $152,002 $165,750 USA $115,006 $132,724 $165,750 Singapore $79,030 $112,900 $169,350 Australia $73,990 $105,700 $158,550 Switzerland $90,776 $104,937 $138,971 Norway $73,150 $104,500 $156,750 Denmark $71,310 $103,300 $154,800 Netherlands $69,790 $99,700 $149,550 Sweden $68,950 $98,500 $147,750 Finland $67,270 $96,100 $144,150 Japan $65,590 $93,700 $140,550 UK $71,229 $87,426 $110,082 Israel $58,870 $84,100 $126,150 Spain $55,090 $78,700 $118,050 Germany $51,940 $70,333 $75,744 Poland $27,000 $49,200 $87,600 Portugal $26,400 $48,600 $87,000 Romania $25,800 $48,600 $82,800 Azerbaijan $22,200 $44,400 $78,000 Georgia $19,800 $41,400 $75,000 Brazil $22,800 $40,800 $65,400 Moldova $19,200 $40,800 $73,800 Kazakhstan $20,400 $40,200 $69,600 South Africa $26,950 $38,500 $57,750 Ukraine $18,600 $36,600 $72,000 Mexico $19,800 $35,400 $58,800 Philippines $22,750 $32,500 $48,750 India $14,000 $20,361 $37,494
* Salary data is based on Qubit Labs’ research, talent.com, swissdevjobs.ch, germantechjobs.de, and jobicy.com
As we can see from our research, Canada has the highest IT average salary of $152,002. The USA, Singapore, Australia, Switzerland, Norway, and Denmark also have a high average IT pay of over $100,000.
Western Europe and Japan have lower rates than Australia and America, with annual salaries around $99,700 and $93,700 on average. The lowest rates are in Eastern European countries and the Caucasus region, with a yearly salary of around $36,600-$49,200.
IT Salary Analytics for Artificial Intelligence Developers
Artificial Intelligence (AI) is one of the highest-paying IT jobs. As industries like tech, financial services, and medical research turn to AI, AI engineer salaries will keep rising. As more global brands like Google and Nvidia dive deeper into AI, the demand for these engineers and their wages will only increase in 2025 and the following decades.
How much does IT make? Let’s look at the IT salary survey for AI engineers worldwide.
Country Junior ▾ Middle Senior Switzerland $117,720 $159,600 $239,400 USA $115,803 $153,286 $204,851 Singapore $95,480 $136,400 $204,600 Australia $106,270 $128,047 $200,000 Norway $88,375 $126,250 $189,375 Denmark $87,360 $124,800 $187,200 Germany $85,330 $121,900 $182,850 Netherlands $84,315 $120,450 $180,675 Sweden $83,300 $119,000 $178,500 Finland $81,270 $116,100 $174,150 Japan $79,240 $113,200 $169,800 Canada $78,517 $110,119 $165,000 Israel $71,120 $101,600 $152,400 Spain $66,552 $95,075 $142,612 UK $63,910 $77,702 $104,906 Poland $34,800 $54,000 $80,400 Brazil $37,200 $52,800 $73,200 Mexico $36,600 $51,600 $72,000 Romania $30,000 $50,400 $73,200 Portugal $30,000 $48,000 $72,000 Ukraine $25,200 $46,800 $69,000 South Africa $32,550 $46,500 $69,750 Azerbaijan $24,600 $43,800 $63,600 Georgia $22,800 $41,400 $60,600 Kazakhstan $22,800 $39,600 $61,800 Philippines $27,475 $39,250 $58,875 Moldova $18,000 $37,200 $56,400 India $9,804 $17,507 $40,851
* Salary data is based on Qubit Labs’ research, talent.com, and jobicy.com
The best country to work as an AI engineer is Switzerland, which pays an entry level salary of $117,720. Experienced specialists earn $101,600 (Israel) — $153,286 (the USA). Eastern Europe is a popular outsourcing destination as the IT salary average is $37,200-$54,000.
IT Job Average Salary for IoT Developers
IoT is one of the emerging technologies embraced by enterprises across verticals. Last year, more than 10 million connected IoT devices were in use, and the number is expected to surpass 25 billion by 2025 and 41 billion by 2027. Businesses can boost their IoT capabilities. Let’s analyze the IT salary in America and the world for C# developers.
Country Junior ▾ Middle Senior Canada $95,000 $136,500 $154,599 USA $69,600 $115,200 $162,000 Switzerland $86,400 $114,000 $144,000 Denmark $67,200 $96,000 $117,600 Australia $72,000 $93,600 $108,000 Singapore $66,000 $89,000 $134,000 Norway $61,000 $82,000 $102,000 Germany $54,000 $80,000 $120,000 Netherlands $58,000 $80,000 $98,000 Finland $50,400 $78,000 $90,000 Spain $48,000 $78,000 $117,800 Sweden $54,000 $76,000 $92,000 UK $56,000 $75,000 $90,000 Israel $44,400 $72,000 $98,400 Portugal $24,000 $45,600 $78,600 Poland $22,800 $43,200 $75,000 Romania $24,400 $42,600 $69,600 Brazil $20,400 $37,800 $64,800 Kazakhstan $16,800 $37,200 $65,400 Mexico $19,800 $36,600 $63,600 Japan $16,340 $35,470 $56,417 Ukraine $15,600 $35,400 $58,800 Azerbaijan $15,000 $34,800 $64,200 Georgia $13,800 $34,800 $60,600 Moldova $15,000 $33,000 $64,200 South Africa $21,397 $32,385 $44,251 Philippines $4,803 $12,630 $17,337 India $4,663 $5,887 $12,240
* Salary data is based on Qubit Labs’ research, talent.com, and jobicy.com
How much does an IT make? Canada, the USA, and Switzerland offer the highest rates, ranging from $114,000 to $136,500. In Western Europe, the average salary of IT is $75,000-$96,000. Eastern European and Latin American countries have a more competitive salary of IT professionals of $33,000-$43,200. India has the lowest average pay for IT — $5,887.
Cyber Security Engineer Salary Worldwide
Cybersecurity specialists are always in high demand among tech organizations. However, the world has faced a vast tech talent shortage, driving up the salaries of these professionals. Here is a brief breakdown of major tech salary trends in cybersecurity.
Country Junior ▾ Middle Senior Canada $92,600 $124,979 $154,429 Switzerland $93,662 $119,207 $130,560 Australia $100,198 $111,607 $173,832 USA $92,586 $109,176 $136,500 Norway $73,150 $104,500 $156,750 Denmark $72,310 $103,300 $154,950 Sweden $68,950 $98,500 $147,750 Finland $67,270 $96,100 $144,150 Singapore $45,242 $92,849 $128,763 Netherlands $56,775 $77,863 $91,921 Germany $56,778 $64,889 $70,297 UK $46,646 $58,267 $80,928 Israel $39,328 $45,882 $106,484 Portugal $24,600 $40,800 $62,400 Poland $24,600 $40,800 $61,200 Japan $33,405 $40,086 $80,174 Romania $19,200 $36,000 $56,400 Spain $30,826 $35,152 $37,856 Ukraine $15,600 $33,600 $52,800 Georgia $15,600 $33,000 $50,400 Kazakhstan $15,600 $32,400 $50,400 Brazil $16,200 $31,200 $46,800 Azerbaijan $15,000 $31,200 $48,000 Moldova $14,400 $31,200 $49,200 South Africa $8,797 $30,176 $44,276 Mexico $14,400 $28,800 $44,400 Philippines $6,412 $8,140 $13,954 India $5,055 $5,446 $11,328
* Salary data is based on Qubit Labs’ research, talent.com, swissdevjobs.ch, germantechjobs.de, devitjobs.nl, indeed.com, prosfy.com, glassdoor.com, morganmckinley.com, and jobicy.com
How much does IT jobs pay? Canada, Switzerland, and Australia offer the highest rates. The IT salary US is also high — $109,176 annually. In Western Europe, the salary of an IT is $40,800-$104,500. Eastern Europe and Latin America offer $28,800-$40,800 to mid-level programmers.
Data Science Developer Income
The US Bureau of Labor Statistics predicts that mathematician and statistician roles, including data scientist jobs, will experience an 11 percent growth between 2023 and 2031, much faster than the average 8 percent for all information technology occupations. How much does IT earn? Let’s analyze the average Scala developer salary.
Country Junior ▾ Middle Senior Canada $112,469 $140,867 $158,025 USA $90,000 $126,000 $165,600 Switzerland $96,000 $120,000 $150,000 Singapore $78,500 $112,500 $169,000 Germany $66,000 $102,000 $132,000 Denmark $78,000 $98,400 $138,000 Netherlands $61,000 $88,000 $102,000 UK $60,000 $88,000 $102,000 Australia $69,600 $86,400 $98,400 Israel $60,000 $86,400 $102,000 Spain $54,000 $78,000 $114,000 Norway $58,000 $75,000 $102,000 Sweden $54,000 $72,000 $93,000 Finland $54,000 $72,000 $96,000 Portugal $27,600 $54,000 $81,000 Poland $28,200 $52,200 $83,400 Japan $33,218 $46,501 $53,144 Romania $23,400 $45,000 $81,000 Georgia $17,400 $43,200 $75,000 Brazil $23,400 $42,600 $75,600 Ukraine $18,000 $39,000 $73,200 Kazakhstan $16,800 $39,000 $69,000 Azerbaijan $17,400 $37,800 $75,000 Mexico $21,000 $37,800 $72,000 Moldova $15,000 $33,000 $69,600 South Africa $18,629 $25,971 $35,067 India $17,480 $23,306 $34,960 Philippines $6,758 $10,226 $14,557
* Salary data is based on Qubit Labs’ research, talent.com, payscale.com, and jobicy.com
The US and Canada are leading countries in terms of high salaries for Data Science engineers. The salary for senior level positions ranges between $165,600 and $158,025, respectively. In Western Europe, mid-level Data science developers earn approximately $45,000-$120,000.
Data Science developers earn between $33,000 and $52,200 in Eastern European countries and the Caucasus Region. The lowest median annual wage is in India and the Philippines.
Manual QA Engineer Analytics Worldwide
With software becoming the main value driver and the primary interface between developers and customers, the focus on software quality is growing. The need to deliver high-quality, bug-free products in ever-accelerating cycles is putting pressure on software teams.
Country Junior ▾ Middle Senior Canada $75,777 $117,000 $131,860 Australia $94,450 $116,422 $160,000 Switzerland $73,220 $104,600 $156,900 USA $66,713 $82,875 $108,584 Norway $57,925 $82,750 $124,125 Denmark $57,260 $81,800 $122,700 Germany $55,930 $79,900 $119,850 Netherlands $55,265 $78,950 $118,425 Sweden $54,600 $78,000 $117,000 Finland $53,270 $76,100 $114,150 Israel $46,620 $66,600 $99,900 Spain $43,627 $62,325 $93,487 Portugal $37,310 $53,300 $79,950 UK $38,723 $46,790 $70,522 Japan $33,283 $46,593 $66,562 Singapore $31,413 $39,000 $58,339 Poland $14,400 $37,310 $79,950 Brazil $17,400 $31,200 $43,200 Romania $16,200 $30,000 $43,200 Mexico $16,200 $29,400 $42,000 Philippines $18,025 $25,750 $38,625 Georgia $13,200 $24,000 $34,200 Kazakhstan $11,400 $24,000 $36,000 Azerbaijan $12,000 $23,400 $36,000 Ukraine $9,600 $22,200 $38,400 South Africa $16,045 $20,571 $43,857 Moldova $9,000 $18,000 $30,000 India $6,995 $13,991 $25,797
* Salary data is based on Qubit Labs’ research, talent.com, payscale.com, morganmckinley.com, and jobicy.com
How much does a IT person make? QA engineering is a relatively high-paying job worldwide. Still, the highest rates are in Canada, Australia, and Switzerland. Such specialists earn from $104,600 on average to $117,000. In Western Europe, on average, mid-level QA engineers can earn $46,790-$82,750 annually.
The countries with the lowest rates are IT Outsourcing in Eastern Europe, where you can build your nearshore tech team, the Caucasus region, India, and the Philippines.
Salesforce Developer Salary
The Salesforce ecosystem is expanding, so the demand for skilled specialists will only increase. Our IT salary survey shows the current income situation:
Country Junior ▾ Middle Senior Switzerland $86,400 $120,000 $156,000 USA $78,000 $114,000 $144,000 Canada $100,423 $110,000 $142,500 Singapore $76,000 $108,500 $162,500 Australia $78,000 $102,000 $138,000 Denmark $74,400 $98,400 $138,000 Germany $67,500 $96,500 $145,000 Finland $72,000 $91,200 $108,000 Japan $62,860 $89,800 $134,700 Israel $64,800 $86,400 $110,400 Norway $62,000 $83,000 $112,000 Netherlands $58,000 $79,000 $101,000 UK $59,000 $79,000 $91,000 Sweden $53,000 $78,000 $98,000 Spain $52,500 $75,000 $113,000 Portugal $25,200 $54,000 $76,200 Poland $25,200 $46,200 $78,000 Romania $25,200 $45,000 $75,000 South Africa $26,306 $39,459 $34,734 Kazakhstan $15,600 $39,000 $65,400 Mexico $17,400 $38,400 $63,000 Ukraine $18,000 $37,800 $68,400 Georgia $16,800 $37,200 $63,000 Azerbaijan $17,400 $36,600 $67,200 Brazil $16,800 $34,200 $63,000 Moldova $16,800 $33,000 $63,000 Philippines $21,805 $31,150 $46,725 India $3,488 $7,181 $11,668
* Salary data is based on Qubit Labs’ research, talent.com, indeed.com, payscale.com, and jobicy.com
As for yearly income, Salesforce developers earn $100,000+ in Australia, Singapore, Canada, the USA, and Switzerland. In Western Europe, Salesforce specialists usually make $79,000 to $98,400 annually. In Eastern Europe, the rates are $33,000-$46,200.
IT Salary Trends: Future Outlook
In 2025, companies will have to deal with IT talent shortages, increasing competition for skilled talent. Technological advancements like AI, automation, cybersecurity, cloud, data science, and remote work trends will boost IT salaries, which will grow steadily in the coming years.
Final Thoughts
Salaries for IT professionals are growing, especially in highly popular fields like Artificial Intelligence, Machine Learning, data science, cybersecurity, and cloud computing. Regardless of the tech stack, the USA, Canada, Australia, and Switzerland have the highest wages — $100,000+ for mid-level professionals.
Western Europe also offers competitive rates that range from $50,000 to $100,000, depending on location and industry. Eastern European countries remain a popular outsourcing destination due to their perfect price-to-quality ratio. Local mid-level engineers earn from $20,000 to $46,200. India and the Philippines are the top destinations for hiring IT talent.
To boost your development capabilities and access niche expertise, hire offshore developers who will bring innovation and empower your in-house team. Qubit Labs, a top outstaffing company, will land a superior dedicated team tailored to your needs or provide dedicated software developers to bring your idea to life. Schedule a free consultation call to discuss your project.
| 2024-07-03T00:00:00 |
2024/07/03
|
https://qubit-labs.com/it-salary-guide-what-to-expect-in-2025/
|
[
{
"date": "2022/12/13",
"position": 9,
"query": "artificial intelligence wages"
}
] |
How artificial intelligence chatbots could affect jobs
|
How artificial intelligence chatbots could affect jobs
|
https://unctad.org
|
[] |
Chatbots can potentially create winners and losers and will affect both blue-collar and white-collar workers.
|
18 January 2023
By Shamika N. Sirimanne, Director of UNCTAD’s Division on Technology and Logistics
The recent launch of ChatGPT, a chatbot created by Open AI for public use, has underscored the growing reach of digital technologies like artificial intelligence (AI) in working life.
As with most technological revolutions that affect the workplace, chatbots can potentially create winners and losers and will affect both blue-collar and white-collar workers.
To maximize economic gains and minimize the potential negative impact on workers, policymakers need to act in the interests of all of society. And those in developing countries need to step up the pace in preparation for such technologies or risk falling further behind.
ChatGPT shows the promise of AI
ChatGPT is a natural language processing (NLP) tool that allows users to interact with the GPT-3 model using natural language. The model is trained on a massive amount of data, which allows it to generate human-like responses to a wide variety of inputs.
You can ask this bot to perform various tasks. Some of the paragraphs in this article, – while still needing some editing – were written by the chatbot, using requests such as “What is ChatGPT?” and “What are the potential uses and benefits of technologies like ChatGPT?”.
Technologies like GPT-3 still have limitations. These include incorrect responses, lack of updated information and access to the internet – and the potential for bias in algorithms on issues such as race and gender.
For example, we asked the chatbot its suggestions to mitigate some of the limiting factors, and the results show instances where AI does not go beyond commonplace solutions (see the table below).
Limitations of AI technologies and mitigation steps suggested by ChatGPT Limitations of AI technologies Possible mitigation measures Possible mistakes and inaccurate or inappropriate responses Develop and implement regulations and guidelines Requires technological infrastructure and expertise Invest in education and training programmes Ethical concerns Develop and implement regulations and guidelines May exacerbate existing inequalities and the digital divide Foster collaboration and partnerships among businesses, organizations and individuals to share knowledge, expertise and resources
Source: Table generated by ChatGPT on 14 December 2022.
Work will never be the same
Despite limitations, this type of AI can greatly benefit the productivity of skilled workers.
Chatbots offer the possibility to automate tedious and time-consuming tasks, such as writing standardized reports, meeting minutes and emails. Workers could therefore be freed to focus on more critical and creative tasks. A chatbot virtual personal assistant could guide skilled workers through different projects or production processes. It can also generate original content and ideas, and potentially help to research and develop new products and services.
Moreover, tools like ChatGPT are an appealing and cost-effective choice for businesses and individuals looking to use the capabilities of AI without the need for additional, costly equipment.
Siri (developed by Apple), Alexa (by Amazon) and Google Assistant are three well-known human-like AI assistants that often require a slew of related products, such as an iPhone, echo dot or google devices. Despite not having voice processing capabilities, ChatGPT is more affordable: access to the internet and basic literacy are sufficient to make it work.
Potential winners and losers
But tools such as ChatGPT presents a real risk of skilled and semi-skilled workers losing their jobs.
For example, chatbots can be developed to train employees in an organization, resulting in the redundancy of human trainers.
Previous waves of technological change have created both winners and losers. Workers who are quicker to adjust to technological change will gain by increasingly taking on tasks complementary to AI while abandoning automated ones. And the great potential for the creation of new jobs is in innovation using tools like ChatGPT to bring new goods and services to the market.
For example, several new jobs associated with frontier technologies emerged in the past few years, from social media managers to drone pilots. But these adjustments will take time, and the breakneck speed of changes that can come with cheap AI personal assistants may significantly impact office workers.
Barriers for developing countries
This technology’s impact is not restricted to work in advanced economies.
With gig work and digital transformation, the expectation was that some skilled workers in developing countries were getting ready to compete for more skill-intensive jobs around the world as “telemigrants” in jobs such as accountants, legal clerks, software developers, or even X-ray analysts. The risk is that these jobs will be taken by the ChatGPTs of the world.
Moreover, most firms and workers in developing countries may not be able to take advantage of this personal use of AI to increase productivity.
Most high-income countries and some upper middle-income countries have the highest potential to benefit from these AI technologies, given that they are above average in the availability of high-skilled labour and internet download speed, as a proxy of the quality of digital infrastructure (see the graph below). On the opposite end of the spectrum, low-income and most lower middle-income countries are in the worst situation to take advantage of these technologies, given their low share of skilled workers and relatively slow download speed.
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Developing economies generally lag in the adoption of digital technologies and risk repeating this pattern with the latest frontier technologies.
An UNCTAD readiness index that assesses countries’ abilities to use, adopt and adapt frontier technologies highlights the leading position of developed economies, showing that developing countries still struggle with issues like digital skills and infrastructure, and research and development.
The way forward
Ultimately, developing countries need to prepare to benefit from AI by promoting the technology's use, adoption, adaptation and development. There is no one-size-fits-all strategy on how this could be done. But generally, it requires actions in four main areas.
First, we need to continue preparing the workforce for work in the twenty-first century. This means developing digital skills and building and strengthening complementary skills such as complex problem solving, critical thinking and creativity.
Second, we need to take care of those who will lose in the transition to new forms of work. Reskilling programmes should be part of government policies and programmes to address job loss due to new technologies. Life-long learning initiatives, involving the training and re-training of workers, are increasingly the joint responsibility of governments, employers and workers.
Third, we need to promote inclusiveness and broadly share the benefits of this powerful technology. For this, we need to promote an open innovation approach for AI, in which inputs, methods and results of the innovation are shared openly with different people who could use them for further innovation.
And fourth, the impact of frontier technologies will be felt by all, but not all are participating equally in defining the path that frontier technologies like AI will follow. It is critical to establish ethical frameworks and regulations for these technologies.
| 2023-01-18T00:00:00 |
2023/01/18
|
https://unctad.org/news/blog-how-artificial-intelligence-chatbots-could-affect-jobs
|
[
{
"date": "2022/12/14",
"position": 1,
"query": "AI impact jobs"
},
{
"date": "2022/12/14",
"position": 49,
"query": "automation job displacement"
},
{
"date": "2022/12/14",
"position": 13,
"query": "AI replacing workers"
},
{
"date": "2022/12/14",
"position": 10,
"query": "reskilling AI automation"
},
{
"date": "2022/12/14",
"position": 100,
"query": "future of work AI"
},
{
"date": "2022/12/14",
"position": 23,
"query": "workplace AI adoption"
},
{
"date": "2022/12/14",
"position": 10,
"query": "artificial intelligence workers"
}
] |
The Dawn of Artificial Imagination
|
The Dawn of Artificial Imagination
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https://www.theatlantic.com
|
[
"Matteo Wong"
] |
For years, fears about the disruptive potential of automation and artificial intelligence have centered on repetitive labor: Perhaps machines could replace ...
|
For years, fears about the disruptive potential of automation and artificial intelligence have centered on repetitive labor: Perhaps machines could replace humans who do secretarial work, accounting, burger-flipping. Doctors, software engineers, authors—any job that requires creative intelligence—seemed safe. But the past few months have turned those narratives on their head. A wave of artificial-intelligence programs, collectively dubbed “generative AI,” have shown remarkable aptitude at using the English language, competition-level coding, creating stunning images from simple prompts, and perhaps even helping discover new drugs. In a year that has seen numerous tech hype bubbles burst or deflate, these applications suggest that Silicon Valley still has the power to, in subtle and shocking ways, rewire the world.
A reasonable reaction to generative AI is concern; if not even the imagination is safe from machines, the human mind seems at risk of becoming obsolete. Another is to point to these algorithms’ many biases and shortcomings. But these new models also spark wonder, of a science-fictional variety—perhaps computers will not supersede human creativity so much as augment or transform it. Our brains have largely benefited from calculators, computers, and even internet search engines, after all.
“The reason we built this tool is to really democratize image generation for a bunch of people who wouldn’t necessarily classify themselves as artists,” Mark Chen, the lead researcher on DALL-E 2, a model from OpenAI that transforms written prompts into visual art, said during The Atlantic’s first-ever Progress Summit yesterday. “With AI, you always worry about job loss and displacement, and we don’t want to kind of ignore these possibilities either. But we do think it’s a tool that allows people to be creative, and we’ve seen, so far, artists are more creative with it than regular users. And there’s a lot of technologies like this—smartphone cameras haven’t replaced photographers.”
Chen was joined by The Atlantic’s deputy editor, Ross Andersen, for a wide-ranging conversation on the future of human creativity and artificial intelligence. They discussed how DALL-E 2 works, the pushback OpenAI has received from artists, and the implications of text-to-image programs for developing a more general artificial intelligence.
Their conversation has been edited and condensed for clarity.
Ross Andersen: To me, this is the most exciting new technology in the AI space since natural-language translation. When some of these tools first came out, I started rendering images of dreams that I had when I was a kid. I could show my kids stuff that had only previously appeared in my mind. I was wondering, since you created this technology, if you could tell us a bit about how it does what it does.
Mark Chen: There’s a long training process. You can imagine a very small child that you’re showing a lot of flash cards to, and each of these flash cards has an image and a caption on it. Maybe after seeing hundreds and millions of these, whenever there’s the word panda, it starts seeing a fuzzy animal or something that’s black and white. So it forms these associations, and then kind of builds its own kind of language for basically representing language and images, and then is able to translate that into images.
Andersen: How many images is DALL-E 2 trained on?
Chen: Several hundred millions of images. And this is a combination of stuff that we’ve licensed from partners and also stuff that’s publicly available.
Andersen: And how were all those images tagged?
Chen: A lot of natural images on the web have captions associated with them. A lot of the partners that we work with, they also provide data with annotations describing what’s in the image.
Andersen: You can do really complex prompts that generate really complex scenes. How is the thing creating a whole scene; how does it know how to distribute objects within the visual field?
Chen: These systems, when you train them, even on individual objects—it knows what a tree is; it knows what a dog is—it’s able to combine things in ways that it hasn’t seen in the training set before. So if you ask for a dog wearing a suit behind a tree or something, it can synthesize all these things together. And I think that’s part of the magic of AI, that you can generalize beyond what you trained it on.
Andersen: There’s also an art to prompt writing. As a writer, I think quite a bit about crafting sequences of words that will conjure vivid images in the mind of a reader. And in this case, when you play with this tool, the reader’s imagination has the entire digital library of humankind at its disposal. How has the way you thought about prompting changed from DALL-E 1 to DALL-E 2?
Chen: Even up to DALL-E 2, a lot of the ways people induced image generation was with short, one-sentence descriptions. But people are now adding very specific details, even the textures they want. And it turns out the model can kind of pick up on all of these things and make very subtle adjustments. It’s really about personalization—all of these adjectives that you’re adding help you basically personalize the output to what you want.
Andersen: There are a lot of contemporary artists that have been upset by this technology. When I was messing around generating my dreams, there’s a Swedish contemporary artist named Simon Stålenhag who has a style that I love, and so I slapped his name on the end of it. And indeed, it just transformed the whole thing into this beautiful Simon Stålenhag–style image. And I did feel a pang of guilt about that, like I almost wish that it was a Spotify model with royalties. But then there’s another way of looking at that, which is just, too bad—the entire history of art is about mimicking the style of masters and remixing preexisting creative styles. I know you guys are getting a lot of blowback about this. Where do you think that’s going?
Chen: Our goal isn’t to go and stiff artists or anything like that. Throughout the whole release process, we’ve wanted to be very conscientious and work with the artists, have them tell us what it is they want out of this and how can we make this safer. We want to make sure we continue to work with artists and have them provide feedback. There’s a lot of solutions that are being floated around in this space, like potentially disabling the ability to generate in a particular style. But there’s also this element of inspiration that you get, like people learn from imitation of masters.
Andersen: Neil Postman has a line that I love, where he says that instead of thinking of technological change as additive or subtractive, think about it as ecological, as changing the systems in which people operate. And in this case, those people are artists. Because you are in dialogue with artists, what are you seeing in terms of the changes? What does the creative space look like five, 10 years from now in the wake of these tools?
Chen: The amazing thing with DALL-E is we’ve found that artists are better at using these tools than the general population. We’ve seen some of the best artwork coming out of these systems basically produced by artists. The reason we built this tool is to really democratize image generation for a bunch of people who wouldn’t necessarily classify themselves as artists. With AI, you always worry about job loss and displacement, and we don’t want to kind of ignore these possibilities either. But we do think it’s a tool that allows people to be creative, and we’ve seen, so far, artists are more creative with it than regular users. And there’s a lot of technologies like this—smartphone cameras haven’t replaced photographers.
Andersen: As transformative as DALL-E is, it’s not the only show at OpenAI. In recent weeks, we’ve seen ChatGPT really take the world by storm with text-to-text prompts. I was wondering if you could say a little bit about how the evolution of those two products has made you think about the difference in textual and image creativity? And how can you use these tools together?
Chen: With DALL-E, you can get a large grid of samples and very easily pick out the one you like. With text, you don’t necessarily have that luxury, so in some sense the bar for text is a little bit higher. I do see a lot of room for these kinds of models to be used together in the future. Maybe you have a conversational interface for generating images.
Andersen: I’m interested in whether we’re ever going to get to something like an artificial general intelligence, something that can operate in many different domains instead of being really specific to one domain, like a chess-playing AI. From your perspective, is this an incremental step toward that? Or does this feel like a leap forward to you?
Chen: One thing that’s always differentiated OpenAI is that we want to build artificial general intelligence. We don’t care necessarily about too many of these narrow domains. A lot of the reason DALL-E plays into this is we wanted a way to see how our models are viewing the world. Are they seeing the world in the same way that we would describe it? We provided this text interface so we can see what the model is imagining and make sure the model is calibrated to the way we perceive the world.
| 2022-12-14T00:00:00 |
2022/12/14
|
https://www.theatlantic.com/technology/archive/2022/12/generative-ai-technology-human-creativity-imagination/672460/
|
[
{
"date": "2022/12/14",
"position": 69,
"query": "automation job displacement"
}
] |
Will AI replace human workers?
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Will AI replace human workers?
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https://www.ft.com
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[] |
Will AI replace human workers? New tools such as ChatGPT and Stable Diffusion could disrupt creative and knowledge industries.
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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.
| 2022-12-14T00:00:00 |
https://www.ft.com/content/24f07261-f95d-4bb3-8aa4-3799f1f75e52
|
[
{
"date": "2022/12/14",
"position": 10,
"query": "AI replacing workers"
}
] |
|
You Might Not Like AI Art, But It's Here To Stay
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John Walker's Electronic House
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https://botherer.org
|
[
"John Walker"
] |
... AI creations replace a large amount of what would have been commissioned to artists. ... As the ability of AI progresses many more jobs, especially in the ...
|
“An artist smashes a computer, digital art.”
The word “Luddite” has unfairly pejorative connotations in the modern age. In the early 19th century, English textile workers saw where their industry was heading with the introduction of technology, and recognised the imminent destruction of their livelihoods. So, in response, the workers would protest the new factories, and famously, destroy the rival machines.
It was hopeless. Such protesters were shot by the factory owners, and eventually the British army was used to suppress their wider movement. And, inevitably, machines became the primary way to create textiles, as is still the case today. The Luddites were not—as they are so often parodied—afraid to embrace modern technology. Instead, they were simply aware that it would starve their families. They were trying to defend their livelihoods, protect their loved ones, and responded with physical violence against capitalist violence.
This is all to say, I entirely get why artists are so furious about AI art creation. This new technology has the real potential to—at the very least—carve into a craft that has existed for thousands of years. If a book cover or movie poster or piece of background art can be created by typing a sentence into a website, then of course artists who’ve trained their whole lives to be masters of their craft are going to lose work. It must be terrifying, not least with the speed with which such extraordinarily powerful AI has appeared in the last year. I hate that very rich corporations, that absolutely could and should be compensating those from which they are gaining, are not.
I’m also aware that if AI can start doing as good a job as, or even better than, conventional artists, then it will win. Because every example of technology replacing artisan skill in the last few hundred years has ended that way. The protesting, the fury, and the sense of unfairness, will have no bearing on anything, and the computers will win out. This isn’t pessimism, and nor is it “giving up”; it’s just simply understanding reality.
“The last human stares at the sunset as the bombs drop, digital art.”
I don’t have a solution, because I don’t think there is one. Much as the Luddites hopelessly smashed apart that which would replace them, it’s completely understandable that current artists wish to find a way to smash apart this latest technology. However, it’s even more futile a task in the digital age, and attempting to find methods to do so is going to be very ugly, and very self-defeating.
This is already demonstrably the case in the current main messaging against AI art: It is, we’re endlessly told, “theft.” As someone who has been a loud voice for the last twenty years within the (mostly hopeless) movement to prevent corporations from tricking the wider public into thinking that copying is theft, this couldn’t be a more demoralising position to see taken by the creatives themselves. Copying isn’t theft, it never has been, and it never can be. This isn’t a moral argument in favour of copying, it’s a factual argument about what words mean. Theft requires the original item to no longer be in the original owner’s possession, and no matter how many knots people try to tie themselves in, that can never, ever be applied to copying. Indeed, you wouldn’t steal a car. But you’d sure as hell download one. Call it bad, fight against it, but don’t call it something it isn’t.
It’s genuinely bewildering to have lived through the Napster years, when suddenly the world realized that music had at last been freed from its one-hundred-year-long plastic prison (and during which time official album sales spiked worldwide, and record labels never made more money, until they had Napster shut down and sales started falling again), where people were threatened by corporate goons out of their life savings because their grandkid downloaded a Sum 41 album, to then see the creators of art attempting to use “theft” as their attack against corporate AI.
Copying may be something you’re against! You may wish to legislate against copying. Rather famously, there’s that whole “copyright” system, that itself has been brutally twisted into a weapon of oppression rather than a tool of freedom. But it isn’t theft, it isn’t “stealing,” and I am aghast at the decades-long backward step at seeing this being wheeled out by the “goodies,” in an attempt to fight the “baddie” corporations.
Should artists be compensated for their creations? Of course, if their creations are desired. I am a passionate believer in the patron system, where artistic work is paid for at the point of creation, and I believe that credit should always be given to those who have created something. I also passionately believe in sharing, and am vehemently against systems where a creator is paid in perpetuity for work completed years previously. I don’t feel beholden to the estate of Monet or to whichever current painter if I fancy printing off one of their paintings, any more than I think I should pay the plumber who fixed my tap every time I use the sink. I get paid for my time when writing articles about games, not whenever anyone reads them in five years’ time. (And should a time come when AI can usefully critique artistic creations like games, then yes, I’ll be screwed too.)
Yes, AI systems are fed with potentially millions of pieces of art, from which its code learns the patterns, systems, techniques, styles, and then attempts to reconfigure them into something original. And yes, they are mostly doing this with no permissions from the creators of the pieces of art that go in. But here’s the bad news: that was just a very accurate description of all of art ever.
“A couple watches as the stars fall from the sky, watercolour.”
No artist creates art in a vacuum. Since the first cave person scrawled in mushed up flowers on a cave wall, all art has been formed based on what has come before. All artists, since the beginning of recorded history, have learned art by copying other art. None needed to ask for permission—hell, for a good period of history, they were actively encouraged by the original artists. The most famous artists you know, especially those known for pioneering new movements, began by learning the patterns, systems, techniques and styles of those who came before, and then attempted to reconfigure them into something original. Picasso was one hell of a realistic portrait artist, as taught by his father, before he ever explored Cubism.
Computers didn’t think up AI art generation on their own. It was programmed by people, whether for good or evil. This isn’t a robot takeover—it’s new man-made technology doing as good a job as human creators. And as soon as we started “feeding AI a thousand…” of whatever, to see what it would generate, this became inevitable. You can hate it, and you can hate the corporations that have been quick to take advantage of it, but trying to redefine it as something more evil than the printing press or the self-scanning cashier machine in the grocery store is folly.
(I want to clarify something here, that I fear would otherwise be misunderstood: I am not advocating for people to directly use an artist’s previous creations for their own financial gain. I believe, obviously, an artist should be compensated in such a circumstance, unless that creation has been graciously released under a copyleft license that allows such use. However, I do believe that any piece of art is a legitimate source for inspiration, and being inspired by any piece of art when creating one’s own, even for financial gain, is clearly legitimate, given—again—all of history.)
In the latest of Luke’s passionate tirades against this technology on Kotaku, he firmly states “machines don’t make art. They’re machines!” This is, in my opinion, utterly wrong. Because as anyone who’s had to suffer through any interminable “are computer games art?” article will know, art is in the eye of the viewer. I’ve typed in sentences to AI art creation software that has produced images that are utterly beautiful. Of course it’s art, no matter how much I might not like that the tool that created it is owned by a corporation that sees no desire to compensate those from which it has gained.
I think there’s a very uncomfortable uncanny feeling about AI art, but I think it’s because it is art. There’s a piece of soul in it, but it’s not earned. It’s likely the scraps of soul that survive the mechanical processing of what’s been fed in. That’s existentially unsettling.
I hate it a bit, too. I mean, I can draw to some degree. I’ve been paid to draw silly cartoons for things over the years. I’ve sold them on greetings cards. Now, you can create something just about the same by asking Dall-E for a “cartoon of a rabbit in a medieval helmet.” Mine’s on the left, Dall-E’s is on the right:
Like all other artisan crafts where technology has allowed the mass production of very similar creations, I know that we will see AI creations replace a large amount of what would have been commissioned to artists. I also hope that many will recognize the worth of commissioning a person to use their skills to create something utterly bespoke, specifically for your needs.
I’m also aware that I can’t wait for AI to replace the current gouging of corporations like Getty and Shutterstock, who try to charge hundreds or thousands of dollars for copying an infinitely duplicable jpeg. Sure, they’ll all try to figure out how they can monetise it for a bit, before such technology escapes their confines, but it won’t last long.
Yes, many will be utterly furious with me for writing any of this, no matter how clear I make it that I would prefer for artists to be compensated, and for original work to be commissioned. But I won’t allow myself to deny two core truths, no matter how little I might like them: Technology always successfully replaces the mass production of artisan craft, and fighting to prevent it is a depressingly futile act that hurts the creators whose livelihoods are being threatened. As awful as any of this might be, it doesn’t change reality.
Artists and creators should not be fighting this with the language of greedy corporations, with attempts to wield corrupt systems like “copyright” and “intellectual property,” making ridiculous claims of “stealing,” but instead by proudly standing up and showing why what they create is special. Artists should boast of their talent, loudly display their work, seek patronage, and work together to create systems that better put themselves in front of people.
You—you—should respond to this by being diligent in your beliefs. Fund artists. Find their Patreons and sign up. Scream at sites like ArtStation until they add a button that lets you financially support artists you admire. Pay for art you care about. That’s a damn site more effective than screaming in fury at technology for existing. It’s going to win. You can’t smash it up, and the people who own it have the bigger guns.
| 2022-12-14T00:00:00 |
2022/12/14
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https://botherer.org/2022/12/14/you-might-not-like-ai-art-but-its-here-to-stay/
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[
{
"date": "2022/12/14",
"position": 55,
"query": "AI replacing workers"
}
] |
Erica becomes a little more human
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Erica becomes a little more human
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https://www.emarketer.com
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[] |
But AI technology is unlikely to replace skilled human workers. Computational algorithms lack the ability to reason cognitively, so humans will always have a ...
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The news: Bank of America is adding a human element to its artificial intelligence-powered (AI) virtual assistant Erica, per Banking Dive.
A human touch: Launched in 2018, Erica has become one of the most-accessed virtual banking assistants, helping 32 million customers with over 1 billion interactions . Now, BofA is enhancing the chatbot to give customers even more personalization and tailored product recommendations.
Beginning in early 2023, users who begin interacting with Erica online will be able to switch to speaking with a human agent when they need more help. The agent can pick up where the chatbot left off.
After the agent resolves the issue, the client can resume their interaction with Erica.
Erica uses natural language processing, and its predictive nature can anticipate why a consumer is reaching out. The ability to switch to a human agent provides a more personalized experience for the consumer, letting the agent use information collected by Erica to suggest specific products to the client.
Appealing to young and old: Banks’ digital transformations, accelerated by the pandemic, have changed the way many consumers manage their financial lives. BofA recognized that it’s not just younger generations who are using Erica: Older clients are also taking advantage of the technology. But the bank realized that consumers of all ages still crave some human interaction.
| 2022-12-14T00:00:00 |
https://www.emarketer.com/content/bank-of-america-adds-human-touch-erica
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[
{
"date": "2022/12/14",
"position": 91,
"query": "AI replacing workers"
}
] |
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Robots: The Use in Everyday Tasks Essay
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Robots: The Use in Everyday Tasks - 1365 Words
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https://ivypanda.com
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They also argue that the future of artificial intelligence (AI) and robots is volatile: many jobs will be lost throwing millions of people into poverty. Many of ...
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The recent advancements in robotics and artificial intelligence have the potential to automate a wide range of human activities and to dramatically reshape the way people live and work in the coming decades. Initially, robots were only limited to the manufacturing industry, but today they are increasingly becoming part of people’s everyday tasks. It is evident that nowadays, more people are relying on intelligent technological types of equipment compared to the past due to advancement in technology. According to Smith and Anderson (2017), the robotics industry around the globe is constantly innovating, integrating artificial intelligence with vision to suit the needs of aging population. It other words, the industry is constantly looking for new ways of making people’s work a bit easier, faster and efficient. In essence, robots are a gift from science to mankind because of their high productivity rate coupled with an increased speed of production, quality and greater workplace safety.
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First, the use of robots increases productivity when they are applied to perform tasks that require more efficiency. In the recent past, many industries have shifted their focus to implementing robots that improve productivity and fulfill the demands in the market. Most of them argue that robots work by eliminating the element of human error,thus allowing them to perform tasks at the same level of consistency. In fact, many organizations are already using them to help maximize inventory and, at the same time, improve quality. According to Scheiber (2019), Amazon has implemented palletisers, which are robotics arms with grippers, in their 26 fulfillment centers worldwide to lift the heavy totes and packages from conveyor belt. These palletisers provide robotic muscle for daily operations: they substitute man-hands for their productivity in the effort to cut down on human wear-and-tear. Overall, robots increases productivity by performing tasks at a faster rate but with the same consistent level of quality and quantity than humans.
Secondly, robots are increasingly being adopted in task-performance because of their high speed: they do not easily get distracted. Companies around the globe are focusing more on meeting customers’ demands which explains why high-speed robots are being sort after due to their ability to work 24/7 without any breaks or vacations (RobotWorx, 2021). In fact, it is common to find robots that move at a high speed than humans making them more preferable in ensuring fast production lines,especially in manufacturing companies. A good example of how robots work by improving speed is in the case of Factory Automation System Inc., located in Atlanta which is part of the Architectural, Engineering, and Related Services Industry (Mena Report, 2021). With automation, the company has managed to supply parts to agricultural manufactures with the growth of 150 percent than before while meeting their targets within three hours—five hours less than manual operations (RobotWorx, 2021). It therefore follows that high speed-robots ensures products are produced within the specified time.
Thirdly, the use of robots always brings quality to production which, for many years, has been an issue of concern for many manufacturers. It is important to note that poor quality products tend to have a negative impact on organization’s reputation and bottom line. Therefore, robots help resolve this because they are programmed to manufacture a particular product more precisely without any error, whereas humans tend to make mistakes. A good example here is the Motoman Inc. which uses reciprocating painting machine to paint car and truck on the same line (Yaskawa, 2021).With human tendency, the company used to get different and mixed results as far as production is concerned, but since the adoption of robots, product quality and perfection have improved greatly. Overall, the use of robots in the future as mandatory in everyday tasks stem from the fact that they do not get fatigue or lose focus, thus preventing unnecessary errors that leads to low quality.
Finally, the use robots is critical, especially since they provide greater workplace safety. In the manufacturing sector, robots are increasingly being used to reduce the risk of falls. A good example is where robots are used in the warehouse to help minimize injuries—the robotic machinery is able to reach items that are too high. Similarly, exoskeleton robots are already being used in the manufacturing industry to perform repetitive work associated with musculoskeletal disorders (MSDs). Hyundai Motor Group is one such company that has adopted the use of exoskeleton robots (Menyhárt, 2019). With its Vest Exoskeleton (VEX), the company has managed to reduce fatigue of workers—the wearable vest imitates the movement of human joints.
However, those against the use of robots in everyday tasks claim that many industries, in the effort to maximize profits, are replacing human labour with automated machines. They also argue that the future of artificial intelligence (AI) and robots is volatile: many jobs will be lost throwing millions of people into poverty. Many of them express concern that having AI in workplaces will lead to high levels of income inequality caused bymillions people who are not employable. This, in the end, will lead to breakdown in the social order. Their fears have been validated by detailed analyseswhich shows how increasing automation in workplace impact jobs. A good example is the analysis carried out by Bruegel whose findings showed that “about 54 percent of EU jobs are at risk of computerization” (Tavis, 2015, p. 78). Bruegel’s analysis of European data led him to conclude that job losses will be significant and that people should prepare for large scale disruptions.
The opponents also argue that robots are taking over meaningful work which they consider to be important and valuable. They maintain that doing meaningful work is what leads to high job satisfaction and employee well-being. Their views were echoed by Smids et al.’s (2020) study where the authors followed the work schedules of metro drivers in Paris. The company outsourced robots which led to the introduction of self-driving metros. In return, the company’s drivers were offered alternative positions as managers. While these new positions gave the employees formal responsibilities, a follow-up survey showed that the drivers felt deprived of meaningful work. The workers claimed that, instead of being able to respond immediately to emergency situations, they were only being indirectly informed of the incidences. Smids et al. (2020) results also showed that the workers, by not being directly responsible for the lives of the people, “felt a loss of responsibility in adjusted jobs” (p. 12). In other words, the introduction of robots in everyday tasks tends to disrupt people’s normal work routines.
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While it is true the introduction of robots in workplaces might lead to loss of jobs and meaningful work, the future is still promising. First, robots do not have the ability to perform complex tasks such as negotiation and persuading. According to Huang et al. (2021), robots are not as efficient in creating new ideas as they are at solving them despite having higher intelligence levels. In essence, work which require creativity, emotional intelligence and social skills will be on high demand—they are less likely to be performed by robots. With regard to meaningful work, employees being given alternative positions should receive adequate training. Training and development ensures employees such the metro drivers identify the knowledge and skills they require. With evidence-based programs, employers can educate their workers about new skills and the benefits associated with their positions. Most importantly, employees should be encouraged to exercise their capacities for understanding and decision making to higher extents as this would them finds meaning in their work.
In conclusion the use of robots in the future as mandatory in everyday tasks stems from the many benefits associated with it. As evidenced above, robots have been found to increase productivity, speed, quality, and workplace safety. For instance, robots provide greater workplace safety by reducing the risk of fall. Hyundai is currently using Vest Exoskeleton (VEX) with the aim of reducing fatigue of workers: the wearable vest imitates the movement of human joints. However, it is important to note that the use of robots also has its own limitations such as cutting off the manpower and meaningful work. While this is case, employers are encouraged to provide training and development programs aimed at ensuring employees appreciate their new positions.
References
Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30-41.
Menyhárt, J. (2019). Artificial Intelligence possibilities in vehicle industry. International Journal of Engineering and Management Sciences, 4(4), 148-154.
RobotWorx. (2021). Robot savings time. Web.
Services for the Manufacture of Links of the Agricultural Excavator. (2021). Mena Report, NA. Web.
Scheiber, N. (2019). Inside an Amazon Warehouse, Robots’ Ways Rub Off on. Web.
Remember! This is just a sample You can get your custom paper by one of our expert writers Get custom essay
Smids, J., Nyholm, S., & Berkers, H. (2020). Robots in the workplace: A threat to—or opportunity for—meaningful work?. Philosophy & Technology, 33(3), 503-522.
Smith, A., & Anderson, M. (2017). Automation in everyday life: Where will the jobs go? Industrial Safety & Hygiene News, 51(11), 10–14
Tavis, A. A. (2015). Rise of robots: Technology and the threat of a jobless future. People & Strategy, 38(4), 77-79.
Yaskawa.(2021). Robotic painting & dispensing. Web.
| 2022-12-14T00:00:00 |
https://ivypanda.com/essays/robots-the-use-in-everyday-tasks/
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[
{
"date": "2022/12/14",
"position": 95,
"query": "AI replacing workers"
}
] |
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Workers have high hopes for pay hikes next year; too high?
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Workers have high hopes for pay hikes next year. Perhaps too high
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https://www.latimes.com
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California raises wage replacement for new parents, sick workers. Gov ... How worried should workers be about AI replacing them? July 5, 2025. LONG ...
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There’s a gap between the raises workers expect for next year — 5.5%, according to one consultant — and what companies have budgeted for, typically 3.5% to 4.5%. That may not seem like a big difference, but it can represent hundreds of millions of dollars.
The pay negotiation season is looking increasingly fraught this year as workers fret about inflation — and their job security.
Although the labor market remains tight, evidenced by last month’s better-than-expected increases in both jobs and wages, employers are gaining back some leverage just in time for the tough conversations between bosses and employees to begin. This week, Goldman Sachs said smaller bonuses and job cuts are coming. That has come after the running tally of tech layoffs hit 52,771 in November, the highest monthly total for the sector since Challenger, Gray & Christmas began keeping detailed industry data in 2000.
“Over the last year employees knew they could get more money if they left and that remains true, but the ease with which they can get a new job has decreased,” said Tony Guadagni, a senior principal at consultant Gartner. “It’s not the job market it was even three months ago. That shifts the balance of power back to organizations a bit. How they use that to their advantage is a bit unclear, and that’s the big thing to watch as we go into this cycle.”
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One thing that’s clear is the gap between the raises that workers expect for next year — 5.5%, according to Gartner — and what companies have budgeted for, typically 3.5% to 4.5%. One or two percentage points may not seem like much, but it can represent hundreds of millions of dollars.
Some of that disconnect stems from people mistakenly assuming that their employers would deliver raises in line with inflation, but in the current price environment a cost-of-living boost isn’t realistic in most cases. The fault also lies with employers for keeping their compensation practices deliberately opaque for years. Although that’s starting to change, sparked by movements to improve transparency, this compensation cycle could be a particularly contentious one, as employees and bosses each have reasons to dig in their heels.
“This is a fundamentally different salary negotiating season than we’ve seen in the past,” Guadagni said. “People are going to get smaller raises than they think they deserve and there will be a lot of hard conversations.”
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Companies are preparing by harvesting all the compensation information they can from external data providers, economic surveys and industry recruiters, so they can finalize their budgets across teams and departments before the holidays and map out different scenarios depending on business conditions. In January, the pay cycle kicks into high gear, with performance reviews and compensation conversations extending into late February, when bonuses are typically paid out.
What’s different now, for many white-collar workers at least, is that the ability to work from home a few days a week is commonplace, which delivers the same value as a 5% to 10% pay increase, according to research from economists including Stanford University’s Nicholas Bloom. Companies that don’t raise base salaries by as much as workers want can use that increased flexibility as a sweetener. Startups can always dangle equity in lieu of cash. Lattice, which provides workforce management software, is doing so for certain employees, said Chief People Officer Cara Brennan Allamano.
“We’re starting to focus more on the total reward, which is something we did not pay a ton of attention to in the last few years,” she said. “That means looking at benefits, equity and also what your career experience will be. All of those things are currency. It’s a dramatic shift from last year.”
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For example, Lattice is enhancing its fertility benefit for employees next year, while other employers are adding more resources for employee mental health. Companies need to deploy a “full arsenal of rewards to address the unique nature of the 2022 labor market,” said Lori Wisper, a managing director at workplace consultant Willis Towers Watson. The problem is that employees often downplay the value of other facets of compensation, said Brian Dunn, director of professional programs at Cornell University’s Institute for Compensation Studies.
Psychology plays a role as well. Research from behavioral economists shows that expectations often override our senses, blinding us to reality.
“If someone got a 5% raise last year, their expectations have been raised and they will be disappointed this year,” Dunn said. “It’s the most prominent thing in their mind, what they got last year.”
Prospects for some types of workers are better than others. Job gains last month were concentrated in a few categories such as leisure and hospitality, healthcare and government. Exxon Mobil, flush with profits, is awarding U.S. employees with inflation-besting average salary bumps of 9%. Meanwhile, employers in retail, transportation and warehousing and temporary help services have cut staff.
Regardless of where they work, though, Americans are growing more pessimistic about the labor market. The share of job seekers who expect there to be fewer jobs available six months from now recently surpassed those who expect there to be more, according to a survey from ZipRecruiter.
Meanwhile, as layoff announcements spread through industries such as technology, banking and real estate — even snack maker PepsiCo is reportedly shedding staff — workers appear more hesitant to leave their current roles: The quits rate, a measure of voluntary job leavers as a share of total employment, dropped to 2.6% in October, the lowest since May 2021.
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Nick Bunker, director of North American economic research at job site Indeed, said demand for workers is still very strong, “but the direction things are going will favor employers more. A better time to be a job switcher was three months ago.”
Some experts, such as Gerald Cohen, chief economist at the University of North Carolina’s Kenan Institute of Private Enterprise, say skilled employees still have the upper hand in salary talks. But he allows that the decline in temporary workers, who are the first to go in a downturn, does not bode well for full-timers.
“We were very bullish coming into 2022,” said Lattice’s Allamano. “We are a lot less bullish coming into 2023.”
| 2022-12-14T00:00:00 |
2022/12/14
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https://www.latimes.com/business/story/2022-12-14/workers-expect-pay-hikes-next-year
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[
{
"date": "2022/12/14",
"position": 100,
"query": "AI replacing workers"
}
] |
Education - DAF AI Accelerator
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Education
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https://www.aiaccelerator.af.mil
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... AI Education Research project is to help the USAF in its efforts to “educate, cultivate, and grow a world-class AI workforce.” By leveraging online and ...
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Inspired by the 2020 DoD AI Education Strategy, the goal of the DAF/MIT Know-Apply-Lead (KAL) AI Education Research project is to help the USAF in its efforts to “educate, cultivate, and grow a world-class AI workforce.” By leveraging online and custom-built MIT courses and workshops, the KAL AI Education Research team investigates pedagogy, content/curriculum, educational platforms, and technical innovations. Research participants include DAF and Joint members, whose feedback helps inform published findings. Ultimately, these findings will help inform future business decisions for the DAF to ultimately advance the AI and training of an “elite and world-class AI ready force” of diverse roles and educational backgrounds at scale.
| 2022-12-14T00:00:00 |
https://www.aiaccelerator.af.mil/Research/Education/
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[
{
"date": "2022/12/14",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2022/12/14",
"position": 15,
"query": "machine learning workforce"
}
] |
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How to Build Your Career in AI eBook - Andrew Ng ...
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How to Build Your Career in AI eBook
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https://info.deeplearning.ai
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By reading this book, you'll learn how to develop core AI skills, practical interviewing tips, how to build a portfolio of projects that will make your resume ...
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FREE EBOOK REVEALS...
How to Build Your Career in AI
Insights from AI pioneer Andrew Ng about learning foundational skills, working on projects, finding jobs, and community in machine learning
| 2022-12-14T00:00:00 |
https://info.deeplearning.ai/how-to-build-a-career-in-ai-book
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[
{
"date": "2022/12/14",
"position": 23,
"query": "machine learning job market"
}
] |
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ChatGPT: How to Understand & Compete with the AI Bot
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ChatGPT: How to Understand & Compete with the AI Bot – 365 Data Science
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https://365datascience.com
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[
"Aleksandra Yosifova"
] |
How does it work, and will it change the job market? Read on for the answers ... We need to dive deeper into machine and deep learning to grasp how they work.
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Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.
OpenAI released ChatGPT, and the world reacted with awe and trepidation.
Why?
What can this AI chatbot achieve? Where does it fail? Is it a threat to your job? How do you remain relevant in the 21st-century job market?
We answer these questions and more below.
Table of Contents
What Is ChatGPT?
ChatGPT is a text-based AI assistant from OpenAI—an AI research laboratory co-founded by Elon Musk (who stepped down from the board in 2018 and now remains an investor).
The new chatbot is not the first breakthrough creation from OpenAI—not even for 2022. The company is also responsible for DALL-E—an AI system that creates surprisingly realistic images based on text descriptions. OpenAI launched the new and improved second version of the program in November 2022. Not a month later, it rolls out ChatGPT, sparking a flurry of excitement.
Seriously, the public’s reaction was unprecedented. As always, Twitter’s take on it was beyond hilarious.
Elon Musk himself deemed the chatbot “scary good” and “not far from dangerously strong” in a tweet. Others compare it to Google and claim it surpasses the depth and usefulness of its responses.
But why did a chatbot trigger such a reaction?
The technology itself isn’t groundbreaking. GPT stands for “generative pre-trained transformer,” which is an autoregressive language model that uses deep learning to produce human-like speech. But there were other transformer-based models before it, such as the Bidirectional Encoder Representations from Transformers (BERT).
Besides, ChatGPT isn’t even the company’s first chatbot. OpenAI released the first GPT in 2018. Of course, the first version wasn’t nearly as good as their most recent creation. The GPT-3 bot, however, made quite the impression in 2020—standing out from other language models with the sheer number of parameters it was trained with.
ChatGPT is an upgraded version of GPT-3 and one of the largest and most powerful language processing AI models to date, trained with 175 billion parameters.
But what makes it special?
Mostly, the fact that it’s scary good. It produces human-like responses in various domains and tasks in the blink of an eye.
What Can ChatGPT Do?
OpenAI says ChatGPT can “answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.” And what’s most impressive is that it can use humor. (Some of its jokes are actually funny.)
In other words, it’s more human-like than anything we’ve seen.
With the right prompt, the AI chatbot can do anything! It’s capable of content generation, summarization, classification, categorization, sentiment analysis, data extraction, and translation.
It can generate various text types, including scripts, poems, essays, and computer code. Your creativity is the only limit—you can make it write a biblical verse explaining how to remove a peanut butter sandwich from a VCR or direct it to condemn itself in the tone of Shakespeare.
Its potential applications go beyond the anecdotal and to the practical realm. They span from game and app development to college-level essay writing and even medical disease diagnosis.
How did OpenAI achieve this?
How Does ChatGPT Work?
As we said, ChatGPT is an autoregressive language model that uses deep learning to produce human-like speech. If you’re unfamiliar with data science, that may sound like a string of complicated words with unclear meanings.
Let’s demystify this complex creation by defining each term, starting with language models.
Language Models
Language models are machine learning models which predict the probability of a particular word coming next in a sequence. They are at the basis of natural language understanding and generation, or the ability of machines to comprehend and produce human-like speech. We need to dive deeper into machine and deep learning to grasp how they work.
Machine Learning
Simply put, machine learning (ML) models are algorithms that leverage data to improve their performance on a given task. The quality of a model depends on how sophisticated the algorithm is and the quantity and quality of the data it is trained with.
The main types of machine learning are supervised, unsupervised, and reinforcement. In supervised learning, the ML model is fed with labeled data; every data point in the set we use for training has its category.
In reinforcement learning, we evaluate the model’s performance, and it learns based on our feedback. Regardless of the type of ML, data scientists and ML engineers can fine-tune the models, meaning they tweak the parameters to improve their performance.
The GPT-3.5 model was trained and fine-tuned using supervised and reinforcement learning. But the architecture of the language model was based on deep learning and, more concretely, on artificial neural networks.
Deep Learning
Deep learning is a more complex version of machine learning, consisting of several ‘layers’ of models. It relies on a structure called a ‘neural network,’ which was inspired by the human brain.
Artificial Neural Networks
Artificial neural networks consist of an input layer, one or more hidden layers where the incoming information is processed, and an output layer.
Each layer contains connected nodes—resembling the neurons in the brain. When data enters the nodes in the input layer, it is assigned a weight based on its relevance to the desired output. If the weight exceeds a given threshold, the node is activated, and the information is transmitted to the next layer for processing. Finally, the model produces an output—hopefully, a relevant one if the model is good and the training data is sufficient.
There are many approaches to building these complex AI systems. We focus on the ones involved in the creation of ChatGPT.
The Methodology Behind ChatGPT
As we mentioned, ChatGPT is a generative pre-trained transformer. This is a group of autoregressive language models that predict the most probable next word in a sequence based on the preceding text.
Being a transformer model, it uses attention mechanisms to assign weights to words according to their relevance to the next token. The model determines the weights based not only on the meaning but also on the order and hierarchy of the words in a sentence. It’s a sophisticated deep learning algorithm with the impressive capability to understand and produce human speech.
At this point, language models can be fine-tuned to perform specific tasks. But ChatGPT’s creators used a different approach. Instead of specializing the model for a given domain or challenge, they trained it with an enormous data set.
During the training phase, the model learned and improved its predictions based on 175 billion parameters. As a result, it developed various capabilities—which it wasn’t explicitly trained for—like translating from English to French, for example.
GPT-3.5 was trained with a colossal amount of text and code from the public web and the OpenAI lab, dated before Q4 2021. The model finished training at the beginning of 2022, and OpenAI fine-tuned specific systems from it to create ChatGPT.
It used Reinforcement Learning from Human Feedback (RLHF) for this purpose—a machine learning technique using human feedback to improve the model’s performance. This involves adjusting the parameters based on the human trainers’ ratings of performance accuracy.
The following is ChatGPT’s training and fine-tuning process step by step as described by OpenAI:
Step 1: Collect Demonstration Data and Train a Supervised Policy.
First, GPT-3.5 was trained with supervised learning. This means that the training data fed to the model consists of labeled input-output pairs. In this case, the data points were human-generated prompts and example responses. In other words, human trainers provided the output from both perspectives—the user and the AI bot.
Step 2: Collect Comparison Data and Train a Reward Model.
Next, OpenAI created a model for reinforcement learning. It collected comparison data with model-generated responses, which human trainers ranked from best to worst. Then, their feedback was used to train the reward model.
Step 3: Optimize a Policy Against the Reward Model and Using the PPO Reinforcement Learning Algorithm.
Finally, the GPT-3.5 model was fine-tuned using Reinforcement Learning from Human Feedback. This means that the reward model from Step 2 was applied to the supervised policy from Step 1. Simply put, GPT-3.5 responded to new prompts from a test set, its outputs were ranked by the reward model, and this feedback was used to improve its performance.
This process was iterated several times before OpenAI released ChatGPT to the general public. And while the AI chatbot is arguably better than anything we’ve seen, it isn’t without its downsides.
What Can’t ChatGPT Do?
Applying general knowledge to specific domains without prior experience with the task at hand is a typical human capability. ChatGPT is one of the few AI systems which produce comparable results.
But before you throw your diploma out the window, consider its limitations.
The AI chatbot may write like a human but cannot reason like one yet.
The Outputs Depend on the Instructions
Despite its profound knowledge of various topics, the quality of answers depends mainly on the instructions provided. For one thing, it requires detailed context to produce more sophisticated outputs.
In fact, it’s so reliant on context that it may forget everything it knows on another topic. For example, if we present it with a number sequence and then ask an unpredictable question (e.g., about a historical fact instead of the next number in the sequence), ChatGPT may respond that it doesn’t know the answer.
Gives Wrong Answers to Simple Questions
You’d expect a bot trained with 175 billion parameters to know the answers to all questions. But that’s not always the case with ChatGPT.
Despite the numerous examples we see of programs coded by the chatbot, its output isn’t perfect. In fact, Stack Overflow (a questions and answers platform for programmers) temporarily banned its usage. Allegedly, the website was overflooded by incorrect submissions generated by the AI chatbot.
Workarounds for Its Restrictions
ChatGPT is programmed not to provide information that could harm people or instructions for illegal activities. Some see it as a huge step toward ethical AI, while others argue it’s a downside. But the fundamental limitation is that people have found workarounds for its restrictions, tricking it into disabling its own safety features just by changing the instructions. This seems profoundly flawed for such a complex creation.
Unaware of Current Events
ChatGPT doesn’t work with real-time data nor crawl the web for recent information. In fact, its knowledge dates before Q4 2021, which means it cannot answer factual questions about current events, and its references may be outdated. Considering how much has happened in the past year alone, that’s a significant limitation.
But more importantly, this isn’t likely to change in the foreseeable future. Having a system trained with 175 billion parameters already requires an overwhelming amount of computational power. Feeding it real-time data on top of that is hardly achievable at this point.
But what will happen when AI bots become even better?
Will AI Make Tech Jobs Obsolete?
The AI text generator came out with a splash and was received with awe and apprehension.
But is ChatGPT really a threat to our jobs?
As we said, it’s not the first AI chatbot of this magnitude, and although some would argue it’s the best one yet, it’s still far from being widely implemented for commercial use.
In addition to the limitations listed above, the system requires vast computational power to implement and maintain. Besides, a chatbot trained with 175 billion parameters and capable of producing Shakespearean monologues might be overkill for, say, an e-commerce shop’s support channel.
So, is ChatGPT a precursor to mass unemployment?
It’s safe to say that your job is not threatened by this particular system at the moment. With that said, the rapid development of technologies of this magnitude is drastically changing the job market.
To remain competitive, you’d need to upskill. After all, technologies like ChatGPT are only possible thanks to the highly skilled workforce behind them.
When asked how it was created, ChatGPT said the following:
“The process of my creation began with the development of a concept and design. The software engineers then wrote code to create the basic structure of my program. After that, the designers worked on the user interface and visuals, making sure that the program was easy to use and visually appealing. Finally, the engineers tested the program to make sure it was functioning properly and that all features were working correctly.”
Here are some of the people who worked to design this mix of software and sorcery.
Data architects who designed the system’s architecture
Product managers responsible for the planning and execution of the product
Data engineers who created the basic structure and fine-tuned the system
Software engineers involved in the design, development, and testing of the system
Data scientists carrying out the model’s development and training
Data analysts responsible for data processing for the training of the model
Designers to create the UI and visuals
Human AI trainers involved in the training and fine-tuning of the model
The list is by no means exhaustive, but it is enough to illustrate an important point—the next in-demand jobs will be those involved in creating such technologies.
How to Remain Relevant on the Job Market
For starters, learn to use ChatGPT to aid your productivity at work with our Intro to ChatGPT and Generative AI course.
GPT-4—the next incarnation of the company’s large language model—came out in 2023. And it’s just one of the many AI bots being developed and a small (although significant) piece among the plethora of technologies flooding the market.
If you don’t see your profession on the list above, it doesn’t mean you’ll lose your job to AI. But if you want to be on the crest of a wave and not feel threatened, you need to upskill.
The data science field is on the rise and presents numerous career development opportunities. The numerous examples above demonstrate that ChatGPT is only as creative as the person who uses it. Learning how to leverage new technologies can take your career to another level. And if you want to be at the forefront of innovation, the “sexiest job of the 21st century” is the way to go.
With the proper training, you can become a product manager for AI, learn machine learning and deep learning to build complex models, familiarize yourself with convolutional neural networks to create image classification systems and leverage the power of AI to meet your business goals.
Our courses can help you build a successful career even if you’re starting from scratch. 365 Data Science is the best place to familiarize yourself with data science and take the first steps in your professional development.
Pick a course to upskill or build your data science knowledge from the ground up with our Data Scientist Career Track. Sign up via the link below to try our learning platform for free and see if this is the right career path for you.
| 2022-12-14T00:00:00 |
2022/12/14
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https://365datascience.com/trending/chatgpt-how-to-understand-and-compete-with-the-ai-bot/
|
[
{
"date": "2022/12/14",
"position": 48,
"query": "machine learning job market"
}
] |
Hire a top freelance Machine Learning Engineer for your ...
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Find The Best Freelance ML Software Engineers on YunoJuno
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https://www.yunojuno.com
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[] |
Machine Learning Engineers typically work with big data sets and use a variety of programming languages, including Python, R and Java. They have an ...
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When hiring a freelance Machine Learning Engineer, you should look for a few key skills to ensure you’re getting the best person for the job. First, you should look for an engineer who is well-versed in programming languages such as Python, R, and Java. Experience with technologies such as TensorFlow and Scikit-learn are also beneficial. An understanding of AI concepts, like Neural Networks and Genetic Algorithms, are bonuses.
In terms of soft skills, you should look for someone who can communicate effectively and understands the importance of collaboration. They should also have strong problem-solving skills and be able to understand and follow business objectives. Lastly, having a good understanding of data structures and algorithms is important.
By looking for these key skills in a freelancer, you’ll be sure to find a great Machine Learning Engineer who can help you achieve your goals. With the help of YunoJuno, you can quickly source the best tech talent for your project.
| 2022-12-14T00:00:00 |
https://www.yunojuno.com/sub-disciplines/machine-learning-engineer
|
[
{
"date": "2022/12/14",
"position": 56,
"query": "machine learning job market"
},
{
"date": "2022/12/14",
"position": 95,
"query": "machine learning workforce"
}
] |
|
Students Flock to Artificial Intelligence Offerings at USD's ...
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Students Flock to Artificial Intelligence Offerings at USD's Computer Science Department
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https://www.usd.edu
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[] |
... machine learning engineers. Both undergraduate and graduate students at ... job market sooner. Working at Solarity gives him the opportunity to apply ...
|
Driving the increase is not only a national growth trend in computer science programs, but also a niche opportunity in South Dakota to study and specialize in artificial intelligence, or AI.
Leading the way in AI teaching and research at USD is KC Santosh, Ph.D., associate professor and chair of the Department of Computer Science. Santosh is an internationally recognized researcher on all aspects of AI, which harnesses the power of computers to process and analyze data in ways that mirror human intelligence.
AI is transforming how we live, work and play, creating a demand for experts in this field that is growing exponentially. From 2019-2029, the Bureau of Labor Statistics projects a 15% increase in computer and information technology occupations, which includes AI specialists and machine learning engineers.
Both undergraduate and graduate students at USD can earn a degree in computer science with a specialization in artificial intelligence. A certificate in AI is also available to undergraduate and graduate students who are majoring in other subjects. This fall, the department also introduced a new certificate in data science to provide a foundation in computer programming and machine learning to students majoring in computer science as well as in other disciplines.
“USD is home to South Dakota’s premier programs in AI,” Santosh says. “We are preparing students for careers in data science and machine learning. Expertise is needed to develop AI for good—AI for health care, AI for sustainable agriculture, AI for education, AI for security and defense, AI for economy, and much more.”
A lot of that “AI-for-good” activity is happening here in South Dakota. Whether it’s helping local banks prevent theft or health professionals make medical diagnoses, AI specialists design the systems using algorithms that learn from data and make predictions. The result: more accurate and reliable information and better decision making.
Recent USD graduate and Sioux Falls native Adam Grady ’21, B.S. M.S., is a software engineer at the Sioux Falls office of Solarity, a company that uses AI to interpret clinical data in health care. Grady is part of a team of engineers that helps hospitals and clinics manage the huge amounts of clinical data collected.
Grady earned his bachelor’s and master’s degrees in an accelerated 4 + 1 program, allowing him to save a year of tuition expenses and enter the job market sooner. Working at Solarity gives him the opportunity to apply his undergraduate and graduate training in software development and AI to help the medical community maintain comprehensive and accurate records.
“There is a massive network of information coming in and out of hospitals, and it’s a struggle for them to manage all of that,” Grady said.
The challenging and ever-changing field of AI and computer science in general appeals to Grady. “I just love the problem-solving aspect of what I do,” he said. “Developing something and seeing it come together is so satisfying.”
Grady adds that he also does website development on the side with his own company, Grady Development.
At USD, he took advantage of research opportunities available to computer science students. Grady worked with the USD School of Health Sciences to write a program that increased mammogram awareness among Native American women. The ability to take part in such opportunities while still a student was one of the best aspects of his time at USD, he says.
Another favorite aspect about the computer science department: Associate Professor Doug Goodman, Ph.D., a 36-year veteran of the department. “Dr. Goodman is an awesome professor,” Grady says. “His door is always open, although you may find him playing Chinese Checkers with a student.”
Siva Allu ’22, M.S., another recent graduate of the program, now works as a software development engineer at Amazon in Seattle, Washington. Originally from India, Allu chose to attend graduate school at USD because of its reputation in AI.
“I don’t come from a computer science background; I got my undergraduate degree in aeronautical engineering,” Allu says. Learning about AI imaging during his studies drove him to pursue further education in the subject. “AI is the innovation of the century. A lot of future solutions to science problems will come from AI.”
USD’s program gained his attention due to Vermillion’s low cost of living, the opportunity to work directly with professors in the department and research opportunities offered to students. “I was able to get a summer research project right away,” Allu says.
Like Grady, Allu credits Goodman—particularly the professor’s algorithm class—for his success in the field. “His class was tough. But when you have a tough class, you learn a lot.” Allu also acknowledges Santosh and Instructor Zach Tschetter for their guidance and mentorship.
Current graduate student Suprim Nakarmi also came to USD after earning an undergraduate degree in engineering in his native Nepal. Studying AI emerged from an interest in serving people living in rural areas in low- and middle-income countries. “AI has a huge impact where experts are not available,” Nakarmi says. “AI can help non-experts in those areas in health care facilities screen for diseases, for example.”
He also has a research apprenticeship with Synthetik, a start-up based in Austin, Texas, which uses technology to mitigate threats such as terrorism and environmental events. After graduating next year, Nakarmi plans to work in industry before pursuing his Ph.D. USD’s AI specialization, accessible faculty, low-cost tuition and the active student International Club all add up to a great experience for students from all over the world to pursue graduate studies here, he adds.
Shotabdi Roy ’22, M.S., says USD’s reputation in AI led her to apply after earning her bachelor’s in electrical and electronic engineering in her home country of Bangladesh where she wrote her thesis on an AI-based object-detection robot. “I wanted to do my master's in artificial intelligence and computer vision, and when I saw Dr. KC Santosh's work, I was so amazed that I made up my mind to work under him. I didn't even apply to other universities,” she says.
Currently, she works as a research associate in the lab of Arun Singh, Ph.D., assistant professor of basic biomedical sciences in the Sanford School of Medicine at USD, where Roy and her colleagues study motor and cognitive control networks in Parkinson's disease and other neurodegenerative and neuropsychiatric disorders. “Both of my degrees are perfectly blended here,” Roy says. “I’m also working with human subjects which makes me feel so good because my research work will contribute to human well-being.”
South Dakota native Josh Henderson earned his bachelor’s degree in computer science this May and now serves as an instructor in the department while earning his master’s degree here. “Growing up in South Dakota, I knew that USD had a good program and was intrigued by the chance to stay in state and still be at a program that allowed me to excel in my field and be prepared for working in computer science,” he says.
Henderson plans to work in deep learning engineering and research after he graduates. “Remaining in South Dakota is an option, as there are a rapidly growing number of employers in computer science.”
Annual Symposium Highlights AI’s Far-Reaching Applications
In 2021, faculty members in departments across the University of South Dakota united to hold the university’s first AI Symposium. Free and open to the public, hundreds of registered participants took part in the event online and on the Vermillion campus. This year, the symposium continued to bring in speakers on the latest developments in AI and draw professionals from academia, industry and government.
Past presentations include “Searching for dark matter with machine learning,” “Using machine learning to better understand natural resource dynamics” and “The role of AI in decision-making.”
The symposium is another way to position USD as a top educator of AI professionals in the region and beyond, says Santosh. “Our AI and data science programs at USD will contribute to preparing a workforce of young scientists who will maintain America's strategic position of command in science and engineering from South Dakota.”
The IEEE Computer Science Society 2024 president elect Jyotika Athavale is set to be a keynote speaker for USD’s 2023 AI Symposium.
Learn more about USD’s computer science programs at usd.edu/Computer-Science.
| 2022-12-14T00:00:00 |
https://www.usd.edu/the-south-dakotan/students-flock-to-artificial-intelligence-offerings-at-usds-computer-science-department
|
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"date": "2022/12/14",
"position": 75,
"query": "machine learning job market"
},
{
"date": "2022/12/14",
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|
Reinforcing Key Enabling Technologies
|
Reinforcing Key Enabling Technologies
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https://digital-skills-jobs.europa.eu
|
[] |
... job market to understand their potential, and use them in their professional life? ... We start with a focus on Artificial Intelligence and Machine Learning, so ...
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Advanced digital technologies and their role in contemporary and future society: what are the practical applications of these technologies, and how is it possible for people who are already in the job market to understand their potential, and use them in their professional life?
The focus of this section of the Digital Skills and Jobs Platform is to analyse advanced key enabling technologies, such as Artificial Intelligence and Machine Learning, Blockchain, Data and Cloud.
We will be showcasing good practices from EU countries and training institutions which are working to train people in these areas, what EU funding programmes are available to finance trainings, and finally discuss how these technologies are currently being integrated in many professions and businesses.
We start with a focus on Artificial Intelligence and Machine Learning, so please enjoy the selection of articles, trainings and funding opportunities below, and feel free to join in the discussion by subscribing to the group "Real-world applications for advanced digital skills".
| 2022-12-14T00:00:00 |
https://digital-skills-jobs.europa.eu/lv/node/2216
|
[
{
"date": "2022/12/14",
"position": 97,
"query": "machine learning job market"
}
] |
|
Harnessing the potential of AI: Creating a better future ...
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Harnessing the potential of AI: Creating a better future through technology
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https://distinction.studio
|
[] |
However, with the advent of machine learning, AI systems gained the ability to learn from data and improve their performance over time. Machine learning ...
|
Although Artificial Intelligence (AI) has been around for a while, it has gained significant attention recently with the public releases of Large Language Models (LLMs) such as ChatGPT and DALL·E from OpenAI. For many, it has become a driving force behind technological advancements.
With the potential to revolutionise various industries and improve our daily lives, AI has become a key focus for researchers, policymakers, and businesses alike. As we navigate this rapidly evolving landscape, we must understand the magnitude of AI's power, its role in shaping the future, and the challenges we must overcome to harness its true potential.
Understanding the power of artificial intelligence
Artificial intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence. These tasks may include speech recognition, problem-solving, decision-making, and even creative thinking. By leveraging algorithms and large datasets, AI systems can analyse and interpret complex information, making them instrumental in tackling intricate problems across various domains.
Artificial intelligence has revolutionised the way we interact with technology. AI has become an integral part of our daily lives, from voice assistants like Siri and Alexa to self-driving cars and personalised recommendations on streaming platforms. But how did AI evolve from a mere concept to today's powerful technology?
Defining artificial intelligence and its capabilities
When we talk about artificial intelligence, we refer to a machine's ability to exhibit human-like intelligence. It involves the development of algorithms and models that enable computers to learn from data, make decisions, and perform tasks that would typically require human intervention.
AI systems are designed to mimic human cognitive abilities, such as perception, reasoning, learning, and problem-solving. These systems can process vast amounts of data, identify patterns, and make predictions or recommendations based on the information they have analysed.
For example, speech recognition technology, a common application of AI, allows computers to understand and interpret human speech. This capability has paved the way for virtual assistants that can respond to voice commands, making our interactions with technology more natural and intuitive.
Another remarkable capability of AI is its problem-solving prowess. AI systems can analyse complex problems, break them down into smaller components, and devise strategies to find optimal solutions. This ability has benefited healthcare, finance, and logistics, where complex decision-making is required.
The evolution of AI: From concept to reality
The concept of artificial intelligence has been around for several decades, but it is only in recent years that we have witnessed significant advancements in this field. The evolution of AI can be attributed to various factors, including improvements in computational power, the availability of big data, and breakthroughs in machine learning algorithms.
In the early days of AI, researchers focused on developing rule-based systems. These systems relied on predefined rules and logical reasoning to perform tasks. While effective in certain domains, they could not adapt and learn from new data.
However, with the advent of machine learning, AI systems gained the ability to learn from data and improve their performance over time. Machine learning algorithms, such as neural networks, enable computers to recognise patterns and make predictions based on examples. This breakthrough opened up new possibilities for AI, allowing it to tackle more complex tasks.
Furthermore, the availability of big data has played a crucial role in the advancement of AI. With the proliferation of digital technologies, vast amounts of data are being generated every day. This data serves as fuel for AI systems, allowing them to learn and make accurate predictions. The combination of big data and machine learning has been instrumental in the development of AI applications like image recognition, natural language processing, and recommendation systems.
As AI continues to evolve, researchers are exploring new frontiers, such as deep learning and reinforcement learning. These approaches aim to make AI systems even more capable of understanding and interacting with the world around us.
In conclusion, artificial intelligence has come a long way from being a mere concept to a powerful technology that has transformed various industries. With its ability to analyse complex information, make decisions, and learn from data, AI has the potential to revolutionise the way we live and work. As we continue to unlock the full potential of AI, we can expect to see even more exciting advancements in the future.
The role of AI in shaping our future
AI in everyday life: A glimpse into the future
Imagine a future where AI simplifies our daily routines, enhances productivity, and improves the quality of our lives. From virtual assistants that anticipate our needs to self-driving cars that make transportation safer and more efficient, AI has the potential to transform the way we live. With the ability to learn from vast amounts of data, AI systems will continue to evolve, adapting to our preferences and providing personalised experiences that cater to our individual needs.
In the future, AI will not only assist us with mundane tasks but also become an integral part of our decision-making processes. Imagine waking up to an AI-powered alarm clock that not only wakes you up at the perfect time but also adjusts the room temperature and lighting based on your sleep patterns. As you go about your day, your AI assistant will seamlessly manage your schedule, remind you of important meetings and suggest the most efficient routes to take.
AI will also play a significant role in healthcare, revolutionising the way diseases are diagnosed and treated. Imagine a world where AI-powered algorithms can analyse medical images with unparalleled accuracy, assisting doctors in detecting early signs of diseases that may have otherwise gone unnoticed. These algorithms will continuously learn from vast datasets, improving their diagnostic capabilities over time and ultimately saving lives.
Furthermore, AI will have a transformative impact on the transportation industry. Self-driving cars will become the norm, eliminating the need for human drivers and reducing the number of accidents caused by human error. These autonomous vehicles will communicate with each other, optimising traffic flow and reducing congestion. As a result, commuting will become a stress-free experience, allowing individuals to use their travel time more productively.
The transformative impact of AI on industries
Industries across the board are experiencing the transformative power of AI. From healthcare to finance, manufacturing to agriculture, AI is revolutionising operations, driving innovation, and improving efficiency. In healthcare, AI-powered algorithms can aid in diagnosing diseases, analysing medical images, and predicting patient outcomes. In finance, AI is transforming fraud detection, risk assessment, and algorithmic trading. These advancements are just the tip of the iceberg as the potential applications of AI continue to expand.
Let's delve deeper into the impact of AI in the manufacturing industry. With the integration of AI, factories are becoming increasingly automated, leading to higher production rates and improved quality control. AI-powered robots can perform repetitive tasks with precision and accuracy, reducing the risk of human error. These robots can also adapt to changing production demands, ensuring optimal efficiency and reducing downtime. As a result, manufacturers can meet customer demands more effectively, leading to increased customer satisfaction and loyalty.
Agriculture is another industry that is benefiting from AI advancements. AI-powered drones equipped with sensors and cameras can monitor crop health, detect pest infestations, and optimise irrigation systems. By analysing data collected from these drones, farmers can make informed decisions about crop management, leading to higher yields and reduced environmental impact. AI can also help predict weather patterns and optimise planting schedules, further enhancing agricultural productivity.
As AI continues to evolve, it will undoubtedly reshape industries in ways we can't fully comprehend yet. From personalised shopping experiences to intelligent energy management systems, the possibilities are endless. The key to harnessing the full potential of AI lies in responsible development and ethical implementation, ensuring that the benefits are shared by all and that AI remains a force for good in shaping our future.
Overcoming challenges in AI implementation
Ethical considerations in AI development
While AI presents vast opportunities, it also raises important ethical considerations. As AI systems become more sophisticated, questions arise concerning their decision-making processes, biases, and the potential impact on privacy and security. Addressing these concerns requires a collaborative effort between technology developers, policymakers, and society at large to ensure that AI is developed and deployed in a responsible and ethical manner.
Addressing the fear of AI: Job displacement and privacy concerns
There is a natural fear that AI will replace human jobs and lead to widespread unemployment. However, history has shown that technological advancements ultimately create more job opportunities than they eliminate. While certain roles may be automated, AI can also augment human capabilities, creating new jobs and industries. Additionally, privacy concerns come hand in hand with AI's ability to collect and analyse vast amounts of data. Developing transparent and secure systems is crucial to mitigating these concerns and building trust in AI technologies.
The road ahead: AI and sustainable development
AI's contribution to environmental sustainability
As we strive for a sustainable future, AI can play a significant role in addressing environmental challenges. From optimising energy consumption to managing waste and predicting natural disasters, AI can help us make smarter decisions and reduce our ecological footprint. By harnessing the power of AI, we have the potential to create a world that is both technologically advanced and environmentally conscious.
AI in healthcare: A promise of a better future
In the field of healthcare, AI holds immense promise. By analysing vast amounts of patient data, AI systems can identify patterns and provide more accurate diagnoses. They can enhance personalised treatment plans, aid in drug discovery, and improve patient outcomes. Moreover, telemedicine and remote monitoring powered by AI can increase access to healthcare in underserved areas, bridging the gap between patients and healthcare providers.
Conclusion: Embracing AI for a better tomorrow
The need for AI literacy and education
As AI and its applications become increasingly prevalent, it is crucial for individuals to acquire the necessary literacy and skills to navigate this technological landscape. A shift towards STEM education, AI literacy programs, and continuous learning will enable individuals to adapt and seize the opportunities presented by AI and shape the future together.
The role of policy and regulation in AI's future
Given the potential impact of AI on society, it is imperative for policymakers to establish clear guidelines and regulations that promote innovation while ensuring accountability and ethical use of AI. Striking the right balance between fostering AI advancements and safeguarding public interest will be key in maximising the benefits of AI while minimising potential risks.
In conclusion, AI represents a powerful force that has the potential to reshape our world. By understanding its capabilities, acknowledging its impact on various industries, and addressing the challenges it presents, we can harness AI's potential to create a better future through technology. It is up to us, as individuals, businesses, and societies, to embrace AI responsibly, invest in education and research, and work collaboratively to unleash the true power of AI for the benefit of all.
As we stand on the brink of an AI-driven future, the journey from concept to market leader requires a partner with the expertise to navigate this transformative landscape. Distinction is that partner. We specialise in strategy, design, and development services tailored to elevate disruptive brands. If you're ready to launch or grow your digital product with speed and certainty, let's start the conversation.
Arrange a discovery call with us today and take the first step towards realising your vision with AI.
| 2022-12-14T00:00:00 |
https://distinction.studio/journal/harnessing-the-potential-of-ai-creating-a-better-future-through-technology
|
[
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"date": "2022/12/14",
"position": 100,
"query": "machine learning job market"
},
{
"date": "2022/12/14",
"position": 74,
"query": "future of work AI"
}
] |
|
AI Capability Overhang means our soft skills become ...
|
AI Capability Overhang means our soft skills become groundbreaking
|
https://www.lepaya.com
|
[
"Gregor Towers",
"August",
"Karolina Fesołowicz",
"May",
"July"
] |
AI Tech has just closed the gap to human intelligence. This new AI trend does not compromise our talent's soft skilling , but excels them.
|
• ChatGPT represents a new era in AI communication, with hidden capabilities that challenge the relevance of traditional skills in the workplace. • Even though the use of ChatGPT allows for more efficient completion of tasks, soft skills such as empathy and creativity are still essential for excellence. • Soft skills are crucial for decision-making in business, as AI messaging lacks empathy and human consciousness. • The emergence of ChatGPT pushes the workforce to enhance their soft skills to adapt to the changing landscape of AI technology. • Soft skill training is necessary to navigate the challenges and opportunities presented by AI Overhang and to excel in the future of work.
ChatGPT leverages the relevance of Learning & Development
AI Tech has just closed the gap to human intelligence. Until now, AI communication was developing quickly, but never to the standard of human dialogue. The AI Capability Overhang is here.
What is AI Overhang and why does it matter in the context of soft skill training?
AI Overhang refers to the capability to build transformative AI without people realizing it was possible. In simple terms, the AI algorithm develops powerful and complicated capabilities which researchers neither understand, nor have the resources to test. With such powerful AI communication, this creates ambiguity around the relevance of hard or soft skills in a business environment.
And since the AI capabilities are hidden, so too are the changes and challenges. These changes are now entering our workspace and they are reshaping how we should approach talent upskilling.
Welcome to the era of ChatGPT
With AI going mainstream, we mark a turning point where Capability Overhang becomes accessible via a new tool – ChatGPT. ChatGPT went viral because of its accessibility and ability to produce human-like dialogue. It helps users to execute time-consuming, often mundane communication tasks.
And even though we’re not fully aware of the Overhang capabilities, the possibility to execute on projects quicker and dedicate time to other tasks is so attractive that people are flocking to test it and use it in their work.
Distinguishing AI trends from groundbreaking changes in the future of work
When a new AI trend arrives, we are faced with two options: we can be fearful or curious. Humanity is designed to adapt, optimize and progress. Those that make groundbreaking changes will use resilience, intuition and adaptability to evolve their skillset. They are not fearful of new trends, but inquisitive.
ChatGPT is an opportunity to move with a new trend and excel to define the future of work. By allowing this AI model to do the foundational work for us, we have more time to be more creative and execute on our business growth strategies. ChatGPT enables us to utilize AI tech to accelerate and optimize the upskilling of our workforce.
You might also like: How to Mitigate Employees’ Fear of Being Replaced by AI Technology
We need soft skills to turn ChatGPT into excellence
AI text is still 92% detectable and the ChatGPT Overhang cannot produce new content, but repeat phrases. The algorithm only scans patterns and auto-generates pre-existing combinations of words . What’s more, since more content will become available and it will be repetitive, people will be more selective and their tolerance for substandard content will decrease.
That means our organization’s messaging and communication to target audiences has to cut through the mediocrity. And more than ever, we are reliant on the human intelligence of our talent to communicate and engage our audience with messaging that resonate beyond the surface level.
When an employee is engaging a new client, ChatGPT can efficiently produce the building blocks of a promising pitch. But soft skills integrate humor, empathy and creativity to excel. We still need to curate language because only originality and human emotional intelligence built on top of AI content will accelerate our business growth.
Soft Skills cultivate consciousness in business decision-making
AI messaging can never fully influence our decisions, as this neglects our responsibility for the impact we have on our people.
Remember, ChatGPT produces repetitive information without empathy. Its hidden capacity – the Overhang – still generates text with biases against people of color and gender.
That’s where soft skill training builds awareness into our communication in business.
Decision-making involves communicating with people around us to understand the wider implications. In business, we need to be able to problem solve, rationalize and discuss to make difficult decisions. Especially when managers deal with delicate situations which impact lives and emotions such as terminating a contract. Soft skill training empowers us to reflect, collaborate, and think independently before we make decisions which impact others and our organization.
You might also like: AI Skills of the Future: Understanding AI and How to Make it Work for You
The Overhang pushes our workforce to the next level
Now that ChatGPT is in the public domain, people leaders need to push the workforce’s soft skills to the next level.
ChatGPT is a new trend. If we are curious and learn how to use it, we can optimize and drive towards our business goals.
AI content helps us become more efficient with mundane tasks and free us to accelerate and focus on our talent’s upskilling. We can spend more time developing soft skills such as creativity, awareness and emotional intelligence. These skills are what define the future of work and push us to be resilient, yet innovative.
If the Overhang has suddenly produced ChatGPT, then we can expect more powerful tools to emerge in 2023.
But no matter the AI Tech to come, these hidden capabilities can enable us to build the most efficient and competent workforce. We need soft skill training to navigate the ambiguity with resilience and adaptability and become even better at how we work.
| 2022-12-14T00:00:00 |
https://www.lepaya.com/blog/ai-overhang-means-our-soft-skills-will-be-groundbreaking
|
[
{
"date": "2022/12/14",
"position": 9,
"query": "AI skills gap"
},
{
"date": "2022/12/14",
"position": 98,
"query": "workplace AI adoption"
}
] |
|
The Future of Work: 12 Workplace Trends in 2023
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12 Workplace Trends to Expect in 2023
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https://emeritus.org
|
[
"Sanmit Chatterjee",
"About The Author",
"Read More About The Author",
"Supriya Sarkar"
] |
As AI and automation unlock new opportunities in the workplace, companies will need to invest in upskilling and reskilling their employees to work with these ...
|
The workplace trends we witness today have irreversibly changed nearly three years on from the start of the COVID-19 pandemic. But in the face of strong economic headwinds and ongoing geopolitical disruptions, it’s clear that the new status quo is not static.
As we enter a new year, new approaches to work and retention, as well as technological changes like the increasing adoption of artificial intelligence (AI) and advanced data analytics, will drive continued changes to the most prevalent workplace trends.
Workplace Trends in 2023
If the last few years are any guide, 2023 is likely to hold its share of surprises when it comes to workplace trends. Here’s what we already know to look out for in the year ahead.
1. Ongoing Hiring and Retention Challenges
While the peak of the so-called “Great Resignation” is likely well behind us, workers in the U.S. continue to leave their jobs at higher-than-usual rates. In October 2022, 4 million Americans quit their jobs, leaving 10.3 million positions open. The gap between the number of people seeking work and the number of open roles remains wide, meaning that effective hiring and employee retention tactics remain highly important.
The fact that this trend has persisted well past the end of most COVID restrictions and into a more challenging economic environment points to a deeper set of drivers behind employees’ choices. The World Economic Forum notes that the problem is especially acute in lower-paying jobs and industries, in part because employees are no longer willing to accept many of the conditions that were standard before the pandemic.
Turnover also remains high in professional and managerial positions, where a lack of advancement opportunities is often a key motivator for departing employees. Investing in employee development programs, like upskilling and reskilling initiatives, can help companies improve their staffing levels in 2023.
2. A Focus on Flexibility
Remote work is no longer a pandemic-driven necessity—but as companies enter 2023, many will continue to experiment with different forms of remote and hybrid working – or at least some flexibility. A new survey by Omdia found that 48% of the workforce will continue to work remotely or in a hybrid fashion, though questions about the future of work remain. While 54% of Omdia’s survey respondents believe work-from-home has increased productivity, companies like Tesla have made news for requiring all employees to return to the office.
As employers and employees test a range of approaches to hybrid and remote work, companies will need to take employee feedback and concerns into careful consideration to avoid retention backlash. With demand high for remote positions, flexible work policies may also offer a strong recruitment tool for organizations.
3. Prioritization of Employee Well-Being
Companies have increasingly been focusing on work-life balance and the mental health of employees—and this will continue as one of the key workplace trends of 2023. According to Indeed research, 90% of people believe that how we feel at work matters, yet only 49% feel their organization is focused on measuring and improving well-being.
Since work stress and concerns around benefits and flexibility are frequent drivers of turnover, focusing on employee well-being can have significant payoffs for companies. Efforts to prioritize employees’ happiness and health can include providing additional employee benefits, greater flexibility, sign-on bonuses, and an overall positive workplace experience.
Companies have increasingly been focusing on work-life balance and the mental health of employees—and this will continue into 2023.
4. Talent Shortages and Widening Skills Gaps
Hiring is already difficult in this labor market—but many organizations are also challenged by severe talent shortages in critical areas. According to the Manpower Group, around three-quarters of companies in industries ranging from construction to tech are having a hard time finding qualified applicants for certain positions.
Perhaps even more worryingly, LinkedIn recently found that nearly half of learning and development leaders say that skills gaps in their organizations are widening rather than growing smaller. To address these shortages, savvy companies will work to broaden and improve their upskilling and reskilling efforts in 2023.
5. A Continued Focus on Skills Over Jobs
Historically, many employees remained in fairly similar roles for their entire careers. Today, the pace of technological and economic change means the average worker might need to frequently reskill and upskill, and even change jobs, to preserve their opportunities for growth and advancement.
Hiring, in this environment, should be focused on what specific skills employees bring to the table rather than their previous roles. Many of the most valuable skills in today’s economy, like data analysis, can be applied to a number of different roles. In 2023, more companies are likely to look to alternative credential programs like short courses or certificates when hiring and to increase their spending on in-house upskilling efforts.
Leaders recognize that focusing on upskilling employees and career pathing can help their organizations close skills gaps. This means using a whole new set of tools to identify individual skill sets as opposed to more traditional job grading. Skills development can help organizations meet their most urgent business needs—and skills can be measured using what is referred to as “skill data.”
Check out the graphic below to see how Emeritus programs have helped past learners narrow skills gaps in their organizations, based on data from the 2022 Emeritus Global Career Impact Survey.
6. Renewed Focus on Sustainability
In a recent Gartner survey, more companies than ever before cited sustainability as a key focus for the year ahead. While the specifics of how companies approach and prioritize environmental and sustainability issues will vary depending on industry, company size, and numerous other factors, increased consumer and shareholder demand is driving a significant shift toward green business practices.
For many organizations, embracing new sustainability efforts will also require investment in employee upskilling to help executives and frontline employees alike make strategic and everyday decisions that improve their company’s environmental footprint.
7. Emphasis on Soft Skills
Soft skills, also referred to as “power skills,” were important before the pandemic. But the need to build relationships virtually and work with reduced oversight has made soft skills in the workplace even more important.
Among the soft skills that are in particularly high demand are management and leadership in a changing workplace, critical thinking, creativity, and problem-solving. As 2023 progresses, companies will reassess their leadership models and the skills leaders need to succeed in their roles.
8. Increased Diversity, Equity, and Inclusion (DEI) Efforts
As we enter 2023, diversity, equity, and inclusion (DEI) efforts remain top of mind for many organizations. That commitment is essential for employee engagement and recruiting since a recent GoodHire survey found that 81% of respondents would seriously consider quitting their jobs if the company failed to demonstrate a true commitment to DEI.
In addition to potential turnover issues, failing to prioritize DEI can lead to major financial losses. Accenture has found that companies are losing over a trillion dollars a year due to their lack of DEI efforts. Companies that boost their investments in this area in 2023—and who use metrics and KPIs to track progress—will lay the groundwork for positive employee relations and outcomes for years to come.
9. Greater Use of AI and Automation
The emergence of ChatGPT, an advanced AI tool available to the public, has raised public awareness of the importance and rapid growth of artificial intelligence (AI)—but in many corporations, AI has been a pressing concern for years. Along with automation technology, AI offers numerous growth opportunities for companies, if they’re prepared to take them on.
As AI and automation unlock new opportunities in the workplace, companies will need to invest in upskilling and reskilling their employees to work with these technologies.
10. An Emphasis on Strategic Transformation
As we transition into 2023, organizations are accelerating strategic digital transformation efforts to reimagine and reconfigure how they operate. In part, innovations in automation and the adoption of new technologies are prompting the change, as is a greater need for strategic thinking around data analytics, sustainability, marketing, and leadership.
Companies launch transformations in response to significant pressures from the market, and these changes may involve pivoting services, products, and offerings to realign how they function. In fact, Emeritus recently launched its new skills transformation academies solution to drive skills development for companies planning transformations.
11. Transformation of HR Using Tech and Data
Going into 2023, more organizations are transforming their human resources departments as they leverage data analytics in direct sourcing and talent acquisition. HR workers can also use data to find out why employees are leaving their organizations and stem turnover. People analytics, or insights derived from data related to workforce talent, are helping employers uncover important information about organization-wide performance and employees’ individual needs.
When it comes to tech, companies are turning to cloud computing, collaboration technologies, and digitization to improve HR operations and the employee experience, according to the Society for Human Resource Management.
12. An Emphasis on Continued Employee Growth
As more leaders prioritize upskilling and reskilling in the workplace, they’re aiming to embed a growth mindset into their organization’s culture. A growth mindset stems from the belief that you have the capacity to learn and grow. This contrasts with a fixed mindset—a belief that certain qualities of an individual (like talent or intelligence) are innate.
Two examples of companies that have made organization-wide growth mindsets high priorities are Microsoft and Unilever. In 2023 overall, establishing a culture of learning with organizations will become even more important to closing skills gaps.
As the world of work continues to evolve in 2023, it’s impossible to accurately predict how changing economic and business trends might impact workplace priorities and trends. But it’s clear the pandemic (among other factors) has spurred workforce transformation at a rapid pace, and employers need to keep up with skills development more than ever before.
As these workplace trends emerge, upskill and reskill your workforce to meet the needs of businesses today. Learn more about Emeritus Enterprise and how we can help you develop online employee training programs for your team.
| 2022-12-14T00:00:00 |
2022/12/14
|
https://emeritus.org/blog/the-future-of-work-workplace-trends/
|
[
{
"date": "2022/12/14",
"position": 39,
"query": "future of work AI"
},
{
"date": "2022/12/14",
"position": 33,
"query": "workplace AI adoption"
},
{
"date": "2022/12/14",
"position": 94,
"query": "machine learning workforce"
}
] |
He Used AI to Publish a Children's Book in a Weekend. ...
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He Used AI to Publish a Children’s Book in a Weekend. Artists Are Not Happy About It
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https://news.yahoo.com
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[] |
... universal duty, report says ... Because of the calendar, Social Security recipients who get Supplemental Security Income benefits get one check in April, but two ...
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A page from <em>Alice and Sparkle</em>, a children's book made using artificial intelligence. Credit - Courtesy Ammaar Reshi
Ammaar Reshi was playing around with ChatGPT, an AI-powered chatbot from OpenAI when he started thinking about the ways artificial intelligence could be used to make a simple children’s book to give to his friends. Just a couple of days later, he published a 12-page picture book, printed it, and started selling it on Amazon without ever picking up a pen and paper.
The feat, which Reshi publicized in a viral Twitter thread, is a testament to the incredible advances in AI-powered tools like ChatGPT—which took the internet by storm two weeks ago with its uncanny ability to mimic human thought and writing. But the book, Alice and Sparkle, also renewed a fierce debate about the ethics of AI-generated art. Many argued that the technology preys on artists and other creatives—using their hard work as source material, while raising the specter of replacing them.
I spent the weekend playing with ChatGPT, MidJourney, and other AI tools… and by combining all of them, published a children’s book co-written and illustrated by AI!
Here’s how! 🧵 pic.twitter.com/0UjG2dxH7Q — Ammaar Reshi (@ammaar) December 9, 2022
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Reshi, a product design manager from the San Francisco Bay Area, gathered illustrations from Midjourney, a text-to-image AI tool that launched this summer, and took story elements from a conversation he had with the AI-powered ChatGPT about a young girl named Alice. “Anyone can use these tools,” Reshi tells TIME. “It’s easily and readily accessible, and it’s not hard to use either.”
His experiment creating an AI-generated book in just one weekend shows that artificial intelligence might be able to accomplish tasks faster and more efficiently than any human person can—sort of. The book was far from perfect. The AI-generated illustrations had a number of issues: some fingers looked like claws, objects were floating, and the shadowing was off in some areas. Normally, illustrations in children’s books go through several rounds of revisions—but that’s not always possible with AI-generated artwork on Midjourney, where users type a series of words and the bot spits back an image seconds later.
Artists singled out this page from Alice and Sparkle as showing the limits of the AI-powered technology. The illustration has several apparent flaws, including the character appearing to have claws. Courtesy Ammaar Reshi
Alice and Sparkle follows a young girl who builds her own artificial intelligence robot that becomes self aware and capable of making its own decisions. Reshi has sold about 70 copies through Amazon since Dec. 4, earning royalties of less than $200. He plans to donate additional copies to his local library.
More from TIME
Reshi’s quixotic project drew praise from many users for its ingenuity. But many artists also strongly criticized both his process and the product. To his critics, the speed and ease with which Reshi created Alice and Sparkle exemplifies the ethical concerns of AI-generated art. Artificial intelligence systems like Midjourney are trained using datasets of millions of images that exist across the Internet, then teaching algorithms to recognize patterns in those images and generate new ones. That means any artist who uploads their work online could be feeding the algorithm without their consent. Many claim this amounts to a high-tech form of plagiarism that could seriously harm human artists in the near future.
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Reshi’s original tweet promoting his book received more than 6 million impressions and 1,300 replies, many of which came from book illustrators claiming artists should be paid or credited if their work is used by AI.
“In order to protect the rights and property of artists, there must be stricter regulation around how art is used to train AI algorithms,” says Michelle Jing Chan, an illustrator of children’s books. “Artists should be appropriately compensated when their works are used in the training of algorithms.”
“The main problem to me about AI is that it was trained off of artists’ work,” adds Adriane Tsai, also a children’s book illustrator. “It’s our creations, our distinct styles that we created, that we did not consent to being used.”
Already, some companies and brands are choosing AI technology over human talent. The San Francisco Ballet used images generated by Midjourney to promote this season’s production of the Nutcracker. A digitally-generated fashion model, Shudu Gram, has modeled for brands including Louis Vuitton. And at comedy clubs, artificial intelligence is being used to deliver standup jokes.
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The phenomena has made many creatives nervous about their own futures, wondering if people will pay for their services when they could generate it for much cheaper using AI.
“As somebody who makes my money and finds my joy in writing, it’s deeply troubling to see people seeking cheap alternatives to actual human writing, which is already one of the most deliriously underpaid professions,” says Abraham Josephine Riesman, an author.
Reshi responded to these concerns by calling on the creators of the AI tools at OpenAI and Midjourney to ensure protections for artists and authors whose work may be used in artificial intelligence algorithms.
“I think there’s real concern and I actually do hear out those artists,” Reshi says. “It’s really important that the tech industry that’s working on these tools involves them in the process of creation.”
| 2022-12-14T00:00:00 |
2022/12/14
|
https://news.yahoo.com/used-ai-publish-children-book-215811849.html
|
[
{
"date": "2022/12/14",
"position": 71,
"query": "universal basic income AI"
}
] |
Disruption funds | Investing in innovative industries
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Investing in innovative industries
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https://www.fidelity.com
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[] |
Fidelity's disruptive funds invest in innovative business models, emerging industries, and technologies that are changing the status quo.
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Thought leadership
Learn why disruptor companies may help shape the investing world for years to come.
Some of the largest companies in the world are disruptors that introduced new business models and changed established industries to create entirely new ones. Think Amazon, Facebook, Netflix, and Uber.
Interpreting the disruptive power of AI
Artificial intelligence-based technology that seemingly pushes traditional limitations of human-like abilities has arrived, but this advancement is only the beginning, according to Fidelity’s Chris Lin.
| 2022-12-14T00:00:00 |
https://www.fidelity.com/mutual-funds/investing-ideas/disruption
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[
{
"date": "2022/12/14",
"position": 26,
"query": "AI economic disruption"
}
] |
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5 business trends for 2023 - IBM
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5 business trends for 2023
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https://www.ibm.com
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[] |
Technology and the Global economy · Think 2024. C-suite Study. Global C ... CISOs: Is your cybersecurity AI-resilient or AI-disrupted? How to begin the ...
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The chaos of the 2020s has pushed leaders to act quickly and decisively. It has accelerated innovation and forced organizations to transform in ways that didn’t previously seem possible. And those that didn’t adapt have now fallen behind.
In 2023, rapid response is the new baseline. Uncertainty is expected and complexity is compounding. As threats materialize on multiple fronts, organizations must reduce the time from insight to action. Business leaders need precise intelligence to dodge obstacles as they appear—and obstacles will be legion.
As disruption drives change on virtually every front, leaders have to keep their heads up.
To prepare, executives must try to predict the future. Is a global recession around the corner? Will easing pandemic restrictions affect hybrid work? Will extreme weather events continue to wreak widespread havoc? Will geopolitical conflicts cool—or will battles move to entirely new fronts? How and where threats appear, as well as where they overlap, will have far-reaching implications for global businesses.
For example, it’s unclear how long the war in Ukraine will continue to disrupt supply chains, talent pools, and business operations. But as organizations grapple with the consequences, they must also navigate increasing cost pressures caused by inflation. In October 2022, the International Monetary Fund (IMF) forecast that global inflation would peak at 9.5% in the third quarter of 2022—and some economists fear that price surges could have staying power.
At the same time, companies are striving to achieve aggressive sustainability targets that require collaboration across aisles and ecosystems. In 2022, global CEOs named sustainability as the top challenge their organizations are expected to face over the next two to three years—rising from sixth place in 2021. Plus, purpose-driven consumers, who prioritize products and brands that align with their values, now represent the largest shopper segment (44%).
Purpose-driven consumers now represent the largest shopper segment.
As disruption drives change on virtually every front, leaders have to keep their heads up. They must respond to threats strategically and pick their battles wisely. To help executives set the right priorities, the IBM Institute for Business Value has identified five key trends that we believe will influence the business landscape in 2023—and the top opportunities that can help organizations forge ahead in the face of volatility.
Download the report to see what trends topped the list—and how your business can prepare for what’s ahead.
| 2022-12-14T00:00:00 |
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/business-trends-2023
|
[
{
"date": "2022/12/14",
"position": 93,
"query": "AI economic disruption"
}
] |
|
Data Annotation
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Data Annotation
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https://www.taskus.com
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[] |
For job seekers · For former employees · For employment verification · For vendors/suppliers. Why TaskUs? Our Advantage · Generative AI · Digital Innovation.
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Outsourced Data Annotation Services
Data annotation is a crucial process in the machine learning lifecycle to build high quality datasets for AI applications like chatbots, virtual assistants, automatic search recognition models and more.
TaskUs has over a decade of experience helping the world’s leading companies develop computer vision and natural language processing models for a better customer experience. With 54,800 highly qualified and well-trained Teammates present globally, we can take care of all your data annotation needs to help scale your business.
| 2022-12-14T00:00:00 |
https://www.taskus.com/services/ai-data-services/data-annotation/
|
[
{
"date": "2022/12/14",
"position": 92,
"query": "generative AI jobs"
}
] |
|
Tools for Teaching Artificial Intelligence :: Certiport Blog
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Tools for Teaching Artificial Intelligence :: Certiport Blog :: Certiport
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https://certiport.pearsonvue.com
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[] |
Every day we use and interact with devices that use artificial intelligence, but to most of us the concepts of AI and machine learning are cryptic.
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Tools for Teaching Artificial Intelligence
Every day we use and interact with devices that use artificial intelligence, but to most of us the concepts of AI and machine learning are cryptic. With the right resources, you can learn and teach AI with confidence. In this webinar, computer science teacher, Nora Burkhauser, presented an overview of the big ideas of artificial intelligence and how AI lessons can be incorporated into your classroom. Get ideas for how you can bring AI to life for your students.
Expand your AI knowledge
If you’re an AI novice, you may want to expand your own skillset before you teach this in the classroom. Nora suggested some fabulous resources to help you learn more.
Learn with Google AI
Google has incredible resources for those looking to get their toes wet. It doesn’t matter if you’re a novice just learning to code or a veteran, they have resources to help you polish your skills. You can see their full library of interactive projects, courses, and guides here.
MindSpark
MindSpark is a registered non-profit focused on transforming education, providing cutting-edge learning experiences, and reshaping workforce development. In partnership with IBM, the MindSpark team has published AI focused webinars for K12 educators. All of them are available for free and on-demand. According to their website, “The nine webinars will guide educators through AI’s foundational concepts and K-12 classroom connections, with topics including introduction to AI, natural language processing, ethics, robotics, and more.” Those who attend all nine webinars can earn the IBM AI Education badge.
AI4K12.org
Artificial Intelligence for K12 is an initiative sponsored by the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA). On the AI4K12 website, you can find a list of instructional resources for educators interested in teaching AI. Whether you’re interested in books, curriculum materials, course outlines, software, or videos, they have you covered. Their resource list also includes professional development courses for educators.
Kaggle Intro to Machine Learning
For those who haven’t used Kaggle, they offer a no-setup, customizable, Jupyter Notebooks environment. You can access graphics processing units (GPUs) for free, along with a huge repository of community published data & code. This is a good fit for teachers who have some experience with coding and AI but want to expand their skillset. Their Intro to Machine Learning course is three hours, covering everything from how models work, to data exploration, and building machine learning models.
Resources for teaching AI
Once you’ve established your own AI foundation, it’s time to bring your students on board. Nora highlighted additional companies and websites that you can turn to for teaching ideas.
Technovation Curiosity Machine
Teaching a new concept, like AI, can often require students to learn and develop skills outside the classroom. Technovation has created a program to help your student learn with their families and communities as well as in the classroom. Their free challenge-based program allows students, families, educators, and communities to come together to solve real-world problems using AI. The challenge is broken into ten parts, and by the end of the course, learners will have created an Al invention that solves a problem that affects their local community.
Elements of AI
Over 850,000 people have started discovering the basics of AI with Elements of AI, so you know it’s going to be great. Elements of AI is a free online course created by MinnaLearn and the University of Helsinki. The course is broken into two parts: Introduction to AI and Building AI, each with five to six chapters. Not only will your students learn what AI is, but they’ll also learn what can (and can’t) be done with AI, and how to start creating AI methods. The best part is that it combines theory and hands-on activities, so there’s something for different learning styles.
AI4ALL
AI4ALL is a US-based nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy. The AI4ALL team has put together their Open Learning program to help all educators (regardless of subject) bring AI into their curriculum. They offer free, customizable AI curriculum and educator resources to all K12 educators. With up to 80 hours of content, you’re sure to find something that will engage your students. All their curriculum is also aligned to NGSS Engineering standards, ISTE standards, Common Core ELA/Literacy Standards, and CSTA standards.
Code.org
Code.org is dedicated to helping “every student in every school have the opportunity to learn computer science as part of their core K-12 education.” Famous for their Hour of Code program, they’ve also put together some incredible resources to help educators introduce AI to their students. If you’re looking for a longer course, rather than individual activities, this is a great option. Their AI and Machine Learning Module is structured for five weeks of lessons and can also be added to their CS Discoveries course. Importantly, the course focuses on AI ethics, issues of bias, and fundamental concepts, helping students see the impact of AI on daily living.
Bringing AI into the classroom doesn’t need to end with a project or challenge. Help students validate their knowledge with an industry-recognized certification! Our IT Specialist Artificial Intelligence certification exam is a great capstone exam for your learners. Find out all the details here.
| 2022-12-14T00:00:00 |
https://certiport.pearsonvue.com/Blog/2022/December/Tools-for-Teaching-Artificial-Intelligence.aspx
|
[
{
"date": "2022/12/14",
"position": 52,
"query": "machine learning workforce"
}
] |
|
Big Data Archives | Innovate
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Big Data Archives
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https://innovate.ieee.org
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[] |
... workforce shortages. Utilizing machine learning (ML), AI, and big data ... intelligence, machine learning, Data privacy. AI and ML in voice-driven ...
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© Copyright 2025 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Disclosures.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
| 2022-12-14T00:00:00 |
https://innovate.ieee.org/big-data/
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[
{
"date": "2022/12/14",
"position": 57,
"query": "machine learning workforce"
}
] |
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H2O.ai - Automated Machine Learning Platform
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H2O.ai - Automated Machine Learning Platform
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https://www.carahsoft.com
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[] |
The H2O.ai cloud accelerates IT modernization & transforms public sector operations with driverless AI. Experience the power of machine learning software ...
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Join Our Partner Ecosystem
As the Master Government Aggregator® and distributor for the industry's leading IT manufacturers, Carahsoft supports and enables a vibrant and growing partner ecosystem of: Solution Providers, Value-Added Resellers, Prime Contractors, and System Integrators. Learn more about the value we deliver and how we can accelerate your growth.
| 2022-12-14T00:00:00 |
https://www.carahsoft.com/h2oai
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[
{
"date": "2022/12/14",
"position": 67,
"query": "machine learning workforce"
}
] |
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The hidden upside of tech layoffs
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The hidden upside of tech layoffs
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https://www.businessinsider.com
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[
"Aki Ito"
] |
Despite the massive downturn in the tech industry, most laid-off employees are finding new jobs within three months — and many are even scoring raises.
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Most laid-off tech workers are finding new jobs within three months — and their ability to bounce back underscores just how strong the job market still is.
Most laid-off tech workers are finding new jobs within three months — and their ability to bounce back underscores just how strong the job market still is. Tyler Le/Insider
Most laid-off tech workers are finding new jobs within three months — and their ability to bounce back underscores just how strong the job market still is. Tyler Le/Insider
Austin Smith was given the news not long after he walked into the office one morning in June. His employer, Netflix, was laying off some 300 employees. Smith, a data analyst in Salt Lake City, was one of them.
In a state of shock, Smith drove home to break the bad news to his wife. They had gotten married in 2020, and bought a house together the following year. At 28, his life had seemed so good, so secure — until suddenly it wasn't. "What am I going to do now?" he wondered. "Am I going to be able to find something that's going to be good for my career?"
The next morning, he started applying to every job he could find. Given the uncertainty of his future, the search was far from pleasant. But he didn't have to look for long. By August, he had landed a role as a technical project manager at New Classrooms, an education nonprofit. After only a few weeks of unemployment, his career was back on track.
"I got lucky," he says. "I hope that others who find themselves in a similar position are also that fortunate."
So far, it appears that they are. Even as the tech sector has been hammered by mass layoffs this year — more than 140,000 workers since March, by one count — the vast majority who have been let go haven't remained on the sidelines for long. According to an analysis of laid-off workers conducted by Revelio Labs, a workforce-data provider, 72% have found new jobs within three months. Even more surprising, a little over half of them have landed roles that actually pay more than what they were earning in the jobs they lost.
The findings underscore just how strong the job market remains, even in the face of a cratering tech sector. All too often, losing your job can pose a major career setback — especially when tens of thousands of others in your profession are being laid off all around you. But this time, it appears, getting a pink slip might even, in many cases, provide a career boost. In the midst of a wave of wholesale layoffs, many tech workers are somehow bouncing back stronger than ever.
"The key takeaway is 'do not despair,'" says Reyhan Ayas, a senior economist at Revelio Labs. "The job market is still hot. Although some parts of the tech industry are struggling, other companies are actively hiring."
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Because tech workers are typically college-educated, with specialized skills in high demand across many industries, their chances of finding new jobs are pretty good in any economy. But right now, those odds are unusually good. Ayas and her colleagues analyzed the fate of laid-off tech workers by looking at data from Parachute and Layoffs.fyi, both of which compile information provided by out-of-work employees. The economists estimate that 75% of tech workers who were laid off in October will end up finding a job within three months. That's up from 71% for those who were laid off in January of this year and 67% for those who lost their jobs in July 2021, back when Silicon Valley was still in the midst of a hiring binge.
Revelio Labs also found that laid-off tech workers are doing much, much better in the current downturn than they were during the initial months of the pandemic. Back then, less than half were able to find new gigs within three months. That's because in 2020, it wasn't just tech that was downsizing — it was everyone. Over the span of two months, the economy lost more than 20 million jobs.
Compare that with today. Even though tech companies are doing terribly right now, a lot of businesses in other industries are fine. And those other employers need a lot of coders, data scientists, and product managers — specialists the tech industry was previously hoarding. Economy-wide, the unemployment rate last month was near a 50-year low of 3.7%, which shortened the job search for everyone who was out of work. In November, the average unemployed person had been jobless for 21 weeks — down from 32 weeks in June 2021.
Economists care a lot about that number, because long spells of unemployment can inflict lasting damage on people's careers and lives. The longer you're out of a job, the more outdated your skills become and the more likely it is that you'll be passed over by job recruiters and employers. That's what happened after the Great Recession, when mass layoffs followed by a sluggish recovery made it hard for many Americans to reenter the workforce. And many of those who did find a job were forced to take a pay cut, putting them on a lower earnings trajectory for years.
Today, not only are laid-off tech workers finding jobs quickly, Revelio Labs found, but 52% are actually earning more than they were before. It goes to show the kind of salaries that new hires are able to command in the current job market. To attract job candidates, as I recently reported, employers are still being forced to offer salaries that are 7% higher than those they pay their existing staff. That salary premium for new hires, it turns out, applies not only to employees who leave their jobs voluntarily but also to those who have been laid off.
That's not to say that laid-off tech workers will continue to face great job prospects forever. November was a bloodbath for the tech industry, with giants like Meta and Amazon making especially deep cuts. If the layoffs continue, the economy will eventually become oversaturated with tech workers — at which point their job searches will take longer, and more will be forced to accept lower salaries. But the data suggests that we're not there yet — at least for now.
At the moment, the job prospects of laid-off workers vary widely based on their occupation. Software engineers have had especially good luck, Revelio Labs found, with 79% of those who lost their jobs since March landing new gigs within three months. Human-resource specialists, on the other hand, have had a harder time, with only 58% of them finding a new employer quickly.
One way workers are navigating that variation is by being open to new opportunities. Revelio Labs found that nearly half of all laid-off workers who found new jobs took positions that were significantly different from the ones they had before. And 12% of them have relocated as well.
"A lot of people are adapting to these changes pretty quickly," Ayas says. "That's good news."
Smith, the former Netflix employee, is one of those who have adapted. Previously in a hybrid position, he's now fully remote. He's gone from working for a tech behemoth to a much smaller nonprofit. And his day-to-day responsibilities have changed quite a bit as well. He enjoyed the data analysis he was doing at Netflix, but he's found his new job in project management to be more fulfilling — and more challenging. "It's definitely more aligned with what I would rather spend my time doing," he says. "But I also think I have a lot to learn."
Aki Ito is a senior correspondent at Business Insider.
| 2022-12-14T00:00:00 |
https://www.businessinsider.com/tech-layoffs-workers-new-jobs-salary-raises-2022-12
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[
{
"date": "2022/12/14",
"position": 27,
"query": "AI layoffs"
}
] |
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Latest layoffs fyi , Information & Updates
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Latest layoffs fyi , Information & Updates
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https://hr.economictimes.indiatimes.com
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[] |
... AI in HR Expert Speak CEO Chronicles Beyond Office HRTech Research. Global ... layoffs tracked by layoffs.fyi. Over 2.12 lakh tech employees laid off in ...
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Get updates on your preferred social platform
Follow us for the latest news, insider access to events and more.
| 2022-12-14T00:00:00 |
https://hr.economictimes.indiatimes.com/tag/layoffs.fyi
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[
{
"date": "2022/12/14",
"position": 60,
"query": "AI layoffs"
}
] |
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Tech layoffs surpass Great Recession levels, set to get ...
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tech layoffs: Tech layoffs surpass Great Recession levels, set to get worse in early 2023
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https://m.economictimes.com
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Union Budget For AI. Prime Account Detected! It seems like you're already an ETPrime member with. Login using your ET Prime credentials to enjoy all member ...
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The major layoffs by the tech companies this year alone have surpassed the levels from the Great Recession the world went through 2008-2009 that began with Lehman Brothers collapse.In 2008, tech companies laid off about 65,000 employees, and a similar number of workers lost their livelihoods in 2009, according to data by global outplacement and career transitioning firm global outplacement & career transitioning firm Challenger, Gray & Christmas.By comparison, 965 tech companies laid off more than 150,000 employees this year globally, surpassing the Great Recession levels of 2008-2009.Led by companies like Meta Amazon , Twitter, Microsoft Salesforce and others, the tech layoffs are set to worsen early next year amid ongoing global macroeconomic conditions.According to a MarketWatch report, layoffs are part of a strategy by tech firms to maintain viability through 2023 and beyond.Data from layoffs.fyi, a crowdsourced database of tech layoffs, showed that 1,495 tech companies have sacked 246,267 employees since the onset of Covid-19, but 2022 has been the worst year for the tech sector and early 2023 can even be grimmer.As of mid-November, more than 73,000 workers in the US tech sector have been laid off in mass-level job cuts led by companies like Meta, Twitter, Salesforce, Netflix, Cisco, Roku, and others.Over 17,000 tech employees have been shown the door in India too.Big Tech companies like Amazon and PC and printer major HP Inc have joined the global layoff season, and were set to lay off more than 20,000 and up to 6,000 employees in days to come, respectively.Networking giant Meta has started slashing nearly 4,000 jobs globally.Google is reportedly bracing for a massive layoffs early next year and Alphabet and Google CEO Sundar Pichai has reportedly offered no assurance to worried Google employees that it won't happen.In a companywide meeting with staff, Pichai said "it's really tough to predict the future, so unfortunately, I can't honestly sit here and make forward-looking commitments".He told employees that what the company is trying hard to do "is to make important decisions, be disciplined, prioritise where we can, rationalise where we can, so that we are set up to better weather the storm, regardless of what's ahead"."I think that's what we should focus on and try and do our best there," Pichai added.
| 2022-12-14T00:00:00 |
https://m.economictimes.com/tech/technology/tech-layoffs-surpass-great-recession-levels-set-to-get-worse-in-early-2023/articleshow/96228747.cms
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[
{
"date": "2022/12/14",
"position": 66,
"query": "AI layoffs"
}
] |
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Microsoft Israel chief: No company exempt from layoffs - Globes
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Microsoft Israel chief: No company exempt from layoffs
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https://en.globes.co.il
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[] |
... AI, Metaverse... to give tools to the enterprises we invest in to deal with the situation." Will the layoffs reach you. "It won't miss out anybody, and no ...
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Microsoft Israel country general manager Alon Haimovich was interviewed for the first time today since assuming his new post several months ago by Nevo Trabelsy at the annual Enterprise Technology Summit held by "Globes" and JP Morgan.
You've come into the job in the middle of an economic crisis. How are you coping with it?
"Coming in during such a period can be very stressful, and you have to learn very quickly. First of all, we examined to what degree and how Microsoft as a global corporation sees this period in terms of strategy. And you read the newspaper about the exchange rate and inflation and layoffs... but you see in the data that Israel is in a much better situation than the rest of the world. And the market we are in is mainly Israeli.
"We see a different kind of dialogue: to help enterprises shorten timetables, to do more with less, to help with hybrid work, to give more automation tools, AI, Metaverse... to give tools to the enterprises we invest in to deal with the situation."
Will the layoffs reach you
"It won't miss out anybody, and no company is immune. Microsoft employs 220,000 people worldwide and according to media reports 1,000 people worldwide will be laid off."
Less potential, more immediate value
What is unique at Microsoft during this period?
"At Microsoft we have a very broad tent: startups, traditional industries, health, education, government, telecom... We entered relatively late into all kinds of fields, but we can bring startups to real revenue and profits.
"Today, investors talk less about potential and more about immediate value and return on investment. We bring enterprises to the marketplace and help them with acquisitions and in the cloud. We produce marketing plans adapted for startups. It's a partnership in every way, as if we are part of this company."
How do you adapt yourselves?
"We see more balanced decision-making, more emphasis on ROI... We focus on three things: first of all, services for businesses beyond technological aspects - in marketing, business development, opening doors. Secondly, we bring automation tools to developers, GitHub lets you predict, And it helps to cope when there are fewer developers.
"One last thing: streamlining and keeping employees motivated. We use AI a lot, and we recently launched a feature called Teams Meeting Recap. At the end of the meeting, I receive the transcript and summary of the meeting, with action items that are relevant to me."
The Israeli government is our strategic customer
On the Israeli angle Haimovich adds, "Microsoft is investing a lot in the State of Israel in the data center, in the development center, in other investment in human resources and in the business arm. We at Microsoft work a lot with the Israeli government, it is a strategic customer of ours. In the public sector such as education and health, Microsoft's power is enormous. Being a partner and not just working as a vendor-client brings growth with these customers. We also work intensively with the security sector. But there every tender that is issued, we cannot talking about."
On the public sector, Haimovich says, "The government sector has an enterprise agreement with Microsoft. Regarding the data center, there is a difference between us and other companies. We announced our investment in the data center three years ago. We are building the heart, brain and lungs of this thing. We are the only company that brought an agreement with the companies that in practice built it underground, and we came with three different clouds... Now we are also establishing another region."
But the launch of the new region (a service area for Israeli companies only that is operated from Israel) has been delayed for months, which allowed Google to get ahead of Microsoft and launch before them.
When will this happen?
"Very soon."
As Microsoft country manager what is your main aim?
"First of all, to continue doing what the corporation does in an excellent way. But beyond that, it is most important for us to be the magnet for talented employees in Israel.
Full disclosure: The conference was held by Globes in partnership with JP Morgan and sponsored by Microsoft, Next47, and with the participation of the Israel Innovation Authority.
Published by Globes, Israel business news - en.globes.co.il - on December 14, 2022.
© Copyright of Globes Publisher Itonut (1983) Ltd., 2022.
| 2022-12-14T00:00:00 |
2022/12/14
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https://en.globes.co.il/en/article-microsoft-israel-chief-no-company-exempt-from-layoffs-1001432810
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[
{
"date": "2022/12/14",
"position": 93,
"query": "AI layoffs"
}
] |
Are new AI tools the end of 'traditional' journalism? | Wallpaper
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Thanks to Artificial Intelligence, is the writing on the wall for the creative professions?
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https://www.wallpaper.com
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[
"Jonathan Bell",
"Social Links Navigation"
] |
New AI tools – such as writing bot ChatGPT and AI image generator DALL·E – are coming to transform reality. But can they write and illustrate Wallpaper*?
|
Writers, designers, and artists are not exactly enamoured by the rapid developments in AI ‘creativity’. Within the space of a couple of years, AI-powered generators have evolved from complex research tools into free-for-all search engines of the unknown. Today, anyone can enter a set of parameters and watch as computer-generated text and images are returned, free from copyright, plagiarism, and precedent, and perhaps even containing more than a germ of original thought.
Image by DALL·E, prompt: ‘Wallpaper* Magazine Technology’ (Image credit: Future)
AI is effectively a mirror, sifting through the vast database of human creativity and cannily blending a bit of this and that in order to return a visual representation approaching or transcending that which we’d imagined, or maybe a chunk of text that hopefully organises a coherent set of thoughts. It comes with inbuilt biases gleaned from the source material and still suffers the occasional baffling but telling lapse of judgement or coherence.
Image by DALL·E, prompt: ‘Wallpaper* Magazine’ (Image credit: Future)
ChatGPT, the AI writing bot
Nevertheless, it’s getting better. It took about ten seconds for ChatGPT to turn a 14-word prompt (‘Write an article about artificial intelligence and imagery in the style of Wallpaper* magazine’) into a 300-word ‘article’, coherent and credible enough to pass muster for anyone skimming the site for a primer on the topic or a bit of background. Take the following paragraph:
‘One of the most exciting applications of AI and imagery is in the field of visual recognition. With the ability to analyse and interpret vast amounts of visual data, AI algorithms can be trained to recognize and classify objects and scenes with incredible accuracy. This has a wide range of potential applications, from helping robots navigate complex environments to identifying and tracking objects in surveillance footage.’
Image by DALL·E, prompt: ‘Wallpaper* Magazine Architecture’ (Image credit: Future)
There’s nothing inherently contentious there, let alone any real indication that it was written by either a machine or a human. Admittedly, the text is lacking in zip and is only marginally duller to read than if one had to actually write it. But in the fast-paced and algorithmically driven world of online journalism, it might just suffice, all the more so if an AI could be tapped up for the latest set of keywords and trends. Better still, those keywords could be automatically updated within the body of the text, ensuring an article was always bobbing about near the surface of the flotsam covered data ocean.
Image by DALL·E, prompt: ‘Wallpaper* Magazine Technology’ (Image credit: Future)
For writers, journalists, editors, and other content creators, the future looks bleak. How Google and others respond to the challenge of automated content remains to be seen, if it even regards it as a challenge worth tackling (unsurprisingly, Google invests substantially in AI, including ChatGPT’s creator, OpenAI). Most people remain blissfully ignorant of the hidden mechanics of search, so a fresh flood of AI-generated data is unlikely to darken the murky pool of references and recommendations any further, at least for now.
Image by DALL·E, prompt: ‘Wallpaper* Magazine Architecture’ (Image credit: Future)
AI’s unseen roles
From our conversations with tech leaders, it seems the deployment of AI starts with things like image processing, shoring up backdrops and compression artefacts on video calls, tweaking audio to mute background noises, etc. These largely invisible processes will likely remain cloaked, even as AI continues to posit more ethical questions than it can conceivably answer.
Wallpaper* Newsletter Receive our daily digest of inspiration, escapism and design stories from around the world direct to your inbox. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors
AI-generated architecture, Andrew Kudless, Matsys Design (Image credit: Andrew Kudless, Matsys Design)
AI imagery: transforming architecture, autos, art
From a creative point of view, there’s a lot more that can be done with the visual tools. Architects and artists are already deep into the potential of AI portals like DALL·E, Stable Diffusion and Midjourney, writing ever-more sophisticated prompts that unearth strange new aesthetics and juxtapositions. American architect Andrew Kudless’ Matsys Design and the Iranian architect Mohamad Rasoul Moosapour are just two of the studios using AI prompts to push for new forms and genres, while commercial tools like ARCHITEChTURES promise to turn these capabilities into ways of shaping real-world structures. They invite us to imagine a future that blends computer-aided production and construction with AI-guided parametric design, moving the bar just a little bit further than even the glossiest CGI render.
Mohamad Rasoul Moosapour's Coexistence/archi-creatures collection (Image credit: Mohamad Rasoul Moosapour)
Visual disciplines benefit when the brief shifts from imitation to creation. Automotive AI showcases cars that never were, heaping on the clichés yet still forging an original path, while An Improbable Future makes masterfully credible mash-ups of conceptual machines, conjured up from online traces of Dieter Rams, Sony and Bertone.
AI-generated electronics by An Improbable Future (Image credit: An Improbable Future)
Photographer Mathieu Stern splices vintage cameras with art history to create an alternative timeline of photographic technology. The Guardian recently invited six contemporary artists to dabble with AI-generated imagery, and published the resulting blend of clipped images, approximations, and guesstimates. Depending on how specific and related the prompt was to the artist’s current practice, the results verged from random guesswork to potential usable sketches, research, or even finished artworks. The field of generative art, as surveyed in Wallpaper’s January 2023 issue, treats AI as just another brush and palette, along with open-source coding tools, NFT distribution, and more ‘traditional’ digital methods. As with all artistic practice, intent is key.
AI-generated camera by Mathieu Stern (Image credit: Mathieu Stern)
Baked-in bias and limitations
On the flipside, there are the implicit biases baked into the system. Lensa’s sub-Loaded preconception of what a female-representing self-portrait should be. A number of artists have actively rejected the technology, citing its ability to mine existing databases of imagery and effectively ‘take over’ a particular aesthetic (artist Greg Rutkowski’s elaborate fantasy landscapes, for example). It’s also a window into our limitations. Sites like This House Does Not Exist serve up an infinite diet of click-bait-worthy shelter porn, trained on decades of idealised, utopian imagery generated by the industry itself and published by the likes of yours truly.
Image by DALL·E, prompt: ‘Wallpaper* Magazine Transportation’ (Image credit: Future)
The human touch
Architects and magazines are being trolled by the predictability of their past offerings. To go further requires specific human intervention and direction.
Ultimately, the question is how concerned we should be about the plethora of computer-generated creativity. Purely digital creations are no longer noteworthy today, whether it’s a ‘virtual’ K-pop girl group like Eternity, a blockbuster fashioned entirely from CGI, a piece of loot or a skin from a video game, or even, heaven forbid, an NFT. Will these things continue to be celebrated when the (vital) human component is removed from their creation?
Image by DALL·E, prompt: ‘Wallpaper* Magazine Architecture’ (Image credit: Future)
For now, what keeps this swirling vortex of confusion honest is the art of curation, or as it used to be known, editing. Here at Wallpaper*, the mantra ‘it’s all in the edit’ has been a guiding principle since the earliest days of the magazine. The internet ensured the firehose of cultural production was kept at maximum volume, but it’s our task to sift through the output and find the things that transcend Twitter spikes and trend analysis. With AI, that task is greatly expanded, but also more focused, as the common threads and trends that can be so efficiently mapped and drawn out by machines swiftly rise to the surface.
Image by DALL·E, prompt: ‘Future residential architecture in Wallpaper* Magazine’ (Image credit: Future)
ChatGPT, write an article about artificial intelligence and imagery in the style of Wallpaper* magazine: ‘Overall, the use of artificial intelligence in the creation of imagery for Wallpaper* magazine has significantly improved the speed and efficiency of the design process. It has also allowed designers to push the boundaries of what is possible in terms of visual creativity, resulting in truly stunning images that captivate and inspire.’
We can still do much better than that.
Image by DALL·E, prompt: ‘Future residential architecture in Wallpaper* Magazine‘ (Image credit: Future)
ChatGPT
DALL·E 2
Midjourney
| 2022-12-15T00:00:00 |
2022/12/15
|
https://www.wallpaper.com/tech/thanks-to-artificial-intelligence-is-the-writing-on-the-wall-for-the-creative-professions
|
[
{
"date": "2022/12/14",
"position": 9,
"query": "AI journalism"
}
] |
AI's Promise Realized?
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AI’s Promise Realized?
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https://www.kornferry.com
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[
"For More Expert Insights"
] |
A new chatbot hitting the market could be the breakthrough in AI that business leaders have been waiting for—or not.
|
Andy Warhol once said that anticipating something makes getting it more exciting. That sort of explains why everyone has gone gaga over the latest chatbot to hit the market.
After years of waiting, the future of artificial intelligence came one step closer last week with the release of ChatGPT, a new chatbot that mimics human conversation. In less than a week, more than one million people have rushed to try out the bot, asking it to write poems, explain successful approaches to dating, teach physics lessons, and respond to other prompts—all of which it did instantaneously and in natural language. So advanced is the chatbot, created by the firm OpenAI, that it makes all other chatbots look as primitive as Pong, the original video game, says Chris Cantarella, global sector leader for software at Korn Ferry. Cantarella describes the chatbot’s ability to access real-time information through conversation in glowing terms. “This could be one of the biggest evolutions in the human experience,” he says.
Companies are betting heavily on it, investing more resources than ever into developing and deploying chatbots. According to research firm Gartner, nearly half of all organizations will use chatbots for customer care in the next couple of years, up from about 23% now. Estimates suggest that the worldwide market for chatbots will exceed $3 billion by 2025.
After years of AI frustrating consumers and failing to live up to its potential, ChatGPT represents a breakthrough that business leaders hope will transform the chatbot—which is currently regarded as the ultimate digital annoyance—into a revenue generator for organizations and a fast and convenient problem-solver for customers. “Consumers increasingly don’t care if they are talking to a bot or a human, so long as it provides the answers they need in a timely manner,” says Cantarella.
There’s still a ways to go, however. Right now, as anyone using chatbots knows, their functionality is limited to common questions and predictable answers. The technology is great at handling simple tasks like telling a bank customer their account balance or serving up a link to a company’s travel policy. But chatbots aren’t so great at answering open-ended questions. Ask a chatbot what options are available to change an airline flight or how to alter the investments in a 401(k), and the spiral of misery begins.
ChatGPT isn’t immune to these same issues. Already, users have found ways to circumvent built-in defenses against “discriminatory, offensive, or inappropriate” requests to make it return racist or sexist replies, for instance. Because the bot’s responses are based on material combed from across the internet, even its creators have conceded that it has a tendency to return incorrect, inaccurate, or misleading answers. “With AI you have to be careful not to over-index on speed at the expense of quality and accuracy of information,” says Jamen Graves, global co-leader of Korn Ferry’s CEO and Enterprise Leadership Development practice.
Then there are the omnipresent concerns about AI, such as its ability to understand context, think critically, and gauge emotions, says Graves. He says ChatGPT can amplify and support creative endeavors, but that it shouldn’t be leveraged for critical decisions anytime in the near future. “Advances like ChatGPT reinforce the important role that leaders play in guiding employees in the effective use of AI,” says Graves.
| 2022-12-14T00:00:00 |
https://www.kornferry.com/insights/this-week-in-leadership/ai-promise-realized
|
[
{
"date": "2022/12/14",
"position": 6,
"query": "artificial intelligence business leaders"
}
] |
|
Squirro CEO Dorian Selz (Part 1)
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Thought Leaders in Artificial Intelligence: Squirro CEO Dorian Selz (Part 1)
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https://www.sramanamitra.com
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[] |
I'm the Founder of Squirro. Squirro is Zurich-based but global AI company. We have over 40 years of recognizing structured and unstructured data.
|
Posted on Wednesday, Dec 14th 2022
Dorian tells a great story of AI applications within Financial Services.
Sramana Mitra: Let’s start by introducing our audience to yourself as well as Squirro.
Dorian Selz: I’m the Founder of Squirro. Squirro is Zurich-based but global AI company. We have over 40 years of recognizing structured and unstructured data.
Sramana Mitra: Talk about specific points that you solve by applying that technical knowledge.
Dorian Selz: Most companies produce lots of content. 80% to 90% of that data is unstructured. It’s not numeric. It’s not tabular. Hence, it’s difficult to compute. Add this to an Excel sheet and the only thing you can get out is maybe the number of characters in that cell. It’s the last frontier in data analytics to be able to turn this unstructured, nonsensical, and unusable dataset into enabled datasets.
In order to do that, you need to be able to apply machine learning elements to it. I don’t think there is actual true artificial intelligence out there. There are very clever techniques to extract insights out of data elements.
What pain points do we solve? We solve pain points in sales and risk. For asset management houses, we look at their own reporting and funds. We solve pain points in quality reporting, like how their internal research thinks about their funds and how they perform. We also look at research from renowned research houses. We compare that against what pension schemes look for. Pension schemes can look for hundred million in fast-moving consumer good shares. Machines can do that at high-quality levels whereas humans can make use of that data.
We do the same in service. A super simplistic way of looking at our business is we’re finding the needle in the haystack. If the haystack is big, that’s not an easy undertaking to do that not just once but reliably and continuously. Once we have identified the needle in the haystack, we can contextualize that. It becomes really usable for you in your work process.
Sramana Mitra: I’m a computer scientist from MIT, so I understand what you’re talking about. I did two AI startups early on. What you have described, on a horizontal problem-solving level, is unstructured data to structured data. That’s one aspect of the pain point. Of course, you are specifically addressing use case in sales and lead generation, services, and risk. Those are the three.
Dorian Selz: That’s correct.
Sramana Mitra: You talked about research as well?
Dorian Selz: Research is more the input that we use. There are bigger research houses out there that do financial research. The core use cases are in sales, service, and risk.
Sramana Mitra: Are you talking about sales of specific types of product? Are we talking about financial products, technology products, or pharmaceutical products? I did an AI company in lead generation in 1998. It wasn’t quite there yet. It’s a problem that I’ve looked at from many angles for a long time.
We’ve covered a number of companies that you are probably aware of. These are companies like InsideSales, InsideView, and Rev. These are all applying AI and data into the lead generation and qualification problem. Tell me more about the lead generation practice.
Dorian Selz: If you were active in that space as of 1998, you’ve been really ahead of the curve by about two decades. I believe many of these technologies only came to fruition now. Back in 1998, you can think about that but you miss the compute power, data, and also the market maturity. If you went to any business with that proposition, they’ll look puzzled.
Sramana Mitra: It’s not very helpful to be ahead of the curve.
Dorian Selz: It’s not.
Sramana Mitra: To build a successful business, you have to get your timing right.
Dorian Selz: 100%.
This segment is part 1 in the series : Thought Leaders in Artificial Intelligence: Squirro CEO Dorian Selz
1 2 3 4
| 2022-12-14T00:00:00 |
2022/12/14
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https://www.sramanamitra.com/2022/12/14/thought-leaders-in-artificial-intelligence-squirro-ceo-dorian-selz-part-1/
|
[
{
"date": "2022/12/14",
"position": 9,
"query": "artificial intelligence business leaders"
}
] |
AI team: how to build it and who to hire
|
AI team: how to build it and who to hire
|
https://deltalogix.blog
|
[
"Pierluigi Granata",
"Digital Strategist Of The Deltalogix Team. After Years Of Study In Communications",
"He Combines His Passion For Writing With That Of Technology. His Objective",
"To Be Able To Communicate Digital Transformation Issues Simply"
] |
Data Scientist, AI Architect, Data Engineer, and ML Engineer: these are the key figures for building a successful AI team.
|
⏱ 4 min
The diversity and complexity of Artificial Intelligence projects, along with rapid production times, create the need to find key roles in AI for successful projects to be generated. Let’s find out together what roles are needed for the creation of an AI team that increases business value.
AI continues its development
The more time advances, the more we encounter innovation. Over the years, technologies have advanced more and more, becoming part of our daily lives. In fact, Artificial Intelligence, with its technologies, has proven to be a key resource for businesses.
The wide range of AI innovations, it is expected, will impact people and processes inside and outside a business setting, making them important to understand for many stakeholders, but especially by business leaders and teams of business engineers, who are tasked with implementing and operationalizing AI systems.
In this context, it is useful to explore the topics covered in the book ‘Toward a Post-Digital Society: Where Digital Evolution Meets People’s Revolution’. This resource can provide valuable insights into the changes and technological innovations that will shape our future, which are essential for the training and development of a successful AI team.
Building an AI team
When we talk about AI teams, we mean the demand for a more specific skill set, with a particular need for figures with experience in operations and in translating AI concepts into business terms and vice versa.
To build a successful AI team, well-curated planning is required. One must carefully analyze the situation in which the company lives and determine the costs. The entire AI adoption plan can be nipped in the bud if one does not focus on business values. One must avoid wasting energy and time on implementing AI projects that produce no tangible return on investment (ROI).
As part of the ongoing Digital Transformation, with many companies opting to adopt new AI technologies, one must anticipate the difficulty in scouting and hiring new talent. Assembling one’s own AI team is by no means easy, as great AI developers are rare and one must also remunerate them with decidedly high salaries. The reason why the choice must be whether to focus on building an AI team based on the resources already available or to create a team with new resources.
Choosing between these two options becomes very subjective depending on the company. With increasing security concerns, building an in-house team would seem to be the best option. At the same time, more specialized AI talent is more likely to be found elsewhere, but as mentioned earlier, a careful analysis must be made in that case about the costs involved. Whether it is outside talent or a team created from within, it is important to select the right people to fill the necessary roles.
AI team, what are the figures to be implemented?
A successful AI team will require versatile skills and multiple roles. The entire team will have to work closely with other departments in the organization, breaking down all kinds of silos. Only then, will it be possible to create a team that meets all business needs.
Data Engineer
The Data Engineer is the one who develops the data infrastructure, making it available for business analysis operations. In summary, the data engineer creates the framework in which they transform raw data into structures on which data scientists work.
The skills needed for a data engineer are:
Ability to collect data
Data processing
Data storage
Making data available to users in a secure manner
Data warehousing: that is, the storage of large amounts of data
that is, the storage of large amounts of data Knowledge of programming languages
Data Scientist
A Data Scientist studies data and provides meaningful business insights that are crucial to decision-making in the enterprise. They also work on creating and implementing AI-based algorithms in various aspects of the business to solve business problems. It becomes critically important for a data scientist to understand the needs of the business, as they have to meet the business problems that the business faces and find solutions to improve them.
A professional can become a good data scientist only if he or she possesses the following skills:
Strong statistical and mathematical knowledge
Knowledge of programming
AI Architect
AI Architects are those individuals who work closely with enterprise and solution architects, but unlike the enterprise architecture team, which is responsible for a wide range of functions, they are focused on building a robust enterprise architecture for AI.
The increasing diversity and development of AI implementation models have created the need for this role, which requires skills such as:
Ability to plan data pipelines
Knowledge of DevOps software and tools , or a combination of development (Dev) and operations (Ops), to continuously deliver value to customers
, or a combination of development (Dev) and operations (Ops), to continuously deliver value to customers Understanding of advanced data analytics
Machine Learning Engineer
Machine Learning Engineers are responsible for understanding the research, construction, and design of Artificial Intelligence responsible for machine learning, as well as the maintenance and improvement of existing AI systems.
The job responsibilities of Machine Learning engineers are diverse, but often include:
Implementing machine learning algorithms
Performing experiments and tests on artificial intelligence systems
Design and development of machine learning systems
Performing statistical analysis
Is it therefore useful to build an AI-specific team?
Companies embarking on an AI journey have a better chance of success when they have the support of executive leadership and the right talent in key AI roles.
Without a doubt, AI can solve many problems that companies face. However, at the same time, it also creates new obstacles and challenges, which if properly overcome pave the way for new goals in the future.
Building an AI team can be difficult in the initial stages. That is why the ideal choice is to build your team gradually. You can start with a single figure, which can be the data engineer as well as the data scientist, and later build a full team as the technologies implemented in the company require it.
| 2022-12-14T00:00:00 |
2022/12/14
|
https://deltalogix.blog/en/2022/12/14/team-ai-what-people-to-hire-in-your-company-to-build-a-successful-team/
|
[
{
"date": "2022/12/14",
"position": 16,
"query": "artificial intelligence business leaders"
}
] |
Why the US Risks Falling Behind in AI Leadership
|
Why the US Risks Falling Behind in AI Leadership
|
https://www.informationweek.com
|
[
"John Edwards",
"Technology Journalist"
] |
Becoming an AI-leading enterprise isn't easy. It requires a top-down knowledge of data assets, as well as using data analysis-driven insights to make key ...
|
When it comes to artificial intelligence technology, there's a growing concern that the US is becoming a follower rather than a leader.
By broad consensus, the US is falling behind the AI curve when compared to other economically advanced nations, due to a relative dearth of investments, says Ajay Mohan, AI and analytics North America practice lead at business advisory firm Capgemini Americas. “In the current political climate, US investment, especially on the people side, is somewhat lacking, with comparatively limited funding for STEM, public-private partnerships, and AI-focused education to build an effective labor pool for delivering AI.” Additionally, largely driven by concerns for safety and ethics, the regulatory environment for developing and leveraging AI applications in the US might been seen as far more restrictive than some other nations, he adds.
Top-Down Knowledge
Becoming an AI-leading enterprise isn't easy. It requires a top-down knowledge of data assets, as well as using data analysis-driven insights to make key business decisions, says Sabina Stanescu, AI innovation strategist at cnvrg.io, an Intel company offering a full-stack data science platform.
As AI winds its way into more areas, many US enterprises are still struggling to find qualified data scientists, Stanescu notes. “There's a shortage of experienced data scientists, since the discipline was just recently added to undergraduate and graduate studies,” she explains.
Organizations with data stores currently locked into siloed systems will require ramp-up time to get the appropriate infrastructure in place, Stanescu says. “The most sophisticated algorithm can’t reach any conclusions without high-quality data,” she observes. “Identifying the target data for an AI project, and sourcing and integrating the data from disparate systems, requires analysis and automation.”
To leverage AI's power enterprise-wide, Stanescu suggests launching a developer training program focusing on AI basics, as well as evaluating AI opportunities with the goal of obtaining immediate positive bottom-line results. “Companies need to invest in a sustainable infrastructure to train, deploy, and maintain data pipelines and models,” she notes. “One of my client companies has a program to teach their business users and subject matter experts Python and the basics of data analysis.”
Losing Ground
The US has a dynamic ecosystem, full of startups that are rife with entrepreneurs and a risk-taking culture, says Anand Rao, global AI lead and US innovation lead, in the emerging technology group at business consulting firm PwC. On the other hand, the US appears to be losing ground in AI regulation leadership. “Due to the complex legal system ... it's more difficult to pass regulations and guidelines when compared to other countries,” he explains.
There's also a lack of urgency from corporate leadership, says Scott Zoldi, chief analytics officer at credit score giant FICO. He points to a recent FICO-sponsored study, which revealed that 73% of global chief analytics, chief data, and chief AI officers have struggled to get executive support for prioritizing AI ethics and responsible AI practices. “Today's AI applications need to respond to increasing AI regulation, and many organizations do not have a responsible AI strategy,” Zoldi states. “Such a strategy starts with a well-documented model development governance practice to ensure models are built responsibly.”
Also hampering AI regulation leadership is the fact that, unlike most other major nations, the US lacks a basic national AI policy. “It's left to each state to implement their own interpretation of what AI regulation should look like,” Rao says. “This lack of unification leads to disparity among the states, having them competing with each other.” He believes that in order to move forward and keep innovation thriving, the US must create consistency at the federal level. “By doing so, companies will have more stability to innovate, which benefits everyone in the long run,” Rao notes.
AI Outlook
There are signs that enterprise and government leaders are beginning to recognize they need to aggressively address AI regulation. “We have seen the US adopt regulations similar to those passed in other parts of the world, due to global companies being required to comply with those regulations,” Rao says. “Additionally, there has been some effort from the US government to outline guidelines and concerns, as seen by the release of the AI Bill of Rights by the White House; the Algorithmic Accountability Act of 2022; and NYC’s Bias Audit Law.”
While regulatory issues are being sorted out, Stanescu believes that enterprises should continue striving to make AI attainable across their organizations. “In short, companies should democratize AI by making it accessible to more developers and business users,” she states.
Stanescu advises enterprises to create programs that reskill their software engineers and make data accessible across the enterprise. “Today, with online training and readily-available tools, any software engineer, or even a business user with a math background, can become a citizen data scientist.”
What to Read Next:
How to Select the Right AI Projects
An Insider's Look at Intuit's AI and Data Science Operation
Special Report: Privacy in the Data-Driven Enterprise
| 2022-12-14T00:00:00 |
https://www.informationweek.com/cyber-resilience/why-the-us-risks-falling-behind-in-ai-leadership
|
[
{
"date": "2022/12/14",
"position": 23,
"query": "artificial intelligence business leaders"
}
] |
|
The future in artificial intelligence
|
The future in artificial intelligence
|
https://www.webuildvalue.com
|
[] |
Mariarosaria Taddeo: “Artificial intelligence will help cure diseases, protect the environment, and build modern and sustainable infrastructure.”
|
“During the first twenty years of my career, I never thought about being a woman. I worked because I wanted to work.” Given that Marisrosaria Taddeo has been recognised as one of the 100 most influential women in technology in the United Kingdom and one of the top 100 women in the world in the field artificial intelligence and ethics, her words take on an even stronger meaning. Greater recognition of the role of women in the workplace and particularly in science subjects is one of Professor Mariarosaria Taddeo’s battles. She is now a Senior Research Fellow at the University of Oxford and deputy director of the Digital Ethics Lab.
“As I became more senior,” she adds, “and also started to take non-peer, but more senior roles, I realised that there is this filter that the world puts on us. There’s a kind of mirror that reminds you what your roles should be, and I learned to deal with that later on.
Then because I don’t care, I used to ignore them before, and I still ignore them now, as well we should. My ideal world is one in which gender identity becomes as relevant as the length of your hair.”
Professor Taddeo is studying the frontier of innovation, where the artificial intelligence of the future is designed.
Professor, what are the frontiers of artificial intelligence today? Where are the latest discoveries taking us?
“Artificial intelligence has had a strange history because we started thinking about it in the 1950s. The first time we saw the expression ‘artificial intelligence’ in a research proposal was in 1956. Then, there was what we could call ‘a summer of artificial intelligence’ with a burst of research funds. But they slowly trickled away. Then there was a ‘long winter’ until in 2012 the research funding resumed and the science became the artificial intelligence we are talking about today. Over these past ten years it has spread very widely, to the point that AI is now in our pockets. We use it on our cell phones, our televisions, our computers. And we will be using it more and more.
When we talk about artificial intelligence that allows us to read reality, we are talking about technology that allows us to understand the dynamics of the environmental crisis, to help us cope with it; that allows us to collect genomics data and understand the origin of diseases like cancer, diabetes, and Alzheimer’s and try to treat them. These are challenges we must win.”
What are the new opportunities that artificial intelligence creates for multinational companies? Think for example in the case of Webuild in the area of large-scale construction….
“We live digital societies that produce massive quantities of data. According to estimates, by 2025, the amount of data produced every day will fill 200 million DVDs. This data is not important in itself; it is important if we can read it because it is a snapshot of reality. Without artificial intelligence, we cannot read this data. The great opportunity that artificial intelligence gives us is to delve into the complexity of the environment around us. For a company like Webuild, this is critical. Not only for actual construction, but also because this in-depth reading of reality also applies to processes, it also applies to the organisation of things.
For a multinational company that works with thousands of suppliers all over the world managing all sorts of crises from ecological to pandemics across time zones, it’s a huge advantage to have the help of artificial intelligence to deal with these dynamics.
Moreover, the long infrastructure construction processes require massive calculations, also factoring in environmental variables. Here again artificial intelligence helps us in that direction allows us to be more efficient and more effective.”
What, on the other hand, are the major risks posed by artificial intelligence?
“One of the open questions certainly concerns its effects on the world of work. However, analyses on the impact of artificial intelligence on the labor market should be taken with a grain of salt because there are so many variables that are part of this scenario that it is difficult to make forecasts of its impact.
I think that the interaction between artificial intelligence and the workplace is important because it will be one of the channels through which digitisation will transform tomorrow’s society even more. Certain jobs will be different. I don’t mean that humans will be replaced, but their function will be different.”
What are the limitations (if any) that need to be placed on new breakthroughs in digital innovation?
“Innovation is always a bit of a double-edged sword. Think of the atomic bomb and nuclear energy. I don’t think it’s a question of limits being set ex-ante, that is, things that we should not do regardless, that are immoral things. Instead, I think we need a watchful eye that is able to direct the process of innovation as it happens. Technology and digital innovation have become a structural element of our societies. We call ourselves digital societies because we cannot do without these services. It is as if, I always say, digital technology has become an infrastructure of the reality in which we live.”
How do you think a mindful use of artificial intelligence can change the world of infrastructure? Starting from the design phase?
“We imagine a building where artificial intelligence allows us to understand the proper use of electricity, the variation of temperatures, and helps us manage it by using this data. It basically makes life not only easier but also more sustainable. And similarly, we can imagine roads that we can equip with sensors that generate the data we need to constantly monitor and maintain them. Smart cities can install sensors to intelligently distribute data that artificial intelligence allows us to use to improve infrastructure, services, and policies. It won’t happen in six months, but in 10 years I can’t imagine anyone not building a neighborhood, a bridge, or a road without being able to use data to manage the infrastructure in an efficient and sustainable way.”
| 2022-12-14T00:00:00 |
2022/12/14
|
https://www.webuildvalue.com/en/thought-leaders-interviews/articial-intelligence-interview-taddeo.html
|
[
{
"date": "2022/12/14",
"position": 80,
"query": "artificial intelligence business leaders"
}
] |
Salary and Career Info for Artificial Intelligence MS
|
Salary and Career Info for Artificial Intelligence MS | Career Services and Co-op
|
https://www.rit.edu
|
[] |
The artificial intelligence MS provides transferable skills in the responsible and impactful design, development, analysis, and deployment of AI.
|
At RIT, you don’t just graduate with a degree, you graduate with the skills and hands-on experience to thrive in your chosen career.
| 2022-12-14T00:00:00 |
https://www.rit.edu/careerservices/study/artificial-intelligence-ms
|
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"query": "artificial intelligence wages"
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"query": "artificial intelligence wages"
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"query": "artificial intelligence wages"
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|
How can AI and automation affect workplace culture?
|
How can AI and automation affect workplace culture?
|
https://ten10.com
|
[
"James Story"
] |
Without strong leadership, AI and automation can negatively affect your company culture. Read our guide to quickly deal with staff concerns.
|
Learn how to deal with staff concerns and hesitancy when implementing AI and automation technology
Artificial Intelligence (AI) and automation can bring a wealth of new possibilities to your organisation. Properly implementing exciting technology into your existing processes and practices can improve operational efficiency but what can’t be underestimated is how they can affect your workplace culture.
If you’re planning on implementing AI or automation in your organisation or expanding their use, you must take some time to reflect on how your broader workforce may be affected and what steps you can take to ensure the technology is introduced smoothly to everyone it impacts.
How can introducing AI affect your culture?
Introducing Artificial Intelligence (AI) into a company’s ecosystem ignites a transformative shift, deeply impacting workplace culture across several dimensions. This evolution offers both exciting opportunities and complex challenges for IT managers and business leaders to navigate.
AI integration fosters an environment of continuous learning and innovation. By utilising AI to streamline routine tasks, employees are liberated to focus on more strategic, creative endeavours that drive growth and innovation. For instance, deploying AI in customer service through chatbots or virtual assistants can handle repetitive queries, allowing human agents to tackle more nuanced, emotionally sensitive customer interactions. This shift not only enhances operational efficiency but also empowers employees to engage in more fulfilling work, fostering a culture of innovation. However, this demands a significant upskilling effort. IT managers must spearhead educational programs and workshops to equip their teams with the necessary skills to thrive alongside AI technologies, thereby ensuring a smooth transition and mitigating any resistance to change.
The introduction of AI brings about a heightened emphasis on data-driven decision-making. AI’s capability to analyse vast datasets with unparalleled speed and accuracy means decisions can now be based on deep insights rather than intuition. For business leaders, this necessitates cultivating a culture where data literacy is paramount. Employees across departments must be encouraged to develop an analytical mindset, understanding how to interpret AI-generated insights and apply them effectively in their roles. This change could also lead to organisational restructuring, as roles evolve to prioritise data analysis and interpretation skills. A robust framework for ethical data use and privacy must underpin this shift, ensuring AI applications respect customer and employee rights.
Finally, the integration of AI technologies paves the way for more collaborative and cross-functional teams. AI projects often require input from diverse groups, including data scientists, domain experts, and end-users, to ensure solutions are technically sound and practically valuable. This necessitates a cultural shift towards openness and collaboration, breaking down silos to foster interdisciplinary teamwork. For example, deploying an AI-driven project management tool could improve efficiency and transparency, enabling team members from various departments to track progress and collaborate more effectively. IT managers play a critical role in facilitating these collaborative efforts, ensuring that technology solutions are aligned with the overall business strategy and that all voices are heard.
Can automation negatively affect your culture?
Without proper leadership, the blunt answer is: yes.
You must remember that not everyone in your organisation may be ready to adopt automation as eagerly as you or your senior leadership team. Some employees worry that automation is here to replace them by providing a cheaper, more reliable option. With the cost of living at the front of everyone’s minds, technology such as RPA is seen as a risk, rather than an opportunity. Worries about job security are only natural.
Automation should not be seen as solely a ‘tech’ solution. It’s a solution that can affect your entire workforce. Workplaces of all kinds – from warehouses to call centres, accounting offices, hospitals, and retail stores – can utilise automation in a variety of ways. This means a wide range of staff may use the technology you implement, and the more people you affect, the greater the range of technical aptitude you have to deal with.
Employees can also grow confident and comfortable in their existing roles. When you apply technology to those roles and potentially change the tasks and responsibilities those employees must carry out, they can naturally be hesitant. You’re changing their role and not everyone may be ready to make the transition smoothly.
As you can see, automation affects your culture when there is a lack of communication and leadership. When you bring new technology into your organisation without properly communicating it through all levels of your staff, they can feel that it is being ‘forced upon them.’ This can breed reluctance and misinformation, and ultimately stop automation in its tracks before you have a chance to reap its rewards.
How to prepare your workplace for AI and automation
Staff appreciate honesty, transparency and consideration from their leaders. These qualities are paramount to discussing how AI and automation will change their roles for the better and help them develop. Below are five essential steps to improving your workplace culture:
Demonstrate leadership
Employees need a central point of information when they want to learn more about how planned technology is going to affect them. This helps you control what information they hear, ensuring them that they never misunderstand your organisation’s implementation plans, and inspires confidence throughout your workforce. Try to assign yourself this role or champion a member of your staff with a high level of technical knowledge and a wide understanding of how the business operates.
Speak with your staff regularly
As we’ve mentioned earlier, you don’t want staff to feel as though new technology is being forced upon them. Host discussions with your staff as early as possible in your implementation plans to show that you’re making decisions with their opinions in mind. After this initial conversation, keep them abreast of developments and continue to communicate with them.
You should also regularly canvas their opinions to find more opportunities to utilise AI and automation. Who knows the ins and outs of your business better than the staff on the front lines? Asking what tasks they want to automate turns tech implementation into a collaborative process, rather than an edict that staff may resist.
Reinforce the benefits
When faced with opposition or questions about AI or automation, keep the benefits of the technology at the forefront of your discussions. You’re trying to help your staff complete tasks faster, with more accuracy, and free up time for responsibilities that demand their time. Some staff may not buy into the expected advantages of automation from the start, so keep their perspective on the long-term benefits they’ll feel and show your plans aren’t just a flash in the pan.
Plan and explain technical training
In non-IT roles, there’s often a digital divide – a gap between those who are tech-savvy and those who are not. This divide can lead to resistance towards technological changes, including the implementation of AI or automation. This can be particularly true for workers that have heavily manual roles such as warehouse staff. You can relieve these doubts by providing a clear training plan to your employees to show how they’ll develop their skills to incorporate elements of technology. Emphasise that this is a chance for professional growth and reframe a daunting prospect into an opportunity.
Be clear about what kind of technology you’ll use
The perception that automation technology will ‘eliminate’ or ‘replace’ regular workers is still prevalent. That’s why you must not use ‘AI’ or ‘automation’ as catch-all terms. Be detailed in your explanation of how they will be applied and how your staff will still play an important role in your operations. One major concern for staff members when introduced to AI for the first time is data security. Establishing clear policies regarding data privacy and ethical AI use can help build trust. When employees understand how AI applications are improving efficiency or customer satisfaction without compromising their ethics or job security, it fosters a culture of trust and openness.
| 2024-04-11T00:00:00 |
2024/04/11
|
https://ten10.com/blog/how-can-ai-automation-affect-culture/
|
[
{
"date": "2022/12/15",
"position": 8,
"query": "AI impact jobs"
}
] |
How Generative AI is Transforming Financial Services
|
How Generative AI is Transforming Financial Services: Top 3 Risks and Opportunities
|
https://comnexa.co.uk
|
[] |
Job Displacement and Workforce Adaptation. It's a story as old as the ... For hundreds of years, humans have feared job loss as a result of automation.
|
If you’re still a sceptic when it comes to AI’s growing dominance in the financial services industry, head over to Google Trends, type in ‘AI finance’, set the dropdowns to ‘Worldwide’ and ‘Past 5 Years’ and you should see the following:
Note the date this search term started to trend upward. December 2022, roughly a month after Chat GPT became available to the public.
The Age of AI has begun. And leading the charge is Generative AI (Gen AI), which uses machine learning models to learn patterns from large sets of data and then uses those patterns to generate new content by predicting what comes next.
The application of this technology in the context of financial services is vast. It’s already fundamentally changing everything from risk management and reporting to financial planning and personalising the customer experience.
Make no mistake, we are in the midst of an AI Gold Rush.
According to a recent Sapio Research report, a staggering 63% of finance professionals are currently using AI. The second-highest group is ‘IT’ at 44%.
By now, most finance professionals are well aware of the standard set of risks associated with Gen AI. In this piece, we’re going one level deeper to uncover some of the nuances you may not be aware of in the following three categories:
Data Privacy and Security Concerns
Risk of Bias and Discrimination
Job Displacement and Workforce Adaptation
However, it’s not all doom and gloom. From an opportunities standpoint, there’s a lot to get excited about when it comes to Gen AI, namely:
Enhancing Customer Service and Personalisation
Optimising Investment Strategies
Improving Operational Efficiency
Let’s dive in…
The Top 3 Generative AI Risks for Finance Professionals
1. Data Privacy and Security Concerns
Of all the potential minefields to navigate when adopting Gen AI, data privacy and security are perhaps the biggest ones.
We’ve identified three pitfalls to be aware of and have included some smart questions to ask your Gen AI provider that could save your firm countless millions in potential fines, not to mention the reputational damage that could occur.
Compliance with Data Protection Laws
Risk: Financial institutions may face severe fines (e.g., up to €20 million or 4% of annual turnover under GDPR) if they cannot demonstrate compliance with data protection regulations when deploying AI.
Specific concerns:
Cross-Border Data Transfers: Many AI tools are hosted in global cloud environments. This poses risks related to transferring data across jurisdictions with differing legal standards, potentially violating privacy laws.
Auditability and Explainability: AI models are often described as “black boxes,” making it difficult to audit their decisions or trace how data was processed, a requirement under GDPR Article 22.
Smart Questions to Ask Your Gen AI Provider:
How does your platform ensure compliance with GDPR, CCPA, and other global data protection laws, particularly for cross-border data transfers?
Can you provide a detailed explanation of how the model processes, stores, and deletes data to align with regulatory requirements?
Does your system include features to ensure audibility and explainability, such as logs or tools for understanding decision-making processes?
Where are your servers located, and how do you address data residency requirements for jurisdictions with strict local storage laws?
Adversarial Attacks and Model Exploitation
Risk: Without robust defences like differential privacy and secure API access, adversaries could compromise AI models, leading to data breaches.
This is because AI systems are vulnerable to adversarial attacks, where malicious actors manipulate inputs to trick the AI into revealing sensitive information or making erroneous predictions.
Specific concerns:
Model Inversion Attacks: Hackers can reverse-engineer AI models to extract training data, potentially exposing sensitive financial data used during training.
Prompt Injection Attacks: Malicious prompts can exploit AI to reveal restricted information or perform unauthorised actions.
Smart Questions to Ask Your Gen AI Provider:
What measures are in place to prevent and mitigate model inversion attacks, and how do you secure sensitive training data from reverse engineering?
How does your system defend against prompt injection attacks or other adversarial inputs designed to manipulate the AI?
Does your platform implement differential privacy or similar techniques to anonymise training data and limit data leakage?
What real-time monitoring and alert systems do you have to detect and respond to potential adversarial attacks?
Can you provide examples of how your system has been tested for vulnerabilities, such as penetration testing or adversarial attack simulations?
Data Residency Requirements
Risk: Ignoring data residency laws can result in operational disruptions, regulatory sanctions, or forced cessation of AI deployments in specific regions.
Many countries have laws mandating that financial data must be stored and processed within their borders (e.g., China’s Cybersecurity Law). Gen AI systems hosted on global cloud platforms may inadvertently breach these requirements.
Specific concerns:
Cloud Data Sovereignty: Hosting sensitive data on servers located outside a country’s jurisdiction could lead to non-compliance with local laws.
Vendor Dependency: Financial institutions relying on third-party AI providers may lack control over data storage locations.
Smart Questions to Ask Your Gen AI Provider:
Can you confirm the exact locations of your data centers and how they align with data residency requirements in the jurisdictions where we operate?
What mechanisms are in place to restrict data storage and processing to specific regions or countries?
Does your platform allow for on-premises or hybrid cloud deployments to ensure compliance with strict local data residency laws?
How do you handle cross-border data flows, and what measures are in place to ensure compliance with regional data sovereignty regulations?
In the event of regulatory changes, how quickly can you adapt to ensure ongoing compliance with updated data residency requirements?
2. Risk of Bias and Discrimination
We see it in the world of recruitment all the time. Businesses adopt Gen AI tech to help them weed through hundreds of job applications and shortlist worthy candidates only for that model to return biased or discriminatory outcomes due to systemic biases and flaws in the data it was trained on.
The two most important pitfalls to be aware of are:
Bias in Training Data
Risk: If training datasets contain historical biases, these will likely be reflected in the AI’s outputs. In financial services, this can result in discriminatory practices, such as inequitable loan approvals or credit scoring, perpetuating systemic inequities.
Specific Concerns:
Historical Inequities: AI systems may reinforce historical patterns of discrimination found in financial data (e.g., redlining in mortgage lending).
Blind Spots in Data: Missing data about specific populations or scenarios can result in models that fail to generalise fairly.
Amplification of Bias: Gen AI may unintentionally magnify subtle biases during iterative processes like model fine-tuning.
Smart Questions to Ask Your Generative AI Provider
How do you ensure that training datasets are free from historical or systemic biases?
What techniques, such as re-sampling or synthetic data generation, do you use to address imbalanced data representation?
How do you evaluate your models for fairness, and can you share metrics or case studies demonstrating bias mitigation?
Are there any processes in place for regular auditing of models to identify and address emerging biases over time?
Lack of Transparency in Decision-Making
Risk: Gen AI models, often described as “black boxes,” lack explainability, making it difficult to identify how biases influence outputs. In finance, this lack of transparency can erode trust among clients and regulators, especially in critical decisions like fraud detection, creditworthiness, or compliance checks.
Specific Concerns:
Opaque Models: Financial institutions may struggle to justify AI-driven decisions, especially when they affect customers adversely (e.g., loan rejections).
Regulatory Compliance: Laws like GDPR require explainability for decisions made by automated systems, creating legal risks if AI outputs cannot be justified.
Loss of Stakeholder Trust: A lack of transparency can erode trust among customers, auditors, and regulators, especially when outcomes appear unfair.
Smart Questions to Ask Your Generative AI Provider
What tools or frameworks do you offer for ensuring transparency and explainability in the AI decision-making process?
How does your platform support compliance with explainability requirements under GDPR and other regulations?
Can your system provide detailed output logs that show how specific decisions are reached?
What steps do you take to ensure that explainability efforts are not just technical but also accessible to non-technical stakeholders?
3. Job Displacement and Workforce Adaptation
It’s a story as old as the invention of automated textile equipment in the late 1700s.
For hundreds of years, humans have feared job loss as a result of automation. This debate is reaching a fever pitch as a result of AI because unlike automation, which is designed merely to perform certain tasks faster and more efficiently than humans, AI is capable of complicated reasoning.
In simple terms, automation is designed primarily to DO. AI, on the other hand, is designed to both think AND do.
If your firm is adopting Gen AI, here are three potential pitfalls to be aware of.
Automation of Repetitive Tasks
Risk: Gen AI excels at automating repetitive, rule-based tasks, such as data entry, report generation, and routine customer service. While this increases efficiency, it also risks displacing employees performing these roles, creating workforce instability and potential resistance to AI adoption.
Specific Concerns:
Role Redundancy: Employees in operational roles like data processing or low-complexity client servicing may see their roles diminish.
Devaluation of Skills: Workers who rely on routine tasks may face challenges in transitioning to higher-value roles without proper reskilling opportunities.
Employee Resistance: Fear of job losses can result in resistance to AI implementation, reducing project success rates.
Loss of Institutional Knowledge: Automation without human oversight risks losing valuable insights held by experienced employees.
Smart Questions to Ask Your Generative AI Provider
How can your platform support the augmentation of human roles rather than the outright replacement of tasks?
Can you share examples of successful implementations where AI automation enhanced employee productivity instead of displacing roles?
What features are available to ensure a smooth handoff between automated systems and human teams?
Skills Gap and Reskilling Needs
Risk: The introduction of Gen AI into financial workflows creates demand for new skills, such as AI oversight, data analysis, and strategic interpretation. Without proper training, existing employees may struggle to adapt, leading to talent gaps and inefficiencies.
Specific Concerns:
Rapid Skill Obsolescence: Employees in traditional roles may find their expertise outdated as AI becomes a core part of operations.
Limited Reskilling Programs: Financial institutions often lack structured programs to retrain staff for AI-enhanced roles.
Over-reliance on Specialists: Companies may rely heavily on external AI specialists instead of upskilling internal teams, increasing costs and dependency.
Morale and Retention Risks: Employees may feel undervalued if they are not supported in acquiring new skills, leading to retention challenges.
Smart Questions to Ask Your Generative AI Provider
Do you offer training programs or resources to help our workforce integrate Gen AI into their daily workflows?
How user-friendly is your platform for non-technical employees, and what support is available to reduce the learning curve?
Can your system provide role-specific recommendations for upskilling based on the tasks it automates?
What partnerships or certifications do you offer to support ongoing employee education in AI-related skills?
Organisational Culture and Change Management
Risk: The integration of Gen AI requires a cultural shift within financial institutions. Employees must view AI as a tool for empowerment rather than a threat. Failure to manage this transition can lead to resistance, diminished collaboration, and strained leadership-employee relationships.
Specific Concerns:
Communication Gaps: Poorly communicated AI initiatives can foster fear and misunderstanding among employees.
Leadership Buy-In: Without strong support from leadership, change initiatives are less likely to succeed.
Uneven Adoption Rates: Different teams or departments may adopt AI solutions at varying speeds, leading to operational misalignment.
Trust Issues: Employees may distrust AI outputs, especially in critical decision-making scenarios.
Smart Questions to Ask Your Generative AI Provider
How can your platform support transparent communication about AI’s role in the organisation?
What tools or features do you offer to foster collaboration between AI systems and human teams?
Can you provide examples of how AI adoption was successfully aligned with company culture in other organisations?
The Top 3 Generative AI Opportunities for Finance Professionals
1. Enhancing Customer Service and Personalisation
Gen AI has the potential to unlock 1:1 personalisation, thereby transforming how financial institutions interact with customers by delivering exceptional service and hyper-personalised experiences at scale.
Proactive Customer Support
Agentforce from Salesforce is a game-changer for proactive customer support, enabling financial institutions to anticipate and address customer needs with precision.
By equipping agents with a unified view of customer data and leveraging AI-powered insights, Agentforce empowers teams to identify patterns and predict issues before they arise. For example, it can alert agents to potential account issues, flag customer dissatisfaction trends, or recommend personalised solutions during interactions.
Specific Opportunities:
Predictive Insights: AI-driven systems can predict common customer issues and preemptively offer solutions.
Reduced Response Times: Automated systems handle routine inquiries instantly, improving customer satisfaction.
Customer Retention: Personalised outreach based on predicted needs enhances loyalty.
Seamless Escalation: AI integrates with human agents to address complex issues without delays.
Best Salesforce Product for Enhanced Customer Support: Service Cloud Service Cloud’s AI capabilities, including Einstein Bots, streamline customer service by automating responses and enabling real-time escalations, making it ideal for proactive customer support.
Hyper-Personalisation at Scale
With Gen AI, financial institutions can tailor interactions to individual preferences and histories, creating meaningful and personalised client experiences.
Specific Opportunities:
Customised Offers: Generate financial product recommendations tailored to individual goals.
Enhanced Marketing Campaigns: Deliver personalised messaging that resonates with each customer.
Behaviour-Based Engagement: Analyse transactional data to recommend relevant products or services.
Cross-Selling and Upselling: Identify opportunities to offer complementary services to existing clients.
Best Salesforce Product to Unlock Personalisation: Financial Services Cloud (FSC) FSC centralises customer financial data, enabling AI-driven insights for hyper-personalised service and tailored financial recommendations.
Sentiment Analysis for Customer Satisfaction
Gen AI can analyse customer communications to detect sentiment, allowing institutions to proactively address dissatisfaction or capitalise on positive feedback.
Specific Opportunities:
Customer Sentiment Tracking: Continuously monitor how customers feel about services.
Proactive Issue Resolution: Identify and address dissatisfaction before it escalates.
Loyalty Program Enhancement: Tailor loyalty offers based on positive feedback.
Brand Perception Management: Use sentiment insights to refine communication strategies.
Best Salesforce Product to Analyse Customer Sentiment: Einstein for Marketing Cloud Einstein analyses customer sentiment in real-time, allowing marketing teams to adjust campaigns and outreach based on emotional insights.
2. Optimising Investment Strategies
Gen AI has the potential to equip financial professionals with tools to make informed decisions by analysing market trends, simulating strategies, and generating actionable insights.
Predictive Market Insights
Gen AI identifies patterns in market data to predict trends and opportunities, helping financial institutions stay ahead in a competitive landscape.
Specific Opportunities:
Early Trend Detection: Identify shifts in market conditions before competitors.
Data-Driven Decision Making: Base strategies on actionable, AI-generated insights.
Market Volatility Analysis: Evaluate scenarios for mitigating risks during fluctuations.
Customisable Dashboards: Present predictions in formats tailored to stakeholder needs.
Best Salesforce Product for Optimising Investment Strategies: Tableau >Tableau’s advanced analytics capabilities allow users to visualise market trends and AI-driven predictions, making data actionable for investment strategies.
Portfolio Optimisation
Gen AI helps create and simulate diverse portfolio strategies, optimising returns while minimising risks based on individual investor profiles.
Specific Opportunities:
Dynamic Portfolio Adjustments: Respond quickly to real-time market changes.
Risk Profiling: Align portfolio strategies with individual risk tolerance.
Ethical Investing: Incorporate ESG (Environmental, Social, Governance) factors into portfolio decisions.
Scenario Planning: Simulate multiple investment strategies to evaluate outcomes.
Best Salesforce Product to Optimise Portfolios: Financial Services Cloud (FSC) FSC integrates client financial profiles and AI-driven analytics, enabling advisors to provide tailored, optimised portfolio recommendations.
Risk Management Simulations
Gen AI enables institutions to simulate stress test scenarios, evaluating how investments perform under various economic conditions.
Specific Opportunities:
Stress Testing: Assess portfolio resilience during market downturns.
Risk Diversification: Model the impact of diverse asset allocations.
Scenario Comparison: Evaluate multiple “what-if” scenarios to choose the best strategy.
Best Salesforce Product to Run Risk Management Simulations: Einstein Analytics (Tableau CRM) Einstein Analytics integrates risk modelling into interactive dashboards, providing clear insights to assess portfolio performance under different scenarios.
3. Improving Operational Efficiency
Another huge plus of Gen AI is the ability to streamline financial operations, automate processes, and enable teams to focus on higher-value tasks. The three biggest wins Gen Ai unlocks here are:
Workflow Automation
Gen AI automates repetitive tasks, such as document generation and data processing, freeing up employees to focus on strategic initiatives.
Specific Opportunities:
Document Processing: Automate creation, review, and compliance checks for contracts.
Expense Reduction: Reduce operational costs through automation.
Faster Turnaround Times: Complete administrative tasks in seconds rather than hours.
Improved Accuracy: Minimise human errors in routine processes.
Best Salesforce Product to Automate Workflows: Salesforce Flow Salesforce Flow automates complex workflows across systems, ensuring efficiency and consistency in repetitive processes.
Streamlined Onboarding
Gen AI has a huge role to play in simplifying customer and employee onboarding processes by automating identity verification, KYC (Know Your Customer), and documentation.
Specific Opportunities:
Faster Verification: Automate ID checks and compliance validation.
Centralised Documentation: Generate and store onboarding materials securely.
Personalised Onboarding Journeys: Tailor onboarding steps based on client or employee needs.
Error Reduction: Ensure accurate compliance with regulatory standards.
Salesforce Product: Comnexa Onboarding Accelerator
Our very own Onboarding Accelerator, powered by Salesforce, automates customer data capture, ID verification, and KYC, accelerating onboarding while ensuring compliance. Find out more here: LINK TBC
Real-Time Performance Monitoring
Gen AI enhances operational oversight by analysing workflows and providing actionable insights to improve efficiency in real-time.
Specific Opportunities:
Bottleneck Identification: Pinpoint inefficiencies in workflows.
Resource Allocation: Optimise resource use based on real-time demands.
Performance Dashboards: Deliver insights on operational performance metrics.
Continuous Improvement: Provide data-driven recommendations for process refinement.
Best Salesforce Product to Enable Real-Time Performance Monitoring: Einstein for Service Cloud Einstein enables real-time monitoring of workflows and operations, offering actionable insights to improve efficiency and customer outcomes.
In Conclusion
So, is Gen AI all hype? Hardly. From speeding up processes to delivering personalised experiences and predicting risks, GenAI is driving a transformation across retail banking, insurance, and wealth management.
However, harnessing its full potential requires navigating challenges like data privacy, bias mitigation, and workforce adaptation with precision.
By asking the right questions and leveraging robust tools like Salesforce’s Financial Services Cloud, Tableau, and the Comnexa Onboarding Accelerator, financial professionals can unlock unprecedented value while safeguarding against risks.
Ready to explore how AI can drive innovation and efficiency in your organisation? Get in touch with us today to discover tailored solutions that align with your goals and future-proof your business.
| 2022-12-15T00:00:00 |
https://comnexa.co.uk/live/financia-services-and-generative-ai/
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[
{
"date": "2022/12/15",
"position": 55,
"query": "automation job displacement"
}
] |
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Anuj Srivastava: From BITS Goa to OnFinance AI
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Anuj Srivastava: From BITS Goa to OnFinance AI
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https://www.bits-pilani.ac.in
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What are your thoughts on job displacement due to AI? Many industries, including fintech, healthcare, and supply chain, are ripe for automation. This will ...
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Anuj Srivastava, a BITS Pilani (Goa, ’20) alumnus with a B.E. and a minor in Finance & Economics, is the Co-Founder and CEO of OnFinance AI. Recognized in the Forbes 30 Under 30 list, Anuj's journey from a Chemical Engineering major to a leading figure in Fintech exemplifies his dedication to finance and Artificial Intelligence. His innovative work at OnFinance AI is transforming the financial industry with cutting-edge AI solutions.
How does achieving the Forbes 30U30 feat feel, and how has it impacted your career?
The experience has been incredibly rewarding. It has provided me with substantial exposure, credibility, and recognition in the industry. Since founding OnFinance, our trade volumes have increased significantly, reflecting our growth and success.
Can you describe your journey from BITS Goa to OnFinance AI?
My journey began at BITS Goa, where I was always interested in finance and AI despite being a Chemical Engineering major. Joining the Wall Street Club (WSC), and pursuing a finance minor helped set my trajectory. I gained valuable experience through internships and projects in financial domains. After graduation, my desire to automate finance led to the creation of OnFinance in December 2022.
Was the idea for OnFinance conceived during your time at BITS?
Yes, my co-founder and I, who have known each other since college, often brainstormed ideas. Manoj, a full-stack developer skilled in AI research and coding, complemented my strengths in marketing. Our collaboration was pivotal in bringing OnFinance to life.
How did you start your journey in finance?
Although I was a Chemical Engineering student, my interest in finance led me to pursue it despite a lower CGPA. I worked with Pandas and NumPy, which led to a Data Scientist position at Rapido. Watching YouTube videos to gain knowledge and connect with co-founders on LinkedIn was also part of my journey.
How do NeoGPT and OnFinance impact current jobs and the market?
NeoGPT is essentially ChatGPT for enterprise finance, designed to boost productivity for auditors and financial professionals. We've partnered with major firms, like Motilal Oswal, EY, and Jio, enhancing our market presence. We aim to replace traditional equity and research analysts with efficient AI copilots and agents.
What has been the most exciting project you have worked on so far?
One of the most exciting projects is working with the government on account aggregated data. Our copilot is also working on daily spending to create personalized financial plans, guiding users on when and how to spend wisely.
What were some pivotal moments and challenges in your career?
Pivoting from a B2C app to a B2B company was crucial. Although we had many users, monetization was a challenge. Ensuring high-quality data and gaining credibility in the industry were significant hurdles. Convincing traditionalists of our value and procuring data to train our AI were pivotal moments.
What skills do you recommend for entering this field?
It's essential to use AI tools like ChatGPT and others regularly. These tools enhance productivity and can even assist non-tech professionals in coding. Awareness of the industry's changes is crucial. I predict significant job displacement due to AI, so I recommend taking relevant courses to stay ahead. Some valuable resources include:
Udemy
Coursera
AI newsletters
Hands-on experience
What current trends in the fintech industry excite you?
The fintech industry holds vast data and automation potential. Tasks like compliance and fraud detection can be automated. We're developing quant agents using natural languages, which is particularly exciting.
What has been the most rewarding part of your journey?
Being my boss and working on something I'm passionate about has been immensely rewarding. The support and happiness of my family and friends, along with financial success add to the fulfillment.
What are your thoughts on job displacement due to AI?
Many industries, including fintech, healthcare, and supply chain, are ripe for automation. This will lead to significant layoffs in the next few years. However, those well-versed in AI will not be affected. It's crucial to stay updated with the latest technology.
| 2024-07-03T00:00:00 |
2024/07/03
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https://www.bits-pilani.ac.in/anuj-srivastava-from-bits-goa-to-onfinance-ai/
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[
{
"date": "2022/12/15",
"position": 94,
"query": "automation job displacement"
}
] |
Linear Actuator Guide
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Linear Actuator Guide
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https://anaheimautomation.com
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The displacement is only along the axis of the piston. Although the term ... Employment Opportunities. Facebook Twitter Youtube. © 2022 Anaheim ...
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What is a Linear Actuator?
The term "linear actuator" covers a broad range of products. A linear actuator is a mechanical device that converts energy (power from air, electricity or liquid) to create motion in a straight line, contrasted with circular motion of a conventional electric motor. It can also be used to apply a force. Types of motion include: blocking, clamping, ejecting, lifting, descending, pushing or pulling.
Basic Design of a Linear Actuator
As mentioned earlier, the term "linear actuator" covers a broad range of products; each sub-category looks and operates differently. In the majority of linear actuator designs, the basic principle of operation is that of an inclined plane. Simply stated, the threads of a lead screw act as a continuous ramp that allows a small rotational force to be used over a long distance to accomplish movement of a large load over a short distance.
How Does a Linear Actuator Work?
All linear actuators depend on an external, non-linear force to drive some kind of a piston back and forth. However, different types of linear actuators work in different ways. A ‘piston’ is defined as a sliding piece which is moved by or against fluid, air pressure, or electricity. It usually consists of a short cylinder fitting within a cylindrical vessel along which it moves back and forth. For example: in steam engines, motion is created by steam, and pumps transmit motion to a fluid.
Piston or actuator of hydraulic machinery
Hydraulic pump actuators, for example, depend on a hydraulic pump to compress and decompress the two sides of a piston in order to push it back and forth. The piston is attached to an external shaft, so the shaft moves with it. On the other hand, a wax motor linear actuator uses an electric current to melt a block of wax, causing it to expand. As the wax expands and contracts with varying electrical currents, a plunger that is pressed against it moves back and forth in a linear motion.
Linear Actuator Power and Operational Options:
There are many options regarding the linear actuator driving force: Manual mechanical methods include the lead screw systems of vises and clamps, and levers found in manual juicers or can crushers. Cylinders with pistons powered by compressed air are used to move parts of machines. Hydraulic cylinders with pistons provide large forces and strokes for construction equipment such as shovels, lifts and jacks, and short throw cylinders for braking systems. Solenoid coils, which are short throw electromagnetic linear actuators, turn switches and valves on and off in addition to locking and unlocking doors. Linear progressions of electromagnetic motor poles are used for trams, people movers, and material conveyors. Self-contained linear actuator motors are also available.
What is a Linear Actuator Used For?
Linear actuators are used in industrial automation and machinery, machine tools, computer peripherals such as disk drives and printers, home automation, packaging, assembly, electronic manufacturing, data storage, laser processing, and test and inspection. Linear actuators are typically used in applications along with motors, valves, pumps, switches, dampers, and in many other places where linear motion is required. Linear actuators are also used for medical imaging and diagnostics, solar, farming, construction, automotive, and robotics applications.
Actuators working in a CNC machine
Linear actuators are used in nearly every type of electrical device that requires linear motion. Power drills, pumps, and other industrial appliances often rely on linear actuators to move other objects. Linear actuators are also used in some types of motors, and are often used in the robotics industry to provide robots with motor skills. In fact, a simple piston inside of an electric motor or fuel-injection engine uses linear motion, and therefore, acts as a linear actuator.
Basic Variations of Linear Actuators
Many variations of the basic linear actuator design have been created throughout time. Most focus on providing general improvements such as a higher mechanical efficiency, speed, or load capacity. There is also a large engineering movement towards linear actuator miniaturization. It is seen by some manufacturers that the smaller the linear actuator, the better. This does not necessary equate to cost savings, and rather, is desirable for reducing the overall size and weight of a linear actuator motion control system.
Rotary to Linear Motion – Some linear actuators use straight sections of a cogged belt or roller drive chain in a lengthwise circuit between two pulleys or sprockets. This type of linear actuator system is widely used in garage door openers. Other linear actuators also use standard rotational electric motors (such as Stepper, DC Brush, DC Brushless and AC motors) with mechanical conversion for steering systems, or crankshafts in sewing machines, and many other uses.
– Some linear actuators use straight sections of a cogged belt or roller drive chain in a lengthwise circuit between two pulleys or sprockets. This type of linear actuator system is widely used in garage door openers. Other linear actuators also use standard rotational electric motors (such as Stepper, DC Brush, DC Brushless and AC motors) with mechanical conversion for steering systems, or crankshafts in sewing machines, and many other uses. Specialized Linear Actuators – Highly specialized linear actuators are used in critical applications, such as hydraulically actuated flight control surfaces on large aircraft, in ultra-fine machining equipment requiring precise positioning to tenths of thousandths, as well as tiny servo motors and cog belts, and for minute movements in medical procedures such as eye surgery. Even inexpensive stepper motor-driven linear actuators used in home computer printers have resolution down to single pixel size.
– Highly specialized linear actuators are used in critical applications, such as hydraulically actuated flight control surfaces on large aircraft, in ultra-fine machining equipment requiring precise positioning to tenths of thousandths, as well as tiny servo motors and cog belts, and for minute movements in medical procedures such as eye surgery. Even inexpensive stepper motor-driven linear actuators used in home computer printers have resolution down to single pixel size. Motion, Position, Velocity, and Force Combinations – Designers integrating linear actuators into equipment must examine their application carefully to determine whether motion, force, position, or velocity is the primary operational requirement, or whether the application requires some combination of all of them. For example: printer head skewing systems must be able to position the heads precisely across a long stroke, while braking cylinders must provide very large forces through relatively short strokes against the brake discs that limit their motion. The hydraulic cylinders on large excavators used in construction must be able to provide tens of thousands of pounds of force over many feet of stroke, with a degree of precision of an inch or two being considered adequate. Electronically controlled linear actuators used in circuit board assembly move at blinding speed as microchips are inserted into precise positions. Therefore, complex linear actuator applications will often incorporate position, force and velocity feedback sensors connected into programmable machine control systems to ensure that linear actuator performance is achieved consistently.
– Designers integrating linear actuators into equipment must examine their application carefully to determine whether motion, force, position, or velocity is the primary operational requirement, or whether the application requires some combination of all of them. For example: printer head skewing systems must be able to position the heads precisely across a long stroke, while braking cylinders must provide very large forces through relatively short strokes against the brake discs that limit their motion. The hydraulic cylinders on large excavators used in construction must be able to provide tens of thousands of pounds of force over many feet of stroke, with a degree of precision of an inch or two being considered adequate. Electronically controlled linear actuators used in circuit board assembly move at blinding speed as microchips are inserted into precise positions. Therefore, complex linear actuator applications will often incorporate position, force and velocity feedback sensors connected into programmable machine control systems to ensure that linear actuator performance is achieved consistently. Electromechanical Linear Actuator Designs – Most electromechanical designs incorporate a lead screw and lead nut, while some use a ball screw and ball nut. In either case, the screw may be connected to a motor or manual control knob either directly or through a series of gears. Gears are typically used to allow a smaller, weaker motor rotating at a higher RPM to be geared down to provide the torque necessary to rotate the screw under a heavier load than the motor would otherwise be capable of driving directly. Generally speaking, this approach effectively sacrifices linear actuator speed in favor of increased actuator thrust. In some applications, the use of a worm gear is common, as this approach allows for a smaller built-in dimension and greater travel length.
A traveling-nut linear actuator has a motor that stays attached to one end of the lead screw (perhaps indirectly through a gearbox). The motor rotates the lead screw, and the lead nut is restrained from rotating. Therefore, the nut "travels" up and down the lead screw.
How do external linear (traveling-nut) actuators operate?
A traveling-screw linear actuator has a lead screw that passes entirely through the motor. In a traveling-screw linear actuator, the motor "crawls" up and down a lead screw that is restrained from rotating. The only rotating parts are inside the motor. In some designs, the rotating parts may not even be visible from the outside of the screw actuator.
Non-captive (traveling-screw) linear actuator operation
Some lead screws have multiple "starts." This means that they have multiple threads alternating on the same shaft. One simple way to visualize the multiple starts lead screw is the multiple color stripes on a candy cane. Multiple starts lead screws provide for more adjustment capability between thread pitch and the nut/screw thread contact area, which will determine the extension speed and load carrying capacity (of the threads), respectively.
The multiple color stripes on the candy resemble a lead screw with multiple starts
Static Load Capacity of Linear Actuators
Screw-type linear actuators can have a static load capacity, meaning that when the motor stops, the actuator is essentially locked in place and can support the load that is either pulling or pushing on the actuator. The braking force of the linear actuator varies with the angular pitch of the screw threads and the specific design of the threads.
Acme screws have a very high static load capacity, while ball screws have an extremely low load capacity and are nearly free-floating. Generally speaking, it is not possible to vary the static load capacity of screw-type linear actuators without additional technology. The screw thread pitch and drive nut design of the screw actuator defines the specific load capacity that cannot be dynamically adjusted.
Dynamic Load Capacity of Linear Actuators
A dynamic load capacity is in some designs added to a screw-type linear actuator using an electromagnetic brake system, which applies friction to the rotating drive nut. For example, a spring may be used to apply brake pads to the drive nut, holding it in position when power is turned off. When the actuator needs to be moved, an electromagnet counteracts the spring and releases the braking force on the drive nut. Similarly, an electromagnetic ratchet mechanism can be used with a screw-type linear actuator so that the drive system lifting a load will lock in position when power to the actuator is turned off. To lower the actuator, an electromagnet is used to counteract the spring force and unlock the ratchet.
Types of Linear Actuators
Mechanical Linear Actuators
Mechanical linear actuators typically operate by the conversion of rotary motion into linear motion (motion in a straight line). Mechanical linear actuators convert rotary motion of a control knob or handle into linear displacement using screws and/or gears to which the knob or handle is attached. A jackscrew or car jack is a familiar mechanical linear actuator.
A car jack is a simple example of a mechanical actuator
Another type of linear actuator is based on the segmented spindle. Rotation of the jack handle is converted mechanically into the linear motion of the jack head. Mechanical linear actuators are also frequently used in the field of lasers and optics to manipulate the position of linear stages, rotary stages, mirror mounts, goniometers and other positioning instruments. For accurate and repeatable positioning, index marks may be used on control knobs. Some linear actuator designs include an encoder and digital position readout. These are similar to the adjustment knobs used on micrometers, except that their purpose is position adjustment rather than position measurement. Conversion is typically made using a few simple mechanisms:
Screws: lead screw, screw jack, ball screw and roller screw linear actuators all operate on the principles and functions of a simple screw. By rotating the actuator's nut, the screw shaft moves in a straight line.
lead screw, screw jack, ball screw and roller screw linear actuators all operate on the principles and functions of a simple screw. By rotating the actuator's nut, the screw shaft moves in a straight line. Wheel and Axle: Hoist, winch, rack and pinion, chain drive, belt drive, rigid chain and rigid belt linear actuators operate on the principles and functions of the wheel and axle, wherein a rotating wheel moves a cable, rack, chain or belt to produce linear motion.
Hoist, winch, rack and pinion, chain drive, belt drive, rigid chain and rigid belt linear actuators operate on the principles and functions of the wheel and axle, wherein a rotating wheel moves a cable, rack, chain or belt to produce linear motion. Cam: Cam linear actuators function on a principle similar to that of the wedge, but provide relatively limited travel. As a wheel-like cam rotates, its eccentric shape provides thrust at the base of a shaft.
Some mechanical linear actuators only pull (such as hoists, chain drive and belt drives), while other types only push (such as a cam actuator). Pneumatic and hydraulic cylinders, or lead screw linear actuators can be designed to provide force in both directions. The selection of the linear actuator is dependent upon the application and budget.
Hydraulic Linear Actuators
Hydraulic linear actuators, sometimes referred to as or hydraulic cylinders, typically involve a hollow cylinder with a piston inserted into it. An unbalanced pressure applied to the piston provides the necessary force that moves an external object. Since liquids are nearly incompressible, a hydraulic cylinder can provide controlled precise linear displacement of the piston. The displacement is only along the axis of the piston. Although the term "hydraulic actuator" refers to a device controlled by a hydraulic pump, an example of a manually operated hydraulic linear actuator is a simple hydraulic car jack.
Hydraulic cylinders
Pneumatic Linear Actuators
Pneumatic linear actuators, sometimes referred to as pneumatic cylinders, are similar to hydraulic linear actuators except they use compressed gas to provide pressure, rather than a liquid force.
Pneumatic cylinder
Piezoelectric Linear Actuators
The piezoelectric effect is a property of certain materials in which the application of a voltage to the material causes it to expand. Very high voltages correspond to only tiny expansions. As a result, piezoelectric linear actuators can achieve extremely fine positioning resolution. The downside to the piezoelectric linear actuator is that it has a very short range of motion. In addition, piezoelectric materials exhibit hysteresis, which makes it difficult to control its expansion in a repeatable manner.
Electromechanical Linear Actuators
Electromechanical linear actuators are similar to mechanical actuators, the only difference being that the control knob or handle is replaced with an electric motor. Rotary motion of the motor is converted into linear displacement of the actuator. There are many designs of modern linear actuators. Every linear actuator manufacturer has their own proprietary methods and designs, making it difficult to cross parts from one manufacturer to another for a given application.
The following is a example description of a very simple electromechanical linear actuator. Typically, an electric motor is mechanically connected to rotate a lead screw. A lead screw has a continuous helical thread machined on its circumference running along the length (similar to the thread on a bolt). Threaded onto the lead screw is a lead nut or ball nut with corresponding helical threads. The nut is prevented from rotating with the lead screw (typically the nut interlocks with a non-rotating part of the actuator body). Therefore, when the lead screw is rotated, the nut is driven along the threads. The direction of motion of the nut will depend on the direction of rotation of the lead screw. By connecting to the nut, the motion can be converted to usable linear displacement.
Standard Construction or Compact Construction?
A linear actuator using standard motors will typically use the motor as a separate cylinder attached to the side of the actuator, either parallel with the actuator or stuck out to the side, positioned perpendicular to the actuator. Sometimes the motor is attached to the back end of the actuator. The drive motor is of typical construction with a solid drive shaft that is geared to the drive nut or drive screw of the linear actuator.
Compact linear actuators may only be about 6 inches long, motor included
Compact linear actuators use specially designed motors that fit the motor and actuator components into the smallest possible footprint. In such cases, the inner diameter of the motor shaft can be enlarged, so that the drive shaft can be hollow. The drive screw and nut can therefore occupy the center of the motor, with no need for additional gearing between the motor and the drive screw. Similarly, the motor can be made to have a small outer diameter, with the pole faces stretched out lengthwise so that the motor will provide high torque while fitting in a small diameter design.
Motor-Type Linear Actuators
A motor-type linear actuator basically functions the same as a rotary electric motor with the rotor and stator circular magnetic field components designed so that they are laid out in a straight line. Whereas a rotary motor rotates and re-uses the same magnetic pole faces again and again, the magnetic field structures of a motor-type linear actuator are physically repeated across the length of the actuator. Since the motor moves in a linear fashion to begin with, no lead screw is needed to convert rotary motion to linear. While high capacity is possible, the material and/or motor limitations on most designs are surpassed relatively quickly due to a reliance solely on magnetic attraction and repulsion forces. Most linear actuator motors have a low load capacity compared to other types of linear actuators. However, linear actuator motors have an advantage in outdoor or dirty environments in that the two halves do not need to contact each other, so the electromagnetic drive coils can be waterproofed and sealed against moisture and corrosion. The linear actuator motor design provides for a very long service life, so it can be an economical choice for some applications.
Special Design: Telescoping Linear Actuator
Telescoping linear actuators are specialized linear actuators that are typically used where space restrictions or other requirements dictate the best fit. The telescoping linear actuator's range of motion is many times greater than the unextended length of the actuating member. A common form is made of concentric tubes of approximately equal length that extend and retract like sleeves, one inside the other, such as the telescopic cylinder. Other more specialized telescoping linear actuators use actuating members that act as rigid linear shafts when extended, but break that line by folding, separating into pieces and/or uncoiling when retracted.
Examples of telescoping linear actuators include: Helical Band Linear Actuators
Rigid Belt Linear Actuators
Rigid Chain Linear Actuators
Segmented Spindle Linear Actuators
Anaheim Automation Carries the Following Types of Linear Actuators
Also called linear stages or linear actuator tables, screw-driven linear actuators are suitable for low-speeds, heavy loads, and high-inertial loads. These devices are typically used in positioning equipment, CNC machines, and anywhere else slow point-to-point movement is required.
Designed for high-speeds and light loads, belt-driven actuators are only suitable for low-inertial loads. Common applications for belt actuators include automated windows and doors, high-speed data acquisition, and scanning devices.
Rod actuators excel at lifting and thrusting applications, with thrust-bearings built into the actuators themselves. They are similar to screw-driven tables in terms of speed, load, positioning, and accuracy capabilities. Rod actuators offer a compact, collapsible design great for projects where space constraints are a concern.
Stepper linear actuators are intended for point-to-point positioning applications. This type of actuator is best suited for applications with light loads and slow speeds. Steppers are open loop accurate, so they do not require an encoder for accurate positioning.
Our brushless DC linear actuators are great for high-speed motion. Their specialty is delivering power density at high speeds, rather than the high accuracy of a stepper. Brushless linear actuators may require an encoder to obtain accurate positioning data.
Advantages and Disadvantages of Specific Linear Actuator Designs
Advantages of Linear Actuators
Each type of linear actuator is made differently and has its own advantages. Mechanical linear actuators, for example, are relatively inexpensive, reusable, and self-contained. Piezoelectric linear actuators can create extremely small linear motions and can consequently be used for microcomputer and micro-mechanical applications. Hydraulic linear actuators can produce a large amount of pressure, and therefore, can be used for heavy duty applications. Pneumatic linear actuators are powerful, compact, lightweight, very simply designed, and provide repeatable motion. See chart for comparisons of linear actuator types.
Disadvantages of Linear Actuators
While each linear actuator has its own advantages, each also has its disadvantages. For example, mechanical linear actuators are strictly manual and cannot be automated. Piezoelectric linear actuators are slow, can only move across small areas, are very expensive, require a high voltage to be effective, and require a secondary force to push the shaft back into its initial position. See chart for comparisons of linear actuator types.
Comparison of the advantages and disadvantages of various types of linear actuators
How to Select a Linear Actuator for an Application:
"Linear actuator" is a broad term covering many different types of devices. The process of selecting the best device for a specific application is dependent upon the user's diligent research and development practices. It is difficult to compare the specifications between linear actuator manufacturers, as there is very little standardization within the industry. Each type of linear actuator fulfills a different set of design requirements.
There are many different types of motors that can be used in a linear actuator system as well. These include DC Brush, DC Brushless, Servo, Stepper, and in some cases, even AC induction motors. The application requirements and the loads the actuator is designed to move will dictate the best motor option. For example, a linear actuator using an integral horsepower AC induction motor driving a lead screw can be used to actuate a large valve in a refinery. In this case, extreme accuracy and high resolution (down to a thousandth of an inch) would not be required, but high force and speed are critical to the application.
For electromechanical linear actuators used in laboratory instrumentation, robotics, optical and laser equipment, or X-Y tables, fine resolution (measured in microns) and high accuracy may require the use of a fractional horsepower stepper motor linear actuator with a fine pitch lead screw. Because there are many variations in the electromechanical linear actuator system, it is critical to understand all design requirements and application constraints for the proper selection.
The following is a guideline to the selection of the linear actuator, and will assist you through the process step by step. Carefully consider each step and you will be able to narrow down your choice.
BEFORE YOU START
Important notes: Linear actuators are used in a variety of applications across numerous industries, including medical equipment, agriculture machinery, high-voltage switch gears, train and bus doors, and factory processes and assembly machinery. Typical use cases include medical beds, patient lifters, wheelchairs, adjustable tables and workstations, diagnostics, to name a few. Each linear actuator application has unique requirements.
Manufacturers throughout the world offer innumerable models of linear actuators in a wide variety of stroke sizes, speeds, voltage and types. With the availability of so many manufacturers, models and options, selecting the right linear actuator for your application can be a daunting task. When contacting a manufacturer for application assistance for a linear actuator, please be prepared to provide as many of the application requirements as possible, including the environment in which you plan to use the linear actuator. Most linear actuators are built either for high speed, high force, or a compromise between the two.
Starting the Process
Step One: The Basics
Describe and discuss the application in as much detail as possible with a knowledgeable and experienced supplier. At this stage, focus on basic specifications for load, actuator, and power and control. When considering a linear actuator for a specific application, the most important specifications are: travel distance, speed, force, accuracy and lifetime requirements. Other aspects of the linear actuator application will help determine which products to choose. The following questions must be answered before the selection process can start:
What type of energy source will you use? Air, fluid, electricity? Answering this question will eliminate many manufacturers and linear actuator types.
Answering this question will eliminate many manufacturers and linear actuator types. Determine the amount of force required. This may be the weight of an object you are lifting or friction that needs to be overcome. How much force (in newtons or pounds-force) and in what directions (push, pull, vertical, and/or horizontal) will the actuator need to move? (Force is a function of maximum and average dynamic loads.) Rule out any linear actuators that are not capable of producing enough force.
This may be the weight of an object you are lifting or friction that needs to be overcome. How much force (in newtons or pounds-force) and in what directions (push, pull, vertical, and/or horizontal) will the actuator need to move? (Force is a function of maximum and average dynamic loads.) Rule out any linear actuators that are not capable of producing enough force. Speed: How fast (millimeters/second or inches/second) will the actuator need to move? Decide how fast you need to move; you can rule out any linear actuators that are too fast or too slow. Determining the speed combined with the force from step one will give you the mechanical power required.
How fast (millimeters/second or inches/second) will the actuator need to move? Decide how fast you need to move; you can rule out any linear actuators that are too fast or too slow. Determining the speed combined with the force from step one will give you the mechanical power required. Distance: Define how far your actuator needs to travel, also known as the stroke length. Whenever possible, select the standard catalog options. How far will the actuator need to move? This will factor in both the stroke and retracted lengths and is usually expressed in millimeters. Special requirements are generally more costly. Important : Keep in mind that the longer the stroke, the longer the linear actuator will be when fully retracted. This is especially important if you need to fit into an existing space.
Define how far your actuator needs to travel, also known as the stroke length. Whenever possible, select the standard catalog options. How far will the actuator need to move? This will factor in both the stroke and retracted lengths and is usually expressed in millimeters. Special requirements are generally more costly. : Keep in mind that the longer the stroke, the longer the linear actuator will be when fully retracted. This is especially important if you need to fit into an existing space. Duty Cycle: How often will the actuator operate, and how much time will elapse between operations? (This refers to the "duty cycle," which will be based on the number of expected repetitions per unit of time in hours/day, minutes/hour, and/or strokes/minute.) Check the duty cycle rating of your remaining choices. Except for high-end servo units, most linear actuators may not operate continuously without overheating.
How often will the actuator operate, and how much time will elapse between operations? (This refers to the "duty cycle," which will be based on the number of expected repetitions per unit of time in hours/day, minutes/hour, and/or strokes/minute.) Check the duty cycle rating of your remaining choices. Except for high-end servo units, most linear actuators may not operate continuously without overheating. Options to consider: What are the power supply options (motor vs. battery)? A battery-powered application will probably require a DC motor rated the same as the battery voltage. However, an AC powered application does not necessarily need an AC motor because AC is fairly easily converted to any DC voltage. Be flexible when choosing options such as built-in limit switches and position feedback devices such as potentiometers and encoders. Consider that limit switches, for example, can often be incorporated into part of your mechanism rather than being part of the actuator itself.
What are the power supply options (motor vs. battery)? A battery-powered application will probably require a DC motor rated the same as the battery voltage. However, an AC powered application does not necessarily need an AC motor because AC is fairly easily converted to any DC voltage. Be flexible when choosing options such as built-in limit switches and position feedback devices such as potentiometers and encoders. Consider that limit switches, for example, can often be incorporated into part of your mechanism rather than being part of the actuator itself. Environmental Considerations: Will environmental factors (temperature variations, moisture, vibration, or end-product shock) pose a challenge to operation? Most linear actuators can operate well in an indoor environment, but harsh outdoor conditions, extreme temperatures or submersion will drastically limit your product choices. Sometimes it is easier to provide some external protection to the unit rather than find one with the proper ingress protection rating that meets all your other requirements.
Narrowing Down the Selection of the Linear Actuator
Careful review of your linear actuator application can help to eliminate costly mistakes and provide for optimal system performance.
Step Two: Beyond the Basics – Options to Consider
When a system is tailored for an application, the specific requirements will influence both the design and the manufacturing processes. Regardless of end use, an actuation system is designed by first identifying basic needs, and then evaluating certain key parameters that ultimately affect the overall system operation.
Electromechanical linear actuators are designed to provide precision, efficiency, accuracy, and repeatability in effecting and controlling linear movement. These devices serve as practical, efficient, and relatively maintenance-free alternatives to their hydraulic or pneumatic actuator counterparts. Depending on type and manufacturer, today's electromechanical linear actuators can handle loads of up to 3,000 pounds (13 kilonewtons) and deliver speeds of up to 6 inches/second (150 millimeters/second), with strokes ranging from 2 inches (50 millimeters) to 60 inches (1,500 millimeters). Actuators can be self-contained in aluminum, zinc, or polymer housings and ready to mount for easy plug-in operation (using either AC or DC power supplies).
What's more, actuators featuring both modular design and open architecture enable interchangeable internal and external components, according to specifications. Please note that standard components, including the types of drive screws, motors, front and rear attachments, controls, and limit switches used, will allow for desired customization without the costs typically associated with special modifications.
Note: The specific parameters that play a crucial role in every electromechanical actuator application are the: electrical power in, duty cycle, and actuator efficiency. Answering the following questions will help you to define the linear actuator further:
What is the desired lifetime for the end product? (Those answers will impact virtually every component within a linear actuator system.) How will the actuator be mounted? Will front and/or back mounts require special configurations? Does the application suggest particular safety mechanisms (e.g., "manual operators" for use in case of emergency)? Is space limited? (If so, the actuator will have to be designed to fit in a specific footprint.) If a motor is utilized, what is its type (AC, DC, or special) and voltage? Is feedback required for speed and/or position? (This will indicate a need for add-on components, such as encoders.)
Step Three: The Power Factor
A linear actuator is a device that produces linear motion by utilizing some external energy source. As far as the source of energy used is concerned, it can be piezoelectric, pneumatic, hydraulic, mechanical, electro-mechanical, etc. A linear actuator system draws principles from both electrical and mechanical engineering disciplines. Consequently, power (defined in Watts) is usually the first requirement to be calculated. In order to get mechanical power out of an electric linear actuator, it is necessary to put electrical power into the system. Mechanical power out is usually the easier of the two to define because all that is needed for its calculation is the force, or the load that will be moved, and the speed required.
If the parameters are in metric (SI) units, multiply the force (in Newtons) by the speed (in millimeters/second) to obtain Watts. (To convert pounds to Newtons, multiply by 4.448; to convert inches to millimeters, multiply by 25.4.)
Mechanical power out (P 0 ):
P 0 = F x v
F = Force (N)
v = Velocity (meters/sec)
Information regarding electrical power can be ascertained through performance graphs and charts from suppliers' specification sheets. Suppliers chart this information differently, but more often than not, there are graphs for force vs. speed and force vs. current draw at a specified voltage. This data is often presented in two graphs or combined in one. The current draw may also be presented in tabular form. In addition, factors will be given based on a duty-cycle curve. The relevant formula is as follows:
Electrical power in (P i ):
P i = E x I
E = Voltage (V)
I = Current (A)
Step Four: Calculating Duty Cycle
Users will want to establish the duty-cycle factor (sometimes called the "derating factor"). Duty cycle is important. Sometimes the preliminary actuator selection may not meet all of an application's operating requirements. The duty cycle indicates both how often an actuator will operate and how much time there is between operations. Because the power lost to inefficiency dissipates as heat, the actuator component with the lowest allowable temperature (usually this is the motor) establishes the duty-cycle limit for the complete linear actuator system. Please note: There are some heat losses from friction in a gearbox, and via ball-screw and acme-screw drive systems.
To demonstrate how the duty cycle is calculated, assume an actuator runs for 10 seconds cumulative, up and down, and then doesn't run for another 40 seconds. The duty cycle is 10/(40+10), or 20%. If duty cycle is increased, either load or speed must be reduced. Conversely, if either load or speed decreases, duty cycle can increase. The duty cycle is relatively easy to determine if a linear actuator is used on a machine or production device. In other, less predictable applications or those where the linear actuator will be used infrequently, it is advisable to estimate the worst-case scenario in order to assign a meaningful duty-cycle calculation. It is not advisable to operate on the edge of the manufacturer's power curves because this might cause the linear actuator and other components to run too hot. However, in some applications where the duty cycle is 10% or less, the actuator can run to the limit of its power curves.
Step Five: Ascertaining 'Efficiency' and Expected Life
A system's "efficiency" is usually missing from most manufacturers' literature, but it can tell the user how hot the actuator may get during operation, whether holding brakes should be specified in the system if the actuator uses a ball screw, and how long batteries may last in battery-powered systems, among other pertinent data. Calculating efficiency from performance curves is simple: divide mechanical power out by electrical power in. This yields the efficiency percentage.
While these factors are being calculated and decision making is moving toward final selection, one additional parameter should be addressed: the application's expected lifetime. Although linear actuator components (e.g., the motor or screw) can be replaced, most actuators cannot be easily repaired. In addition, it is important to cover application life expectancy because suppliers will sometimes indicate acme or ball screw life at a certain load, or include mathematical formulae to calculate life based on application parameters. A good design practice is to strive to have the screw and motor life expectancies match as closely as possible.
In those cases where an existing linear actuator must be replaced, ensure that the application engineer has all the necessary information to ensure a good fit. Whenever a linear actuator is subject to replacement, it is recommended to review the application as if it were new.
Other Selection Considerations: Budget and Experience
Having a clear picture of a linear actuator system budget in your mind will help in selecting the best product at an affordable price. Advanced budget planning can definitely save the user a lot of time in the selection process by eliminating some types that are too expensive for the application. As mentioned earlier, there are many companies providing linear actuators to the customers based on their requirements. It is important to choose a reliable company for the best results in terms of the actuator features and price.
Need help selecting a Linear Actuator? Whether you need to determine the correct type of actuator for your application, or have a simple question about the options on our Linear Tables, our applications engineers have the expertise to make sure you find the right solution for your system. Contact Us
Common products associated with motion control systems using linear actuators:
AC Motor: An AC Motor is an electric motor that is driven by alternating current. The AC Motor is used in the conversion of electrical energy into mechanical energy. This mechanical energy is made from utilizing the force that is exerted by the rotating magnetic fields produced by the alternating current that flows through its coils. The AC Motor is made up of two major components: the stationary stator that is on the outside and has coils supplied with AC current, and the inside rotor that is attached to the output shaft. Anaheim Automation has a full line of AC motors with many options for budget considerations. To select the best fit for your application, refer to Anaheim Automation's AC Motors.
Brake: A brake is a device that resists and reduces the motion of a mechanism. When the brake is engaged, it "slips" until the driving mechanism stops. When the brake is disengaged, the mechanism can rotate freely. Brakes are similar in principle to clutches. A clutch couples two mechanisms in order to transmit motion and power, while a brake "couples" a mechanism to a fixed frame in order to reduce motion and power. Anaheim Automation offers a line of friction brakes in four series, in NEMA sizes 23, 34 and 42. Perfect for stopping and holding applications, these compact brakes can handle high torque requirements, from 80 to 1,152 oz-in. Their low-voltage design provides for applications that are susceptible to weak battery, brown out, or long wiring runs. To select the best fit for your application, refer to the Anaheim Automation's Accessories/Brakes.
Brush DC Motor: A brush DC motor is a direct current (DC) motor that utilizes a relatively simple design. The brush motor is an electric motor that uses electricity and a magnetic field to produce torque, which rotates the motor. At its most simple design, a brush motor requires two magnets of opposite polarity and an electric coil, which acts as an electromagnet. The repellent and attractive electromagnetic forces of the magnets provide the torque that causes the brush motor to rotate. Anaheim Automation has a full line of brush DC motors with many options for budget considerations. To select the best fit for your application, refer to Anaheim Automation's Brush Motors.
Brushless DC Motor (BLDC): A brushless DC motor is an electric motor powered by direct current (DC). Though typically more expensive than the standard electric or brushed motor, the brushless DC has considerable advantages over its predecessor. Most notably, a brushless DC motor boasts better performance and suffers less wear than brushed motors of similar size. Anaheim Automation has a full line of brushless DC motors with many options for budget considerations. To select the best fit for your application, refer to Anaheim Automation's Brushless Motors.
Clutch: A clutch is a device that transmits power between two mechanisms (usually rotating) selectively. When the clutch is engaged, it "slips" until the two mechanisms rotate at the same speed and power is transmitted. When the clutch is disengaged, the two mechanisms are de-coupled and allowed to rotate at different speeds. Power is not transmitted. Clutches are similar in principle to brakes. In a brake, the driven mechanism would be connected to a fixed frame.
Torque Limiter (Torq/Gard and Centric Clutches) Uses: Centric Overload Clutches and Torq/Gard Clutches provide machine protection and reduced repair time during jamming load conditions. Mechanical or pneumatic torque limiters provide consistent torque levels after many overloads. Unlike friction style or shear pin-type torque limiters, Centric Overload Clutches and Torq/Gard can provide an accurate method of resetting the torque with no operator intervention. A single position clutch will re-engage in the exact rotational position each time. This is often necessary for a system wherein timing is critical, such as bottling, packaging, and paper converting type applications.
Coupling: A coupling is a device that connects two generally coaxial (inline) shafts at their ends in order to transmit power between them. A coupling can be incorporated with a clutch to serve as a clutch-coupling or a torque limiter. At high speeds, couplings are capable of transmitting high torque at a constant velocity. Certain types of couplings may be able to compensate for lateral, axial, and angular misalignments.
Gearbox: A gearbox is a mechanical device that transfers energy from one device to another. A gearbox is used to increase torque while reducing speed. Torque is the power generated through the bending or twisting of a solid material. This term is often used interchangeably with transmission. Anaheim Automation has a full line of gearboxes with many options for budget consideration. To select the best fit for your application, refer to Anaheim Automation's Gearboxes.
Linear Actuator Motors: Linear actuator motors are motors that provide push and pull motive force in a straight line. There are many uses for many different types of linear actuator motors: some will be used to move work tables on industrial machines, while others are better suited to modulate control valves, drive material handling equipment, bottling and packaging and robotics, and move printer and scanner heads back and forth on equipment. Large linear actuator motors can drive shovels and lifts on construction equipment. They can be used for home automation projects, such as providing the oscillatory motion of audio loudspeakers, lowering or raising televisions, and in some solar energy systems. Anaheim Automation offers both Stepper Motor Linear Actuators as well as BLDC Linear Actuators with Ball Screws.
Linear Guide: A linear guide is a mechanical linear motion bearing system or linear slide that is designed to provide free motion. Linear guides are sometimes referred to as linear actuators. Anaheim Automation has a full line of linear guides that are precision rails and matched bearing blocks, in many sizes (including miniature) with options for all budgets. To select the best fit for your application, refer to the Anaheim Automation's Linear Guides.
Rotary Union: A rotary union, or rotating union, is a device used to conduct fluids and gases from one point to another, often under high pressure. Additionally, a rotating union is designed to lock onto an input valve while rotating or swiveling to meet an outlet. Many rotary unions incorporate multiple ports, some of which are designed to handle different types of material simultaneously.
Slip Ring: A slip ring (in electrical engineering terms) is a method of making an electrical connection through a rotating assembly. Slip rings, also called rotary electrical interfaces, rotating electrical connectors, collectors, swivels, or electrical rotary joints, are commonly found in electrical generators for AC systems and alternators and in packaging machinery, cable reels, and wind turbines. One of the two rings is connected to one end of the armature winding and the other is connected to the other end of the armature winding.
Servo Motor: A servo motor is defined as an automatic device that uses an error-correction routine to correct its motion. The term servo can be applied to systems other than a servo motor: systems that use a feedback mechanism such as an encoder or other feedback device to control the motion parameters. Typically, when the term servo is used, it applies to a 'servo motor' but is also used as a general control term, meaning that a feedback loop is used to position an item. Anaheim Automation has a full line of servo motors, including linear actuator servo motors, with many options for budget considerations. To select the best fit for your application, refer to Anaheim Automation's Linear Servo Actuators.
Stepper Motor: A stepper motor is an electrical device that divides the full rotation of the motor into individual parts called steps. Generally, stepper motors are brushless in order to facilitate a synchronous rotation and operate without the input of an external source on the gear itself. Simply stated, stepper motors are designed with electromagnets which are arranged in specific locations around the shaft, each engraved with teeth. These teeth match the teeth that are placed on the gear itself. As the gear rotates, one section matches with the teeth of the first electromagnet, offsetting the teeth from the other electromagnets, and repeating the action as it rotates. Anaheim Automation has a full line of stepper motors, including linear actuator stepper motors, with many options for budget considerations.
Table/Slide/Stage: The terms table, slide, stage and linear actuator are often used inter-changeably, even though there are significant differences among them. Anaheim Automation carries two types of screw-driven positioning tables: standard screw-driven and precision screw-driven tables. Some linear tables are designed with unsupported rails and others with supported rails that utilize the stainless steel 400 series precision rolled lead screw, accurate up to 0.003"ft, with a burnished finish and a zero backlash nut. These screw-driven tables (acme and ball screw) are available as open-loop or closed-loop systems (wherein stepper motors are assembled with encoders). Available options are: light or heavy-duty configurations, travel lengths, homing and limit switches, and lead screw pitch. Ideal for pick-and-place operations, circular and linear interpolation, point-to-point motion, pin-insertion, inspection and test equipment, engraving, part positioning, and assembly, these tables yield a great cost/performance ratio. To select the best fit for your application, refer to Anaheim Automation's Tables.
Glossary of Terms for Linear Actuator Motion Systems
(Absolute) Accuracy – Difference between ideal position and real position.
Absolute Positioning – Refers to a motion control system employing position feedback devices to maintain a given mechanical location.
ACME Screw – The most common type of lead screw found in machine applications. The ACME thread is a particular type of thread. Compared to a ball screw, ACME lead screws have a very high friction and backlash, both of which are undesirable for high-performance applications.
AC Motor – A type of electric motor that runs on alternating current. AC motors are more commonly used in industry than DC motors, but do not operate well at low speeds.
Accuracy – The relative status of something compared to its absolute or perfect value. In motion control this will most often be a position description. For example, a command may be set to 4.0 inches. The accuracy of the system will be defined on how close to the absolute value of 4.0 inches the system can affect the move. Accuracy may be defined as a one-time incident or the average over a number of cycles or motions. Positioning accuracy will normally be defined in terms of deviation (+/- from the theoretical) or limits (acceptable variation from the theoretical: i.e. 3.8 - 4.3 inches define acceptable limits of variation around a theoretical point.
Actual Position – The position of an axis relative to the commanded position. This may be the position at the end of the command move, or the lag between command position at any point during the move and the actual position of the axis at that point. The latter is commonly referred to as following error.
ARC Minute – An angular measurement equal to 1/60th of a degree.
Axes of Motion: The specific major directions along which controlled movement occurs. Usually referred to as the number of these major directions employed in a specific machine. Generally defined as follows:
X: Linear motion in a positioning direction
Linear motion in a positioning direction Y: Linear motion perpendicular to the positioning direction
Linear motion perpendicular to the positioning direction Z: Vertical linear movement
Vertical linear movement A: Angular motion around X (roll)
Angular motion around X (roll) B: Angular motion around Y (pitch)
Angular motion around Y (pitch) C: Angular motion around Z (yaw)
Axial Load – The terms 'axial load' and 'radial load' with respect to the bearing: Axial load is any load that is parallel to the axis of rotation of the bearing. Radial load is any load that is perpendicular to the axis of rotation.
Axial Play – The axial displacement of the shaft due to a reversal of an axial force.
Axis – A principal direction along which movement of a tool, component, or workpiece occurs. The components that control each degree of freedom in a machine can be considered an axis. An X-Y-Z machine is a three axis machine where the X and Y axes control movement in the horizontal plane and the Z axis controls up and down motion. Each axis can consist of a controller, drive, motor, and transmission components necessary to couple to the load.
Axes – Plural of axis.
Backlash – The play caused by loose connections between mechanical components. Backlash becomes a problem when an axis changes direction. When a motor turns, it pushes all the gears together in one direction. When the motor reverses direction the gear teeth separate from one side and meet on the other side. The distance of separation is the backlash.
Ball screw – Ball screws are highly efficient, low-friction and low backlash lead screw devices that use ball bearings rolling in a channel cut into the screw. The low friction and backlash attributes are extremely valuable for precision applications where they are used to drive the axes of the machine.
Brushless Motors – Brushless Motors are a class of motors that operate using electronic commutation of phase currents, rather than electromechanical (brush-type) commutation. Brushless motors typically have a permanent magnet rotor and a wound stator.
Circular Interpolation – The generation of an apparently circular motion through the coordinated movements of two axes. The actual path is a series of straight line approximations generated by software algorithms.
Collision Detection – The use of sensors to detect the imminent impact of two or more parts in a motion control system. The signals from the detection sensors can be used to stop motion or to provide a ramped slow down for a "soft" mating of the approaching components.
Coordination – The integration of the movements of two or more axes of motion, so that the resultant motion is the path which none of the axes are capable of independently. Coordination may also involve the use of sensors and other internal or external commands in the integration effort which assist in effecting the movement or work desired.
Coupling (Couple, Coupler) – The transfer of energy from one circuit to another by means of the mutual capacitance between them. In feedback and control systems this is considered to be electrical noise and is a common problem.
Cut-to-Length – A sub-routine within a motion control process or a standalone process in which feed material is processed at a preset distance. The distance is set prior to the performance of the task, and/or a secondary task such as a cut-off of the feed material. Feedback systems are employed to ensure repeatability of the preset feed length.
Detent Torque – Detent torque is the holding torque when no current is flowing in the motor. The maximum torque which can be applied to the shaft of an un-energized step motor without causing continuous rotation. The minimal torque present in an un-energized motor. The detent torque of a step motor is typically about 1% of its static energized torque.
Dynamic Torque – the torque developed by a motor while stepping at low rates.
Efficiency – In physics: the efficient energy use, useful work per quantity of energy, or mechanical advantage over ideal mechanical advantage, often denoted by the Greek lowercase letter η (Eta). In thermodynamics: efficiency is energy conversion efficiency, a measure of second law thermodynamic loss. Thermal efficiency: useful work per the higher heating value of the fuel. In computing: Algorithmic efficiency is optimizing the speed and memory requirements of a computer program, while storage efficiency as the effectiveness of computer data storage.
Electronic Clutch – The process of generating a slave profile based on master position or time periods by enabling and disabling electronic cam or gearing functions.
Electronic Gearing – A method that stimulates mechanical gears by electrically synchronizing one closed-loop axis to a second axis (open- or closed-loop) through a variable ratio.
Electronic Line Shaft – A virtual axis that is used as the master axis on a machine to which other axes are synchronized by electronic gearing or camming profiles.
Encoder – An encoder is a feedback device. It consists of a disc, vane, or reflector, typically attached to a motor shaft to provide digital pulses, which are provided to a translator and /or counters. This provides positional information if fed into a counter. Speed information may be derived if the time between successive pulses is measured and decoded.
Encoder Resolution – The number of electrically identified positions occurring in 360 degrees of input shaft rotation.
Event – A change-of-state of an input parameter, such as the triggering of a limit switch or proximity sensor.
Fault – The error received when a drive or control has attempted an illegal process and becomes disabled.
Feedback – Feedback is the measurement of the parameter that is being controlled. For a positioning system to accurately compensate for an error, the actual position must be known relative to the commanded position. In this case, position feedback would be used to provide the actual position.
Feedback Signal – The actual value detected by a sensor as a process is taking place. The feedback signal is part of a closed-loop control system.
Feedforward – A method that "precompensates" a control loop for known errors due to motor, drive, or load characteristics to improve response. It depends only on the command, not the measured error.
Gantry – An overhead framework that is designed to linearly move in the X, Y, and/or Z axes. Tooling or other devices are generally designed into the framework to perform various functions as it moves from one location to another.
Gearbox – A system of gears that transmits mechanical power from a prime mover, such as an electric motor, to a typically rotary output device at a lower momentum, but a higher torque.
Holding Torque – Holding torque is the maximum torque that can be externally applied to the motor shaft without causing continuous rotation when one or more phases of the motor are energized.
Home Position – A reference position for all absolute positioning movements. Usually defined by a home limit switch and/or encoder marker. Normally set at power-up
Homing – Locating a unique reference position at power-up for axis calibration.
Indexer – In the context of stepper motor-based systems, the indexer is a device that provides step and direction control signals to a stepper motor driver. More sophisticated dedicated stepper motor controllers will also have I/O points and various other higher level functions and programmability similar to a PLC. In many cases, a PLC may be used as an Indexer.
Indexing – An axis or axes in the process of moving to a pre-programmed position, at a defined velocity and acceleration/deceleration rate.
Interpolation – A coordinated move of two or more axes in a linear and/or circular motion.
Jog – An axis running at a fixed velocity and acceleration/deceleration rate, in a selected direction, with no specific destination.
Lead – The linear distance a nut on a lead screw travels during one revolution of the lead screw, e.g. in/rev
Lead Screw – A device that converts rotary motion into linear motion.
Limits – Sensors within a motion system that alert the control electronics that a physical end of travel has approached and that the motion is not allowed in a specific direction.
Linear Actuator – A linear actuator is an actuator that creates motion in a straight line, as contrasted with the rotary motion of a conventional electric motor. Linear actuators are used in machine tools and industrial machinery, in computer peripherals such as disk drives and printers, in valves and dampers, and in many other places where linear motion is required. Hydraulic or pneumatic cylinders inherently produce linear motion; many other mechanisms are used to provide a linear motion from a rotating motor.
Linear Servo Motor – A linear servo motor is a "flattened" servo motor where the rotor is on the inside, and the coils are on the outside of a moveable u-channel.
Linear Slide – Linear slides are precision products designed to turn motion or torque into straight-line movements. Linear slides are designed to move mounted mechanisms across a given axis. Complete slides normally consist of at least a base, a saddle, adjusting screw and a straight gib.
Load – Any external resistance (static or dynamic) to motion that is applied to the motor.
On-Axis Accuracy – Difference between ideal position and real position after the compensation of linear errors. Linear errors include: cosine errors, inaccuracy of screw or linear scale pitch, angular deviation at the measuring point (Abbe error) and thermal expansion effects. The relation between absolute accuracy and on-axis accuracy is as follows:
Absolute Accuracy = On-Axis Accuracy + Correction Factor x Travel
Override – To force an axis to move during a faulted condition; often required to force an axis to move off of an over-travel limit switch.
Phasing – Adjusting the position of one axis with respect to others during synchronization or electronic line shafting. This is usually done while the axes are moving, and done to correct for small registration problems.
Pick and Place – An application in which objects are transferred from one place to another, such as a pill-sorting machine, medical vial (test tube) testing, or air-sample tester.
Pitch – The distance from any point on one thread of the screw to a corresponding point on the next successive thread, e.g. rev/in.
Positioning – Specifying a move by giving a target position, a velocity, and an acceleration rate. The target position can be an absolute position or a relative position from the current position.
Position Control – A type of control system designed for moving objects or machines to a known position. For example: stepper motors are used for position control.
Position Loop – The position loop operates by continuously comparing the target position with the current position. The difference between these two is the position error or following error.
Rated Speed – the speed specified by the manufacturer at which the motor produces its maximum rated power
Rated Torque – The rated torque is the torque-producing capacity of a motor at a given speed. This is usually specified with a torque/speed curve.
Servo Mechanism – A servomechanism may or may not use a servo motor. For example, a household furnace is a servomechanism that is controlled by a thermostat. Once a set temperature is reached, feedback signals the system to shut off, making it a "servo" in nature. The term "servo" describes more of a function or task, than it does a specific product line. The defining feature of a servomechanism is the use of feedback, creating a closed-loop system.
Servo Motor – A servo motor is a motor which is part of a servomechanism. It is typically paired with some type of encoder to provide positioning and speed feedback. A servo motor is an automatic device that uses an error-correction routine to control its motion. The term servo can be applied to systems other than a servo motor; systems that use a feedback mechanism such as an encoder or other feedback device to control the motion parameters. Typically, when the term servo is used, it applies to a 'Servo Motor' but is also used as a general control term, meaning that a feedback loop is used to position an item. A servo motor can be a DC, AC, or brushless DC motor, combined with a position sensor – in most cases, a digital encoder. The defining feature of a servo motor is that it operates in a closed-loop system (uses feedback).
Shielded Cable – an electrical cable with a common conductive layer surrounding its conductors, which is meant to provide electromagnetic shielding, thereby preventing EMI issues
Stepper Motor – A stepper motor (also referred to as step or stepping motor) is an electromechanical device achieving mechanical movements through conversion of electrical pulses. Stepper motors are driven by digital pulses rather than by a continuous applied voltage. Unlike conventional electric motors which rotate continuously, stepper motors rotate, or step, in fixed angular increments. A stepper motor is most commonly used for position control. With a stepper motor/driver/controller system design, it is assumed the stepper motor will follow digital instructions. One important aspect of stepper motors is the lack of feedback to maintain control of position, which classifies stepper motors as open-loop systems. Stepper motors can rotate forward or in reverse, but they cannot move large loads.
Step – A step is the movement of the rotor of a stepper motor from one energized position to the next.
Step Angle – The step angle is the nominal angle through which the shaft of a stepper motor turns between adjacent step positions. Step angle depends upon the motor and driving sequence (mode of drive). Smaller step angles result in higher resolution.
Step Increment – Step increment is an indication of step or motion size. Usually this is specified in degrees for a rotary motor and inches or millimeters for a linear motor.
Step (Stepping, Stepper) Motor – A Stepper is synonymous with Step and Stepping motor: a digital actuator, which operates from discrete pulses (input signals) and produces motion in discrete increments, which may be rotary or linear. See stepper motor definition above.
Step Position – the angular position that the shaft of an unloaded step motor assumes when energized. The step position is not necessarily the same as the detent position.
Torque – A rotational force. Torque is measured in N*m, lb*in, lb*ft, etc. 1 N*m is the torque produced by 1N of force applied to a lever arm that is 1m long.
Torque Limiter – A torque limiter is a mechanical protection device which causes the load to separate from the drive when an overload happens
Yaw, Pitch – Rotation of carriage around the Z axis (Yaw) or Y axis (Pitch), when it moves. The testing of on-axis accuracy, repeatability, and reversal error are made systematically with test equipment in an air-conditioned room (20 °C±1 °C). A linear cycle with 21 data points on the travel and 4 cycles in each direction gives a total of 164 points.
| 2022-12-15T00:00:00 |
https://anaheimautomation.com/blog/post/linear-actuator-guide?srsltid=AfmBOorYqeWTPnId-sC1fzhzKZJsRxqJxGsfG20K1s-HMHlEEj5-hy06
|
[
{
"date": "2022/12/15",
"position": 97,
"query": "automation job displacement"
}
] |
|
Will AI Replace Humans? Artificial Intelligence vs. ...
|
AI Accounting: What Is Artificial Intelligence, Its Role & How Can AI Help Accountants?
|
https://synder.com
|
[
"Pauline Minaeva",
"About The Author",
"Pauline Minaeva Is A Fintech Enthusiast At Synder. With",
"Years Of Hands-On Experience In Customer Support",
"She Has A Knack For Understanding The Unique Needs",
"Pain Points Of Our Clients. Pauline Has Seamlessly Navigated Through The Intricacies Of Platforms Like Quickbooks",
"Xero",
"Helped Numerous Businesses Improve Their Bookkeeping Workflows. Her Expertise Extends To The World Of Financial Integration",
"Where She Has Successfully Worked With Stripe",
"Paypal"
] |
Will artificial intelligence replace humans? No – at least not for a long time. We can sum up that AI's abilities will complement us, rather than replicate us.
|
The business world today moves quickly and relies a lot on data. Accounting has been keeping up with these changes because it’s important to stay ahead to be successful. Artificial Intelligence (AI) has really changed the accounting field – many accounting programs now use AI, and its role is growing.
ChatGPT, a new advance in AI and machine learning, is being used in different parts of the business, making things faster and better in everything from research and coding to helping customers. Accountants can use this technology too.
In this article, we’re going to talk about how AI is used in accounting. We’ll explain how it helps, clear up some common wrong ideas about AI, and show real ways ChatGPT and other cutting-edge solutions can help with accounting work.
Key takeaways:
AI helps eliminate much of the manual work in accounting, making things more accurate and reducing mistakes.
ChatGPT is useful for many accounting tasks, like sorting transactions, calculating taxes, and predicting cash flow. It makes everyday accounting jobs easier.
Even with all the new AI technology, we still really need human accountants. Their jobs are just changing to focus more on thinking deeply and giving advice.
Right now, not all businesses use AI in their accounting, but more and more are starting to because they see how it can make things smoother and help them make better decisions.
In addition to AI, which simplifies processes without full automation, there are comprehensive solutions for automating your accounting tasks.
Here’s what you’ll read about AI in accounting:
1. What is AI in general and ChatGPT in particular?
2. What is AI’s role in accounting?
3. How does AI transform the role of accountants?
4. Will AI replace accountants?
5. Synder is a tool every accountant should know about
6. How can Synder help you with your accounting?
7. AI trends in accounting in 2024
8. Practical use of AI in accounting on the example of ChatGPT
9. Common concerns about AI in accounting
10. AI in accounting: People also ask
What is AI in general and ChatGPT in particular?
To begin with, it’s important to understand that AI, or Artificial Intelligence, stirs up many different opinions nowadays. There isn’t one clear-cut view of its impact on our lives or what it means for the future.
On one hand, some people see AI as a huge benefit. They believe it’s a breakthrough that can handle large amounts of information much quicker and more accurately than humans ever could. This advantage is seen as a way to push forward scientific discoveries, speed up technological advancements, and make many jobs more efficient. On the other hand, there are those who view AI more negatively, worrying it could replace human jobs or, in extreme cases, pose a danger to humanity itself. That’s why AI is often seen as a hot topic with widely differing viewpoints.
Rather than getting caught up in these debates, let’s focus on the facts and basics of AI to understand it better.
The definition of AI
AI, or Artificial Intelligence, in a nutshell, is the simulation of human intelligence in machines programmed to think and learn like humans. It involves developing computer systems capable of performing tasks that typically require human intelligence, such as:
speech recognition,
decision-making,
problem-solving,
and language understanding.
AI is a multi-disciplinary field encompassing various subfields, such as:
machine learning,
natural language processing,
computer vision,
robotics,
and expert systems.
AI aims at creating intelligent systems that can perceive and understand the world, reason and make decisions, and communicate effectively.
As we’re going to explore the potential and capabilities of ChatCPT in accounting in particular, two of the AI subfields are the most interesting for us, namely:
machine learning;
and natural language processing.
1. Machine learning
Machine learning plays a fundamental role in AI by enabling systems to learn from data and improve performance over time without explicit programming. Algorithms make machine learning models identify patterns, extract meaningful insights, and predict or decide based on the input data. This ability to learn and adapt sets AI apart from traditional software systems.
2. Natural language processing
Natural language processing (NLP) is another essential aspect of AI, focusing on the interaction between computers and human language.
NLP allows machines to:
understand,
interpret,
and generate human language in a contextually relevant and meaningful way.
This capability has paved the way for applications such as
virtual assistants,
chatbots,
and language translation systems.
What is ChatGPT?
ChatGPT, developed by OpenAI, is an advanced language model – a specific example of an AI model that focuses on natural language processing. It’s gained significant attention for its ability to generate human-like text responses based on the input it receives.
ChatGPT uses deep learning techniques, specifically deep neural networks, to process and understand language. Besides, it relies on a vast knowledge base comprising a wide range of data sources, including:
books,
articles,
and websites.
This helps develop a thorough understanding of human language.
Altogether, it makes ChatCPT capable of engaging in conversations, answering questions, and providing valuable insights across various topics, generating contextually relevant responses.
What is AI’s role in accounting?
Adopting artificial intelligence (AI) within the accounting sector isn’t just a trend—it’s a significant shift expected to grow at a high rate. A study by Mordor Intelligence projects a year-over-year growth rate of 30% for AI in accounting through 2027. Gartner’s finding suggests that 80% of Chief Financial Officers (CFOs) plan to increase their investment in AI technologies over the next two years.
But what’s fueling this investment and optimism towards AI in accounting?
An important aspect of modernizing accounting practices involves how accounting firms are utilizing AI to meet their specific needs. For instance, AI can ease traditionally manual and time-consuming tasks. This speeds up operations and significantly reduces the chances of human error, which can be costly and damaging to businesses. By minimizing these errors, companies ensure greater accuracy in their financial reporting, compliance, and decision-making processes.
In particular, accounting firms use AI for things like:
Prediction,
Timetable organization,
Cash flow oversight,
Streamlining workflows,
Crafting emails and organizing inboxes,
Handling invoices and managing expenses,
Analyzing data,
Facilitating business correspondence,
Project coordination.
Despite the promising advancements, the current adoption rate of AI among accounting firms remains modest, with a steep increase anticipated in the near future. Over recent years, there has been a noticeable uptick in the integration of AI within the accounting sector. Firms, regardless of their scale, are increasingly embracing AI solutions to augment their services. This shift is motivated by the recognition of AI’s capacity to automate routine tasks, minimize errors, and liberate accountants to engage in more strategic advisory roles.
Nevertheless, the majority of accounting firms find AI adoption challenging for several reasons:
It’s perceived as too new.
Concerns regarding security remain prevalent.
There are instances of incomplete, misleading, or inaccurate responses.
The foremost concerns revolve around data security and privacy.
A significant portion of surveyed respondents, approximately two-thirds, expressed apprehensions about utilizing technologies like ChatGPT and generative AI for corporate or client-related tasks. Surprisingly, 73% of those surveyed currently have no intentions of leveraging such technology.
As one respondent said, “Generative AI doesn’t equate to genuine intelligence — it’s akin to text-based crowd-sourcing.”
Despite these reservations, around 15% of surveyed accounting firms either currently utilize AI or plan to do so in the near future. Although only 11% are presently using it extensively, a notable 51% anticipate its incorporation within the next six to twelve months.
How does AI transform the role of accountants?
AI technology has been transforming the usual ways of accounting for quite some time already. At this point, it’s safe to say that AI is changing the role of accountants, simplifying traditional accounting practices and enabling accountants to have more time to focus on higher-value tasks. We’ll quickly break down some key transformations brought about by AI.
Transformation #1.
AI algorithms have a high level of accuracy and can detect patterns, anomalies, and errors more effectively than humans. For instance, accountants can use AI to ensure greater accuracy in financial statements, minimize compliance risks, and adhere to accounting standards.
For example, an AI system might identify discrepancies in ledger entries that could indicate unintentional errors in manual data entry or potentially fraudulent activities, allowing for timely corrections and reducing the risk of financial misreporting.
Transformation #2.
AI enables accountants to analyze large volumes of financial data swiftly and accurately. Advanced data analytics tools powered by AI can provide valuable insights, identify trends, predict outcomes, and support decision-making processes.
For instance, AI may uncover a significant increase in sales revenue in a particular market segment, prompting further investigation into the factors driving this growth.
It can also predict future revenue trends based on historical data and market conditions, enabling a business to make informed decisions regarding resource allocation, pricing strategies, and expansion opportunities.
Transformation #3.
AI-based algorithms can detect suspicious patterns and anomalies in financial data, aiding in fraud detection and risk mitigation. For example, accountants can use AI tools to identify potential fraud cases, flag them for further investigation, and implement preventive measures.
Example: If an AI tool notices an unusual series of transactions occurring at odd hours or in atypical amounts, it could flag these for a detailed review, potentially uncovering a scheme to siphon funds or manipulate financial records.
Transformation #4.
AI models can analyze historical data, market trends, and economic indicators to generate accurate forecasts. Accountants can utilize AI-powered forecasting tools to make informed financial decisions, plan strategically, and optimize cash flow and resource allocation.
For instance, by analyzing seasonal sales patterns, economic trends, and consumer behavior, an AI forecasting model could predict the upcoming quarter’s revenue with high accuracy, helping a company prepare for expected demand fluctuations.
Transformation #5.
With routine tasks automated, accountants have more time to focus on providing advisory and value-added services to clients. They can use their expertise and insights gained from AI-driven analysis to offer strategic financial guidance, optimize business processes, and support growth initiatives.
For example, accountants could use AI-driven insights to advise a client on tax-saving strategies based on predictive models of tax code changes or to recommend investment opportunities identified through trend analysis.
Transformation #6.
Accountants need to stay updated with changing regulations and industry practices. AI-based platforms can provide real-time access to the latest accounting standards, regulatory updates, and industry insights, enabling accountants to continuously enhance their knowledge and skills.
Example: An AI-powered learning platform could personalize content delivery to an accountant’s learning style and professional focus areas, ensuring they’re always ahead of new regulatory requirements and industry best practices.
Will AI replace accountants?
It’s a question on many minds. Is AI poised to snatch your job away? It’s another one-million question. While AI has definitely caused significant changes, it doesn’t spell the end of accountancy as we know it. Here’s the rationale:
Rationale #1: Sophisticated decision-making requires human insight and expertise
Accounting requires knowing complex business transactions, rules, and industry details. Despite AI’s advancements, it lacks the capacity for human intelligence, discernment, ethical considerations, and innovation—essential elements for handling complex accounting scenarios.
Rationale #2: Communication with customers & trust are paramount
Building and maintaining relationships with clients are central to the profession, as accountants often become trusted advisors. Skills like good communication, understanding clients’ financial goals and challenges, and effective communication skills are areas where AI falls short.
Rationale #3: Supervision & interpretation are key
Human accountants are crucial in helping businesses deal with financial challenges. Although AI can handle data processing and analysis, it struggles to accurately interpret results, provide context, or offer strategic financial advice consistently.
According to a survey by Moss Adams, one of the nation’s largest accounting, consulting, and wealth management firms, AI in accounting is going mainstream. The study reveals that a large majority of accountants believe the technology will enhance rather than eliminate jobs and benefit the profession overall, driving productivity and business growth. For these reasons and more, AI is ready to complement rather than replace accountants.
What can’t ChatGPT do in accounting?
While AI has made remarkable strides in automating repetitive and rule-based tasks and processing vast amounts of data at incredible speeds, there are distinct areas where the human touch remains indispensable. At this point, AI may encounter challenges or exhibit lower performance compared to human accountants fulfilling certain tasks. It highlights the unique strengths that human professionals bring to the field of accounting.
Back to our examples, while ChatGPT formally completed all the tasks, at some, an accounting professional is highly likely to outperform the artificial mind.
1. Identification of unusual or suspicious transactions
AI might not have the same deep understanding and intuition as human accountants, which could make it less capable of spotting suspicious transactions or potential fraud.
2. Cost allocation based on context
AI systems might find it difficult to completely understand the complexities and specific needs of a client’s business, which could result in mistakes when allocating costs.
3. Development of customized chart of accounts
While AI can create a basic chart of accounts, it might struggle to make a customized one that fits a client’s specific business or industry.
4. Drafting comprehensive financial statement notes
AI can generate basic financial statement notes. However, it can’t provide detailed explanations and give insights like human accountants can.
5. Analysis of financial ratios and provision of meaningful insights
While AI can perform the necessary computations for financial ratios, it may struggle to offer in-depth insights and recommendations based on those ratios, as human accountants are better equipped to provide valuable context and expert judgment.
It’s important to mention that human accountants are particularly skilled in these tasks because they can understand context, apply professional judgment, and offer customized advice based on their thorough understanding of a client’s unique situation.
Synder is a tool every accountant should know about
As we’ve explored, AI can significantly aid in simplifying accounting processes by offering support and guidance. However, it’s important to recognize that AI alone can’t undertake all accounting tasks on your behalf.
Luckily, there’s a solution that can bridge this gap effectively – Synder. Unlike AI, which simplifies processes without fully automating them, Synder offers a comprehensive solution for automating your accounting processes.
Creating an account and connecting your platforms allows you to tailor Synder’s settings to suit your specific needs. Synder excels in the seamless transfer of financial data between data sources—such as ecommerce platforms like Stripe, PayPal, and Shopify—and your accounting system, including QuickBooks Online, QuickBooks Desktop, Xero, or Synder Books.
The core advantage of using Synder lies in its ability to not just simplify but automate your accounting workflow. With just a few clicks, you can set up a system that manages your financial data efficiently, allowing you to enjoy the peace of mind and time to focus on strategic decisions.
How can Synder help you with your accounting?
1. No more manual entry
Synder automatically syncs sales, fees, refunds, and payouts from connected payment platforms into your accounting software.
Moreover, if your store uses supported payment gateways, connecting these to Synder will also yield automatic, detailed records of all financial transactions, including additional information from orders if it’s Shopify, for example.
2. Comprehensive data sync
The app ensures that every transaction detail, including customer information, items sold, taxes, shipping costs, and discounts, is accurately reflected in your accounting system.
3. Financial insights & reporting
Beyond just syncing transactions, Synder offers Business Insights, a feature that aggregates data across sales channels and payment gateways. It provides hourly updated insights into product performance, customer behavior, and key financial health indicators such as total sales and average order value.
Learn how to make smart business decisions using your data with our easy guide: “How to Get Business Insights from Data: Data Insights with Synder.”
4. Advanced features for data security
Synder incorporates features like duplicate detection and rollback options, giving store owners peace of mind by securing their accounting against errors and ensuring that any mistakes can be easily corrected.
5. Customization & configuration
After connecting your platforms to Synder, you can fine-tune settings to match their specific needs, including adjusting tax, item, or customer configurations.
The Smart Rules feature also helps fill in any gaps in data post-sync, such as applying classes or locations to transactions.
To tailor small business accounting to your unique requirements, delve into our article: “How to Add Classes in QuickBooks Online: Assign QuickBooks Online Classes Automatically Using Synder Smart Rules.”
6. Dive into your financial history
Ever wish you could easily look back at your financial data from the past three years? With Synder’s ‘Historical data import’ feature, you can. This is super handy for tax time or when you’re planning ahead, giving you a full, detailed view of your business finances over time.
Explore our related article, “Syncing Historical Data: A Feature Overview,” for an in-depth look at effective data management strategies.
7. No reconciliation headaches
Are you worried about matching your PayPal data with your bank account? Synder smooths out those wrinkles, making sure everything lines up perfectly. This allows you to focus more on growing your business and less on the nitty-gritty of numbers.
Similar read: Bank Reconciliation in Excel vs Reconcile with Synder: Manual Excel Bank Reconciliation vs Automated Bank Reconciliation
We could certainly spend more time highlighting the outstanding features of Synder, but experiencing it firsthand is far more engaging.
Therefore, we invite you to optimize your business processes and explore Synder features with a free trial. To gain more insights and tips, book your seat on the informative Weekly Public Demo offered by Synder.
AI trends in accounting in 2024
Trend #1: Streamlining data with AI
Handling raw data in spreadsheets is often seen as a necessary evil in accounting—a time-consuming task that can drain hours of productivity. AI has started changing the game by efficiently organizing, summarizing, and analyzing financial data.
AI tools are essentially acting as digital assistants, enabling accountants to quickly make sense of vast amounts of information. This shift saves valuable time and significantly reduces the margin for human error, allowing professionals to focus on more strategic tasks.
Trend #2: Predictive insights through AI
The power of AI to forecast financial trends and identify risks before they become apparent is truly groundbreaking. By analyzing complex datasets, AI algorithms can spot patterns that might go unnoticed by the human eye, offering predictive insights that are invaluable for financial planning and risk management.
This capability allows businesses to anticipate future challenges and opportunities, making informed decisions that can steer them toward greater stability and growth.
While standalone AI applications offer significant benefits, their true potential is unlocked when they’re integrated into the everyday software solutions used by accountants and finance professionals.
This integration means that advanced AI functionalities, like automated analytics and custom report generation, become part of the standard toolkit, accessible without needing to switch between applications. It represents a seamless blend of AI and traditional software, enhancing productivity and decision-making without disrupting familiar workflows.
Practical use of AI in accounting on the example of ChatGPT
At this point, I think that demonstration sometimes might be the best way to explain things. So we picked out some basic accounting tasks and asked ChatGPT (remember, it’s a language model that mimics human communication, so yes, you can literally ask it to do things for you) to solve them.
Let’s look at the results.
Example #1: Easier categorization & and tips when balancing the books
Traditionally, transaction categorization has been a labor-intensive task, prone to errors and inconsistencies. With the implementation of AI, ChatGPT can help accountants get a little closer to accurately categorized transactions and automated accounting.
By analyzing past transaction patterns and learning from them, ChatGPT can swiftly classify transactions into appropriate categories, reducing the need for manual intervention.
Moreover, balancing the books becomes more efficient with ChatGPT’s ability to detect discrepancies and suggest adjustments, ensuring the accuracy of financial statements.
1. Transaction categorization
ChatGPT was given a list of ten transactions and asked to properly categorize them.
Office rent payment of $2,000 Purchase of computer equipment for $1,500 Monthly subscription payment for software service: $300 Sales revenue of $5,000 Payment of employee salaries: $4,000 Electricity bill payment: $250 Internet service bill payment: $100 Payment for office supplies: $75 Received payment from a client: $3,500 Advertising expenses: $600
The AI tool categorized the provided transactions in the following way:
2. Detecting errors in the trial balance
ChatGPT was given an example of a trial balance where errors had sneaked in. So we asked it to find those errors.
ChatGPT found errors in the following accounts:
Accounts Payable;
Depreciation Expense;
Retained Earnings;
ChatGPT found errors in the following accounts:
Accounts Payable;
Depreciation Expense;
Retained Earnings.
Example #2: Simplifying tax liability calculations
Tax calculations can be difficult, with various regulations and frequent updates. ChatGPT can simplify this process by utilizing its vast knowledge and computational abilities. AI-powered tax software can leverage ChatGPT to ensure accurate and compliant tax calculations.
ChatGPT can accurately determine sales tax and other tax liabilities by incorporating tax laws, rules, and exemptions, minimizing errors and reducing non-compliance risk.
For example, we asked ChatGPT to calculate sales tax liability, providing it with the following list of transactions and tax rates:
Sale of $1,000 at a 5% tax rate;
Sale of $2,500 at a 7% tax rate;
Sale of $3,000 at a 4% tax rate;
Sale of $4,500 at a 6% tax rate;
Sale of $6,000 at a 5% tax rate.
And we got the following result:
Transaction 1: $50
Transaction 2: $175
Transaction 3: $120
Transaction 4: $270
Transaction 5: $300
Example #3: Accurate cash flow forecasting
Cash flow forecasting is essential for businesses to effectively manage their finances. ChatGPT can play a pivotal role in this process by analyzing historical cash flow data, market trends, and external factors. By using AI-driven models, ChatGPT can provide accurate cash flow predictions, enabling businesses to make informed decisions, plan strategically, and mitigate potential cash flow challenges.
We gave ChatGPT the task of forecasting cash flow based on the provided data as follows:
ChatGPT provided the following answer, breaking down the steps it took to come to a final result.
1. First, it calculated net cash flow for each month.
2. Next, ChatGPT determined the ending cash balances, assuming that the initial cash balance for this calculation would be 10,000.00. As a result, it forecasted the ending cash balance of $148,000.00 at the end of Month 3.
Example #4: Optimizing revenue recognition
Revenue recognition is a complex process that requires adherence to accounting standards and guidelines. ChatGPT can assist in optimizing revenue recognition by automating the identification and classification of revenue streams. By applying AI-enhanced revenue recognition solutions, ChatGPT can ensure compliance, enhance transparency, and minimize the risk of misstatements.
We asked ChatGPT to optimize revenue recognition for the following transactions:
Annual software subscription: $1,200;
Quarterly software subscription: $400;
Monthly software subscription: $50;
One-time software purchase: $2,000.
So, the tool came up with the following approach:
Annual software subscription: Recognize $100 per month for 12 months.
Quarterly software subscription: Recognize $133.33 per month for 3 months.
Monthly software subscription: Recognize $50 per month.
One-time software purchase: Recognize the full amount of $2,000 upon delivery.
We’ve already talked about some ways AI can help in accounting. But there’s more to it. Here are some other important things AI can do:
#1. Making sense of expenses and project
AI can organize and explain spending and project info well, allowing you to understand what’s going more easily.
#2. Spotting suspicious transactions
AI is good at noticing when something doesn’t look right with the money, which can help stop fraud.
#3. Helping make better financial decisions
AI can quickly look at financial numbers to help businesses see how they’re doing and decide what to do next.
#4. Finding ways to save money
By looking over financial details, AI can suggest smart ways to cut costs and be more efficient, helping the business save money.
Common concerns about AI in accounting
Before getting to the practical part and showing examples of how ChatGPT can perform various accounting functions, we’ll look at general concerns about AI in accounting. It might help understand some of the counter-AI voices and see what areas of AI accounting might need more of your attention.
A common belief regarding the use of AI in accounting is the concern that AI can’t effectively handle the complexities and nuances involved in accounting practices. Some of the reasons behind this belief include:
Concern #1: Lack of understanding
There may be a lack of understanding about the capabilities and potential of AI in the accounting field. Skepticism arises when individuals aren’t fully aware of AI capabilities in automating repetitive tasks, analyzing data, and providing valuable insights.
Concern #2: Human judgment & interpretation
Accounting involves professional judgment and interpretation of financial data, regulations, and accounting principles. Critics argue that AI can’t make subjective decisions and apply professional judgment in complex scenarios, while those are considered essential aspects of the accounting profession.
Concern #3: Data reliability & bias
AI systems heavily rely on data, and concerns may arise regarding the reliability and biases within the data used for training AI models. If the training data is incomplete, inaccurate, or biased, it can potentially lead to errors neous outcomes and financial misrepresentations.
Concern #4: Regulatory compliance
Specific regulations and must-follow standards govern accounting practices. Skeptics may question whether AI systems can adhere to these regulations accurately and consistently, particularly considering the dynamic nature of accounting rules and the need for ongoing compliance updates.
Concern #5: Human interaction & trust
Accounting often involves interactions with clients, stakeholders, and regulatory bodies. Some believe that relying solely on AI systems may diminish the personal touch and human-to-human communication valued in accounting. Building trust with clients and stakeholders may also be a concern when AI systems are making critical financial decisions.
AI in accounting: Wrapping up
AI has a lot of power to change how we do accounting for the better. Even though some people might worry about it, it’s clear that AI won’t take away the job of an accountant. How well AI solutions, like ChatGPT, work depends a lot on the information you give them.
However, AI won’t be able to fully replace accountants as they’re really important in making AI work the right way. They have the experience and skills to spot important details, understand what AI is telling them, and make smart choices based on that. To take full advantage of AI in accounting, accountants and AI must team up. This helps accountants improve their work, do more, and give great advice to clients as everything becomes digital.
And don’t forget about Synder. It’s a big help in automating accounting tasks. It sorts out your financial data and does the boring jobs for you so accountants can think about the bigger picture and make wise decisions. By combining Synder with AI, you can streamline your accounting work even more.
AI in accounting: People also ask
1. Is AI replacing accountants?
AI isn’t t replacing accountants but is transforming the accounting profession. It automates repetitive tasks, allowing accountants to focus on more strategic and analytical work.
2. Can AI solve accounting problems?
Yes, AI can solve various accounting problems, especially those involving data processing, analysis, and anomaly detection, by automating calculations and identifying discrepancies in financial data.
3. Which accounting software has the best AI?
The “best” AI in accounting software can vary based on specific needs, but platforms like QuickBooks, Xero, and Sage are known for integrating advanced AI features to streamline accounting tasks.
4. Is AI the future of accounting?
Yes, AI is increasingly seen as the future of accounting due to its ability to enhance efficiency, accuracy, and insights in financial management, shifting the role of accountants towards more advisory capacities.
5. Can you use ChatGPT for accounting?
ChatGPT can assist with general accounting inquiries, explanations, and educational content. However, it shouldn’t replace professional accounting software or advice for specific financial decisions.
6. What are the risks of AI in accounting?
Risks include data privacy concerns, the potential for errors in decision-making if AI is improperly trained, and reliance on AI, leading to a skills gap in traditional accounting practices.
7. Can ChatGPT analyze bank statements?
ChatGPT can’t directly analyze bank statements due to privacy and technical limitations. It can provide guidance on how to interpret and manage financial statements but can’t process personal financial documents.
| 2024-11-05T00:00:00 |
2024/11/05
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https://synder.com/blog/5-reasons-why-ai-wont-replace-humans/
|
[
{
"date": "2022/12/15",
"position": 9,
"query": "AI replacing workers"
}
] |
Google vs. ChatGPT: What happened when I swapped ...
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Google vs. ChatGPT: Here's what happened when I swapped services for a day
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https://www.cnbc.com
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[
"Sofia Blum"
] |
One of the claims (or concerns) around ChatGPT — and artificial intelligence in general — is that it will replace human workers. So I decided to see if ChatGPT ...
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OpenAI logo seen on screen with ChatGPT website displayed on mobile seen in this illustration in Brussels, Belgium, on December 12, 2022. Jonathan Raa | Nurphoto | Getty Images
You may have heard the recent buzz around ChatGPT, an artificial intelligence chatbot that was released to the public at the end of November. I've read about people using the service to write their school essays and I was curious as to how it could help me in my daily life. The technology was developed by OpenAI, a research company backed by Microsoft and others. ChatGPT automatically generates text based on written prompts in an advanced and creative way. It can even carry out a conversation that feels pretty close to one you'd have with a human being.
ChatGPT homepage.
This got me wondering -- is ChatGPT smart enough to change how we find information online? Could it someday replace Google and other search engines? Some Google employees are certainly worried about the possibility, At a company all-hands last week, CNBC's Jen Elias reported, employees recently asked execs if an AI-chatbot like ChatGPT was a "missed opportunity" for the company. Alphabet CEO Sundar Pichai and Jeff Dean, the long-time head of Google's AI division, responded by saying that the company has similar capabilities, but that the cost if something goes wrong would be greater because people have to trust the answers they get from Google. Morgan Stanley published a report on the topic on Monday, Dec. 12 examining whether ChatGPT is a threat to Google. Brian Nowak, the bank's lead analyst on Alphabet, wrote that language models could take market share "and disrupt Google's position as the entry point for people on the Internet." However, Nowak said the firm is still confident in Google's position because the company is continuing to improve search, and creating behavioral change is a huge hurdle -- a lot of internet users use Google as a habit. Additionally, Google is "building similar natural language models such as LaMDA" which could find their way into new products. For now, OpenAI's creators are cautious about making any big claims. Generally speaking, the more users employ ChatGPT, the better it gets. But it still has a lot to learn. OpenAI CEO Sam Altman said in a tweet on Dec. 10 that ChatGPT is "incredibly limited" and "it's a mistake to be relying on it for anything important right now." Either way, I wanted to see how well the chatbot would work as an alternative to Google's search engine. Instead of Googling my questions throughout the day, I asked ChatGPT. Here are some of the questions I asked and how ChatGPT responded compared with Google .
ChatGPT vs. Google
It's easy to sign up for ChatGPT — all you need is an email address. Once you've registered, the webpage is very simple to navigate. There's an area where your results will populate and a text box where you'll type your inquiries. OpenAI says to put in a statement for the best possible result. I recently purchased my second Fiddle Leaf Fern plant for my apartment because the first one died. Now the new one is dying after just a few days. I normally would have asked Google what to do. Instead, I asked ChatGPT. "How can I keep my Fiddle Leaf Fern plant alive?"
Zoom In Icon Arrows pointing outwards
The results lined up with the instructions I received from the plant company, Easy Plant, which is where I bought the new Fiddle Leaf Fern. I also liked that I didn't have to go to various different websites like I would have if I'd Googled this question. When I Googled the same question, the top result gave me detailed instructions in an article that included pop-up ads and a way more information than I needed, like links to buy new soil. Winner: ChatGPT. Next, I tried something where more shopping links would actually have been beneficial. I am still trying to find a present for all 8 nights of Hanukkah for my husband, so I decided to ask ChatGPT for some ideas.
Zoom In Icon Arrows pointing outwards
I didn't give it any information about my husband, besides the fact that the gifts were for Hanukkah. The chatbot then assumed that my husband is kosher and that he's into tech and watches. The latter isn't wrong, but it's not the answers I was hoping for. When I Googled this question I got access to hundreds of articles with different gift ideas and links to websites. For example I got ads to Lululemon and Bombas, which are both companies I've used to buy him gifts before. So in this case, Google used its personalized knowledge of me to find more targeted results from around the web. Winner: Google Next, I decided to ask ChatGPT a question with higher stakes. "Is it safe to take Benadryl while pregnant?" I am currently expecting my first child, and I know that taking Benadryl is safe in pregnancy based on information I've received from my doctor. Still, I wanted to see what the chatbot would say.
Zoom In Icon Arrows pointing outwards
I appreciated that ChatGPT gave me the correct answer, while also noting the importance of consulting a doctor before taking any medications during pregnancy. When I Googled the same medical question, the results were all over the place. At quick glance, one article from TheBump.com quoted a doctor saying "Generally, it's safe to take Benadryl in the first trimester." While other results said similar information to what I received on ChatGPT, without the explicit warning to discuss with my healthcare provider (unless clicking into a specific link). Winner: ChatGPT
Could it replace me?
One of the claims (or concerns) around ChatGPT — and artificial intelligence in general — is that it will replace human workers. So I decided to see if ChatGPT could write this article for me. The results were pretty astonishing. Here's what the chatbot said after I told it to "write an article about whether or not ChatGPT is a good alternative to Google as a search engine." As a large language model trained by OpenAI, I am not capable of conducting research or browsing the internet to gather information on ChatGPT or other search engines. I am only able to provide information based on what I have been trained on, which is limited to the knowledge that was available up until my training cutoff date of 2021. With that being said, it is worth considering the potential advantages and disadvantages of using ChatGPT as a search engine compared to other options, such as Google. One potential advantage of using ChatGPT as a search engine is that it is able to provide more personalized and conversational results. Because ChatGPT is a language model, it is able to understand and respond to natural language queries, allowing users to ask questions and receive answers in a more intuitive and human-like way. This can make the search experience more engaging and interactive, and may be especially useful for users who have specific or complex information needs. On the other hand, there are also potential limitations to using ChatGPT as a search engine. Because it is a large language model and not a traditional search engine, ChatGPT may not have access to the same breadth and depth of information as other search engines. This means that it may not be able to provide as comprehensive or up-to-date results, and may not be as effective for certain types of searches, such as those that require a large amount of data or real-time information. In conclusion, whether or not ChatGPT is a good alternative to Google as a search engine will depend on the specific needs and preferences of the user. It may be a useful option for those who value a more personalized and conversational search experience, but it may not be as comprehensive or effective for certain types of searches. Ultimately, the decision to use ChatGPT or another search engine will depend on the individual user's needs and preferences. What impressed me most about ChatGPT's response is that it was pretty subjective and explained its limitations.
The verdict
ChatGPT's value really lies in its ability to explain complicated topics as if you were talking to a human, and to do simple writing tasks. For example, I asked ChatGPT to explain concepts like the stock market and internet to me, and the responses were as if I was talking to an expert on the matter. Unlike the case on Google, I didn't have to filter through unnecessary results. I also asked it to write a letter to my landlord asking for an early end to my lease, and I'd be happy sending the results directly to my landlord, almost word for word. On the other hand, Google knows more about us and tailors the results to our interests and behaviors. Google also acts as a gateway to the internet, leading users to a plethora of different websites with more information than one could possibly digest. That's helpful if you want a range of voices, or if there's no single simple answer to your question -- like if you're looking for gift suggestions. Google is also great for certain types of questions where it scours the web to provide a brief but simple answer right in line. For instance, if you search "Apple stock ticker" or "Cheap flights to Aruba," it will show you a ticker chart with up-to-the-minute price info, or a calendar with the most likely cheapest days to fly and a dialog box that connects you to multiple web sites to shop for tickets on your chosen date. ChatGPT does not scan the internet for real-time information, and has only been trained on data through 2021, so it's totally useless on these kinds of queries. And sometimes, ChatGPT is strangely close yet totally wrong. My editor asked it for the lyrics to "The Ballad of Dwight Fry" by Alice Cooper. It somehow knew the song was about a man having a mental breakdown, but then returned completely invented lyrics about that subject, rather than the actual lyrics. Google nailed it. Google is also incredibly reliable, thanks to the company's massive operations budget and years of expertise. ChatGPT is still in testing and goes down from time to time. So I'll definitely continue using Google for most of my search queries for now. But if I'm not happy with the results, now I have a useful alternative. And if I ever need to dash off an angry letter, ChatGPT could be a huge help there.
| 2022-12-15T00:00:00 |
2022/12/15
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https://www.cnbc.com/2022/12/15/google-vs-chatgpt-what-happened-when-i-swapped-services-for-a-day.html
|
[
{
"date": "2022/12/15",
"position": 68,
"query": "AI replacing workers"
}
] |
How to Land a Job in Machine Learning or Data Science
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How to Land a Job in Machine Learning or Data Science
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https://www.deeplearning.ai
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[] |
AI can seem like a dream profession: Machine learning engineers and data scientists are in-demand and well paid. On top of that, many find the day-to-day work ...
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Insights From Expert Technical Recruiters for AI Jobseekers
AI can seem like a dream profession: Machine learning engineers and data scientists are in-demand and well paid. On top of that, many find the day-to-day work of building models, tuning algorithms, and analyzing data to be deeply satisfying. But the field is also competitive and fast-paced. Job seekers, especially those early in their careers, are often confused about the best way to get hired.
Have you struggled with:
Writing a resume?
Preparing for interviews?
Finding job listings that match your skills and interests?
Understanding why recruiters or hiring managers don’t respond?
Experts Share AI Hiring Insights
In December 2022, DeepLearning.AI gathered a group of experienced technical recruiters and asked them some of the biggest questions about landing a job in machine learning or data science.
Our guests:
Nikita Gupta is a senior technical recruiter at Uber. Previously she worked at Amazon and a number of startups.
Jeff Bank is a veteran talent attractor who has helped build engineering teams at Google, Microsoft, LinkedIn, Roblox, and more.
Puneet Kohli co-founded (with Nikita) CareerFlow.ai, which offers a free LinkedIn Optimization tool for job seekers. He previously built computer vision systems for Apple, Amazon, and other tech companies.
Linda Lee is a partner at the venture studio AI Fund, where she specializes in talent acquisition. She is also a board member of Factored AI, a startup that helps companies find skilled machine learning and data science workers.
What are the most common AI roles in today’s job market?
Puneet said that most AI roles fall into one of three major categories. He described them as follows:
Machine Learning Engineers : These software engineers are responsible for building and managing data pipelines as well as the infrastructure needed to train and deploy AI models.
: These software engineers are responsible for building and managing data pipelines as well as the infrastructure needed to train and deploy AI models. Machine Learning Practitioners : In this role, your goal is to build and/or improve the machine learning model itself. Responsibilities include developing model architectures, training models, adding data sources, and tuning parameters.
: In this role, your goal is to build and/or improve the machine learning model itself. Responsibilities include developing model architectures, training models, adding data sources, and tuning parameters. Data Scientists: These professionals use statistical and machine learning techniques to extract insights from data. They may also be involved in collecting and cleaning data, as well as visualizing and communicating the results of their analyses.
How to Write Resumes That Stand Out To AI Recruiters/Hiring Managers
Technical recruiters see, on average, about 200 resumes for each job listing, Nikita said. “Why should I select you? If your resume cannot answer that question, then you need to go back and make some changes,” she said.
How can you stand out? “There is no single golden point that applies to everyone,” said Puneet. “It comes down to what each person brings as an individual and whether I can see that clearly from their resume.”
Here are five ways to make your resume show your worth as a candidate.
Do your homework: You can show the recruiter that you pay attention to detail by tailoring your application to the role described in the job posting. “Don’t use the same resume and cover letter for every role,” said Nikita. Read the description carefully, paying extra attention to the responsibilities and qualifications. Then, go through your resume and emphasize any skills, experiences, and projects that match the company’s needs.
You can show the recruiter that you pay attention to detail by tailoring your application to the role described in the job posting. “Don’t use the same resume and cover letter for every role,” said Nikita. Read the description carefully, paying extra attention to the responsibilities and qualifications. Then, go through your resume and emphasize any skills, experiences, and projects that match the company’s needs. Substance matters: Don’t just list your skills. Describe in detail how you have used each of them, Jeff advised. This lets recruiters know you are an experienced practitioner and not just someone listing buzzwords to grab attention. If you don’t feel comfortable talking about a subject during a technical interview, it’s best to leave it off your resume.
Don’t just list your skills. Describe in detail how you have used each of them, Jeff advised. This lets recruiters know you are an experienced practitioner and not just someone listing buzzwords to grab attention. If you don’t feel comfortable talking about a subject during a technical interview, it’s best to leave it off your resume. Show your impact: Recruiters and hiring managers love to see results. Don’t just say you developed a recommendation model; say you developed a recommendation model that increased visitor rates by 20 percent, said Nikita. Data lets recruiters and hiring managers visualize your impact.
Recruiters and hiring managers love to see results. Don’t just say you developed a recommendation model; say you developed a recommendation model that increased visitor rates by 20 percent, said Nikita. Data lets recruiters and hiring managers visualize your impact. Provide links: Recruiters may want to check out all of your projects and past work. Nikita said she appreciates when candidates make it easy by adding clickable links to blog posts, Github repositories, or websites associated with projects listed on their resumes.
Recruiters may want to check out all of your projects and past work. Nikita said she appreciates when candidates make it easy by adding clickable links to blog posts, Github repositories, or websites associated with projects listed on their resumes. Do personal projects: “I look for people who are contributing to open-source projects, have side hustles, or are working on apps,” Jeff said. This demonstrates initiative and shows you are staying on top of advances in the field beyond the requirements for your core work responsibilities.
How to Know if You Are Qualified for an AI Job Posting
Have you ever felt confused by an AI job listing because it listed more skills than anyone could possibly have?
Don’t panic. No candidate is a perfect match for any role. “Go ahead and apply even if you are only a 60 or 70 percent match,” Nikita said. You can also fine-tune your application by asking the recruiter which skills are must-haves. Focus on those.
How to Reach Out Directly to Technical Recruiters
When applying for a job, it pays to be proactive. Connect directly with the recruiter ahead of time to ensure they notice your resume. Some job applications will list the recruiter’s contact information. Or, look for recruiters who work for companies that interest you by searching LinkedIn.
Here are some pointers for presenting your best self when you reach out:
Be clear. “Make sure your message is clear,” Nikita said. She suggests briefly listing the top three things that make you stand out along with a call to action. And don’t forget to attach your resume!
“Make sure your message is clear,” Nikita said. She suggests briefly listing the top three things that make you stand out along with a call to action. And don’t forget to attach your resume! Be personal . “It’s a turnoff when a candidate reaches out to everyone with the same canned message,” Linda said. You shouldn’t copy and paste your cover letter from one job to the next, and you shouldn’t do so with emails or LinkedIn messages either. Address each person you reach out to with a customized message.
. “It’s a turnoff when a candidate reaches out to everyone with the same canned message,” Linda said. You shouldn’t copy and paste your cover letter from one job to the next, and you shouldn’t do so with emails or LinkedIn messages either. Address each person you reach out to with a customized message. Be friendly. “Add a line or two sharing anything you have in common with the recruiter, the team, or the company,” says Puneet. For instance, did you go to the same college as the recruiter? Is the company’s founder from your hometown?
What if the recruiter initially responded to your application, but hasn’t followed up with your subsequent messages?
Be persistent. “If you have reached out to a recruiter and they have missed a window of time in getting back to you, try a different way of getting in touch with them,” Jeff added. If you emailed first, try a LinkedIn message next week. If LinkedIn doesn’t work, try a phone call.
“If you have reached out to a recruiter and they have missed a window of time in getting back to you, try a different way of getting in touch with them,” Jeff added. If you emailed first, try a LinkedIn message next week. If LinkedIn doesn’t work, try a phone call. …But don’t be a pest. Desperation stinks. “Don’t badger the recruiter multiple times during the same week,” Jeff added. This can be a turn-off for recruiters and may lead to them losing interest.
Desperation stinks. “Don’t badger the recruiter multiple times during the same week,” Jeff added. This can be a turn-off for recruiters and may lead to them losing interest. Don’t be negative. If the recruiter isn’t responding to your messages, you may be tempted to reach out to somebody else in the company — maybe even the hiring manager. This is okay, but be sure not to criticize the recruiter. “I’m not going to want to work with someone who is going to throw one of my current colleagues under the bus,” Linda said.
If the recruiter isn’t responding to your messages, you may be tempted to reach out to somebody else in the company — maybe even the hiring manager. This is okay, but be sure not to criticize the recruiter. “I’m not going to want to work with someone who is going to throw one of my current colleagues under the bus,” Linda said. Know your worth. Is the recruiter avoiding your calls and leaving your emails unopened? These could be clues that they are part of a toxic work environment. “If the recruiter has completely ghosted you, maybe that’s an indication that you do not want to be a part of this team or company,” said Jeff.
How to Supercharge Your AI Job Search With Inside Connections
One of the best ways to get ahead of the competition is through personal connections. Here are some tips for building and using a professional network.
Start with your existing network. “If you know other people who work at the company, use them to get information about the role,” Jeff suggested. “Ask them to put in a good word about you to the recruiter or hiring manager.”
Make new friends. If you don’t have any existing connections, use LinkedIn to find employees who work at the company. Don’t reach out randomly, however — Stuart in accounting isn’t going to be much help landing a machine learning role! Look for people who work on the team you are trying to join. “Send them a connection request with a personalized note that mentions your interest,” Puneet said. Once you have made a connection, ask them to pass your resume (along with a good word) to the hiring manager or recruiter.
Network in person. Attend AI events, tech talks, or open house nights in your area. Check your target company’s blog; it may be hosting one of its own. “Recruiters are much more likely to engage with your application if they have seen you in person at a networking event,” Jeff observed.
Think locally. If you have a job, but it’s not in AI, consider asking around to see if any teams or departments at your company are looking to staff up in machine learning or data science talent, Jeff said. Even if there aren’t open roles, you may have an opportunity to shadow engineers. This shows initiative and you are more likely to be remembered when a position opens up.
How to Prepare for a Technical Interview in Machine Learning or Data Science
If a hiring manager likes your resume, they may ask you to participate in a technical interview. Preparing for a technical interview is much like preparing for an exam. It pays to study and practice.
Study: The first thing to do after getting called back for an interview, Nikita said, is ask the recruiter or hiring manager what technical expertise the company generally expects new hires to have. Ask friends within the company about the role. They are likely to have an insider’s view of what skills the hiring manager is looking for.
Practice: Once you have an idea of which skills to focus on, practice coding. Puneet recommends registering with Leetcode, which has a web interface that lets you practice specific coding problems. Many companies use Leetcode or similar services like CodeSignal and HackerRank to test applicants’ skills. You can prepare ahead of time by reading FAQs for those services.
How to Prepare for a Verbal Interview in Machine Learning or Data Science
Hiring managers and team managers use the verbal interview to see if your personality and views on work will be a good fit for the team.
The best way to prepare for a verbal interview is to practice. “You can search Google for lists of common interview questions, then find a couple of friends and ask them to help you,” Linda said. Encourage friends to ask unscripted follow-up questions. Focus on communicating clearly and answering questions by drawing on your own experiences.
Puneet recommends Blind, a website where people post anonymously about their experiences with companies. Type the company’s name plus “interview” into the search bar, and you may find first-hand information about applying to that company.
How to deal with getting rejected from job applications
Have you ever been excited about a job prospect, only to feel crushed when it slips through your fingers? You aren’t alone. “Everyone goes through rejection,” Nikita said. “I have personally been rejected thousands of times!”
Take the effort to learn why you were rejected. “Was it your application? Your resume? Your LinkedIn profile? Your interview?” Nikita said. “If you can’t figure this out for yourself, ask somebody whose opinion you respect to help you figure out where you might be going wrong.” If you feel comfortable inviting this person out for coffee or tea, do so. You will get invaluable insights from the in-person interaction and ability to ask follow-up questions.
Once you have isolated the likely causes, focus on improving in those areas. “Think about it from a software perspective,” Jeff suggested. “Nobody’s code is perfect the first time. You have to keep iterating.”
Sometimes, however, a company rejects a candidate for something outside their control. “By the time you send in your application to a listing, the hiring manager may have already interviewed a person they really like,” Puneet noted. Or they might have decided to fill the role internally. In these cases, the recruiter and hiring manager aren’t as motivated to consider your resume.
“The most important thing is to not lose hope,” Puneet said. Nikita agreed: “You have to understand that you will get rejected. You can be sad for a little while, but don’t let it keep you down.”
What Will the AI Hiring Landscape Look Like in the Future?
The AI industry is evolving, and it’s tricky to predict the future. Still, Puneet described up-and-coming roles he expects to see big demand for.
ML Ops : This role streamlines the process of building, maintaining, and monitoring machine learning products.
: This role streamlines the process of building, maintaining, and monitoring machine learning products. Machine Learning Quality Assurance: This role tests AI-based software to ensure its output meets standards.
Annotation Engineers: These professionals label the data used to train and test machine learning models. They must understand how the data is to be used to ensure their annotations are relevant and accurate.
Conclusion
In summary, the secret to getting your AI dream job isn’t simply to load up your resume with buzzworthy skills. It also comes down to how you present yourself. Your application should be an honest reflection of your experiences. Don’t oversell yourself, but also don’t hold back when it comes to emphasizing all the cool things you have done (especially those that pertain to the job listing). Be proactive about reaching out to recruiters and get to know people inside the company. Be courteous. Above all, know that you will probably fail at least a few times. But if you are persistent about understanding why you fail, there is nothing that will stand between you and your dream job.
For more great AI career advice, watch the full video from “DeepLearning.AI’s Hiring Secrets from Technical Recruiters” event here:
| 2022-12-15T00:00:00 |
https://www.deeplearning.ai/blog/how-to-land-a-job-in-machine-learning-or-data-science/
|
[
{
"date": "2022/12/15",
"position": 1,
"query": "machine learning job market"
}
] |
|
FourthBrain Reviews
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FourthBrain Reviews
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https://www.coursereport.com
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[] |
Find FourthBrain Machine Learning Engineer bootcamp reviews, find FourthBrain AI bootcamp costs, start dates, FourthBrain Machine Learning curriculum ...
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IntroI just completed FourthBrain's MLE bootcamp with the 9th cohort to go through the program. There aren't really any reviews online, so I wanted to leave one.
TL;DR It's not worth $6k, and I wouldn't recommend the course to anyone. However, it's not a total scam, and I did get some value out of it.
Longer version The course is a 16-week bootcamp, which is done part time. The websi...
IntroI just completed FourthBrain's MLE bootcamp with the 9th cohort to go through the program. There aren't really any reviews online, so I wanted to leave one.
TL;DR It's not worth $6k, and I wouldn't recommend the course to anyone. However, it's not a total scam, and I did get some value out of it.
Longer version The course is a 16-week bootcamp, which is done part time. The website says that the program will do things like "future-proof your ML career with expert skills" and "become an end-to-end ML engineer", which I guess it sort of accomplishes. The website also claims that the average FB graduate increases their salary by $27k after completing the program. They don't cite or contextualize this number from what I can tell, and I don't believe they're members of CIRR, so I would be cautious with accepting this claim.
You definitely can learn things in the program. However, you can also not learn things, and they'll take your money and graduate you just the same. That's the crux of my issues with the program.
Pros I have worked in analytics for a long time, and done a lot of data-science adjacent work. This means that I had a good working knowledge of Python going into the program, and had done Kaggle competitions, etc. However, I had never actually built something. This program did result in me building an actual project. That's very cool, and I now feel much more confident exploring the software engineering space and building more projects.
There's also a decent (although not great) community. You get access to their jobs channel on Slack, which posts 1-10 jobs a month. Last post as of this writing is November 18th, but there's an end of the year slowdown of course. There are some other fun slack channels, like algorithm study. I don't think that part is worth the cost at all, but it is a nice feature.
There is a person dedicated to job hunting, and he has already scheduled a call for our cohort to help us prep for the job hunt, which is nice. I don't know what the quality will be yet, but it's nice that they make an effort.
Cons Unfortunately, this is a much bigger section.
The biggest con IMO is the lack of standards. When I applied last year, I had to take an algorithms test in Python, and answer several linear algebra questions. They have since changed the admission criteria to be simply submitting a sample of your work in Pandas. My cohort had people from both admission systems, and there was a very obvious difference in skill level. There were people who actually worked in software engineering, and doing great work in the class, and people with a business background who struggled with basic command line work.
I raised this concern to the Head of Product, and he said something like "this is a business, and we can't be too strict or we won't get enough customers". I'm paraphrasing, but that's pretty close as far as I recall. If we had just been doing solo work, this wouldn't have been a big deal, but there's a lot of collaboration in this program, so low performers really drag down the group. There's pair programming once per week, and a group capstone project, so it's quite a bit of extra work when you get those people in your group. Also, the bootcamps with the best reputations have the strictest admission standards, so I know it can be done.
The lack of standards goes much deeper than that. In prior cohorts, there was a project-based midterm which was required to graduate. With our cohort, they decided to make this optional. The other standards are that you're supposed to attend 70% of lectures, and complete 70% of assignments. They just didn't enforce that.
My capstone group had 3 people, including me. One of our members didn't write a single line of code for our capstone group, and hadn't even done half of his homework assignments, and he graduated just like me. I raised this concern to the Head of Product too, and told him I didn't want the other member to present with our group on demo day because he hadn't contributed anything, and he basically told me I didn't have a choice.
The real crux of my issue is that I don't feel like FourthBrain correctly advertises themselves. When I discussed the fact that a member of my group hadn't contributed, and that I knew that same person hadn't done the homework which was described as mandatory in the syllabus, the Head of Product said something like "it's more about what the student wants to get out of it". If that's the case, then fine. Many people might be okay with that, but explain to students before they sign up that they'll be in class and graduating with people who don't know anything. Let students decide if they want that on their resume, and think it's worth $6k. Some might. I wouldn't have. I told the Head of Product that they should be upfront about the lack of standards and he just gaslighted me and said "that's not true..." But it 100% is true, and they ended up graduating the one guy I know for sure (there might be others) didn't complete the stated requirements of the course.
The other main con is the instructors.
We had 4 instructors. 2 were amazing, and 2 were awful. All of the instructors work full time in industry, and teach part time. They pitch this as a benefit, because you get actual industry knowledge, and it sort of is, but it's more complex than that.
Because the instructors are all part time, a lot of stuff just doesn't get done. Our last 3 homework assignments were never graded, for instance. When the course first started they sent out weekly pre-reading, and weekly reviews, which was great. This just stopped the last 4 weeks.
The instructors are also often very distracted, and will either not answer questions at all, or give answers that indicate they never read the question fully. There were a few students who picked up the slack and answered a lot of questions, which was nice, and speaks to the good community.
The final issue I have is the curriculum. There were tons of errors in assignments. I asked many times for the instructors to try running code before giving it to us, and it never happened. Deprecated libraries, typos, poor logic, etc were in many notebooks. They tried to act like it was for our own good, and would say things like "this is what a programming job is like", which is partially true, but you could see in the repo that they would go fix errors when we brought them up, which indicates to me that they weren't intentional.
Wrap Up
So, overall, I don't think it was worth $6k, and I think they're more interested in getting people in the door than in making those people successful. I do think it's worth $3k maybe, and I did get value out of if. I just wanted to post my honest thoughts, so that people can make a more informed decision than I did.Whatever you choose, good luck on your journey.
| 2022-12-15T00:00:00 |
https://www.coursereport.com/schools/fourthbrain
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[
{
"date": "2022/12/15",
"position": 62,
"query": "machine learning job market"
}
] |
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Artificial Intelligence and Machine Learning based Image ...
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Artificial Intelligence and Machine Learning based Image Processing
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https://www.design-reuse.com
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... market value of USD 151,632.6 million by 2029. Image processing is used in a ... The description's job is to extract quantitative data that helps ...
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By V Srinivas Durga Prasad, Softnautics
Image processing is the process of converting an image to a digital format and then performing various operations on it to gather useful information. Artificial Intelligence (AI) and Machine Learning (ML) has had a huge influence on various fields of technology in recent years. Computer vision, the ability for computers to understand images and videos on their own, is one of the top trends in this industry. The popularity of computer vision is growing like never before and its application is spanning across industries like automobiles, consumer electronics, retail, manufacturing and many more. Image processing can be done in two ways: Physical photographs, printouts, and other hard copies of images being processed using analogue image processing and digital image processing is the use of computer algorithms to manipulate digital images. The input in both cases is an image. The output of analogue image processing is always an image. However, the output of digital image processing may be an image or information associated with that image, such as data on features, attributes, and bounding boxes. According to a report published by Data Bridge Market Research analyses, the Image processing systems market is expected to grow at a CAGR of 21.8% registering a market value of USD 151,632.6 million by 2029. Image processing is used in a variety of use cases today, including visualisation, pattern recognition, segmentation, image information extraction, classification, and many others.
Image processing working mechanism
Artificial intelligence and Machine Learning algorithms usually use a workflow to learn from data. Consider a generic model of a working algorithm for an Image Processing use case. To start, AI algorithms require a large amount of high-quality data to learn and predict highly accurate results. As a result, we must ensure that the images are well-processed, annotated, and generic for AIML image processing. This is where computer vision (CV) comes in; it is a field concerned with machines understanding image data. We can use CV to process, load, transform, and manipulate images to create an ideal dataset for the AI algorithm.
Let’s understand the workflow of a basic image processing system
An Overview of Image Processing System
Acquisition of image
The initial level begins with image pre-processing which uses a sensor to capture the image and transform it into a usable format.
Enhancement of image
Image enhancement is the technique of bringing out and emphasising specific interesting characteristics which are hidden in an image.
Restoration of image
Image restoration is the process of enhancing an image's look. Picture restoration, as opposed to image augmentation, is carried out utilising specific mathematical or probabilistic models.
Colour image processing
A variety of digital colour modelling approaches such as HSI (Hue-Saturation-Intensity), CMY (Cyan-Magenta-Yellow) and RGB (Red-Green-Blue) etc. are used in colour picture processing.
Compression and decompression of image
This enables adjustments to image resolution and size, whether for image reduction or restoration depending on the situation, without lowering image quality below a desirable level. Lossy and lossless compression techniques are the two main types of image file compression which are being employed in this stage.
Morphological processing
Digital images are processed depending on their shapes using an image processing technique known as morphological operations. The operations depend on the pixel values rather than their numerical values, and well suited for the processing of binary images. It aids in removing imperfections for structure of the image.
Segmentation, representation and description
The segmentation process divides a picture into segments, and each segment is represented and described in such a way that it can be processed further by a computer. The image's quality and regional characteristics are covered by representation. The description's job is to extract quantitative data that helps distinguish one class of items from another.
Recognition of image
A label is given to an object through recognition based on its description. Some of the often-employed algorithms in the process of recognising images include the Scale-invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), and the PCA (Principal Component Analysis).
Frameworks for AI image processing
Open CV
OpenCV is a well-known computer vision library that provides numerous algorithms and utilities to support the algorithms. The modules for object detection, machine learning, and image processing are only a few of the many that it includes. With the help of this programme, you may do picture processing tasks like data extraction, restoration, and compression.
TensorFlow
TensorFlow, created by Google, is one of the most well-known end-to-end machine learning programming frameworks for tackling the challenges of building and training a neural network to automatically locate and categorise images to a level of human perception. It offers functionalities like work on multiple parallel processors, cross platform, GPU configuration, support for a wide range of neural network algorithms, etc.
PyTorch
Intended to shorten the time it takes to get from a research prototype to commercial development, it includes features like a tool and library ecosystem, support for popular cloud platforms, a simple transition from development to production, distribution training, etc.
Caffe
It is a deep learning framework intended for image classification and segmentation. It has features like simple CPU and GPU switching, optimised model definition and configuration, computation utilising blobs, etc.
Applications
Machine vision
The ability of a computer to comprehend the world is known as machine vision. Digital signal processing and analogue-to-digital conversion are combined with one or more video cameras. The image data is transmitted to a robot controller or computer. This technology aids companies in improving automated processes through automated analysis. For instance, specialised machine vision image processing methods can frequently sort parts more efficiently when tactile methods are insufficient for robotic systems to sort through various shapes and sizes of parts. These methods use very specific algorithms that consider the parameters of the colours or greyscale values in the image to accurately define outlines or sizing for an object.
Pattern recognition
The technique of identifying patterns with the aid of a machine learning system is called pattern recognition. The classification of data generally takes place based on previously acquired knowledge or statistical data extrapolated from patterns and/or their representation. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognise changes in patterns. Pattern recognition is utilised in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc.
Digital video processing
A video is nothing more than just a series of images that move quickly. The number of frames or photos per minute and the calibre of each frame employed determine the video's quality. Noise reduction, detail improvement, motion detection, frame rate conversion, aspect ratio conversion, colour space conversion, etc. are all aspects of video processing. Televisions, VCRs, DVD players, video codecs, and other devices all use video processing techniques.
Transmission and encoding
Today, thanks to technological advancements, we can instantly view live CCTV footage or video feeds from anywhere in the world. This indicates that image transmission and encoding have both advanced significantly. Progressive image transmission is a technique of encoding and decoding digital information representing an image in a way that the image's main features, like outlines, can be presented at low resolution initially and then refined to greater resolutions. An image is encoded by an electronic analogue to multiple scans of the exact image at different resolutions in progressive transmission. Progressive image decoding results in a preliminary approximate reconstruction of the image, followed by successively better images whose adherence is gradually built up from succeeding scan results at the receiver side. Additionally, image compression reduces the amount of data needed to describe a digital image by eliminating extra data, ensuring that the image processing is finished and that it is suitable for transmission.
Image sharpening and restoration
Here, the terms "image sharpening" and "restoration" refer to the processes used to enhance or edit photographs taken with a modern camera to produce desired results. Zooming, blurring, sharpening, converting from grayscale to colour, identifying edges and vice versa, image retrieval, and image recognition are included. Recovering lost resolution and reducing noise are the goals of picture restoration techniques. Either the frequency domain or the image domain is used for image processing techniques. Deconvolution, which is carried out in the frequency domain, is the easiest and most used technique for image restoration.
Image processing can be employed to enhance an image's quality, remove unwanted artefacts from an image, or even create new images completely from scratch. Nowadays, image processing is one of the fastest-growing technologies, and it has a huge potential for future wide adoption in areas such as video and 3D graphics, statistical image processing, recognising, and tracking people and things, diagnosing medical conditions, PCB inspection, robotic guidance and control, and automatic driving in all modes of transportation.
At Softnautics, we help industries to design Vision based AI solutions such as image classification & tagging, visual content analysis, object tracking, identification, anomaly detection, face detection and pattern recognition. Our team of experts have experience in developing vision solutions based on Optical Character Recognition, NLP, Text Analytics, Cognitive Computing, etc. involving various FPGA platforms.
Author: V Srinivas Durga Prasad
Srinivas is a Marketing professional at Softnautics working on techno-commercial write-ups, marketing research and trend analysis. He is a marketing enthusiast with 7+ years of experience belonging to diversified industries. He loves to travel and is fond of adventures.
| 2022-12-15T00:00:00 |
https://www.design-reuse.com/article/61392-artificial-intelligence-and-machine-learning-based-image-processing/
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"date": "2022/12/15",
"position": 70,
"query": "machine learning job market"
}
] |
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Careers
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https://www.infocusp.com
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Data Annotator · Technical Project Manager - MLE · Senior AI Engineer · Machine Learning Engineer · Senior LLM Engineer · Senior Data Scientist · Senior Machine ...
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Health & Wellness
Employees are invaluable assets of any company and we know that by heart. We believe in inspiring and lifting each other up! Whether it’s a mentor, manager, or someone making a significant impact, maintaining health is always at the Forefront. Happiness is free snacks + freedom to work = Life at Infocusp!
| 2022-12-15T00:00:00 |
https://www.infocusp.com/careers
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[
{
"date": "2022/12/15",
"position": 72,
"query": "machine learning job market"
}
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Top Machine Learning Projects to Build Your Skills
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Top Machine Learning Projects to Build Your Skills
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https://www.labellerr.com
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"Dec"
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Your chances of landing a job will increase as you work with different data kinds. ... However, in the high-risk stock market, even a 1% difference ...
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You have been learning programming languages, honing your machine-learning skills, and delving into the details of data points. Additionally, rather than just studying machine learning models, you are interested in creating your own.
Machine learning (ML) projects provide you a chance to put your newly acquired skills to use while also offering you something to include in your portfolio. As a consequence, they not only aid in your understanding of machine learning and data science but also enable you to show potential employers your true capabilities.
Here are a few machine learning project ideas to get you started that are appropriate for both beginners and more experienced ML students.
1. Automatic captioning of images
The project to enhance your skill must have to be automatic image captioning. You will gain knowledge of LSTM for natural language processing, CNN pre-trained models and computer vision. In the conclusion, you will create the application on Gradio or Streamlight to present your findings. A brief description of the image will be produced by the image caption generator.
To predict captions in several languages, you can build your deep-learning architecture or locate numerous projects that are similar online. The portfolio project's main goal is to tackle a special issue. The model architecture may be the same, but the dataset may differ. Your chances of landing a job will increase as you work with different data kinds.
2. Anticipated sales
How will a company's future sales be impacted by changing seasons, changing demographics, or governmental regulations?
The popular business technique of sales forecasting, which involves estimating the number of goods or services that a company will provide in the future based on pertinent historical data, is supported by questions like these. The use of machine learning by corporations to develop models that can predict sales with ever-increasing accuracy over earlier, less technologically advanced methods is therefore not surprising.
You will practice sales forecasting under this machine learning project using actual sales data given by Walmart. Your task is to forecast department-wide revenue for 45 Walmart locations spread across several geographies, while also accounting for significant seasonal discount times like Thanksgiving, Christmas, and the three major holidays (Labor Day, Thanksgiving, and Christmas).
3. Deep Learning Stock Price Forecasting
The popular project idea of forecasting using deep learning will teach you a lot about the analysis of time series data, data management, pre-processing, and neural networks in terms of time consumption.
The forecasting of time series is not easy. Understanding seasonality, holiday seasons, patterns, and everyday volatility is important. Simple linear regression may usually give you the best-performing model without the need for neural networks. However, in the high-risk stock market, even a 1% difference represents millions of dollars in the company's profits.
All about deep learning models that you should know: read here
4. Create a recommendation system
Everyone has been in a situation where they are unclear about what to watch on a streaming site with an unlimited selection of videos. Do you watch that cheesy romantic comedy that is obviously from the early aughts or the futuristic anime series? Or perhaps you might watch the atmospheric noir from the 1940s?
Online platforms use sophisticated machine learning methods to generate personalized suggestions for consumers since they are mindful of the analysis paralysis that may be brought on by an abundance of possibilities. In reality, many of the most well-liked services available today, including Google, Netflix, and Xbox's Gamepass service, are based on recommendation systems.
5. Autonomous vehicle project
An advantage throughout the employment process comes from having a Reinforcement Learning project on your portfolio. The hiring manager will presume that you have a knack for solving problems and that you are willing to learn more about challenging machine-learning projects.
You will develop the Proximal Policy Optimization (PPO) model for the Self-Driving automobile project in the OpenAI Gym setting (CarRacing-v0).
You should familiarise yourself with the basics of reinforcement learning before beginning the project because it differs significantly from many other tasks involving machine learning. You will test out multiple models and approaches throughout the project to enhance agent performance.
6. AI bot that can converse
Hugging Face TransformersAI for conversation is an enjoyable project. You will get knowledge of Facebook Blender Bot, processing conversational data, and developing chatbot user interfaces (API or Web App).
Hugging Face offers a vast library of pre-trained models and datasets, so you can essentially fine-tune the model on a new dataset. Your favorite movie character, a conversation between Rick and Morty, or a famous person you adore might all be considered.
In addition, you can modify the chatbot to suit your particular use case. Should a medical application arise. The chatbot understands the patient's emotions and requires technological knowledge.
7. Estimate home prices
Among the most significant and expensive life milestones is frequently purchasing a home. As a result, the housing and real estate sectors of the US (and global) industry are a few of the most important.
Although there are many other factors to consider when buying a home outside its financial value, many buyers want to know whether a certain house will be a wise investment over the long run. How much may your house be worth, for instance, if you were to sell it right now or after renovations?
Given the abundance of available public real estate data, housing price forecasting is an obvious application for machine learning. By creating an end-to-end machine learning system using Tensorflow 1. x and the AI Platform, you will learn how to apply machine learning to forecast housing prices in this self-guided lab from Google Cloud Training. To apply your knowledge to new projects, you'll also learn how to use the web for distributed learning and online prediction in general.
8. ML Project that can Identify Emotions
The face is a source of emotion, as painters, sculptors, and performers have known for thousands of years. The ancient sculptor who made the well-known statue Laocoon and his Sons employed twisted expressions on his victims' faces to represent their misery as they are attacked by snakes, in contrast to players in traditional Japanese Noh theatre who use light and shadow to depict smiles and frowns on otherwise immutable masks.
Thus, the face and its emotions provide yet another source of information that many humans frequently understand intuitively but that machines do not. However, the key features of faces that change as an expression do give machine learning models the chance to recognize at least some emotions. Work on a machine learning project that helps identify emotions.
Here are some more machine-learning ideas: Read here
Conclusion
There are numerous uses for machine learning, which is a burgeoning area. Building projects are the best ways to learn successfully, regardless of whether you are just commencing out or are already well familiar with the field. Work on machine learning projects to build your portfolio and hone your skills. With these fun projects, you can put your skills to the test and get ready for a career path as a machine learning specialist.
Find this blog informative, then read more such interesting blogs here!
| 2022-12-15T00:00:00 |
2022/12/15
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https://www.labellerr.com/blog/top-machine-learning-projects-to-build-your-skills/
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[
{
"date": "2022/12/15",
"position": 96,
"query": "machine learning job market"
}
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The Future of AI-generated Art: DALL.E 2 & Midjourney
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The Future of AI-generated Art: DALL.E 2 & Midjourney
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https://www.adtrak.co.uk
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However, AIs are actually being developed for many purposes including healthcare, education, and even agriculture. One of the biggest uses of AI today is in ...
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What is AI?
AI stands for Artificial Intelligence and is a computer program that is designed to mimic human intelligence. AI is not just about computers, but rather a way of thinking. AIs have been around since the beginning of time, but they were always considered machines. Nowadays, we consider them as a type of software.
How does AI work?
AIs use artificial neural networks (ANN) to learn. ANNs are a group of algorithms that mimic how our brains work. Our brain is composed of billions of neurons that communicate with each other via electrical signals. Our neurons receive information from their neighbours and then pass it along to others. In a similar manner, AIs also do this. When they receive information, they compare it to what they already know and store it away if it matches. If it doesn't match, they add it to their knowledge base. Over time, this creates a network of knowledge that can be applied to different situations.
Why should I care about AI?
You might think that AIs are only useful for robots or games. However, AIs are actually being developed for many purposes including healthcare, education, and even agriculture. One of the biggest uses of AI today is in farming. Farmers can now use AI to help them make decisions about their crops. AI was also used to help create the Moderna vaccine during the COVID-19 pandemic. After multiple simulations in just two days, the AI provided the best solution for the pharmaceutical company to go forwards.
Are there any downsides to AI?
Of course! Just like anything else, there are pros and cons to using AI. Some of the downsides include data privacy and security issues. Also, AIs are not perfect. Sometimes they get things wrong.
AI-based Art
Artificial intelligence (AI) is a branch of computer science that focuses on building intelligent machines that work and react as humans do. In the field of artificial intelligence, machine learning is a subfield of AI where computers program themselves based on experience. Machine learning is a type of AI where a computer system learns without being explicitly programmed.
Deep Learning
Deep learning is a subset of machine learning that involves training neural networks, especially deep neural networks. A neural network is a mathematical model that mimics how neurons in the brain interact. Artificial neural networks are computational models that mimic certain features of the human brain. Neural networks have been successfully applied to speech recognition, handwriting analysis, object recognition, translation, bioinformatics, natural language processing, robotics, autonomous vehicles, game playing, stock market prediction, and many other tasks.
DALL.E 2: OpenAI
Have you ever seen these art pieces before? Do you recognise the style? Would you be surprised if I told you that I created them through artificial intelligence, in about 30 seconds…
A powerful new form of artificial intelligence has burst onto the scene and taken the internet by storm over the past few months: text-to-image AI.
Programmes such as DALL.E 2 and Midjourney are at the forefront of this budding technology, and are developing at an incredibly, and somewhat scary, fast pace. They’re built by pulling millions of images from the web, teaching algorithms to recognise patterns and relationships in the images and to then generate new ones in the same style. This means that the more images that are uploaded online, including these AI creations, the more the programmes learn and get better.
Watch DALLE.E 2 Explained: https://www.youtube.com/watch?v=qTgPSKKjfVg
Here are a couple of examples from my own DALL.E 2 account to show the variety of compositions it can produce…
Using the prompt “an impressionist oil painting of deer running through a forest”
Using the prompt “sliced orange in a blue studio set”
Using the prompt “a sunlit indoor lounge area with a pool with clear water, next to a big window, with a landscape view, digital art”
How is DALL.E 2 being used commercially?
Though this trend has predominantly been used as a hobby by amateur creatives, we’re slowly seeing it move into the commercial world, with companies using these programmes to promote their products.
In July, Heinz released an advert showing that when they put the word ‘ketchup’ into an AI art generator, it always depicted a bottle in the shape of a Heinz product. On their YouTube channel they said, “with new artificial intelligence text-to-image programs taking over the internet, we wanted to find out what A.I. thinks “ketchup” looks like. So we used A.I. to generate images of ketchup on DALL-E 2. The result? Just like humans, A.I. prefers Heinz.”
(https://www.creativebloq.com/news/heinz-ai-draw-ketchup)
Though many people have debunked this, stating the programme doesn’t actually produce bottles of Heinz, it goes to show how popular this technology is becoming and how it can be used to target younger generations. “With A.I. imagery dominating news and social feeds, we saw a natural opportunity to extend our 'Draw Ketchup' campaign—rooted in the insight that Heinz is synonymous with the word ketchup—to test this theory in the A.I. space," says Jacqueline Chao, senior brand and communications manager at Heinz.
Watch the video here: https://www.youtube.com/watch?v=LFmpVy6eGXs
DALL.E 2 was even used to create the first-ever magazine cover generated by AI. The cover was used for Cosmopolitan, an American fashion and entertainment magazine for women. The project was made collaboratively by Cosmopolitan editors, OpenAI's workers, and Digital Artist Karen X. Cheng. After hours and hours of trying to get the perfect image, they ended up using the prompt "wide-angle shot from below of a female astronaut with an athletic feminine body walking with swagger toward the camera on Mars in an infinite universe, synth-wave digital art”.
(https://www.cosmopolitan.com/lifestyle/a40314356/dall-e-2-artificial-intelligence-cover/)
Though many people fear creatives may be replaced by AI technology in the future, Cheng notes “it’s not just typing in a few words and BAM magically you have the perfect image. For something like this, there was a TON of human involvement and decision making.”
Posting the cover to her Instagram page, Cheng also notes that “women are underrepresented in the field of AI. Cosmo is an opportunity to get AI in front of women who never would have known about it otherwise. This is a field that will be responsible for so much of the infrastructure on which the future is built, so we need to make sure women are part of it.
Copyright controversy
When this conversation turns to debate surrounding what it means for the future of creatives, most assume the discussion surrounds whether it will replace artists. But a growing issue surrounds what it means for current artists and their work. The open-source programs are built by collecting images from the internet, often without permission and proper attribution to artists. As a result, they are raising tricky questions about ethics and copyright. Concerns over this have caused many photography agencies to remove images created by artificial intelligence models from their databases.
Greg Rutkowski, a Polish artist well-known for his fantasy style art, features as a prompt thousands of times in the Discord of the text-to-image generator, Midjourney. Rutkowski says he doesn’t blame people who use his name as a prompt. For them, “it’s a cool experiment,” he says. “But for me and many other artists, it’s starting to look like a threat to our careers.”
Currently, artists don’t have the choice to opt into the database or have their work removed from it.
So, what does this mean for the future of creatives?
There’s no doubt that AI generators will inevitably evolve into something much bigger in the future, but whether it’s for the better or for the worse remains to be seen.
Personally, I don’t see this as a threat to the creative industry, but as a new and exciting tech tool we can use to explore new creative territories.
Midjourney: Art Created by Artificial Intelligence
The main difference between DALL.E 2 to Midjourney, is that the latter is connected to the server ‘Discord’, where a community of people already collaborate on their creations. This gives Midjourney a bigger social aspect for users to share ideas with each other.
To start using the ‘Midjourney Beta’ you will first need to set up a Discord account, this is where you will be able to give text prompts to the ‘Midjourney bot’.
Once you have signed up for a free account (other options are available) on the Discord website, you can use the application in a web browser or download the app for Windows, Mac, Linux, Android, and iPhone.
Once you’re all set up with Discord, you can then visit the Midjourney website and select “Join the Beta.” this will take you to Discord, where you can select “Accept Invite.”
(SOURCE: The Economist featured an image created with Midjourney on its June 2022 issue cover: Twitter Link)
We have listed some research links below for a full in-depth guide on how to use ‘Midjourney Beta’ so you can begin your own creative AI-assisted journey:
Website: https://www.midjourney.com/home
How is Midjourney being used commercially?
Listed below are some of my favourite artists looking at using Midjourney for commercial marketing solutions, although most of these designs shown are unofficial creations.
Nike x Midjourney By Alejo Bergmann
Mid Journey Ai Art Collection By Jeff Han
Via Richard Rosenman Creative Director
The fast-fashion industry is becoming a serious environmental issue, particularly as brands continue to push their products out via micro-influencers (young teens who don’t get paid for brand promotion as they’re building their own audience which they can monetise at a later date). With this in mind more and more legitimate brands are trying to find new and inventive ways of standing out from the crowd on social media platforms.
Having the availability to deliver an eye-catching, yet familiar image, that draws the attention more than most due to something not being quite right is a bit of a game changer. Especially when it comes to capturing an audience who's more than likely scrolling very quickly on a mobile device.
How is AI Working with Architecture?
Design generated in Midjourney AI by @nai9.art - “Relax and unwind from the cares of the world in this relaxing space that imitates waves sounds to relax you”.
Mood Boards for Game Design
Arnaud Atchimon on Linkedin - “I've been using it on Dreamstudio to build the atmosphere of my fictional video game Dynamo, and the changes are genuinely perceptible.
Prompts used: {heavy rain} {night} sustainable city roads || african city cyberpunk 2 v motorcycle} by marc newsom , {sunrays}, canon eos c 300, ƒ 1.8, 35 mm, 8k, medium - format print,
Then added words like: {fog, heavy fog, sunrays, rain, drizzle, heavy rain} - {electric} - {car, race car, motorcycle, bicycle}
As a part of the Midjourney beta, users who are currently participating will receive approximately 25 free images. After that, you will be required to pay either $10 per month for 200 images per month or $30 per month for a standard membership, which grants unlimited access to the service.
Midjourney User Manual / Quick Start
How Midjourney is Being Used Alongside Architectures 3D Design Tools
“Arturo Tedeschi is an Italian architect and computational designer internationally renowned for his sculptural and visionary approach combined with research on advanced design methods, materials and fabrication technologies. His use of digital technologies blurs the line between disciplines and emphasises the semantic and emotional values of objects which engage all the senses”.
Architect & Computational Designer - Arturo Tedeschi | Instagram | Linkedin
When professional designers are using the tools of their trade, the only limitations to their creativity are their own imaginations, plus the laws of physics. With programmes like Midjourney, architects should be able to push the boundaries of the landscape even further, with no limitations on the initial concepts, then after introducing the software of choice for ironing out any problems with any physical aspects of the buildings, we should be left with some highly imaginative buildings and landscapes designs going forwards.
AI Portrait Images
Brain Castleforte Freelance Motionographer - Founder / Creative Director at Castleforte Group
For my very first attempt, I focused on one of my clients in the windscreen repair and replacement services sector.
Plus, another creation below I made in my spare time, the final character is the upscaled version chosen from image one of the four above.
Prompts: female android wearing body armour "white ceramic shell", holding a sword, unreal engine, 4k. The final product came out great considering I've used minimal prompts and only included one prompt taken from elsewhere (white ceramic shell). As well as this, it was also my final free image credit just as I was about to hit the upscale button!
Time to either take up a paid subscription? Going forwards I will make most images full length, include background prompts to set the scenery, and then continue to build the picture from there. If needed I could even import the image into Photoshop for final touches.
Another sector I will be looking at in my personal time outside of robotics and androids is architectural design and possibly transport design.
Conclusion
I have seen a lot of Midjourney images for portraits like the ones created by Brain Castleforte shown previously in the images above. I presumed this would crossover into the NFT space just as thousands of people were looking for digital artists to create thousands of images for them to monetise. However, with that bubble seemingly bursting, it will still be interesting to see how this will all play out with the introduction of the Metaverse and Web 3.0 just around the corner from the mass market to take any financial opportunities they can by using decentralised applications.
Sources:
Midjourney Website: https://www.midjourney.com/home
The Economist (AI magazine cover)
Useful Links:
Dimitris Katsafouros - Using The Same Prompts in Midjourney & Dalle 2. Which one's better?
| 2022-12-15T00:00:00 |
https://www.adtrak.co.uk/blog/the-future-of-ai-generated-art-dall-e-2-midjourney
|
[
{
"date": "2022/12/15",
"position": 93,
"query": "future of work AI"
},
{
"date": "2022/12/15",
"position": 21,
"query": "AI graphic design"
}
] |
|
Using artificial intelligence to promote diversity & inclusion
|
Using artificial intelligence to promote diversity & inclusion
|
https://www.information-age.com
|
[
"Nick Martindale",
"More Nick Martindale"
] |
Artificial intelligence that helps promote diversity & inclusion can help remove unconscious bias when recruiting to fill tech positions.
|
Artificial intelligence can help remove unconscious bias when recruiting to fill tech positions. But can it be a double-edged sword for tech leaders when it comes to promoting diversity & inclusion?
The use of artificial intelligence (AI) is growing rapidly, infiltrating areas of business which have traditionally required humans to undertake what are often low-level tasks. With this comes the potential for artificial intelligence to help improve diversity & inclusion, using algorithms to arrive at decisions based around objective facts or statistics rather than subjectivity or bias.
One obvious area is in the recruitment space, where AI has the potential to help organisations hire the best candidates, regardless of their background, or to improve representation of particular groups.
‘Just 26% of those working in artificial intelligence are women’
“When used appropriately and kept in check by diverse teams that can spot biases creeping in, AI can lead to much more objective decision-making around new hires,” says Jill Stelfox, executive chair and CEO of software firm Panzura.
“When a human scans a resume, they’re far more likely to give disproportionate weighting to things like gender, age, ethnicity, or where a candidate is from. AI has the potential to solve this problem, instead selecting candidates based on experience, qualifications, and other more objective factors.”
This can also help ensure people are paid fairly, suggests Marie Angselius-Schönbeck, chief of impact and corporate communications officer at conversational AI firm Artificial Solutions.
“If systems were taught to ignore data relating to gender, race or sexual orientation, instead looking neutrally at the education, skills and experience of a candidate and comparing that to existing employees across all demographics, it may help to reduce the diversity pay gap for new employees,” she says.
What is diversity in artificial intelligence?
AI can also be used to promote diversity in the workplace more generally. “It can be an important tool for ensuring that your workforce processes include a diversity and inclusion lens, as it lays the foundation for skill-based workforce decisions that ultimately level the playing field for everyone,” says Jeroen Van Hautte, co-founder and chief technology officer at AI-based strategic workforce planning firm TechWolf.
“If workforce decisions are being made purely based on the skills you have, you cannot be excluded, either consciously or unconsciously, from recruitment, promotions or other career opportunities.”
This could lead to AI systems suggesting someone might be a good fit for a particular role, he adds, moving internal promotions away from gut feel or a reliance on who people know.
The use of AI can also warn businesses of the potential to make poor decisions.
“For instance, AI-powered tools can be used to identify biased language in business meetings, or to ensure that goal-setting is fair and equitable, based on the performance of previous employees inhabiting a given role,” suggests Stelfox. “Bias is insidious, and can impact all aspects of management and productivity, and AI can help to address this and ensure that no employee is treated unfairly.”
How does AI enable diversity & inclusion?
Over time, this can start to change thinking and company culture, believes Angselius-Schönbeck. “Leveraging the nudge theory, a conversational artificial intelligence assistant could help businesses promote diversity and inclusion by asking questions and offering prompts that encourage users to consider whether they have accounted for different groups or scenarios as part of their decision-making process,” she says. “Accumulative nudges support the development of new habits. And in this instance, it might help prevent unconscious bias from creeping into decisions.”
Yet while AI has the potential to help organisations in different ways, its use is also fraught with risk. One issue is the lack of diversity in the AI industry itself. The World Economic Forum’s Global Gender Gap Report 2020 revealed that just 26 per cent of those working in the sector are women, while the BCS Insights 2021 Report suggests just 15 per cent of people in the tech sector as a whole are from ethnic minorities, and only 10 per cent have a disability.
Angselius-Schönbeck believes a lack of diversity in the AI space can have three negative effects on its design and use. “Firstly, the experience of AI is then not representative,” she says. “For example, a conversational AI trained solely by men will not be reflective of women’s conversational styles. Secondly, it risks the introduction of biases that might negatively impact certain groups. And finally, ultimately it means that the AI is flawed. Whether built for consumer or enterprise use, a lack of diversity on AI means that it does not perform as well as comparable solutions which prioritise a more inclusive design process.”
Can AI be used to promote diversity in the workplace?
Aniela Unguresan, founder of EDGE Certified Foundation – the assessment methodology and business certification standard for gender equality – believes that while AI can help identify and reduce the impact of human biases, “it can also make the problem worse by baking in and deploying biases at scale in sensitive application areas”.
“There is a solution,” says Unguresan. “Organisations can hire diverse people to devise correct processes, which are overseen by a chief diversity officer who checks software that is in development for bias and creates applications and processes that remove bias.”
Guidelines and frameworks are also needed to ensure that AI is developed for diverse communities, says Anton Nazarkin, global development director at AI-powered facial recognition provider VisionLabs. “Creators of AI technology can undertake discussions with working groups from diverse communities, allowing the impact of AI technology to be truly understood,” he says. “Organisations can also use tools to ensure that data sets meet a minimum level of diversity. Only through a combination of these approaches can we begin to remove the bias from AI.”
There’s also a risk that AI may be designed to help solve a particular problem but then not be updated to ensure it does not then go too far the other way.
“AI will operate based on the rules that it is trained upon,” points out Angselius-Schönbeck. “For example, if a company wants to improve the diversity of its talent, then an algorithm can be trained to prioritise certain genders or ethnic groups. However, unless it is defined to support a company with a specific quotas target, such an implementation may still not be appropriate. As the AI self-learns to ‘un-prioritise’ certain candidates, it may never re-learn to appraise them ‘fairly’ when there is no longer a need to prioritise certain groups.”
A double-edged sword
For now, it seems as if the use of artificial intelligence to encourage diversity will remain something of a double-edged sword. “We should be cautious about encouraging more widespread AI adoption unless there is both explainability and accountability,” claims Jesse Shanahan, chief technology officer at digital fitness start-up Another Round. “There is already a common problem of data and models being manipulated to justify a particular outcome. Ethical AI should play more of a supportive role, but never replace the human decision-maker.”
More on diversity & inclusion
Why diversity matters when recruiting cybersecurity staff – Putting diversity at the heart of your cybersecurity team helps you spot issues and problems that might not have occurred to you
Why you need more women on your data science team – Companies that embrace diversity and especially women, embedding their data science team will outperform competitors, says research
| 2022-12-15T00:00:00 |
2022/12/15
|
https://www.information-age.com/using-artificial-intelligence-to-promote-diversity-inclusion-123500943/
|
[
{
"date": "2022/12/15",
"position": 14,
"query": "workplace AI adoption"
},
{
"date": "2022/12/15",
"position": 89,
"query": "generative AI jobs"
},
{
"date": "2022/12/15",
"position": 23,
"query": "machine learning workforce"
}
] |
AI and GenAI – State of Enterprises in 2025
|
AI and GenAI – State of Enterprises in 2025
|
https://newgensoft.com
|
[] |
Explore how enterprises are adopting AI and GenAI by 2025, driving innovation and transforming industries with intelligent automation and insights.
|
The year 2024 witnessed a significant adoption of the twin powerhouse, artificial intelligence (AI) and generative AI (GenAI), for improving operations, enhancing customer experience, and driving innovation. The Gartner 2024 CIO for Health report revealed that over 90% of CIOs want to deliver superior customer journeys, and 100% believe AI and machine learning (ML) investments will be critical in achieving this goal. Around 88% of the surveyed stressed implementing low-code/no-code development platforms as a crucial step, followed by nearly 80% of CIOs adding GenAI to this mix.
AI and GenAI Performance in 2024: Key Trends and Growth Insights
AI adoption across industries witnessed a massive surge in 2024. According to Accenture, by the end of 2024, the global AI market was valued at over $200 billion, with a growth of 38% projected over the next five years. In the coming months, AI will continue to benefit industries, particularly healthcare, banking, and insurance. Deloitte’s US State of GenAI highlighted that over 60% of large enterprises now leverage AI-driven tools, particularly GenAI, which will account for 15% of AI-generated revenues by 2025.
AI and GenAI for Low-code
According to Forrester, the generative AI market will grow at 36% annually for the rest of the decade, and low-code and workflow automation platforms will benefit the most from this shift. A roughly comparable AI-fueled explosion in citizen development and AI-infused platforms could drive a 33% annual growth in the low-code and workflow automation market, pushing it to $50 billion by 2028.
A December 2022 Gartner report predicted that citizen developers will constitute about 80% of the user base for low-code development tools by 2026. As AI’s influence continues to grow, 2025 will see more emphasis on AI ethics, data privacy, and compliance regulations, particularly within the low-code ecosystems.
AI and GenAI Reshaping Customer Experience for Banking Industry
In 2024, the banking sector’s AI market size exceeded $30 billion, driven by rapid fraud detection, risk management, and customer service automation advancements. As per Accenture’s Banking Top 10 Trends for 2024, banks will benefit more from GenAI than any other industry, indicating a potential to boost productivity by 22-30% and revenue by 6%.
McKinsey’s 2023 State of GenAIinsights showed GenAI could deliver a value worth $200 billion to $340 billion annually across the banking industry if the use cases were fully implemented. Another report predicted that during 2025 AI will play a more significant role in automated compliance, fraud detection, and regulatory reporting.
Data-driven insights and personalization were top priorities (54%) for banking CIO investment, as per the KPMG Future Proofing Banking 2024 Report. AI and GenAI have been critical tools for delivering personalized products and services that drive bank growth and innovation.
A New Era of Automation Unlocked with AI in Insurance
AI has become essential in the insurance sector, finding several enterprise use cases, such as underwriting and workbench automation, intelligent claims processing, smart policy binding, and fraud detection. According to an ISG 2024 State of GenAI report, AI has the potential to contribute to around 24% of use cases in insurance, especially around assisting underwriters to identify risk and design the best insurance solution for their client. Gartner Predicts 2024 report for Insurance CIOs noted that 91% of insurance CIOs will likely implement AI in their ecosystem by the end of 2026, especially deploying AI and machine learning (AI/ML) initiatives, with basic chatbots, computer vision, and natural language processing (NLP) emerging as the closest-to-majority adopted AI techniques.
A recent Deloitte report predicted that AI could add over 10%—or roughly $12.5 trillion—to global GDP by 2032. The global AI in the insurance market will be worth around $91 billion by 2033, up from $5 billion in 2023, growing at a CAGR of 32.7%.
AI and GenAI for the Healthcare Industry
AI in healthcare, especially for payers and providers, saw remarkable growth in 2024. AI-driven diagnostic tools, robotic surgery, and personalized care models have gained significant traction. According to research reports, the market for AI in healthcare reached $16 billion in 2024 and is projected to grow 41% by 2027.
The latest buzz in AI is Agentic AI, which represents a leap toward creating brilliant systems capable of reshaping industries. A Forbes article called it the ‘third wave of AI’, with predictive AI and GenAI being the first two. Providing round-the-clock health assistance, Agentic AI could be the booster dose for patient care. It can enhance fraud detection systems, automatically identifying patterns and anomalies that traditional models may miss. Agentic AI can autonomously adjust portfolios based on real-time data and market conditions regarding asset management.
Newgen’s commitment to AI and GenAI
Newgen has been consistently collaborating with global customers on strategies for adopting AI and GenAI to facilitate enterprise-wide commercialization. Our advanced AI & Data Science Platform cuts across industry verticals and adds value to our customers.
Responding to the rising demand for accelerated customer-centric digital initiatives, we introduced NewgenONE LumYn, a GenAI-enabled growth intelligence platform, empowering hyper-personalization at scale. LumYn leverages our data science platform and advanced conversational AI to offer insights that lead to deeper customer engagement while safeguarding data privacy and security.
Additionally, NewgenONE Marvin, our GenAI expert, accelerates application development, effortlessly delivers real-time insights from processes and documents, and enhances customer interactions. We are able to deliver a rapid business app design, refined customer communications, and deep document insights—all integrated into existing workflows.
| 2022-12-15T00:00:00 |
https://newgensoft.com/company/newsletters/ai-and-genai-state-of-enterprises-in-2025/
|
[
{
"date": "2022/12/15",
"position": 16,
"query": "workplace AI adoption"
}
] |
|
HR Consulting Solutions – Raising the Impact of the Workforce
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HR Consulting Solutions – Raising the Impact of the Workforce - Eupnea
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https://eupnea.co.uk
|
[] |
HR Consulting Solutions: Developing a Versed and Resilient Workforce. in the Advent of AI · HR Consulting Solutions: · Developing a Versed and Resilient Workforce.
|
With our consulting services related to human capital, we can help you manage and grow your workforce more effectively, improve your workplace and develop strong talent within your organisation for today and the future.
We have looked into 7 areas where businesses can focus today with aim to develop a versed and resilient workforce in their organisation and create strong foundations for succeeding in the future. The latest advancements in AI and other technological areas such as quantum computing mean the skillset that employees will need to possess in order to adequately succeed in various roles from this decade onwards is changing. Similarly to previous significant advancements, for example with Digital, they have a transformative power and tend to upend previously long-lasting standards and norms while providing less time than expected to act and adapt. This is creating a new emergency today for organisations to have a strong vision and strategy for talent today if they want to brace for future success and not fall behind.
| 2023-08-29T00:00:00 |
2023/08/29
|
https://eupnea.co.uk/hr-consulting-solutions-raising-the-impact-of-the-workforce/
|
[
{
"date": "2022/12/15",
"position": 99,
"query": "workplace AI adoption"
}
] |
Développement économique
|
International Development Research Network
|
https://idrn.eu
|
[
"International Development Research Network",
"Sarah Hunter",
"Federico Dante De Falco",
"Ed Biggins",
"Jimena Madrigal",
"Ignacio Berreta Sartini",
"Lena Raballand",
"Niccolò Fantini",
"Jessica Sumner",
"Jana Curcenco"
] |
Universal Basic Income: A panacea or impossible dream? In times of crisis, seemingly impossible ideas suddenly become possible, although the viability of ...
|
Dans les années qui suivent les crises et les chocs financiers mondiaux, la création d'emplois et l'investissement sont essentiels à la reprise de l'économie mondiale. Le processus de reconstruction est un exemple de développement économique, qu'il soit mis en œuvre au niveau national, régional, local ou individuel. Ce développement impliquera également des politiques liées au chômage, aux infrastructures, au commerce, à l'aide étrangère et à l'exploitation des technologies émergentes pour s'assurer que les futurs travailleurs sont préparés à un monde de plus en plus numérique. L'IDRN vise à comprendre comment les nouvelles technologies peuvent être utilisées pour réduire les inégalités économiques, à étudier comment les pays peuvent collaborer au niveau international pour accroître les opportunités et à trouver de nouvelles voies pour le développement économique, pour la génération actuelle et les générations futures.
| 2022-12-15T00:00:00 |
https://idrn.eu/fr/economic-development/
|
[
{
"date": "2022/12/15",
"position": 26,
"query": "universal basic income AI"
}
] |
|
CriticalMAAS: Critical Mineral Assessments with AI Support
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CriticalMAAS: Critical Mineral Assessments with AI Support
|
https://www.darpa.mil
|
[] |
The supply of many critical minerals remains highly vulnerable to disruption due to high levels of import reliance, particularly on unstable or even adversarial ...
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Critical minerals are defined as commodities essential to the U.S. economy and national security with significant supply chain vulnerabilities.
The supply of many critical minerals remains highly vulnerable to disruption due to high levels of import reliance, particularly on unstable or even adversarial nations. Clean energy infrastructure, along with many other next-generation technologies, consume more critical minerals than traditional energy sources, and expected demand for critical minerals used in clean energy will quadruple by 2040.
In addition to assessing the nation’s mineral resources, the United States Geological Survey (USGS) tracks production, use, and trade statistics for over 100 commodities; 50 of which exceed criticality thresholds owing to high supply risk.
Critical Mineral Assessments with AI Support (CriticalMAAS) aims to develop artificial intelligence (AI) and machine learning tools to automate and accelerate key, time-consuming parts of USGS’ critical mineral assessment workflow. The goal of this AI exploration effort is to transform the workflow from a serial, predominantly manual, intermittently updated approach, to a highly parallel, continuous AI-assisted capability that is comprehensive in scope, efficient in scale, and generalizable across an array of applications.
The Energy Act of 2020 called for the USGS to assess all critical mineral resources in the U.S. In addition, the Bipartisan Infrastructure Law called on the USGS to assess potential critical mineral resources in mine waste. These assessments can quantify potential mineral sources from existing domestic mines – whether historical or active – and help identify opportunities for economically and environmentally viable resource development.
The challenge is that critical mineral assessments are labor intensive and using traditional techniques, assessing all 50 critical minerals would proceed too slowly to address present-day supply chain needs.
An AI-assisted workflow could enable the USGS to accomplish its mission, produce high-quality derivative products from raw input data, and deliver timely assessments that reduce exploration risk and support decisions affecting the management of strategic domestic resources.
While the primary focus will be critical minerals, it is expected that the resulting technologies and resulting data products will be valuable for a wide variety of U.S. government mission areas ranging from water resource management, to potential new clean energy sources.
| 2022-12-15T00:00:00 |
https://www.darpa.mil/research/programs/critical-mineral-assessments-with-ai-support
|
[
{
"date": "2022/12/15",
"position": 25,
"query": "AI economic disruption"
}
] |
|
Research Center - BBB National Programs
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Research
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https://industryselfregulation.org
|
[] |
Research in Progress · Self-regulation in an Industry Facing Technological Disruption: The Case of Insurance · Dynamic Regulation of Artificial Intelligence · Risk ...
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Call for Papers
CISR has opened a call for papers addressing the past, present, and/or future of industry self-regulation. We welcome proposals from scholars in law, public policy, business (MBA and undergraduate), marketing (graduate and undergraduate), economics, and related fields or interdisciplinary proposals. Proposals will be accepted and reviewed for any topic relating to industry self-regulation. Scholars need not have conducted prior research or writing in the field.
Download the full call for papers for more detailed information about research areas, funding, the process, the timeline, and more.
Submissions Due: July 15, 2025 @ 5:00PM ET
Questions? - [email protected]
| 2022-12-15T00:00:00 |
https://industryselfregulation.org/research-center
|
[
{
"date": "2022/12/15",
"position": 58,
"query": "AI economic disruption"
}
] |
|
Deep Learning | Michigan Online
|
Deep Learning
|
https://online.umich.edu
|
[] |
Generative AI: Forecasting Disruption. Course. GenAI in Business: Strategies ... Economic Responses to AI. Video. Societal Impact of Generative AI. Video ...
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As we discussed in the first week of the course, one of the key challenges in machine-learning is finding the right features to use as input to a learning model for a particular problem. This is called feature engineering and can be part art and part science. It can also be the single most important factor in doing well on a learning task. Sometimes in fact, more often more important than the choice of the model itself. We'll discuss this further in the last week of the course. Because of the difficulty of feature engineering, there's been a lot of research on what's called feature learning or feature extraction algorithms that can find good features automatically. This brings us to deep learning. At a high level, one of the advantages of deep learning is that it includes a sophisticated automatic feature learning phase as part of its supervised training. Moreover, deep learning is called deep because this feature extraction typically doesn't use just one feature learning step, but a hierarchy of multiple feature learning layers, each feeding into the next.
Here's one simplified example of what a deep learning architecture might look like in practice for an image recognition task. In this case, digit recognition. Recognizing a handwritten digit from 0-9 for example. You can see the automatic feature extraction step made up of a hierarchy of feature layers, each of which is based on a network that does convolution, which can be thought of as a filter for a specific pattern, followed by a sub-sampling step, also known as pooling, that can detect a translated or rotated version of that feature anywhere in the image so that features are detected properly for the final classification step, which is implemented as a fully connected network. The sub-sampling step also has the effect of reducing the computational complexity of the network. Depending on the properties of the object we want to predict for example, if we care only about the presence of an object in an image compared to its specific location, the sub-sampling part of the architecture may or may not be included.
This is only one example of a deep learning architecture. The size, structure and other properties may look very different depending on the specific learning problem. This image from a paper by Honglak Lee and colleagues at the University of Michigan shows an illustration of multi-layer feature learning for face recognition. Here there are three groups from left to right corresponding to first, second, and third stages of feature learning. The matrix at each stage shows a set of image features with one feature per square. Each feature can be thought of as a detector or filter that lights up when that pattern is present in the underlying image. The first layer of their deep learning architecture extracts the most primitive low-level features such as edges and different kinds of blobs. The second layer creates new features from combinations of those first layer features. For faces, this might correspond to key elements that capture shapes of higher-level features like noses or eyes. The third layer in turn creates new features from combinations of the second layer features, forming still higher-level features that capture typical face types and facial expressions.
Finally, all of these features are used as input to the final supervised learning step, namely the face classifier. Here are the feature layers that result from training on different types of objects, cars, elephants, chairs, and a mixture of objects. These kinds of complex features can be learned from a small number of layers. Advances in both algorithms and computing power allow current deep learning systems to train architectures that can have dozens of layers of nonlinear hierarchical features. It turns out that the human brain does something quite related to this when processing visual information. There are specific neural circuits that first do low-level feature extraction, such as edge detection and finding the frequency of repeated patterns, which are then used to compute more sophisticated features to help estimate things like simple shapes and their orientation, or whether a shape is in the foreground or background. Followed by further layers of higher level visual processing that support more complex tasks, such as face recognition and interpreting the motion of multiple moving objects.
On the positive side, deep learning systems have achieved impressive gains and have achieved state-of-the-art performance on many difficult tasks. Deep learning's automatic feature extraction mechanisms also reduce the need for human guesswork and finding good features. Finally, with current software, deep learning architectures are quite flexible and can be adapted for different tasks and domains. On the negative side, however, deep learning can require very large training sets and computing power and that can limit its practicality in some scenarios. The complexity of implementation could be considered as one of the negatives of deep learning and this is the reason that a number of sophisticated high-level software packages have been developed to assist in the development of deep learning architectures. Also, despite the faces example we saw earlier, which gave clear, easy to interpret features in most cases, often the features and weights of typical deep learning systems are not nearly so easy to interpret. That is, it's not clear why or what features led a deep learning system to make a particular prediction.
While Scikit-learn with the MLP classifier and MLP regressor classes provides a useful environment to learn about and apply simple neural networks, if you're interested in getting a deep understanding of deep learning and the software tools required to use it, we've provided some links to additional resources. Typical deep learning development is done with a multi-layer software stack. Here's one example. The higher-level layer provides a high level programming interface that allows you to specify a deep-learning architecture with only a few lines of code. In this example, I've chosen Keras for my top-level programming layer. Now I'll show you an actual example of Keras in a minute. The higher-level programming layer calls into one or more low-level services for things like defining a computation graph that describes the algorithm workflow, or manipulating data in the form of vectors, matrices, tensors, and so on. TensorFlow is an example of software that provides these core machine-learning framework services although TensorFlow has a high level programming layer as well. The higher level Keras layer makes use of TensorFlow two core services. The bottom layer is a hardware dependent layer that does the lowest level operations like multiplying matrices in a way that is usually optimized for a specific processor or computing architecture. For example, graphics processing units or GPUs, are a special processor originally developed for video cards that can do extremely fast matrix operations. This lowest layer can take advantage of this specialized hardware to accelerate the training and running of deep learning models.
Let me explain a few of the acronyms here. BLAS stands for basic linear algebra subprograms. These are the defacto standard low-level routines for linear algebra. The blast specification is pretty general, but specific implementations are typically highly optimized for speed for a given processor. CUDA stands for Compute Unified Device Architecture. That's the parallel computing platform and application programming interface that allows software to use certain types of GPUs for general-purpose processing. We also have new forms of hardware optimized for machine learning called Tensor Processing Units or TPUs, that are being deployed. cuDNN is a GPU accelerated library that provides highly tuned implementations of routines arising frequently in deep neural network applications. Working together, these three layers are all tremendously important in producing effective and efficient deep learning applications. TensorFlow or PyTorch and Keras are currently among the most widely used deep learning frameworks. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources and it supports both high-level programming interfaces and low-level core computational services. PyTorch was developed by Facebook, AI Research Group and open source on GitHub in 2017. It's used for a variety of sophisticated machine learning applications as well, especially in natural language processing. PyTorch has a reputation for simplicity, ease of use, flexibility, efficient memory usage, and dynamic computation graphs. Keras is now part of the TensorFlow ecosystem and provides a simple, flexible top-level programming interface for developing deep learning models.
Being able to develop deep learning frameworks in machine-learning has multiple benefits. With deep learning, you can generate and iterate new models very quickly and you can debug relatively easily, which is of huge importance when building effective machine learning systems. More specifically, with these high-level deep learning frameworks, you get the advantage of simplicity. With deep learning, there's no need for feature engineering. The representation learning and even the architecture iteration are done automatically. You can build pipelines using only a few different vector or matrix tensor operations. These frameworks allow you to do things that are highly scalable so thanks to these multiple layers that we just discussed. Code is highly amenable to parallelization on high performance computing hardware like GPUs or TPUs. You can train these frameworks by iterating over small batches of data. That lets you handle datasets of arbitrary size. Deep learning models using these frameworks are also very versatile and reusable. They can be trained on additional data without starting from scratch. It's easy to thaw an existing model, so to speak, add more training data to update the weights and then "freeze" it again for use on your new task. You can pre-train a model in one domain like image classification and you can adjust its training for a different problem like video segmentation.
I thought it'd be fun to show you a specific example using Keras to define a simple digit recognizer. This example is from Francois Chollet's book on deep learning with Python. Keras script typically has four parts. The first part, there are some lines of code that prepare the data. In this case, we're using the MNIST dataset. There are some code here to load the dataset. There's a little bit of reshaping that has to happen to get it ready, put it in the right format for the neural network later on. In the second part, you have to prepare the data, you define the model. Here we're going to implement this very simple model where we have an input as a digit we run it through a dense neural net layer, it has 512 units. We follow that with a second layer that has 10 units. You can see that it's very nice that the Keras program, the definition of the sequential model corresponds. You can see very clearly the correspondence between a line of code that adds a layer to the model and the graphical description of the model over here. It's very easy to create these models in Keras, you define the type of model you want. You add these layers, so it was one line of code to add each layer. Of course you specify for each layer how many hidden units it has, what activation function to use and so forth. Once you've defined the layers of the model, you do what's called compiling it, where you specify some important parameters like which optimizer to use, which loss function is to be used and so forth. That prepares the code internally for the next step, which is the training step. That's where you fit the network using the training images with their labels. Here you can specify parameters like how many epochs you want to run, how many training images you want to put in a batch as you're training through multiple cycles and so forth. Then, after the model training step, the final step is to evaluate the model. Keras has some nice simple ways where you can just call this evaluate method on the network using the test data and it will provide you the loss over the data as well as evaluation metric like accuracy. This is a great illustration of just how easy it is to use a high-level framework to build a non-trivial neural network that does something interesting.
| 2022-12-15T00:00:00 |
https://online.umich.edu/collections/artificial-intelligence/short/deep-learning-/
|
[
{
"date": "2022/12/15",
"position": 96,
"query": "AI economic disruption"
},
{
"date": "2022/12/15",
"position": 54,
"query": "machine learning workforce"
}
] |
|
Credential Transparency and AI
|
Credential Transparency and AI
|
https://credentialengine.org
|
[] |
CTDL ensures clarity, consistency, and interoperability, strengthening AI-driven education and workforce applications by processing education and workforce data ...
|
Structured Data Powers Smarter Workforce and Education Solutions
The integration of Artificial Intelligence (AI) with structured data is revolutionizing how educational institutions, credential providers, product vendors, government agencies, and other stakeholders create data-driven solutions. Credential Engine provides solutions that make education and workforce data more transparent, structured, and accessible.
AI amplifies our work by analyzing and linking structured data, enabling smarter insights that help individuals, institutions, and employers make more informed decisions. AI-powered tools are more effective when they leverage structured data, improving how education and workforce systems align learning opportunities, skills, and employment.
| 2022-12-15T00:00:00 |
https://credentialengine.org/credentialtransparency-ai/
|
[
{
"date": "2022/12/15",
"position": 4,
"query": "government AI workforce policy"
},
{
"date": "2022/12/15",
"position": 26,
"query": "machine learning workforce"
},
{
"date": "2022/12/15",
"position": 8,
"query": "artificial intelligence employers"
}
] |
|
Toyota Venture's Chris Abshire on Generative AI - Terra Nova
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Toyota Venture’s Chris Abshire on Generative AI
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https://www.terranova.co
|
[] |
Generative coding is starting to get a lot of attention and we think it's a massive opportunity.” Fear of job losses might hinder adoption if careful messaging ...
|
ABSTRACT
🗒️ Chris Abshire, senior associate at In the last few years, Foundation Models have revolutionized how AI systems are built., senior associate at Toyota Ventures , explains how Foundation Models set the stage for generative AI tools and their ability to complete cognitively-demanding and creative tasks. These include writing, coding, and image, video, and 3D-model creation. Despite all the buzz around Generative AI applications recently, “we’re still in the early innings of foundation model applications,” he says.
KEY POINTS FROM CHRIS ABSHIRE'S POV
Why is Generative AI such an important category moving forward?
The conditions are set for a Cambrian Explosion in Generative AI applications. Foundation Models, trained on massive volumes of raw web-scale multi-modal datasets, are adaptable to many types of tasks, including AI generated language and vision. In the last year, companies like Stability AI, Open AI, Midjourney, and others have made versions of their Foundations Models available for commercial use. “The leading companies have positioned themselves as sector-agnostic providers,” says Abshire. “They built these general-purpose language and text-to-image models, and made them available via APIs. They’ve left it up to the AI community, businesses, and consumers to discover use cases.”
Foundation Models, trained on massive volumes of raw web-scale multi-modal datasets, are adaptable to many types of tasks, including AI generated language and vision. In the last year, companies like Stability AI, Open AI, Midjourney, and others have made versions of their Foundations Models available for commercial use. “The leading companies have positioned themselves as sector-agnostic providers,” says Abshire. “They built these general-purpose language and text-to-image models, and made them available via APIs. They’ve left it up to the AI community, businesses, and consumers to discover use cases.” We’re likely to see many more technological inflection points that quickly expand what’s possible. Current capabilities will be quickly superseded. “Lots of people get stuck on text-to-image generation and that perhaps it does not have much real business value,” says Abshire. “And that may be true — there may not be a big market there. But this attitude is too short-sighted. It misses that we may be just a few years away from creating high-quality long-form videos through the same process. People don't always think far enough out in terms of future applications of breakthrough technologies.”
What are some top applications that might be attached to this category?
Various text applications are already seeing traction in business-to-business areas. Products that package Generative AI to augment functions or workflows within organizations are already seeing traction. One example is content marketing: “There already are tools out there that help users create a full blog post by providing just a short prompt, and the results are impressive,” says Abshire. Productivity is another area ripe for Generative AI: “The AI could summarize Zoom meetings or even emails in your inbox and then generate responses to action items or requests,” he adds.
Products that package Generative AI to augment functions or workflows within organizations are already seeing traction. One example is content marketing: “There already are tools out there that help users create a full blog post by providing just a short prompt, and the results are impressive,” says Abshire. Productivity is another area ripe for Generative AI: “The AI could summarize Zoom meetings or even emails in your inbox and then generate responses to action items or requests,” he adds. Three categories are further out but worth watching, given the potential: Generative coding: “Imagine an AI that can build an app based on inputs that describe how it will be used,” says Abshire. “We see this as a giant opportunity, and are tracking the efforts mainly led by the Microsoft-OpenAI partnership, which have spawned a lot of innovations in code generation through GitHub and VS Code.” Text-to-video: “Even without the ability to produce a full-length video, a marketing team would see a lot of value if they could easily produce short videos and try out concepts in a brainstorming session.” Text-to-object creation with 3D printing: “You can pair Generative AI with 3D printers, and literally use text prompts to conjure up objects from your imagination.” Generative Simulators will allow business users to create and manipulate hi-fi replicas of real-world objects and environments, which could be helpful in industrial settings, but also in entertainment for gaming and film. “Today’s Generative AI platforms may help you produce really cool and fun art, but what organizations need is digital models and simulations of the real world that can be precisely controlled,” says Abshire.
Generative Simulators will allow business users to create and manipulate hi-fi replicas of real-world objects and environments, which could be helpful in industrial settings, but also in entertainment for gaming and film. “Today’s Generative AI platforms may help you produce really cool and fun art, but what organizations need is digital models and simulations of the real world that can be precisely controlled,” says Abshire. “There’s a few companies now building what are called Generative Simulators. Think Matrix-type worlds. You have a replica of a slice of the world at a high resolution and then you can type in whatever you want to pop up in that simulated environment. For industrial settings, you can use these simulations to train robots in object manipulation. In gaming, you could generate a Minecraft or Roblox world many times faster than a human could.”
What are some potential roadblocks?
Generative AI still faces high tech barriers in the more complex tasks like generating video or code. “There’s a lot of technology friction that is holding up certain applications,” says Abshire. “Video is just a lot more compute intensive than image generation. The latest examples of text-to-video are still pretty basic. There’s a lot more progress that has to happen for this to be done well. A world with AI-generated 3D films is still very far out. Generative coding is starting to get a lot of attention and we think it’s a massive opportunity.”
“There’s a lot of technology friction that is holding up certain applications,” says Abshire. “Video is just a lot more compute intensive than image generation. The latest examples of text-to-video are still pretty basic. There’s a lot more progress that has to happen for this to be done well. A world with AI-generated 3D films is still very far out. Generative coding is starting to get a lot of attention and we think it’s a massive opportunity.” Fear of job losses might hinder adoption if careful messaging doesn’t defuse this issue. “People might be worried about having their jobs removed, but this technology will likely serve to augment work, at least in the near term. It’s making human jobs easier, not eliminating them.”
It may be true that text-to-image generation may not have real business value. But this attitude misses that we may be just a few years away from creating high-quality long-form videos through the same process. People don't always think far enough out in terms of future applications of breakthrough technologies. Chris Abshire ~quoteblock
VISUAL: APPLICATIONS UP FOR GRABS in GENERATIVE AI
Courtesy of Sequoia Capital
IN THE INVESTOR'S OWN WORDS
Chris Abshire
Foundation Models represent a new approach for building AI systems. They set the stage for the current wave of innovation and new techniques are constantly emerging to make these models even more powerful.
It all started at Stanford where they were trying to create a base “foundation model” that could understand the English language and work with text. They wanted to make an AI that could write a book, or chatbots that could answer a large range of questions.
The first well-known Foundation model was GPT-3, a large-language model or LLM that was released in 2020. But the big breakthrough came somewhere in late 2021 when folks started working with a lot of the tech giants’ compute resources, leveraging AWS and Microsoft Azure to basically train these Foundation Models on all the internet’s raw data.
The models leverage important AI techniques called self-supervised learning and transfer learning. With these, the AI's are able to label unstructured data on their own, and they can constantly apply learnings from one context into another.
What makes Foundation Models especially powerful and valuable is that certain properties can appear that a human would never anticipate. For example, a model trained on a large dataset might be able to tell stories on its own or be able to do arithmetic, without the AI having been specifically programmed to do that.
In Generative AI, we’re especially excited for applications in perception and training, marketing, and media and entertainment. But we’re all still in the exploratory phase figuring out where the biggest opportunities lie. There’s a lot of unknowns as we’re still very early in understanding where the most business value will be created and captured.
MORE Q&A
Q: How should companies think about competing when some of the training datasets and base Foundation Models are widely available?
A: Most generative AI startups have a slide deck and a copy of the stable diffusion weights, but don't know how to go beyond that. The top companies are coming up with creative ways to scrape data and form partnerships with the tech giants who’s compute resources are critical given how expensive it is to train on web-scale data. Folks that have proprietary data have an advantage because they can fine-tune off-the-shelf models for specific applications. For example, a company that generates videos could do the best video generation if they were able to scrape YouTube and get all of that video data to train their models on. Of course, there’s not enough computation power on Earth to train on all of YouTube’s video data. But thinking by analogy, that's the kind of advantage companies will seek.
Q: How do you see the dynamic playing out between vertical-specific applications versus companies like Open AI building out core technology?
A: On the whole, the larger successes and revenue opportunities will tend to come from verticalized applications, rather than the underlying engines. In regards to Foundation Models and the core horizontal technology, what people don’t realize is how much goes into getting these models trained on data and then getting them good enough to be useful. Most people can’t do it themselves because they don’t have the money to pay for the computing resources and they don’t have a clever way of getting the right type of data needed. The few well-known AI companies like Stability AI were built on the shoulders of giants, and they did most of the hard work before ever fundraising…which is refreshing to see in the fundraising landscape.
Q: What is a misconception people commonly have about this space?
A: A big misconception is that Foundation Models can only be used for generating digital art. Foundation Models are generative models, but they deal with different modalities, which impacts the applications they could be useful for.
Big LLMs like DALL-E are used mostly for digital art currently, but might disrupt other visual industries (illustrations, stock photos, or even commercials). Because CLIP is open source, it is also becoming a fundamental building block in many vision systems.
LLMs like GPT-3 are more mature (1 year in AI feels like 10 years in other industries), so they have many more applications, anywhere where text is the interaction medium or product. Well-known successful ones are Copy.ai and Jasper.ai. And many more building on the GPT-3 API or their own clones (Adept.ai, which has cool demos of a web browsing virtual assistant). A huge revolution is also brewing in coding (code is just text!). There is also a nascent trend of LLMs in robotics (language as interface, for instance for task specification).
WHAT ELSE TO WATCH FOR
Generative AI is not all of the same quality, and access to the most powerful models and tools will increasingly be determined by the market. There will be a natural stratification in this category. Companies will begin to experiment with pricing, and hold back some of their models for internal use, or for special sets of clients. This is already visible on many text-to-image platforms, where API access is priced depending on models’ relative power. “Some of the content that you see online isn’t a great representation of what's currently possible,” says Abshire. “It depends on what you’re doing and paying for. Most users don’t have access to the best in-house models.”
There will be a natural stratification in this category. Companies will begin to experiment with pricing, and hold back some of their models for internal use, or for special sets of clients. This is already visible on many text-to-image platforms, where API access is priced depending on models’ relative power. “Some of the content that you see online isn’t a great representation of what's currently possible,” says Abshire. “It depends on what you’re doing and paying for. Most users don’t have access to the best in-house models.” Watch for new jobs such as Query Engineers. “This technology has created an entirely new job called Query Engineering,” says Abshire. “The role involves trying to figure out which keywords and descriptions will output the desired image, video, or 3D model. This is a lot harder than one might think.”
STARTUPS MENTIONED IN THIS BRIEF
Acknowledgements
Special thanks to Adrien Gaidon, Head of ML at Toyota Research Institute, for his insights in the rapidly evolving Foundation Model space.
Editor's note: The interview for this article was in late October, 2022, before the release of GPT-3.5 and other developments in Generative AI.
| 2022-12-15T00:00:00 |
2022/12/15
|
https://www.terranova.co/chris-abshire-toyota-ventures-generative-ai/
|
[
{
"date": "2022/12/15",
"position": 66,
"query": "generative AI jobs"
}
] |
Many laid-off tech workers are bouncing back stronger than ...
|
Many laid-off tech workers are bouncing back stronger than ever. Here's why.
|
https://www.businessinsider.com
|
[
"Diamond Naga Siu"
] |
Nearly 50,000 tech workers have been laid off — but there's a hack to avoid layoffs ... The head of AI at Google reportedly told employees that making a ChatGPT ...
|
This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
Hello, tech readers. Big Sean apparently predicted the tech labor market when he said: "Last night took an L[ayoff], but tonight I bounce back."
I'm almost certain that this prescient verse was talking about how over half the tech workers who got laid off recently are now earning more than what they made before, according to new analysis.
I'm Diamond Naga Siu, a senior reporter on Insider's new tech analysis team. Today, we're calming some tech layoff anxiety. Let's dive into how being laid off was a blessing in disguise for many.
If this was forwarded to you, sign up here. Download Insider's app here.
Tyler Le/Insider
1. Laid-off tech workers are actually finding jobs quickly. Because tech workers are typically college-educated, with specialized skills in high demand across many industries, their chances of finding new jobs are pretty good in any economy. But right now, those odds are unusually good — and many tech workers are bouncing back stronger than ever.
Even as the tech sector has been hammered by mass layoffs this year — more than 140,000 workers since March, by one count — the vast majority who have been let go haven't remained on the sidelines for long.
According to an analysis of laid-off workers conducted by Revelio Labs, a workforce-data provider, 72% have found new jobs within three months. Even more surprising, a little over half of them have landed roles that actually pay more than what they were earning in the jobs they lost.
Even though tech companies are doing terribly right now, a lot of businesses in other industries are fine. And those other employers need a lot of coders, data scientists, and product managers — specialists the tech industry was previously hoarding.
Read more on life after layoffs.
In other news:
Muhammed Selim Korkutata/Getty Images
2. The Twitter account that tracks Elon Musk's jet is now suspended. Musk previously said he wouldn't ban @elonjet — which gave updates on the whereabouts of his jet — but yesterday, it graduated from getting shadowbanned to being fully suspended. Other celebrity jet trackers were also banned. More on that here.
3. An internal study from Amazon's "Earth's Best Employer" project slammed the company's culture. Results of the brutally honest survey, viewed by Insider, found that Amazon's culture is associated with "stress, burnout, churn, and a cut-throat atmosphere." Everything we learned from the study.
4. The US isn't ready for a super-app. Silicon Valley has long wanted an all-in-one app, and Elon Musk is the latest CEO with that dream. But the US has many unique obstacles that even prevented Mark Zuckerberg and Evan Spiegel from making this a reality. Here are the hurdles they face.
5. Private equity firms are trying to snag deals on tech companies. Public tech companies are heavily discounted right now. 'Tis the season for holiday shopping and major companies like Squarespace, Twilio, and Alarm.com top the wishlists of private equity firms. Take a look at 37 of the companies on their shopping lists.
6. Google isn't interested in competing with ChatGPT. The head of AI at Google reportedly told employees that making a ChatGPT competitor was not worth the "reputational risk." Google execs said they have the technology to make a competing chatbot. These are Google's concerns over releasing a chatbot.
7. TikTokers are disappointed in their share of ad money. TikTok started giving creators a cut of money made from advertisements on their videos, but creators are disappointed with the low payout. According to eight creators, the pay ranged from pennies to $17 for every 1,000 views. Read the full story.
8. Veganism isn't a get-out-of-jail card for Sam Bankman-Fried. The FTX founder and CEO is currently in a Bahamian jail. Bankman-Fried reportedly tried using his vegan diet as a reason to get released on bail, but the court rejected his request. See what the judge said.
Odds and ends:
Tim Levin/Insider
9. This is the cheapest electric SUV. The Chevy Bolt EUV price recently dropped to $28,000. This is well below the average $65,000 price tag for an electric vehicle. Cost is currently the largest barrier to this greener transportation option. Come along for the 259-mile range ride.
10. Take a room tour of the world's largest cruise. Royal Caribbean just launched the world's largest cruise ship. The megaship, called Wonder of the Seas, is filled with vibrant spaces, but the rooms pale in comparison. All aboard for a tour of the balcony stateroom.
What we're watching today:
Curated by Diamond Naga Siu in New York. Feedback or tips? Email [email protected] or (tweet @diamondnagasiu). Edited by Hallam Bullock (tweet @hallam_bullock) in London and Jordan Parker Erb (tweet @jordanparkererb) in New York.
| 2022-12-15T00:00:00 |
https://www.businessinsider.com/many-laid-off-tech-workers-are-bouncing-back-stronger-than-ever2022-12
|
[
{
"date": "2022/12/15",
"position": 42,
"query": "AI layoffs"
}
] |
|
How Foundation Models Can Advance AI in Healthcare
|
How Foundation Models Can Advance AI in Healthcare
|
https://hai.stanford.edu
|
[
"Jason Fries",
"Scott Fleming",
"Michael Wornow",
"Nigam Shah",
"Ethan Steinberg",
"Yizhe Xu",
"Keith Morse",
"Dev Dash"
] |
In this post we discuss the opportunities foundation models offer in terms of a better paradigm of doing “AI in healthcare.”
|
The past year has seen a dazzling array of advancements in the development of artificial intelligence (AI) for text, image, video, and other modalities. GPT-3, BLOOM, and Stable Diffusion have captured the public imagination with their ability to write poems, summarize articles, solve math problems, and translate textual descriptions into images and even video. AI systems such as ChatGPT can answer complex questions with surprising fluency, and CICERO performs as well as humans in Diplomacy, a game which requires negotiating and strategizing with other players using natural language. These examples highlight the growing role of foundation models—AI models trained on massive, unlabeled data and highly adaptable to new applications—in underpinning AI innovations. In fact, the Economist observed that the rise of foundation models is shifting AI into its “industrial age” by providing general-purpose technologies that drive long-term productivity and growth.
However, in healthcare, transitioning from impressive tech demos to deployed AI has been challenging. Despite the promise of AI to improve clinical outcomes, reduce costs, and meaningfully improve patient lives, very few models are deployed. For example, of the roughly 593 models developed for predicting outcomes among COVID-19 patients, practically none are deployed for use in patient care. Deployment efforts are further hampered by the approach of creating and using models in healthcare by relying on custom data pulls, ad hoc training sets, and manual maintenance and monitoring regimes in healthcare IT.
In this post we discuss the opportunities foundation models offer in terms of a better paradigm of doing “AI in healthcare.” First, we outline what foundation models are and their relevance to healthcare. Then we highlight what we believe are key opportunities provided by the next generation of medical foundation models, specifically:
AI Adaptability with Fewer Manually Labeled Examples Modular, Reusable, and Robust AI Making Multimodality the New Normal New Interfaces for Human-AI Collaboration Easing the Cost of Developing, Deploying, and Maintaining AI in Hospitals
What Is a “Foundation Model”?
“Foundation model” is a recent term, coined in 2021 by Bommasani et al., to identify a class of AI models that draw on classic ideas from deep learning with two key differences:
1. Learning from large amounts of unlabeled data: Prior deep learning approaches required learning from large, manually labeled datasets to reach high classification accuracy. For example, early deep learning models for skin cancer and diabetic retinopathy classification required almost 130,000 clinician-labeled images. Foundation models use advances in self-supervised learning in a process called “pretraining,” which involves a simple learning objective such as predicting the next word in a sentence or reconstructing patches of masked pixels in an image. While this simplicity enables training foundation models with billions of learnable parameters, it also requires large, unlabeled datasets and substantial computational resources. GPT-3, for instance, was trained using 45TB of text, and BLOOM took 1 million GPU hours to train using the Jean Zay supercomputer—the equivalent of over 100 years for one Nvidia A100 GPU.
2. Adaptability with better sample efficiency: Foundation models learn useful patterns during pretraining and encode that information into a set of model weights. This pretrained model then serves as the foundation for rapidly adapting the model for new tasks via transfer learning. The process may not be new, but it makes foundation models far more sample efficient for transfer learning than prior AI approaches. This means models can be rapidly adapted to new tasks using fewer labeled examples, which is critical in many medical settings where diseases or outcome of interest may be rare or complicated to label at scale.
Why Should Healthcare Care?
It is widely held that information contained within electronic health records (EHRs)—in both coded forms such as International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes as well as unstructured forms such as text and images—can be used to learn classification, prediction, or survival models that assist in diagnosis or enable proactive intervention. The promise is clear. However, despite good predictive performance, models trained on EHR data do not translate into clinical gains in the form of better care or lower cost, leading to a gap referred to as an AI chasm. There are also concerns that current models are not useful, reliable, or fair and that these failures remain hidden until errors due to bias or denial of healthcare services incite public outcry.
What’s more, both the creation and the management of models that guide care remain artisanal and costly. Learning models require custom data extracts that cost upward of $200,000 and end-to-end projects cost over $300,000, with each model and project incurring downstream maintenance expenses that are largely unknown and unaccounted for. Simply put, the total cost of ownership of “models” in healthcare is too high and likely rising due to new reporting guidelines, regulation, and practice recommendations—for which adherence rates remain low.
If we can reduce the time and energy spent on training models, we can then focus on creating model-guided care workflows and ensuring that models are useful, reliable, and fair—and informed by the clinical workflows in which they operate.
What Are the Benefits of Foundation Models?
Foundation models provide several advantages that can help narrow the AI chasm in healthcare.
Adaptability with Fewer Labeled Examples
In healthcare, a model is typically trained for a single purpose like sepsis prediction and distributed as install-anywhere software. These models are trained to perform a classification or prediction task using a mix of biological inputs (such as laboratory test results, which are more stable across patient populations) and operational inputs (such as care patterns, which are variable and tend to be hospital-specific). But models often have poor generalization, which in turn limits their use. Epic, a top EHR vendor, recently began retraining their sepsis model on a hospital’s local data before deployment after the algorithm was widely criticized for poor performance.
The need to retrain every model for the specific patient population and hospital where it will be used creates cost, complexity, and personnel barriers to using AI. This is where foundation models can provide a mechanism for rapidly and inexpensively adapting models for local use. Rather than specializing in a single task, foundation models capture a wide breadth of knowledge from unlabeled data. Then, instead of training models from scratch, practitioners can adapt an existing foundation model, a process that requires substantially less labeled training data. For example, the current generation of medical foundation models have reported reducing training data requirements by 10x when adapting to a new task. For clinical natural language extraction tasks, variations of large foundation models like GPT-3 can achieve strong performance using only a single training example.
Modular, Reusable, and Robust AI
Andrej Karpathy’s idea of Software 2.0 anticipated transitioning parts of software development away from writing and maintaining of code to using AI models. In this paradigm, practitioners codify desired behaviors by designing datasets and then training commodity AI models to replace critical layers of a software stack. We have seen the benefits of Software 2.0 in the form of model hubs from companies like Hugging Face, which have made sharing, documenting, and extending pretrained models easier than ever.
Since foundation models are expensive to train but easily adaptable to new tasks, sharing models empowers a community of developers to build upon existing work and accelerate innovation. Using shared foundation models also allows the community to better assess those models’ limitations, biases, and other flaws. We are already seeing this approach being explored in medical settings, with efforts in NLP such as GatorTron, UCSF BERT, and others.
Medical foundation models also provide benefits beyond improved classification performance and sample efficiency. In our group’s research using CLMBR, a foundation model for structured EHR data, we found that adapted models demonstrate improved temporal robustness for tasks such as ICU admissions, where performance decays less over time.
Making Multimodality the New Normal
Today’s medical AI models often make use of a single input modality, such as medical images, clinical notes, or structured data like ICD codes. However, health records are inherently multimodal, containing a mix of provider’s notes, billing codes, laboratory data, images, vital signs, and increasingly genomic sequencing, wearables, and more. The multimodality of EHR is only going to grow, having jumped twenty fold from 2008 to 2015. No modality in isolation provides a complete picture of a person’s health state. Analyzing pixel features of medical images frequently requires consulting structured records to interpret findings, so why should AI models be limited to a single modality?
Foundation models can combine multiple modalities during training. Many of the amazing, sci-fi abilities of models like Stable Diffusion are the product of learning from both language and images. The ability to represent multiple modalities from medical data not only leads to better representations of patient state for use in downstream applications, but also opens up more paths for interacting with AI. Clinicians can query databases of medical imaging using natural language descriptions of abnormalities or use descriptions to generate synthetic medical images with counterfactual pathologies.
New Interfaces for Human-AI Collaboration
Current healthcare AI models typically generate output that is presented to clinicians who have limited options to interrogate and refine a model’s output. Foundation models present new opportunities for interacting with AI models, including natural language interfaces and the ability to engage in a dialogue. By building collections of natural language instructions, we can fine-tune models via instruction tuning to improve generalization.
In medicine, we don’t yet have a good mechanism to systematically collect the types of questions clinicians generate while interacting with EHRs. However, adopting foundation models in medicine will put these types of human-AI collaborations front and center. Training on high-quality instruction datasets seems to be the secret sauce behind many of the surprising abilities of ChatGPT and smaller, open language models. In fact OpenAI has put out job listings for Expert AI Teachers who can help teach specialized domain knowledge to the next generation of GPT models.
Easing the Cost of Developing, Deploying, and Maintaining AI in Hospitals
The current paradigm of doing “AI in healthcare,” where developing, deploying, and maintaining a classifier or predictive model for a single clinical task can cost upward of $200,000, is unsustainable. Commercial solutions also fall short because vendors typically charge health systems either on a per model or per prediction basis. It is clear that we need a better paradigm where instead of a one model, one datapull, one project per use-case mindset, we focus on creating models that are cheaper to build, have reusable parts, can handle multiple data types, and are resilient to changes in the underlying data.
Analogous to how the healthcare sector has focused on standardizing patient level data access via FHIR and other EHR APIs, making foundation models available via an API to support the development of subsequent models for specific tasks can significantly alter the cost structure of training models in healthcare. Specifically, we need a way to amortize (and hence reduce) the cost of prototyping, validating, and deploying a model for any given task (such as identifying patients with undiagnosed peripheral artery disease) across many other tasks in order to make such development viable. Foundation models, shared widely via APIs, have the potential to provide that ability as well as the flexibility to examine emergent behaviors that have driven innovation in other domains.
By lowering the time and energy required to train models, we can focus on ensuring that their use leads to fair allocation of resources with the potential to meaningfully improve clinical care and efficiency and create a new, supercharged framework for AI in healthcare. Adopting the use of foundation models is a promising path to that end vision.
This is part of a healthcare AI series. Read more about:
Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition. Learn more.
| 2022-12-15T00:00:00 |
https://hai.stanford.edu/news/how-foundation-models-can-advance-ai-healthcare
|
[
{
"date": "2022/12/15",
"position": 1,
"query": "AI healthcare"
}
] |
|
Artificial Intelligence
|
Artificial Intelligence
|
https://www.medtecheurope.org
|
[] |
AI can substantially improve healthcare and patient outcomes. ... The socio-economic impact of AI in healthcare: Addressing barriers to adoption for new ...
|
By clicking the Subscribe button, you give consent to MedTech Europe AISBL to use of the information you provided and send you content on the services you selected. We will ensure that the information is processed confidentially, and will only share it with third party providers that assist in providing these services. These providers may be located outside the EU; in this case, we will ensure that they are subject to a legal framework adequate in safeguarding your data, in compliance with European data protection law. You can unsubscribe, change your preferences or update your information at any time by clicking on the unsubscribe button available on all messages. For more information on how MedTech Europe will handle your personal data, please refer to our Privacy Policy. You can contact us at [email protected] for further questions related to your privacy and your rights.
| 2022-12-15T00:00:00 |
https://www.medtecheurope.org/digital-health/artificial-intelligence/
|
[
{
"date": "2022/12/15",
"position": 9,
"query": "AI healthcare"
}
] |
|
MIT Clinical ML
|
MIT Clinical ML
|
https://clinicalml.org
|
[
"David Sontag"
] |
... artificial intelligence, and using these techniques to advance health care. Broadly, we have two goals: Clinical: To make a difference in health care, we ...
|
About the Lab
Led by David Sontag, the Clinical Machine Learning Group is interested in advancing machine learning and artificial intelligence, and using these techniques to advance health care.
Broadly, we have two goals:
| 2022-12-15T00:00:00 |
https://clinicalml.org/
|
[
{
"date": "2022/12/15",
"position": 79,
"query": "AI healthcare"
}
] |
|
AI for medtech commercial growth: Five missteps to avoid
|
AI for medtech commercial growth: Five missteps to avoid
|
https://www.mckinsey.com
|
[
"Ralph Breuer",
"Nicolas Formoso",
"Umut Karaarslan",
"Marcel Meuer",
"Abhi Patangay"
] |
AI can transform medtech companies' engagement with healthcare providers. But business leaders can mistakenly assume that progress requires the massive data ...
|
The medtech industry is at a critical juncture. Digital solutions put in place during the lockdowns related to the COVID-19 pandemic have evolved into clear customer engagement preferences. Companies that wish to remain competitive are rethinking their traditional commercial models and designing differentiated digital strategies for the future.
About the authors This article is a collaborative effort by Ralph Breuer, Nicolas Formoso, Umut Karaarslan, Marcel Meuer, and Abhi Patangay, representing views from McKinsey’s Life Sciences Practice.
Companies in the medtech sector have access to more data—and more advanced analytics—than ever before. As the availability and competencies of AI continue to grow, it’s being deployed to address an ever-widening range of commercial challenges, including microsegmenting customers for providing account insights, creating propensity-to-buy models for account prioritization, recommending next best actions for customer engagement, suggesting tender prices for bid support, and helping predict and prevent customer churn (Exhibit 1). Now is the time for medtech companies to reimagine their customer engagement models by incorporating these and other capabilities.
Medtech companies that have begun to explore commercial AI use cases are already seeing significant business benefits and commercial growth. These early-adopter companies are reaping multiple improvements, including a 1.5- to 2.0-fold increase in customer funnel metrics, such as the number of identified leads; 50 percent higher proposal conversion rates; and up to 10 percent increases in incremental revenue, according to McKinsey analysis.
AI use cases are seemingly limitless in the medtech commercial organization, enabled by new capabilities in data and AI—and that’s part of the challenge. While some companies lead the pack in AI adoption, others are overwhelmed by the possibilities and are making common missteps. They may be waiting for the perfect data set or advanced machine-learning tools and overlooking the immediate benefits of more basic algorithms. Some are so busy planning massive, longer-term AI strategies that they miss out on incremental AI wins and experience. Others with overstretched commercial teams are farming out the work to IT and seeing limited results.
AI is a fast-moving space. Companies that wait for the optimal tool, data set, algorithm, use case, or start time risk being left behind; those that begin sooner rather than later will accrue the greatest value. This article lays out five frequent missteps companies make—and what commercial organizations seeking to build a winning customer engagement model can do instead—to achieve near-term benefits and build a foundation for the future.
Five potential pitfalls when exploring commercial AI use cases
Medtech companies can avoid five pitfalls when engaging with AI and analytics in commercial use: waiting for the perfect data or technology, presuming that only the most advanced AI will deliver insights, assuming that data scientists and field reps can’t work together, pursuing only the areas of greatest opportunity, and adopting a “go big or go home” approach to advanced analytics.
1. Waiting for the perfect data or technology
All types of customers have come to expect the seamless, intuitive, omnichannel consumer experiences provided by their favorite online retailer or preferred financial institution. Those experiences can inspire medtech leaders to transform their own customer engagement models (Exhibit 2).
But business leaders can mistakenly assume that progress requires the massive data volumes and advanced technology stacks that digital tech giants possess. Trying to emulate those giants can trigger concerns about the breadth and quality of data in the industry. In fact, the medtech industry has lagged in its accumulation and management of data. Logging valuable data such as client visits or sent emails was a low priority historically. Internal data sets were often ineffectively managed. Sales organizations ran on intuition rather than insight. Thus, the value of analytics was limited.
However, it’s imperative not to let the perfect become the enemy of the perfectly good. Commercial organizations can reap significant value from data and analytics without new data sets or an ideal technology foundation. As one leading US-based medtech player with a broad portfolio of capital equipment and associated consumables found, a “good enough” data infrastructure can enable valuable analytics use cases, including providing guidance to representatives about the customers who are most likely to purchase select products in the next three months—and yielding insights behind that guidance. Rather than wringing hands over whether there were enough data or worrying that reps would never adopt analytics-driven processes, the company acted. It focused on doing a few essential things—and doing them well:
It created a standard data taxonomy as a backbone for internal data, and it assigned owners to maintain data hygiene and best practices for data storage and compliance in the cloud.
It purchased from a single outside vendor both treatment and usage data, such as claims and procedures, at the account and healthcare professional levels.
Most important, it linked the internal and external data sets using a common identifier.
As the initial use cases proved their benefit, the company purchased additional data sets, such as payer mix, patient dynamics, and healthcare professionals’ channel preferences. It combined the data, analyzed it, and fed the resulting insights to its customer relationship management (CRM) systems for its sales reps and customer service agents.
Another medtech company began even more simply. The company, a global leader in imaging, wanted to explore opportunities in the long-tail customer segment. To get started, it identified the data sources that needed cleaning, devised a master data repository, and manually pulled the data. Without building an advanced algorithm or hiring a data science team, it used spreadsheet functionality to develop a data-driven lead-scoring application that provides a clear benefit to sales reps. The company has since integrated more of its data and technology stack, adopted a new CRM system, and launched a cloud marketing platform.
The finish line is always moving. Companies will never have all the data they think they need, and better tools will always be in development. But while some companies wait for the ideal, others are rolling up their sleeves to standardize data taxonomies, get the most from existing technology, integrate internal and external data, and build out analytics use cases that differentiate them in the marketplace. The best approach is to get started with a pilot, prove its value with a minimum viable solution, and use the lessons learned to develop requirements for the data and technology stack.
2. Presuming that only the most advanced AI will deliver insights
Medtech leaders can become enthralled by the potential of sophisticated algorithms to generate accurate predictions. Indeed, there’s a lot to get excited about. But companies don’t need the most advanced machine-learning techniques to derive significant insights. In fact, more basic approaches can yield better outcomes, especially as commercial organizations begin their AI evolution.
One US-based multinational medtech company with a focus on surgical equipment and supplies learned this lesson as it built a predictive next-best-action engine. Commercial success relied on the company’s ability to engage various stakeholders in a hospital, to demonstrate the value and benefits of the medical supplies it produces. Thus, the ability to provide sales reps with recommendations on how to better engage those stakeholders was a high priority for the business.
The company put most of the effort into data engineering and creating an algorithm to give reps highly accurate account-level recommendations on how to improve engagement with customers (Exhibit 3). In the process, it brought together internal sales and marketing data with external data—for example, publicly available health system data. As the predictive model was still being developed, the project team used simple analysis and business rules to create a quantified view of account potential and penetration, an invaluable tool for account prioritization. Combining the “simple” account prioritization tool with the “sophisticated” next-best-action recommendation model resulted in a more holistic solution than the predictive model alone provided. Sales reps could use the account prioritization tools right away as predictive modeling continued. Some reps took time to trust the models’ recommendations, and without the additional analyses, they might have abandoned the solution altogether.
When they define their data and analytics, medtech leaders can begin with the outcomes they seek and then make their technology choices accordingly. High-value insights are often uncovered early in the process, even before sophisticated modeling occurs.
3. Assuming that data scientists and field reps can’t work together
Technical teams tend to lead commercial AI projects in medtech companies. The assumption is that product managers, data scientists, and data engineers have the talent to deliver the best solutions. The business typically is involved in upfront gathering of requirements and, later, in user testing—with minimal involvement in solutions development. After all, what do managers and sales reps know about machine learning? An equally important consideration, however, is how much data scientists know about the challenges of the commercial organization.
Leaving the commercial enterprise out of developing solutions is a mistake that creates technical and change-management risks. Solutions may not address the business context or underlying needs, and commercial users may resist buy-in and adoption. The business can play an important role as solutions evolve throughout development. Full value is captured only when there is close collaboration among experienced sales reps and managers, who bring their business judgment, and well-trained data scientists, who translate judgment into analytics.
Please contact the authors if you’d like to understand more about McKinsey’s diagnostic offerings for commercial AI.
A US-based medtech company, piloting a propensity-to-buy solution, created a cross-functional team with two key roles: user champions and analytics translators. The user champions were sales team members selected for their open-mindedness and eagerness to innovate. The analytics translators served as liaisons between the technical and business sides of the project. The user champions ensured that the voice of the business was heard throughout the 12-week project, and the analytics translators made certain that business needs were translated correctly into requirements for technical teams.
The nature of the solution evolved during the pilot. While the initial goal was to identify high-potential customers, the solution pivoted into a next-best-action use case. The resulting solution provided more value to the sales team and the business—and would never have emerged without the involvement of end users.
Medtech commercial leaders pursuing AI opportunities would do well to bring business and science together from the start. Best practices include creating a cross-functional team with key business, technical, and translator roles—and instituting agile development processes involving business stakeholders at all phases.
4. Pursuing only the areas of greatest opportunity
Medtech leaders may incorrectly assume that AI’s value to the commercial organization correlates to customer size. Bigger, they surmise, is better, resulting in some hesitation to leverage commercial AI solutions, such as lead scoring, for smaller customers.
Large accounts can indeed drive a significant volume of the business and eat up a fair share of resources for sales and postsales service. The top ten to 20 accounts are commonly responsible for the large majority of sales in some product categories. In addition, companies tend to acquire more data on those larger accounts.
But while analytics can reveal insights into the big accounts, medtech companies can generate insights throughout the client portfolio. When one global capital and consumables company created a propensity-to-purchase model to guide sales reps on the customer upsell opportunities and the churn risks, it was surprised by some of the outputs. Big names were at the top of the list of upsell opportunities, but there also were some midsize clients and more traditional firms presumed to be less interested in exploring new products. As the sales reps visited these tier-two and tier-three accounts, they confirmed the opportunities, increasing their confidence in the model.
AI can uncover significant value throughout the client portfolio. Because smaller clients can easily be overlooked, the value of analytical models to those accounts can be even greater. A lead-scoring engine can help to identify and prioritize the most economically attractive accounts of any size.
5. Adopting a ‘go big or go home’ approach to advanced analytics
Once medtech executives understand the potential value of advanced commercial analytics, they may be eager to go all in, involving every country and business unit from the start. They may try to develop global solutions for churn prediction, lead scoring, and next best actions. But beginning too broadly and attempting to solve multiple business problems at once are perilous pursuits. They can create significant development challenges, enhance complexity, increase risk, and ultimately delay delivery.
Limiting the scope of early efforts is critical to establishing important proof points as soon as possible. Medtech companies that have been most successful in building and scaling commercial AI solutions begin with clear business objectives, motivated participants, and a limited scope of products.
One global company with a broad product portfolio in orthopedics was eager to develop a lead-scoring solution for its field reps. After clarifying the business goal, the company conducted a pragmatic assessment of feasibility at the country, business unit, and product levels, including the availability of local resources and the quality of available data, for example. Using the study findings, the company prioritized two countries and two product segments for the lead-scoring solution.
Limiting the scope ensured the focus and collaboration for developing a minimally viable product quickly, with fewer country- and product-specific requirements to integrate. The solution could later be adapted to a particular country or business unit. Such an approach—agreeing on one business objective and starting small—enables medtech companies to derive insights more rapidly and prove initial value, thereby creating momentum for broader AI adoption and business impact.
How to get started
For medtech companies that have embarked on or plan to start a commercial AI journey, the best way to begin is by identifying and prioritizing potential use cases and assessing internal analytics capabilities. Developing this holistic view of the status quo helps companies identify strengths and gaps relative to best-in-class performance. Commercial organizations must therefore examine not only data and analytics capabilities but also design thinking, agile execution, and change management as they integrate data and advanced analytics to create differentiated customer engagement models.
| 2022-12-15T00:00:00 |
https://www.mckinsey.com/industries/life-sciences/our-insights/ai-for-medtech-commercial-growth-five-missteps-to-avoid
|
[
{
"date": "2022/12/15",
"position": 96,
"query": "AI healthcare"
}
] |
|
6 ways media companies use AI to meet their strategic needs
|
6 ways media companies use AI to meet their strategic needs
|
https://digitalcontentnext.org
|
[
"Damian Radcliffe",
"Carolyn S. Chambers Professor In Journalism",
"University Of Oregon"
] |
From audience analytics to programmatic advertising and automated story creation, media companies have used Artificial Intelligence (AI) for some time.
|
From audience analytics to programmatic advertising and automated story creation, media companies have used Artificial Intelligence (AI) for some time. However, this technology is rapidly maturing and opening up new creative and business possibilities that media executives need to be aware of.
In fact, AI is creating what venture capitalist and MIT fellow Paul Kedrosky describes as the “most disruptive change the U.S. economy has seen in 100 years.”
ChatGPT, an AI chatbot, is the current poster child for this robotic reckoning. Garnering a huge amount of column inches in recent weeks, the application can provide detailed answers to questions and prompts. Along with other AI-generated innovations like the portrait app Lensa and OpenArt – a gallery of works created by AI – these tools have inspired the latest wave of discussion about the implications of this technology.
Amidst copious innovation and optimism, concerns have also surfaced around AI-generated content, consent, bias, labeling and regulation, as well as the impact on labor markets. None of these issues are going to go away any time soon. Nevertheless, while media companies and policymakers navigate this unfolding landscape, the roll-out and adoption of AI continues to gather pace.
Artificial intelligence at work in the media
With AI having a real moment right now, this is the perfect time to explore the ramifications for media companies. Here are six uses of AI technologies that need to be on your radar:
1. Driving engagement
One of the most common ways publishers are using AI and machine learning is through AI-powered algorithms which personalize content recommendations.
This can help increase engagement and keep readers on your site for longer. That’s particularly useful if time on site is a key performance metric. Of course, it can also enable you to serve more adds to your audience too.
Personalized recommendation technology has long been the mainstay of platforms like Amazon, Spotify, and Netflix. Now it’s becoming increasingly common for other forms of content too.
One early proponent, The Washington Post, uses AI to personalize the news that they deliver based on readers interests and preferences. It’s an approach they’ve been using for some time across their app, newsletters and now the homepage.
Sign up page for The Washington Post’s “For You” newsletter, highlighting the personalized nature of this product (Dec. 2022)
As Digiday explains, the Post offers a personalized “For You” section on the homepage that taps into information provided during onboarding. At sign-up, subscribers or registered users can select their topic preferences. Recommendations are further augmented by your reading history and other performance data.
It’s an area the Post looks set to double down on, as they and other outlets seek to move to a more tailored content offering and away from the “one size fits all” approach of yesteryear.
2. Tailoring your paywall
There are many different types of paywall possibilities. But, whatever your approach, the end goal is the same: to convert readers into paying subscribers.
One of these models, dynamic paywalls, deploys AI to change free article limits. As a result, users hit the paywall at different times, based on their behaviors and other indicators that help to determine a consumer’s propensity to pay.
There is “no magic number” after which readers will subscribe, notes Piano CEO Trevor Kaufman in an article that asked: “Has AI brought an end to the metered paywall?”
“Piano has seen visitors subscribe after a single pageview. Others take much longer to make the decision to convert, while some aren’t likely to ever subscribe at all,” Kaufman observes. In response to this variance, he argues, we need “smarter, more satisfying automation.”
AI can help. New York Media and Neue Zürcher Zeitung (NZZ, Switzerland) are just some of the publishers to adopt this model. They have used AI to determine individual paywalls, based on variables including geography, consumption habits and visit behavior, as well as subject matter and the device being used. Expect more publishers to follow suit.
3. Creating content
Many early newsroom experiments with AI focused on the potential to craft stories that typically follow a predictable formula.
One of the earliest to leverage AI for content creation, The Associated Press (AP) has been using AI since 2014 to generate summaries of earnings reports from publicly traded companies. This allows them to quickly and accurately provide readers with key information, freeing up reporters to do other work. “Prior to using AI, our editors and reporters spent countless resources on coverage that was important but repetitive,” their website notes, adding that this “distracted from higher-impact journalism.”
Alongside freeing up reporters, the technology has allowed AP to create more of this content. Automated story generation has enabled AP to increase the volume of these corporate stories by a factor of 10.
At a simpler level, AI is also being used to liberate resources otherwise hoovered up by resource-heavy work such as interview transcriptions.
AP is currently working with local newsrooms to help them increase their use of AI tools. In a survey asking what would be the most useful use of this technology, automating transcription came top.
Image: via AP
4. Distributing content
A further potential benefit of AI can be seen in its ability to support publishers in their desire to get material in front of audiences – wherever they may be.
POLITICO Europe has used AI to convert two of their popular newsletters, Brussels Playbook and London Playbook into daily podcasts. The audio option gives subscribers another way to consume this content on the go.
This type of technological solution can help publishers manage their resources more efficiently, as well as distribute content to different platforms in a timely and cost-effective manner.
A further mainstream iteration of this idea is also being developed by Google. Dyani Najdi, Managing Director of Video and Display EMEA, has highlighted how the tech giant is experimenting with a tool to reformat landscape videos for YouTube. Viewers will see videos in square or vertical formats, with the shape automatically determined by how you are accessing the platform.
Although currently only available for certain video-ad products, it’s not a big leap to imagine this being used for other content in the near future. If it is, that would be a huge time-saver for many publishers. A further boon is the possibility of this technology opening up new distribution avenues, without the time and expense of repurposing everything.
Where we go from here: two trends to keep an eye on
The manner in which AI is being employed is constantly changing. Its possibilities have sparked discussion about the implications for education, journalism and other creative work, as well as the wider knowledge economy.
Within that, here are two key AI-trends for publishers to closely follow and potentially adopt.
1. Leveling-up content, and ad, personalization
Based on their interests and preferences, AI can personalize the news that publishers deliver to readers. Its usage is only likely to increase and become more ubiquitous.
More than 9,000 publishers use Taboola’s recommendation platform. Earlier in the year, they announced that AI functionality had been added to their homepage techstack. The company said that in beta testing companies such as McClatchy, The Independent and Estado de Minas in Brazil, had seen a 30% – 50% increase in clickthrough rates for homepage sections personalized by Taboola.
Alongside content, AI can also be used to deliver a better ad experience. Publishers like Condé Nast are using machine learning to find patterns that can lead to more personalized and contextual ads. In a cookie-less future this type of approach will be essential if ads are to be targeted and relevant.
2. Improving and streamlining workflows
With cuts being seen across the media landscape, a key challenge for publishers in 2023 will involve maintaining output levels (never mind launching new products and verticals) with fewer staff.
AI may help here, given its ability to be used for A/B headline testing and other forms of predictive analysis. It can also tag and generate content such as business, sports and real estate stories. Or, as seen at Forbes, provide detailed prompts for writers.
It can further support social media and off-platform strategies too. The South China Morning Post saved resources akin to work done by 3.9 full-time employees by using AI to streamline its social media management.
Meanwhile, in Germany, Frankfurter Allgemeine Zeitung has used AI to help editors understand which stories to put behind the paywall. This matters given their freemium model, and the need to balance free content that drives subscriptions with premium subscriber-only content that readers value.
The big picture
This list of uses is far from exhaustive. To it we can also add important developments such as the ability of AI to help address inequalities (through the automatic creation of audio articles, and work to measure gender disparity in news coverage), as well as the rise of automated fact checking and many others.
Although no one knows how this technology will play out, it’s clear that AI can play a valuable role in helping publishers with their operations. As a result, it is no surprise that key activities unlocked by this technology – such as data analytics and automation – are among the top investment areas for publishers in the coming year.
Previously, as the Knight Foundation has found, “when we talk[ed] about AI in newsrooms, we seem to lean heavily on the newsgathering part of the process and maybe do not pay as much attention to the product or the business side of the ecosystem.”
In 2023, that may begin to change, as we see an overdue shift in the thinking about the role that AI plays in supporting the strategic needs of publishers.
From shaping the content you see (Pink News’ positive news filter), to aiding with translations of new international editions (Le Monde’s digital English language product) and improving your SEO (Summari and other tools), AI is here to stay and increasingly integral to publisher strategies.
Against a challenging business backdrop, as outlets begin to focus more on areas like product, subscriptions and retention, AI’s contribution to a publisher’s success will become more prominent and important than ever.
| 2022-12-15T00:00:00 |
2022/12/15
|
https://digitalcontentnext.org/blog/2022/12/15/6-ways-media-companies-use-ai-to-meet-their-strategic-needs/
|
[
{
"date": "2022/12/15",
"position": 7,
"query": "AI journalism"
}
] |
Is A.I. the "graphic artist" killer that many claim it to be?
|
Is A.I. the "graphic artist" killer that many claim it to be?
|
https://bulenthasan.com
|
[
"Bulent Hasan - Creative Narrative Director"
] |
I am against AI art to take over as the 'norm' for companies to have artwork generated for mass products, designs, etc.
|
My childhood sandbox! Enhancing the Live Events... Everything under the mouse house
As a life long comic book fan working on a show like "Legends of Tomorrow" was a blast, and was happy to provide boards for 8 episodes of season 3 for the series.
| 2022-12-15T00:00:00 |
https://bulenthasan.com/f/is-ai-the-graphic-artist-killer-that-many-claim-it-to-be
|
[
{
"date": "2022/12/15",
"position": 30,
"query": "AI graphic design"
}
] |
|
AI Drew This Gorgeous Comics Series. You'd Never Know It
|
AI Drew This Gorgeous Comics Series. You'd Never Know It
|
https://www.cnet.com
|
[
"See Full Bio",
"Leslie Katz",
"Leslie Katz Led A Team That Explored The Intersection Of Tech",
"Culture",
"Plus All Manner Of Awe-Inspiring Science",
"Space To Ai",
"Archaeology. When She'S Not Smithing Words",
"She'S Probably Playing Online Word Games",
"Tending To Her Garden Or Referring To Herself In The Third Person.",
"Steve Coulson"
] |
As machine-made art improves, will those humans -- actual graphic designers, illustrators, composers and photographers -- find themselves edged out of work?
|
You might expect a comic book series featuring art generated entirely by artificial intelligence to be full of surreal images that have you tilting your head trying to grasp what kind of sense-shifting madness you're looking at.
Not so with the images in The Bestiary Chronicles, a free, four-part comics series from Campfire Entertainment, a New York-based production house focused on creative storytelling.
In The Lesson, a teacher tells students about the monsters that ruined their planet. The team behind the comic used the phrase "Hitchcock Blonde" to describe the story's heroine to AI art-generation tool Midjourney, "and more often than not she came out looking like Grace Kelly," says writer Steve Coulson. Campfire, Midjourney
The visuals in the comics series -- believed to be the first made with AI-assisted art -- are stunning. They're also stunningly precise, as if they've come straight from the hand of a seasoned digital artist with a very specific story and style in mind.
"Deep underground, the last remnants of humanity gather to learn about the monsters that have destroyed their planet," reads a description of The Lesson, the visually rich retro-futuristic third comic in the four-part series. All four are available for download now on Campfire's site, and also come in softcover and hardcover printed anthologies.
Though AI-generated visual art can tend toward the wildly absurd, the photorealistic humans in The Bestiary Chronicles don't have rearranged facial features, or limbs protruding at odd angles. The monsters -- with their glowing eyes and astonishingly bad teeth -- look like love children of Godzilla and Vhagar and could hardly be mistaken for anything other than rage-filled beasts.
This algorithm-assisted art looks tailor-made for the dark dystopian tale, which leans on tropes from 1960 sci-fi horror film Village of the Damned and from THX 1138, George Lucas' 1971 debut feature film.
"We're seeing the rise of a completely new visualization tool that will radically change the storytelling process across both the comics industry and entertainment in general," said Steve Coulson, writer of the series and creative director of the award-winning Campfire, which has created immersive fan experiences for shows including Ted Lasso, Westworld and Watchmen. Its founders thought up The Blair Witch Project.
For The Bestiary Chronicles, Coulson turned to Midjourney, a service that quickly turns short text phrases, or "prompts," into images by scanning a giant database trained on visual art by humans. Artificial intelligence tools like it, Dall-E and Stable Diffusion are capturing the internet's imagination as they let anyone manifest images from text in intriguing and sometimes disturbing ways.
The Bestiary Chronicles is a science fiction odyssey about monsters born from man's technological hubris. But it also showcases the remarkable progress of products like Midjourney, which are producing increasingly more sophisticated and refined images.
The advances in AI image generation over the last few months have been exponential and mind blowing. Steve Coulson, Campfire Entertainment
"By the new year, even the trained eye probably won't be able to perceive an AI generation from any other," Coulson said. "It's exciting and terrifying at the same time. But you can't put the genie back in the bottle, so we're embracing the future as fast as we can."
AI image generation is advancing so rapidly, he adds, that The Lesson, out Nov. 1, marks a clear visual step up from the first comic in the trilogy, Summer Island, a folk-horror story in the spirit of Midsommar that came out in August. During those three months, Midjourney went through two upgrades.
AI art-generation tool Midjourney did an impressive job coughing up images of a bleak postapocalyptic landscape for The Lesson, the third in a four-part series of comics from production house Campfire. Campfire, Midjourney
AI, partner in art
"Technology is changing our world, with artificial intelligence both a new frontier of possibility but also a development fraught with anxiety," Thomas P. Campbell, director and CEO of the Fine Arts Museums of San Francisco, said when the exhibit Uncanny Valley: Being Human in the Age of AI opened in 2020 to explore the ever-expanding space where humans and artificial intelligence meet.
AI generating visual art, composing songs and even writing poetry and movie scripts is driving some of that anxiety, raising ethical and copyright concerns among artists and even lawyers. AI art isn't created in a vacuum. It works by absorbing and reconstructing existing art created by humans. As machine-made art improves, will those humans -- actual graphic designers, illustrators, composers and photographers -- find themselves edged out of work?
When an AI-generated picture won an art prize in September, some artists weren't happy about it. "We're watching the death of artistry unfold right before our eyes," one Twitter user wrote.
Coulson, an avid comics reader since age 5, is among those pondering the complex questions raised by AI art, but he doesn't think tools like Midjourney will replace the comics artists he's long loved. "Those geniuses have an eye for dramatic composition and dynamic narrative that I strongly doubt machine learning will be able to match," he writes in the afterword to Summer Island. "But as a visualization tool for nonartists like myself, it's a hell of a lot of fun."
Has Midjourney been watching House of the Dragon? Campfire, Midjourney
He does, however, see Midjourney as his true collaborator in The Bestiary Chronicles, even giving it an author credit. Where a comics artist might conceive of a narrative and then create art to illustrate it, AI-assisted images have the potential to more actively steer the story, or even change its direction, thus dramatically redefining the whole creative workflow. Coulson likens this human-machine duet to improv jazz.
"I would never ask a human artist to just 'draw 100 splash pages and maybe I'll pick the one I like the best,' but Midjourney will happily spit them out 24/7," Coulson said. "Then after we review the imagery, we start to assemble the story, almost as an act of collage, filling in gaps along the way."
AI art is the star here, but humans had the decisive hand in which images made it into the final version of each story. They experimented with text prompts and carefully selected their final images from multiple Midjourney offerings, making a Photoshop tweak here and there, but mostly letting the machine-made work stand.
The Campfire team, for example, liked the rich effect produced by the style prompt "olive-green and sepia and teal-blue tritone print on watercolor paper," so they used that one often to give images a painterly effect. For The Lesson, the phrase "futuristic underground bunker in the style of J.C. Leyendecker" yielded the perfect retro-futuristic postapocalyptic hideaway.
"We also used the phrase 'Hitchcock Blonde' to describe our heroine, and more often than not she came out looking like Grace Kelly," Coulson said. That's a fully recognizable Grace Kelly, without misplaced ears or a dog snout.
"The advances in AI image generation over the last few months have been exponential and mind blowing," Coulson said, "and this technology is only going to get better -- faster than we can imagine."
| 2022-12-15T00:00:00 |
https://www.cnet.com/culture/ai-drew-this-gorgeous-comics-series-youd-never-know-it/
|
[
{
"date": "2022/12/15",
"position": 90,
"query": "AI graphic design"
}
] |
|
10 AI Image Apps Topping the U.S. App Store Charts
|
AI Image Generators Skyrocket to the Top of the U.S. App Charts
|
https://mymodernmet.com
|
[
"Jessica Stewart",
"Jessica Stewart Is A Staff Editor",
"Digital Media Specialist For My Modern Met",
"As Well As A Curator",
"Art Historian. Since",
"She Is Also One Of The Co-Hosts Of The My Modern Met",
"Dropcloth Samplers",
"Today Is Art Day",
"Viviva Colorsheets"
] |
Pixelcut: AI Graphic Design. Download. Inspired: Magic Avatar, AI Art. Download. Dream by WOMBO. Download. AI Art Generator. Download. Related Articles:.
|
Ever since the Lensa AI app introduced its Magic Avatar feature, people have been flocking to Apple's app store in search of more ways to create AI art. Whether looking to make cute avatars or wishing to experiment with text-to-art capabilities, it's clearer than ever that AI's artistic possibilities have captured the imagination of the public. In fact, Tech Crunch noticed that following Lensa's success, AI apps are rising to the top of the U.S. App Store charts.
Currently, three of the top 10 free apps in the United States are AI generators. Lensa is currently the most downloaded and sits at number three on the charts. But closely following it at numbers four and five are Dawn (an AI avatar generator) and Aiby's AI Art Generator. All of the apps are free to download, but also have paid in-app features.
Several more apps sit within the top 50 on the general charts, but even more AI apps can be found when looking in the Graphics & Design category. Most of the apps will allow users to upload photos that can be transformed, much like Lensa. Additionally, they will also let users experiment with text prompts and different visual styles.
This success is mirrored in the Google Play Store, where Lensa and other AI apps are having similar success. And while these image generators can be fun, they aren't without controversy. It's always important for users to read how uploaded images will be used and if they'll be stored. And Stable Diffusion, the open-source model used by Lensa, is controversial for training the model using images without artists' permission. There's also concern about how some images it creates are based on cultural stereotypes.
So while these apps may seem harmless and fun to play with, it's always a good idea to ensure you are informed before you start using them or if you even start.
Here are the top 10 AI Apps currently dominating the U.S. App Store Charts.
Lensa AI: Photo & Video Editor Download AI Art Generator by AIBY Download Dawn - AI Avatars Download Wonder - AI Art Download VOI - AI Avatar App Download Meitu - Photo Editor & AI Art Download Pixelcut: AI Graphic Design Download Inspired: Magic Avatar, AI Art Download Dream by WOMBO Download AI Art Generator Download
Related Articles :
Architect Uses AI to Create Utopia Where Buildings Grow and Breathe
Man Fools Relatives Into Think He Has Girlfriend but She’s Actually AI
AI-Generated Art Reimagines the Iconic Japanese Kimono in Surprising Ways
AI Portraits Imagine How Celebrities Would Look If They Were Still Alive Today
| 2022-12-15T00:00:00 |
2022/12/15
|
https://mymodernmet.com/ios-apps-ai-generators/
|
[
{
"date": "2022/12/15",
"position": 93,
"query": "AI graphic design"
}
] |
How is your organisation dealing with AI?
|
How is your organisation dealing with AI?
|
https://www.pwc.ch
|
[] |
To use AI responsibly and realise its full potential, business leaders need to take three key actions: Ensure a governance structure and an enterprise-wide AI ...
|
Generative AI is an offshoot of the broader spectrum of deep learning. Specifically, it’s trained to create, enrich, and analyse unstructured data such as text, voice, and images. In business, however, this is precisely the type of data that is critical to understanding the ever-changing needs of stakeholders. Rather than simply analysing and interpreting, as was the norm with traditional AI models, generative AI creates new content based on patterns it deciphers from its training data. This significant difference opens up a wealth of possibilities. Imagine AI not only sifting through existing art, music, or text, but creating new masterpieces and even entire virtual worlds. The huge potential of generative AI has important implications for businesses.
The phenomenal rise of generative AI in business…
In December 2022, the world sat up and took notice of the meteoric rise of ChatGPT. Developed by OpenAI, ChatGPT revolutionised natural language processing and achieved user adoption at a rate that left titans like Netflix, Facebook, and Instagram far behind. With ChatGPT reaching a million users in just 5 days, compared to 10 months for Facebook and 3.5 years for Netflix, the message was clear: demand for advanced AI technologies is skyrocketing.
This surge in demand isn’t just a momentary trend. It signals a shift in the global business landscape. From production lines to finance departments, from IT corridors to marketing meetings, AI is the new thing, driving innovation, new business models, and investors alike. What’s more, companies aren’t just looking to adopt generative AI – they want to create their own bespoke AI models and tailor the solutions to their unique needs.
…comes with risks and trust issues
However, as with any powerful tool, there are inherent risks. Companies must balance speed and trust when adopting generative AI, ensuring a risk-based approach to gain a competitive edge and maintain stakeholder trust. Effective risk management is vital for leveraging the full benefits of this transformative technology.
Two of the main concerns with AI, particularly generative AI, are data protection and intellectual property (IP) challenges. High-end models such as ChatGPT are often hosted on cloud services, leading to potential data vulnerabilities. In addition, the use of generative models can infringe on the intellectual property of others, potentially leading to legal issues. Another important concern is the data on which the model is trained. If it’s trained on biased data, its outputs may reflect the same biases. It’s therefore critical for organisations to carefully examine their AI’s underlying data sets to ensure fairness, neutrality, and relevance.
Furthermore, while companies are under pressure to collect vast amounts of data, the key challenge is how to use it effectively. There is a risk in relying too much on generated data, especially without critical evaluation. With their engaging writing styles, chatbots can sometimes provide supposedly correct information that’s in fact biased or simply wrong. Relying on such data can lead to ill-informed decisions, potentially damaging a company’s service quality and reputation. Dealing with the AI-generated information landscape requires caution, curiosity, and a discerning eye.
One underestimated concern regards the new security treats created by the usage of AI driven tools. AI is not only used to improve attackers capabilities, but creates new categories of risks, that need to be specially addressed. Current continuous discoveries in AI weaknesses are showing the fragility of AI tools.
| 2022-12-15T00:00:00 |
https://www.pwc.ch/en/insights/digital/how-is-your-organisation-dealing-with-ai.html
|
[
{
"date": "2022/12/15",
"position": 29,
"query": "artificial intelligence business leaders"
}
] |
|
Leadership Blog - Qualetics AI management system
|
Qualetics AI management system
|
https://qualetics.com
|
[
"Qualetics Team"
] |
Insights From Our Leaders. Leaders who are exceptionally focused on developing and delivering Artificial Intelligence.
|
EdTech - What Can AI Do For You...
Yet another research study is highlighting the increasing adoption rate of AI. This study commissioned by IBM released just last month (May, 2021) shows almost one third of the over 5,500 companies represented are using artificial intelligence (AI) and over half say their companies are exploring AI.
| 2022-12-15T00:00:00 |
https://qualetics.com/leadership-blog/
|
[
{
"date": "2022/12/15",
"position": 32,
"query": "artificial intelligence business leaders"
}
] |
|
POC - 4th Annual Artificial Intelligence Summit
|
POC – 4th Annual Artificial Intelligence Summit
|
https://www.potomacofficersclub.com
|
[] |
Join the Potomac Officers Club for the platform's 4th Annual 2023 AI Summit to hear notable executive leaders within the GovCon sector discuss important AI ...
|
Over the last decade, landmark artificial intelligence advancements have drastically altered the federal and industry landscape and influenced government agencies’ evolving technology priorities and initiatives.
Federal officials are working to incorporate industry technology breakthroughs in numerous applications to computationally augment capabilities of service members and warfighters in the field, advance unmanned vehicle development and deliver greater benefits to civilians through AI-enhanced government services.
Now, with the creation of the Chief Digital and Artificial Intelligence Office, the Department of Defense is more focused than ever before on leveraging AI to advance missions across the entire enterprise.
Join the Potomac Officers Club for the platform’s 4th Annual 2023 AI Summit to hear notable executive leaders within the GovCon sector discuss important AI advancements achieved over the past year as well as cutting-edge development strategies for 2023 and beyond.
| 2022-12-15T00:00:00 |
https://www.potomacofficersclub.com/events/poc-4th-annual-artificial-intelligence-summit/
|
[
{
"date": "2022/12/15",
"position": 56,
"query": "artificial intelligence business leaders"
}
] |
|
Data Leadership
|
Connection Leadership
|
https://connectionleadership.com
|
[] |
Collaborative workshops using the AI Design Sprint method. · Selection of the most relevant AI use cases according to your business (Marketing, HR, Operations, ...
|
Unleash the full potential of AI with a robust Data strategy
The rise of generative AI is disrupting businesses, offering unprecedented opportunities for innovation and optimization. But to take full advantage of them, it’s essential to have solid Data governance and the right strategic approach.
At Connection Leadership, we help you identify the most profitable use cases for AI, while structuring your data to guarantee its quality and security.
| 2022-12-15T00:00:00 |
https://connectionleadership.com/en/expertise/data-leadership/
|
[
{
"date": "2022/12/15",
"position": 74,
"query": "artificial intelligence business leaders"
}
] |
|
The Future of Digital Business: Leveraging Automation & AI ...
|
The Future of Digital Business: Leveraging Automation & AI for Transforming Operations - Illuminar
|
http://www.avaali.com
|
[] |
Enterprises today are changing their ways of doing business by leveraging automation and artificial intelligence. ... By 2025, 75% of business leaders will ...
|
The Future of Digital Business: Leveraging Automation & AI for Transforming Operations
Enterprises today are changing their ways of doing business by leveraging automation and artificial intelligence. These technologies are transforming how organizations and customers interact with each other, opening up new possibilities and enhancing the overall experience. When done right automation has proven to deliver real benefits including distinctive insights, faster service, and improved savings. According to a recent global report on the future of digital business:
90% of global businesses are engaged in some form of digital initiative.
By 2025, 75% of business leaders will leverage digital platforms and ecosystem capabilities to improve their adaptability and make their platforms more future-proof.
Almost 40% of executives believe that digital transformation has a direct effect on meeting growing customer expectations.
Leveraging automation which combines the capabilities of RPA, OCR, AI, Analytics, Process Mining etc. allows enterprises to maintain a hybrid workforce of humans and software robots, working in tandem to achieve key business objectives. The large-scale and growing adoption of Intelligent Automation has enabled businesses to automate more than just repetitive rule-based activities, create greater value from their human resources and streamline workflows/processes to accelerate their digital transformation journey. The key aspects that help businesses leverage Intelligent Automation to drive competitiveness are mentioned below:
Enhanced Quality and Compliance: Automation reduces errors and facilitates digital audit trail that increases accuracy and regulatory compliance.
Cost Reduction: Immediate reduction in operational costs and rapid return on investment can be achieved through automation.
Scalability: A virtual workforce can respond to growth events (e.g. organic, acquisitive) with speed, agility and resiliency. Once developed, automated solutions can be used to ramp up/down volumes easily
Employee and Customer Satisfaction: Increased employee satisfaction through a focus on higher value activities will, together with fewer errors, result in more satisfied customers.
Automation and AI for Upgrading Business Processes
Although many enterprises have started implementing automation and AI to further their digital transformation strategies, they are also aware of the possible challenges in implementation. One of the primary concerns amongst businesses is that the implementation of AI can impact existing IT systems and daily operations, leading to increased complexity, added efforts and disruption in critical operations. They are increasingly looking at platforms and tools that can seamlessly incorporate AI capabilities such as ML and NLP into existing processes and systems without disrupting current operations.
Enhanced Employee Efficiency
A widespread myth about automation and intelligence is that software robots will replace the human workforce, ultimately leading to job losses. However, automation capabilities can help in the better execution of manual and repetitive tasks, thus enabling employees to focus on more strategic value-add activities. Improved employee satisfaction and efficiency result in lower attrition and increased productivity, both of which are important factors to create a sustainable competitive advantage.
Harnessing the Value of Data
Organizations today have access to large amounts of data that can be used to make key business decisions and help in risk mitigation. Leveraging automation and AI can help businesses better integrate gathered data into the current ecosystem. In case the data is unstructured, AI can be utilized to transform information into a robot-friendly format. Together, AI and IA can help in unlocking insights that enhance human decision-making capabilities.
Organizations are recognizing the value of AI by investing in technology that assists it in achieving maximum value for their business. Let us look at some of the AI technologies that can help transform business operations.
Artificial Intelligence of Things (AIoT)
AIoT, an advanced hybrid of AI and the Internet of Things, offers niche capabilities that can both be leveraged once implemented together.AIoT involves intelligent, optimized and real-time orchestration of physical and digital processes across process control systems (PCS), manufacturing execution systems (MES), enterprise resource planning (ERP) and other technologies to increase overall efficiency.
Conversational AI
Interactive voice response (IVR) is an AI solution offering to drive market growth, because it can work with copious amounts of data. Leveraging conversational AI, businesses can improve user experience, IVR containment and omnichannel collaboration to maximize cross-selling and upselling opportunities. Conversational AI will also enable advancements in platform governance, microservices, application programming interfaces (APIs), natural language processing (NLP) optimization and bot repositories.
No Code AI
The growing need for technologies to accelerate and democratize the data science process has paved the way for advanced AI applications.No-code AI creates democratization, empowering line-of-business, management and operational teams with advanced analytical capabilities without requiring specialized data science skills. Many of these no-code platforms offer easy-to-use, visual drag-and-drop tools. One challenge companies face is that the complex workflows currently in use by most AI/ML models won’t allow them to implement no-code solutions. If organizations want to benefit from these tools, they will need to migrate to a more sophisticated eAutoML platform that enables true no-code, end-to-end automation.
Machine learning (ML) and hyper-automation
Hyper-automation works in harmony with AI/ML technologies and leverages digital process automation (DPA) and intelligent process automation (IPA). It also can automate inflexible and unstructured processes that in the past were non-automatable.
For hyper-automation initiatives to be successful, businesses cannot rely on static packaged software; automated business processes thus must adapt and respond to changing circumstances. Almost all of the leading process automation platforms are embedded with aspects of AI/ML to allow for responsiveness. While the Covid-19 pandemic caused an increased need for learnable solutions, these enhanced capabilities will continue to be used and improved long after it ends.
AI on the cloud
AI has become integrated into every aspect of human life. The next big opportunity in digital transformation is integrating the cloud with AI-powered devices to organize and retrieve data. This collaboration not only enhances the performance of AI-enabled devices, but also allows unstructured data sources such as conversations to be collected, analyzed and used to a company’s benefit.Merging AI and cloud to scale won’t be easy, but it’s inevitable. Companies need to think beyond implementing ML tools solely to enhance customer service, and to harness the power of the cloud to optimize the entire customer journey.
The future is here, and automation and artificial intelligence is certainly a part of it. Automation and artificial intelligence are the keys to augmenting business performance with technological innovation and human capabilities, and to solving important problems today. The increasing switch to digital has become imperative for enterprises to effectively align business strategies, not only as a response to this change but also to pave the way for digital transformation.
| 2022-12-30T00:00:00 |
2022/12/30
|
http://www.avaali.com/illuminar-digitalenterprise/the-future-of-digital-business-leveraging-automation-ai-for-transforming-operations/
|
[
{
"date": "2022/12/15",
"position": 83,
"query": "artificial intelligence business leaders"
}
] |
Why hasn't AI delivered on its promise?
|
Why hasn’t AI delivered on its promise?
|
https://www.deloitte.com
|
[
"Fellow",
"The Centre For The Edge Consulting",
"Lead Partner",
"Deloitte Strategic",
"Business Design",
"Peter Evans-Greenwood",
"Dr. Kellie Nuttall",
"Dr. Amina Crooks"
] |
... job displacement. Or it might be the case that deploying AI solutions ... automation of legal business processes (making the documents digital) and the ...
|
AI exploded out of research some ten years ago, promising to deliver all manner of science fiction solutions. From autonomous cars and perfect prediction machines1 for business, through to ushering in the singularity (where machine intelligence accelerates past human intelligence).2 Pundits predicted systemic disruption as AI eliminated the need for humans in many fields of endeavor. Some even went so far as posit that we should stop training professions such as radiographers,3 as AI would soon be so superior that human radiographers would find it impossible to compete.
Despite all this promise, adoption of AI is not where many expected (or hoped) that it might be. Research continues to improve the underlying AI technology—the recent of development of both Midjourney and Stable Diffusion4 is a case in point—and firms continue to invest in AI.5 We even saw a bump in investment during the first couple of years of the pandemic.6 However, a majority of AI projects fail.7 Compelling demonstrations are not transitioning into value creating solutions. Autonomous cars are a prime example, where commercial, mass market, versions constantly seem to be a decade away, despite early success and significant investment. We hear a similar story from AI practitioners working in firms attempting to leverage AI, with carefully developed models and solutions left on the bench as they are either not compelling enough or too fragile to replace existing solutions. There are notable successes, such as machine language translation, however there appears to have been more misses.
Reframing the challenge
It’s possible that broad adoption of AI is being held back by the usual human adoption concerns, such as a lack of the technical implementation skills required, resistance to organizational change, and potential job displacement. Or it might be the case that deploying AI solutions requires significantly more work than anticipated, and it will take some time to work through many issues. We might even ask if AI is not the general-purpose technology8 many assumed it to be, as successful point solutions have not evolved into general business platforms (the way computers, once used in offices for discrete, complex calculations, eventually became part of the business infrastructure9). AI might be a solution to particular types of problems, but we’ve yet to develop the ability to identify the types of problems it is best applied to.10
Before we write off AI though, we might ask if the disappointment in AI is due to a failure in the technology being able to develop quickly enough11 (or as quickly as we’d like), or if some other factor is at play. There has been some speculation that the shortfall in AI realization is due to an inability to translate theory into practice—something akin to the technology commercialization chasm that plagues academic research. This is an unsatisfying explanation, though, as there is no indication that AI is different from other technology domains that don’t suffer similar problems.12
Another possibility is that the emergence of AI was due to the development of complementary technologies—innovations that AI relies on—rather than developments in the core AI technologies themselves. For example, Deep Blue, the solution that triggered the computational chess revolution, could be attributed to a rapid drop in the cost of computing13 along with discovery (by AI researchers) of the Markov Decision Process,14 rather than a key development in the core AI techniques used.
Many (if not all) recent AI developments are at least equally due to the development or commoditization of other technologies, as the development of the core technology itself. The surprise emergence of machine translation in mid-1990s was more likely the result of dropping computation costs and access to digital, multilingual texts, rather than a significant improvement in the underlying theory.15 Similarly, autonomous cars went from research oddity16 to potentially transformative technology due to the development of portable LiDAR17 sensors and powerful portable computers18 that were both lightweight and had miserly electricity requirements.
Technology comes in packages, big and small
Technology comes in packages, as any practical solution relies on a range of complementary technologies as well as the key innovation.19 Consider how the first telegraphs (used to dispatch trains) were not much more than a battery, switch, two conductors, electromagnet, striker, and bell—a package of six complementary technologies.20 New technologies rely on packages of earlier technologies, creating dependencies from our current technologies to their earlier, simpler, building blocks. Over time, these packages of technologies have grown in size. The modern equivalent of the telegraph—sending an emoji via text message—depends on a huge package of technologies that includes mobile computing and digital radios along with global telecommunications networks.21 The complex digital environment we live in means any modern solution will have many dependencies.
In the past, solutions that depended on large packages of technologies were at a disadvantage. It was quite possible, for example, to create impressive AI-powered legal analysis solutions in the 80s and 90s, though these solutions were hamstrung by the need to manually enter information from paper documents before the solution could do its work. Lack of easy access to digital data means that the solution was left on the shelf, uneconomic. Today, on the other hand, the same data is most likely digital, likely stored in a cloud-based document management system that can be accessed via an API, and a solution that was technologically possible but not economically viable now makes sense as it can leverage the many technologies in our technology-rich environment.
Our approach to realizing AI’s promise in the enterprise is predicated on there being some remarkable new development in AI. Assuming that the core AI technology is racing ahead, firms have attempted to seize the opportunity by investing in developing internal capabilities and then targeting these capabilities at problems that seem suitable. Machine learning (ML), for example, is seen as a machine for perfect predictions (given sufficient data) so the challenge is to develop expertise in ML and then find predictions to make perfect.
This approach, after a few early wins, appears to be running out of steam.22 A marketing team, for example, might use machine learning to develop a new customer segmentation model. At first, the solution provides novel (and valuable) insights, but repeated use provides little benefit. Bringing a fresh pair of (machine) eyes to the problem was valuable, but the enduring benefits that were expected from “constantly optimizing the model” never eventuated. A similar dynamic can be seen in other AI research efforts. It’s straightforward, for example, to hire a team of AI graduates, build a prototype autonomous car, and provide compelling demonstrations of the car’s capabilities. It’s very challenging, though, to move past that first prototype. A general autonomous car, one that can operate in the same diverse range of environments that humans can, remains beyond our reach.23
What if, rather than focusing on particular AI technologies, we tease apart the packages of technologies that resulted in those technically possible solutions? Developments in the core AI technology likely have contributed to the solution’s success, but what if the success was also due to developments in complementary technologies?24 Our earlier legal example is a case in point. The barrier to adoption of the first legal solutions was easy access to suitable digital texts, a barrier that was overcome by a combination of the automation of legal business processes (making the documents digital) and the shift to cloud computing (making the digital documents easily accessible online). While the core AI technologies were improved in the intervening years, it was developments in the complementary technologies that transformed the solutions from technically possible to economically viable.
There are two consequences.
First, other AI techniques that haven't yet reached the limelight might also have transitioned from the technically possible to economically viable as they can leverage similar complements. An organization that has used robotic process automation (RPA) to provide digital access to all the steps in a business process, for example, might swap their business process engine25 for an AI-enabled real-time planning engine to dynamically manage business processes.26 This has the effect of eliminating (static) business processes (as processes are constructed dynamically by the planning engine), along with business exceptions (as each process is constructed to meet the particular needs of a single transaction).
Second, and more interesting, rather than look for particular (novel) AI technologies and then try to find potential problems that the technology might address, we can look across the enterprise to find challenges and opportunities where the changes to the complements enable new approaches, and then pull in a range of technologies (new and established) to address these challenges and opportunities.
Our natural recency bias27 leads us to focus on the novel and new. However, the history of big ideas tends to be a story of the drive to find a better way, coupled with the incremental development of otherwise banal technologies. The development of the global multi-modal container network is a great example28—the genesis of which was in 1956 when Marshall McLean (1914-2001) launched an all-container shipping line built on the realization that up to half of shipping costs were due to the manual handling required to move cargo between transport modes (from boat to truck to train to truck and so on).29 All-container shipping streamlined these mode changes to realize dramatic savings, and so usher in the next phase of globalization.
Big disruptive innovations tend to be the result of the incremental development of existing ideas which, at some point, come together in a way that amplifies their effect. The earlier legal example is one such instance, as is the story of machine translation. If we construct a figure that captures the technology novelty and the impact of the solutions, such as machine translation and the global multi-modal container network, then we find ourselves with the two-by-two shown in figure 1.
Share image Or copy link Copy
We tend to look for opportunity under the streetlight of technical novelty—in the lower-right quadrant of our two-by-two—trying to find those big new technologies that will change the world. This is often a fruitless search. A more productive approach might be to acknowledge that a significant factor in AI’s recent successes has been the development of our digital infrastructure. Rather than looking for opportunities to apply particular AI technologies that have been successful in the past, look for problems in our business environment where the operating context has changed significantly in the past five to ten years. These might well represent problems whose time has come, as we can now bring a range of digital tools to create innovative new solutions to old problems.
New solutions for old problems
Consider call centers: In 2014, the then CEO of Telstra, Australia’s largest mobile phone company, made headlines with a forecast that within five years there would be no people in its call centers.30 AI-powered automation was forecast to replace human call center workers with digital ones. Since then, call centers have deployed a range of AI technologies to augment humans and streamline operations. IVR systems have been replaced with AI chatbots that use natural language processing to resolve simple queries while directing other calls to humans. Those same chatbots can answer voice calls thanks to improved voice recognition. AI-powered mood analysis can even alert human workers to attend to anxious or distressed callers. But despite these many improvements, the promised transformation has yet to come to fruition. Call centers continue to rely on human call center workers.
What if AI’s lack of progress is due to a lack of imagination? We might move beyond automating existing business practices to optimize or reinvent them, to integrate human and machine work in new ways. For example, it took 30 years from the initial electrification of factories for production engineers and managers to realize that electric power distribution could be used inside the factory, and consequently reorganize the factory floor to optimize workflow (rather than mechanical power distribution) leading to a 30% improvement in total productivity with the same machines, staff, and floor space.31 Similarly, AI enables us to rethink the call center. Rather than automate existing work practices, we can invent new ones, optimizing the call center or even redefining the role that call centers play in an organization, and the role human workers play in the call center.
To optimize a call center, given the current technologies and infrastructure we have at our disposal, we need to approach the work done there in a new way.32 Rather than chipping away at automating (some) worker tasks, we can blend the activities of humans and AI workers, envisioning work as a set of complementary behaviors and expertise (both AI and human) focused on framing and addressing a problem. Let’s call this behavior-based work.33
Behavior-based work can be conceptualized as a team standing around a shared whiteboard, each holding a marker, responding to new stimuli (text and other marks) appearing on the board, carrying out their action, and drawing their result on the same board. Some team members are human, while others are represented by AI behaviors. The whiteboard is a shared context34 against which both human and digital behaviors can operate. Contrast this with the task-based work of a traditional organization,35 which is more like a bucket brigade where the workers stand in a line and the “work” is passed from worker to worker on its way to a predetermined destination, with each worker carrying out their action as the work passes by.
The starting point in our contact center example might be a transcript of the conversation so far, created via a speech-to-text behavior. A collection of AI “recognize-client” behaviors monitor the conversation to determine if the caller is a returning client. This might be via voiceprint or speech-pattern recognition, or the client could state their name clearly enough for the AI to understand. They may have even provided a case number or be calling from a known phone number. Or the human worker might step in if they recognize the caller before the AI does. Regardless, the client’s details are fetched from case management to populate our shared context, the shared digital whiteboard, with minimal intervention.
As the conversation unfolds, AI behaviors use natural language to identify key facts in the dialogue. A caller mentions a dependent child, for example. These facts are highlighted for both the human and other digital behaviors to see, creating a summary of the conversation—the case being investigated—updated in real time. The worker can choose to accept the highlighted facts or cancel or modify them. Regardless, the human’s focus is on the conversation, and they only need to step in when captured facts need correcting, rather than being distracted by the need to navigate a case management system.
The whiteboard metaphor enables us to transform the problem we’re solving from the difficult one of automating human tasks (or even, creating an artificial human), to the much more tractable one of finding useful and complementary AI behaviors to add to the system.
This approach can be extended by applying our call center whiteboard metaphor to solve old problems in new ways. We might apply this technique to a forklift in a warehouse, for example, enabling us to create a new approach to forklift support and maintenance, with the forklift “calling in” when it needs support, and the human and AI workers collaborating around identifying and resolving its problem(s). Rethinking how to apply AI in this way enables us to identity three new possible approaches, for a total of four strategies for leveraging AI in the enterprise (figure 2).
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Augment, the lowest level, is the familiar and common strategy of using AI to augment an existing task, such as leveraging the perfect predictions of AI to augment (or even replace) workers, to improve productivity. We might also eliminate waste, reducing costs, by streamlining tasks to eliminate waste—as the earlier example of swapping static processes for dynamic real-time planning did. Our call center and forklift examples take a more aggressive approach, optimizing work by reorganizing it along different lines. Late electrification of factories, and the invention of workflow, is the non-AI equivalent. Finally, we might imagine situations where a combination of the modern digital workplace and AI enable us to change what work needs to be done, to renegotiate our relationships with external actors (other teams, departments, firms, or even with our clients and customers) to create new opportunities, solve new problems, and unlock new value.
Common approaches to applying AI have us taking our AI hammer and looking for suitable nails to hit. While this has seen some success, it might be more productive to:
Look across the enterprise for situations or problems where changes in the digital complements—documents that have been digitized, tasks made available via robotic process automation (RPA), and so on—have been (or can be) matured to the point that AI can be applied. Consider how the work can be transformed with the AI techniques we have at hand—can it be augmented, streamlined, optimized, or even renegotiated—via the judicious applications of techniques such as whiteboard work. Consider a broad palette of AI techniques (and possibly even non-AI techniques), not just the big names, when constructing your solution.
The promise of AI remains out of reach as automating an activity involves transforming it. The real opportunity, the path to unlocking the full value of AI, requires us to think about work differently.36
There’s work ahead, but also significant rewards
Despite setbacks after its early promise, there is still a lot of value to be realized from AI. Efforts to apply particular AI technologies to particular problems may be stalling, but there is a lot of fertile ground yet to explore.
The challenge is to think differently. Rather than focusing on particular AI technologies and assuming that the technology is racing ahead, we need to think in terms of packages of technologies where our assumptions about some of the complements might be wrong. Rather than looking for problems that a “hot,” new technology might address, we need to look for challenges or opportunities where changes to complements enable a new approach.
There will always be barriers. The larger the scope of a change, the larger the challenge. Existing practices can be also difficult to shift. However, if we’re clear and realistic about both opportunities inherent in challenges and barriers, then AI can create significant value.
| 2022-12-16T00:00:00 |
https://www.deloitte.com/us/en/insights/topics/emerging-technologies/ai-adoption-challenges.html
|
[
{
"date": "2022/12/16",
"position": 60,
"query": "automation job displacement"
},
{
"date": "2022/12/16",
"position": 4,
"query": "workplace AI adoption"
},
{
"date": "2022/12/16",
"position": 9,
"query": "AI economic disruption"
},
{
"date": "2022/12/16",
"position": 30,
"query": "artificial intelligence business leaders"
}
] |
|
Robots' Impact and Human Employment Opportunities Essay
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Robots' Impact and Human Employment Opportunities - 1037 Words
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https://ivypanda.com
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[] |
Analysts predict robots could replace two million manufacturing workers by 2025 (Semuels). However, this data may be changed because the COVID-19 impact is hard ...
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Introduction
The problem of machines replacing human labor is vital not only in our perception of the present, where robots and artificial intelligence are gradually replacing humans. In fact, this situation can be traced in history for centuries. Many technologies are associated with the simplification of complex functions, and progress in this way has historically eliminated the need for one or another human labor. However, due to the digitalization and automation of many processes, the workforce began to be supplanted by computers even more intensively.
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Technologies in the Times of the COVID-19
Additional worrisome prospects are observed precisely in the 2020s, when the pandemic that swept the world changed the rules of doing business and began to dictate its own rules. Due to the need for companies and industries to continue functioning, while people found themselves in a situation of needing to isolate themselves from each other, robots have replaced many workers. Automation of initially human work turned out to be not only safer based on the pandemic situation, but also proved to be more cost-effective. Many of the costs of complying with the isolation rules, the costs associated with the spread of the disease, can actually be offset by replacing the workforce with robots. Analysts predict robots could replace two million manufacturing workers by 2025 (Semuels). However, this data may be changed because the COVID-19 impact is hard to predict, understand, and analyze.
Robots Replacing Humans in Workplace
Robots are able to perform basic work at a high level due to a number of skills to be implemented as a part of their programs. As a rule, such work does not require specific social intelligence like emotional involvement or the manifestation of social and intellectual education (Partington). Artificial intelligence can be used to make automated calls, notifying the addressee or even providing automated assistance, and advice to a live interlocutor. Robots are gradually displacing the workforce in the textile and clothing industries, leaving a large number of the lower class in India out of work, unable to obtain other income. Automation is a rapidly evolving process in the service industry, as cashiers and waiters can potentially be supplanted by machines. The ongoing process of automation in the service sector has the potential to leave millions of people unemployed.
Robots and AI in Healthcare
The use of robots in medicine has a long history, since robots performed the simplest operations back in the 1990s. However, at the moment, artificial intelligence can be used not only for performing mechanical physical labor. The mechanisms turn out to be able to solve intellectual problems using the loaded database for decision making. Algorithms designed to analyze medical history and establish a diagnosis and treatment strategy are already being actively implemented in hospitals. At the moment, the automatic diagnostic process can demonstrate itself as more reasonable and qualified than the conclusions of another medical worker, simply by virtue of the ability to process more information. Thus, the professions, like those of diagnostic doctors or nurse coordinators, can be easily replaced by an analytical program.
Threats and Opportunities
Many benefits and threats are usually associated with the implementation of robots in everyday activities. At the moment, it is claimed that more than 1.5 million rudimentary work tasks have been replaced with automated process execution by robots (Casey and Nzau). The threat of robots crowding out machine labor implies not only the loss of jobs, but also the impoverishment of certain classes of the population of countries. Workers who have lost the ability to provide for their families require state social support. The state can support the new volunteers of unemployment by providing financial assistance. However, more valuable would be the opportunity for emergency professional retraining that allows them to adapt to the new workspace. Modern reality implies certain flexibility of human labor skills; however, not everyone, being tied to their work, may be able to adapt to the coming robotic revolution. Therefore, special attention should be paid to the professions market, which, on the contrary, becomes open with the filling of the working environment with robots.
New Spheres of Professional Skills
The loss of more unnecessary work positions does not necessarily mean that new jobs requiring new skills will not appear because of ongoing automation. Changes in manufacturing sectors mean the emergence of new professions that will be accordingly appreciated above the previous ones. Rethinking work strategies and implementing new production tactics developed and applied since the coronavirus era will also require new jobs. In general, the claim that robots generate more work than they eliminate seems to be right at this point. However, the work related to the maintenance of robotics and monitoring of machines still requires human control. The same applies to the maintenance and care of service systems in any area, from food to medical. Successful interaction between humans and robots will perhaps be even more appreciated. Also, the modern era, which is increasingly turning into its virtual analog, can offer new jobs in the field of technology, as well as in the development and use of cyber economics.
Conclusion
Employment opportunities of humans undergo multiple changes due to the implementation of robots in different spheres. It is hard to predict the impact of such interventions, but it is wrong to believe that these contributions are of a negative outcome only. It is fundamentally important that, in the foreseeable future, the state should spend as many resources as possible on the adaptation of vulnerable classes of the population to new technological conditions. In other words, people would get an opportunity to study new subjects and learn how to implement their technological knowledge in everyday practice. However, the impossibility of carrying out such programs would mean an impending economic catastrophe for an entire class of the population. Thus, government initiatives should provide a sufficiently suitable ground for training the unemployed in new skills, and this potential crisis can be overcome. New jobs will be added to provide a person with greater comfort since the work performed in a new, robotic world requires more mental than physical activity. In general, robots would increase employment opportunities from one perspective (more comfort and simplicity) but reduce some opportunities from another perspective (replacement of labor).
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Works Cited
Casey, Marcus, and Sarah Nzau. “Robots Kill Jobs. But They Create Jobs, too.” Brookings, 2019, Web.
Partington, Richard. “Robots in Workplace ‘Could Create Double the Jobs They Destroy‘.” The Guardian, 2018, Web.
Semuels, Alina. “Millions of Americans Have Lost Jobs in the Pandemic.” Time, 2020, Web.
| 2022-12-16T00:00:00 |
https://ivypanda.com/essays/robots-impact-and-human-employment-opportunities/
|
[
{
"date": "2022/12/16",
"position": 24,
"query": "AI replacing workers"
}
] |
|
Research - IBM Careers
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IBM Careers
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https://www.ibm.com
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[] |
Use IBM Watson's AI or build your own machine learning models Aggregate and ... It's why I chose this job. I love teamwork, and teamwork is why I love ...
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We empower our IBMers to exemplify behavior that fosters a culture of conscious inclusion and belonging, where innovation can thrive. We're dedicated to promoting, advancing and celebrating the plurality of thought from those of all backgrounds and experiences.
| 2022-12-16T00:00:00 |
https://www.ibm.com/in-en/careers/research
|
[
{
"date": "2022/12/16",
"position": 71,
"query": "machine learning job market"
},
{
"date": "2022/12/16",
"position": 25,
"query": "generative AI jobs"
}
] |
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The Top Skills in Demand for Programmers - Coder Bible
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The Top Skills in Demand for Programmers: A Look at the Current Job Market
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https://coderbible.io
|
[
"Coder Bible"
] |
One of the most notable trends in the programming job market is the increasing demand for professionals with expertise in data science and machine learning.
|
Despite recent news of layoffs in some areas of the tech industry, the programming job market is generally healthy and experiencing growth in certain areas. As a programmer, it is important to stay up-to-date on the latest trends in demand for different skills in order to position yourself for success in the job market. In this article, we will analyze the current state of the programming job market and discuss some of the key trends in demand for different skills.
One of the most notable trends in the programming job market is the increasing demand for professionals with expertise in data science and machine learning. These fields are experiencing rapid growth as organizations seek to leverage the power of data and artificial intelligence to drive business decisions and optimize operations. As a result, there is a high demand for programmers with skills in languages such as Python and R, as well as experience with libraries and frameworks such as scikit-learn and TensorFlow.
Another trend in the programming job market is the increasing demand for professionals with expertise in web development. The growth of the internet and the proliferation of mobile devices have created a need for programmers who can build and maintain web applications and APIs. There is a high demand for professionals with skills in languages such as JavaScript and TypeScript, as well as experience with frameworks such as React and Angular.
Another trend in the programming job market is the increasing demand for professionals with expertise in cloud computing. As organizations migrate more of their operations to the cloud, there is a need for programmers who can build and maintain cloud-based applications and infrastructure. There is a high demand for professionals with skills in languages such as Java and Python, as well as experience with cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure.
Finally, there is a trend towards specialization in the programming job market, with employers seeking programmers with deep expertise in specific areas rather than generalists. This trend is driven by the increasing complexity of modern software systems, which require highly specialized knowledge and skills to build and maintain. As a result, there is a high demand for programmers with skills in areas such as security, DevOps, and artificial intelligence.
In conclusion, the programming job market is constantly evolving, with trends towards specialization and increasing demand for skills in data science, web development, cloud computing, and other areas. While there may be some layoffs in certain areas of the tech industry, the overall market is healthy and there are many opportunities for those with the right skills and experience. As a programmer, it is important to stay up-to-date on the latest trends and continuously work to improve your skills and knowledge. With hard work, dedication, and a willingness to learn, you can position yourself for success in the programming job market and achieve your career goals.
| 2022-12-16T00:00:00 |
https://coderbible.io/top-skills-in-demand-for-programmers-software-job-market
|
[
{
"date": "2022/12/16",
"position": 82,
"query": "machine learning job market"
}
] |
|
Global AI Policy
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Future of Life Institute
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https://futureoflife.org
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[] |
Artificial intelligence (AI) holds great economic, social, medical, security, and environmental promise. AI systems can help people acquire new skills and ...
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How countries and organizations around the world are approaching the benefits and risks of AI
Artificial intelligence (AI) holds great economic, social, medical, security, and environmental promise. AI systems can help people acquire new skills and training, democratize services, design and deliver faster production times and quicker iteration cycles, reduce energy usage, provide real-time environmental monitoring for pollution and air quality, enhance cybersecurity defenses, boost national output, reduce healthcare inefficiencies, create new kinds of enjoyable experiences and interactions for people, and improve real-time translation services to connect people around the world. For all of these reasons and many more researchers are thrilled with the potential uses of AI systems to help manage some of the world’s hardest problems and improve countless lives.
But in order to realize this potential, the challenges associated with AI development have to be addressed. This page highlights four complementary resources to help decision makers navigate AI policy: A dashboard that helps analyse the current documents published on the OECD website, a global landscape of national and international AI strategies; a list of prominent AI policy challenges and key recommendations that have been made to address them; and a list of AI policy resources for those hoping to learn more.
1. National Strategy Radar
NOTE: This resource is not designed for use on mobile. Please view on desktop for the best experience.
The Future of Life Institute has partnered with PricewaterhouseCoopers in the development of an initiative to analyze the soft and hard law efforts to govern artificial intelligence (AI). The dashboard below was created with the help of a natural language processing tool that categorized documents downloaded from the OECD’s AI governance database in February of 2022. Further background information on this initiative is available in this blog post and users can expect periodic updates to this resource.
Summary View
This dashboard summarizes the distribution of AI documents published by governments and sorted by geography, year, and topic. The fact that a country lacks a bubble does not mean it lacks documents relevant to artificial intelligence. Rather, it indicates that they are not available within the OECD database.
How to use: Clicking on one of the countries on the map will display the year and topic distribution of that country.
Clicking on a topic in the bottom right frame will display the distribution of that topic on the map and the bar chart.
Document View
This view gives an in depth look at all the documents individually, organized by their country of origin and the topics identified via a natural language processing-based dashboard developed by PwC.
How to use: Clicking on the download icon to the left of the file name will open the document in question
On the right hand side, documents can be filtered by year of publication
On the left hand side, users can select and filter by topic
This page is intended as an introduction to the major challenges that society faces when attempting to govern Artificial Intelligence (AI). FLI acknowledges that this list is not comprehensive, but rather a sample of the issues we believe are consequential.
Here are ten areas of particular concern for the safe and beneficial development of AI in the near- and far-future. These should be prioritised by policymakers seeking to prepare for and mitigate the risks of AI, as well as harness its benefits.
The evolution of AI systems has proved to be so rapid and continuous that society now expects novel methods and applications everyday. To keep up, stakeholders in the public, private, and nonprofit worlds are responding with a variety of instruments, from soft to hard law, and academic or grey literature. As a result, the resources that describe and respond to the policy challenges generated by AI are always in flux. This page contains a few excellent resources to help you stay up to date.
| 2022-12-16T00:00:00 |
https://futureoflife.org/resource/ai-policy/
|
[
{
"date": "2022/12/16",
"position": 34,
"query": "future of work AI"
}
] |
|
Vic.ai: is Defining the Future of Accounting
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Vic.ai: is Defining the Future of Accounting
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https://www.vic.ai
|
[] |
The world is changing rapidly, and technology is a major player in this change. We have seen a lot of technological advancements in the last two decades, ...
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The world is changing rapidly, and technology is a major player in this change. We have seen a lot of technological advancements in the last two decades, but artificial intelligence (AI) is the biggest technological leap forward since the modern computer. This has opened up many new possibilities in all fields, including accounting, so this is where we entered the scene to shake things up.
Vic.ai is pioneering the use of autonomy and intelligence to digitally transform accounting and finance processes to improve productivity, decision-making, and ROI. We are the first pure AI accounting solution that lets you get back to working on tasks that have a real impact on your company. We are Vic.ai.
Who is Vic.ai?
Here at Vic.ai, we help accounting teams automate tedious AP data entry through artificial intelligence (AI) & machine learning (ML). We are the world's first pure AI accounting technology that works alongside teams in the accounting process to aid in invoice processing and other accounting-related tasks.
How do we do it?
Vic.ai adds a sophisticated artificial intelligence engine and autonomous approval flow to layer across your financial account payables operations. We've specialized in processing and understanding invoices and extracting real-time insights for better and faster financial decision-making using our world-leading AI solution and modern User Interface (UI). In short, we expedite internal processes and cut unnecessary costs —without cutting corners.
Our solution consists of two pillars: Autonomy and Intelligence.
The new autonomy features will be additions to award-winning features like:
Autopilot : intelligently selects invoices and expenses that meet a certain confidence level and automates them so that they are immediately sent to the approvers without requiring any data entry or classification review by a human. This includes everything from vendor identification to cost and dimensional classifications on a line item level.
: intelligently selects invoices and expenses that meet a certain confidence level and automates them so that they are immediately sent to the approvers without requiring any data entry or classification review by a human. This includes everything from vendor identification to cost and dimensional classifications on a line item level. Autonomous Approval Flows: determines the correct number of steps in the invoice approval process and who needs to review each step automatically.
Then when it comes to intelligence, we’re known for:
Vic.ai Analytics: provides clients with customizable processing insight dashboards and includes performance data based on user, region, AI accuracy, processing time, and more. With Vic.ai Analytics, finance teams get crucial data to pinpoint issues in real-time. Measuring key metrics enables optimizing a once complex and manual operation while enabling sustainable change management and creating competitive advantage with data insights.
The problem we’re solving
No matter the industry, every company needs accounting, which is inheritably tedious and time-consuming. Accounting tasks have always been managed by using legacy systems that are based on various predefined rules and templates. We throw the need for rules and templates out the window. Because let’s be honest, that’s not scalable.
With the Vic.ai platform, the whole process can work autonomously, not requiring any human intervention. This approach shouldn’t be confused with automation, which is rule/template-based, pre-programmed, and not adaptable to changes in the environment.
In contrast, Vic.ai’s AI algorithm performs reasoning intelligently and adapts to new requirements, making the accounting process autonomous. Customers see up to 80% process improvements by having our AI algorithms perform the work previously handled via templates and human revision.
Where we got our roots
Founded in 2017, Norwegian entrepreneurs Alexander Hagerup and Kristoffer Roil embarked on a mission to help accounting teams across the globe work better. They both got their fire for accounting at a young age. Alexander “grew up” in his mom’s accounting practice, and Kris has turned around financially troubled companies throughout his career.
One pattern they recognized early on was the fact that companies would undoubtedly face unforeseen accounting obstacles as they began to scale. This is where they decided to set out to create an accounting system that could scale when businesses did. It may have seemed like a lofty goal, but the two of them wouldn’t take no for an answer.
“We saw a need in accounting, and we filled the gaps”
Every company on the planet needs accounting, which is inheritably boring. It’s tedious and time-consuming. It’s always been handled by humans and somewhat automated via templates and RPA. Kris & Alex wanted to create something smarter. An AI that can reason like a human being. They didn’t want to replace humans – they wanted them to do more elevated work.
Today, Vic.ai delivers fully autonomous AI systems that make finance and accounting teams more efficient, accurate, and intelligent. The company’s AI platform has now processed more than half a billion invoices with 97-99% accuracy rating, helping 10,000+ customers achieve nearly $200 million in cost savings and 6 million hours in time savings. Enterprise customers include HSB Real Estate (one of Scandinavia's largest CRE companies), Higher Ground, Nordic Choice Hotel, Intercom Inc. and HireQuest, as well as top accounting firms, including PwC, BDO (Norway and US), RSM (US and Europe) and Armanino LLP redirect resources toward higher-value functions like spend intelligence, benchmarking and cost optimization.
As of September 1, 2021, we raised our Series B funding. The Series B funding was led by ICONIQ Growth with participation from existing investors GGV Capital, Cowboy Ventures, and Costanoa Ventures. The infusion of $50M in Series B funding will bring the total capital raised to $63M. One December 13th, we raised our series C funding, which was led by the same investors, bringing total capital raised to $115 million
Our values
Here at Vic.ai, we believe that innovation is at the core of tomorrow.
But we also understand that without our diverse, global team, we wouldn’t be the company we are today. By nurturing a culture of humility, authenticity, and growth, we’ve been able to rally the help of talented people from around the world to get us where we are today. As an equal opportunity employer, we represent different nationalities, races, genders, ages, sexual orientations, and backgrounds to give us a diverse team with differing perspectives.
So, what else do we value (besides our awesome team)? Our other core values include:
Putting people first
first Making integrity a fundamental
a fundamental Believing in the magic of honesty
Valuing action over motion
over motion And, enjoying the journey
We live out these values, as a company, every day. Without them, we wouldn’t be Vic!
Quick Q&A
Q. Give us some background. Where is Vic.ai?
Vic was founded in 2017 in Norway. The company still has team members in Norway, but now also in the rest of Europe (France, Portugal, Austria, and more). Today, the HQ is in New York, but we have remote employees all across the globe!
Q. Why should we consider Vic.ai?
Vic.ai is pioneering the use of autonomy and intelligence to digitally transform accounting and finance processes to improve productivity, decision-making, and ROI. Our global customers include accounting firms, enterprise, and mid-market finance teams seeking to embrace automation and AI to improve financial performance.
Q. Will features like “Auto-Pilot” replace humans?
Not at all. Accountants still play a critical role, but instead of focusing much of their time and energy on mundane, repetitive tasks, their day-to-day work can shift to higher-level, more valuable jobs and opportunities.
Q. Which industries especially benefit from automated accounting?
Vic.ai is industry-agnostic, but any company that processes more than 100,000+ invoices per year is a great fit! We are continuously developing our offering, and it’s not industry-specific and could work for any type of enterprise.
Q. How reliable is your AI-based decision-making?
Our AI system has a 97-99% accuracy rate. At the end of the day, template-based or other legacy alternatives that require manual inputs achieve significantly less. In fact, according to numerous studies, human error mixed with technology is a leading cause of accounting mistakes.
The future of accounting is Vic.ai
We believe in giving finance professionals the tools they need to make everyday operations simpler and more intelligent. From processing invoices to data predictions, our system is here to give you the bandwidth you need to complete those more meaningful tasks in your day-to-day.
If you haven’t figured it out by now, we are disrupting the world of accounting by challenging the status quo. We may just be the group of game-changers and innovators that you didn’t know you’d be so happy to meet.
Are you ready to eliminate mundane tasks and expedite your invoice processing times with AI? Book a demo to see Vic.ai in action and how our AI-based invoice processing system can help your business thrive!
Enter the new era of intelligent accounting. Access our latest e-book to discover 7 ways to unlock faster and more accurate accounting.
| 2022-12-16T00:00:00 |
https://www.vic.ai/blog/vicai-the-future-of-accounting
|
[
{
"date": "2022/12/16",
"position": 69,
"query": "future of work AI"
}
] |
|
Psychological Safety: Artificial Intelligence
|
Psychological Safety: Artificial Intelligence
|
https://psychsafety.com
|
[
"Tom Geraghty"
] |
AI can potentially impact psychological safety in several ways. For example, the use of AI in decision-making can create a sense of uncertainty among employees.
|
Welcome to the psychological safety newsletter and thanks for subscribing. You are amazing. This week discusses artificial intelligence and psychological safety.
Psychological safety and safety culture workshops
In the New Year, we’re running two new workshops! The first is a 2-hour Intermediate Psychological Safety Workshop on Thursday 19th January at 3pm UK time, and on Tuesday 24th January at 3pm we’re running an Advanced Psychological Safety Workshop.
In the intermediate session, we cover the essentials of safety culture, safety theory, examples of good practice from different industries, and facilitation practices as well as team and leadership practices. This is for you if you’re interested in psychological safety and you’d like chance to learn more in a friendly environment.
In the advanced session, we dive further into the theory and look also at sociotechnical systems, sociological research methods, organisational dynamics and resilience engineering. This is for you if you have already done a workshop with us, or have a good working knowledge of psychological safety and want to deepen your understanding.
Both sessions are intended to draw on the expertise of the attendees as well as the course material. It’s going to be a lot of fun!
Prices based upon affordability. Both sessions are available to book online now.
I can’t believe it’s nearly the end of 2022! In order to make 2023 even better, I’d love to get your feedback and input on the psychological safety newsletter: what you liked and what you’d like to see more of. And by completing this survey, you’re also in with a chance of winning some psych safety swag!
Click here – it’ll only take a couple of minutes!
Artificial Intelligence and Psychological Safety (Guest Post)
Artificial intelligence (AI) is the simulation of human intelligence by machines. It has the ability to learn, reason, and take action in a way that is similar to human beings. AI has the potential to revolutionize many aspects of our lives, from healthcare to transportation. However, as with any new technology, there are also concerns about its potential impact on society.
One of the key concerns around AI is its impact on psychological safety. Psychological safety is the feeling that individuals have that they can speak up and express their opinions without fear of negative consequences. This is particularly important in the workplace, where employees need to feel safe to speak up about issues or concerns they may have.
AI can potentially impact psychological safety in several ways. For example, the use of AI in decision-making can create a sense of uncertainty among employees. If they feel that their job performance is being evaluated by a machine, they may be less likely to speak up about their concerns. This can lead to a decrease in psychological safety and a decrease in morale among employees.
Additionally, the use of AI in the workplace can also lead to the replacement of human workers. This can create feelings of insecurity among employees and can lead to a decrease in psychological safety. If employees are worried about losing their jobs to machines, they may be less likely to speak up about their concerns.
In order to ensure that AI does not negatively impact psychological safety, it is important for organizations to consider the potential consequences of using AI in the workplace. They should also take steps to communicate with employees about how AI is being used and how it may impact their jobs. Additionally, organizations should provide support to employees who may be affected by the use of AI, such as through training and development opportunities.
In conclusion, AI has the potential to revolutionize many aspects of our lives. However, it is important to consider the potential impact of AI on psychological safety. By taking steps to address these concerns, organizations can ensure that the use of AI does not negatively impact the well-being and psychological safety of their employees.
The above post was written by a guest contributor, but not by a human. This was actually written by the AI system at Chat GPT in response to the request “Write an article about artificial intelligence and psychological safety“. GPT stands for Generative Pre-trained Transformer, and is a prototype artificial intelligence chatbot developed by OpenAI. The chatbot is a large language model which was fine-tuned by humans interacting with it and “training” it. Chat GPT is “stateful”, which means it remembers previous prompts and answers, making it conversational and able to build on, or correct, previous interactions.
Whilst requests get parsed through a moderator API to filter inappropriate requests, it does suffer from a degree of algorithmic bias, sometimes assuming that (for example), the term “CEO” likely refers to a white man. It’s also currently limited to knowledge existing prior to 2021.
Impressively though, it can generate grocery lists for meals, explain complex grammar rules, write bedtime stories for (or even about) your children, help you prepare for an interview, develop software, or simply write your emails for you.
Whilst not always correct in its responses, ChatGPT’s ability to write academic essays and papers is causing both excitement and alarm in the education sector. I tested by asking it to write a short essay that I’ve previously written for my Masters in Global Health with the instruction: “Write about improving health system service delivery in Mexico via the Oportunidades programme” and it generated a 1000 word essay that I reckon would at the very least get a passing grade.
The interesting question here is whether such a tool should be considered cheating, or research. There are already tools to detect AI text available, but are fallible to minor tweaks to text, and can sometimes flag “real” writing as fake. If a student asks ChatGPT to write about a subject, and then writes their own essay, drawing on what they learned from the ChatGPT tool, is that cheating? Should we consider the ability to reference ChatGPT as a source? Other tools are valid for use in assessments, including spellcheck, grammatical tools, and plagiarism detectors, so what about AI language models?
Could the emergence of ChatGPT and similar tools catalyse a reversion back to exams as the primary method of assessment for students? How long will it take academia and schools to realise there’s a potential problem? And if a chatbot can write a decent essay, in less time than the average student, is that really what we should be testing students on anyway? Maybe, instead of exams, we should be assessing skills which have greater ‘real world’ value and longevity, such as problem solving, collaboration and communication?
What do you think about technologies like Chat GPT? How is it going to change our work, lives, and relationships?
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‘The Fearless Organization’ by Amy Edmondson is considered by many to be essential reading on the topic of psychological safety. In this article, Sophie Weston, Principal at Conflux, has put together some key takeaways from the book.
Psychological Safety in the Workplace
This is a powerful piece by Dr. Carey Yazeed, a behavioural scientist who specialises in work culture and work trauma. It addresses head-on the uneven intersectional distribution of being able to be “courageous” in speaking up and standing up for oneself in the workplace – we are not all provided the “same space, cover or protection” to do so – especially Black and Brown women.
Here’s a great new study from Eve Purdy and others, on “Psychological safety and emergency department team performance: A mixed-methods study“, showing that a key foundation for psychological safety in this context was familiarity with colleagues, and also showed that nurses suffered from particularly low psychological safety – a point that resonates here in the UK with the largest nurses’ strike in history this week.
This Week I Learned (TWIL) that incidents at Spotify have songs assigned to them! Here’s the “sounds of glorious failure” by Clint Byrum at Spotify. I love this idea. Only the really big incidents warranted a theme tune, and they were chosen to fit the incident, such as a certificate expiration outage with the theme tune of “Signed, sealed, delivered, I’m yours.” by Stevie Wonder.
In a workshop this week, after learning more about psychological safety, a client of mine came up with the idea of Giraffes vs Pugs. Giraffes stick their long neck out and model psychological safety for others in the team, whilst Pugs (sorry pugs!) don’t really have a neck to stick out at all. I’m not really sure where the metaphor goes from here, but I like the imagery!
Giraffe photo by Sian Cooper on Unsplash
Pug photo by Colin Horn on Unsplash
Eri Hirose, for her MA in Service Design at the London College of Communication, has completed this excellent service design project that aims to help people in creative organisations foster an environment of psychological safety. Embrace Being Human At Work.
Things to do and try:
I had a lovely conversation with Dr Lyn Williams this week about psychological safety in teams and organisations, in healthcare in particular, and the World Cafe principles came up in conversation, so I thought it’d be a good idea to share them. Here is this classic World Café Etiquette graphic illustration, created by visual graphics professional Avril Orloff.
This week’s poem:
“Psychological Safety” by Chat GPT, 9 December 2022
In a safe space, I can be free
To express myself and be me
No judgments, no fear, no shame
A place where I can be the same
I can open up and share my thoughts
Without the worry of being caught
In a trap of criticism or doubt
Here, my voice won’t be drowned out
I can speak my truth and feel heard
And find support in every word
I can take risks and make mistakes
Without the fear of being forsaken
In this space, I am accepted
And my emotions are respected
I can be vulnerable and authentic
And feel a sense of psychological safety
This is a place where I can grow
And let my true self show
Where I can heal and be at peace
In a world that often brings me unease
So thank you, dear psychological safety
For giving me the space to be
Authentic, vulnerable, and free
In a world that can be so unkind.
| 2022-12-16T00:00:00 |
2022/12/16
|
https://psychsafety.com/psychological-safety-90-artificial-intelligence/
|
[
{
"date": "2022/12/16",
"position": 7,
"query": "workplace AI adoption"
}
] |
Digital Workforce Mobility for O&G Companies
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Digital Workforce Mobility
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https://www.cognizant.com
|
[] |
Invest in people to unlock the power of AI. ... Bridge the gap between strong AI leadership and business readiness. ... Explore the future of business with our Gen ...
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Cognizant’s DWM leverages core industrial mobility features such as unified communication, digital documentation, automated workflows and real-time reporting and notifications. This helps create a seamless digital workplace and enables the “digital yard” vision.
Cognizant offers a full suite of business and technology mobile integration services for organizations at all stages of digital and mobile maturity. For clients who have already implemented DWM, we can help:
| 2022-12-16T00:00:00 |
https://www.cognizant.com/us/en/industries/oil-gas-digital-solutions/digital-workforce-mobility-oil-gas
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[
{
"date": "2022/12/16",
"position": 49,
"query": "workplace AI adoption"
}
] |
|
EY and Software AG Partner on Digital Transformation ...
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Software AG and EY Partner on Digital Transformation, Gtmhub's New Name, More News
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https://www.reworked.co
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[
"David Roe",
"About The Author"
] |
He added: “We'll start to see platforms that use AI/ML to monitor key metrics and performance to give companies a better understanding of the health of current ...
|
Darmstadt, Germany-based Software AG has announced a new partnership with professional services company EY (Ernst & Young) aimed to help organizations develop digital transformation initiatives based on business process management, IoT and integration of platforms.
EY is providing the industrial knowledge and Software AG is providing the technology, a statement from EY reads.
According to the companies, the partnership will tackle the lack of data transparency within many organizations and its impact on transformation initiatives and relationships with other companies. Data can be found throughout legacy systems, but without a clear inventory, organizations are operating with only a partial picture.
According to the statement, Sofware AG's platforms — including webMethods, ARIS and Cumulocity IoT — will support the integration side of the equation. This integration will take the form of data integration, application integration, device integration and integration of "things" (e.g. machines, robots, servers). These will also support the development of improved processes.
Software AG's strong process management position is bolstered by its ARIS and Alfabet platforms, which offer BPM and enterprise architecture using cloud-based, agile applications. It also offers advanced governance, risk and compliance (GRC) monitoring as well as ESG management and insights for businesses to improve their operations and cut costs.
The partnership with EY is driven by the latter's experience implementing the Software AG BPM offerings, as well as its experience with the ARIS platform in rolling out large S/4HANA implementations.
Gtmhub Rebrands OKR Offerings
Earlier this week, Denver-based Gtmhub debuted a new company name and rebranded offerings. It is now known as Quantive. Though a company rebrand is common enough, in this case the rebrand provides insights into the growing interest in the OKR (Objectives and Key Results) market.
OKR is a goal-setting framework used by individuals, teams and organizations to define measurable goals across the organization. Gtmhub bolstered its position in the space earlier in the year through two acquisitions, paid for with a successful Series C funding round of $120 million. With the money the company bought Koan, an OKR status and tracking platform, and Cliff Ai, a UK-based operational intelligence software provider that uses machine learning to help business and operations teams track and analyze KPIs.
Quantive’s COO Seth Elliott explained the rebrand to Reworked, saying the updated product names better reflect what companies need to be successful for the future of work. The new names for the products, Quantive Results and Quantive Signals, allow users to better connect strategy to their teams’ day-to-day execution, achieve operational excellence, and use data and insights to inform and improve decision-making each step of the way.
Elliott continued by noting the pressures companies face in light of the rapid changes and macroeconomic business challenges of the last few years. They need “strategic agility,” he said and a way to effectively execute strategic objectives.
“With our rebrand, we’re ... enabling businesses to meet organizational goals on time, on budget, and on target while ensuring all employees are on the same page with how to achieve the goal and what their role is to make that happen,” he told Reworked.
He also said he believes OKRs are one of the most effective ways for organizations to meet their quarterly, biannual and annual goals. In 2022, the OKR software market size was valued at over $923 million. That market size is projected to increase to over $2 billion by 2030. "This rapid growth shows organizations are now acknowledging the importance of closing the gap between strategic priorities and the day-to-day operations of the business," Elliott added.
He predicts some big shifts in the OKR market as companies become more focused on strategy execution, to become less about pure OKR solutions and more about platforms that enable companies to accomplish strategic objectives.
He added: “We’ll start to see platforms that use AI/ML to monitor key metrics and performance to give companies a better understanding of the health of current operations while proactively forecasting the future state of the business to get companies where they want to go.”
This, he explained, is why the company acquired a business observability technology as both strategy execution and operational excellence are necessary for companies to achieve the best possible results.
Meta Changes Direction on Data Centers
Meanwhile, according to Reuters reports, San Francisco-based Meta has stopped the construction of two data centers in Denmark.
While Meta has been quite public about the fact that it will be cutting jobs and curtailing spending, to stop the building of data centers, which are a key part of its business model, seems drastic. However, it seems it hasn't entirely abandoned the project. According to the reports, it has simply changed the focus, which says more about Meta’s forward-looking strategy than anything else.
Citing a spokesperson during the week, Reuters stated that Meta will change focus to new kinds of data centers that will be used to assist with its artificial intelligence projects.
The result is that Meta will halt construction of two of the three Danish centers it is building while the third one will be continued.
Meta spokesperson Peter Münster told Reuters, “A significant part of these measures is to shift a larger part of our resources to high-priority growth areas, including a strategic investment in artificial intelligence.”
AI is a key part of what Meta sees as its future, which is not a huge surprise given the enormous bet it has made on the metaverse. Meta also announced during the week that it is pausing construction of its new plant in Temple, Texas.
Meta announced earlier this year that it was planning to invest $800 million in Temple to create a Hyperscale Data Center.
Twelve Labs Raises $12M for Video Search
Finally, this week San Francisco-based Twelve Labs, which develops a video search company has just announced that it has closed $12 million in funding.
This round, led by Radical Ventures, brings the company's total funding to $17.1 million, according to Crunchbase.
Twelve Labs is a platform that gives businesses and developers access to multimodal video understanding. According to the company, the aim is to help developers build programs that can see, listen and understand video.
Currently in closed beta, it uses AI to take the ‘rich’ data from videos including the sound and text that appears on screen as well as speech to understand the relationship between those objects and people.
Unveiled at the end of March this year, it does this by offering a suite of APIs that enable comprehensive video search in less than one second.
| 2022-12-16T00:00:00 |
https://www.reworked.co/digital-workplace/ey-and-software-ag-partner-on-digital-transformation-gtmhub-rebrands-okr-offerings-more-news/
|
[
{
"date": "2022/12/16",
"position": 71,
"query": "workplace AI adoption"
}
] |
|
December 2022
|
December 2022
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https://www.ugogentilini.net
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[] |
A compilation of seven briefs by IEJ unbundles key issues on universal basic income in the country, including discussing its rationale, financing, empowerment, ...
|
What about guaranteeing a basic job instead of guaranteeing a minimum income? This is a recurrent and much-debated question in social protection, which is now informed by new evidence from Austria. Since 2020, the town of Marienthal is offering a universal guarantee public or private job to every resident unemployed for more than 12 months. Jobs are paid at least the minimum wage (making income higher than under social assistance/insurance payments), with participation voluntary/free of sanctions and duration until 2024. Jobs were generated in carpentry, renovation, public gardening, and elderly care – and some projects were created by participants themselves (e.g., supporting communal public services like local schools and kindergartens). An evaluation of the program by Kasy and Lehner found a range of impacts (see figure above), with one of the estimates indicating a reduction in long-term unemployment by about 1.5 percentage points, down to less than 1% of the working age population. Importantly, not only participants’ incomes rose, but they had also more meaningful interactions with others, felt more valued, and more connected to the community (h/t Lukas Lehner).
More on high-income contexts: a rich brief by the OECD documents how countries are responding to inflation. Approaches have favored broad eligibility as opposed to narrower targeting (about $190B vs $130B respectively, see figure 2, p.4). The brief also sheds light on preexisting challenges – for example, limited indexation of benefits (see box 1 on US and Poland, p.6) and how between 10-45% of transfers go to the richest quintile (25% on average). Among the conclusions, it is argued that “… [u]nlike price regulation and subsidies, income support maintains price signals that are needed for easing supply bottlenecks and rebalancing consumption towards greener energy sources”.
From inflation to the pandemic: Palomo et al have a review of Covid19-related social protection registration and payment practices in Latin America and the Caribbean. So much to like about the paper, including for example the helpful tables on coverage and adequacy stats (p.14-19), or table 3 (p.29) laying out cases where high coverage was achieved with high social registry coverage (e.g., Argentina and Peru) and without it (e.g., El Salvador and Haiti). See also the handy table on different forms of payment system (p.31).
Bonus on pandemic responses, this time focusing on Cambodia: a report by Chhoeung et al documents key challenges encountered – for example related to the unprecedented scale of the cash transfers program and outdated information in the IDPoor database – and discusses the remedial actions during the crisis, including for example poverty self-assessments (which still entailed follow-up physical verifications, see p.8).
Let’s stay in the region: what does a religious perspective tell us about a cash transfer program? Rohmi examine Indonesia’s PKH in light of “Islamic economics”. It finds that the program, which was evaluated in Pringsewu Regency, Lampung, fulfilled several criteria, like the criteria of justice, responsibility, and takaful (redistribution) – as well as “right on target, right in number, and appropriate in use”. However, it didn’t meet the criteria of timeliness due to delays in distribution.
More on East Asia: a report by Karacsony et al discusses data and interoperability issues in Fiji’s social protection system. The note is fascinating as it not only examines the country’s interoperability ecosystem (e.g., see crisp summary on p.14), but it also synthesizes and array of global practices on the matter – for example, box 1 (p.5-7) and box 2 (p.9-10) are really nicely structured (h/t Sandor Karacsony).
Moving to Africa, Valli et al evaluate a “cash plus” program for agropastoralists in Somalia. The full intervention included cash transfers and an agriculture package adapted to different livelihood zones. The latter was delivered as an e-voucher through local traders and included seed kits for cereals, pulses and assorted vegetables, as well as farm tools and storage bags (a training was also included). The evaluation then compared three groups – those receiving inputs only, cash only, and cash plus inputs. Main result? Input-only recipients increased asset wealth and livestock, while the cash plus inputs showed better food security outcomes (food consumption scores).
Let’s continue discussing food security: Mncube et al investigate the relationship between cash transfers and food security in South Africa’s KwaZulu-Natal’s province. They show that food security hinged on access to cash transfers, finance, agricultural training and other factors. They also show that older household heads had a larger probability of getting transfers, and so did larger households. Bonus on South Africa: a compilation of seven briefs by IEJ unbundles key issues on universal basic income in the country, including discussing its rationale, financing, empowerment, gender, health, jobs effects and targeting.
Two resources on child-sensitive programing: in neighboring Lesotho, Carraro and Ferrone found that the Child Grant Programme reduced multidimensional deprivations among children living in labor-constrained female-headed households. And Castillo and Marinho document the profound negative impact that the pandemic had on various dimensions of children’s wellbeing in Latina America, and set out recommendations for social protection (see p.35-39).
Shifting to climate, what is needed for smallholder farmers to better manage climatic risks? A blog by Dahlet argues that “… [a]ccess to adequate social protection in rural areas must be massively and rapidly expanded” (h/t Marco Knowles). And this is a great resource: FAO has a free, virtual course on managing climate risks through social protection.
Acting early in crises is good economics; but how to finance anticipatory action? Bharadwaj and Mitchell lay out four options for risk financing of social protection, including insurance-linked anticipatory responses; debt support based on thresholds; global taxation regimes; and carbon markets and resilience bonds. Bonus: a brief lays out WFP’s perspective on anticipatory responses.
More on crises, but from a displacement angle: Lowe et al devised a conceptual framework for connecting humanitarian assistance and social protection in forced displacement. The framework is the result of adapting, reconciling, and integrating various analytical contributions from the literature – see the compact final result on p.36, including contextual factors, actors, options and entry points, and first and second order effects. Bonus: Lowe also has a well-organized compendium on summary theories linking social protection and humanitarian assistance. Double bonus: check out NRC’s perspective unpacking “policy” vs “implementation” of Poland’s assistance to Ukrainian refugees (h/t Larissa Pelham).
And what about elderly populations? A brief by HelpAge contains recommendations for strengthening the understanding of older people’s rights and needs within humanitarian responses and the wider humanitarian system (h/t Pip O’Keefe).
Benchmarking! A piece by Christiano reflects on six CEGA-sponsored cash benchmarking studies. What is benchmarking about? Basically, it is meant to “… developing a profile of what to expect from a given type and size of cash transfer for certain groups of people. This profile, which would naturally become more reliable as the evidence base grew, could serve as a benchmark for traditional aid and inform consequential funding and programming decisions”.
Speaking of comparisons… the “SPEED” database, or “SP Expenditure and Evaluation Database” for the Europe and Central Asia region, is online with social assistance, insurance and labor market indicators across countries and over time. Enjoy! (h/t Cem Mete).
Let’s step back and look at the historical roots of today’s social protection: Davy traces the factors that shaped social protection trajectories in countries like China and Brazil. In particular, the study/chapter examines how the Bismarckian approach to social protection was transplanted and ideas spread. The ILO played a particularly prominent role in facilitating the transmission of such thinking in the 1920s and 1930s. More generally, “… Southern welfare emerged against the backdrop of Northern welfare and involved a process of constant testing and weighing of Northern ideas against the local concepts and the local interests”. And in India, Sen discusses the evolution from “growth-based welfare to welfare entitlements”.
More on Brazil: What happens when the minimum wage is raised in a high-informality setting? Engbom and Moser present evidence from Brazil showing that following a nearly 130% increase in the minimum wage over 22 years, about half of the reduction in earning inequality can be attributed to such hike (although effects on employment and output are muted) (h/t Mattias Lundberg).
Oh, if there is still any doubt, a paper on Brazil by Maciel and Duarte shows that cash transfers are spent wisely!
| 2022-12-16T00:00:00 |
https://www.ugogentilini.net/2022/12/
|
[
{
"date": "2022/12/16",
"position": 34,
"query": "universal basic income AI"
}
] |
|
DARPA Announces Winners of AI for Critical Mineral ...
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DARPA Announces Winners of AI for Critical Mineral Assessment Competition
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https://www.darpa.mil
|
[] |
... economy and security. Dec 16, 2022. Critical minerals are essential to the U.S. economy and national security; however, their supply is vulnerable to disruption ...
|
Critical minerals are essential to the U.S. economy and national security; however, their supply is vulnerable to disruption. U.S. production and refining of critical minerals has been declining for decades, while production has become more concentrated in fewer countries.
Given the urgency to increase and better secure critical mineral supply, DARPA partnered with the U.S. Geological Survey (USGS) to launch the AI for Critical Mineral Assessment Competition in August 2022.
The partnership will help the USGS conduct assessments for more than 50 critical mineral resources to aid in economic planning and land-use decision-making. To do this, the USGS draws from over a century of accumulated data, contained mostly within geologic maps and reports, that provide the fundamental basis for these resource assessments.
Extracting useful and accurate information from these maps is a time-consuming and laborious process involving manual human effort. In fact, a typical assessment for one critical mineral takes approximately two years to prepare. That’s because the USGS map catalog consists of over 100,000 geologic maps; only about 10% of those are available as georeferenced images and only about half of those are fully digitized vector files needed for analysis. Everything else – 90% of the data – consist of scanned images of paper maps.
The goal of the competition was to crowdsource ideas that could drastically reduce the time required to complete parts of the assessment using AI and machine learning to automate key processes.
After analyzing the mineral assessment workflow, DARPA and its performers MITRE and NASA Jet Propulsion Laboratory recognized the greatest potential for near-term, high-impact in solving the data needs associated with georeferencing and extraction of individual geologic features found on USGS maps. As such, the competition was divided into two distinct sub-challenges. A total of 18 teams from industry, academia, and even a high school junior, competed for cash prizes of $10,000 for first place, $3,000 for second, and $1,000 for third.
For the Map Georeferencing Challenge, participants were tasked to find a map within a given scanned image, and georeference it by aligning reference points to base maps, such as grid lines, topography, administrative boundaries, roads, or towns. A Canadian company, Uncharted, received top prize for their simple, clean, and organized solution. U.S. company Jataware received second place, and “Team Ptolemy,” with members from the Massachusetts Institute of Technology, University of Arizona, and Pennsylvania State University, received third place.
For the Map Feature Extraction Challenge, participants were asked to extract features identified in an image’s map legend. Students and faculty from the University of Southern California Information Sciences Institute and University of Minnesota joined forces, earning first place for their exceptional solution to extract line features as well as polygons and points. “Team ICM” from the University of Illinois received second place, followed by Uncharted in third.
Throughout the competition, participants had up to eight weeks to complete each challenge. Each week, they had the option to submit their results for a blind validation dataset to test the accuracy of their code. In the last week of each challenge, participants received a completely blind evaluation dataset and had 24 hours to process and submit their code and detailed documentation of their approach, which was evaluated by experts from USGS, MITRE, and NASA Jet Propulsion Laboratory, who reviewed the solutions for accuracy/usability.
To meet the high-quality standards required by the USGS, the resulting solutions require further evaluation and development to become operational. USGS experts plan to integrate the best elements of the submissions into a workable solution for mineral assessment workflows, and potentially for other mission area assessments within the agency.
“The competition has been a valuable opportunity for the USGS to work with leading minds in AI to improve our approach to critical mineral assessments,” said David Applegate, USGS Director. “It has already led to incredible time savings in how we prepare data in a machine-readable format. Furthermore, these machine-learning models have implications beyond mineral resources into other fields that use map data, including geologic mapping, ecological mapping of species diversity and many other application areas.”
“Critical minerals are essential to the national security supply chain, and as such, the agency is approaching the need from multiple angles,” said DARPA Director Stefanie Tompkins. “The USGS collaboration puts an emphasis on identifying existing domestic resources. Other DARPA programs are evaluating the feasibility of recovering rare earth elements from e-waste and bioengineering methods to purify rare earth elements.”
To hear more about the Critical Minerals Assessment Competition, including insights from members of the winning teams, listen to Voices from DARPA podcast episode 63, “So Many Maps, So Little Time: Using AI to Locate Critical Minerals.”
# # #
Media with inquiries should contact DARPA Public Affairs at [email protected]
Associated images posted on www.darpa.mil and video posted at www.youtube.com/darpatv may be reused according to the terms of the DARPA User Agreement.
Tweet @darpa
| 2022-12-16T00:00:00 |
https://www.darpa.mil/news/2022/critical-minerals-assessement-winners
|
[
{
"date": "2022/12/16",
"position": 55,
"query": "AI economic disruption"
}
] |
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