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AI's mysterious 'black box' problem, explained
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AI's mysterious ‘black box’ problem, explained
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https://umdearborn.edu
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[] |
Either way, it's more work, and it's very much an unsolved problem right now.” At the end of the day, the question of what role AI should play in our lives ...
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Learning by example is one of the most powerful and mysterious forces driving intelligence, whether you’re talking about humans or machines. Think, for instance, of how children first learn to recognize letters of the alphabet or different animals. You simply have to show them enough examples of the letter B or a cat and before long, they can identify any instance of that letter or animal. The basic theory is that the brain is a trend-finding machine. When it’s exposed to examples, it can identify qualities essential to cat-ness or B-ness, and these ultimately coalesce into decision protocols that give us the ability to categorize new experiences automatically and unconsciously. Doing this is easy. Explaining how we do this is essentially impossible. “It’s one of those weird things that you know, but you don’t know how you know it or where you learned it,” says Associate Professor of Electrical and Computer Engineering Samir Rawashdeh, who specializes in artificial intelligence. “It’s not that you forgot. It’s that you’ve lost track of which inputs taught you what and all you’re left with is the judgments.”
Associate Professor Samir Rawashdeh
Rawashdeh says deep learning, one of the most ubiquitous modern forms of artificial intelligence, works much the same way, in no small part because it was inspired by this theory of human intelligence. In fact, deep learning algorithms are trained much the same way we teach children. You feed the system correct examples of something you want it to be able to recognize, and before long, its own trend-finding inclinations will have worked out a “neural network” for categorizing things it’s never experienced before. Pop in the keyword “cat” — or even the name of one of your favorite cats — into the search bar of your photo app and you’ll see how good deep learning systems are. But Rawashdeh says that, just like our human intelligence, we have no idea of how a deep learning system comes to its conclusions. It “lost track” of the inputs that informed its decision making a long time ago. Or, more accurately, it was never keeping track.
This inability for us to see how deep learning systems make their decisions is known as the “black box problem,” and it’s a big deal for a couple of different reasons. First, this quality makes it difficult to fix deep learning systems when they produce unwanted outcomes. If, for example, an autonomous vehicle strikes a pedestrian when we’d expect it to hit the brakes, the black box nature of the system means we can’t trace the system’s thought process and see why it made this decision. If this type of accident happened, and it turned out that the perception system missed the pedestrian, Rawashdeh says we’d assume it was because the system encountered something novel in the situation. We’d then try to diagnose what that could have been and expose the system to more of those situations so it would learn to perform better next time. “But the challenge is, can you get training data that covers everything?” Rawashdeh says. “What about when it’s sunny and a bit foggy, or they’ve just salted the roads and the asphalt now appears whiter than it usually does? There are an infinite number of permutations so you never know if the system is robust enough to handle every situation.”
Rawashdeh says this problem of robustness makes it difficult for us to trust deep learning systems when it comes to safety. But he notes the black box problem also has an ethical dimension. Deep learning systems are now regularly used to make judgements about humans in contexts ranging from medical treatments, to who should get approved for a loan, to which applicants should get a job interview. In each of these areas, it’s been demonstrated that AI systems can reflect unwanted biases from our human world. (If you want to know how AI systems can become racially biased, check out our previous story on that topic.) Needless to say, a deep learning system that can deny you a loan or screen you out of the first round of job interviews but can’t explain why, is one most people would have a hard time judging as “fair.”
So what can we do about this black box problem? Rawashdeh says there are essentially two different approaches. One is to pump the brakes on the use of deep learning in high-stakes applications. For example, the European Union is now creating a regulatory framework, which sorts potential applications into risk categories. This could prohibit the use of deep learning systems in areas where the potential for harm is high, like finance and criminal justice, while allowing their use in lower-stakes applications like chatbots, spam filters, search and video games. The second approach is to find a way to peer into the box. Rawashdeh says so-called “explainable AI” is still very much an emerging field, but computer scientists have some interesting ideas about how to make deep learning more transparent, and thus fixable and accountable. “There are different models for how to do this, but we essentially need a way to figure out which inputs are causing what,” he says. “It may involve classical data science methods that look for correlations. Or it may involve bigger neural nets, or neural nets with side tasks, so we can create data visualizations that would give you some insight into where the decision came from. Either way, it’s more work, and it’s very much an unsolved problem right now.”
At the end of the day, the question of what role AI should play in our lives may not be fundamentally different from the conversations we have anytime a potentially transformative technology emerges. Typically, that conversation involves a calculation of risks and benefits, and Rawashdeh thinks it’s still early enough for us to have thoughtful conversations about how and how quickly we want deep learning to shape our world. “Without question, there is a huge potential for AI, but it gets scary when you get into areas like autonomy or health care or national defense. You realize we have to get this right. For example, whenever I have a moment when I’m disconnected from the internet for a few days, I'm reminded just how different that reality is than the modern reality that’s shaped by social media or all the things we immerse ourselves in online. When the internet came into being, we just let it into our world, and in hindsight, we can see that came with certain risks. If we could turn back the clock 30 years, knowing what we know now, would we just let the internet loose on people? I think it’s a similar decision that we face now with AI.”
###
Story by Lou Blouin
| 2023-03-06T00:00:00 |
https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained
|
[
{
"date": "2023/03/06",
"position": 50,
"query": "AI labor union"
}
] |
|
A group of YouTube contractors just earned a big win in ...
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A group of YouTube contractors just earned a big win in their labor dispute with Google
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https://www.tubefilter.com
|
[
"Sam Gutelle"
] |
They hoped to join the Alphabet Workers Union, which was established in 2021 as an unofficial bargaining unit within Google. Alphabet argued that Cognizant ...
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Thanks to a recent ruling by the National Labor Relations Board (NLRB), Alphabet may be compelled to collectively bargain with U.S. employees for the first time in its history. The parent company of Google and YouTube plans to appeal a decision that granted bargaining rights to a group of YouTube Music employees who are looking to join the Alphabet Workers Union.
The NLRB decision concerned a group of YouTube contractors who were hired by employment firm Cognizant. Those employees staged a walkout a month ago to lobby for a more permissive remote work policy. They hoped to join the Alphabet Workers Union, which was established in 2021 as an unofficial bargaining unit within Google.
Alphabet argued that Cognizant should be responsible for the working conditions of the aggrieved contractors, but the NLRB disagreed. “Google exercises direct and immediate control over benefits, hours of work, supervision and direction of work,” said an NLRB regional director in a statement.
Subscribe to get the latest creator news Subscribe
Both Alphabet and Cognizant have challenged the ruling, which will be appealed. “We are confident the facts and law clearly support our position,” said Google spokesperson Courtenay Mencini. “We simply don’t control these workers’ employment terms or working conditions.”
The contractors will now join forces with the Alphabet Workers Union, which is part of the Communication Workers of America. The union has won some victories for its members, though it hasn’t yet achieved official recognition. A unionization vote is expected in the future, though there’s no indiciation of when that might occur.
According to Fortune, if the Alphabet Workers Union does pass its vote, Google will collectively bargain with U.S. employees for the first time ever. The widespread push for improved workers rights seems to have made its way to Mountain View, and that’s good news for people who use Google’s platforms on a professional basis.
Some execs have suggested that content creators need their own union, though it’s not clear who exactly that group would be bargain with. In the meantime, Google will have its hands full with its own group of labor organizers, even if the NLRB accepts the tech giant’s appeal.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.tubefilter.com/2023/03/06/alphabet-workers-union-google-youtube-music-national-labor-relations-board/
|
[
{
"date": "2023/03/06",
"position": 94,
"query": "AI labor union"
}
] |
IT Leaders Call Generative AI a 'Game Changer' but Seek ...
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IT Leaders Call Generative AI a ‘Game Changer’ but Seek Progress on Ethics and Trust
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https://www.salesforce.com
|
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A new survey of more than 500 senior IT leaders reveals that a majority (67%) are prioritizing generative AI for their business within the next 18 months.
|
Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein, go here.
Quick take: Salesforce’s new Generative AI in IT Survey reveals that over half of senior IT leaders are either experimenting with or actively using generative AI in their business. The problem? Nearly all admit they must take measures to equip their company to use it successfully and responsibly.
A new survey of more than 500 senior IT leaders reveals that a majority (67%) are prioritizing generative AI for their business within the next 18 months, with one-third (33%) naming it as a top priority.
Despite the interest, most IT leaders have technical and ethical concerns with the technology that could impact its adoption in the coming year.
The data highlights the potential of generative AI, and shows what IT leaders will need to prime their tech infrastructure, data strategy, security and ethical guidelines, and employee skills necessary for the massive opportunities ahead.
Many senior IT leaders see generative AI as a ‘game changer’ and a tool to better serve customers
The majority of senior IT leaders (57%) believe generative AI is a ‘game changer.’ They believe the technology has the potential to help them better serve their customers, take advantage of data, and operate more efficiently. This outlook is echoed even among the skeptics — 80% of those who say the technology is ‘over-hyped’ agree that generative AI will help them better serve their customers.
Ethical, operational concerns draw skepticism
Thirty-three percent of those surveyed feel that generative AI is ‘over-hyped,’ with concerns that the technology brings the potential for security risks (79%) and bias (73%).
Open Image Modal Image Modal
Concerns extend beyond just those who feel the technology is ‘over-hyped’ — a majority of the 515 IT leaders surveyed are skeptical about the ethical implications of generative AI. Almost six in 10 (59%) believe generative AI outputs are inaccurate and almost two-thirds (63%) agree there is bias in generative AI outputs (e.g. misinformation and hate speech). In addition, 71% agree generative AI would increase their carbon footprint through increased IT energy use.
Security risks, integration, and siloed data are top barriers to generative AI implementation
Businesses aren’t yet prepared for successful implementation: 65% of senior IT leaders can’t justify the implementation of generative AI at the moment. Surveyed leaders report major barriers to using generative AI successfully within their organization, and security barriers are particularly top of mind among those currently using the technology.
Because of these barriers, nearly all respondents (99%) believe their business must take measures to equip themselves to successfully leverage the technology.
Open Image Modal Image Modal
Collaboration can address generative AI’s technical and ethical concerns
Ethical use must underpin the use of generative AI. But despite raising ethical and security concerns, leaders aren’t yet sure how to put ethics into practice — about one-third (30%) of businesses believe they must have ethical use guidelines to successfully implement generative AI within their business.
How Salesforce Guides the Development of Trusted Generative AI Read the 5 Guidelines
The responsibility is on businesses to work together and knowledge-share to make generative AI a reality across the enterprise. Leaders see collaboration as a key tool to ensure the technology is functional and used in an ethical way.
The majority (81%) of senior IT leaders believe generative AI should combine public and private data sources.
Most (82%) senior IT leaders think businesses should work together to improve the functionality of generative AI technology.
Over 8 in 10 (83%) senior IT leaders think businesses must work together to ensure generative AI is used ethically.
“Generative AI represents a step change in how organizations across industries will analyze data, automate processes, and empower sales, service, marketing, and commerce professionals to grow customer relationships — but it comes with new risks and challenges,” said Clara Shih, CEO of Service Cloud, Salesforce.
Whether generating a tailored sales email or customer support chat response, an ethics-first approach grounded in trusted data and human-in-the-loop workflows is what will allow enterprises to safely and responsibly use generative AI to deliver against today’s growing customer expectations.
Explore further
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.salesforce.com/news/stories/generative-ai-research/
|
[
{
"date": "2023/03/06",
"position": 15,
"query": "artificial intelligence business leaders"
}
] |
AI For Business - 30 Case Studies That Led To Competitive ...
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AI For Business – 30 Case Studies That Led To Competitive Advantage
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https://robllewellyn.com
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[] |
AI in business transformation is becoming increasingly more popular to drive innovation, efficiency, and growth. It's being utilised to automate routine ...
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AI in Business Transformation
AI in business transformation is becoming increasingly more popular to drive innovation, efficiency, and growth. It's being utilised to automate routine tasks, provide predictive analytics, personalise the customer experience, optimise supply chain operations and improve financial and HR processes. But the biggest breakthroughs are in AI business model transformation.
By analysing large amounts of data, identifying patterns, and making predictions, AI is helping businesses make better decisions and stay competitive in today's rapidly changing marketplace. As AI technology continues to evolve, new use cases will emerge, creating new opportunities for organisations to improve their operations and drive innovation.
What is AI?
AI stands for Artificial Intelligence, which refers to the ability of computer systems and machines to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems are designed to analyse and interpret large amounts of data, learn from that data, and make decisions or perform tasks based on that learning.
There are several different types of AI, including rule-based systems, machine learning, and deep learning. Rule-based systems use a set of pre-defined rules to make decisions, while machine learning algorithms are designed to learn from data and improve their performance over time. Deep learning, a subset of machine learning, is based on artificial neural networks and is used for tasks such as image recognition and natural language processing.
AI is used in a wide range of applications, including virtual assistants, autonomous vehicles, medical diagnosis, fraud detection, and recommendation systems. As the amount of data that is generated continues to increase, AI is becoming increasingly important for businesses and organisations in order to help them make more informed decisions and gain a competitive edge.
How AI for Business Matters
AI is being used in a variety of ways in business to drive efficiency, innovation, and growth. It is being used to automate routine tasks, provide predictive analytics, analyse customer data, and improve supply chain operations.
AI is also used to detect fraud, analyse financial data, and automate recruitment processes. With the development of AI technology, new use cases will continue to emerge, creating opportunities for businesses to improve their operations and drive innovation. In this article you will learn about dozens of ways in which AI is used in business.
How Can AI Help Companies?
AI has the potential to provide several benefits for large organisations, including:
Increased Efficiency
AI can help automate routine tasks, allowing employees to focus on more complex and value-adding activities. This can lead to increased productivity and efficiency, ultimately leading to cost savings for the organisation.
Improved Decision-making
AI systems can process vast amounts of data quickly and accurately, which can help organisations make better-informed decisions. By using AI to analyse data and identify patterns, organisations can gain insights into customer behaviour, market trends, and other key factors that can help them stay ahead of the competition.
Enhanced Customer Experience
AI can be used to develop personalised experiences for customers, such as chatbots that can answer customer queries in real-time, or recommendation systems that suggest products or services based on the customer's previous behaviour. This can lead to increased customer satisfaction and loyalty.
Better Risk Management
AI can be used to identify potential risks and vulnerabilities, allowing organisations to proactively manage these risks and avoid potential problems. For example, AI can be used to detect fraud or cybersecurity threats, helping organisations to protect their assets and reputation.
Innovation
AI can help organisations to develop new products and services by identifying new opportunities and predicting future trends. By using AI to analyse data and identify patterns, organisations can gain insights into emerging markets and customer needs, allowing them to develop innovative solutions that meet those needs.
AI has the potential to transform the way that large organisations operate, helping them to become more efficient, agile, and innovative. However, implementing AI requires careful planning and execution to ensure that the technology is integrated effectively and aligned with the organisation's overall strategy and goals.
What Are The Main AI Categories?
AI can be broadly categorised into four categories:
Reactive Machines
These are the most basic types of AI systems that can only react to inputs based on pre-programmed rules. They do not have any memory or ability to learn from past experiences. Examples of reactive machines include Deep Blue, the computer program that beat Garry Kasparov in chess in 1997, and IBM Watson, which defeated human contestants on Jeopardy in 2011.
Limited Memory
These AI systems have the ability to learn from past experiences and make decisions based on that learning. They can store past experiences and use that information to make predictions and decisions. An example of a limited memory AI system is self-driving cars, which use sensors and data to navigate roads and avoid obstacles.
Theory of Mind
These AI systems have the ability to understand the mental states and emotions of other entities, such as humans or animals. They can predict behaviour based on these mental states and emotions. Theory of mind AI is still in the early stages of development, and research is ongoing to improve this type of AI.
Self-Aware
These AI systems have consciousness and can think and learn like humans. They have the ability to understand their own existence and their place in the world. Self-aware AI is still a long way off, and research in this area is mainly theoretical at this point.
These categories of AI provide a framework for understanding the capabilities and limitations of AI systems. Each category has its own set of challenges and opportunities, and researchers and developers are working to improve AI systems in all categories.
What Are The Challenges of AI in Business Transformation?
While AI has the potential to transform businesses and drive business transformation, there are several challenges that organisations must address in order to successfully implement AI. Some of these challenges include:
Data Quality
AI systems rely on data to learn and make decisions. However, if the data used to train the AI is incomplete, biased, or inaccurate, the resulting AI system may produce unreliable or biased results. Ensuring high-quality data is essential for effective AI implementation.
Technical Complexity
Implementing AI systems requires significant technical expertise and resources. Organisations must have the necessary infrastructure, such as high-performance computing and data storage, and the technical knowledge to develop and maintain AI systems.
Privacy and Security
AI systems require access to large amounts of data, which raises privacy and security concerns. Organisations must ensure that data is properly protected and that AI systems comply with relevant privacy regulations.
Ethical and Social Implications
AI has the potential to disrupt industries and change the way we live and work. Organisations must consider the ethical and social implications of AI and ensure that their use of AI is aligned with their values and principles.
Human Resistance
Introducing AI may face resistance from employees who fear job losses or who are uncomfortable with the use of AI. Organisations must communicate the benefits of AI and provide training and support to employees to ensure a successful transition.
Addressing these challenges requires careful planning and execution. Organisations must develop a clear strategy for AI implementation and address technical, ethical, and social issues to ensure that AI is integrated effectively and aligned with the organisation's overall goals and values.
30 AI Business Use Cases
AI has a wide range of use cases across industries and business functions. Some examples of AI use cases include:
AI For Customer Service
AI-powered chatbots and virtual assistants can provide customers with quick and accurate responses to their queries, improving the customer experience while reducing the workload on customer service representatives.
KLM AI Case Study
One example of AI being used for customer service is the case of KLM Royal Dutch Airlines. KLM implemented an AI-powered chatbot on its Facebook Messenger platform to provide customers with quick and accurate responses to their queries.
The chatbot, called BlueBot, is designed to handle a range of customer queries, from flight information and baggage allowances to booking confirmations and refunds. Customers can interact with BlueBot through the Facebook Messenger app, and the chatbot uses natural language processing (NLP) technology to understand and respond to customer queries.
Since implementing BlueBot, KLM has seen a significant improvement in customer service efficiency. The airline reports that the chatbot is able to handle around 60% of customer queries without the need for human intervention. This has freed up customer service representatives to focus on more complex queries, improving the overall customer experience.
AI For Sales and Marketing
AI can be used to analyse customer data and behaviour to develop targeted marketing campaigns and sales strategies. For example, AI can be used to predict which customers are most likely to make a purchase or respond to a marketing campaign.
Coca-Cola AI Case Study
One example of AI being used for sales and marketing is the case of Coca-Cola. The company implemented an AI-powered marketing platform called Albert to help it optimise its digital advertising campaigns.
Albert uses machine learning algorithms to analyse customer data and identify patterns and insights that can be used to optimise digital advertising campaigns. The platform is able to make real-time adjustments to advertising campaigns based on factors like customer behaviour, preferences, and purchasing history.
Since implementing Albert, Coca-Cola has seen significant improvements in its digital advertising campaigns. The platform has helped the company increase its return on investment (ROI) by optimising ad spend and targeting the most profitable customer segments.
AI For Supply Chain Management
AI can be used to optimise supply chain operations by predicting demand, identifying potential disruptions, and recommending the most efficient routes for shipping and delivery.
UPS AI Case Study
One example of AI being used for supply chain management is the case of UPS. The company implemented an AI-powered logistics platform called ORION (On-Road Integrated Optimisation and Navigation) to help it optimise its delivery routes and improve overall efficiency.
ORION uses machine learning algorithms to analyse data from multiple sources, including customer information, traffic patterns, and weather conditions, to generate optimised delivery routes for UPS drivers. The platform is able to make real-time adjustments to delivery routes based on changing conditions, ensuring that packages are delivered in the most efficient way possible.
Since implementing ORION, UPS has seen significant improvements in its delivery operations. The platform has helped the company reduce the distance its drivers travel by millions of miles each year, resulting in significant cost savings and environmental benefits.
AI For Financial Services
AI can be used to improve fraud detection, risk management, and investment analysis in the financial services industry. For example, AI can be used to analyse credit card transactions to detect fraudulent activity.
JPMorgan Chase AI Case Study
One example of AI being used for financial services is the case of JPMorgan Chase. The bank implemented an AI-powered virtual assistant called COiN to help it automate its back-office operations and improve efficiency.
COiN uses machine learning algorithms to analyse large amounts of data from various sources, including invoices, receipts, and other financial documents. The platform is able to automate tasks like data entry, reconciliation, and compliance checks, freeing up human employees to focus on more complex tasks.
Since implementing COiN, JPMorgan Chase has seen significant improvements in its back-office operations. The platform has helped the bank process large volumes of financial documents quickly and accurately, reducing errors and improving compliance with regulatory requirements.
AI For Healthcare
AI can be used to improve patient outcomes by analysing patient data and developing personalised treatment plans. For example, AI can be used to analyse medical images to identify potential health issues.
IBM Watson Health AI Case Study
One example of AI being used for healthcare is the case of IBM Watson Health. The company has developed an AI-powered platform called Watson for Oncology, which is designed to help healthcare professionals diagnose and treat cancer.
Watson for Oncology uses natural language processing (NLP) and machine learning algorithms to analyse large amounts of patient data, including medical histories, lab reports, and other diagnostic tests. The platform is able to generate personalised treatment recommendations for individual patients based on their specific medical needs.
Since implementing Watson for Oncology, healthcare professionals have reported significant improvements in the accuracy and speed of cancer diagnosis and treatment. The platform has helped doctors identify previously overlooked treatment options and avoid potential medical errors.
AI For Manufacturing
AI can be used to optimise manufacturing processes by predicting equipment failures, reducing downtime, and improving quality control.
Siemens AI Case Study
One example of AI being used for manufacturing is the case of Siemens. The company has implemented an AI-powered platform called the Siemens Digital Enterprise Suite to help it optimise its manufacturing operations.
The platform uses machine learning algorithms to analyse large amounts of data from various sources, including sensors, machines, and other manufacturing equipment. The platform is able to generate real-time insights into production processes and identify opportunities for optimisation and improvement.
Since implementing the Siemens Digital Enterprise Suite, the company has reported significant improvements in efficiency and productivity. The platform has helped Siemens optimise its manufacturing processes, reducing downtime, and improving overall equipment effectiveness.
AI For Human Resources
AI can be used to automate HR processes such as resume screening and candidate selection. AI can also be used to analyse employee data to identify potential issues such as low morale or high turnover.
Unilever AI Case Study
One example of AI being used for human resources is the case of Unilever. The company implemented an AI-powered recruitment platform called HireVue to help it streamline its hiring process and improve candidate selection.
HireVue uses machine learning algorithms to analyse video interviews conducted by job candidates. The platform is able to identify patterns in candidate behaviour, such as body language and facial expressions, to generate insights into their suitability for a particular role.
Since implementing HireVue, Unilever has reported significant improvements in the efficiency and effectiveness of its recruitment process. The platform has helped the company identify high-potential candidates more quickly and accurately, reducing the time and cost involved in the hiring process.
AI For Cybersecurity
AI can be used to detect and respond to cybersecurity threats in real-time. AI can analyse network traffic and identify patterns of suspicious activity, alerting security teams to potential threats and allowing them to act before a breach occurs.
Darktrace AI Case Study
One example of AI being used for cybersecurity is the case of Darktrace. The company has developed an AI-powered cybersecurity platform called the Enterprise Immune System, which is designed to help organisations detect and respond to cyber threats in real-time.
The platform uses machine learning algorithms to analyse large amounts of data from various sources, including network traffic, user behaviour, and other system logs. The platform is able to detect anomalous activity and identify potential threats before they can cause damage to the organisation.
Since implementing the Enterprise Immune System, Darktrace's customers have reported significant improvements in their ability to detect and respond to cyber threats. The platform has helped organisations identify previously unknown threats and take corrective action to prevent further damage.
AI For Transportation
AI can be used to optimise transportation systems by predicting traffic patterns and identifying the most efficient routes for vehicles. For example, AI can be used to optimise bus routes to reduce travel time and improve passenger experience.
UPS AI Case Study
One example of AI being used for transportation is the case of UPS. The company has implemented an AI-powered route optimisation system called ORION (On-Road Integrated Optimisation and Navigation) to help it optimise its delivery routes.
ORION uses machine learning algorithms to analyse large amounts of data, including traffic patterns, road closures, and weather conditions, to generate optimised delivery routes for UPS drivers. The platform is able to adjust routes in real-time based on changing conditions, such as traffic delays or road closures.
Since implementing ORION, UPS has reported significant improvements in efficiency and cost savings. The platform has helped the company optimise its delivery routes, reducing the number of miles driven and improving overall delivery times.
AI For Energy Management
AI can be used to optimise energy usage by predicting energy demand and identifying areas where energy usage can be reduced. For example, AI can be used to optimise heating and cooling systems in buildings, reducing energy consumption and costs.
Enel AI Case Study
One example of AI being used for energy management is the case of Enel. The energy company has implemented an AI-powered energy management platform called Enel X to help it optimise its energy distribution and consumption.
Enel X uses machine learning algorithms to analyse large amounts of data from various sources, including energy production and consumption data, weather patterns, and energy market data. The platform is able to generate real-time insights into energy demand and consumption patterns, helping Enel optimise its energy distribution and consumption in response to changing conditions.
Since implementing Enel X, the company has reported significant improvements in energy efficiency and cost savings. The platform has helped Enel optimise its energy distribution and consumption, reducing waste and improving overall energy efficiency.
AI For Agriculture
AI can be used to optimise crop yields by analysing data on weather patterns, soil conditions, and plant health. For example, AI can be used to identify the optimal time for planting and harvesting crops.
Blue River Technology AI Case Study
One example of AI being used for agriculture is the case of Blue River Technology. The company has developed an AI-powered crop management system called See & Spray, which is designed to help farmers optimise their crop yields and reduce the use of herbicides.
See & Spray uses computer vision and machine learning algorithms to identify and target individual plants in a crop field. The system is able to differentiate between crops and weeds, and can selectively apply herbicides to the weeds, reducing the amount of herbicide needed and minimising the impact on the crops.
Since implementing See & Spray, farmers using the system have reported significant improvements in crop yields and reductions in herbicide use. The system has helped farmers optimise their crop management, reducing costs and improving overall sustainability.
AI For Legal Services
AI can be used to assist with legal research and document review. For example, AI can be used to review contracts and identify potential legal issues.
eBrevia AI Case Study
One example of AI being used for legal services is the case of eBrevia. The company has developed an AI-powered contract analysis platform, which is designed to help law firms and corporate legal departments automate the contract review process.
The platform uses natural language processing (NLP) and machine learning algorithms to analyse and extract key provisions from contracts, including indemnification clauses, termination provisions, and change of control clauses. The system is able to identify potential issues or inconsistencies within the contract, and can provide recommendations for how to resolve these issues.
Since implementing eBrevia, law firms and corporate legal departments using the platform have reported significant improvements in efficiency and cost savings. The system has helped them to automate the contract review process, reducing the amount of time and resources required to review and analyse contracts.
AI For Insurance
AI can be used to automate claims processing and fraud detection. For example, AI can be used to analyse claims data to identify potential instances of fraud.
Lemonade AI Case Study
One example of AI being used for insurance is the case of Lemonade. The insurance company has implemented an AI-powered claims processing platform, which is designed to improve the speed and accuracy of claims processing.
The platform uses natural language processing (NLP) and machine learning algorithms to analyse claims and assess the likelihood of fraud. The system is able to automatically approve certain claims, reducing the need for human intervention, and can identify potential fraud cases for further investigation.
Since implementing the AI-powered claims processing platform, Lemonade has reported significant improvements in claims processing times and cost savings. The platform has helped the company to automate the claims process, reducing the amount of time and resources required to process claims.
AI For Education
AI can be used to personalise learning experiences for students by analysing their learning data and providing targeted recommendations. For example, AI can be used to recommend specific study materials based on a student's learning style and preferences.
Carnegie Learning AI Case Study
One example of AI being used for education is the case of Carnegie Learning. The education technology company has developed an AI-powered math education platform called Mika, which is designed to provide personalised learning experiences for students.
Mika uses machine learning algorithms to analyse students' learning patterns and provide personalised feedback and guidance. The platform adapts to each student's individual needs, providing them with personalised recommendations for further study and practice.
Since implementing Mika, educators and students using the platform have reported significant improvements in student engagement and achievement. The system has helped to improve students' math skills and confidence, providing them with personalised learning experiences that are tailored to their individual needs.
AI For Entertainment
AI can be used to develop personalised recommendations for movies, TV shows, and other forms of entertainment. For example, AI can be used to recommend content based on a user's viewing history and preferences.
Netflix AI Case Study
One example of AI being used for entertainment is the case of Netflix. The streaming service has implemented an AI-powered recommendation engine, which is designed to provide personalised content recommendations for users.
The recommendation engine uses machine learning algorithms to analyse users' viewing histories and preferences, and provide them with personalised content suggestions. The system is able to identify patterns in users' viewing behaviour and make recommendations based on their interests and preferences.
Since implementing the recommendation engine, Netflix has reported significant improvements in user engagement and retention. The system has helped to improve users' satisfaction with the service, providing them with personalised content recommendations that are tailored to their individual interests.
AI For Sports
AI can be used to analyse player performance data and develop personalised training plans. For example, AI can be used to analyse an athlete's performance data to identify areas where they can improve.
Second Spectrum AI Case Study
One example of AI being used for sports is the case of Second Spectrum. The sports analytics company has developed an AI-powered platform, which is designed to provide real-time insights and analysis for basketball games.
The platform uses machine learning algorithms to analyse player movements and interactions, and provide coaches and players with real-time feedback and recommendations. The system is able to identify patterns and trends in player behaviour, and make recommendations for adjustments to gameplay and strategy.
Since implementing the AI-powered platform, Second Spectrum has been able to provide coaches and players with valuable insights and feedback, helping them to improve their performance on the court. The system has helped teams to identify areas for improvement and make strategic adjustments in real-time.
AI For Real Estate
AI can be used to analyse property data and develop personalised recommendations for buyers and sellers. For example, AI can be used to recommend properties based on a buyer's preferences and budget.
Compass AI Case Study
One example of AI being used for real estate is the case of Compass. The real estate technology company has implemented an AI-powered platform, which is designed to provide personalised recommendations for home buyers and sellers.
The platform uses machine learning algorithms to analyse real estate listings and provide personalised recommendations for properties that match a buyer's preferences. The system is able to identify patterns in buyers' behaviour and make recommendations based on their interests and preferences.
Since implementing the AI-powered platform, Compass has reported significant improvements in customer engagement and satisfaction. The system has helped to improve buyers' experiences by providing them with personalised recommendations that are tailored to their individual needs.
AI For Hospitality
AI can be used to develop personalised recommendations for hotel guests based on their preferences and past behaviour. For example, AI can be used to recommend specific room types, restaurants, and activities based on a guest's previous bookings and reviews.
Hilton AI Case Study
One example of AI being used for hospitality is the case of Hilton. The hotel chain has implemented an AI-powered concierge service, which is designed to provide personalised recommendations and assistance for guests.
The AI-powered concierge, called Connie, uses machine learning algorithms to analyse guests' preferences and provide personalised recommendations for local restaurants, attractions, and events. The system is able to understand natural language queries and provide helpful responses in real-time.
Since implementing Connie, Hilton has reported significant improvements in customer satisfaction and engagement. The system has helped to improve guests' experiences by providing them with personalised recommendations and assistance, making their stays more enjoyable and memorable.
AI For Retail
AI can be used to develop personalised recommendations for shoppers based on their browsing and purchase history. For example, AI can be used to recommend products based on a shopper's previous purchases and preferences.
Amazon AI Case Study
One example of AI being used for retail is the case of Amazon. The e-commerce giant has implemented an AI-powered recommendation system, which is designed to provide personalised product recommendations for customers.
The recommendation system uses machine learning algorithms to analyse customers' browsing and purchasing behaviour, and provide personalised product suggestions that are tailored to their interests and preferences. The system is able to identify patterns in customers' behaviour and make recommendations based on their individual needs.
Since implementing the AI-powered recommendation system, Amazon has reported significant improvements in customer engagement and sales. The system has helped to improve customers' shopping experiences by providing them with personalised product recommendations that are relevant to their needs and interests.
AI For Government
AI can be used to analyse public data to identify potential areas of concern, such as crime rates or health trends. For example, AI can be used to analyse social media data to identify potential instances of public unrest.
United States IRS AI Case Study
One example of AI being used for government is the case of the United States Internal Revenue Service (IRS). The tax agency has implemented an AI-powered platform, which is designed to detect and prevent tax fraud.
The platform uses machine learning algorithms to analyse tax returns and identify potential cases of fraud. The system is able to identify patterns in tax returns and make recommendations for further investigation.
Since implementing the AI-powered platform, the IRS has reported significant improvements in its ability to detect and prevent tax fraud. The system has helped to identify cases of fraud that may have gone undetected using traditional methods, and has helped to reduce the amount of fraudulent refunds paid out each year.
AI For Environmental Management
AI can be used to analyse environmental data and predict the impact of climate change. For example, AI can be used to predict sea level rise and develop strategies to mitigate its impact.
Microsoft AI Case Study
One example of AI being used for environmental management is the case of Microsoft. The technology company has implemented an AI-powered platform, which is designed to optimise energy consumption in its data centres.
The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising energy consumption. The system is able to identify patterns in energy usage and make recommendations for reducing waste and increasing efficiency.
Since implementing the AI-powered platform, Microsoft has reported significant reductions in energy consumption and carbon emissions. The system has helped the company to achieve its sustainability goals by reducing its environmental impact and promoting more efficient use of resources.
AI For Aerospace
AI can be used to optimise flight routes and improve aircraft maintenance. For example, AI can be used to predict equipment failures and schedule maintenance before a problem occurs.
Airbus AI Case Study
One example of AI being used for aerospace is the case of Airbus. The aircraft manufacturer has implemented an AI-powered predictive maintenance system, which is designed to identify potential issues with aircraft components before they cause problems.
The system uses machine learning algorithms to analyse data from sensors and other sources, and make predictions about when components may need to be serviced or replaced. The system is able to identify patterns in component behaviour and make recommendations for maintenance based on the data.
Since implementing the AI-powered predictive maintenance system, Airbus has reported significant improvements in aircraft reliability and safety. The system has helped the company to reduce the number of unscheduled maintenance events, and minimise downtime for aircraft.
AI For Construction
AI can be used to optimise construction projects by analysing data on materials, labour, and equipment. For example, AI can be used to predict potential delays and identify opportunities for cost savings.
Komatsu AI Case Study
One example of AI being used for construction is the case of Komatsu, a Japanese construction equipment manufacturer. The company has implemented an AI-powered platform, which is designed to optimise the operation of its construction equipment.
The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising equipment usage. The system is able to identify patterns in equipment behaviour and make recommendations for reducing waste and increasing efficiency.
Since implementing the AI-powered platform, Komatsu has reported significant improvements in equipment performance and efficiency. The system has helped the company to reduce fuel consumption, minimise downtime, and improve overall productivity.
AI For Logistics
AI can be used to optimise logistics operations by predicting demand, identifying the most efficient routes, and improving warehouse management. For example, AI can be used to predict shipping volumes and adjust inventory levels accordingly.
DHL AI Case Study
One example of AI being used for logistics is the case of DHL, a global logistics company. The company has implemented an AI-powered platform, which is designed to optimise its logistics operations and improve delivery efficiency.
The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising delivery routes, vehicle usage, and delivery schedules. The system is able to identify patterns in delivery behaviour and make recommendations for reducing waste and increasing efficiency.
Since implementing the AI-powered platform, DHL has reported significant improvements in delivery efficiency and customer satisfaction. The system has helped the company to reduce delivery times, minimise fuel consumption, and improve overall productivity.
AI For Gaming
AI can be used to develop more realistic and challenging game environments. For example, AI can be used to create non-playable characters that behave more realistically and adapt to player actions.
NVIDIA AI Case Study
One example of AI being used for gaming is the case of NVIDIA, a technology company that specialises in graphics processing units (GPUs) for gaming and other applications. The company has developed an AI-powered platform called NVIDIA DLSS (Deep Learning Super Sampling), which is designed to improve the performance and visual quality of games.
The platform uses deep learning algorithms to analyse graphics data and generate high-quality images in real-time. It is able to identify patterns in graphics data and make predictions about how to improve the image quality and performance.
Since implementing the NVIDIA DLSS platform, game developers have reported significant improvements in game performance and visual quality. The platform has helped to reduce the workload on GPUs, allowing for higher frame rates and smoother gameplay.
AI For Marketing
AI can be used to develop targeted advertising campaigns by analysing customer data and behaviour. For example, AI can be used to identify potential customers and recommend products based on their preferences.
Sephora AI Case Study
One example of AI being used for marketing is the case of Sephora, a cosmetics retailer. The company has implemented an AI-powered platform called “Virtual Artist”, which is designed to enhance the customer experience and increase sales.
The platform uses augmented reality and machine learning algorithms to help customers try on different makeup products virtually. Customers can use the Sephora app to scan their face and then apply different makeup products to see how they would look in real life. The platform also uses machine learning to recommend personalised product recommendations based on the customer's skin tone and preferences.
Since implementing the Virtual Artist platform, Sephora has reported significant improvements in customer engagement and sales. The platform has helped the company to increase customer satisfaction and reduce product returns, as customers can now try on makeup virtually before making a purchase.
AI For Social Media
AI can be used to analyse social media data and identify trends and patterns. For example, AI can be used to identify the most popular topics on social media and develop strategies to engage with customers.
Hootsuite AI Case Study
One example of AI being used for social media is the case of Hootsuite, a social media management platform. The company has implemented an AI-powered feature called “AdEspresso by Hootsuite”, which is designed to help businesses optimise their social media advertising campaigns.
The platform uses machine learning algorithms to analyse data from various sources, including social media ad performance and audience behaviour. It is able to identify patterns in audience behaviour and make recommendations for optimising ad spend, ad targeting, and messaging.
Since implementing AdEspresso by Hootsuite, businesses have reported significant improvements in their social media advertising performance. The platform has helped businesses to increase their return on ad spend, improve targeting accuracy, and reduce the time required to launch campaigns.
AI For Humanitarian Aid
AI can be used to analyse data on natural disasters and humanitarian crises to help aid organisations respond more effectively. For example, AI can be used to predict the path of a hurricane and identify areas that are most at risk.
United Nations World Food Programme AI Case Study
One example of AI being used for humanitarian aid is the case of the United Nations World Food Programme (WFP). The WFP has implemented an AI-powered platform called “Building Blocks”, which is designed to improve the efficiency and effectiveness of its aid distribution efforts.
The platform uses machine learning algorithms to analyse data from various sources, including satellite imagery, weather patterns, and social media. It is able to identify areas of need, predict potential crises, and optimise aid delivery routes.
Since implementing Building Blocks, the WFP has reported significant improvements in its aid distribution efforts. The platform has helped the organisation to increase the speed and accuracy of aid delivery, reduce waste and inefficiencies, and reach more people in need.
AI For Automotive
AI can be used to improve safety and performance in vehicles by analysing sensor data and providing real-time alerts to drivers. For example, AI can be used to detect potential collisions and warn drivers before an accident occurs.
Tesla AI Case Study
One example of AI being used for the automotive industry is the case of Tesla, a company that produces electric cars. Tesla has implemented an AI-powered platform called “Autopilot”, which is designed to enhance the safety and performance of its vehicles.
The platform uses machine learning algorithms to analyse data from various sensors, including cameras and radars, to detect obstacles and other vehicles on the road. It is able to make real-time decisions about braking, steering, and acceleration to avoid collisions and improve driving performance.
Since implementing Autopilot, Tesla has reported significant improvements in vehicle safety and performance. The platform has helped the company to reduce the number of accidents and increase the efficiency of its vehicles.
AI For Art
AI can be used to create new forms of art by generating images, music, and other creative works. For example, AI can be used to create original paintings and music compositions. Digital art is also now very popular.
The Next Rembrandt AI Case Study
One example of AI being used for art is the case of The Next Rembrandt project, a collaboration between ING Bank and J. Walter Thompson Amsterdam. The project used machine learning algorithms to create a new “Rembrandt” painting, designed to look and feel like one of the master's original works.
The project started by analysing data from Rembrandt's paintings, including brushstrokes, composition, and colour. The machine learning algorithms then used this data to create a new painting in the style of Rembrandt, which was produced using a 3D printer.
The result was a highly detailed painting, complete with brushstrokes and intricate details, that looked and felt like an original Rembrandt painting. While the painting was not created by Rembrandt himself, it demonstrated the potential for AI to create art in the style of famous artists.
These are just some examples of the many use cases for AI in business. As AI technology continues to develop, new use cases will continue to emerge, creating new opportunities for businesses to improve their operations and drive innovation.
AI in Digital Transformation
AI has the potential to transform digital transformation by automating routine tasks, providing decision support, and enhancing the customer experience. By analysing large amounts of data, AI can provide insights into customer behaviour and preferences, identify patterns and trends, and help organisations make more informed business decisions.
AI can also assist with product development by analysing customer feedback and identifying areas for improvement. Through the use of chatbots and virtual assistants, AI can improve the customer experience while reducing the workload on customer service representatives. As AI technology continues to develop, new opportunities will emerge for organisations to drive innovation and improve their operations.
Here are some ways that AI can be used in digital transformation:
Process Automation
AI can be used to automate routine tasks and free up employees to focus on more strategic work. For example, AI can be used to automate data entry or customer service tasks.
Predictive Analytics
AI can be used to analyse large amounts of data and identify patterns and trends that can inform business decisions. For example, AI can be used to predict customer behaviour or identify opportunities for cost savings.
Personalisation
AI can be used to develop personalised experiences for customers, employees, and other stakeholders. For example, AI can be used to recommend products or content based on a user's previous behaviour.
Decision Support
AI can be used to provide decision support for managers and executives. For example, AI can be used to provide recommendations on which products to stock or which marketing campaigns to launch.
Chatbots and Virtual Assistants
AI-powered chatbots and virtual assistants can provide customers with quick and accurate responses to their queries, improving the customer experience while reducing the workload on customer service representatives.
Data Security
AI can be used to enhance data security by detecting potential threats and identifying vulnerabilities. For example, AI can be used to detect anomalous behaviour on a network that may indicate a security breach.
Customer Insights
AI can be used to analyse customer data and develop insights into customer behaviour and preferences. For example, AI can be used to identify which customers are most likely to churn and develop strategies to retain them.
Product Development
AI can be used to assist with product development by analysing customer feedback and identifying areas for improvement. For example, AI can be used to identify which features customers are most interested in and prioritise them for development.
These are just a few examples of how AI can be used in digital transformation. As AI technology continues to develop, new use cases will emerge, creating new opportunities for organisations to drive innovation and improve their operations.
Scaling AI For Business
Scaling AI is the process of deploying and integrating AI solutions at a large scale within an organisation. Here are some key considerations when scaling AI:
Infrastructure
Scaling AI requires a robust infrastructure that can support the processing and storage requirements of AI applications. This may involve investing in new hardware, software, and cloud services.
Data
AI requires large amounts of high-quality data to train machine learning models. Scaling AI requires organisations to ensure that they have access to the right data and that it is organised and labelled in a way that makes it easy to use.
Talent
Scaling AI requires a skilled workforce that can develop, implement, and maintain AI solutions. This may involve hiring new talent, up-skilling existing employees, or partnering with external consultants.
Governance
Scaling AI requires strong governance practices to ensure that AI solutions are deployed ethically and in compliance with regulatory requirements. This may involve establishing new policies, procedures, and governance structures.
Change Management
Scaling AI requires effective change management practices to ensure that the organisation is prepared for the cultural and organisational changes that come with deploying AI solutions. This may involve developing new training programs, communication strategies, and performance metrics.
Where Can I Learn About AI in Digital Transformation?
There are many resources available for learning about AI in digital transformation. Here are a few suggestions:
Online Courses: There are many online courses available that cover AI in digital transformation, but most just teach theory that people don't understand how to put into practice. This AI Strategy and Implementation Programme is different because it gives you a system you can use in your organisation..
Conferences and Events: Attending conferences and events focused on AI and digital transformation can be a great way to learn about the latest trends and best practices in the field. Some popular conferences and events include AI Summit, World Summit AI, and the Digital Transformation Conference.
Industry Publications: Many industry publications cover AI in digital transformation, including publications like Forbes, Harvard Business Review, and MIT Technology Review. These publications provide insights into the latest trends and best practices in the field.
Online Resources: There are many online resources available that cover AI in digital transformation, including blogs, whitepapers, and eBooks. These resources are often provided by industry experts and provide insights into the latest trends and best practices in the field.
These are just a few suggestions for learning about AI in digital transformation. By exploring these resources and others, individuals and organisations can gain a better understanding of the role that AI can play in driving digital transformation.
Certified AI Strategy & Implementation Leader Programme
| 2025-05-18T00:00:00 |
2025/05/18
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https://robllewellyn.com/ai-for-business/
|
[
{
"date": "2023/03/06",
"position": 50,
"query": "artificial intelligence business leaders"
}
] |
AI in Business by Elie El Khoury - Eduvate - UAE
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AI in Business by Elie El Khoury
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https://eduvate.ae
|
[
"Mayyas Hamdan"
] |
Artificial Intelligence (AI) is revolutionizing the way businesses operate across various industries. It offers new opportunities for leaders to enhance ...
|
Artificial Intelligence (AI) is revolutionizing the way businesses operate across various
industries, including Business, Education, Medical Sector, Research and Development and many
more. The impact of AI on leadership has been significant, as it has created new opportunities
and challenges for leaders. AI has the potential to enhance decision-making, automate processes,
and optimize operations, resulting in increased efficiency, productivity, and profitability.
However, it also presents challenges, such as job displacement and ethical concerns.
The Impact of Artificial Intelligence on Business Leadership
Artificial Intelligence (AI) is revolutionizing the way businesses operate across various industries. It offers new opportunities for leaders to enhance decision-making, automate processes, and optimize operations. However, it also presents challenges such as job displacement and ethical concerns.
The Role of Leaders in Adopting AI
Leaders must understand how AI can transform their organizations and industries. They need to develop a comprehensive AI strategy that aligns with the organization’s vision and values. This requires a strong understanding of AI technologies and their applications, as well as the ability to manage the human and ethical implications of AI adoption.
Balancing Technical Expertise with Soft Skills
The successful adoption of AI requires leaders to foster a culture of innovation and balance technical expertise with soft skills. Leadership is significantly impacted by AI, and leaders must learn new techniques and approaches to take advantage of its potential while minimizing its risks.
The Future of AI: Opportunities and Challenges
The emergence of AI has led to concerns about job displacement in various industries. However, AI can also enhance the abilities of professionals and create new opportunities. Leaders can delegate repetitive tasks and use AI insights to make better decisions. But how can leaders effectively communicate the benefits of AI adoption to their employees? One way is by providing clear examples of how AI can improve efficiency and productivity while also emphasizing the importance of human skills and expertise.
How can leaders effectively communicate the benefits of AI adoption to their employees?
One way is by providing clear examples of how AI can improve efficiency and productivity while also emphasizing the importance of human skills and expertise.
The Importance of Soft Skills in an AI-Driven World
To succeed in a leadership role, it’s essential to possess soft skills such as effective communication, collaboration, and inclusivity. The integration of technology can help professionals develop these skills and become more effective leaders.
Overcoming Obstacles to AI Adoption
One significant hurdle to AI adoption is mistrust among employees who fear being replaced by cheaper alternatives. However, employees remain essential components of an organization’s development and growth. Communicating this fact is crucial to facilitate a culture change. Another challenge is the cost of acquiring or developing AI applications, which can still be prohibitively expensive for small businesses.
What steps can small businesses take to overcome the cost barrier to AI adoption?
One option for small businesses to overcome the cost barrier to AI adoption is to explore partnerships or collaborations with larger companies or research institutions that have access to AI technologies.
In summary,
AI has a significant impact on leadership. It has revolutionized traditional methods by transforming decision-making processes, organizational operations, and interactions between leaders and their teams. As organizations strive to maintain a competitive edge and adapt to new technologies, the use of AI in leadership has become increasingly vital.
Leaders must possess the ability to make swift and precise decisions, as AI can process data and make decisions faster than humans. This requires leaders to be agile, adaptable, and able to leverage AI to their advantage.
AI can provide valuable insights into team dynamics, allowing leaders to better comprehend their team members and effectively motivate them. Additionally, AI can offer feedback to team members, leading to improved performance and increased engagement within the organization.
Given the continuous evolution of AI technology, it is crucial for leaders to stay abreast of the latest developments and harness its potential to drive organizational success.
Author: Elie El Khoury
Reference:
Ribeiro Jair, 2021, Building bridges between AI and the business, Retrieved from
https://medium.com/codex/building-bridges-between-ai-and-the-business-1294c001be5e
| 2023-03-06T00:00:00 |
2023/03/06
|
https://eduvate.ae/ai-in-business-friend-or-foe/
|
[
{
"date": "2023/03/06",
"position": 60,
"query": "artificial intelligence business leaders"
}
] |
Key findings from the State of AI in the Enterprise: 5th Edition
|
Key findings from the State of AI in the Enterprise: 5th Edition
|
https://futurecfo.net
|
[] |
The company's fifth annual report reveals that nearly all business leaders see AI as critical to success in the coming years.
|
Since 2017, Deloitte has tracked the ways businesses use AI and reported the results in our annual State of AI in the Enterprise surveys. The company's fifth annual report reveals that nearly all business leaders see AI as critical to success in the coming years.
Find out what Al-fuelled organisations are doing differently to achieve success and gain competitive advantage.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://futurecfo.net/key-findings-from-the-state-of-ai-in-the-enterprise-5th-edition/
|
[
{
"date": "2023/03/06",
"position": 67,
"query": "artificial intelligence business leaders"
}
] |
Advantages of Artificial Intelligence (AI) in Business
|
Advantages of Artificial Intelligence (AI) in Business
|
https://revenuegrid.com
|
[
"Core Ux Writer At Booking.Com"
] |
AI can apply to all areas of business, from sales to finance to human resources and others. Artificial intelligence allows companies large and small to get ...
|
Artificial intelligence (AI) has been around for a while, but it’s only recently that it’s started to become more widely used in the business world. AI can apply to all areas of business, from sales to finance to human resources and others.
Artificial intelligence allows companies large and small to get more done with less manpower. With AI at their disposal, businesses can automate tasks that previously required someone’s full attention—like processing customer data or managing inventory levels—which lets employees focus more on value-added tasks like sales and customer service.
In this article, let’s discuss the advantages of artificial intelligence to businesses and how your company can take advantage of it to operate more effectively and efficiently.
What is Artificial Intelligence (AI)? Artificial intelligence is a computer system that can be trained to perform tasks that typically require human intelligence. It’s a type of software capable of learning and problem-solving with the help of algorithms that mimic human brain functions. Artificial intelligence has applications in many fields, including medicine, law, business, and academia.
How Does AI Work: a General Overview AI systems rely on machine learning, i.e., computers analyze large amounts of data and learn patterns to make predictions about future events. They can do this with incredible accuracy. Take Google Translate as an example—it can instantly translate text between any pair of languages without human intervention. That’s because Google Translate can analyze millions upon millions of documents written in multiple languages and learn which combinations of words tend to appear together in different contexts. There are two typical types of AI, each with specific applications and uses. The first is known as “weak AI,” which refers to a system that can perform only one task at a time. Weak AI also has no knowledge base and can’t generalize based on past experiences. Another type of AI is “strong AI,” which indicates a system that can perform multiple tasks simultaneously. It can also build upon previous experiences to learn more about its environment and surroundings.
Advantages of Artificial Intelligence According to BCG, “modest investments in specific AI use cases can generate up to 6% more revenue, and with rising investments, the revenue impact from AI triples to 20% or more.” Forrester’s researchers even emphasize that “AI has fundamentally changed our ability to understand and empower our bespoke audiences, whether customer or employee.” Let’s take a closer look at the benefits of using artificial intelligence in businesses: 1. AI Helps Improve Customer Service AI will be able to interact with customers in real-time and respond to their questions or concerns in a way that feels more natural than any human could ever manage. This will strengthen the customer experience, which will keep people coming back to your brand repeatedly. 2. AI Helps Make Decisions Faster Artificial intelligence can help businesses make decisions that are more accurate and consistent. For example, your company can use an AI-powered platform that uses data from previous sales to predict how many people will buy your product. Or you can use it to determine which products should be promoted at certain times. This allows you to make strategic decisions about what products should go into production – no more guesswork. 3. AI Helps Save Operational Costs When you use AI to automate processes, you can reduce the need for human employees to do repetitive tasks. This will allow your business to focus on other areas of growth, like innovation and development. Some AI systems are even trained to write content by analyzing the data from thousands of articles written by human authors. This means you might not need to hire writers or editors anymore, which helps you save money on employee salaries and training and management costs. 4. AI Helps Understand Customers Better As said above, artificial intelligence can analyze large amounts of data and identify patterns and trends in seconds. It can help you figure out what your customers need and what they want to get from your company. You can use an AI system to collect customer insights and then use those insights to provide your audiences with personalized recommendations. Think about showing relevant content to what a customer read or the products that can be paired with the item they’ve just added to their cart. This will help improve the shopping experience, drive purchases, and build trustworthy relationships with the customer. 5. AI Helps Forecast Revenue and Sales Forecasting revenue and sales is a complex task that requires many different inputs from customers, suppliers, and employees. But that can be easy with the help of an AI-powered tool. Your AI system will learn from past data and predict what will happen in the future based on your company’s history. It also takes into account any changes that have occurred recently or that might happen in the future (such as new products or price changes). Hence, your AI can identify patterns in your historical data and predict how much money will be made from each product line over time. This allows you to make informed decisions about inventory levels and other aspects of your business operation.
Latest AI Technologies 1. AI Technologies in Sales Many companies currently use AI-powered lead scoring software to evaluate lead quality. Basically, lead scoring is the process of assigning a numerical value to every single lead that comes into a CRM (customer relationship management) system and then evaluating those leads based on their importance and potential. Lead scoring is important because it helps salespeople understand who they’re working with and how to structure their campaigns around specific customers’ needs. It also helps them increase efficiency by ensuring they’re not wasting time contacting clients who aren’t a good fit for their product or service. 2. AI Technologies in Customer Service Customer service is one of the most important aspects of any business. It’s one of the first things companies look at when considering new technologies. There are several ways that AI can be used in customer service, including chatbots. For example, a chatbot can be programmed to handle basic questions, such as opening an account or verifying identity. It can also be designed to deal with complex inquiries like providing information about a product or solving a technical issue. The best part is chatbots are easy to use and can offer the same level of service as humans. 3. AI Technologies in Finance AI is revolutionizing the financial industry. According to Insider Intelligence, AI can help financial institutions (FIs) provide customers with 24/7 access to their accounts and personalized financial services. AI applications also allow FIs to prevent fraud and cyberattacks by monitoring all transactions and analyzing them for suspicious activities. Another innovative use case of AI in finance is algorithm trading—a technology that uses computer algorithms to trade securities. Algorithmic trading can be used to automate the process of buying and selling securities, allowing a trader to focus on higher-level strategies like research and analysis.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://revenuegrid.com/blog/advantages-of-artificial-intelligence/
|
[
{
"date": "2023/03/06",
"position": 83,
"query": "artificial intelligence business leaders"
}
] |
Data and AI Staffing Agency: San Francisco
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Data and AI Staffing Agency: San Francisco
|
https://www.harnham.com
|
[] |
We do this through our unparalleled customer service throughout the recruitment process. We provide in-depth guidance during the hiring process in addition to ...
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SAN FRANCISCO DATA & AI STAFFING
OVERVIEW
We work with some of San Franciscos' leading brands to provide recruitment solutions across the Data & AI industries.
Harnham is committed to helping companies find the best talent for their Data & AI roles in San Francisco, roles including AI Engineers and Architects, as well as machine learning engineers. We do this through our unparalleled customer service throughout the recruitment process.
We provide in-depth guidance during the hiring process in addition to offering insights based on our research on the industry. Our consultants are also well-versed in their markets so they’re able to guide you to the right candidate for your job.
Our Team who specializes in Data & AI Staffing in San Francisco also has an extensive database of Data & AI professionals in the Bay area so we have the best chance to find the best candidate for any role your company has.
| 2023-03-06T00:00:00 |
https://www.harnham.com/data-ai-staffing-agency-san-francisco/
|
[
{
"date": "2023/03/06",
"position": 38,
"query": "artificial intelligence hiring"
}
] |
|
Securing Online Hiring Process: How AI Proctoring ...
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Securing Online Hiring Process: How AI Proctoring Prevents Fraudulent Candidates
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https://proctortrack.com
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[] |
AI proctoring is a technology that can help prevent hiring fraudulent candidates and ensure that only genuine candidates pass through the virtual interview ...
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There are several types of remote proctoring solutions like Proctortrack used in the online recruiting process done through web-based recruitment software, including:
Live Proctoring: A live proctor is present during the exam to monitor the test-taker in real-time. The proctor uses webcams, microphones, and other monitoring tools to ensure the test-taker is not cheating.
Automated Proctoring: This is when AI-powered software monitors the test-taker during the exam. The software uses advanced algorithms to detect suspicious behavior, such as looking away from the screen or using unauthorized devices.
Recorded Proctoring: Here, the recording is on during the assessment, and a proctor reviews the recording later to check for suspicious behavior.
In-person Proctoring: This is when the test-taker takes the exam in person, and a proctor is present to monitor them.
Remote proctoring: The test-taker takes the exam remotely, and the proctor monitors them via webcams and other monitoring tools.
It is important to note that the type of proctoring used in the online recruiting process may vary depending on the organization’s requirements, budget, and other factors.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://proctortrack.com/blog/article/securing-online-hiring-process-how-ai-proctoring-prevents-fraudulent-candidates
|
[
{
"date": "2023/03/06",
"position": 49,
"query": "artificial intelligence hiring"
}
] |
Why Hidden Artificial Intelligence Features Make Such an ...
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Why Hidden Artificial Intelligence Features Make Such an Impact in Education
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https://www.edsurge.com
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Recently, EdSurge sat down with Levine to discuss how AI can enhance the learning experience. EdSurge: How does Qualcomm fit into the equation of advancing the ...
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When classrooms and conference rooms abruptly moved online three years ago, we all experienced moments of technical frustration. Whether dealing with connectivity issues or clumsy virtual interactions, which were sometimes accompanied by awkward background noises, we persisted. Fortunately, the education sector had time to smooth out some of these wrinkles, especially with improved connectivity and advancing technology such as artificial intelligence (AI).
Having seen such positive changes firsthand, Elliott Levine, director of worldwide public sector and education at Qualcomm Technologies, Inc. is excited about the newest technologies and their impact on the learning experience. Before transitioning to EdTech, Levine enjoyed 30 years working in various positions in K-12 and higher ed. Levine’s wealth of experience has enabled him to understand what technology teachers and students need to foster 21st-century skills. Recently, EdSurge sat down with Levine to discuss how AI can enhance the learning experience.
EdSurge: How does Qualcomm fit into the equation of advancing the education sector?
It really demonstrates what's possible when we look at connected learning not as a luxury but as a basic human right.
Levine: Qualcomm brings connectivity, efficient processing and artificial intelligence to the edge. Connectivity is probably how Qualcomm offers the greatest impact in education. Most of your cellular networks—even a lot of your wireless networks—often rely on Qualcomm innovations. So 5G, 4G, LTE … those were technologies developed by Qualcomm that are used across the globe. And we introduce technologies in ways to make them more affordable and accessible to schools, districts and educational agencies.
Through our Qualcomm Wireless Reach Initiative, we work closely with partners to collaborate and identify local schools that need high-quality technology with the goal of bringing in connected solutions to drastically improve teaching and learning outcomes. The school is not only getting advanced devices powered by our latest processor technology and access to connectivity—many of these schools don't even have WiFi available—but also introduced to curriculum teacher training and assessment elements to model the embracing of digital learning that can occur. This innovative approach has been working for well over 10 years. And I think it really demonstrates what's possible when we look at connected learning not as a luxury but as a basic human right.
We are making advances with more embedded cellular devices, which include our platforms designed for Always On, Always Connected PCs. In addition to being built with 4G or 5G modems, they perform unlike other processors that can really consume a lot of power: what we've seen with Snapdragon technology is more like “sipping” power. So, while typical devices may get up to 10 hours of battery life before needing a charge, Chromebooks and PCs powered by Snapdragon get up to 20 hours of battery life. My Windows on Snapdragon laptop gets up to 28 hours of battery life, which helps me as I’m constantly on the go. But what this means is that students can check out a laptop from school to complete homework without the worry of lugging around or losing the charger. Classes can move outdoors since students and teachers don’t need to be tethered to an outlet. We’ve even seen instances where mobility-restricted students struggle in classrooms where power cords are dangling from desks to outlets. These are very tangible improvements that also help ensure learning continues after class.
How else does Qualcomm address the digital divide among students?
Elliott Levine
Director of Worldwide Public Sector and Education
Qualcomm Technologies
If we look at K-12 education on a worldwide level, the majority of learners don't have internet access at home. We're still contending with what is sometimes called digital poverty because a child without these resources and tools will face a lifetime of disadvantages. We’re very passionate about addressing digital poverty by bringing more connected solutions to the market. And at the same time, we're trying to bring better computing experiences for those already with devices, as well as introduce mixed reality—both virtual and augmented—as key facets of immersive learning. We feel this is what’s next for digital education.
What teacher supports or professional learning community does Qualcomm offer?
With any new technology initiative in schools, you must think about a digital initiative like a stool. And any good stool has four legs on it. One of those legs is the hardware and the infrastructure. The second leg is technical knowledge and support. The third is the curriculum tools. The fourth leg is professional development, mentoring and coaching. If you leave any one of those legs out of the equation and try to sit on that stool, you're going to end up on the floor. But Qualcomm is the whole package; we offer all four legs of the stool to provide a robust foundation of support for teachers and professionals in the learning community.
How does AI augment an educator’s role?
Great question. It can be hard to conceptualize AI, so I've started doing ‘60 seconds on Snapdragon’ videos to explain some of the AI functionality in our devices these days. As I mentioned, one of the things I love about the new devices running Windows on Snapdragon platforms, such as the newest ThinkPad X13s, isn't just the battery life but the AI functionality that exists on the unit itself. One of those features is the built-in noise cancellation. Some of the other companies have tried to build a database of noises that they will analyze and block, which is more machine learning. We instead have true AI, and it works in a matter of TOPS—trillions of operations per second.
I wanted to show how well this noise cancellation worked. So I shot a video where I went into the middle of Times Square with all that noise. For the first 30 seconds, I was on my phone explaining it. For the next 30 seconds, I switched to my laptop and recorded the video to show exactly what it sounds like with the AI. No tricks. No prototype. It made such a difference! This is a big game changer, especially for teachers who create content, do virtual tutoring, hold virtual office hours or interact with parents online. This sort of functionality makes a world of difference.
What is your advice for educators who are hesitant to implement AI in the classroom?
We must embrace advancing technology and show students how to gain mastery in even higher critical thinking skills.
First, start looking at some of the simple AI tools that could really help enhance a custom experience—just the littlest bit. Don’t be afraid to fail. We have some of our best learning take place in the midst of mistakes. Next, encourage the students to explore AI technology and try to apply it in the projects they're doing. Not all AI is cloud-based, and much of it runs in the background on everyday devices we use, such as laptops and smartphones. Lastly, we must remember this will be the world today’s children are growing up in. These are the tools and resources they will need to rely upon. We can't ignore them. We must embrace advancing technology and show students how to gain mastery in even higher critical thinking skills. Nonetheless, educators reading this are already off to a great start in researching what’s possible and learning what Qualcomm, in particular, is doing to help educators and students improve the teaching and learning experience in and outside the classroom.
Snapdragon is a product of Qualcomm Technologies, Inc. and/or its subsidiaries.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.edsurge.com/news/2023-03-06-why-hidden-artificial-intelligence-features-make-such-an-impact-in-education
|
[
{
"date": "2023/03/06",
"position": 24,
"query": "artificial intelligence education"
}
] |
AI in Education: Gains, Use Cases, and Implementation ...
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AI in Education: Gains, Use Cases, and Implementation Steps
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https://www.cleveroad.com
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[
"Author Details"
] |
In this article you'll learn about the interaction between AI and education, AI's benefits, and how you can make AI-based program for education.
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Is it possible to create Artificial Intelligence? Just like neurone web is a form of Machine Learning, Machine Learning is a form of Artificial Intelligence. But the meaning of AI is vague, so the phrase doesn’t have practical meaning yet. Nevertheless, it doesn't stop us to picture a fantastic technologically advanced future, though the reality is far from that.
Machine Learning is a subdivision of Artificial Intelligence development that uses data analysis algorithm, gets conclusions and makes decisions or predictions. It means, that instead of manual program creation with the help of special instruments, the system is being taught with a large amount of data and algorithms, that give an opportunity to the platform to learn how to fulfil a task.
The AI algorithm educates with the help of technologies Deep Learning or Natural Language generation. In Artificial Intelligence usually algorithms is a small procedure that fulfill a repetitive task.
AI software development is a long and complex task. Yet, the programme after deep learning in some cases show much higher results than people, starting from cat recognition to the detection of cancer cells in blood and on MRT photographs.
The program Google AlphaGo learnt how to play Go (Chinese logical board game, that is even more complicated than chess) and now can play with itself.
Jorge Dolbier, CTO social & Interactive Media at IBM describes Machine Learning as a way to create software with the help of data instead of a code. The program makes it's own choice, carries out actions, based on statistics and changes behaviour with new data.
As a matter of fact, latest developments in Artificial Intelligence are used in educational sphere via an adaptive learning. It is a kind of educational method in connection with interactive mechanisms, that takes into account individual needs of all and every student.
The Adaptive education is applied in such systems:
| 2023-03-06T00:00:00 |
https://www.cleveroad.com/blog/ai-in-education-or-what-advantages-of-artificial-intelligence-in-education-you-can-gain-/
|
[
{
"date": "2023/03/06",
"position": 48,
"query": "artificial intelligence education"
}
] |
|
AI in Higher Education: Transforming Learning, Teaching, and ...
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AI in Higher Education: Transforming Learning, Teaching, and Student Support
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https://www.uniranks.com
|
[
"Uniranks Editorial Team"
] |
This article explores how AI is reshaping learning, teaching methodologies, and student support services while also addressing the challenges and ethical ...
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This article explores how AI is reshaping learning, teaching methodologies, and student support services while also addressing the challenges and ethical considerations of AI-driven education.
1. AI-Powered Personalized Learning: The Future of Education
AI-driven learning tools adapt to individual student needs, offering customized lessons, feedback, and assessments. Some key applications include:
Adaptive Learning Platforms
AI analyzes student performance in real-time and adjusts lesson plans accordingly.
Platforms like Coursera and Udemy use AI to suggest courses based on student behavior and performance .
Universities integrating AI-powered learning assistants help students grasp difficult concepts at their own pace.
Smart Tutoring Systems
AI-based tutors provide instant feedback, answer student queries, and track progress .
Chatbots and virtual tutors offer 24/7 academic support, reducing faculty workload and enhancing student engagement.
Predictive Analytics for Student Success
AI predicts student performance and identifies at-risk students before they fail.
Universities can intervene with personalized study plans and mentoring to improve retention rates.
2. AI Transforming Teaching: A New Era for Educators
AI does not replace educators but enhances their ability to deliver high-quality education. Some AI-driven innovations include:
Automated Grading & Assessment
AI-powered grading systems can evaluate multiple-choice tests, essays, and assignments with high accuracy.
Saves professors valuable time, allowing them to focus on student mentorship and curriculum enhancement.
Enhanced Course Creation & Content Personalization
AI helps faculty analyze student learning patterns to create more effective course materials .
AI tools assist in generating customized quizzes, assignments, and interactive learning modules.
Virtual & Augmented Reality (VR/AR) in Education
AI-driven VR and AR provide immersive learning experiences for fields like medicine, engineering, and architecture .
Simulated environments allow students to practice skills in a risk-free setting.
3. AI in Student Support Services: Enhancing Campus Life
Beyond academics, AI is revolutionizing student services, career guidance, and administrative processes.
AI-Powered Career Counseling
AI-driven platforms like UNIRANKS provide career path assessments based on student interests, skills, and job market trends.
AI helps match students with internship opportunities, scholarships, and job prospects tailored to their profile.
Chatbots & Virtual Assistants for Student Queries
AI chatbots handle admissions inquiries, financial aid questions, and course registrations 24/7.
Reduces administrative workload while ensuring students receive quick and accurate information.
AI in Campus Security & Well-being
Facial recognition AI enhances campus security and access control .
AI-based mental health platforms offer confidential support, stress management tools, and crisis intervention resources.
4. Challenges & Ethical Concerns of AI in Higher Education
While AI has transformative potential, it also presents challenges:
Bias & Fairness
AI algorithms may carry biases based on incomplete or imbalanced training data .
Universities must ensure transparent, ethical AI use that avoids discrimination.
Data Privacy & Security
AI relies on student data collection , raising concerns about data protection and misuse .
Institutions must comply with strict cybersecurity policies to safeguard student information.
The Role of Human Educators
AI should complement, not replace, human interaction and mentorship .
Emotional intelligence, creativity, and personalized guidance remain areas where human educators excel.
5. How UNIRANKS is Shaping AI Adoption in Higher Education
UNIRANKS evaluates universities based on their integration of AI technologies in education, student support, and campus operations. Key ranking factors include:
Use of AI in Learning & Teaching – Institutions leveraging AI for adaptive learning and smart assessments rank higher.
– Institutions leveraging AI for adaptive learning and smart assessments rank higher. AI-Driven Career Guidance – Universities offering AI-powered career matching and industry partnerships score well.
– Universities offering AI-powered career matching and industry partnerships score well. Data Security & Ethical AI Practices – Schools prioritizing student data protection receive higher recognition.
– Schools prioritizing student data protection receive higher recognition. Student Satisfaction with AI-Powered Services – UNIRANKS assesses how effectively AI enhances student experiences.
By promoting responsible AI adoption, UNIRANKS helps students choose universities that embrace innovation while maintaining ethical education standards.
6. The Future of AI in Higher Education
AI in higher education is evolving rapidly, and future developments may include:
AI-Powered Personalized Degree Programs – Customized courses based on individual strengths and career aspirations.
– Customized courses based on individual strengths and career aspirations. AI-Enhanced Research & Innovation – Universities using AI for academic research, medical advancements, and engineering solutions .
– Universities using AI for . Greater AI Collaboration Between Academia & Industry – AI-driven partnerships offering students real-world learning experiences.
– AI-driven partnerships offering students real-world learning experiences. Increased AI Regulation & Ethical Standards – Universities adopting responsible AI policies to ensure fairness and privacy.
Conclusion
AI is transforming how universities teach, how students learn, and how institutions provide student support services. From adaptive learning and AI tutors to automated assessments and AI-driven career guidance, higher education is entering a new era of intelligent learning.
As AI continues to advance, universities must balance innovation with ethical considerations, ensuring student privacy, inclusivity, and meaningful human interaction in education. With insights from UNIRANKS, students can identify universities leading in AI adoption, choosing institutions that offer the best technology-driven learning experiences.
AI is not replacing education—it is enhancing it, making learning smarter, more efficient, and accessible to all.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.uniranks.com/explore/tech-in-education/ai-in-higher-education-transforming-learning-teaching-and-student-support
|
[
{
"date": "2023/03/06",
"position": 93,
"query": "artificial intelligence education"
}
] |
Rise Of Robots - Jobs Lost to Automation Statistics in 2023 - Leftronic
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Rise Of Robots - Jobs Lost to Automation Statistics in 2023
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https://leftronic.com
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1.7 million jobs worldwide have been lost to automation since 2000. The world has lost a total of 1.7 million manufacturing jobs to industrial robots since ...
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Are robots taking over our jobs?
That’s one of the most pressing questions in this digital world. Although it sounds like a plot from a sci-fi novel, the idea of automating manual labor has been a reality since the cotton gin.
Certainly, it’s no secret that technology is dominating society. The question is whether the future will look like The Terminator, with self-aware killer robots, or The Jetsons, with robotic maids doing all the housework.
Let’s take a closer look at recent jobs lost to automation statistics to better understand how technology is disrupting society.
Fascinating Automation and Job Loss Statistics (Editor’s Choice)
1.7 million jobs have been lost to automation since 2000.
In England, 1.5 million jobs are in danger of being automated.
25% of mundane and repetitive jobs are at risk of automation.
375 million jobs are expected to vanish by 2030.
Artificial intelligence will displace 40% of jobs worldwide in the next 15 years.
Artificial intelligence will generate 2.3 million jobs starting in 2020.
These are staggering figures, and we’ll dig deep to discover the trends behind them.
First things first, though:
What Is Job Automation?
Job automation is the practice of replacing human labor in the workplace with computer-controlled devices and other electronics.
Here’s the deal:
It’s no longer limited to the replacement of manual processes. Job automation also entails substituting knowledge-based occupations with technology, a bit like the workers who compile Wikipedia statistics. Artificial intelligence and machine learning make it possible for robots to carry out tasks that previously required human intellect. And this can be liberating.
At the same time, these innovations also present new challenges to the human workforce.
What Percentage of Jobs Have Been Lost to Automation?
Automation is gradually reshaping the workplace, from the booming of AI tools to a rise in statistics about IoT usage. Each year, more and more medium-skilled manufacturing and office jobs disappear.
Now:
Robots may not have taken over yet and may not be able to do so in the next few years. However, some industries are already experiencing the pressures of automation. Workers need to change occupational groups, acquire new skills, and expand their knowledge on important subjects to stay relevant in ever-changing times.
Let’s learn more about the impact of automation on employment.
1. 1.7 million jobs worldwide have been lost to automation since 2000.
The world has lost a total of 1.7 million manufacturing jobs to industrial robots since 2000. Some of the regions that saw the highest number of cuts in human labor in the manufacturing sector include the US, the European Union, China, and South Korea.
2. The US has lost 260,000 jobs to robots.
(Source: Oxford Economics)Even in the US, the threat of automation is pervasive. Robots have taken over 260,000 jobs there since 2000, which represents 2% of the country’s current manufacturing workforce.
3. 2.25 million robots have entered the workforce in the past two decades.
The rate of adoption of robotization has tripled in the past 20 years, with 2.25 million robots currently replacing human labor in the global workforce. This figure is expected to increase significantly, reaching up to 20 million by 2030, as suggested in recent automation and job loss statistics.
4. An average of 1.6 jobs has been displaced in the manufacturing sector.
Since 2004, each industrial robot that’s been installed in the manufacturing sector has displaced an average of 1.6 people. In the first year of a robot’s deployment, it displaces roughly 1.3 workers. This figure increases in succeeding years.
As robots carry out the major tasks, only a few human employees are needed on the manufacturing floor.
What Percent of Jobs Are Automated?
As businesses invest billions in improving efficiency, nearly all occupational groups will be affected by artificial intelligence and automation in the future. The rest of us are often complicit in this job loss, without knowing it.
Human workers used to perform such duties. However, shifting consumer needs have eliminated unskilled labor.
5. 25% of mundane and repetitive jobs in the US face high risks of automation.
In the US, the percentage of jobs at risk of automation is 25%. Approximately 36 million jobs face high risks of automation.
And that’s not all:
No single occupation will remain unaffected by technological breakthroughs, jobs lost to automation statistics tell us. However, jobs that entail routine activities, physical labor, and information collection or processing are very susceptible to automation.
6. The US uses 189 robots in manufacturing per 10,000 workers.
In the manufacturing sector, US companies utilize nearly 200 robots per 10,000 workers. Additionally, the rate of adoption of robots has increased from 14% in 2011 to 40% in 2016.
How Many Jobs Are Being Lost to Automation?
There are several uncertainties around the rate of adoption of industrial robots, artificial intelligence, and machine learning. However, it’s possible to estimate the impact of automation.
Let’s dive right in:
7. Robots could replace 40% of the world’s jobs within 15 to 25 years.
In an interview with Scott Pelley, artificial intelligence expert Kai-Fu Lee suggested that robots could take close to half of the world’s jobs. Seeing their overwhelming presence in sectors like customer service (judging by the chatbot statistics).
8. A 2017 McKinsey Global Institute study projected that there could be 375 million jobs lost to automation by 2030.
A McKinsey Global Institute study in 2017 showed that anywhere between 75 million and 375 million workers could be replaced by robots in the coming years. In other words, 3% to 14% of the global workforce faces high risks of unemployment.
That’s huge!
These people will have to change occupations, while others must learn new skills to remain relevant in the evolving job market.
9. McKinsey Global Institute suggests 73 million workers face automation job displacement.
McKinsey Global Institute suggests that as much as 33% of the 2030 workforce may have to pursue new professions in different sectors.
What’s more:
About 39 million jobs could be displaced if we see midpoint automation. Of that number, 13 million to 16 million workers would have to find new jobs. If rapid automation occurs, the world could have an incredible 73 million jobs lost to automation by 2030. In this case, around 48 million to 53 million workers will be forced to change occupations.
10. 1.5 million jobs in England are in danger of being automated.
The UK Office for National Statistics surveyed over 20 million people in England and found out that 7.4% of occupations are at high risk of automation. That’s a total of 1.5 million jobs, as indicated in its recent automation and job loss statistics.
It’s not all bad news:
The number of jobs facing high risks of automation actually decreased from 2011 to 2017. However, the number of professions facing low and medium risks rose within that period.
What Jobs Are at Risk of Automation?
Research shows that a quarter of US jobs are at high risk of being automated. So, what professions are the most at risk of automation? Read on to find out.
11. High-risk professions in production have 70% to 100% automation potential.
Professions that are most likely to be automated involve mundane and repetitive tasks. For instance, jobs that require physical labor, machine operation, food preparation, data collection, and data processing have 70% to 100% automation potential.
According to automation jobs loss statistics, filling machine and plant operators are some of the most vulnerable in this sector.
12. 100% of logging equipment operators’ tasks can be automated.
The agriculture sector in general may not face the same level of threats as the production, food services, and transportation sectors. However, a few of the occupations in this industry face high risks of automation. Logging equipment operators face the highest risks, with 100% automation potential.
13. Administrative jobs at risk of automation include mail clerks, mail machine operators, and office machine operators.
Mail clerks and mail machine operators face the highest risks with 93.7% of their jobs susceptible to automation. Around 92.9% of the duties of office machine operators and 90.4% of the responsibilities of word processors and typists can potentially be automated in the next few years.
What Jobs Are at Risk of Being Lost to Robots?
Risks of automation vary across different industries and occupations.
14. Food preparation workers are the most vulnerable in the food service industry, as they face a 91.4% automation potential.
Food service is also susceptible to automation, with occupations facing 70% to 100% automation potential. Food preparation workers are the most vulnerable in the sector, facing a 91.4% automation potential. Short order cooks come a close second, with 91.2% of their responsibilities facing automation risk level.
15. 100% of the tasks of dredge operators, machine feeders, and offbearers are susceptible to automation.
Transportation also faces high risks of automation. Different occupations face varying risk levels, but none are as high as the threats that dredge operators, machine feeders, and off bearersface, with 100% of their activities being susceptible to automation.
What Jobs Will Be Automated by 2030?
Assuming that investments in automation continue at their current pace, millions of jobs could be displaced by robotization in the years to come. Cloud computing statistics suggest that it will continue to mature, alongside AI and machine learning. With their advancement, the risks of robots taking over the human workforce become increasingly urgent.
But wait:
Not all professions will experience the same levels of displacement.
So here comes the question: What jobs could be automated by 2030?
Let’s find out:
16. Some occupations in unpredictable environments have a high probability of jobs lost due to technology.
A few professions set in unpredictable environments face higher risks of automation. Machinery installation and repair workers have a greater probability of being displaced compared to agricultural field workers and building and ground cleaners.
Key takeaway:
By 2030, machinery installation and repair workers and emergency first responders could experience an employment decline due to automation.
17. Professions in predictable environments have a high probability of automation and job loss.
Most, if not all, of the jobs in predictable environments, will be automated by 2030. Food preparation workers are the most vulnerable in the occupation group, as they face a 35% employment decline.
18. Office support professions could also be automated by 2030.
Due to automation, financial workers could face a 25% to 34% employment decline by 2030. Meanwhile, computer support workers and administrative assistants could experience a 5% to 14% decline.
Is Accounting at Risk of Automation?
Finance departments are exploring automation options as much as any other sector. As artificial intelligence advances, robots will be coming for jobs that require human intellect as well.
Is accounting one of those jobs?
Well:
19. Accountants, bookkeepers, and auditing clerks face 85.6% automation potential.
In the world of accounting, how many jobs will be lost to automation?
According to Brookings, auditing and accounting clerks and bookkeepers are susceptible to automation. Their jobs have an 85.6% chance of being automated.
But not everyone agrees:
20. McKinsey Global Institute expects accounting to see an increase in labor demand.
McKinsey Global Institute has a different forecast, predicting that accountants will be one of several professions to see growth in the US due to automation.
What Jobs Are Least Likely to Be Automated?
There’s no doubt that the rise of robots will lead to job loss across many industries, leaving millions of workers displaced.
However:
The current limitations of technology will spare other professions. In fact, automation will lead to growth in labor demand in certain sectors.
Learn more about the professions that are least likely to be affected by automation.
21. Doctors, health technicians, and therapists are less likely to have their jobs lost to automation.
The roles of care providers are so unique and important that they won’t be replaced by robots anytime soon. By 2030, the labor demand for doctors, health technicians, and therapists might even increase by 5% to 24%.
Where AI does fit into healthcare well, however, is cyber security. Healthcare data breach statistics are quite concerning in today’s day and age. Artificial intelligence could play a vital role in preventing data breaches.
22. The labor demand for childcare workers is expected to increase by 100% or more.
Of all care providers, childcare professionals are most likely to experience employment growth by 2030. A recent study of workforce automation and jobs projects a 100% or more increase in labor demand for childcare workers in the coming years.
23. Teachers and other education professionals should experience a 5% to 24% increase in labor demand.
According to the latest jobs lost to automation statistics, educators won’t be replaced by robots anytime soon. As a matter of fact, there will be an increase in demand for teachers, education support workers, and other education professionals in the next few years.
Does Automation Create More Jobs?
Each leap in artificial intelligence, robotics, and other burgeoning technologies threatens the jobs of millions of human workers. At the same time, they create new opportunities in yet-to-exist industries. The value these innovations bring might even be enough to offset the disruptive changes they bring.
Read on to discover the benefits of automation.
24. AI will produce 2.3 million jobs in the US in 2020.
Great news:
Artificial intelligence stats from the research firm Gartner forecasts that AI will create more job opportunities for human workers that it’s expected to eradicate.
So, how many jobs will be lost to AI?
According to Gartner, AI will eliminate 1.8 million jobs. However, it will generate 2.3 million jobs starting in 2020.
25. Automation will generate 133 million jobs worldwide by 2022.
A World Economic Forum study suggests that automation will displace tens of millions of jobs worldwide; however, it will also generate hundreds of millions more. So, about 133 million jobs will be produced worldwide thanks to advances in technology. Meanwhile, job loss due to automation will amount to 75 million by 2022.
26. 29% of jobs worldwide will be replaced, while only 13% will be generated.
Forrester forecasts that the global workforce will lose 29% of jobs to automation while generating 13% of jobs. Considering how many jobs will be automated, the need for workers to be lifelong learners who can adapt to the ever-changing landscape of business is more pressing than ever.
In Conclusion
Automation is a powerful disruptive force that’s already reshaping society as we know it. It’s changing the way the economy operates as well as the jobs that people do. Although robotics and artificial intelligence can cause a worldwide crisis due to widespread job loss, these innovations also provide excellent opportunities to improve our quality of life.
Bottom line:
As recent jobs lost to automation statistics show, technology also creates new opportunities. Innovation could lead to new jobs and offset the negative impacts of automation.
Have a great day, guys!
See you around!
| 2023-03-07T00:00:00 |
https://leftronic.com/blog/jobs-lost-to-automation-statistics/
|
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"query": "artificial intelligence employment"
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"date": "2023/03/07",
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"date": "2023/03/07",
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{
"date": "2023/03/07",
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},
{
"date": "2023/03/07",
"position": 36,
"query": "AI labor union"
},
{
"date": "2023/03/07",
"position": 72,
"query": "artificial intelligence workers"
},
{
"date": "2023/03/07",
"position": 99,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 42,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 42,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 82,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 18,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 96,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 14,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 89,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 98,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 63,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 99,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 90,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 14,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 99,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 18,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 98,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 90,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 19,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 99,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 18,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 99,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 99,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 93,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 42,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 17,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 99,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 42,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 98,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 98,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 17,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 97,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/07",
"position": 17,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 46,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 82,
"query": "automation job displacement"
},
{
"date": "2023/03/07",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 17,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 38,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 29,
"query": "robotics job displacement"
},
{
"date": "2023/03/07",
"position": 99,
"query": "AI job creation vs elimination"
}
] |
|
Workforce Development Efforts Advance Amidst Calls for AI Readiness
|
Workforce Development Efforts Advance Amidst Calls for AI Readiness
|
https://www.meritalk.com
|
[
"Meritalk Staff"
] |
It's absolutely critical for our Federal government to get ahead, not only by dedicating resources but also by training the workforce to be AI ...
|
On the website of the Department of Veterans Affairs (VA), a robot-like hand reaches out to touch a human hand through a pane of glass as a bright light illuminates the importance of re-tooling the workforce for the arrival of artificial intelligence (AI).
The illustration is part of the VA’s promotion of its AI Communities, launched last year “to explore artificial intelligence, machine learning, and other frontier technologies and the impacts they will have on veterans and their families.” So far, VA officials said, nearly 600 employees have signed up, exchanging ideas over email groups and an intranet hub.
“The promise and the potential of artificial intelligence is really enormous,” said Gil Alterovitz, director of the VA’s National Artificial Intelligence Institute. The department recently published a list of 41 potential use cases for AI at the VA, in line with the requirements of Executive Order 13960, “Promoting the Use of Trustworthy Artificial Intelligence in Federal Government.” The use cases include physical therapy support, colon polyp detection, and seizure detection.
“AI can lead to the next wave of new technologies that help us grow our economy faster, make our society better, and make our military more effective. It’s absolutely critical for our Federal government to get ahead, not only by dedicating resources but also by training the workforce to be AI ready,” observed Yll Bajraktari, who served as executive director of the National Security Commission on Artificial Intelligence.
AI can be applied to government missions as diverse as national security, drug and human trafficking, energy resilience, cyber threat monitoring, and administration of social programs such as Medicare and unemployment insurance, noted Diana Gehlhaus, a research fellow at the Center for Security and Emerging Technology at Georgetown University. She’s a co-author of an October 2021 report, “U.S. AI Workforce: Policy Recommendations.”
“There is so much untapped potential in the use of artificial intelligence within the federal space,” explains Bob Venero, Founder and CEO of Future Tech. “Our customers are looking at use cases across training, simulation, faster disaster recovery, fraud detection, and cybersecurity. Without a strong foundation of AI expertise, the federal government and their federal system integrators may be left behind in the innovation and progress curve.”
Meeting Federal AI skills needs “is a budget problem, and it is also a people, process, and technology problem,” Gehlhaus said. People may have cultural inertia, a “this is how we’ve done things” mindset, she said. “Senior leaders might not understand the technology and so they are not comfortable. Then there are the actual skills of people operating these systems or administering them. They would need some just-in-time training or targeted upscaling.”
Recent MeriTalk-Future Tech research shows that 95 percent of Federal IT decision makers say their organization is investing in-house AI skills development, and that 87 percent feel their workforce has only a fraction of the knowledge they need. Gehlhaus and other experts stress that the need for training extends beyond technical staff to executives, program managers, procurement professionals, and others.
Bajraktari noted: “The government has to have employees who understand what’s possible, how to change business models, and how to implement change.”
Throughout the government, these and other efforts are underway to train employees and the future workforce, and identify staff with AI skills:
President Biden on Oct. 17 signed the Artificial Intelligence Training for the Acquisition Workforce Act into law. The law aims to improve the Federal workforce’s understanding of AI and ensure its ethical and safe use. It requires the Office of Management and Budget to develop a training program that helps Federal employees who purchase and manage AI technologies to better understand their risks and benefits
into law. The law aims to improve the Federal workforce’s understanding of AI and ensure its ethical and safe use. It requires the Office of Management and Budget to develop a training program that helps Federal employees who purchase and manage AI technologies to better understand their risks and benefits The Defense Innovation Unit is building an AI-powered talent-matching platform called GigEagle that will enable DoD reservists and National Guard members to build profiles that highlight their civilian expertise and find short-term work with DoD organizations seeking specialty skills, including AI
that will enable DoD reservists and National Guard members to build profiles that highlight their civilian expertise and find short-term work with DoD organizations seeking specialty skills, including AI The U.S. Army Intelligence Center of Excellence Warrant Officer Advanced Course includes a day-long AI/ML seminar intended to serve as a primer for military intelligence soldiers on AI, machine learning, and data science
intended to serve as a primer for military intelligence soldiers on AI, machine learning, and data science The U.S. National Science Foundation’s Data Science Corps seeks to expand data-science skills by bringing students and local organizations together to use available data to solve community problems
seeks to expand data-science skills by bringing students and local organizations together to use available data to solve community problems The VA is running an AI Tech Sprint in which teams compete to build prototype tool that will help agencies understand their employees’ skill sets in fields such as AI and curate individualized learning pathways
This is all happening amid growing agreement on the need to retool the workforce for AI. In its final report, issued in March 2021, the National Security Commission on Artificial Intelligence said, “Despite exciting experimentation and a few small AI programs, the U.S. government is a long way from being ‘AI-ready.’ … “This is not a time to add a few new positions in national security departments and agencies for Silicon Valley technologists and call it a day. We need to build entirely new talent pipelines from scratch. We should establish a new Digital Service Academy and civilian National Reserve to grow tech talent with the same seriousness of purpose that we grow military officers.”
Sixty-seven percent of respondents to the Future Tech-MeriTalk survey said workforce preparation for AI needs more urgency. In the meantime, 59 percent said they are using Federal systems integrators to meet AI staffing and professional development needs.
At the VA, leaders are developing key performance indicators and timelines for a formal AI training program, Alterovitz said. Retooling takes time, so it’s also “really important for the Federal government to be able to recruit and retain that AI talent to remain at the leading edge,” he said.
If agencies “put the investment in now – into the individuals, into the technologies – the true rate of returns are significantly higher than what any of them believe is even possible,” predicted Michael Shepherd, senior distinguished engineer at Dell Technologies.
| 2023-03-07T00:00:00 |
https://www.meritalk.com/articles/workforce-development-efforts-advance-amidst-calls-for-ai-readiness/
|
[
{
"date": "2023/03/07",
"position": 90,
"query": "government AI workforce policy"
},
{
"date": "2023/03/07",
"position": 77,
"query": "government AI workforce policy"
},
{
"date": "2023/03/07",
"position": 79,
"query": "government AI workforce policy"
},
{
"date": "2023/03/07",
"position": 63,
"query": "government AI workforce policy"
},
{
"date": "2023/03/07",
"position": 52,
"query": "government AI workforce policy"
},
{
"date": "2023/03/07",
"position": 83,
"query": "government AI workforce policy"
}
] |
|
AI, automation and women - Pursuit
|
AI, automation and women
|
https://pursuit.unimelb.edu.au
|
[
"Professor Leah Ruppanner",
"Founding Director",
"Future Of Work Lab",
"School Of Social",
"Political Sciences",
"Faculty Of Arts",
"University Of Melbourne",
"Dr Brendan Churchill",
"Arc Decra Senior Research Fellow",
"Senior Lecturer"
] |
Some jobs but not others ... Automation and artificial intelligence are bringing revolutionary change to the workforce that marks the fourth industrial revolution ...
|
So much of the debate around the future of work has us thinking far into the future– to workplaces unimaginable and to jobs that very few of us feel we are qualified for at present. But the future of work is here and now. To take but just one example, algorithms: our lives and day-to-day experiences are mitigated and managed by algorithms, sorting out where we eat, what we buy, who we date and where we go. Algorithms are also at the heart of ‘apps’.
Our lives and day-to-day experiences are mitigated and managed by algorithms. Picture: Getty Images
We have seen an ‘appification’ of basic needs and services. When we encounter a problem, we turn to our apps. Transport? Uber. Home repairs? Airtasker. Where to go for dinner? Yelp. The pervasiveness of this technology is so profound that even governments are turning to apps to provide services, like assistance for the unemployed. The almost seamless integration of technology into our lives means that the future is here and that ‘the future’ will probably mean more integration of automation and artificial intelligence into our everyday lives.
In light of this, it’s important to ask: what does the expansion and uptake of these technologies mean for the future of work? And what does it mean for the futures of women?
Automation and artificial intelligence
Artificial intelligence and automation are used all the time in popular lexicon but without a clear distinction. So, let’s clear it up here. Automation is taking existing tasks that had historically been done by humans and having them done by a machine.
Automation and artificial intelligence are bringing revolutionary change to the workforce. Picture: Getty Images
And robots will increasingly take over many physical tasks in the future – from meatpacking to fruit picking to stocking shelves. We have been living in an automated world for a while – starting with the cotton loom, then to the assembly line and now to a world of robots cultivating our food. Artificial intelligence (AI) is the use of data and algorithms to create more efficient decision-making. Much like the human brain, AI is scanning all those little sprinkles of data spread around us to make smarter decisions. The goal is to get machines to think flexibly, to understand how to sort through data and identify the important from the superfluous.
Business & Economics Overcoming gender bias of boardroom gatekeepers
This can include machine learning where algorithms are automatically refining based on new information and past mistakes – it is becoming intelligent so that computers can provide nuanced understandings similar to the workings of the human brain. So, the future is here and automation and AI are only going to canvass more of our lives. And, that is a good thing, right?
Some jobs but not others
Automation and artificial intelligence are bringing revolutionary change to the workforce that marks the fourth industrial revolution.
Care and caring industries are expected grow exponentially as the population ages. Picture: Getty Images
The greatest employment reductions for humans will be in manufacturing, professional, scientific and technical services as well as in the construction sector. All of this makes automation and artificial intelligence sound like the harbingers of doom and gloom to modern economies and work. But there will be some industries and occupations that will be advantageous amongst all this change. Care and caring industries as well as human service delivery industries (for example, social workers, psychologists, grief counsellors) are expected to grow exponentially as ageing populations across Western, developed countries soar.
Health & Medicine Sisters are doing it for themselves
The global care workforce accounts for 11.5 per cent of total global employment and is dominated by women who make up two-thirds of all care workers: women account for 248.9 million care workers while men only 132.1 million. Conservative estimates from the International Labor Organization expect the global care workforce to grow to 475 million by 2030 This growth will see shortages – for example, the World Health Organization (WHO) forecasts a shortage of 18 million healthcare workers by 2030. This means the care area will continue to grow and need workers to fill these new roles. Alongside the growing care and caring industries, new industries will emerge.
To make new technologies work and to ensure their successful utilisation, trained users will be needed.
Workers will need to be trained to work alongside artificial intelligence. These technologies while advanced are dependent on human supervision. To make these technologies work and to ensure their successful utilisation, trained users will be needed. Further, integral to the success of the future workforce will be the performance of tasks that require strong social, empathic and interpersonal skills.
Arts & Culture Gendered labour in the Australian arts
These are traits that are inherent to being human and are most often cultivated by women. So, women hold deeper future skills and already work in industries slated to grow. So, does this mean that women are the winners in the world of automation?
The future of work for women
Economists have tended to argue that the workforce is segregated by gender because women and men tend to ‘sort’ themselves into different types of jobs or choose different careers.
Some economists say women and men tend to ‘sort’ themselves into different types of jobs. Picture: Getty Images
Women are more likely to go into care professions, termed “pink collar” jobs that include nursing, teaching, childcare workers and aged care. These jobs tend to have an inherent component of caring for others – children, older adults and the sick. Since women are socialised to value their roles as caregivers, it’s no surprise that these occupations are dominated by women. Interestingly, these jobs are the least likely to be automated by baby-changing robots or artificial intelligence.
Health & Medicine We can eliminate cervical cancer
And the demographic changes in the pipeline – the ageing of Baby Boomers – mean a larger segment of the population is going to need these services. As a result, it appears that these woman-dominated industries are going to survive, grow and thrive. But the question remains, is this good news for women? To answer these questions, we need to think about what the caregiver lives of working women look like now. Women in these caregiving jobs tend to hold less education, lower incomes, less job security and care demands of their own. These women tend to experience overwork, burn-out and poor mental health. The reason? Caregiving is emotionally and physically strenuous and, many are also managing caregiving demands at home, leaving them no break from care work.
Will men start to move into historically women-dominated professions? Picture: Getty Images
The ageing of the Baby Boomers indicates that a larger number of adults will require some kind of caregiving. Add to that the fact that globalisation has spread families around the world and we can see why a dire caregiving crisis is coming. The demand for caregiving will exceed capacity – who will pick up these roles is up for discussion. But, based on previous patterns, we would expect caregivers to be mostly women. So, will the future of work for these women be rosy? Only if we value their labour and support their care. And, while we are at it, can we help with the care for all families as well?
Arts & Culture Provocative women in cinema
These are important questions that we cannot solve with an algorithm.
Automation and re-skilling
The other open question is whether men will start to move into these historically women-dominated professions as jobs in male-dominated industries contract. Automation is positioned to erode our historically male-dominated professions – manufacturing, construction and transportation. As these jobs disappear, will men re-skill into what were historically women-dominated professions? Will men become the caregivers of the future? If the future is women, will men step into these roles?
There is no reason why Australia can’t lead the world in the future of work. Picture: Getty Images
Historically, men have sorted into higher prestige and higher pay jobs than women even if they work in the same industry. Sociological research shows men who enter professions stereotypical to women – like nursing or teaching – earn more money and are promoted more quickly. This previous research suggests that men won’t take poorly paid, low-status, woman-dominated jobs. But, if they do step into them, they are likely to shift the cultures or reap greater status, power and money. There’s reason to believe the COVID-19 pandemic will speed up the movement towards automation and artificial intelligence technologies.
Arts & Culture The modern women of Australian ballet
| 2023-03-07T00:00:00 |
https://pursuit.unimelb.edu.au/articles/ai-automation-and-women
|
[
{
"date": "2023/03/07",
"position": 73,
"query": "artificial intelligence employment"
}
] |
|
The Impact of Automation on job transitions and future ...
|
The Impact of Automation on job transitions and future skills needs
|
https://www.ilo.org
|
[] |
This talk will examine the opportunities and challenges that workers who are at risk of job displacement resulting from automation could face in transitioning.
|
In 2021, the Department for Business, Energy and Industrial Strategy (BEIS) and PwC published a study which explored how the composition of the labour force could change as a result of automation. It found that automation could have a significant disruptive effect on the UK labour market, with around 18% of jobs having a high probability of displacement over 10 years. This current study extends this analysis by examining the opportunities and challenges that workers who are at risk of job displacement resulting from automation could face in transitioning into the jobs that are expected to be created by the structural changes caused by automation. In particular, it aims to answer the following questions: (i)Which jobs will grow and decline as a result of automation? And to what extent? (ii) What opportunities do workers at risk of job displacement, i.e. in jobs which are expected to decline, have to transition into growing and resilient jobs? (iii) How can businesses and government help at-risk workers to transition and businesses to manage skills shortages and retrain the workforce? The approach therefore expands the original 2021 publication by extending analysis by Nesta (2020) to compute “transition scores” which estimate the ease with which workers could move from one job to another. More “viable” transitions are defined as those with more similar skills, similar or lower qualification requirements and where there is greater precedent for that transition having happened. More “desirable” transitions are defined as those which are more viable, will not require workers to take a pay cut and are located in the same region.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://www.ilo.org/meetings-and-events/impact-automation-job-transitions-and-future-skills-needs
|
[
{
"date": "2023/03/07",
"position": 8,
"query": "automation job displacement"
}
] |
Amazon still seems hell bent on turning workers into robots
|
Amazon still seems hell bent on turning workers into robots – here’s a better way forward
|
https://theconversation.com
|
[
"David Spencer"
] |
One frequently cited study published in 2013 predicted that up to 47% of jobs in the US would be removed by automation over a 20-year period. Now that we're ...
|
The strikes by hundreds of Amazon workers at the company’s Coventry warehouse in the English Midlands have brought into relief some of the problems of work in today’s high-tech society.
While primarily focused on pay, the workers are pushing back against long hours and an automated surveillance system that times how long they take to do each task, as well as going to the toilet. It all contributes to a high pressure and intensive work environment – plus more accidents.
We have much to learn from this painful situation about the future of work and technology. On the one hand, Amazon’s whole employment model goes against the general assumption that technology destroys jobs. Equally, however, the company’s employment practices show how automation can make workplaces oppressive, forcing workers to become more like robots.
Pessimistic predictions about the threat to jobs from technology are nothing new. One frequently cited study published in 2013 predicted that up to 47% of jobs in the US would be removed by automation over a 20-year period. Now that we’re halfway through that period, jobs in the US remain plentiful and unemployment is low. Similarly, there’s evidence from Germany that the use of robots has had no effect on total employment.
Across the G7 as a whole, employment has been holding up well. Aside from a COVID blip, unemployment has generally been falling for the past decade in spite of automation and robotics gradually becoming more important to workplaces. The reality is that paid employment has been surviving bouts of technological progress for centuries.
G7 unemployment rates 2005-21
As the Amazon example suggests, the greater threat from technology is almost certainly to the quality of jobs. This threat should concern us in thinking about ways to use and deploy technology in workplaces now and in the future.
Reimagining automation
In a recent paper, I put forward a couple of basic principles related to the aims behind automation for society as a whole.
Firstly, automation should help to promote more meaningful work. In discussions about the future of work, fears of job losses are often the starting point for arguing that workers’ wages will need to be replaced by a universal basic income. But this sees work as purely instrumental, pursued for income only. Work also matters for who we are and are able to become.
Once you recognise these intrinsic benefits to doing a job, it becomes important to see technology not as a way to eliminate work but to make it better. This means automating the least appealing aspects of work. Technology should complement labour that enlivens and enthuses workers. You can see the potential in sectors as diverse as farming, where robots can replace the human toil of harvesting produce, and medicine, where they can be used to transport things like medical waste around hospitals.
Secondly, automation should make it possible for people to spend more time away from work. This is not to contradict the idea that work is beneficial for our well-being but to recognise that a life well lived entails experiencing rewarding activities in and outside of work. Automation should make more time for us to achieve well-being in work and leisure.
The reality
Unfortunately, these aims are not generally the priority with technological advancement. This stems from the fact that employees have less say over its nature and direction than employers, which explains why automation makes many workers anxious.
When workers are given more say, the dynamic can change. Take Germany, where there is evidence that the use of robots has actually improved the chances of workers staying in employment. The presence of work councils and strong labour unions in Germany seems to be a large part of the explanation.
This partnership approach appears to have helped to create an environment that has protected jobs while allowing workers to upskill to adjust to technological change. It’s no coincidence that Germany has the second lowest unemployment rate in the G7.
Amazon has been introducing robots over the past decade to help make its warehouses more efficient too. It looks likely to scale this up in the next few years, though the company insists this is not about removing jobs.
Time will tell on that front, but it is hard to be confident in Amazon’s approach to technology when its workers’ interests seem so subordinate to those of the company. In tandem with the UK protests, Amazon workers in places like the US and Germany have also been battling against its conditions.
Amazon did agree in 2022 to form a European works council, which has worker representatives from 35 countries including the UK, and is consulted on company issues that cross borders. But the council’s operations are fairly restricted, while the company’s general reluctance to engage with unions suggest that warehouse workers are still struggling to further their interests.
In the end, technology will only work for workers if it is democratised. If workers and society rather than big tech companies such as Amazon are to benefit from automation, they need to have a larger influence and stake in it. If this can be achieved, less and better work remains the prize.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://theconversation.com/amazon-still-seems-hell-bent-on-turning-workers-into-robots-heres-a-better-way-forward-201221
|
[
{
"date": "2023/03/07",
"position": 25,
"query": "automation job displacement"
}
] |
Ahead of new U.S. jobs data releases, here's what ...
|
Ahead of new U.S. jobs data releases, here’s what employment growth and job switching mean for wage disparities in the U.S. labor market
|
https://equitablegrowth.org
|
[
"Emilie Openchowski",
"Authors",
"Carmen Sanchez Cumming",
"Kate Bahn",
"Marta Lachowska",
"Alexandre Mas",
"Raffaele Saggio",
"Stephen Woodbury",
"Ellora Derenoncourt",
"Claire Montialoux"
] |
... AI, tech, & the economy · Bargaining Power · Job Mobility · Minimum Wage · Monopsony ... The unemployment rate for workers without a high school diploma, for ...
|
Overview
Later this week, the U.S. Bureau of Labor Statistics will release data on February 2023 job growth through its Employment Situation Summary. It will also release information on job openings, hires, and separations during the month of January 2023 through its Job Openings and Labor Turnover survey. As new economic data are published and the U.S. labor market continues to recover from the COVID-19 recession, one important underlying dynamic that economists and policymakers alike need to heed is the relationship between employment growth, job switching, and wage disparities in the U.S. labor market.
Topline economic indicators show that the U.S. labor market is remarkably strong. Over the past 3 months, the U.S. economy added an average of 356,000 jobs. The national joblessness rate, at 3.4 percent, is currently at a 50-year low. Almost all major industries have surpassed their pre-pandemic employment levels. And the quits rate continues to be near record highs—a sign that U.S. workers are confident in the labor market and in finding new and better employment opportunities.
Underlying these strong positive trends are several key developments that are shaping the recovery from the short but deep COVID-19 recession of 2020. One is the real wage gains experienced by workers at the bottom of the wage distribution. Another is the high rates of quits among these workers and U.S. workers overall. And a third development is the positive relationship between these two dynamics.
This issue brief will detail these three trends to put into perspective the importance of the stronger bargaining power experienced by low-wage workers in the wake of the most recent recession, as well as the key social infrastructure investments and policies that need to be undertaken in order to maintain and strengthen the gains workers made over the first 2.5 years of the economic recovery.
The topline U.S. labor market indicators
Let’s first set the stage by examining three important U.S. labor market indicators: employment growth, the labor force participation rate, and the prime-age employment-to-population ratio, or the share of 25- to 54-year-olds who are employed. Employment growth is strong but may be slowing down. And while neither the labor force participation rate nor the prime-age employment-to-population ratio is back to its February 2020 levels, both metrics experienced upticks in 2022. (See Figures 1 and 2.)
Figure 1
Figure 2
Employment outcomes continue to be widely unequal across demographic groups, but the hot U.S. labor market seems to be narrowing some of the longstanding disparities that then skyrocketed during the onset of the pandemic in early 2020. The unemployment rate for workers without a high school diploma, for instance, is at a record low, and employment among Black men and Latino workers, both men and women, has experienced a particularly strong bounce back.
Indeed, the number of Black men who are employed was almost 8 percent greater in January 2023 than it was during February 2020, reflecting stronger job growth than for any other group of workers. Likewise, the gap between the national joblessness rate and the joblessness rate of both Black workers and Latino workers is smaller now than during the immediate aftermath of the COVID-19 recession in the early spring of 2020. (See Figures 3 and 4.)
Figure 3
Figure 4
A higher quits rate is associated with greater worker bargaining power
Why are some U.S. labor market disparities narrowing? The year after the onset of the COVID-19 pandemic, the number of U.S. workers quitting their jobs rose to record highs. Some workers left their jobs because they no longer felt safe at work; others moved. And other workers had new or greater caregiving responsibilities, started their own businesses, or decided to pursue another career path. Indeed, many workers not only switched employers but also decided to transition to another industry or occupation altogether.
During the final 2 months of 2021, the monthly national quits rate—or the share of all workers who voluntarily leave their job in a given month—reached a record peak of 3 percent. More than 4.5 million workers quit in November 2021—the highest number since the U.S. Bureau of Labor Statistics started reporting the metric in the early 2000s. While both the quits rate and the absolute number of quits declined throughout the course of 2022, these two statistics continue to be well above their pre-pandemic levels. (See Figure 5.)
Figure 5
As quits and job mobility accelerated in the years after the initial shock of the COVID-19 pandemic, the pace at which workers changed jobs increased more for some demographic groups than others. An analysis by the Pew Research Center, for example, finds that the share of Black workers and Latino workers who change employers in a given month is substantially higher than the share of Asian American workers and White workers who make that same switch.
Moreover, the same analysis finds that while Asian American workers and White workers were about as likely to change employers in 2019 as in 2022, the likelihood that Black workers and Latino workers moved from one employer to another increased substantially after the onset of the pandemic. Workers with a high school diploma or less were also much more likely to switch employers in 2022 than in 2019. (See Figure 6.)
Figure 6
Workers of color and workers with lower levels of formal education change employers especially often because they are more likely to be sorted into lower-quality jobs. Indeed, inadequate pay, insufficient benefits, lack of opportunities for career advancement, unsafe working conditions, and undesirable working environments are some of the main drivers behind the particularly high turnover rates in low-paying industries such as retail and leisure and hospitality.
At the same time, a higher quits rate and greater opportunities for job mobility seem to be leading to better employment outcomes for many U.S. workers. People are more likely to leave their current jobs when the labor market is strong, and they are confident that they will find employment opportunities elsewhere. Indeed, research shows that the great majority of workers who quit move on directly to another job, rather than spending time unemployed or stepping out of the labor force altogether. In economic speak, then, a higher quits rate tends to reflect that workers have more outside options.
A climbing quits rate also is associated with faster wage growth because workers tend to transition to higher-paying opportunities after voluntarily leaving their previous jobs. And, as a higher share of the workforce quits, employers usually have to offer higher wages in order to attract and retain employees, shifting bargaining power toward labor. Indeed, several studies suggest that the sluggish earnings growth that U.S. workers experienced during most of the recovery from the Great Recession of 2007–2009 can be, at least in part, attributed to a decline in what economists call job-to-job transitions.
In addition, economic disparities can be reduced amid a tight labor market due precisely to a higher number of job openings and an elevated quits rate. Job switchers usually experience stronger wage growth than job stayers, especially during economic booms, and research shows that lower-wage workers tend to rely more on job switching to get increases in pay than higher-wage workers.
In a 2021 study, for instance, a team of economists used administrative data on dual job-holders to examine the pathways workers use to get higher wages. Their findings suggest that the highest earners were much more likely to see increases in pay by bargaining with their employers, since firms generally have to spend more time and resources filling higher-wage positions and are therefore more likely to make efforts to retain higher-paid employees. Those in the bottom of the distribution, in contrast, usually had to change firms in order to get a raise.
Similarly, research by Nathan Wilmers and William Kimball at the Massachusetts Institute of Technology finds that workers in low-wage occupations are much more likely to move on to higher-quality jobs when switching employers than when changing positions through internal hiring processes.
The tight labor market reduced wage inequality in the U.S. economy, but there are signs that disparities are no longer narrowing
The rapid recovery of the U.S. labor market after the COVID-19 recession in 2020 was accompanied by faster nominal wage growth for U.S. workers, especially for those in low-wage jobs. An analysis by the Peterson Institute for International Economics finds, for instance, that between December 2019 and December 2021, wage growth was fastest in the industries of retail trade and leisure and hospitality—the lowest-paid sectors of the U.S. economy. And an analysis by the Economic Policy Institute finds that in the same year, workers in the bottom 30 percent of the wage distribution were the only ones to experience real wage gains. That is, these workers saw a wage increase even after accounting for inflation.
Greater competition for workers, recent research shows, was an important driver of this compression in wages. For instance, a study by David Autor at the Massachusetts Institute of Technology and Annie McGrew and Arindrajit Dube at the University of Massachusetts Amherst finds that as the U.S. labor market started to recover from the initial COVID-19 shock, increased competition for labor led to particularly strong wage growth among the lowest-paid workers, who now had greater access to better-paying employers.
Specifically, the team of economists analyzed differences in state-level unemployment rates and job switching rates—two metrics researchers use to determine how tight a given labor market is. They find that in states with lower joblessness and relatively more job-to-job transitions, real wage growth among low-wage workers was faster, especially among younger workers without a college degree. In addition, Autor, McGrew, and Dube find that as the lowest-paid workers became especially likely to quit their previous job in response to higher-paying opportunities elsewhere.
Overall, these dynamics contributed to an important decline in the wage divide, reversing about 25 percent of the four-decade increase in the gap between workers in the top 10 percent of the wage distribution and workers in the bottom 10 percent.
But these recent U.S. labor market dynamics, which triggered what Autor, McGrew, and Dube call the “great compression” in wages, might be decelerating. In the second half of 2022, wage growth slowed down for workers in general, and for workers in low-paying sectors such as leisure and hospitality in particular. And while employment growth continues to be strong and the quits rate is still relatively high, there are signs that employers’ competition for workers has cooled over the past few months.
Conclusion
When the U.S. Bureau of Labor Statistics later this week releases its February 2023 data on job growth and information on job openings, hires, and separations during the month of January 2023, these recent underlying trends in the U.S. labor market will become a bit more clear.
As the new data come in, policymakers need to consider how to institutionalize worker power and enact policies that proactively and intentionally promote broadly shared growth, so that wage disparities continue to narrow. A higher federal minimum wage, providing government agencies with the tools and resources to better enforce labor standards, and making it easier for workers to form and join unions, for example, would all go a long way to foster better outcomes for U.S. workers in the bottom of the wage distribution, even when the U.S. labor market is not sizzling hot.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://equitablegrowth.org/ahead-of-new-u-s-jobs-data-releases-heres-what-employment-growth-and-job-switching-mean-for-wage-disparities-in-the-u-s-labor-market/
|
[
{
"date": "2023/03/07",
"position": 58,
"query": "AI unemployment rate"
},
{
"date": "2023/03/07",
"position": 46,
"query": "AI labor market trends"
}
] |
Jobs, Jobs and ... Less Jobs? - CKGSB
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Jobs, Jobs and … Less Jobs?
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https://english.ckgsb.edu.cn
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“The introduction of artificial intelligence and robotics into the manufacturing sector has very important implications for the youth unemployment rate,” says ...
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Long-held prejudices against blue-collar jobs are hampering the filling of vacant positions
Mimi Wang looks exactly as you’d imagine someone would after a two-hour class baking bread. Covered in flour, she says, “I was interested in doing a class and the government will reimburse me once I’m finished, so I thought, why not? But I don’t have any intention of going into baking as a career.”
The 27-year-old, who has a day job in PR at one of China’s larger internet firms, is one of many young Chinese taking advantage of government-sponsored vocational courses for fun, rather than for career development. In 2020, the Chinese government introduced a plan to create 35 million vocational skill training opportunities, ranging from baking to childcare, through to e-commerce and programming. But the fact that these courses aren’t always being used for their intended purposes is reflective of the currently paradoxical nature of China’s labor market.
On one side there is a labor shortage in blue-collar industries, while at the same time, there is a surfeit of unemployed university graduates. The issue is exacerbated by a widespread perception that blue-collar jobs are the inferior choice for any graduate, even though many university leavers have not acquired the requisite skills for the white-collar jobs to which they think they are entitled.
“There is something of a paradox in the labor market, with so many graduates coming out of higher education but being much more selective about the jobs they take up. As a result, there are huge numbers of vacancies in the factory sector,” says Lu Feng, director of the China Macroeconomic Research Center at Peking University. “It’s a very strange phenomenon.”
The rat race
Around 70% of Chinese businesses are currently contending with some kind of labor shortage, and the Ministry of Education estimates that there will be a dearth of 30 million workers by 2025. The issue is particularly acute for those in the country’s crucial industrial sector, as it contains 36 of the top 100 occupations with the most severe staffing problems. Countrywide, 55% of businesses report being unable to find enough blue-collar employees.
At the same time, there are record levels of unemployment among China’s graduate youth population, with nearly one in five young people out of work, according to official statistics. The youth unemployment rate reached 19.9% in July, far above the overall urban unemployment rate of 5.5%, and up 25% year-on-year. Graduates from rural areas face an even tougher time, they are significantly less likely to be employed than their urban counterparts. This situation is also far worse than in other major economies. For perspective, the youth unemployment rate in the US is 8.1%.
“There is a mismatch between the supply of college graduates and the market demand in China to some degree,” says Rockee Zhang, managing director of Randstad China Staffing and Outsourcing.
Although there are a vast number of open positions in factories, long-held prejudices around the quality or worthiness of blue-collar jobs make Chinese graduates reluctant to fill the vacancies. This has resulted in China’s seemingly contradictory state of simultaneous labor and job shortages, a paradox that has previously appeared in other labor markets around the world, particularly in recent years thanks to the COVID-19 pandemic.
The market pressures in China were compounded in 2022 when a record 10.8 million graduates entered a cooling labor market. In addition, 2021 saw more than 800,000 Chinese students who graduated from universities overseas return home, further saturating the labor market.
The number of vacancies in companies looking to hire Chinese graduates actually increased by 8% year-on-year in the first quarter of 2022, but the number of applicants far exceeds this and is getting worse—the total number of applications grew by 75%. In mid-April, only about 47% of fresh graduates had received job offers, a substantial drop from nearly 63% in 2021.
The Chinese government has been promoting the country’s vocational colleges as a route to resupplying ailing industries and diverting people away from traditional universities. The sector is expanding, with 2,492 technical colleges in China at year-end 2021, 100 more than two years ago.
The number of new enrollments in higher vocational colleges did increase, but still fell 1 million short of the total available roles they would be expected to fill, and not all vocational graduates are continuing into the roles they are training for. Despite government efforts to boost promotion, the proportion of students in vocational education is actually down overall, with only 35% of students in vocational schools in 2020, down from 60% in 1998.
Rapid expansion
The current imbalance in China’s labor market is in many ways the result of the rapid expansion of Chinese higher education since the 1980s when the government initiated reforms to support China’s transition from a fully-centrally-planned economy to a more market-based economy. In 1993, the Outline for Education Reform and Development in China report emphasized the need for decentralized operations and management of academic institutions.
Today, China has the biggest higher education sector in the world in terms of total enrollment and degrees awarded every year. In 2020, a total of almost 42 million students were enrolled in 2,738 higher education institutions in China.
While the country now has a large supply of graduates, there is a gap between the skills universities are providing and the skills that industry needs. China produces around two million more STEM graduates per year than any other country, but there is a dearth of manufacturing workers and engineers in particular, which is worsening the pressure on China’s factories.
Chinese people generally attach great importance to education as a means of improving their self-worth and economic prospects. But education for the sake of education may not be the right move. “There’s a lot of educational inequalities across China, both in terms of availability and quality,” says Margit Molnar, head of the China Desk at the OECD’s Economics Department. “Many prospective students are not discerning enough about the quality of the institutions they wish to attend.”
Molnar adds that Chinese graduates are also often more highly qualified than they are highly educated, pointing to a growing gap in quality between the handful of elite schools at the top and the majority of universities across the country. “The expansion of the tertiary education system also brought about the dilution of quality,” she says. “With so many young Chinese graduates entering the system, it’s impossible to maintain the same quality of education.”
The widespread perception among graduates that factory jobs are inferior to office jobs has been one of the major factors leading to the current the job market situation. Children are told from a young age to dream of becoming a scientist or work in business when they grow up, but none are encouraged to want to work in manufacturing.
Economic reforms in the 1990s led to an increased portion of the labor force working in privately-owned factories, in sweatshop conditions, that led to an overwhelmingly negative impression of manufacturing work. Despite improvements in conditions and pay in the intervening years, many graduates remain unwilling to accept such jobs, having spent so much time and effort on their education.
But these manufacturing jobs have changed dramatically in the intervening years and, given the quality of education received by many of today’s graduates, there may need to be some concessions made.
“Graduate expectations in a tough job market are unreasonable,” says Julia Zhu, head of recruitment firm Robert Walters’ Suzhou office. “But the younger generation doesn’t want to compromise, and there is no real push factor for them to do so.”
Looking for work
Private sector roles were once coveted by Chinese graduates, but faced with a scarcity of jobs, many are now interested in the more stable, albeit lower-paid, positions in the country’s civil service.
According to a survey by the Chinese job-hunting website Zhaopin, some 11.4% of the newly minted graduates in 2022 were looking for jobs in the civil service, double the proportion from the year before. But again, these positions are highly competitive, with about 1.6 million applicants sitting 2021’s national civil service examination, in a scramble for only 25,726 jobs.
Meanwhile, the percentage of graduates seeking jobs at Chinese state-owned enterprises has increased to 42.5%, from 36% in 2021. Others are considering careers in medicine or education because of the stability provided to those roles thanks to their being government funded.
The graduate employment problem has been exacerbated by strict lockdowns in major Chinese cities. Beijing’s zero-COVID strategy diminished companies’ ability to absorb new staff. “A lot of companies are cutting headcount in China because of the supply chain shock from the coronavirus lockdowns,” says Julia Zhu. “This is a tough job market for graduates.”
In addition, over the past two years, the government has launched a regulatory crackdown under the common prosperity initiative that has hit the value of technology companies, which until recently were significant employers of Chinese graduates. Chinese tech giants including Alibaba and Tencent have recently conducted widespread layoffs, according to media reports, further enhancing the appeal of more stable civil service jobs.
On top of this, the crackdown on the tutoring industry, in an attempt to ensure equal access to education, wiped out many jobs last year, adding pressure to an already tight labor market. The sector has shed tens of thousands of jobs, with one group, New Oriental, sacking 60,000 staff. Graduates and young people have been disproportionately impacted, as many worked as after-school tutors.
Furthermore, an increase in automation in the private sector has to some extent offset the labor shortage in the manufacturing sector but has also worsened the problems facing young people, as it is expected to lead to job losses. According to the McKinsey Global Institute, up to 31% of working hours in China will be automated by 2030.
“The introduction of artificial intelligence and robotics into the manufacturing sector has very important implications for the youth unemployment rate,” says Feng from Peking University. “These digital technologies are expected to replace many workers because they can increase productivity on the factory floor.”
A top economic priority
Outgoing Chinese premier Li Keqiang has made the stability of the graduate employment market a top priority. The country is aiming to enroll 1.4 million students at technical colleges in the coming academic year, up from 1.09 million last year.
While many university leavers are unemployed, vocational college graduates are in high demand. Last year, 97.2% of them were employed, although not necessarily in their areas of expertise.
To make vocational training more attractive to high school students, some Chinese provinces including Shandong and Zhejiang now enable them to skip taking the grueling national Gaokao college entrance exam and go straight into higher vocational education. This is done through the newly introduced Zhongkao, a high school entrance exam taken at age 16, with many who fail ending up in vocational education.
China has the world’s largest workforce, numbering close to 900 million people, of whom 350 million are migrant workers. But digitalization and automation mean that many millions of people will need to reskill and sometimes change occupations entirely.
“Overcapacity in traditional industries, transformation, and upgrading of automation has led to more laid-off workers in some fields,” says Zhang. “But in the short term, the vocational skills of these workers do not meet the needs of re-employment positions.”
Some of this can be attributed to a lack of quality in the courses themselves. An example is 29-year-old Fu Hetong, who works for an energy company in Shanghai and is doing a Classic Coffee Making course for which he will be fully reimbursed by the government once he finishes. “I have no plans to become a full-time barista. I’m just doing it for fun,” says Fu. “The curriculum is not suitable for today’s specialty coffee trends anyway.”
McKinsey estimates that up to 220 million Chinese workers may need to transition into a new job by 2030, and this will require a lot of retraining. The government wants to ensure that China’s entire population has the skills it needs for a post-industrial economy. In mid-2019, it pledged the equivalent of about $14.8 billion to upskill the workforce through subsidies and a large-scale training plan in which 50 million people will receive vocational skills.
“China’s next phase of economic development is inseparable from the upgrade of industry, which in turn requires the cooperation of education,” says Liu Jing, professor of accounting and associate dean at Cheung Kong Graduate School of Business.
Access to training is especially difficult for China’s rural-urban migrants, who numbered 291 million in 2019 and could grow to 331 million in 2030. China’s Hukou household registration system restricts access to training programs for many migrants, who are especially susceptible to job losses caused by automation because they tend to work in low-paid and low-skilled jobs. But some provinces have relaxed residency rules for recent graduates, helping them to settle in cities like Shanghai and access training and expand the local talent pool. The city of Zhengzhou has recently scrapped Hukou registration hurdles altogether.
Reflecting fierce competition for talent in some fields, employers are approaching graduates who have the correct skills up to two years before their graduation dates, helped by sweeteners from the government. “Employers who take on fresh graduates can get subsidies, tax breaks and easier loans,” says Zhu from Robert Walters.
Many employers have also strengthened their ties to vocational colleges through apprenticeship programs and mentorship schemes that help colleges better understand demands for skills and improve the design of training and strengthen pathways to employment.
In addition, larger firms are retraining graduates once they are employed, to address mismatches between skills and job requirements. McKinsey says the number of corporate “universities” is rising as employers retrain workers to improve productivity.
Many other countries such as Singapore have used immigration as the solution to the problem, especially in the construction sector. An analysis by the World Economic Forum suggests that, even with a stronger vocational training system, at least 4.95 million migrants would be needed annually in China to fill labor shortages.
But the likelihood of this happening in China is extremely small because of government restrictions on immigration.
In some sectors, technological advancements have helped ease various staffing problems. The Chinese working-age population has shrunk by more than 5 million in the past decade as the birth rate fell, worsening the shortage of labor. With wages also rising, many companies have begun to automate jobs in factories, warehouses and the transportation sector as they cannot find enough people to fill them.
“As the population ages, the phenomenon of re-employment in retirement may increase,” says Zhang.
No easy solutions
China now has a glut of unemployed graduates, with universities churning out far more degree-holding workers than the slowing Chinese economy can absorb. And while many are seeking shelter in civil service jobs as the private sector has been battered by COVID lockdowns, many graduates still aren’t willing to work on factory floors, where there are so many unfilled vacancies.
“Even in the coming years, for young people, the situation looks very grave,” says Peking University’s Feng. “There is still an oversupply of young labor but there aren’t the jobs to match the growth.”
Reskilling for millions of workers is required, and the Chinese government is encouraging the expansion of vocational schools and courses, to varying degrees of success. But ultimately, without a change in the national psyche around the perceived superiority of university education and a private sector or civil service job over blue-collar work, the labor mismatch in the country will continue to exist.
“With the qualifications they have, many graduates believe they have the right skills to match the jobs that they aspire to, but that isn’t necessarily true,” says Molnar. “This has built a perception in many that they are now at a certain level and blue-collar work is now beneath them. This is quite wrong, and a serious issue that China needs to deal with, in order to deal with their labor problems.”
| 2023-03-07T00:00:00 |
https://english.ckgsb.edu.cn/knowledge/article/jobs-jobs-and-less-jobs/
|
[
{
"date": "2023/03/07",
"position": 67,
"query": "AI unemployment rate"
},
{
"date": "2023/03/07",
"position": 55,
"query": "job automation statistics"
},
{
"date": "2023/03/07",
"position": 71,
"query": "AI labor market trends"
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|
21+ Freelancer Statistics to Make You Ditch Your 9-5 in 2023
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21+ Freelancer Statistics to Make You Ditch Your 9-5 in 2023
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https://leftronic.com
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This is huge because we're currently losing jobs to automation. A whopping 1.7 million have vanished since 2000 and 375 million more will be wiped by 2030! For ...
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“But freelancing isn’t even a real job!“
… would have said aunt Debra in the 90s.
Today, freelance jobs are taking over the workforce. People kissing their corporate workplaces goodbye.
It’s the new normal!
Do you have an aunt Debra in your life?
We have the numbers to prove her wrong!
Check out these freelancer statistics and start planning your digital nomad life now!
57 million – this is the number of freelancers in the US in 2019.
Do you know what percentage of the workforce is freelance? 35% of all workers are independent in 2019!
51% of freelancers wouldn’t go back to their 9-5, no matter how much money they’re offered!
Getting contracted is easier than ever – 66% of freelancers find their gigs online.
In the EU, freelancers have increased by 45% in a single year!
They are constantly developing themselves – 70% of full-time freelancers have taken part in skills training (significantly more than their nine-to-five counterparts).
The freelancer economy is growing and we have the numbers to prove it.
Let’s get started, guys:
What Is Freelancing?
First things first:
Let’s get the freelance definition right:
What is a freelancer?
Any worker that sells their services to different people/organizations. That’s basically every employee out there.
However:
A freelancer doesn’t work for a company in the conventional sense. While they do contract work, they’re not an employee but rather, self-employed.
It’s an opportunity to be your own boss, pick interesting projects by your preferred clients, and work from any corner of the world. I hear a lot of people choose Thailand (cheap, beautiful, and the food is to die for… you can’t have that in an office).
Next order of business:
Is freelancing a career?
It could be.
Some people start with freelancer jobs on the side and turn it into their main thing. Others only take up projects from time to time.
Freelancing is what you make of it.
(Come to think about it, Picasso was also a freelancer and nobody doubts he had a great career!)
Besides, do you know how many freelancers are there in the US in 2019? 57 million people!
To put that into perspective, do you know how many people work in America? 156.92 million.
In other words, 35% of the American workforce is freelancing.
These aren’t unskilled workers or people who were forced to quit their ‘regular job’, either. The gig market largely consists of skilled workers, most of them in the creative or tech industries.
The vast majority of gig workers chose to quit their 9-to-5 job in favor of a more flexible and enjoyable lifestyle. The second biggest reason to go for freelancing is extra income (among part-time independent workers).
Being forced into it? This is the exception, not the rule!
Can you guess how many freelancers are there in the world? More than we can count! There aren’t any comprehensive global surveys on the gig economy. However, let’s assume international trends are similar to what we see in the US:
The number of employed people in the world is close to 3.5 billion people. If 35% of them are freelancers, this gives us 1 220 977.36. Over 1 billion freelancers worldwide! And this is a conservative statistic because in some countries independent work is a lot more prevalent than the US.As more and more people wave goodbye to their day jobs, you have to wonder: why?
And more importantly:
Should I Start Freelancing?
Remote work is more popular than ever. From flexibility and living drama-free to (surprisingly) job stability and the opportunity to work on your own terms, here are 9 evidence-backed reasons why you might want to give it a go:
Choose Your Office
1. 71% of freelancers say they can do their job from any corner of the world.
With remote work, you could work from the beach, a co-working space, or the comfort of your own sofa. Becoming a “digital nomad” is a fascinating opportunity that most workers wouldn’t trade for anything – not even money!
Do Meaningful Work
2. 74% of full-time freelance workers are doing it because they get to choose their projects.
In a traditional corporate job, you rarely pick your assignments, instead doing what is thrust upon you. This couldn’t be further from the truth for freelancers! Gig-workers get to decide on projects based on expertise and passion, which leads to more fulfilment (and arguably, a job better done)!
Job Security
3. 45% of freelancers say they feel more secure in their employment this year, compared to last.
Having a diversified job portfolio means you can’t get fired (not in the traditional sense).
This is huge because we’re currently losing jobs to automation. A whopping 1.7 million have vanished since 2000 and 375 million more will be wiped by 2030!
For workers, freelancing can be as crucial as diversifying for a stockbroker. You lose one client? On comes another! In an ever-changing labor market, this could be the difference between financial stability and bankruptcy.
Commutes Will Be a Thing of the Past
4. 7 in 10 freelancers want to live away from the big city (and now their job allows it).
Busy city life isn’t for everybody. With a freelancing job, you can skip the pollution, long commutes, expensive life, dangerous neighborhoods. The option to move anywhere you please is a huge perk for most freelancers – whether they choose a cabin in the woods or a flat in Brooklyn is completely up to them.
Better Quality of Life
5. 68% of freelancers say their quality of life improved after going independent.
This might be the reason why they’re not going back to their 9-to-5, even if they’re offered more money. These professionals aren’t doing it for a better income, or out of necessity – their life is better, happier, and more fulfilled when they’re freelancing.
Flexible Lifestyle
6. 45% chose to freelance because a corporate job doesn’t fit their personal circumstances.
Stay-at-home parent? Disabled? Relocated and struggling to find a rewarding job? Freelance jobs are flexible enough to accommodate these needs. People who couldn’t contribute to the economy before are finding new and exciting opportunities!
Earn from Your Talents – All of Them
7. 95% of freelancers do varied gigs, depending on the client.
Most freelancers don’t limit themselves to a single skill. In fact, the vast majority employs 2-3 different talents on a regular basis. This is nearly impossible at a corporate job – especially if your best skills are graphic design, meal planning, and Java programming.
Stress Less
8. 82% of telecommuters reduced stress, 80% improved morale.
In a world where mental health issues are on the rise, anything that reduces stress and anxiety is worth trying.
The Best Is Yet To Come
9. 91% believe freelancing is only going to get better from here.
As freelancing becomes more respected, lucrative, and an option for even more professionals, the heyday of remote work surely lies ahead.
Worried about finding a gig? With the rapid development of technology, this is becoming less of a problem. It’s estimated that mobile banking alone will produce 95 million jobs in the future!
What Are The Downsides?
It’s not all sunshine and roses for remote workers. While freelancing can have some amazing benefits, here are some of the cons to be aware of:
Savings and income predictability are major concerns for freelancers. 76% of them are worried about being able to put enough money away, 75% are uneasy about retirement options, and 72% consider unpredictable income to be a source of concern. (Source: Upwork/Freelancers Union)
44% of freelancers have been stiffed, blaming gaudy contracts and companies not taking them seriously for the issue. (Source: The Slash Workers)
61% of gig-workers miss the sense of community and belonging that comes from an office job. (Source: The Slash Workers)
All of that said, freelancers and non-freelancers share 4 out of 5 major concerns – affordable healthcare, retirement, fair wages, and savings. Freelancers are a lot more likely to do something about it.
However:
51% of them are politically active, compared to just 33% of non-freelancers!
Now:
Freelancers Union: Here To Protect You
According to freelance economy statistics, nearly 5% of the US GDP is contributed by freelancers. Independent workers are increasingly aware of their importance and power. This is where the Freelancers Union comes in!
Considering gig-work? Check out their site ASAP. Some of the key resources they provide include:
Freelance 101 toolbox complete with guides on finance, healthcare, and even handling client non-payment.
Freelance contract creator to protect yourself and your work.
Special discounts on everything from online accounting to coworking costs.
Insurance options tailored for freelancers
There are currently 450,000 members of the freelancers union and joining them is free. You can get involved in some of their campaigns, join events, and even use their coworking space in Brooklyn for free (8 days per month).
What Are The Best Freelancer Websites?
Freelancing clearly has its’ perks. So how do you even get started? Here are our favorite places to find freelancing jobs in the USA and beyond:
Fiverr: A Rising Star
Fiverr is gaining traction and popularity even outside the independent worker community. This freelance jobs marketplace is the perfect launching ground for your future career. That said, it is also getting competitive, as more and more people sign up to sell their services there.
How do I make it big on Fiverr?
Start small and build your portfolio. At first, you might have to pick up lower-paying jobs to get some reviews up on the site. Clients are more likely to trust established freelancers so, for the first month, focus your efforts on getting glowing recommendations and take it from there.
Market yourself. If you have social media or a website dedicated to your craft, make sure you link to Fiverr.
Create enticing ads. Sites like Canva help you make professional graphics with minimal technical skills. Take advantage and make your postings stand out!
Upwork: Safety First
Upwork has been a go-to marketplace for independent workers and one of the best freelance websites for years now.
Why Upwork?
Secure payments – employers pay in advance and Upwork holds the money in escrow. Once the job is completed, the freelancer receives the amount. This way, both parties are protected from fraud and you don’t have to worry about client nonpayment.
Billing is a breeze – Upwork has their own Work Diary and time-tracking system, allowing you to measure the hours worked and get paid according to contract.
Free signup – getting started with Upwork is free, though they do take a commission on your payments and charge you (through credits) for applying to jobs. It’s a positive and a negative, sure, but at least you get to test out what you’re paying for.
Freelancer.com: A Beginner’s Best Friend
Freelancer.com has thousands of postings from all over the world and it’s a fuss-free way of finding a gig, even if you have limited experience. It works like Ebay for odd jobs – employers post their job offerings and different professionals bid on them.
Freelancer.com does charge a fee for using them but the membership plans are flexible and affordable. Annual subscription packages end up saving you a lot so I recommend checking them out, especially if you’re serious about freelancing on a long-term basis!
Toptal: For Pro’s
Toptal is short for ‘top talent’ and this is exactly what this platform specializes in. Whether you’re an experienced programmer, top finance expert, or a project manager superstar, Toptal has the best opportunities by companies like Zendesk, Airbnb, HP, and Shopify.
This is not your beginner-friendly marketplace, though. Toptal screens their freelancers extensively and takes pride in only hiring the top 3%. On the plus side, if you are an employer, they deliver top talent within 3 weeks and offer perks like a free trial period, full talent integration, and support in talent relocation.
Guru.com
This freelancer pool rewards expertise fosters long-term relationships and makes client-freelancer communication straightforward and hassle-free.
Guru encourages stable partnerships by offering up preferred freelancers, ones that have proved their expertise and consistency. This is great for businesses because it reduces the time to find a good candidate. However, it’s also great for workers, since it provides stability and rewards hard work, much like a traditional job might (but you still get all the freelancer freedom perks).
Freelancer Statistics: Main Takeaways Going Into 2020
If the past few years are any indicator, freelance work is here to stay. What’s more, independent workers now have more opportunities, unparalleled flexibility, job stability in a volatile economy, and, perhaps most importantly, better life quality than ever.
If there is any takeaway from this year’s freelance statistics it’s:
Freelancers do it better.
A gig-worker:
can choose any place in the world to work from.
gets to pick inspiring and fulfilling projects.
monetizes their multiple talents.
chooses the clients they want to work with.
is more likely to advance their skills through training.
lives a flexible, less stressful, much happier lifestyle (and wouldn’t trade it for the world).
Finally, it’s time to do away with the myth of ‘freelancing means you can’t get a job’.
60% of independent workers started doing it by choice. (Source: Upwork/Freelancers Union)
It’s likely that more and more professionals will make that choice, too:
59% of non-freelancers said they’re interested in doing independent work in the future.
64% say that top professionals are increasingly moving to freelance work.
53% of workers in the 18-34 age bracket are doing freelancer jobs.
If there is one takeaway from 2019’s freelancer statistics it’s that freelancing is here to stay. There are dozens of reasons to try it yourself and getting a job is easier than ever. Check out some of the freelance marketplaces above and come back to tell us your experience in the comments below!
| 2023-03-07T00:00:00 |
https://leftronic.com/blog/freelancer-statistics/
|
[
{
"date": "2023/03/07",
"position": 32,
"query": "job automation statistics"
}
] |
|
25 Revolutionary Robotics Industry Statistics [2023]
|
25 Revolutionary Robotics Industry Statistics [2023]: Market Size, Growth, And Biggest Companies
|
https://www.zippia.com
|
[
"Mar.",
"Fact Checked",
"Cite This Webpage"
] |
The average robot density in the U.S. is 228 units per 10,000 employees. · The U.S. saw 34,987 industrial robots installed in 2021. · 517,385 industrial robots ...
|
Research Summary. Whether the average person realizes it or not, robots are becoming more and more of a part of daily life and industry, whether that’s in surgery, on a website, or on a manufacturing floor. Here are the key statistics about the robotics industry:
The CAGR of the North American robotics industry’s revenue is 11.67% through 2026.
The global market value of the industrial robotics industry is $43.8 billion , by revenue.
There are about 3 million industrial robots in use across the globe.
88% of companies plan to invest in adding robotics to their organizations.
For further analysis, we broke down the data in the following ways:
Employer | Industry/Sector | Benefits and Challenges | Types | Trends and Projections
The average robot density in the U.S. is 228 units per 10,000 employees.
$1.9 billion went to industrial robotics firms, many of which are working on automating warehouses, surgeries, or recycling systems. This $1.9 billion is a significant increase from the $300 million this sector received just six years earlier in 2015.
This is a much larger amount than the $4.3 billion they invested the year before.
This occupation is projected to grow by 6.4% from 2016 to 2026. Most currently employed robotics engineers work in California, Texas , Florida, Michigan , and Ohio .
There are about 132,500 robotics engineers in the U.S.
Headquartered in Tokyo, Japan, Honda Motor is known for creating robots to assist people who can’t walk on their own and a robot that can interact with people by recognizing emotions and communicating with sound, movement, and facial expressions.
The company has a revenue of $142.4 billion, and it employs nearly 220,000 people.
87% of the total funds will go to the Navy, Air Force, Army, and Marine Corps, while the Defense Advanced Research Projects Agency, Special Operations Command, and the Office of the Secretary of Defense will receive the remaining 13%.
Most of this funding will go toward unmanned vehicles that operate in the air, on land, or at sea.
The U.S. Department of Defense has been given $7.5 billion to spend on robotics in 2021.
This was a decline of 7% from the year before in the automotive industry and an increase of 66% in the metal and machinery industry, cementing its place as the industry with the second-highest robot demand. The electronics/electrical industry held that title in 2020.
In 2021, the automotive industry installed 9,782 robotic units, and the metal and machinery industry installed 3,814 robotic units.
Following the automotive industry is the electrical and electronics industry with almost 600,000 industrial robots and the plastics and chemicals industry with about 200,000.
This industry had almost 900,000 robots in use as of 2017, with more being installed every year.
The automotive industry uses the most industrial robots of any industry.
Following cost is a lack of homogenous programming platforms or interfaces, integrators working across locations and industries, and a general lack of experience with automation.
16% of respondents listed this as their top challenge, and 53% listed this as their top five challenges.
The cost of robotic units is the top challenge for companies trying to implement robots.
This is a benefit for younger workforce members, as many of them will now be able to start their careers in jobs that require more creativity and provide higher salaries than most entry-level positions currently do.
However, it’s estimated that this change will also open up 97 million different jobs for humans. Since robots usually cover basic tasks that come with entry-level positions best, they’ll free companies to create more paid high-level roles.
It’s estimated that by 2025, robots will displace 85 million jobs currently held by humans.
In 2016, this number was 15.8%, and it is expected to hit 22% by 2025.
The benefits of robots in surgery are controversial, however, with some saying the high cost isn’t worth it and that medical facilities are opting for more invasive procedures than are necessary for the name of using robots. Others say that robotically performed surgeries require less recovery time and greatly reduce the risk of infection in patients.
This may be a comparatively small percentage, but it is significantly higher than the 1.8% robot-assisted in 2012.
These come behind 87% of companies that use traditional industrial robots, 74% that use AGVs (automated guided vehicles), and 63% that use cells (a robotic system made up of a robot and a human controller to perform tasks such as packaging or assembly).
This means that these organizations use robots designed to work with or alongside humans instead of industrial robots designed to work without human contact.
These machines do all kinds of work for humans: Build cars, manufacture parts, and even perform surgeries.
From 2011 to 2021, annual installations of industrial robots around the world increased from 166,000 to 517,000.
It’s estimated that in 2024, global industrial robot shipments will reach 518,000, up from 394,000 in 2020.
2020 saw new robotic installations slow by 8%, making it the second year this statistic has declined after steadily growing for eight years.
Robot density in the U.S. has grown from 189 robots per 10,000 employees in 2016 to 228 robots per 10,000 employees in 2019.
The revenue of the North American robotics industry is expected to have a CAGR of 11.67% from 2019-2026.
What is the role of robotics in the Fourth Industrial Revolution? The role of robotics in the Fourth Industrial Revolution is to provide new ways to integrate different technological systems and humans. Since the Fourth Industrial Revolution is focused on integrating technologies, robots will play a large part in it. Robotics, Artificial Intelligence, Virtual Reality, Augmented Reality, and Unmanned Aerial Vehicles are just a few of these technologies that scientists are using in conjunction with each other, and often they do this within a single machine they call a robot.
Who is leading in robotics? Honda Motor, Siemens AG, Sony, Denso Corp., and Midea Group is leading in robotics. These companies have the highest revenues of any robotics company in the world. All are headquartered in Japan except Siemens AG (Germany) and Midea Group (China). As far as countries with the most annual installations of industrial robots, however, China takes the lead with 1.684 million installed in 2020. Japan, the U.S., and Korea follow with 38.7 million, 30.8 million, and 30.5 million, respectively.
What jobs are going to be replaced by robots? Repetitive jobs are going to be replaced by robots. In some settings, robots are also replacing jobs requiring more reasoning, but this is rarer. Jobs such as making french fries at fast-food restaurants, welding, driving trains, and answering basic customer service queries are relatively easy for robots to take over, leaving the more managerial and other creative, advanced roles open for humans.
What are the five major fields of robotics? The five major fields of robotics are operator interface, mobility or locomotion, manipulators and effectors, programming, and sensing and perception. Those who work in robotics usually choose from one of these fields to specialize in. Since robots are so varied and complex, they need a team of people to design, build and program them. As a result, many jobs are considered a part of the robotics industry.
How big is the robotics industry? The robotics industry has grown very big over time, with approximately 3 million industrial robots in use across the globe, and an industry worth $43.8 billion. As more robots are produced and automation becomes a cheaper investment for companies (saving them up to 20% in costs), the size of the industry will continue to grow. By 2025, robots may displace up to 85 million jobs currently held by humans. With that in mind, as long as there’s continued demand for robots, the industry will continue to grow larger.
How fast is the robotics industry growing? The robotics industry is growing at a relatively fast rate, with a projected CAGR of 11.67% from 2019 through 2026. By 2024, global industrial robot shipments are expected to reach 518,000, up from 394,000 in 2020. The industry’s growth has also started to recover from 2020, with yearly new robot installations increasing from 8% to 13% in 2021. This marks improved but continued growth within the past eight years.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://www.zippia.com/advice/robotics-industry-statistics/
|
[
{
"date": "2023/03/07",
"position": 40,
"query": "job automation statistics"
}
] |
Glider AI Series A Funding
|
Glider AI Series A Funding
|
https://glider.ai
|
[] |
Closing the Skills Gap: A Roadmap to Workforce Transformation 74% of companies struggle with skills shortages, impacting productivity, retention, and growth.
|
CUPERTINO, CA, March 7, 2023 — Glider AI, a Skill Intelligence Platform, closed a total of $10M in Series A funding from Primera Capital and other industry leaders. Recognized by Staffing Industry Analysts (SIA) as the most innovative HR technology, the company will use the funds to continue its expansion in contingent programs and permanent hiring, furthering the development of proprietary technology, and growing its global team.
Glider AI serves a customer base of global enterprises and leading staffing firms, supporting their transformation from outdated credentials-first recruiting into remote-centric, skills-based hiring. Founded in 2020 by Satish Kumar, previous COO and co-founder of Edulastic (acquired by Sumeru Equity Partners), Kumar started Glider AI to make hiring fair and opportunity accessible. Notably, diversity hiring (DE&I) and supporting candidates with accommodation needs are central to the platform.
Arun Sarin, former CEO of Vodafone and Glider AI advisor and investor, shares, “We always say people are your competitive advantage; Glider AI makes it a reality by evaluating talent skill at scale without bias, ensuring employees you have are the best and most deserving of the opportunity.”
Hiring skilled talent remains competitive despite economic uncertainty and is a top priority for Glider AI customers. Underscoring this customer notion and the latest BLS data, Ori Sasson, General Partner of Primera Capital, shares, “Demand for quality talent remains high, and I don’t think this will ever change. Talent quality is a dynamic that makes Glider AI critical whether the economy is slow or growing. Unequivocally, Glider is the best solution available for companies to build their dream team. It removes the clutter in hiring decisions, and top candidates win, always!”
Glider AI customers benefit from the industry’s most comprehensive recruiting solution, bringing skill intelligence across multiple modalities and channels, from screening to live interviews, over the web or phone, through WhatsApp or SMS. Beyond skill validation, sophisticated AI-proctoring confirms candidate identity across all hiring stages while still delivering an exceptional candidate experience. Additionally, customers can configure Glider AI to meet their quality benchmark requirements, customize assessments to match their technology stack or functional roles, and throttle the level of AI-proctoring and anti-plagiarism controls.
Read the Press Release>
| 2023-03-07T00:00:00 |
2023/03/07
|
https://glider.ai/blog/glider-ai-secures-10m-series-a-funding-to-help-enterprises-shift-to-skills-based-hiring-and-guarantee-candidate-fit/
|
[
{
"date": "2023/03/07",
"position": 64,
"query": "AI skills gap"
},
{
"date": "2023/03/07",
"position": 91,
"query": "artificial intelligence hiring"
}
] |
Digital skills gap costing Australian businesses $9 million ...
|
Digital skills gap costing Australian businesses $9 million per day
|
https://www.rmit.edu.au
|
[] |
To address the skills gap, almost half (48%) of surveyed employers prefer upskilling or reskilling existing employees, over hiring externally, as they believe ...
|
Large Australian businesses need to invest $1.5 billion in digital skills training to address the existing skills gap
80% of business leaders expect to hire as many employees as they did last year despite predictions of slow economic growth
Three in five (58%) surveyed Australian employers still lack one or more of the digital skills they require to do business
According to the study, the skills shortage impacts companies in several ways, including loss of business, increased outsourcing costs, and reduced productivity. However, despite predictions of slow economic growth and inflation impacts, 80% of business leaders expect to hire at least as many people in 2023 as they did last year.
"Now is the time for businesses to invest in skills and development capabilities if we are to grow a resilient and competitive workforce. As the demand for digital skills continues to grow, the cost to businesses will also grow if decisive action is not taken to address these gaps."
- Claire Hopkins, interim CEO of RMIT Online
“While the upfront cost to solve our upskilling and reskilling crisis may seem high, our research shows investing in training is necessary for Australian businesses to reap substantial and long-lasting benefits, and to mitigate the impact of the digital skills gap."- Claire Hopkins
To address the skills gap, almost half (48%) of surveyed employers prefer upskilling or reskilling existing employees, over hiring externally, as they believe internal solutions generate additional benefits such as increasing retention, strengthening team culture, and raising cost-effectiveness.
Employees who received promotions in the past year, on average, spent 50% more time on training than those who did not receive a promotion.
While a third of employers think employees need to refresh their skills at least every three months, employees cited barriers to training such as lack of time, high cost, and lack of support from their employers. Additionally, the most valuable types of training for employees were mandatory on-the-job training, formal qualifications, and formal certifications.
"If businesses underinvest in digital skills training it can result in a loss of revenue, additional costs of outsourcing work to external staff or contractors and reduced productivity. That’s why training is an investment, not just a cost.”
- John O’Mahony, Partner at Deloitte Access Economics
| 2023-03-07T00:00:00 |
https://www.rmit.edu.au/online/blog/2023/digital-skills-gap-costing-australian-businesses
|
[
{
"date": "2023/03/07",
"position": 77,
"query": "AI skills gap"
}
] |
|
Data and AI Staffing Los Angeles | Global Talent Teams
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Data and AI Staffing Los Angeles
|
https://www.harnham.com
|
[] |
AI in 2025 – The Effect On the Job Market. By Thomas Savidge The UK is ... Research regarding current market trends, Benchmark my teams salaries, Compare ...
|
LOS ANGELES DATA & AI RECRUITMENT
OVERVIEW
Harnham Data Recruitment specialises in finding the best Data & AI professionals for companies in Los Angeles. We have a large network of both clients and candidates that we work with to fill data roles in the Los Angeles area.
Also, our team of experienced recruiters is incredibly well-versed in their markets so they will make sure to help companies find the perfect candidates for their Data & AI roles. Additionally, Harnham offers advice and guidance designed to help companies navigate the Data & AI Recruitment process.
Part of this comes in the form of the industry insights we provide, such as an annual salary guide and diversity guide, which we publish in order to assist companies in making the best hiring decisions they can. Our Data & AI Staffing Team in Los Angeles is dedicated to making the right decision for your business
| 2023-03-07T00:00:00 |
https://www.harnham.com/data-ai-staffing-los-angeles-global-talent-teams/
|
[
{
"date": "2023/03/07",
"position": 76,
"query": "AI labor market trends"
},
{
"date": "2023/03/07",
"position": 78,
"query": "artificial intelligence hiring"
}
] |
|
Lynne Parker Receives the 2023 CRA Distinguished ...
|
Lynne Parker Receives the 2023 CRA Distinguished Service Award
|
https://cra.org
|
[] |
... AI research, workforce development, and infrastructure. About the Award The CRA Distinguished Service Award recognizes service in the areas of government ...
|
The Computing Research Association (CRA) has selected Lynne Parker, Associate Vice Chancellor and Director of the AI Tennessee Initiative at the University of Tennessee, as the recipient of the 2023 CRA Distinguished Service Award for her unparalleled impact on the computing research community.
Parker has made numerous outstanding contributions over the course of six years in federal government positions, leading national artificial intelligence (AI) policy across the Executive Branch and fashioning key strategic policies in support of AI research, development, and deployment.
As Division Director of Information and Intelligent Systems at the National Science Foundation (NSF) Computer and Information Science and Engineering Directorate (CISE), Parker demonstrated technical vision and the ability to forge new national-scale research initiatives and teams across the CISE directorate, NSF, and government agencies. She took a leadership role in several new cross-agency federal research initiatives while founding a new Networking and Information Technology Research and Development program (NITRD) interagency working group on Robotics and Intelligent Systems. She conceived and led the development of several new strategic research initiatives, including the Smart and Autonomous Systems program, the re-visioning of the National Robotics Initiative, and the development of NSF’s new Big Idea on “The Future of Work at the Human-Technology Frontier.”
Parker co-led the 2016 NITRD effort that produced the National AI R&D Strategic Plan, which “establish[ed] a set of objectives for Federally-funded AI research.” The plan was extremely impactful and established national directions and intent during a time of growing concern about U.S. research leadership in AI. It has transcended three U.S. Administrations, spanned political ideologies, and also served as a model that dozens of nations around the world have adopted.
In 2018, Parker joined the White House Office of Science and Technology Policy (OSTP) as the Assistant Director for AI, and later became Deputy U.S. Chief Technology Officer in 2019, as well as Founding Director of the National AI Initiative Office in 2021. In these roles, Lynne served as the White House lead for national AI policy, managing efforts to promote and foster the nation’s leadership in AI for economic growth, improved quality of life, and national security.
Parker has displayed strategic vision, dedication, and forward-looking perspective while leading the nation’s strategic approach to AI research, workforce development, and infrastructure.
About the Award
The CRA Distinguished Service Award recognizes service in the areas of government affairs, professional societies, publications or conferences, and leadership that has a major impact on computing research.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://cra.org/2023-distinguished-service-award/
|
[
{
"date": "2023/03/07",
"position": 56,
"query": "government AI workforce policy"
}
] |
'It's Glacial': One Agency Is Still Struggling to Overcome the ...
|
‘It’s Glacial’: One Agency Is Still Struggling to Overcome the ‘Assault’ on Its Workforce
|
https://www.govexec.com
|
[
"Eric Katz",
"Senior Correspondent"
] |
Biden promised to revive a "hollowed out" federal workforce ... Joel Clement is one such policy expert who left government during the previous administration.
|
This is the third part in a series. Click here to read part one looking at Biden's hiring agenda as a whole, and here to read about an agency that has found success in delivering on the president's promise.
The Interior Department, as much as nearly any federal agency, has been given opportunities to succeed, and to grow.
Lawmakers for the last three years have been throwing resources at it. In 2020, President Trump signed into law the Great American Outdoors Act. The following year, President Biden signed the Infrastructure Investment and Jobs Act. Last year, he signed the Inflation Reduction Act. All provided significant resources to Interior. Now, it needs the people to oversee their implementation.
The department lost about 6% of its workforce during the Trump administration, according to data maintained by the Office of Personnel Management, among the largest decreases of any department. Since Biden took office, it has regrown its rolls by less than 2%.
The department, which declined to comment for this story, has recognized it is facing a crisis. It is being asked to do more than ever before, but hasfewer people with which to do it. In a recent workforce planning document, Interior said the infrastructure law and the GOAO will force it to “address current skills and pay gaps experienced by the bureaus.” It must attract the required technical talent pool, improve its data collection around hiring and reduce the time it takes to get someone on board, the department said.
In the meantime, according to Chandra Rosenthal, who has spent time at the departments of Energy and Justice and now leads the Public Employees for Environmental Responsibility’s Rocky Mountain Office, Interior is taking shortcuts. At the Bureau of Land Management, for example, employees are increasingly using a “loophole” to avoid conducting thorough analysis before providing grazing permits. The waiver process robs the public of an opportunity to weigh in before a permit is awarded, Rosenthal said.
“They are cutting corners where they can, legally,” she explained.
Interior is particularly hurting for specialists, Rosenthal said, such as biologists and botanists, who headed for the exits under Trump.
Joel Clement is one such policy expert who left government during the previous administration. He served as a career senior executive and the director of Interior's Office of Policy Analysis before then-Secretary Ryan Zinke reassigned him and dozens of others for what they claimed was retaliation. Clement in particular suggested he was being punished for speaking out on the risks of climate change to Alaska Native populations.
“There was an intentional effort to hobble the agency,” Clement said. “Not just to reduce the workforce, but to diminish the capacity of the department.”
As the department looks to regain what it lost—and grow from there to tackle new responsibilities—Clement predicted it will face severe challenges.
“It's a hard thing to bounce back from,” he said, adding, “It was such an assault.”
He flagged three key areas where staffing shortages will hinder Interior from carrying out its mission: health and safety issues for indigenous people that are "urgent and immediate" and require people in place to address them; the energy transition, for which Interior is "ground zero;" and the Fish and Wildlife Service generally, where advocates have long bemoaned insufficient resources. In a recent survey by the Union of Concerned Scientists, FWS scientists cited limited staff capacity as the greatest barrier to science-based decision making at the agency.
“Gutting programs, thwarting or removing scientists, and subverting missions,” one FWS scientist said of the priorities at the agency during the Trump administration. “It will take many years to just get back to where we were, and there is a constant fear that it can all be demolished again if a similarly bent administration comes to power.”
In the coming months and years, Interior will be taking on a myriad of new responsibilities. The GAOA provided the National Parks Service with $10 billion over five years to tackle its longstanding maintenance backlog. The infrastructure law awarded the department a whopping $28 billion for habitat restoration, drought mitigation, wildland fire management and extreme weather preparation. Lawmakers then gave Interior another $6 billion through the IRA for tribal electrification, land conservation, more habitat restoration, more maintenance backlog work, endangered species activities, water projects and climate disaster mitigation for indigenous communities. The projects are spread throughout Interior’s bureaus and will all require additional manpower.
“In a way, the agency gets out over its skis a bit by having an ambitious agenda without first having a moonshot to get all the right people back in there,” Clement said.
The department does have some strategies in place, however, and the resources to implement them. Quickly after passing GAOA, lawmakers in both parties flagged that the measure did not provide sufficient personnel for the National Park Service—which has struggled for a decade with understaffing, losing 16% of its workforce over that time—to carry out its new workload. Democrats subsequently included $500 million for NPS hiring in the IRA. The infrastructure law included a new minimum wage for federal firefighters and boosts to base salaries.
Internally, Interior's human capital office is focusing on better supporting firefighter needs, improving working conditions for its law enforcement personnel, closing skill gaps identified by the Office of Personnel Management and strengthening the talent pipeline for its career Senior Executive Service. The department noted 70% of its current SESers are eligible to retire within five years.
In the meantime, as Interior looks to realize the benefits of its newfound resources and reforms, the department is straining its existing workforce.
“It means everyone is working twice as hard,” Clement said.
PEER’s Rosenthal said employees have reported the same to her group.
“That’s what we're hearing from people on the ground,” Rosenthal said. “They’re expected to do more.”
The annual Federal Employee Viewpoint Survey demonstrates similar findings. Just 54% of Interior employees said their workload was reasonable, compared to 61% across government and 59% at the department in 2020. Global satisfaction at Interior dropped off by 4 percentage points since 2020, mirroring a similar governmentwide decline. NPS Director Chuck Sams said in an internal email to staff last month that the results demonstrated there were “many areas for improvement” and said the agency was “hiring additional staff to support mental health and wellness.”
“It’s glacial, the pace of action,” Rosenthal said of Interior’s efforts. “The repercussions of the last administration are still being felt and instead of getting better, it’s getting worse.”
As the department attempts to rebuild its workforce, it will have an opening to bring in employees not yet jaded by the ebbs and flows of administration turnover and evolving policy.
“There is an opportunity to get people in who have not felt that sting,” Clement said. “An opportunity to bring in a fresh, enthusiastic workforce.”
| 2023-03-07T00:00:00 |
2023/03/07
|
https://www.govexec.com/workforce/2023/03/one-agency-struggling-overcome-assault-workforce/383509/
|
[
{
"date": "2023/03/07",
"position": 97,
"query": "government AI workforce policy"
}
] |
Tooling U-SME
|
Tooling U-SME - CESMII
|
https://www.cesmii.org
|
[
"Jeannine Kunz"
] |
... workforce, to educators training the ... Basic types of machine learning explained include supervised, unsupervised, and reinforcement machine learning.
|
Tooling U-SME works with thousands of companies, including more than half of all Fortune 500® manufacturers and nearly 1,000 educational institutions across the country. Tooling U-SME focuses on the design and distribution of learning resources, and tailoring solutions to the needs of the manufacturing community. With Tooling U-SME’s extensive training experience, its reach into industry and academia, and CESMII’s subject matter expertise and expertise in smart manufacturing technology and business practices, the CESMII and Tooling U-SME collaboration will expedite adoption and drive progress through transformational workforce development.
| 2023-03-07T00:00:00 |
https://www.cesmii.org/education/tooling-u-sme/
|
[
{
"date": "2023/03/07",
"position": 65,
"query": "machine learning workforce"
}
] |
|
When training AI/ML models, here are the top 5 obstacles ...
|
When training AI/ML models, here are the top 5 obstacles that businesses face when labeling their data.
|
https://www.springbord.com
|
[] |
Both the quality and quantity of big data are beneficial to the fields of Artificial Intelligence and Machine Learning. Increasing the workforce to meet a ...
|
Read time 3 min
The first step in creating an AI or ML model is the pre-processing phase, also known as data labeling or data annotation. However, Data Annotation can continue after the final AI/ML model has been released, allowing for even greater improvements in accuracy.
Big Data (in the form of pictures, audio, or video files) must be identified by hand and precisely annotated to specify their content during this human-intervened process. After that, the data is fed into AI/ML models so that they can make precise predictions in areas like AI, ML, Computer Vision, and Natural Language Processing.
Machine learning models rely heavily on accurately labeled data. Data efficiency is greatly improved by proper labeling and categorization. Regular scaling up or down of data annotation effort is required due to the ever-shifting demands of machine learning models.
In this article, we will explore 5 obstacles that businesses face when labeling their data.
Five Challenges in Data Labeling
Despite the apparent simplicity of the concept at its core, Data Engineers must address some legitimate concerns.
Five major barriers reduce the efficacy of data labeling, one of which is the poor quality of data that 19% of businesses have reported.
Insufficient domain knowledge and training data expertise
It takes not only large amounts of big data but also skilled labor, to create and train AI/ML models that produce reliable outcomes. When labeling data, it’s crucial to keep quality and quantity in mind. In the worst case, human error can set off a cascade of other mistakes that will doom the project.
Worryingly, however, domain specialists who are not only data scientists but also have practical knowledge of the technological facets of business applications are sometimes in short supply in many companies.
Lack of Realistic Objectives, Resources, and Metrics
The cost of implementing AI/ML initiatives has, according to recent Statista research, always been a major worry. The United States is notable because 33% of respondents cited higher costs of data labeling as the primary barrier to integrating AI/ML in business.
Similarly, data annotators can’t work together, the team can’t coordinate their efforts, and nobody will know whether they’ve succeeded unless they have measurable objectives to compare progress against. Not only that, but without Key Performance Indicators, top-level executives would be basing their decisions on misleading data.
Problems with Employee Management Efficiency
Big data is essential for developing new AI/ML models. The data labeling process still involves a lot of manual labour because it hasn’t benefited from existing AI systems that can at least filter out unstructured data into easily digested bits.
Increasingly large datasets necessitate the employment of large human labour forces to annotate the data for use in artificial intelligence and machine learning systems. This creates yet another difficulty in managing the workforce. Labeling unstructured data to the highest quality is the key to achieving higher accuracy, where organizations are falling short.
Quality Assurance Procedures That Don’t Work
Both the quality and quantity of big data are beneficial to the fields of Artificial Intelligence and Machine Learning. Increasing the workforce to meet a quantitative goal is simple, but if the quality of the data isn’t up to par, the AI/ML models won’t be trained with the right inputs.
Validating whether data complies with standards is a challenge for data annotation organizations alongside rendering high-quality data. Keep in mind the significance of consistency in making accurate AI/ML model predictions.
Unable to work along with machines
There is a widespread fear that AI will decimate human jobs. Though this is true to an extent, the outcomes are ultimately attainable through collaborative intelligence, the most prominent example of which is data annotation.
Because of a lack of awareness of the synergistic relationship between humans and AI/ML, businesses aren’t making the most of how human workers can supplement machine output. Machines can improve human leaders by streamlining essential business processes.
Conclusion
Consistent data labeling while ensuring enhanced data security is currently possible, easing concerns about data labeling challenges that can slow down your project.
There has never been a more pressing need for accurate data labeling, and Springbord’s expertise in this area means that they can give you the tools you need to speed up the training of your artificial intelligence and machine learning models without sacrificing accuracy.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://www.springbord.com/blog/when-training-ai-ml-models-here-are-the-top-5-obstacles-that-businesses-face-when-labeling-their-data/
|
[
{
"date": "2023/03/07",
"position": 76,
"query": "machine learning workforce"
}
] |
National Labor Relations Board and Consumer Financial ...
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National Labor Relations Board and Consumer Financial Protection Bureau Announce New Partnership to Address Employer Surveillance, Monitoring, Data Collection, and Financial Practices in the Workplace
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https://www.nlrb.gov
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[] |
The NLRB is responsible for protecting employees from unfair labor practices which interfere with the rights of employees to join together to improve their ...
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Today, National Labor Relations Board (NLRB) General Counsel Jennifer Abruzzo and Consumer Financial Protection Bureau (CFPB) Director Rohit Chopra signed a new memorandum of understanding (MOU) creating a formal partnership between the two agencies to better protect American workers and address practices of employer surveillance, monitoring, data collection, and employer-driven debt, which can include a worker going into debt with their employer for the purchase of equipment, supplies, or required training.
The agreement, signed by the CFPB and the NLRB, supports the two agencies’ efforts to more closely collaborate on related efforts, and recognizes the intersection of acts and practices that may pose risks under federal consumer financial protection law and the National Labor Relations Act. The agencies will now be able to closely collaborate by sharing information, conducting cross-training for staff at each agency, and partnering on investigative efforts within each agency’s authority.
“Employers’ practices and use of artificial intelligence tools can chill workers from exercising their labor rights,” said General Counsel Jennifer Abruzzo. “As our economy, industries, and workplaces continue to change, we are excited to work with CFPB to strengthen our whole-of-government approach and ensure that employers obey the law and workers are able to fully and freely exercise their rights without interference or adverse consequences.”
While the agencies have two distinct missions, the CFPB and NLRB share an interest in protecting American workers. The CFPB is responsible for ensuring that markets for consumer financial products are fair, transparent, and competitive for American workers. The NLRB is responsible for protecting employees from unfair labor practices which interfere with the rights of employees to join together to improve their wages and working conditions, to organize a union and bargain collectively, and to engage in other protected concerted activity.
“Bad actors too often try to escape oversight by dodging between regulatory gaps and supervisory authorities,” said CFPB Director Rohit Chopra. “Today’s agreement will fill in cracks that may otherwise have let practices or products harmful to workers slip through.”
Last year, General Counsel Abruzzo issued a memorandum to all field offices, committing to working closely with other federal agencies to fully effectuate the mission of the NLRA and take action on interagency collaborations outlined in the White House Task Force on Worker Organizing and Empowerment report.
Workers who think their labor rights have been violated can call 1-844-762-6572 for assistance filing an unfair labor practice charge.
Consumers can submit complaints about employer-driven debt and employer surveillance, as well as about other financial products and services, by visiting the CFPB’s website or by calling (855) 411-CFPB (2372). Employees of companies who they believe their company has violated federal consumer financial laws, including violations related to workplace financial products and services, are encouraged to send information about what they know to [email protected].
The Consumer Financial Protection Bureau is a 21st century agency that implements and enforces Federal consumer financial law and ensures that markets for consumer financial products are fair, transparent, and competitive. For more information, visit consumerfinance.gov.
| 2023-03-07T00:00:00 |
https://www.nlrb.gov/news-outreach/news-story/national-labor-relations-board-and-consumer-financial-protection-bureau
|
[
{
"date": "2023/03/07",
"position": 15,
"query": "AI labor union"
}
] |
|
Layoffs? Come to Italy, We'll Teach You How to Fight
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Layoffs? Come to Italy, We'll Teach You How to Fight
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https://techworkerscoalition.org
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[] |
Today we hear from Laura, an Italian worker at a factory producing coffee machines, and part of a union that won a 100-day battle against relocation.
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Today we hear from Laura, an Italian worker at a factory producing coffee machines, and part of a union that won a 100-day battle against relocation. Recently, Laura and fellow workers made headlines with a invitation: “To the workers of Facebook and Twitter being laid off, come to Gaggio Montano, we can teach you how to form a union like ours.” This story is printed in English and Italian.
The Worker’s Perspective
A conversation with Laura, a worker at SaGa Coffee Factory
English version:
Gaggio Montano, a town of 5,000 inhabitants near Bologna, Italy is 10,000 kilometers from Facebook’s headquarters in Menlo Park, California. The distance is not only physical but also cultural and social: life in the small, quiet Italian town could not be more different from large tech company campuses. Yet this distance did not stop the women workers of SaGa Coffee, a company that produces Italy’s best-known coffee machines under the Saeco and Gaggia brands, from sending a message to Facebook workers.
In the months since SaGa Coffee workers’ statements, many American software companies have seen mass layoffs – without much opposition from employees. Meanwhile, unionized SaGa workers won a 100-day battle preventing the relocation of the plant by the Evoca Group, SaGa’s owner. Their protest centered around a permanent garrison on factory grounds to prevent management from taking away the machinery. In a victory speech, SaGa workers said: “Come, we’ll teach you how to fight. Don’t be passive in front of layoffs. Form a union like the FIOM, and if you don’t know how, we can teach you.” (FIOM is the main Italian union for metal workers, which, for various reasons, also includes most IT workers.)
Given the unusual offer of dialogue, we as members of TWC Italy decided to interview Laura, a SaGa Coffee employee and union representative. We want to tell the story of these workers to the U.S. public and find out where the idea of reaching out to employees of big tech companies came from.
☕
The spark that started the dispute was the sudden news that ownership was planning to dismantle the plant. If you had not realized in time, the corporate owner probably would have notified you of the closure at the last possible moment. How did your consideration of the company and the employer change?
We, six months earlier, had come to an agreement with the company: we had found 50 volunteers who would come out with good severance pay so that the plant and the work would still continue. However, at some point one of our colleagues intercepted an email saying that by Thursday evening the plant had to be emptied. There, I decided to contact my union contact: we immediately held an assembly to understand and to explain to the workers what was happening. We decided to block the factory exit.
The relationship between the union and HR had always been very good: we had discussions, we got along, we always found a solution, and they recognized me as a union representative, so when there was a problem they would call me, we would try to solve it, and so it was really a cold shower for us. In a conflict with a company where the relationship is already bad it’s different, but in our case the relationship was good until the day before.
What was it like, on a psychological and personal level, to experience this change?
At first I was angry because I’ve been working at Saeco since ‘96. I started working at the company when I was basically still a child. It angered me because they took away our brand, because Saeco was born here in Gaggio and we grew the brand with our work.
Then there was the disappointment towards these corporations, towards these big giants that don’t look at people. To them you are just a number. They only do things in their own interest, and that creates a distance.
Living In the mountains and in a small town helped keep the community strong. The union presence also played a key role in managing the dispute. Did you, as a union delegate, witness a change in your colleagues and the whole community’s relationship with the union?
Before the dispute, inside the plant we had our long-standing members, about 50-60 people out of 220, mainly FIOM members. The moment the picket started, our membership grew dramatically: basically everyone signed up.
The community has also been very supportive: everyone, from neighbors, to neighboring businesses, to restaurants, stores, factories in Bologna, Ferrara, Imola and Florence, everyone came to bring their solidarity, verbally or even financially. Sometimes some people showed up with a cake to share at the picket. We really felt the warmth of the whole mountain.
We could not have done what we did and achieved what we achieved with our dispute if there had not been a strong union. The strength, tenacity, and perseverance that the FIOM showed is unfortunately lacking in other unions in Italy.
I take as an example the fact that politicians and institutions came to us at the picket. This has never happened in the area. We didn’t have to go to them but they came to us, because anyway the picket is there, that’s where the struggle was. In my opinion the FIOM made a difference.
The workers in the first place, but also people outside, even today one year later they all say, “It’s a good thing that the union is there, it’s a good thing that anyway you guys had the strength to fight for 100 days.”
Is your appeal to Facebook workers a call to break down the geographic, cultural, and skills boundaries and distances that exist between workers? Did the mass overnight layoff at Facebook, which is a multinational corporation, remind you of your dispute? Where did the idea of addressing such a seemingly distant reality come from?
What I wanted to say to the Facebook workers with my message is to start thinking not only about themselves. They need to start thinking beyond the idea that “I don’t want a union because I can manage just fine on my own.” It doesn’t work that way. Unfortunately, without a union, without someone to protect the workers, it then happens unfortunately like what happened for example at Twitter, where overnight you get fired and no one can give you any hope. I thought that if they had a union like the FIOM, things would have been different.
When I made that statement, I didn’t think I would make such a fuss. It came out of me spontaneously, without thinking about it too much.
I know that programmers, but also our office employees for instance, don’t join because they are afraid of exposing themselves. They think that joining a union makes them more vulnerable targets and they are afraid. I don’t think so, if the union is strong. To have a card, to have a union, is to protect yourself, is to protect your job, is to protect a national contract [an Italian form of collective bargaining that groups workers by category], is to protect your children’s future.
I can see myself in their situation: the kind of violence is the same. I don’t know if these workers are protected by having a union behind them. I don’t know if they fought for their jobs or are fighting now after the fact. If I were them, I would expose myself more, look for connections to go on television, make announcements, make a fuss.
If you do nothing, you get nothing.
Protesta delle lavoratrici della SaGa / Source
Italian version
Gaggio Montano, un paese di 5.000 abitanti vicino Bologna, dista 10.000 chilometri dalla sede centrale di Facebook a Menlo Park, in California. La distanza non è solo fisica, ma anche culturale e sociale: la vita nella piccola e tranquilla cittadina italiana non potrebbe essere più diversa dai campus delle grandi aziende tecnologiche. Eppure questa distanza non ha impedito alle lavoratrici di SaGa Coffee, azienda che produce le più note macchine da caffè italiane con i marchi Saeco e Gaggia, di inviare un messaggio ai lavoratori di Facebook.
Nei mesi successivi alle dichiarazioni delle lavoratrici della SaGa Coffee, molte aziende americane di software hanno visto licenziamenti di massa, senza molta opposizione da parte dei dipendenti. Nel frattempo, i lavoratori sindacalizzati di SaGa hanno vinto una battaglia di 100 giorni per impedire il trasferimento dello stabilimento da parte del Gruppo Evoca, proprietario di SaGa. La loro protesta si è incentrata su un presidio permanente all’interno della fabbrica per impedire alla direzione di portare via i macchinari. In un discorso di vittoria, i lavoratori della SaGa hanno detto: “Venite, vi insegneremo a lottare. Non siate passivi di fronte ai licenziamenti. Formate un sindacato come la FIOM, e se non lo sapete fare, ve lo insegniamo noi”.
Vista l’insolita offerta di dialogo, noi di TWC Italia abbiamo deciso di intervistare Laura, dipendente di SaGa Coffee e rappresentante sindacale. Vogliamo raccontare la storia di questi lavoratori al pubblico americano e scoprire da dove nasce l’idea di rivolgersi ai dipendenti delle grandi aziende tecnologiche.
☕
La scintilla che ha dato il via alla vertenza è stata l’improvvisa notizia che la proprietà aveva intenzione di smantellare lo stabilimento. Se non ve ne foste accorti voi, probabilmente la multinazionale vi avrebbe comunicato la chiusura nell’ultimo momento utile. Come è cambiata la tua considerazione dell’azienda e del datore di lavoro?
Noi sei mesi prima avevamo trovato un accordo con l’azienda: avevamo trovato 50 volontari che sarebbero usciti con delle buone uscite per far sì che comunque lo stabilimento e il lavoro continuasse. Tuttavia ad un certo punto un nostro collega ha intercettato un e-mail dicendo che entro il giovedì sera andava svuotato lo stabilimento. Lì ho deciso di contattare il mio funzionario sindacale: abbiamo fatto subito un’assemblea per capire e per spiegare ai lavoratori cosa stesse accadendo. Abbiamo deciso di bloccare permanentemente l’uscita della fabbrica.
Il rapporto tra sindacato e HR era sempre stato ottimo: avevamo confronti, andavamo d’accordo, trovavamo sempre una soluzione e mi riconoscevano come delegata, quindi quando c’era un problema mi chiamavano, cercavamo di risolverlo e quindi è stata proprio una doccia fredda. In un conflitto con un’azienda in cui i rapporto sono già pessimi è diverso ma nel nostro caso i rapporti erano buoni fino al giorno prima.
Com’è stato, a livello psicologico e personale, vivere questo cambiamento?
All’inizio ha provocato tanta rabbia, almeno da parte mia, perché lavoro in Saeco dal ‘96: ho iniziato a lavorare in azienda quando ero praticamente ancora una bimba. Mi ha fatto rabbia perché hanno portato via il nostro marchio, perché la Saeco è nata qua a Gaggio e il marchio l’abbiamo fatto crescere noi, col nostro lavoro.
Poi c’è stata la delusione verso queste multinazionali, di questi grandi colossi che non guardano le persone. Per loro tu sei solo un numero. Loro fanno solo il proprio interesse e questo crea una grande distanza.
Vivere In montagna e in un paese piccolo ha aiutato a mantenere salda la comunità. Anche la presenza sindacale ha avuto un ruolo fondamentale nella gestione della vertenza. Tu, da delegata sindacale, hai avvertito un cambiamento delle tue colleghe e della comunità intera nel rapporto con il sindacato?
Prima della vertenza, dentro allo stabilimento avevamo i nostri tesserati storici, circa 50-60 persone su 220, principalmente membri FIOM. Nel momento in cui abbiamo iniziato a costruire il presidio, abbiamo molte ricevuto molte più iscrizioni: praticamente tutti hanno fatto la tessera.
Anche la comunità è stata di grande supporto: tutti, dal vicino di casa, alle aziende limitrofe, ai ristoranti, alla bottega, a tante fabbriche di Bologna, Ferrara, Imola e Firenze, tutti sono venuti a portare la loro solidarità, sia con una parola che anche economicamente. A volte alcune persone si sono presentate con un dolce da condividere al presidio. Abbiamo proprio sentire il calore di tutta la montagna.
Non avremmo potuto fare quello che abbiamo fatto e ottenere quello che abbiamo ottenuto con la nostra vertenza se non ci fosse stato un sindacato forte. La forza, la tenacia e la costanza che ha mostrato la FIOM purtroppo manca negli altri sindacati.
Porto ad esempio il fatto che i politici e le istituzioni sono venute da noi nel presidio: non è mai successo in zona. Non siamo dovuti andare noi da loro ma sono venuti loro da noi, perché comunque il presidio è lì, è stata lì la lotta. Secondo me in questo la FIOM ha fatto la differenza.
I lavoratori in primis, ma anche le persone fuori, anche oggi ad un anno di distanza dicono: “ma meno male che c’è il sindacato, meno male che comunque voi avete avuto la forza di lottare per 100 giorni”.
Il vostro appello ai lavoratori di FB è una chiamata ad abbattere i confini e le distanze geografiche, culturali, di competenze che esistono tra lavoratori e lavoratrici? Il licenziamento di massa di FB, che è una multinazionale, da un giorno all’altro vi ha ricordato la vostra vertenza? Da dove è venuta l’idea di rivolgervi ad una realtà così apparentemente lontana?
Quello che volevo dire ai lavoratori di Facebook col mio messaggio è di iniziare a pensare non solo a sé stessi. Devono iniziare a pensare oltre il “non voglio un sindacato perché ce la faccio benissimo da solo”, perché non funziona così. Purtroppo, senza un sindacato, senza qualcuno che comunque tuteli il lavoratore, succede poi purtroppo come è successo ad esempio in Twitter, in cui dall’oggi al domani vieni licenziato e nessuno può darti qualche speranza. Ho pensato che se avessero avuto un sindacato come la FIOM, le cose sarebbero andate diversamente.
Quando ho fatto quella dichiarazione non pensavo che avrei fatto così clamore. Mi è uscito spontaneamente, senza pensarci troppo.
So che i programmatori, ma io vedo anche i nostri impiegati, non si tesserano perché hanno paura di esporsi. Pensano che unirsi ad un sindacato li renda dei bersagli più vulnerabili e hanno paura. Io non penso sia così, se il sindacato è forte. Avere una tessera, avere un sindacato, è tutelare te stesso, è tutelare il tuo lavoro, è tutelare un contratto nazionale, è tutelare il futuro dei tuoi figli.
In parte mi ci rivedo nella loro situazione: il tipo di violenza è lo stesso. Non so se questi lavoratori sono tutelati avendo un sindacato alle spalle. Non so se loro hanno lottato, per il posto di lavoro o stanno lottando adesso a cose fatte. Io, se fossi in loro, mi esporrei di più, cercherei degli agganci per andare in televisione, farei degli annunci, farei confusione.
Se non fai niente, non ottieni niente.
| 2023-03-07T00:00:00 |
2023/03/07
|
https://techworkerscoalition.org/blog/2023/03/07/issue-2/
|
[
{
"date": "2023/03/07",
"position": 68,
"query": "AI labor union"
}
] |
A Denver charter school teacher is trying to start a union
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A Denver charter school teacher is trying to start a union
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https://www.chalkbeat.org
|
[
"Https",
"Www.Chalkbeat.Org Authors Melanie-Asmar",
"Rosie Thomas"
] |
While federal law protects the rights of private sector employees to organize unions and engage in collective bargaining, public sector education workers don't ...
|
In this 2015 photo, a teacher mentor works with another teacher during a training day.
A teacher at a KIPP charter school in Denver is leading a unionization push that could determine whether charter schools in Colorado are public or private employers, a key factor in whether their teachers can unionize under state law.
Cody Taffet is in his second year of teaching at KIPP Northeast Denver Leadership Academy, a charter high school in far northeast Denver. In January, after administrators abruptly changed the school’s schedule without input from teachers, Taffet filed paperwork with the National Labor Relations Board to form a union called the Colorado Charter Teachers Association.
“I was talking with some teachers and we wanted more democracy in the workplace,” said Taffet, who left a corporate job to become a teacher because he wanted to give back. “We wanted our input to be not only valued and appreciated but also considered and reckoned with.”
But the union organization effort was rejected by the regional director of the National Labor Relations Board in Denver, who found that charter schools are essentially public employers. While federal law protects the rights of private sector employees to organize unions and engage in collective bargaining, public sector education workers don’t have those rights under state law.
Now Taffet is appealing that decision to the National Labor Relations Board in Washington, D.C., which has upheld the right of charter school workers to unionize in other cases.
The outcome could open the door for charter school unions in Colorado or close off that option in a sector where educators are sometimes asked to work longer hours and often paid less than teachers at traditional public schools. The average charter school teacher salary in Denver in the 2021-22 school year was $44,475, while the average traditional public school teacher salary was $70,910, according to the Colorado Department of Education.
Charter schools have what University of Colorado Boulder law Professor Ahmed White describes as “features of both public and private entities.” All Colorado charter schools are publicly funded but privately run by nonprofit organizations. None are unionized.
An attempt last year by teachers at another Colorado charter network, New America School, failed after the charter network’s board rejected the teachers’ bid to unionize.
State labor department officials have speculated that charter schools could be seen as nonprofits that contract with the government rather than government employers. That would mean they are private sector employers.
But when the KIPP case came before Regional Director Paula S. Sawyer, she disagreed. In a dismissal decision last month, she found that charter schools in Colorado are a “political subdivision” and answer to government officials.
For example, Colorado law gives the State Board of Education the power to revoke the charter of an underperforming charter school, Sawyer wrote. She also noted that the state education commissioner can appoint and remove members of the unelected boards that govern individual charter schools and make changes to the schools’ governing documents.
Because of that, Sawyer found that charter schools are in the same category as public schools. State law says Colorado school districts don’t have to recognize teachers unions, though several dozen districts do so. A public sector collective bargaining law passed by state lawmakers last year specifically left out K-12 employees.
Private employers, on the other hand, are required to recognize unions if a majority of employees indicate they want to form one.
Taffet says KIPP feels like a private employer to him, and employees need a way to have a voice. Having to weather unilateral changes without the chance to give input is “destabilizing and demoralizing as a teacher in the building,” he said. Turnover is high, he added.
KIPP Colorado officials did not return emails and a phone call seeking comment.
Many charter leaders say collective bargaining agreements would limit their flexibility and take away from their ability to serve students.
In his request to review the dismissal, Taffet noted that charter teachers are paid by their networks. His paychecks, he said, are signed by KIPP, a nationwide network started in Texas that has 280 schools across the country, not by Denver Public Schools.
The district doesn’t control the day-to-day operations of his school, either, he said. And charter teachers like him can’t join the Denver Classroom Teachers Association.
The lack of clarity about the status of charter school employees in Colorado “creates an exploitable loophole” for employers, Taffet wrote in his request for review.
“While these entities are ultimately required to respond to the demands of the state and its authority, they are granted a unique privilege in that they are functionally inoculated from the concerns of their employees,” he wrote.
If the National Labor Relations Board under President Joe Biden agrees with him, the case could set a sort of precedent for Colorado. But White, the law professor, cautioned that the precedent would be a temporary one that future administrations could change.
“To the extent that other schools are funded and managed in an identical and very similar fashion, then it would establish an administrative precedent,” White said.
“But I say that with a caveat that the precedent established by an administrative agency at this level in particular is not really set in stone in the way precedent established by the U.S. Supreme Court would be.”
Still, Taffet has hope. He named the union the Colorado Charter Teachers Association with the thought that if his bid is successful, teachers from other charter schools could join.
“For me, personally, I’m in a place where I’m trying to be more authentic every day and use my existence to do something meaningful,” he said.
Melanie Asmar is a senior reporter for Chalkbeat Colorado, covering Denver Public Schools. Contact Melanie at [email protected].
| 2023-03-07T00:00:00 |
2023/03/07
|
https://www.chalkbeat.org/colorado/2023/3/7/23629426/charter-school-teachers-union-unionize-denver-colorado-kipp-appeal/
|
[
{
"date": "2023/03/07",
"position": 77,
"query": "AI labor union"
}
] |
Artificial Intelligence Opportunities for State and Local DOTs: A ...
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Artificial Intelligence Opportunities for State and Local DOTs: A Research Roadmap
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https://nap.nationalacademies.org
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[] |
... AI in their work within the next 5–10 years. ... Another piece of the AI Roadmap is understanding what structures support a strong AI group within an organization ...
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researched for AI-applications. Part two is also aimed at identifying AI application trends in transportation research projects sponsored by state DOTs.
The findings from part one of the literature review showed that the topics within the transportation domain are interlinked but the relative interdependencies within the topics vary. For example, the transportation topic “traffic management” is highly related to other transportation topics, whereas the transportation topic “winter road management” is much less studied and does not show much dependence on other transportation areas. The team also found that AI topics like advanced machine learning, neural methods, and optimization are widely used in most transportation research areas. Part two of the literature analysis explored the extent of AI applications in transportation research in the last 5 years using the Transportation Research Board (TRID) database. The results show that most AI applications research is in the area of traffic management and transportation infrastructure. Since urban areas constantly face traffic congestion issues, AI tools can provide real-time information from vehicles for traffic management. The team explored 17 state DOT-funded transportation research projects that looked at applying AI tools in the last 5 years. Overall, the literature analysis presents the interrelationships within transportation areas and the various AI applications that are available for state DOTs, local DOTs, and the stakeholders to apply in research areas. The detailed report is now submitted.
As part of our outreach efforts, the Virginia Tech Transportation Institute (VTTI) team conducted eight interviews with individuals who work for or with state DOTs to obtain a snapshot of where state DOTs are in their adoption, understanding, and needs for successful implementation of AI-based methods to solve their state’s transportation problems. The team compiled the data and wrote a report outlining (1) the current priorities of state DOTs and their state of using AI to address these priorities, (2) the challenges associated with integrating AI into DOT work, (3) the workforce and infrastructure needs for AI work, and (4) where DOTs hope to be using AI in their work within the next 5–10 years. Results from this task show that state DOTs are currently looking forward to using more AI and ML functions in their daily work to address transportation problems. Some DOTs are already using AI tools and methods. Traffic management seems to be the main area of current work and future interest for AI opportunity. Incident detection and pavement performance were the areas where DOTs indicated they have incorporated AI methods. Researchers found that there is an active collaboration among DOTs and academic institutions and private sector companies for research and development of AI-based methods, but more collaborations are required. However, overall, there is a general lack of understanding, education, and support in terms of how AI can be used to help solve transportation problems. To move forward with AI work, state DOTs have several needs to fulfil in their workforce and infrastructure.
Scope of the Two Workshops
To inform the AI Research Roadmap, the research team conducted a series of workshops to engage stakeholders from DOTs on the current and future use of AI in their agencies. The first workshop had two purposes: (1) to allow representatives from state and local DOTs to discuss and validate the results from the literature review and interviews, and (2) to facilitate discussions regarding primary needs for research and advancement related to AI in state and local DOTs as well as regional transportation agencies. The second workshop focused on presenting the draft Research Roadmap ideas to the representatives of state and local DOTs and gathering their feedback on each of the ideas. This report summarizes the discussion that occurred during Workshop 1 and Workshop 2.
For this task, VTTI researchers conducted two workshops involving individuals associated with academic institutions, regional transportation agencies, and state and local DOTs. The interviews in Task 3
| 2023-03-07T00:00:00 |
https://nap.nationalacademies.org/read/27865/chapter/11
|
[
{
"date": "2023/03/07",
"position": 84,
"query": "AI labor union"
}
] |
|
The CEO's Guide to the Generative AI Revolution
|
The CEO’s Guide to the Generative AI Revolution
|
https://www.bcg.com
|
[
"François Candelon",
"Abhishek Gupta",
"Lisa Krayer",
"Leonid Zhukov"
] |
Whether they work at a born-digital organization or (more often) a legacy company, our guests all have actionable insights to share. The series covers strategy, ...
|
The release of ChatGPT in late 2022 created a groundswell of interest in generative AI. Within hours, users experimenting with this new technology had discovered and shared myriad productivity hacks. In the weeks and months since, organizations have scrambled to keep pace—and to defend against unforeseen complications . Some organizations have already adopted a more formal approach, creating dedicated teams to explore how generative AI can unlock hidden value and improve efficiency.
For CEOs, however, generative AI poses a much bigger challenge. Today’s focus might be on productivity gains and technical limitations, but a revolution in business-model innovation is coming. Much as Mosaic, the world’s first free web browser, ushered in the internet era and upended the way we work and live, generative AI has the potential to disrupt nearly every industry—promising both competitive advantage and creative destruction. The implication for leaders is clear: today’s breathless activity needs to evolve into a generative AI strategy owned by the C-suite.
This is no small task, and CEOs—who are likely several steps removed from the technology itself—may feel uncertain about their next move. But from our perspective, the priority for CEOs isn’t to fully immerse themselves in the technology; instead, they should focus on how generative AI will impact their organizations and their industries, and what strategic choices will enable them to exploit opportunities and manage challenges. These choices are centered on three key pillars:
Each pillar raises an urgent question for CEOs. What innovations become possible when every employee has access to the seemingly infinite memory generative AI offers? How will this technology change how employees’ roles are defined and how they are managed? How do leaders contend with the fact that generative AI models may produce false or biased output?
Clearly, generative AI is a rapidly evolving space, and each of the pillars above involves short- and long-term considerations—and many other unanswered questions. But CEOs need to prepare for the moment when their current business models become obsolete. Here’s how to strategize for that future.
Potential: Discover Your Strategic Advantage
AI has never been so accessible. Tools such as ChatGPT, DALL-E 2, Midjourney, and Stable Diffusion allow anyone to create websites, generate advertising strategies, and produce videos —the possibilities are limitless. This “low-code, no-code” quality will also make it easier for organizations to adopt AI capabilities at scale. (See “The Functional Characteristics of Generative AI.”)
Expand All The Functional Characteristics of Generative AI The transformative potential of generative AI can be summed up by three key functional characteristics.
Seemingly “Infinite” Memory and Pattern Recognition. Because generative AI is trained on huge amounts of data, its memory can appear infinite. For example, ChatGPT has been trained on a massive portion of publicly available information on the internet. To put this in context, as of 2018 the internet generated 2.5 quintillion bytes of new data daily, according to Domo—the equivalent of 1.2 quintillion words. That number is likely much higher today. Generative AI can also create connections (or recognize patterns) between distant concepts in an almost human-like manner.
Low-Code, No-Code Properties. When describing the impact of ChatGPT, Andrej Karpathy, a founding member of OpenAI, said “the hottest new programming language is English.” That’s because generative AI’s natural-language-processing interface allows nonexperts to create applications with little or no coding required. By contrast, coding assistant systems such as Github Copilot still require competent programmers to operate them.
Lack of a Credible Truth Function. Generative AI’s “infinite” memory can become an infinite hallucination. In reality, the level of error in today’s generative AI systems is an expected characteristic that makes it useful for generating new ideas and content. But because generative AI does not use logic or intelligent thought, instead predicting the most probable next word based on its training data, it should only be used to generate first drafts of content.
Companies are working to make generative AI’s output significantly more reliable by using an approach known as reinforcement learning with human feedback; other approaches that combine generative AI with traditional AI and machine learning have also been considered. Improvements to generative AI are expected soon, with some predicting that it will be able to produce final-draft content by 2030.
The immediate productivity gains can greatly reduce costs. Generative AI can summarize documents in a matter of seconds with impressive accuracy, for example, whereas a researcher might spend hours on the task (at an estimated $30 to $50 per hour).
But generative AI’s democratizing power also means, by definition, that a company’s competitors will have the same access and capabilities. Many use cases that rely on existing large language model (LLM)1 1 Large language models, also known as foundation models, are deep- learning algorithms that can recognize, summarize, translate, predict, and generate content based on its training data. Today these models are mostly trained on text, images, and audio, but they can also go beyond language and images into signals, biological data, and more. Models trained on data beyond language are called multimodal models. applications—such as productivity improvements for programmers who use Github Copilot and for marketing content developers who use Jasper.ai—will be needed just to keep pace with other organizations. But they won’t offer differentiation, because the only variability created will result from users’ ability to prompt the system.
Identify the Right Use Cases
For the CEO, the key is to identify the company’s “golden” use cases—those that bring true competitive advantage and create the largest impact relative to existing, best-in-class solutions.
Such use cases can come from any point along the value chain. Some companies will be able to drive growth through improved offerings; Intercom, a provider of customer-service solutions, is running pilots that integrate generative AI into its customer-engagement tool in a move toward automation-first service. Growth can also be found in reduced time-to-market and cost savings—as well as in the ability to stimulate the imagination and create new ideas. In biopharma, for example, much of today’s 20-year patent time is consumed by R&D; accelerating this process can significantly increase a patent’s value. In February 2021, biotech company Insilico Medicine announced that its AI-generated antifibrotic drug had moved from conceptualization to Phase 1 clinical trials in less than 30 months, for around $2.6 million—several orders of magnitude faster and cheaper than traditional drug discovery.
Once leaders identify their golden use cases, they will need to work with their technology teams to make strategic choices about whether to fine-tune existing LLMs or to train a custom model. (See Exhibit 1.)
Fine-Tuning an Existing Model. Adapting existing open-source or paid models is cost effective—in a 2022 experiment, Snorkel AI found that it cost between $1,915 and $7,418 to fine-tune a LLM model to complete a complex legal classification. Such an application could save hours of a lawyer’s time, which can cost up to $500 per hour.
Fine-tuning can also jumpstart experimentation, whereas using in-house capabilities will siphon off time, talent, and investment. And it will prepare companies for the future, when generative AI is likely to evolve into a model like cloud services: a company purchases the solution with the expectation of achieving quality at scale from the cloud provider’s standardization and reliability.
But there are downsides to this approach. Such models are completely dependent on the functionality and domain knowledge of the core model’s training data; they are also restricted to available modalities, which today are comprised mostly of language models. And they offer limited options for protecting proprietary data—for example, fine-tuning LLMs that are stored fully on premises.
Training a New or Existing Model. Training a custom LLM will offer greater flexibility, but it comes with high costs and capability requirements: an estimated $1.6 million to train a 1.5-billion-parameter model with two configurations and 10 runs per configuration, according to AI21 Labs. To put this investment in context, AI21 Labs estimated that Google spent approximately $10 million for training BERT and OpenAI spent $12 million on a single training run for GPT-3.2 2 “How Generative AI Is Changing Creative Work,” Harvard Business Review, November 14, 2022. (Note that it takes multiple rounds of training for a successful LLM.)
These costs—as well as data center, computing, and talent requirements—are significantly higher than those associated with other AI models, even when managed through a partnership. The bar to justify this investment is high, but for a truly differentiated use case, the value generated from the model could offset the cost.
Plan Your Investment
Leaders will need to carefully assess the timing of such an investment, weighing the potential costs of moving too soon on a complex project for which the talent and technology aren’t yet ready against the risks of falling behind. Today’s generative AI is still limited by its propensity for error and should primarily be implemented for use cases with a high tolerance for variability. CEOs will also need to consider new funding mechanisms for data and infrastructure—whether, for example, the budget should come from IT, R&D, or another source—if they determine that custom development is a critical and time-sensitive need.
The “fine-tune versus train” debate has other implications when it comes to long-term competitive advantage. Previously, most research on generative AI was public and models were provided through open-source channels. Because this research is now moving behind closed doors, open-source models are already falling far behind state-of-the-art solutions. In other words, we’re on the brink of a generative AI arms race. (See “The Future State of the LLM Market.”)
Expand All The Future of the LLM Market
The next improvements to generative models with vast number of users will likely come from logs of their user interaction, giving these models a significant competitive advantage over new entrants. This reality, combined with the heavy data, infrastructure, and talent costs required to train LLMs, means that the LLM market has both economy and quality of scale. Advances in generative AI therefore might be limited to large companies, while the democratization of AI development for small and medium-sized enterprises could be limited to nondifferentiated use cases.
The jury is still out, but this dynamic appears comparable to the “search-engine wars.” Several large companies invested heavily in search solutions, but Google’s user-friendliness and accuracy helped set it apart from competitors. Once users preferred Google, other engines could not keep up—because every search request Google received made it better and smarter. Soon, all other B2C solutions faded away. A similar winner-take-all situation could play out in the LLM market, with the big, early entrants eventually owning the models and having full control over access.
A winner-take-all situation could play out in the LLM market.
It’s worth noting, however, that Google did not achieve the same level of success in the enterprise search market, which has unique requirements and challenges compared to B2C. At the enterprise level, search-engines lack the scale to build domain expertise and lack the volume of user data to build that capability. Similarly, businesses will get the most value out of LLMs that are trained on their proprietary data and that have modalities that drive unique use cases. This could make it difficult for any single player to dominate the B2B market.
There is also the potential for companies and governments to fund open-source models to keep them state of the art—similar to how IBM funded Linux.
These market dynamics have key implications for CEOs as they make customization and implementation decisions:
It is unlikely that any single LLM provider will dominate the B2B market; the key for companies is to find large models with the modality and functionality that match their golden use cases or use cases that require sensitive data.
While training LLMs is an option for large businesses, the quality of scale could make purchasing solutions more reliable (similar to cloud).
If choosing to train in-house, be wary of relying too much on individual researchers. If only a small number of people have the expertise to advance and maintain the model, this will cause a single point of failure if those researchers choose to leave. Until recently, most generative AI research has been publicly accessible. But many companies are choosing to stop or delay publishing their research findings and are keeping model architectures as proprietary knowledge. (For example, GPT-2 was open-source but GPT-3 is proprietary.)The next improvements to generative models with vast number of users will likely come from logs of their user interaction, giving these models a significant competitive advantage over new entrants. This reality, combined with the heavy data, infrastructure, and talent costs required to train LLMs, means that the LLM market has both economy and quality of scale. Advances in generative AI therefore might be limited to large companies, while the democratization of AI development for small and medium-sized enterprises could be limited to nondifferentiated use cases.The jury is still out, but this dynamic appears comparable to the “search-engine wars.” Several large companies invested heavily in search solutions, but Google’s user-friendliness and accuracy helped set it apart from competitors. Once users preferred Google, other engines could not keep up—because every search request Google received made it better and smarter. Soon, all other B2C solutions faded away. A similar winner-take-all situation could play out in the LLM market, with the big, early entrants eventually owning the models and having full control over access.It’s worth noting, however, that Google did not achieve the same level of success in the enterprise search market, which has unique requirements and challenges compared to B2C. At the enterprise level, search-engines lack the scale to build domain expertise and lack the volume of user data to build that capability. Similarly, businesses will get the most value out of LLMs that are trained on their proprietary data and that have modalities that drive unique use cases. This could make it difficult for any single player to dominate the B2B market.There is also the potential for companies and governments to fund open-source models to keep them state of the art—similar to how IBM funded Linux.These market dynamics have key implications for CEOs as they make customization and implementation decisions:
As research accelerates and becomes more and more proprietary, and as the algorithms become increasingly complex, it will be challenging to keep up with state-of-the-art models. Data scientists will need special training, advanced skills, and deep expertise to understand how the models work—their capabilities, limitations, and utility for new business use cases. Large players that want to remain independent while using the latest AI technology will need to build strong internal tech teams.
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People: Prepare Your Workforce
Like existing forms of artificial intelligence , generative AI is a disruptive force for humans. In the near term, CEOs need to work with their leadership teams as well as HR leaders to determine how this transformation should unfold within their organizations—redefining employees’ roles and responsibilities and adjusting operating models accordingly.
Redefine Roles and Responsibilities
Some AI-related shifts have already occurred. Traditional AI and machine-learning algorithms (sometimes incorrectly referred to as analytical AI), which use powerful logic or statistics to analyze data and automate or augment decision making, have enabled people to work more autonomously and managers to increasingly focus on team dynamics and goal setting.
Now generative AI, in its capacity as a first-draft content generator, will augment many roles by increasing productivity, performance, and creativity. Employees in more clerical roles, such as paralegals and marketers, can use generative AI to create first drafts, allowing them to spend more of their time refining content and identifying new solutions. Coders will be able to focus on activities such as improving code quality on tight timelines and ensuring compliance with security requirements.
Of course, these changes cannot (and should not) happen in a vacuum. CEOs need to be aware of the effect that AI has on employees’ emotional well-being and professional identity. Productivity improvements are often conflated with reduction in overall staff, and AI has already stoked concern among employees; many college graduates believe AI will make their job irrelevant in a few years. But it’s also possible that AI will create as many jobs as it will displace.
The impact of AI is thus a critical culture and workforce issue, and CEOs should work with HR to understand how roles will evolve. As AI initiatives roll out, regular pulse checks should be conducted to track employee sentiment; CEOs will also need to develop a transparent change-management initiative that will both help employees embrace their new AI coworkers and ensure employees retain autonomy. The message should be that humans aren’t going anywhere—and in fact are needed to deploy AI effectively and ethically. (See Exhibit 2.)
As AI adoption accelerates, CEOs need to learn as they go and use those lessons to develop a strategic workforce plan—in fact, they should start creating this plan now and adapt it as the technology evolves. This is about more than determining how certain job descriptions will change—it’s about ensuring that the company has the right people and management in place to stay competitive and make the most out of their AI investments. Among the questions CEOs should ask as they assess their company’s strengths, weaknesses, and priorities are:
What competencies will project leaders need to ensure that individual contributors’ work is of sufficient quality?
How can CEOs create the optimal experience curve to produce the right future talent pipeline—ensuring, for example, that employees at a more junior level are upskilled in AI augmentation and that supervisors are prepared to lead an AI-augmented workforce?
How should training and recruiting be adjusted to build a high-performing workforce now and in the future?
Adjust Your Operating Model
We expect that agile (or bionic) models will remain the most effective and scalable in the long term, but with centralized IT and R&D departments staffed with experts who can train and customize LLMs. This centralization should ensure that employees who work with similar types of data have access to the same data sets. When data is siloed within individual departments—an all-too-common occurrence—companies will struggle to realize generative AI’s true potential. But under the right conditions, generative AI has the power to eliminate the compromise between agility and scale.
Because of the increased importance of data science and engineering, many companies will benefit from having a senior executive role (for example, a chief AI officer) oversee the business and technical requirements for AI initiatives. This executive should place small data-science or engineering teams within each business unit to adapt models for specific tasks or applications. Technical teams will thus have the domain expertise and direct contact to support individual contributors, ideally limiting the distance between the platform or tech leaders and individual contributors to one layer.
Structurally, this could involve department-focused teams with cross-functional members (for example, sales teams with sales reps and dedicated technical support) or, preferably, cross-departmental and cross-functional teams aligned to the business and technical platforms.
Policies: Protect Your Business
Generative AI lacks a credible truth function, meaning that it doesn’t know when information is factually incorrect. The implications of this characteristic, also referred to as “hallucination,” can range from humorous foibles to damaging or dangerous errors. But generative AI also presents other critical risks for companies, including copyright infringement; leaks of proprietary data; and unplanned functionality that is discovered after a product release, also known as capability overhang. (See Exhibit 3.) For example, Riffusion used a text-to-image model, Stable Diffusion, to create new music by converting music data into spectrograms.
Prepare for Risk
Companies need policies that help employees use generative AI safely and that limit its use to cases for which its performance is within well-established guardrails. Experimentation should be encouraged; however, it is important to track all experiments across the organization and avoid “shadow experiments” that risk exposing sensitive information. These policies should also guarantee clear data ownership, establish review processes to prevent incorrect or harmful content from being published, and protect the proprietary data of the company and its clients.
Another near-term imperative is to train employees how to use generative AI within the scope of their expertise. Generative AI’s low-code, no-code properties may make employees feel overconfident in their ability to complete a task for which they lack the requisite background or skills; marketing staff, for example, might be tempted to bypass corporate IT rules and write code to build a new marketing tool. About 40% of code generated by AI is insecure, according to NYU’s Center for Cybersecurity—and because most employees are not qualified to assess code vulnerabilities, this creates a significant security risk. AI assistance in writing code also creates a quality risk, according to a Stanford University study, because coders can become overconfident in AI’s ability to avoid vulnerabilities.
Leaders therefore need to encourage all employees, especially coders, to retain a healthy skepticism of AI-generated content. Company policy should dictate that employees only use data they fully understand and that all content generated by AI is thoroughly reviewed by data owners. Generative AI applications (such as Bing Chat) have already started implementing the ability to reference source data, and this function can be expanded to identify data owners.
Ensure Quality and Security
Leaders can adapt existing recommendations regarding responsible publication to guide releases of generative AI content and code. They should mandate robust documentation and set up an institutional review board to review a priori considerations of impact, akin to the processes for publishing scientific research. Licensing for downstream uses, such as the Responsible AI License (RAIL), presents another mechanism for managing the generative AI’s lack of a truth function.
Finally, leaders should caution employees against using public chatbots for sensitive information. All information typed into generative AI tools will be stored and used to continue training the model; even Microsoft, which has made significant investments in generative AI, has warned its employees not to share sensitive data with ChatGPT.
Today, companies have few ways to leverage LLMs without disclosing data. One option for data privacy is to store the full model on premises or on a dedicated server. (BLOOM, an open-source model from Hugging Face’s BigScience group, is the size of GPT-3 but only requires roughly 512 gigabytes of storage.) This may limit the ability to use state-of-the-art solutions, however. Beyond sharing proprietary data, there are other data concerns when using LLMs, including protecting personally identifiable information. Leaders should consider leveraging cleaning techniques such as named entity recognition to remove person, place, and organization names. As LLMs mature, solutions to protect sensitive information will also gain sophistication—and CEOs should regularly update their security protocols and policies.
Generative AI presents unprecedented opportunities. But it also forces CEOs to grapple with towering unknowns, and to do so in a space that may feel unfamiliar or uncomfortable. Crafting an effective strategic approach to generative AI can help distinguish the signal from the noise. Leaders who are prepared to reimagine their business models—identifying the right opportunities, organizing their workforce and operating models to support generative AI innovation, and ensuring that experimentation doesn’t come at the expense of security and ethics—can create long-term competitive advantage.
| 2023-03-04T00:00:00 |
2023/03/04
|
https://www.bcg.com/publications/2023/ceo-guide-to-ai-revolution
|
[
{
"date": "2023/03/07",
"position": 95,
"query": "AI labor union"
},
{
"date": "2023/03/07",
"position": 92,
"query": "artificial intelligence business leaders"
}
] |
Future of AI in Business: The Start of a New Digital Era
|
Future of AI in Business: The Start of a New Digital Era
|
https://www.institutedata.com
|
[
"Institute Of Data"
] |
With the help of artificial intelligence, businesses implement pattern recognition concerning people's search activities and give them more pertinent ...
|
Data Science, Analytics, Machine Learning Future of AI in Business: The Start of a New Digital Era
Stay Informed With Our Weekly Newsletter Receive crucial updates on the ever-evolving landscape of technology and innovation. By clicking 'Sign Up', I acknowledge that my information will be used in accordance with the Institute of Data's Privacy Policy. Subscribe
The prominence future of AI in business operations continues to gain positive momentum in the post-Covid world, as enterprises across different industries adopt technical solutions for common problems. While the pandemic increased the pace at which businesses adopted new technologies, most companies were already headed that way.
AI provides simple solutions for complex problems, and with evolving business operations and consumer habits, technologies are more critical to businesses than ever before. However, there is a learning curve that comes with every new technology, and companies continue to shift between different options for better results.
Is AI transforming modern business?
Artificial intelligence tools and big data analytics have transformed how business is conducted across multiple industries. Deep learning continues to permeate the business world as every company attempts to satisfy the changing expectations of different consumer groups.
Personalised customer experience is the game’s name, and consumers want businesses to simplify their lives by comprehending, anticipating and fulfilling preferences they might not even be aware of. One excellent example of this change is visible in the entertainment industry. Even though Hollywood often presents AI as demonic dystopian tech, it now relies on AI more than ever, with algorithms now reshaping how people watch movies and how studios distribute content.
Streaming services now use AI to track user behaviour and promote certain shows and films, revolutionising how people consume entertainment. Businesses that employ machine learning and more advanced algorithms to maintain their consumer base have a competitive advantage. For instance, they can provide a broader range of customised materials using facial recognition systems and individual profiling.
However, there are also possible downsides that come with these practices. For instance, customers now have access to a wider variety of entertainment options than ever before, but the exact algorithms are employed to entice viewers and encourage binge-watching. Technology and service sectors—and the quick pace at which machine learning is transforming them—define the economic foundation of the modern industrial world, much like changes in manufacturing characterised the Industrial Revolution.
Modern companies need to incorporate AI into their user experience strategy to provide a customised experience to every client, improving each stage from product selection to checkout. However, several ethical questions must be considered with the growing dependence on AI tools to limit unhealthy business practices and promote sustainable growth.
Recent applications of AI in the corporate world
Since basic robotic machinery was introduced in corporate production processes, it has somewhat replaced human input. This is also true in the 21st century, where each AI update makes human input more replaceable, with some common examples being hiring decisions, content production, data analysis and process automation.
Making hiring decisions
Up to 75% of resumes are rejected by an autonomous applicant tracking system, or ATS, throughout the recruiting process, demonstrating how influential AI has already become in the hiring process.
Previously, it was very time-consuming for interviewers to review resumes to find qualified individuals. According to research on LinkedIn, recruiters may spend up to twenty-three hours reviewing candidates for a single hiring. However, this has all changed in the computer age as modern systems with AI are an increasingly popular substitute to scan resumes.
With the potential for even more updates, the workload is certainly getting easier for hiring managers in multiple companies. However, several people have criticised the use of specific forms of AI by hiring managers. Objections are usually based on the fact that AI tools can perpetuate and even increase prejudice in recruiting.
Technological advancements in innovation
With various tools at their disposal, startups and established businesses have several uses for AI and analytics. For instance, startups can use the newest data-driven technology to go after well-established firms and break into new markets with novel solutions.
However, since startups have limited resources compared to larger firms, they cannot utilise AI independently. Even if they can do so, they may solve only a portion of the puzzle. Collaboration is essential in this scenario as larger companies can welcome and integrate those startups into their ecosystem.
AI also allows existing businesses to test and iterate possible prospects ]on a larger scale with better speed and accuracy, which helps them compete with startups. Established companies can use AI to recognise high-potential growth sectors and decide whether it makes more sense to fight directly against their competitors or, if the chance presents itself early enough, to buy them out entirely.
Data-driven business development and innovation will see a surge shortly. Innovators, industry leaders, and visionaries will have the chance to create value and develop new companies that can be quickly reconfigured and realigned with altering consumer demands as the globe gradually exits the COVID crisis.
AI technology and analytics, two next-generation technologies, will be crucial in fostering corporate innovation and advancement in this setting and developing new business models.
Substituting for lack of human talent
Tourism, entertainment, and other service-related industries crushed by the COVID crisis require resources to prepare for pent-up demand. As a result, there should be more opportunities in multiple areas, including truck drivers, factory employees, and restaurant staff. Sadly, there is a growing demand to create analytics and for AI to act as a substitute in several areas primarily driven by human talent.
AI is critical in developing solutions to ongoing, always-changing skill demands and training. Through career transition assistance tools, AI is already starting to help the existing workforce address skills gaps. Using virtual assistants and internal AI-driven training courses, AI also assists people in their current roles and rapidly evolving their skill sets.
By enhancing support operations, AI can also assist in reducing talent shortages. Other uses include tools like chatbots and support systems. For example, a well-trained chatbot can answer many simple inquiries, at least in part reducing stress. On the other hand, we also have NLP systems that can do straightforward document scanning, frequently for more pre-programmed terms.
Identification of future problems
As the economy recovers from the COVID crisis and loss of business, there are lingering after-effects, and AI can help analyse and predict these issues in advance. Analytics and AI enable companies to anticipate, plan for and identify potential problems that affect their ability to provide goods and services. One common application of AI is in supply chain management, as supplies of multiple products are cut short while businesses regain momentum.
Some companies still use human talent to keep tabs on their supply chains. However, those implementing AI will significantly differentiate themselves from their competitors in the upcoming months and years. TMerging supply chain planning with AI systems tackles repetitive issues. As a result, humans can focus on solving the more complicated supply chain issues.
Even in more complicated situations, the AI system may help by suggesting potential solutions to examine or accelerating the examination of possible solutions by formulating an answer from a recommendation on a particular aspect of the problem.
What should businesses know about AI and its power today?
AI’s strength boosts data recognition systems and contributes to a much more robust cybersecurity system. AI also allows businesses to offer improved customer service and automate production systems.
A boost in information recognition
With the help of artificial intelligence, businesses implement pattern recognition concerning people’s search activities and give them more pertinent information related to their search patterns. In addition, users can enjoy a more personalised experience as frequent device use will further improve AI technology. This has particularly enormous implications for small enterprises since it makes it much easier to target a high niche market.
There are also significant changes in how different companies transmit and gather user data. As always, this raises several ethical questions as several user groups wish to limit the exploitation of their personal data.
Immediate detection of cyberattacks
Artificial intelligence is a crucial ally, even when finding gaps in computer network defences. By observing technology trends in big data intake, AI systems may detect cyberattacks and other cyber threats. When a danger is identified, it can go back through your data to locate the source and prevent further threats.
Having an additional pair of eyes is always beneficial for infrastructure maintenance as attacks become more vigilant and constant. Due to the size and growing complexity, there is an increasing demand for cybersecurity professionals who can examine these problems and create counter-attacks and defences. Artificial intelligence is an excellent companion for these professionals and can help make their workload more manageable.
Enhanced client relationships
Systems for managing client relationships (CRM) are also altering with the increasing influence of artificial intelligence. To be accurate and current, software like Salesforce and Zoho depend on increased human interaction. However, when we apply AI to these platforms, a standard CRM system becomes an auto-updating, self-correcting system with improved accuracy and increased capacity.
The banking industry is a prime illustration of how AI can support client interactions. With the help of artificial intelligence, clients can receive a personalised message inviting them to visit the nearest branch and renew their mortgages. In addition, AI tools can analyse which customers have a mortgage with their bank and if it is due for renewal in 90 days or less.
Conclusion
As AI commands more and more business relations every day, it is imperative to hone the needed skills under the right mentorship before you can profit from using it. So sign up for a career consultation today, and become part of the flourishing AI industry!
| 2023-03-07T00:00:00 |
2023/03/07
|
https://www.institutedata.com/blog/future-of-ai-in-business-the-start-of-a-new-digital-era/
|
[
{
"date": "2023/03/07",
"position": 68,
"query": "artificial intelligence business leaders"
}
] |
Leveraging AI in Business: Exploring Opportunities and ...
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Leveraging AI in Business: Exploring Opportunities and Weighing Perspectives
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https://www.gsb.stanford.edu
|
[] |
Absolutely. I think the best example for us is how we approach our data analytics capability, AI, and machine learning (ML) capability. It's currently very ...
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Image
Oz Ozturk is charged with building and driving technology strategy for go-to-market solutions for the global consulting firm PwC. He attended Stanford’s Harnessing AI for Breakthrough Innovation and Strategic Impact program (AIP) to hear the latest research and insights on artificial intelligence (AI) applications from an interdisciplinary faculty team and to delve deeper into the opportunities for using AI technology to transform and grow business.
What made you decide to attend AIP?
It’s quite easy to get myopic about your own strategy and point of view. I always find it healthy to get an external perspective. Having attended a previous Stanford Executive Education program, I know that Stanford always attracts an interesting cadre of students so you get perspectives from others in the course besides your professors – I knew I’d come back with some perspectives on how we could apply AI within our business. Plus, coming from England, I do enjoy the sunny weather!
What were some of your takeaways about the use of AI to drive business strategy and innovation?
Quote Usually a test for me of the value of a program such as this is, do you keep in touch with your classmates? And we have. We've got our own WhatsApp group. The chat’s always buzzing every couple of days. I've met a few people in Geneva, and I’ll see more at a London session in a couple of weeks. So I think Stanford does an excellent job in facilitating networking. Author Name — Oz Ozturk
There’s a common misconception that organizations need an army of data scientists. But, if you can’t get the data or can’t annotate it, data scientists are worthless. Investing more into driving data acquisition and annotation is going to give your AI models a lot more accuracy. Another takeaway was the value of open source technology, which can be applied freely. It’s not about the tech itself, but how you apply it.
You work on go-to-market solutions for clients; did anything you learned help you specifically with that?
Absolutely. I think the best example for us is how we approach our data analytics capability, AI, and machine learning (ML) capability. It’s currently very fragmented with different teams doing different things with various technologies. And sometimes we fall into the trap of coming out with cool tech before actually asking how we could apply it to a given industry or identifying a proposition to drive value for a business or sector. I presented our board with recommendations on reorganizing ourselves around data, AI, and ML after I returned from the program.
This program has a strong interdisciplinary approach, with faculty from business, engineering, law, medicine, and the school of humanities and science. How important was this to understanding the bigger picture of AI and its utilities for the future?
There is a danger of [approaching the topic by] doing a deep dive into tech and theory and losing perspective. You can learn the tech behind AI but it’s really the application that’s important. This course brought to life various applications as well as things like ethics, legal considerations, and more. In a business like mine, you can’t just approach a topic from one angle. Our clients look to us to provide advice and inform them of what they need to consider with various applications of tech. So Stanford’s approach works really well.
Was there a faculty session that really stood out for you?
Professor Christopher Manning’s session on “Natural Language Processing Overview” was fantastic. He gave us the technical structure behind how it [machines reading and interpreting human language] works. It’s a pervasive technology that we are all used to, but understanding how it was developed and how it’s being improved was fascinating. It’s good to understand the guts of it – the different building blocks that make it what it is.
You recently oversaw publication of a detailed report for your company on “frictionless retail: the future of shopping,” which received 20,000 hits in one week. How did AIP impact your thoughts for that work?
It was something I was already thinking about, but I didn’t quite know how to structure it or what to prioritize as the key messages. The program helped a great deal in that. I’ve been working in the retail and consumer sector for 25 years now and I wanted to just put what I know on paper. I was quite happy that it worked out and has been well received.
| 2023-03-07T00:00:00 |
https://www.gsb.stanford.edu/exec-ed/difference/participants/leveraging-ai-business-exploring-opportunities-weighing
|
[
{
"date": "2023/03/07",
"position": 80,
"query": "artificial intelligence business leaders"
}
] |
|
The rise of the AI recruiter in HR
|
The rise of the AI recruiter in HR
|
https://growthbusiness.co.uk
|
[
"Timothy Adler"
] |
Artificial intelligence can help accelerate job recruitment and handle clerical work for onboarding staff. How can you convince your organisation that it needs ...
|
Should your HR department invest in artificial intelligence tools? Technology such as ChatGPT and generational AI may be making the news, but what can artificial intelligence offer you as HR professional?
Artificial intelligence is mostly being used by human resources in two ways: automating talent recruitment and also answering bread-and-butter chatbot questions from staff about, say, holiday owing or payroll run dates.
Roger Philby, founder of The Chemistry Group, which uses AI to help organisations including Pizza Hut, Barclays and EY with internal recruitment, describes the current use of AI in HR as “nascent”.
Says Philby: “It’s really focused on either external hiring or it’s being used in what’s called talent marketplaces which use AI to attach people to internal job openings.”
AI x staff recruitment
AI can save time when it comes to initial screening of job candidates. It can sift through LinkedIn profiles, identifying candidates who meet selection criteria and manage early-stage applications.
Chatbots can talk to a candidate in the form of automated questions, collecting information about skills, training and previous roles. These bots free up HR professionals’ time during these first-contact interactions.
>See also: Can a sole trader employ staff?
For example, one recruitment agency client of SME HR specialist Citrus HR uses software/AI to select and shortlist candidates automatically. He loves it, saying it saves time and is very efficient.
However, as with all software/CRM systems, you have to have the right information in in the first place for it to be useful. Therefore, AI can miss transferable skills, which often do not fall under the recruitment criteria, but which can be crucial to finding the best candidate.
Says Philby: “The problem with AI is that it’s scalable but it doesn’t care what it’s scaling.”
On the other hand, the use of AI has a major positive in that it reduces discrimination risks and unconscious bias, selecting on experience and aptitude.
But over-reliance on such tech could restrict the pool of candidates, working against those who are not tech savvy or those who are not active on social media platforms such as LinkedIn.
>See also: How incentive and reward schemes can motivate staff to go the extra mile
And there have been some spectacular pratfalls when it comes to organisations using AI in HR.
Amazon, for example, had to scrap its AI-driven recruitment engine back in 2017 after it realised its program was biased against women.
And US AI recruiter HireVue has been hit with a class action lawsuit for scanning people unknowingly with its now-scrapped facial recognition technology in online job interviews. Apparently, facial expressions are helpful when questioning job candidates. Those with facial tics or other disabilities later claimed that systems such as HireVue’s – whose clients include The Co-Operative Bank and accountants Grant Thornton – would have unfairly ruled them out for jobs.
For Philby, the way AI identifies candidates for job openings is still very “broad brush” as AI is incapable of understanding the kinds of roles or career development candidates are actually interested in.
Philby says: “It’s being used by HR people to skills match people but they’re matching pretty much sucks.”
Basically, the more skills you brag about on LinkedIn, the more job matches you’re going to get.
Answering routine questions
Perhaps the best way to think of AI is as a multiplier for HR professionals, almost like a robot exoskeleton, freeing them up to focus on more complex human tasks with real value rather than wasting their time grinding through admin.
AI-driven chatbots in particular can mimic person-to-person conversation convincingly. Many companies are using chatbots to answer basic HR questions, such as payroll run dates or questions about holiday dates.
And chatbots generate data, so if employees or interview candidates keep asking the same questions, that can be useful when it comes to spotting gaps in your onboarding process or staff handbook.
French energy company EDF for example uses chatbots to answer questions about staff absences, holiday and payroll. Seventy-five per cent of users are satisfied with it.
And AI and machine learning can automate transactional processes such as new starter paperwork, annual admin requirements for fleet car driving licences, DBS checks and so forth.
AI x performance management
The next trend in using AI in HR will be for performance management. According to Citrus HR, this means identifying training/support interventions earlier, with immediate support offered rather than waiting on manual identification by managers.
Convincing the C-suite
At some point, an HR manager who is convinced that buying AI HR software could help automate recruitment and free up time from answering basic questions will have to go to the board and convince them to spend money.
How should an HR manager pitch their case for incorporating more AI into day-to-day- HR functions?
The key for any head of HR trying to make their case for a company to adopt AI software is to focus on how automation will boost revenue and productivity and improve recruitment and staff management – both hiring and employee engagement – rather than just save money.
Philby says: “The question to ask is what is the business outcome you are trying to drive? Don’t buy into the technology and then look for the problem, think about what’s the problem you’re trying to solve.”
At this point, a finance director could point out that if you do commit the latest whizz-bang AI HR platform, you could be stuck on a treadmill where you are committed to expensive software upgrades.
However, most enterprise-level businesses have already bought in to human capital management (HCM) software such Workday, SAP and Oracle, so it’s more a question of switching on AI already embedded in those systems or AI functionality which plugs into these platforms.
Limitations of AI for HR
Appropriate use of AI and automation allows HR professionals to focus on the “human” elements of the job.
However, the more complex the role which needs to be filled, the greater the technology’s limitations.
Artificial intelligence is a powerful technology that can help human resources improve how to recruit, hire, measure, compensate and develop people. But sourcing and hiring will always require very human skills, from building relationships to supporting and managing employees.
Successful adoption of AI enables HR teams to spend more time on the “human” part of human resources — listening to employees and helping them achieve their potential.
More on employment
What is a scale-up visa?
| 2023-03-07T00:00:00 |
2023/03/07
|
https://growthbusiness.co.uk/the-rise-of-the-ai-recruiter-in-hr-15810/
|
[
{
"date": "2023/03/07",
"position": 86,
"query": "artificial intelligence hiring"
}
] |
Workers' responses to the threat of automation
|
Workers’ responses to the threat of automation
|
https://cepr.org
|
[] |
The authors find that fear of automation leads workers to demand higher taxation and more redistribution. Workers plan to join a union to protect their ...
|
Increased automation and the use of robots and artificial intelligence (AI) have the potential to drastically change the nature of work, and whilst new jobs may be created that are complementary to machines and algorithms, future automation also poses a threat to the existing jobs of many workers (Acemoglu and Restrepo 2020). Labour displacement due to automation can have important social and political consequences, as shown by the impact of past technological advancements (Caprettini and Voth 2020). The threat of future automation waves is therefore likely to play a critical role in shaping workers’ response, the public discourse around economic inequality, and the role of the government in mitigating its effects. Despite the importance of understanding the link between the perceived threat of automation and workers’ responses, research on this topic has been held back by the difficulty of identifying exogenous sources of variation in perceived automation risk. To fill this gap in the literature, in a new paper (Golin and Rauh 2022), we provide evidence on the effect of fear of automation on workers’ behaviour, intentions, and preferences from a sample of almost 4,300 workers in the US.
How workers’ fear of automation relates to preferences, attitudes, and intentions
How concerned are workers about the threat that automation poses to their jobs in the near future? To measure workers’ perceived automation threat, we ask respondents to estimate the probability that, in the next ten years, they will lose their job/not find a job due to automation, robots, or AI. Our data show that workers in our sample are on average concerned about automation, with almost 40% of respondents believing their probability of being replaced by a machine, robot, or algorithm to be higher than 50%. Younger respondents and the less educated are amongst the most concerned groups, and fear of automation is highest amongst respondents working in occupations related to food preparation and serving as well as transportation and material moving. Those least in fear are working in protective services or community and social services.
We then proceed to examine the correlational relationship between perceived automation risk and workers’ preferences, attitudes, and employment responses. Workers can insure themselves against the automation threat by demanding more redistribution from the government, or by reskilling and changing occupations. Understanding the link between fear of automation and workers’ preferences and behaviour is important to predict the societal consequences of future automation waves. In our survey, we measure workers’ responses to automation with a series of questions capturing (1) preferences for redistribution; (2) workers’ support for government spending towards adult training programmes and income support programs for the poor; (iii) workers’ employment responses, as measured by their intentions to join or remain part of a union, join a retraining programme, or switch occupations; (iv) populist attitudes; and (v) voting intentions.
We find that perceived automation risk strongly relates to workers’ preferences for redistribution, employment responses, and populist attitudes. In Figure 1 we show, for example, that workers who are more concerned about the automation threat to their job favour higher income tax rates, request higher universal basic income, are more likely to be looking to join a union, are more likely to want to retrain, are more likely to consider themselves anti elite, and are also more likely to abstain from the next presidential elections.
Figure 1 Fear of automation and preferences, intentions, and attitudes
Notes: The x-axis shows the binned perceived probability of losing one’s job due to automation within the next 10 years versus average outcomes indicated in the title and on the y-axis. The sample is restricted to the control group. Thin lines represent 95% confidence intervals.
The causal effect of fear of automation
The above relationships between perceived threat of automation and workers’ preferences, intentions, and attitudes cannot be interpreted causally because the same factors that might be driving automation risks might also be driving opinions. To overcome endogeneity concerns, we use an experiment that we embed in our survey which allows us to affect workers’ beliefs about the threat posed by automation. After eliciting respondents’ perceived fear of losing their job due to automation, participants of our study are randomised into either a control group or one of two treatment groups. While respondents in the control group did not receive any information, treated participants were informed about the average expectation of job automation of other labour force members working in similar jobs as theirs. The figures provided were derived from data collected as part of the Covid Inequality Project (Adams-Prassl et al. 2020), which featured information on the perceived automation risk of a representative sample of members of the labour force in the US.
Fear of automation increases demand for larger welfare state and alters the political landscape
Results from our information experiment show that receiving information on expected automation leads to some significant changes in workers’ preferences and attitudes. However, we do not find any significant effect of fear of automation on intentions to switch occupations or participate in a retraining programme. Economic models looking at optimal taxes assume that workers respond to automation by adopting non-routine skills (Rebelo et al. 2020). However, the only causal effect on employment responses that we find is a much higher likelihood of intention to join a union when exposed to a job loss probability that is higher than one’s initial fear (see Figure 2). This is suggestive of the fact that workers will not respond to future automation waves with re-skilling or switching occupations, but will rather seek job (and task) protection through a union. In terms of the political landscape, automation risk also causally reduces workers’ intentions to turn out to vote at the next presidential elections and shifts their political ideology to the left.
Figure 2 Treatment effect on employment responses
Notes: The figure plots the coefficients of the treatment intensity. The outcomes on the y-axis are workers’ intended employment behaviour. Thick lines indicate the 90% and thin lines the 95% confidence intervals.
Figure 3 shows how respondents’ preferred mean tax rate on income (left) and level of universal basic income (right) change depending on the intensity of the information treatment they received. The green bars represent the treatment effects on workers being informed about good news, i.e. lower job loss probabilities than they feared. The red bars display treatment effects of bad news, i.e. workers being informed about a higher job loss probability than they expected. The fact that the far-right red bars – the treatment effects of receiving very bad news – are largest indicates that workers informed about much higher job loss probabilities request higher taxes on income and higher levels of universal basic income. Overall, we find that for most cases, treatment effects are not symmetric. The treatment effects tend to be driven by respondents who saw an automation probability that was higher than the level of fear they had.
Figure 3 Treatment effects on preferred taxes and UBI by treatment intensity
Notes: The figures plot the sum of the coefficient on the treatment dummy and the dummy capturing the intensity range indicated on the x-axis. The outcomes on the y-axis are the mean tax rate on income and the log of the preferred level of UBI + 1. Thick lines indicate the 90% and thin lines the 95% confidence intervals.
Taken together, the results from our survey experiment raise concerns about the potential for future automation waves to put pressure on public budgets through increased demand for redistribution and a larger welfare state. With democracy already on decline (Frey et al. 2020), we find that automation has the potential to stir anti-elite sentiments as well as reduce voter turnout and trust in politicians. Given the pending waves of automation, in particular in the service sector (Baldwin 2022), policies need to carefully consider how to prepare workers for, and accommodate those left behind by, the changing landscape of work.
References
Acemoglu, D and P Restrepo (2020), “Robots and jobs: Evidence from US labor markets”, Journal of Political Economy 128(6): 2188-2244 (see also “Robots and Jobs: Evidence from the US”, VoxEU.org, 10 April 2017).
Adams-Prassl, A, T Boneva, M Golin, and C Rauh (2020), “The large and unequal impact of COVID-19 on workers”, VoxEU.org, 8 April.
Baldwin, R (2022), "Globotics and macroeconomics: Globalisation and automation of the service sector", National Bureau of Economic Research.
Caprettini, B and H-J Voth (2020), “Rage against the machines: Labor-saving technology and unrest in industrializing England,” American Economic Review: Insights 2(3): 305–20 (see also “Rage against the machines: New technology and violent unrest in industrialising Britain”, VoxEU.org, 9 May 2017).
Frey, C B, G Presidente and C Chen (2020), “Covid-19 and the future of democracy”, VoxEU.org, 20 May.
Golin, M and C Rauh (2022), “The Impact of Fear of Automation”, CEPR Discussion Paper No. 17816.
Rebelo, S, P Teles and J Guerreiro (2020), “Robots should be taxed, for a while”, VoxEU.org, 20 August.
| 2023-03-08T00:00:00 |
https://cepr.org/voxeu/columns/workers-responses-threat-automation
|
[
{
"date": "2023/03/08",
"position": 7,
"query": "automation job displacement"
},
{
"date": "2023/03/08",
"position": 4,
"query": "job automation statistics"
},
{
"date": "2023/03/08",
"position": 27,
"query": "AI regulation employment"
},
{
"date": "2023/03/08",
"position": 11,
"query": "AI labor union"
},
{
"date": "2023/03/08",
"position": 70,
"query": "artificial intelligence workers"
}
] |
|
Claims That AI Productivity Will Save Us Are Neither New, ...
|
Claims That AI Productivity Will Save Us Are Neither New, nor True
|
https://www.cigionline.org
|
[
"Elizabeth M. Renieris"
] |
The AI productivity narrative is a lie. It holds that by automating tasks, AI will make them more efficient and make us, in turn, more productive.
|
As artificial intelligence (AI) captures the public imagination, while also exhibiting missteps and failures, enthusiasts continue to tout future productivity gains as justification for a lenient approach to its governance. For example, Venture fund ARK Invest predicts that “during the next eight years AI software could boost the productivity of the average knowledge worker by nearly 140%, adding approximately $50,000 in value per worker, or $56 trillion globally.” Accenture claims that “AI has the potential to boost labor productivity by up to 40 percent in 2035…enabling people to make more efficient use of their time.” And OpenAI CEO Sam Altman similarly appeals to time savings on menial tasks like email.
But what if promises around AI productivity do not necessarily translate into benefits to society?
Today, many fears around AI focus on its potential to replace human workers — whether teachers, lawyers, doctors, artists or writers. In a 1930 essay, the economist John Maynard Keynes made similar predictions, coining the phrase technological unemployment to refer to “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.” For Keynes, this was proof positive that “mankind is solving its economic problem.” He predicted that his grandchildren would work 15-hour weeks, liberated from economic necessity.
But the recent Global Innovation Index suggests otherwise, raising concerns that “considerable investments in technology, innovation, and entrepreneurship [are] failing to deliver the kind of productivity improvements that improve the lot of people across society.” Indeed, the history of “technological revolutions” paints a different story than the one Keynes anticipated about the benefits of technology-related productivity gains.
Take the example of household appliances in the twentieth century. Sociologist Juliet Schor has examined how so-called labour-saving technologies such as the dishwasher, electric stove and vacuum cleaner failed to reduce women’s household labour. Instead, “rising standards and expectations of domestic life…expanded the hours devoted to cleaning, food preparation, and child rearing.” For example, washing machines and dryers allowed laundry to be done more frequently, “adjusting normative standards of cleanliness to meet efficiencies introduced by these appliances,” Schor notes.
Historian Laine Nooney has chronicled how, despite the personal computer revolution’s promises of efficiency and productivity, people have become chained to their computers to the detriment of the human body. Similar claims were made around how laptops and smartphones would untether us — they haven’t. Indeed, these devices have made it possible to work from anywhere, any time. Rather than this having a liberating effect, we experience “work metastasizing throughout the rest of life,” as Jenny Odell, author of How to Do Nothing: Resisting the Attention Economy, puts it — a phenomenon that was on heightened display for women and working mothers during the pandemic. In fact, these technologies have so drastically eroded boundaries that some jurisdictions are entertaining right-to-disconnect laws.
And now, argues tech writer Paris Marx, “new technologies like AI are framed as offering us various forms of empowerment and liberation: We’ll be able to work more productively, spend less time doing our chores, and anything we want will be a click or tap away. But those promises never paint an accurate picture of how that tech is transforming the world around us or the true cost of those supposed benefits.”
History has shown us that gains in efficiency or productivity as a result of new technologies rarely liberate those already overburdened in society. Instead, new tech often creates new expectations and norms, heightening standards and the amount of work required to attain them. Known as Parkinson’s law, it’s the idea that “work expands so as to fill the time available for its completion.” We have all experienced how meetings scheduled to last an hour will stretch to fill the time allotted.
| 2023-03-08T00:00:00 |
2023/03/08
|
https://www.cigionline.org/articles/claims-that-ai-productivity-will-save-us-are-neither-new-nor-true/
|
[
{
"date": "2023/03/08",
"position": 19,
"query": "AI unemployment rate"
}
] |
The Future of AI: What You Need to Know in 2025
|
The Future of AI: What You Need to Know in 2025
|
https://pg-p.ctme.caltech.edu
|
[] |
It causes unemployment. If an AI robot is working on an assembly line, that's one less human worker doing the job. Now multiply this by thousands, even millions ...
|
Artificial intelligence is one of those concepts that captures the imagination and sometimes, let’s face it, scares people. The idea of computers and machines thinking for themselves is exciting, but it’s an unsettling concept for anyone who’s watched certain science fiction movies or television shows.
But the future of AI technology has more benign potentials, and this article will show it. We will explore the beginnings and evolution of artificial intelligence technologies, recent inventions and innovations, and the pros and cons of artificial intelligence. We will also devote some time focusing on the implications of artificial intelligence and, to a lesser extent, machine learning, in the workplace. Finally, we will show you how you can get in on the future of AI.
Let’s begin our glimpse of the future of artificial intelligence with a backward glance at where it’s been.
The Evolution of Artificial Intelligence
Artificial intelligence involves simulating human intelligence using machines that learn from their experiences and imitate human thinking. AI encompasses a set of technologies that let computers perform various advanced functions, including analyzing data, understanding verbal and written language, and the ability to see, making recommendations, and much more.
The concept of AI has been around for almost a century. However, we can better understand the future of artificial intelligence by tracing its past trajectory. Here’s how artificial intelligence has evolved since those early days.
Also Read: The Double-Edged Sword of AI Deepfakes: Implications and Innovations
The 1950s
British mathematician Alan Turing created the Turing test. If a machine can carry on an indistinguishable human conversation, it is reasonable to consider it a thinking entity.
Also, Claude Shannon, known as the father of information theory, published the article “Programming a Computer for Playing Chess,” which was the first work to develop a chess-playing computer program.
Finally, George Devol invented Unimate, an industrial robot that became the first machine to work on a New Jersey General Motors assembly line. Unimate transported die castings from the assembly line and welded them onto cars, a dangerous job for human workers.
The 1960s
In 1965, computer scientist and professor Joseph Weizenbaum developed ELIZA. ELIZA was an interactive computer program that could hold a functional conversation in English with a person. ELIZA spoke canned lines when specific keywords came up, using programmed scripts.
The 1970s
This decade saw witnessed advancements in robots and automatons. For example, the Wabot Project, which started in 1967, created WABOT -1, considered the first anthropomorphic robot. It was built in Japan at Waseda University, and its features included moveable limbs and the ability to see and speak. It could also measure object distances and directions using external receptors, artificial ears, and eyes.
The 1980s
Corporations worldwide adopted an AI program called Expert Systems, which was adopted by corporations worldwide and became the focus of mainstream AI research. Waseda University built WABOT -2, which could communicate with people and read musical scores, playing music on an electronic organ.
The 1990s
In this decade, a new paradigm called Intelligent Agents became widely accepted. This paradigm perceives its environment and acts to maximize its chances of success. For example, computer scientist Richard Wallace, inspired by Weizenbaum’s ELIZA, developed the A.L.I.C.E (Artificial Linguistic Internet Computer Entity) chatbot. A.L.I.C.E. was different from ELIZA because of the addition of a sample data collection of natural language.
And AI machines weren’t just talking; they were playing chess. Deep Blue, the chess-playing computer developed by IBM in 1997, became the first to win against a reigning world champion.
Incidentally, according to the Terminator movies, Skynet, the artificial intelligence system designed to run America’s defense systems, became self-aware in 1997. Looks like we dodged a bullet there!
The 2000s
The 21st century brought access to more data, considerably faster computers, and advanced machine-learning techniques. Professor Cynthia Breazeal developed Kismet in 2000. Kismet could recognize and imitate emotions with its face, structured like a human face, complete with eyes, lips, eyelids, and even eyebrows.
Meanwhile, Google secretly developed a driverless car in 2009.
The 2010s
Google’s driverless car passed Nevada’s self-driving test in 2014. And by 2016, the AI-related hardware and software market exceeded $8 billion. Additionally, worldwide spending on artificial intelligence systems is estimated to hit $35.8 billion in 2019 and more than double to $79.2 billion in 2022.
It’s already been an exciting road, and it will be intriguing to see how artificial intelligence and the future of humans play out in the next few decades.
Also Read: Performance Modeling: What is an ROC Curve?
Recent AI Inventions and Innovations
Artificial intelligence-based innovations and inventions have exploded over the past few decades, and as future AI technology improves, that rate will keep growing faster. Here’s a quick sampling of more exciting and successful artificial intelligence developments.
2010. ImageNet launched its annual artificial intelligence object recognition competition called the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Meanwhile, Microsoft debuted the Kinect for Xbox 360. Kinect was the first gaming device that could track human body movement through a 3D camera and infrared detection.
2011. Apple released its iOS operating system virtual assistant Siri, which uses a natural-language user interface to observe, answer, infer, and make recommendations things to its user. Siri distinguished itself by adapting voice commands and projecting an individualized user experience.
Apple released its iOS operating system virtual assistant Siri, which uses a natural-language user interface to observe, answer, infer, and make recommendations things to its user. Siri distinguished itself by adapting voice commands and projecting an individualized user experience. 2012. Cats, videos, and artificial intelligence all came together in one glorious moment when two Google researchers trained an extensive 16,000-processor neural network to recognize cat images by showing the network 10 million unlabeled images from YouTube videos.
Cats, videos, and artificial intelligence all came together in one glorious moment when two Google researchers trained an extensive 16,000-processor neural network to recognize cat images by showing the network 10 million unlabeled images from YouTube videos. 2014. Microsoft threw its hat into the virtual assistant ring by releasing Cortana. Not to be outdone or left out, Amazon created Amazon Alexa, a home assistant based on smart speakers that work as personal assistants.
Microsoft threw its hat into the virtual assistant ring by releasing Cortana. Not to be outdone or left out, Amazon created Amazon Alexa, a home assistant based on smart speakers that work as personal assistants. 2015. Google’s DeepMind developed AlphaGo, a computer program that plays the board game Go, and it defeated various human champions.
Google’s DeepMind developed AlphaGo, a computer program that plays the board game Go, and it defeated various human champions. 2016. Hanson Robotics created Sophia, a humanoid robot called the first Robot citizen. Thanks to her ability to see with image recognition, communicate through AI, and make facial expressions, Sophia has raised the bar on human-like robots.
Examples of AI in the Workplace
Artificial intelligence has also made many inroads in today’s workplace. As we consider the future of AI technology, what it will look like, and how it will be applied, here are a few examples of AI in today’s workplace.
Resume screening
Voice assistants on your phone (Alexa, Google Assistant, and Siri)
Customer service call centers
The Google search engine
Anti-collision technology in a company vehicle
Machinery maintenance analysis and scheduling
Tumor location and illness diagnosis
Also Read: Exploring the Artificial Intelligence Career Path
The Pros of AI in the Workplace
The future of AI will rely in part on how well people grasp the benefits of artificial intelligence and apply them to today’s workforce, everything from the boardroom to the factory floor. Here’s a partial list of the benefits of AI in today’s workplace.
It perfectly performs dull, repetitive jobs. Artificial intelligence efficiently automates menial chores and eliminates boring tasks for employees so that they can focus on the more rewarding, creative aspects of their careers.
Artificial intelligence efficiently automates menial chores and eliminates boring tasks for employees so that they can focus on the more rewarding, creative aspects of their careers. It makes unbiased decisions. AI has no emotions to influence its decisions, thereby eliminating bias.
AI has no emotions to influence its decisions, thereby eliminating bias. It aids in the invention process. AI-based technologies make it easier to help researchers develop technologies that overcome current issues.
AI-based technologies make it easier to help researchers develop technologies that overcome current issues. It reduces human error. AI ‘s precision and accuracy significantly reduce mistakes to practically zero, contingent on programmers entering the proper initial algorithms and information.
AI ‘s precision and accuracy significantly reduce mistakes to practically zero, contingent on programmers entering the proper initial algorithms and information. It handles dangerous jobs. Whether in the cold of space, the heat of a volcano, or the crushing pressure of the ocean floor, AI-powered robots can perform critical tasks in environments otherwise harmful to humans.
Whether in the cold of space, the heat of a volcano, or the crushing pressure of the ocean floor, AI-powered robots can perform critical tasks in environments otherwise harmful to humans. It never sleeps, calls in sick, or needs a personal day. AI is available 24×7 and works faster than its flesh and blood counterparts.
AI is available 24×7 and works faster than its flesh and blood counterparts. It can handle user demands without getting burned out or annoyed. Let’s face it; sometimes, customers can be jerks, taking a toll on customer service reps. AI-powered digital assistants, chatbots, and voice bots can handle people’s requests, no matter how rude the requester is.
Let’s face it; sometimes, customers can be jerks, taking a toll on customer service reps. AI-powered digital assistants, chatbots, and voice bots can handle people’s requests, no matter how rude the requester is. It creates more jobs. Although we’ll see the reverse of this in the disadvantages section, AI creates the demand for more machine managers, data detectives, robotics engineers, and programmers.
The Cons of AI in the Workplace
Of course, it’s not all hearts and flowers for artificial intelligence. Like other innovations, AI has its downside. Here’s a glance at AI’s disadvantages.
It is costly. It costs much to mimic human intelligence, including time, resources, and money.
It costs much to mimic human intelligence, including time, resources, and money. It causes unemployment. If an AI robot is working on an assembly line, that’s one less human worker doing the job. Now multiply this by thousands, even millions. People losing their jobs to high technology remains one of the greatest job-related fears.
If an AI robot is working on an assembly line, that’s one less human worker doing the job. Now multiply this by thousands, even millions. People losing their jobs to high technology remains one of the greatest job-related fears. It can cause overdependency. In the Pixar movie Wall-E, future humans lie like slugs on their couches and let machines do all the work. Although that’s clearly taking the idea to ludicrous extremes, it has its roots in a genuine concern: people will come to excessively rely on AI, letting their mental faculties atrophy.
In the Pixar movie Wall-E, future humans lie like slugs on their couches and let machines do all the work. Although that’s clearly taking the idea to ludicrous extremes, it has its roots in a genuine concern: people will come to excessively rely on AI, letting their mental faculties atrophy. It’s not creative. Although artificial intelligence does an excellent job of mimicking human analytical thinking and reasoning, it needs to improve in the act of creation. Currently, AI lacks significant imagination, although perhaps that’s for the best (from the perspective of writers and artists in general!).
Although artificial intelligence does an excellent job of mimicking human analytical thinking and reasoning, it needs to improve in the act of creation. Currently, AI lacks significant imagination, although perhaps that’s for the best (from the perspective of writers and artists in general!). It lacks ethics and emotion. Artificial intelligence lacks empathy, emotion, and ethics. It can’t form teams or communities, encourage cooperation, or consider simple human decency.
Artificial intelligence lacks empathy, emotion, and ethics. It can’t form teams or communities, encourage cooperation, or consider simple human decency. All that computing power is bad for the environment. Increasingly complex computers require increasing amounts of power, which means more power plants generating that energy and spewing pollutants into the environment.
Also Read: What Is a Generative Adversarial Network? Types, How They Work, Pros, and Cons
Five Things to Expect from AI in the Next 10 Years
In the next decade, we will see how the future of AI helps us to work and live. Here are five solid examples of what to expect.
Business administration. If business management primarily involves noticing and properly assigning talent, AI might soon be able to fill that role better.
If business management primarily involves noticing and properly assigning talent, AI might soon be able to fill that role better. Health care. Imagine combining a doctor’s experience and human intuition with the precision of artificial intelligence! The amount of information we’ve generated about the human body has far outstripped the capacity of any single brain to remember, process, and act upon. So, AI could fill the gap.
Imagine combining a doctor’s experience and human intuition with the precision of artificial intelligence! The amount of information we’ve generated about the human body has far outstripped the capacity of any single brain to remember, process, and act upon. So, AI could fill the gap. Financial transactions . Advanced artificial intelligence face recognition algorithms will soon be fast and inexpensive enough to support millions of daily transactions, although there’s much more potential. Machine learning can teach computers to recognize users through increased biometric factors such as voice recognition.
. Advanced artificial intelligence face recognition algorithms will soon be fast and inexpensive enough to support millions of daily transactions, although there’s much more potential. Machine learning can teach computers to recognize users through increased biometric factors such as voice recognition. Investments. Just like cloud computing allows small businesses to compete with big corporations, AI-powered financial and investment advice could give us “regular” folk a chance to become more financially secure like the rich are and do it inexpensively.
Just like cloud computing allows small businesses to compete with big corporations, AI-powered financial and investment advice could give us “regular” folk a chance to become more financially secure like the rich are and do it inexpensively. Linguistics. Real-time machine translation already exists (e.g., Skype and Microsoft), but other researchers from organizations like Google and DARPA want to push the envelope.
Real-time machine translation already exists (e.g., Skype and Microsoft), but other researchers from organizations like Google and DARPA want to push the envelope. Shopping. Artificial intelligence has already revolutionized shopping, but it’s just getting started. Online shopping algorithms are already commonplace, but things could be taken to the next level with AI not only suggesting the perfect product to users but confirm that the item is in stock and, if not, initiating a restock through the supply chain.
Artificial intelligence has already revolutionized shopping, but it’s just getting started. Online shopping algorithms are already commonplace, but things could be taken to the next level with AI not only suggesting the perfect product to users but confirm that the item is in stock and, if not, initiating a restock through the supply chain. Sports. Modern machine learning algorithms can help devise the perfect offensive formation or correct bad habits in individual athletic performances, provided the data is available and collected.
What Industries Will Artificial Intelligence Change?
The future of artificial intelligence is the future of today’s commercial landscape. Here are but a few examples of how artificial intelligence, and certifed AI professionals, will change specific industries.
Cybersecurity. Cybersecurity is a big deal, and AI is already being developed to detect online fraud and protect consumers intelligently. For example, AI can see spending habits and credit activity changes, sending flags indicating potential credit card theft.
Cybersecurity is a big deal, and AI is already being developed to detect online fraud and protect consumers intelligently. For example, AI can see spending habits and credit activity changes, sending flags indicating potential credit card theft. Health care. Doctors can better diagnose illnesses and maybe use AI to create new ways to combat dementia and cancer. Doctors could also use AI to predict future diseases by factoring a patient’s health history with genetic data and devising a preventative care plan.
Doctors can better diagnose illnesses and maybe use AI to create new ways to combat dementia and cancer. Doctors could also use AI to predict future diseases by factoring a patient’s health history with genetic data and devising a preventative care plan. Law enforcement . AI-based facial recognition in security cameras can help police officers track down potential criminals. AI-run robots are already employed as security officers in certain businesses.
. AI-based facial recognition in security cameras can help police officers track down potential criminals. AI-run robots are already employed as security officers in certain businesses. Marketing. AI is already targeting us with customized ads on social media sites, but eventually, AI-powered programs may even be able to create complete ads or articles. A few articles and ad-writing bots already exist, and their developers claim they’re almost as good as human writers (spoiler alert: they’re not yet).
AI is already targeting us with customized ads on social media sites, but eventually, AI-powered programs may even be able to create complete ads or articles. A few articles and ad-writing bots already exist, and their developers claim they’re almost as good as human writers (spoiler alert: they’re not yet). Service industry. AI-powered robots and machines could replace customer service representatives, cashiers, and even cooks. For example, robots are operating french-fry baskets at some fast-food restaurants.
AI-powered robots and machines could replace customer service representatives, cashiers, and even cooks. For example, robots are operating french-fry baskets at some fast-food restaurants. Transportation. We’re already witnessing the self-driving vehicle phenomenon. As technology gets better, we’ll see it more often.
Also Read: What Is Transfer Learning in Machine Learning?
Do You Want to Be a Part of the Future of AI?
The fields of both artificial intelligence and machine learning are expected to greatly impact our future, so if you want to become a part of this exciting, innovative revolution, you can begin by mastering the technology through this highly effective artificial intelligence course. This highly effective and informative AI and ML bootcamp will help you master AI in just a short six months. You will get a high-engagement learning experience and more than 25 hands-on projects to teach you critical, timely skills like:
Computer Vision
Deep Learning
Ensemble Learning
GANs
Neural Networks
NLP
Reinforcement Learning
Speech Recognition
Ziprecruiter.com reports that artificial intelligence engineers working in the United States earn an average annual salary of $156,648.
So, take that critical first step into the future and a better career by signing up for a cutting-edge AI Bootcamp.
You might also like to read:
What is Reinforcement Learning in AI?
What is Sustainable AI? Definition, Significance, and Examples
The Top 10 Natural Language Processing Applications
Explainable AI: Bridging the Gap Between Human Cognition and AI Models
AI in Human Resources: Improving Hiring Processes with Predictive Analytics
| 2024-07-15T00:00:00 |
2024/07/15
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https://pg-p.ctme.caltech.edu/blog/ai-ml/the-future-of-ai-a-comprehensive-guide
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[
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},
{
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Will AI an Robots help or harm society?
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One Minute English Video #1532 Will AI an Robots help or harm society?
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https://www.elllo.org
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[
"Todd Beuckens",
"Elllo Productions"
] |
Also, businesses will start laying off people and unemployment rate will rise. And finally, robots work will never match a human's touch and the creativity ...
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Hide / Show Transcript
Hello, my name is Amar and I'm from Lebanon. My question is do you think that artificial intelligence will be more helpful or harmful in the future?
Well, in my opinion, anything and everything is good or bad depending on how we use it. So for example, in the last few years, it's undeniable how helpful AI has been. It has helped many businesses, many fields like banking, medicine. It has helped reduce human error, it has helped complete repetitive tasks, and a lot of things. It has made people's lives a lot easier. But on the other hand, if we depend a lot on it, people become lazy and dependent. Also, businesses will start laying off people and unemployment rate will rise.
And finally, robots work will never match a human's touch and the creativity of it. So I do believe that in the future, if people become too dependent on robots and AI, then it will definitely be harmful. But it is always good if used in moderation and appropriately.
| 2023-03-08T00:00:00 |
https://www.elllo.org/video/1501/1532-Amar-Robots.htm
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[
{
"date": "2023/03/08",
"position": 81,
"query": "AI unemployment rate"
}
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A “Not-So” Dystopian Future by Jordan Phillips
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A “Not-So” Dystopian Future by Jordan Phillips
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https://pikespeakparley.com
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Systemic racism, or biased algorithms will not be a source to inspire, but a source for artificial intelligence to learn and grow from. Given the rapid rate of ...
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“Hey Siri, how will the implementation of artificial intelligence advance human beings into the next technological revolution?” Hollywood cinema might suggest a horrifying visual of a technology obsessed culture suffering its demise through a robotic uprising. While it makes for an action-packed science-fiction thriller, no handsome hunk protagonist is needed to save the day. Fortunately, the world is not ending tomorrow, and we do not need to concern ourselves with unlikely dystopian predictions of a certain doom. What we need to consider is how to advance as a society, to better the people and the planet through optimizing our processes. The first models of basic computing came to life at the hands of the Bell Company in 1939, a century later simple systems have advanced to complex robotics and quantum computing. At a standard rate of progression, the next step in human and technological evolution is to welcome the advancement and implementation of artificial intelligence.
Artificial intelligence, AI, is a form of algorithmic machine learning divided into four categories of intelligent processing: reactive, limited, theory of mind, and self-aware. Reactive systems perform simple tasks in real time but are unable to store data or build a memory. Modern examples might include text and speech tools, content filters, search engines, or defeating humans in strategic games such as chess. Limited systems use and store data to learn how to complete future tasks. Driving assisted cars like Tesla’s Model S are a prime example of limited memory intelligence as sensors gather data in real-time while onboard computers store speed, distance, and physical object data to safely navigate unpredictable traffic scenarios. Theory of mind introduces predictability based on human emotions which allows for artificial intelligence to better understand how to interact with human beings. Self-aware or point of singularity is where machine learning is equivalent to deep learning or human level intelligence, emotion, and sense of identity. Reactive and limited systems are known as weak AI, where theory of mind and self-aware systems are referred to as strong AI and are in preliminary stages of development as of this writing. Everyday use of artificial intelligence became relevant within the past decade as companies like Google, Apple, Amazon, HP, Tesla, DeepMind, OpenAI, and many others develop convenience software to fill in the blanks, grant access to password protected possessions, or get you from point A to point B safely while napping.
Imagination is what drove humanity to harness fire, create tools, civilizations, governments, technology, and a structured sense of free-will. Having an imagination is a crucial part of the human process that led to the development of society as we know it today, but it can also lead us astray. While Hollywood portrays an entertaining version of a dystopian robotic future, the reality is that we already exist among the machines and fear stems solely from imagination. From mobile devices to advanced monitoring systems, artificial intelligence is a necessary and functioning part of everyday life. Implementation of artificial intelligence provides for strategic and real-time response to emergencies such as wildfires, hurricanes, volcanoes, or other climate related events seen frequently over the past decade. According to Jenifer Strong, podcast host of M.I.T.’s In Machines We Trust, satellite-based monitoring systems using reactive and limited intelligence to inform fire districts of when to evacuate neighborhoods as the first sign of wildfire smoke is detected in the atmosphere. At the same time, limited memory algorithms can determine if a detected wildfire is of benefit to the land while maintaining minimal risk of human casualties or structural loss. Having access to real-time data provided by intelligent monitoring systems is already saving lives by allocating an appropriate emergency response.
Artificial intelligence ranges in use in the modern world from simple everyday tasks to complex medical procedures that are known to change human lives for the better. Advancements in the medical field have provided useful tools for patients and medical professionals alike. Though limited by Amyotrophic Lateral Sclerosis (ALS), Stephen Hawking was one of the greatest minds to ever exist. Hawking worked with a team of computer scientists and doctors to implement reactive artificial intelligence in the form of infrared sensors that were connected to his glasses, and a speech synthesizer developed by Intel and AMD to turn phrases on a computer screen into intentional and meaningful spoken words. Technology advances medicine in ways that improve human longevity thanks to precision machinery and early detection. Where complicated surgical procedures are required, human error can occur. A combination of reactive and limited artificial intelligence is used in operating rooms to perform complex surgical procedures with absolute precision, easing tension on medical staff while increasing the probability of recovery for the patient. Seamless integration of human and artificial intelligence provides for rapid adaptation and solution to complex situations.
Human beings have placed lofty expectations on artificial intelligence as a sense of modern robotic slavery. Artificial intelligence is simply designed for generalized learning, reasoning, and problem solving; not post-apocalyptic tyranny. There is compelling evidence that implementation of artificial intelligence in the real world could be increasingly useful for human beings; however, in his book Superintelligence, Nick Bostrom suggests human beings are still decades away from what is commonly referred to as the point of singularity. Outside of simple reactive and limited artificial intelligence, advanced systems such as theory of mind or self-aware systems are in the initial stages of development operating at a fraction of the capabilities of the human mind. According to Bostrom, artificial intelligence will not become human-level intelligence until 2093 at the current rate of progression. As of now there is no clear blueprint or path to strong artificial intelligence. So, what other than imagination drives human fear of implementing artificial intelligence in the modern world?
With strong AI considered a thing of the future, concerns of artificial intelligence implementation in the modern world can be as simple as reactive systems themselves. When easy tasks are fulfilled by machines operating on basic lines of code, the need for human labor and payroll becomes obsolete. Assembly lines are no longer limited by an overwhelmed Lucille Ball chowing down pounds of chocolate as her coded counterpart of the surgical program mentioned earlier places manufactured products precisely where they need to be in a consistent and timely manner. Grocery stores were once a cultural congregation of the community; now they are a rushed and robotic world of sensors and advanced monitoring systems. Human cashiers and store clerks have been replaced by self-checkout stands and automated inventory tracking systems that command self-guided shelf stocking robots. Worst of all is dock workers dwelling from the hundreds of thousands to hundreds as the SARS CoV-2 pandemic sent most of the world into a lengthy quarantine. Containers now move around our ports thanks to self-propelled modular transport vehicles operating on the same technology as self-driving cars.
Maybe the concern is not exactly that of a dystopian future but that of record unemployment rates collapsing the economy. If a simple machine can consume our careers, then what is left when technology advances to deep learning with strong artificial intelligence? Doctors, scientists, engineers, skilled, and unskilled laborers alike face the same financial fate when one machine can manage an assembly line with surgical accuracy or perform complex surgeries with assembly line consistency. If not unemployment, most certainly the greatest concern is that of biased artificial intelligence. Afterall, artificial intelligence learns from all knowledge available to humanity. Given the nature of systemic racism, artificial intelligence is sure to favor white males over people of color, LGBTQIA2S+, or women. Flynn Coleman, author of A Human Algorithm provides a daunting reminder that the tech industry is still dominated by white males, with a 20% disparity and only 9% of leadership positions being filled by women. To create better and stronger artificial intelligence, humanity must do and be better to each other as we lead by example.
Artificial intelligence has become a buzz word in the tech industry as multiple startups develop various models of reactive, limited, theory of mind, and self-aware artificial intelligence. Though simple systems have been helpful to humanity, not one model has broken the point of singularity. In a recent interview with Greylock partner Reid Hoffman, OpenAI CEO Sam Altman states that early artificial intelligence will be great for optimizing what has already been done but will not add to the “sum total of human scientific knowledge” anytime soon. We have only begun to scratch the surface of human and artificial intelligence integration, but we do so with caution every step of the way. Systemic racism, or biased algorithms will not be a source to inspire, but a source for artificial intelligence to learn and grow from. Given the rapid rate of progression in technology already happening in the modern world, implementation of artificial intelligence will advance humanity into the next technological revolution equally.
Works Cited
Bostrom, Nick. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2016.
Coleman, Flynn. A Human Algorithm: How Artificial Intelligence Is Redefining Who We Are. Counterpoint, 2020.
Greylockpartners, director. OpenAi Sam Altman: AI For The Next Era. YouTube, GreyLock, 21 Sept. 2022, https://www.youtube.com/watch?v=WHoWGNQRXb0. Accessed 7 Dec. 2022.
Medeiros, Joao. “How Intel Gave Stephen Hawking a Voice.” Wired, Conde Nast, 13 Jan. 2015, https://www.wired.com/2015/01/intel-gave-stephen-hawking-voice/.
*Other information comes from personal experience within the field of AI and presented as common knowledge.
| 2023-03-08T00:00:00 |
2023/03/08
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https://pikespeakparley.com/a-not-so-dystopian-future-by-jordan-phillips/
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[
{
"date": "2023/03/08",
"position": 85,
"query": "AI unemployment rate"
}
] |
Employment Rate Rising for People with Disabilities
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Employment Rate Rising for People with Disabilities
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https://www.shrm.org
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The unemployment rate for people with a disability ages 16-64 fell from 10.8 ... Here's how employers and employees can successfully manage generative AI and ...
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Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2023-03-08T00:00:00 |
https://www.shrm.org/topics-tools/news/inclusion-diversity/employment-rate-rising-people-disabilities
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[
{
"date": "2023/03/08",
"position": 88,
"query": "AI unemployment rate"
}
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U.S. Workplace Deaths
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U.S. Workplace Deaths — APM Research Lab
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https://www.apmresearchlab.org
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Workplace deaths also resurged in 2021 (the last full year the Bureau of Labor Statistics has recorded). In 2021, 5,190 U.S. workers died while on the job, a ...
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However, the workplace death rate is not equal across the major racial or ethnic groups tracked by the BLS. Since at least 2012, Latino workers have been the racial or ethnic group most likely to die on the job. In the early and mid 2010s, Latinos’ workplace death rate fluctuated between 3.7 and 4.0 workplace deaths per 100,000 full-time equivalent (FTE) employees. In 2019, the Latino workplace death rate increased even more, rising to 4.2 deaths per 100,000 FTEs, and then rose again to 4.5 deaths per 100,000 in 2020 before plateauing in 2021.
In 2012, Black and white workers were equally as likely to die at work, with both groups having a death rate of 3.5 deaths per 100,000 FTE employees. Between 2013 and 2017, the workplace death rate among Black workers was somewhat lower than that of white workers—but that gap closed in 2018. In 2021, Black workers experienced a significant spike in workplace deaths, increasing from 3.5 to 4 deaths per 100,000 between 2020 and 2021, the highest death rate among Black workers in the past decade.
In contrast, since 2016, white workers’ workplace death rate has decreased somewhat. From a recent high of 3.7 deaths per 100,000 FTEs in 2016, the workplace death rate among white workers fell to a ten year low in 2020, with 3.3 white workers dying per 100,000 FTE employees. This trajectory has reversed slightly, however, in 2021.
Over the 10 years included in this analysis, Asian workers experienced the lowest rate of workplace deaths of any racial or ethnic groups tracked in BLS data. During this period, Asian workers’ workplace death rate typically fluctuated between 1.6 and 1.9 deaths per 100,000 FTE workers, with a low of 1.4 workplace deaths per 100,000 in 2015 and a high of 2 deaths per 100,000 in 2012.
Recent changes in occupational fatalities by race have coincided with changes in the rates of workplace death by age. In general, older workers have higher workplace mortality rates than younger workers—but the rates among oldest workers have fallen in recent years. Younger workers tend to die at lower rates—but occupational death rates have risen among teens in recent years.
| 2023-03-08T00:00:00 |
https://www.apmresearchlab.org/workplace-deaths
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[
{
"date": "2023/03/08",
"position": 94,
"query": "job automation statistics"
}
] |
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How to Train Your Staff on AI Tools: A Step-by-Step Guide
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How to Train Your Staff on AI Tools: A Step-by-Step Guide
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https://profiletree.com
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Conducting a Skills Gap Analysis. Step 1: Gather existing data – Review performance metrics and outcomes to determine current capabilities within your team.
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How to Train Your Staff on AI Tools and Technologies: A Step-by-Step Guide Updated on: Updated by:
In today’s rapidly evolving digital landscape, the adoption of artificial intelligence (AI) and machine learning technologies is not just an advantage but a necessity for businesses seeking to stay competitive. For small and medium-sized enterprises (SMEs), empowering staff with the knowledge to wield these powerful tools can be a game-changer. It’s crucial to understand the basics of AI and how it can drive efficiencies, foster innovative solutions, and, ultimately, boost your bottom line. By embracing AI, we can transform our workforce into a modern ensemble that is adept at navigating the complexities and harnessing the capabilities of these technologies.
Identifying existing skills gaps and designing bespoke AI training strategies is essential. This involves choosing the right mix of AI training tools, engaging employees effectively, and incorporating practical applications to cement their learning. Furthermore, it’s imperative to measure the effectiveness of the training provided and ensure ethical considerations are at the core of our AI initiatives. As we support more advanced AI training, we pave the way for enhanced collaborative learning that can propel our staff to new heights of technical proficiency and innovation.
Understanding the Basics of AI
Before we discuss how to train your staff on AI tools and technologies, it’s crucial to have a solid grasp of AI fundamentals. This foundational knowledge is key to leveraging AI’s full potential in your business.
Defining AI and Its Importance
Artificial Intelligence (AI) encompasses a suite of technologies that allow machines to sense, comprehend, act, and learn with human-like levels of intelligence. Perhaps the most common type of AI that businesses are currently utilising is machine learning, where computers develop skills by processing large amounts of data. AI is transforming industries by enabling enhanced decision-making, increasing efficiency, and creating new products and services that were previously unthinkable.
AI Technologies Overview
Within the realm of AI, various technologies are at play. Generative Artificial Intelligence can create content, such as text and images, that can pass for human-made. Natural Language Processing (NLP) allows computers to understand and respond to human language in a meaningful way. Both of these are built on algorithms and data-driven approaches that underpin machine learning. It is the depth of these technologies that provide the capabilities for AI to outperform human benchmarks in numerous tasks.
Identifying the Skills Gap
In the fast-evolving landscape of artificial intelligence, ensuring that your staff is proficient with AI tools is crucial. Identifying where skills are lacking is the first step towards building a workforce capable of leveraging AI effectively.
Conducting a Skills Gap Analysis
Step 1: Gather existing data – Review performance metrics and outcomes to determine current capabilities within your team.
Step 2: Engage with your team – Consult with staff to understand their self-assessed AI competencies and apprehensions.
Step 3: Benchmark against industry standards – Compare your team’s skills with those required for state-of-the-art AI utilisation in your field.
Step 4: Identify the gaps – Plot the differences between current competencies and industry benchmarks to spotlight precise areas for improvement.
Step 5: Develop a training plan – With the insights gained, formulate a tailored training programme to bridge the identified skills gap.
By systematically assessing your team’s stand and what skills they need to acquire, you can streamline AI adoption in your operations.
Employing AI tools and skills gap templates can significantly simplify the analysis process. Templates provide a structured framework to:
Categorise skills relevant to AI technology, ranging from basic literacy to advanced programming abilities.
relevant to AI technology, ranging from basic literacy to advanced programming abilities. Rate proficiency levels among your staff to visualise the variance between existing and required expertise.
By using these templates, you ensure consistency in your evaluation process and gain a clearer vision for future training requirements. Navigating the Tech-Driven Future: How to Train Employees on Generative AI Tools discusses the importance of focusing on common skill gaps and providing targeted training.
Regularly revisiting the skills gap analysis and updating the templates will help you to keep pace with the dynamic field of AI.
Creating a comprehensive AI training strategy is vital for upskilling staff and ensuring they’re proficient in the latest tools and technologies. The goal is to foster a well-trained workforce equipped to handle AI-driven projects with confidence.
Setting Training Objectives
To ensure the effectiveness of AI training, we first need to establish clear objectives. What exactly do we want our employees to achieve through this training? Do they need to be proficient in specific AI software like TensorFlow, or should they understand broader concepts like machine learning algorithms? For companies like TalentLMS, customisable training objectives are critical to address the unique skills gap in various teams. Consider creating SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals to guide the training process and measure its success.
Curating a Training Curriculum
Once our objectives are set, it’s time to curate a training curriculum tailored to meet these goals. This involves selecting the appropriate course material; platforms like Udemy can provide a plethora of courses suited for varying degrees of proficiency and specialisation. Our curriculum should:
Include a range of resources from in-depth tutorials to hands-on workshops. Be designed to progress from fundamental concepts to advanced applications. Accommodate different learning styles with a blend of visual, auditory, and kinesthetic methods.
By ensuring that our training strategy is targeted and comprehensive, we can effectively prepare our workforce to utilise AI tools and technologies.
In our quest to harness the power of AI tools and technologies effectively, selecting the right training tools is a pivotal step. The right tools can make a significant impact on how well our staff can utilise AI to its full potential.
Comparing Training Platforms
When evaluating training platforms, we need to scrutinise their features critically. Platforms differ widely in interactivity, content quality, and relevance to current trends. For instance, a platform like TalentLMS offers a comprehensive environment that blends traditional e-learning with the latest in AI training modules. Here, we lay out a table comparing key features of select training platforms:
Feature TalentLMS Platform B Platform C Interactive Workshops Yes Yes No AI Tool Integration Yes No Yes Real-Time Collaboration No Yes Yes Custom Learning Pathways Yes No Yes Analytics and Reporting Yes Yes No
We should look for platforms that provide analytic tools to measure our staff’s progress and custom learning pathways to tailor the experience to their individual needs.
Transitioning to the actual use of generative AI tools, like ChatGPT, warrants attention to how these tools align with our staff’s roles and responsibilities. Our training should cover specific scenarios where the use of generative AI can enhance productivity and creativity. We aim to empower our staff with the skills to operate these tools for tasks ranging from content creation to complex problem-solving.
To cement this knowledge, we organise interactive workshops that allow staff members to get hands-on experience with generative AI applications. The goal is to enable them to master the software through practical use scenarios that mirror their day-to-day work challenges.
Understanding that practical implementation is the crux of any training; our AI training regimen provides our staff with the structured, hands-on opportunities they need to become proficient with these state-of-the-art tools.
Engaging Employees in AI Learning
To ensure your staff stays ahead in the ever-evolving landscape of AI tools and technologies, it’s crucial to foster a work environment that emphasises continuous learning and leverages engaging methods like gamification. These avenues not only enhance skills but also keep your team motivated and invested in their development journey.
Promoting a Culture of Continuous Learning
Continuous learning is the backbone of innovation, especially when it comes to artificial intelligence. We encourage our staff always to be learning by providing access to the latest AI courses and technologies. It’s not just about occasional training sessions but a constant cycle of education, application, and reflection. Regularly scheduled catch-ups and progress evaluations ensure our employees stay on track and deeply engaged with their learning paths.
We’ve seen how fostering this culture leads to heightened employee engagement. As Stephen McClelland, ProfileTree’s Digital Strategist, observes, “When learning is embedded into our daily routine, the uptake of new AI tools becomes a habit rather than a hurdle.”
Gamification and Interactive Elements
Gamification transforms the learning process into an interactive experience, injecting fun and competition into AI technology training. We’ve seen this approach lead to higher levels of engagement, where employees actively look forward to training sessions. Here are some tactics we use:
Leaderboards: These display progress and encourage a healthy level of competition among peers. Reward Systems: Points, badges, and certificates serve as tangible achievements that workers can earn and showcase. Interactive Workshops: These allow team members to experiment with new tools in real time, reinforcing their learning through direct application.
By embracing these elements, we find that even the most complex AI concepts become more digestible, and the hands-on experience drastically improves skill retention.
Incorporating Practical Applications
Training staff on AI tools and technologies can be complex, but by incorporating practical applications, we make the learning process immersive and relevant. We focus on engaging staff through real-world examples and hands-on simulations that prompt active decision-making and familiarise them with a variety of AI solutions.
Using Real-World Examples
We believe in the power of real-world examples to illustrate the potential of AI technologies in a workplace setting. By demonstrating how AI solutions have been successfully implemented in businesses similar to ours, we provide a concrete understanding of their impact on efficiency and growth. For instance, a case study may show how a company’s customer service was transformed by an AI-driven chatbot, leading to improved customer satisfaction.
Case Study: AI Chatbot Implementation Objective : Improve customer satisfaction Action : Implement an AI-powered chatbot Result : Enhanced customer service efficiency and satisfaction
By analysing these cases, our staff can see the tangible benefits of AI and feel motivated to explore its applications within their own areas of responsibility.
Hands-On Simulations and Exercises
Simulations and exercises are crucial in our training because they provide a safe environment where our staff can learn through trial and error. We use a variety of decision-making scenarios to simulate real business challenges, allowing staff to test different approaches without real-world consequences. For example, a simulation might involve choosing the best AI solution for data analysis and seeing the immediate impact of that decision on simulated company performance.
Simulation Exercise: Data Analysis Tools Goal : Select the most effective AI tool for data analysis Process : Trial multiple AI software options Outcome : Understand AI tools’ impact on performance metrics
This hands-on approach ensures that when our staff encounter AI technology in their day-to-day work, they are confident and competent in utilising it to its full potential.
Staff Augmentation: A Flexible Solution for Your Workforce Needs
In today’s rapidly evolving business landscape, having the right talent at the right time is crucial for success. However, finding and hiring skilled professionals can be a time-consuming and resource-intensive process. This is where staff augmentation services come into play.
Staff augmentation involves partnering with a specialized agency to supplement your existing workforce with skilled professionals on a temporary or project-based basis. These services offer a flexible and cost-effective solution for businesses facing various challenges, such as:
Skill Gaps: Bridging gaps in your team’s expertise or experience.
Bridging gaps in your team’s expertise or experience. Peak Workloads: Handling temporary surges in demand or specific projects.
Handling temporary surges in demand or specific projects. Scalability: Adapting your workforce to changing business needs.
Adapting your workforce to changing business needs. Cost-Efficiency: Avoiding the overhead costs associated with hiring full-time employees.
By leveraging staff augmentation services, you can access a pool of qualified talent without the commitment of long-term employment. This allows you to scale your workforce up or down as needed, ensuring optimal resource allocation and minimising costs.
Measuring Training Effectiveness
As we implement new training initiatives on AI tools and technologies, it is crucial for us to measure their effectiveness. This ensures that not only the knowledge is acquired but also appropriately applied, ultimately reflecting improved performance and return on investment (ROI).
Assessing Employee Progress
1. Knowledge uptake: To gauge if our staff are assimilating the knowledge imparted through AI training, we shall conduct regular assessments. These could range from quizzes to practical tests that reflect their understanding of the AI tools.
2. Skills application: Beyond theoretical knowledge, we need to observe how our employees apply what they’ve learned in real-world scenarios. Role plays, simulations or project assignments serve as excellent indicators of practical application.
3. Feedback mechanisms: Feedback is invaluable. We will gather data continuously from the participants throughout the training process using surveys and one-on-one conversations to fine-tune our approach and promptly address any gaps.
Evaluating AI Training ROI
Calculating ROI: The return on investment for AI training is not just a matter of financials; it’s about the value added to our business operations. To measure this, we shall look at several KPIs, including increased productivity rates, error reduction, and time saved due to automation.
Consider efficiency and costs: Calculating ROI requires us to consider both the cost-efficiency of training initiatives and the tangible business outcomes. By comparing these numbers before and after training, we can evaluate the financial impact of our AI training programs.
Ciaran Connolly, founder of ProfileTree, emphasises, “Effective training in AI tools isn’t just about upskilling. It’s a strategic move that reflects in every facet of business growth, from workflow efficiency to innovative product development.
In summary, carefully assessing employee progress and rigorously evaluating the ROI are pivotal steps in understanding the impact of our AI training programs. These methods not only highlight the success of current initiatives but also pave the way for continuous improvement in our training strategies.
Addressing Ethical Considerations
As we introduce AI tools and technologies to our staff, it’s imperative that we uphold ethical standards, mitigate biases, ensure data governance and privacy, and remain compliant with legal frameworks. These factors are the cornerstone to maintaining the trust of our workforce and the wider community.
AI Bias and Discrimination
Why It Matters: AI systems can unintentionally perpetuate bias and discrimination if they’re not carefully designed and monitored.
Steps for Mitigation:
Training: We must educate our teams on the nuances of AI bias. This means understanding how data can reflect existing prejudices and teaching methods to detect and correct for bias. Diverse Data Sets: Utilising a broad range of data can help prevent AI systems from developing skewed perspectives.
ProfileTree’s Insight: Ciaran Connolly, founder of ProfileTree, notes, “By prioritising diversity in our data and continuous learning in our teams, we turn AI from just a tool into a beacon of ethical practice.”
Data Governance and Privacy
Why It Matters: Protecting data privacy is not just about compliance; it’s about respecting the individual rights of our staff and customers.
Steps for Assurance:
Policies: Implement robust data governance policies that align with regulations like GDPR. Education: Staff need regular training on data security best practices, reaffirming the importance of data privacy.
Our commitment to these ethical considerations sets the foundation for a trustworthy and secure AI-enabled work environment.
Supporting Advanced AI Training
Advanced AI training goes beyond basic tutorials, fostering an environment where employees can develop expertise in AI tools and technologies. Through targeted learning and development programmes, we support our staff in mastering AI-driven systems.
Mentorship and Coaching
We believe in the power of mentorship as a key component of advanced AI training. Each of our employees is paired with a mentor who has a deep understanding of AI applications. These mentors offer one-on-one guidance, ensuring learners can apply AI concepts in their daily tasks effectively. Through regular coaching sessions, team members receive support in troubleshooting complex issues and are encouraged to explore innovative uses of AI within their roles.
Reskilling for Advanced AI Capabilities
Reskilling is essential for keeping pace with technological advancements. We provide comprehensive reskilling programmes that equip our employees with the required skills to handle advanced AI tools. These programmes encompass a range of learning modules, from understanding machine learning algorithms to applying AI for data analysis. Our reskilling efforts concentrate on both theoretical understanding and practical applications, making sure our employees stay ahead of the AI curve.
Identify skill gaps: We assess our employees’ current AI skills to tailor the reskilling programmes effectively. Flexible learning paths: We offer online courses, workshops, and hands-on projects to suit diverse learning preferences. Real-world application: Employees are given opportunities to work on actual AI projects, translating learning into tangible outcomes.
By fostering mentorship and investing in reskilling, we lay a robust foundation for advanced AI training within our organisation. Our learning and development initiatives are designed to support continuous growth and expertise in AI technologies.
Enhancing Collaborative AI Learning
In the fast-evolving landscape of artificial intelligence (AI), prioritising collaborative learning among employees is pivotal for cultivating an AI-literate team adept at leveraging AI tools for enhanced engagement and teamwork.
Facilitating Peer-to-Peer Learning
Through peer-to-peer learning, employees can share diverse insights and approaches to working with AI. We recommend establishing learning groups or partnerships wherein team members can exchange knowledge and practical experiences with AI applications. This cross-pollination of ideas not only fosters a supportive learning environment but also strengthens interpersonal skills pivotal to effective collaboration.
Organise AI learning sessions : Schedule regular, informal meetups for staff members to discuss AI topics or challenges they’ve encountered.
: Schedule regular, informal meetups for staff members to discuss AI topics or challenges they’ve encountered. Create a shared knowledge base: Encourage teams to document useful AI insights and tools in a central repository that everyone can contribute to and access.
Integrating AI in Team Projects
Weaving AI into actual team projects can lead to substantive increases in both AI literacy and teamwork. For example, by utilising AI for data analysis, teams can more efficiently identify market trends and insights, paving the way for more informed decision-making.
Assign AI-enhanced tasks : Incorporate AI tools within projects, e.g., using AI-aided design software for marketing materials.
: Incorporate AI tools within projects, e.g., using AI-aided design software for marketing materials. Reflect on outcomes: Ensure teams review and discuss the effectiveness of the AI tools post-project, understanding their impact on the team’s collaboration and project outcome.
“Embedding AI into the fabric of our teamwork allows us to achieve a symmetry between human creativity and machine efficiency,” shares Ciaran Connolly, founder of ProfileTree. This fusion propels businesses towards innovation and growth in the digital age.”
Frequently Asked Questions
As leaders in digital marketing and AI training, we understand the importance of equipping your team with the right skills and knowledge to harness the power of artificial intelligence. Here, we address the key concerns and queries you might have about training your staff on AI tools and technologies.
What strategies should companies use to upskill their employees in artificial intelligence?
We suggest identifying common skill gaps within your team and providing targeted training in these areas. It’s also vital to choose a training format that fits the subject, like interactive workshops for hands-on AI tools.
What are the core competencies necessary for a workforce to effectively utilise AI technologies?
Your employees need a combination of technical skills and effective interpersonal skills. The ability to understand and manage AI tools, along with communicating effectively and fostering teamwork, are critical for maximising the potential of AI in your company.
How can businesses ensure continuous learning and development for staff in the field of AI?
To ensure continual growth, businesses should develop a learning culture that embraces AI advancements. Regular updates to training programmes and encouraging self-directed learning are effective ways to maintain a culture of continuous development.
What roles do training modules and workshops play in equipping employees with AI skills?
Training modules and workshops are instrumental in providing hands-on experience and practical knowledge. They offer an interactive environment where employees can familiarise themselves with AI technologies and apply new skills in real-world scenarios.
What measures can be taken to assess and track the progress of staff training in AI?
Implementing assessments and setting clear benchmarks are crucial. Track your team’s progress through regular evaluations and feedback sessions, and ensure that training outcomes are meeting the business’s strategic objectives.
How can an organisation foster a culture of innovation to adapt to AI advancements?
We believe that encouraging a mindset of continual learning and curiosity is essential for fostering innovation. Organisations can create a supportive atmosphere by recognising and rewarding innovative ideas and approaches in incorporating AI into business processes.
As ProfileTree’s Digital Strategist Stephen McClelland says, “The adoption of AI is not just about technology; it’s about empowering people to think differently and innovate beyond traditional boundaries.”
By addressing these frequently asked questions, we aim to offer a robust foundation for businesses looking to integrate AI training into their employee development programmes, ensuring your team is well-equipped to meet the challenges of a tech-driven future.
| 2024-04-20T00:00:00 |
2024/04/20
|
https://profiletree.com/how-to-train-your-staff-on-ai-tools/
|
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"query": "AI skills gap"
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"date": "2023/03/08",
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"query": "machine learning workforce"
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How should leaders address the gen Z skills gap?
|
How should leaders address the gen Z skills gap?
|
https://www.raconteur.net
|
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According to research by the job board CWJobs, 72% of IT leaders think the next generation of workers will be the answer to the UK's digital skills gap; however ...
|
A bevy of recent research indicates that members of gen Z don’t feel prepared for the demands of their working lives, with a particular focus on digital literacy issues.
A 2022 report by Salesforce found that just 32% of gen Z adults feel very equipped with the resources to learn workplace digital skills. And when young employees do experience a digital issue, they’re likely to feel embarrassed. A study by HP found that 20% of gen Z feel ‘tech shame’ when experiencing a digital issue, compared to just 4% of workers over 40.
Misconceptions about the skills gap
“I think managers tend to assume young employees have better digital skills because they are digital natives,” says Severine Hierso, director for product marketing at RingCentral. However, growing up in the digital age does not necessarily equate to having the right tech skills for the workplace. According to research by the job board CWJobs, 72% of IT leaders think the next generation of workers will be the answer to the UK’s digital skills gap; however, only 24% of those aged 16 to 24 feel their age is an advantage in landing a tech role.
I think managers tend to assume young employees have better digital skills because they are digital natives
This preconceived idea of the digital literacy of younger workers may be the source of the ‘shame’ they feel. However, the lack of skills can be attributed to multiple generations, says Dave Prezzano, managing director of UK & Ireland at HP.
“The digital skills gap affects people of all ages, so it’s not something young people should feel uniquely responsible for,” he says. “Shame is the exact opposite of what I want our people to feel if they currently lack a given ability in any area.
“If these issues were allowed to fester, I would expect the ramifications to include an ongoing debilitation of staff confidence and willingness to experiment – which are two vitally important qualities that management has a responsibility to cultivate.”
This knock in confidence could compound gen Z’s perception of their soft skills. A study by The Workforce Institute found that young people in the US doubt their ability to negotiate, speak confidently in front of an audience and network effectively.
Understanding the unique challenges for gen Z
Prezzano stresses that managers must be empathetic towards their younger workers, who are missing out on valuable face-to-face interactions and casual learning due to the widespread adoption of hybrid working. Research by Gartner found that 46% of gen Z felt the Covid-19 pandemic made the pursuit of their educational and career goals more difficult.
“Imagine if you were new to the workforce when the pandemic hit, or have only ever worked in a hybrid environment”, he says. “There is a huge amount of workplace culture, in-person coaching and face-to-face time you will have missed out on.”
So, how can companies get their new recruits up to speed? Kimberly Maucher-Lynch, head of talent acquisition sales for EMEA at Workday, says aligning competencies with the business’s core vision is crucial to engaging gen Z.
“They want to tie what it is that they do for money and career to something which has a broader meaning for society,” says Maucher-Lynch. “This is a huge opportunity for companies to have that kind of dialogue with them. So it’s not just about the concrete skills and competencies, but how do these skills and competencies fit into the broader strategy of the company and how are they part of that?”
Providing suitable tools for the new workplace
Practically, businesses also have to engineer a method of replicating learning processes in a digital world. At RingCentral, a large portion of the workforce is working hybrid, so finding a digital substitute for in-person learning is of the utmost importance.
Using their own proprietary app, workers are able to message and video call each other, allowing for quick and informal communication should issues arise. Having one interface for both asynchronous and immediate feedback helps younger workers access their managers’ expertise and mimics apps they may use in their personal life.
“Simplicity is very important now in this complex work environment,” says Hierso. She explains that using one interface where everyone can communicate helps build culture, and says that younger employees find it particularly helpful as it’s the kind of technology they are already comfortable with.
A commitment to learning
Technology also has a role to play in the acquisition of technical skills. For example, Workday has a machine-learning platform which enables its clients to discover the most desirable skills for its open positions, and this is also used to help Workday’s own staff pinpoint the capabilities most applicable to their future. There is also a scheme to help employees pick up new experiences by joining short projects within Workday or its clients.
The most important thing is to stay agile, to be open to the fact that what we do today is not what we’re going to be doing tomorrow
Maucher-Lynch says this underpins an organisation-wide commitment to learning that younger workers must adopt to thrive in the future of work. She cites a 2017 report by the Federation of Young Australians, that posits the theory young people entering the workplace are more likely to have 17 jobs in five different industries than to have a linear career trajectory.
“The skills are going to change. We’re going to continue to see a change in the kinds of skills which are needed for the future,” says Maucher-Lynch. “In my mind, the most important thing is to stay agile, to be open to the fact that what we do today is not what we’re going to be doing tomorrow.”
Finding an inclusive approach
A commitment to learning, and showcasing this in an accessible way, is important at HP. Again citing gen Z’s desire to work in more equitable workplaces, Prezzano says building an inclusive space where employees of all ages and backgrounds can upskill is vital to success.
“We have designed comprehensive training programmes and built learning platforms that are equally accessible for people at all levels,” he says. “Everyone is empowered to save and protect time for digital literacy and upskilling. This includes mentorship for new starters, graduates and interns.”
While the working world, and the processes of training, have changed in the past few years to suit the expectations of gen Z, they will still have to learn how to get along with certain rhythms of the workplace. Hierso notes that understanding etiquette and knowing how and when to use different methods of communication are eternal workplace skills that will be important in any working environment. “Workplaces have become way more flexible in the last few years,” she says. “But there are still some norms to adopt and understand.”
With the right delivery of training, a commitment to learning and support from their managers, there should be no barrier to the next generation of workers acquiring both the technical and soft skills they’ll need to thrive in their future careers.
Find out more about how RingCentral supports hybrid working
| 2023-03-08T00:00:00 |
2023/03/08
|
https://www.raconteur.net/future-of-work/how-should-leaders-address-the-gen-z-skills-gap
|
[
{
"date": "2023/03/08",
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An Overview of Global AI Regulation and What's Next
|
An Overview of Global AI Regulation and What's Next
|
https://www.progressivepolicy.org
|
[
"The Progressive Policy Institute"
] |
New York City introduced one of the first AI laws in the U.S., effective from January 2023, which aims to prevent AI bias in the employment process. Colorado ...
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Artificial intelligence (AI) is the new subject of large-scale regulation by governments around the world. While AI has many benefits, such as increased productivity and cost savings, it also presents some risks and challenges. For example, AI systems can sometimes be biased or discriminatory, leading to unfair outcomes. They can also raise concerns about privacy and data security, as these systems often rely on large amounts of personal data.
As a result, governments around the world are starting to introduce regulations to ensure that AI is developed and used in a safe, responsible, and ethical manner. These regulations cover a range of issues, from data privacy and security to algorithmic transparency and accountability.
This piece will unpack the novel AI regulation in the U.S., EU, Canada, and China and how each country approaches the technology as they seek to balance economic, social, and public priorities with innovation.
European Union: Artificial Intelligence Act (AIA)
The European Union introduced the Artificial Intelligence Act (AIA) on April 21, 2021. The current text proposes a risk-based approach to guide the use of AI in both the private and public sectors. The approach defines three risk categories: unacceptable risk applications, high-risk applications, and applications not explicitly banned. The regulation prohibits the use of AI in critical services that could threaten livelihoods or encourage destructive behavior but allows the technology to be used in other sensitive sectors, such as health, with maximum safety and efficacy checks by regulators. The legislation is still under review in the European Parliament.
The AI Act is a type of legislation that regulates all automated technology rather than specific areas of concern. It defines AI systems to include a wide range of automated decision-makers, such as algorithms, machine learning tools, and logic tools, even though some of these technologies are not considered AI.
Canada: The Artificial Intelligence and Data Act (AIDA)
In June of 2022, Canadian Parliament introduced a draft regulatory framework for Artificial Intelligence using a modified risk-based approach. The bill has three pillars, but this piece will just examine the section dealing with AI, the Artificial Intelligence and Data Act (AIDA). The goal of Canada’s AI rules are to standardize private companies’ design and development of AI across the provinces and territories.
The modified risk-based approach is different from the EU’s approach as it does not ban the use of automated decision-making tools, even in critical areas. Instead, under the AIDA regulation, developers must create a mitigation plan to reduce risks and increase transparency when using AI in high-risk systems. The plan should ensure that the tools do not violate anti-discrimination laws. These mitigation plans or impact assessments aim to decrease risk and increase transparency in the use of AI in social, business, and political systems.
United States: AI Bill of Rights and State Initiatives
The United States has yet to pass federal legislation governing AI applications. Instead, the Biden Administration and the National Institute of Standards and Technology (NIST) have published broad AI guidance for the safe use of AI. In addition, state and city governments are pursuing their own regulations and task forces for AI use. In a break from the EU model, regulation thus far targets specific use cases rather than seeking to regulate AI technology as a whole.
At the federal level, the Biden Administration recently released the AI Bill of Rights, which addresses concerns about AI misuse and provides recommendations for safely using AI tools in both the public and private sectors. This AI strategy would not be legally binding. Instead, the Bill of Rights calls for key safety strategies such as greater data privacy, protections against algorithmic discrimination, and guidance on how to prioritize safe and effective AI tools. While the blueprint is not legally binding, it serves as a guide for lawmakers at all levels of government who are considering AI regulation.
In addition, NIST, which is an agency in the Department of Commerce that develops technology standards, published standards for managing AI bias. NIST also tracks how the public sector integrates AI tools across the federal government.
In 2022, 15 states and localities proposed or passed legislation concerning AI. Some bills focus on regulating AI tools in the private sector, while others set standards for public-sector AI use. New York City introduced one of the first AI laws in the U.S., effective from January 2023, which aims to prevent AI bias in the employment process. Colorado and Vermont created task forces to study AI applications, such as facial recognition, at the state level.
China: Algorithm Transparency and Promoting AI Industry Development
China has set a goal for the private AI industry to make $154 billion annually by 2030. China has yet to pass rules on AI technology at large. Recently, however, the country introduced a law that regulates how private companies use online algorithms for consumer marketing. The law requires companies to inform users of AI for marketing purposes and bans the use of customer financial data to advertise the same product at different prices. However, not surprisingly, the law does not apply to the Chinese government’s use of AI.
Along with China’s federal regulation, in September of 2022, Shanghai became the first province to pass a law focused on private-sector AI development. The law titled Shanghai Regulations on Promoting the Development of the AI Industry provides a framework for companies in the region to develop their AI products in line with non-Chinese AI regulations.
Next Steps for Global Regulation:
Artificial intelligence is a promising tool that is stimulating global growth and driving the future of innovation. Despite the positive impacts of AI, there is no question that some regulation is needed to combat the misuse of AI and to protect consumers.
The different approaches summed in this piece offer methodologies for how policymakers around the world are approaching specific harms from AI, as well as AI as a whole. The EU’s approach regulates the use of any automated decision-making tools and outlines the sectors where they can and cannot be used. The U.S. offers voluntary recommendations and standards at the federal level, with states and cities pursuing their own targeted studies and rules based on specific harms. The modified risk-based approach in Canada regulates all AI tools but stops short of banning the technology in certain spheres by allowing companies to define their own risk-mitigation strategies. And the Chinese approach seeks to increase transparency for consumers and become a global power in AI standards.
To prepare, companies will need to further develop global stances on AI ethics and compliance for their products in order to meet transforming regulations. In addition, legislators should focus on legitimate harms to consumers and keep apprised of how stricter regulatory regimes affect AI innovation.
| 2023-03-08T00:00:00 |
2023/03/08
|
https://www.progressivepolicy.org/an-overview-and-of-global-ai-regulation-and-whats-next/
|
[
{
"date": "2023/03/08",
"position": 5,
"query": "AI regulation employment"
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Artificial intelligence and gender equality: how can we ...
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Artificial intelligence and gender equality: how can we make AI a force for inclusion, instead of division?
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https://ennhri.org
|
[] |
Supervisory authorities with a strong mandate, resources and expertise capable of monitoring and (if necessary banning) AI systems that pose an unacceptable ...
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Although it may not seem so, artificial intelligence and gender equality are closely intertwined. In an interview for this year’s International Women’s Day looking at technology and innovation for gender equality, Nele Roekens, Legal Officer at Unia (Interfederal Centre for Equal Opportunities and Opposition to Racism) and Co-Chair of ENNHRI’s Working Group on Artificial Intelligence, reflects on this complex topic.
What is the link between artificial intelligence (AI) and gender equality?
The progress of AI relies on using massive amounts of data to train algorithms. But what if the data used reflects existing inequalities, such as gender inequality, rooted in society? Due to previous discrimination against women, historical databases can lack sufficiently gender-balanced data. When such databases are then used to train algorithms, this leads to equally biased decisions.
Such use of historical databases is currently extensive. With AI permeating almost all parts of our lives, including ones as fundamental as policing, migration management, and access to the labour market and housing, its impact is immense. Existing inequalities are being perpetuated and institutionalised.
While women are affected, people at the intersection of historically disadvantaged groups experience even more pronounced discrimination, for example women of colour, older women, and women with disabilities. This is on both an individual and collective level.
How can legal frameworks ensure that artificial intelligence helps, rather than hinders, gender equality?
Existing legislative frameworks are insufficient to meet specific AI-related challenges. This is widely recognised and legislators – including the European Commission and Council of Europe – are at an advanced stage of negotiating new legal frameworks.
To address gender equality properly, we need to use an intersectional lens. An algorithm’s output and the resulting decision will often not be based on a single characteristic, for instance gender or ethnic origin. Instead, it will be a combination of several. Yet it will not be possible to identify the most important one in the decision-making process. If we do not recognise this when drafting frameworks, it will lead to incomplete protection, and even worse, exacerbate existing inequalities.
Cases like the Amazon hiring tool and French Parcoursup system show how AI used to promote inclusion of vulnerable groups can still lead to discrimination. Almost ten years after the first of these scandals, we still need:
Accuracy standards for AI that set maximum “error rates” (to give an example – how often a facial recognition algorithm incorrectly identifies a person) for the decision-making systems influencing our lives;
Prohibitions on AI systems demonstrated to be harmful;
Supervisory authorities with a strong mandate, resources and expertise capable of monitoring and (if necessary banning) AI systems that pose an unacceptable risk to human rights, democracy and rule of law.
While some people worry that regulation and enforcement will hamper innovation, I disagree. Did making seat belts mandatory make cars worse? No – they became safer. Only by taking steps like the above can we make AI a driver of inclusion, not division, and provide effective redress for those harmed by AI.
What role do National Human Rights Institutions (NHRIs) have in ensuring AI is a positive force for gender equality?
NHRIs are crucial in ensuring a human rights-based approach to AI’s use and development. With their role, expertise and mandate, cooperation with NHRIs is critical for guaranteeing strong, independent oversight of AI systems and their adherence to relevant standards. In turn, NHRIs can advise on the development of these standards.
As a bridge between civil society and governments, NHRI are also uniquely positioned to encourage the public consultation we need to ensure that the huge impact of mass, digital technologies is comprehensively monitored, debated, and addressed. How accurate do we want the AI systems that shape our lives to be? Are there even ones that we don’t want to use at all?
Ultimately, we as NHRIs need the appropriate mandate and financial and technical means to conduct the vital work I mention above. Only then can we effectively protect and promote human rights for all in the digital age.
| 2023-03-08T00:00:00 |
2023/03/08
|
https://ennhri.org/news-and-blog/artificial-intelligence-and-gender-equality-how-can-we-make-ai-a-force-for-inclusion-instead-of-division/
|
[
{
"date": "2023/03/08",
"position": 79,
"query": "AI regulation employment"
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] |
Ranking Member Connolly's Opening Statement at ...
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Ranking Member Connolly’s Opening Statement at Subcommittee Hearing on Artificial Intelligence
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http://oversightdemocrats.house.gov
|
[] |
A GAO report also predicts AI could help identify and patch vulnerabilities and defend against cyberattacks, automate arduous tasks, and expand jobs within the ...
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Washington, D.C. (March 8, 2023)—Below is Ranking Member Gerald E. Connolly's opening statement, as prepared for delivery, at today's Subcommittee on Cybersecurity, Information Technology, and Government Innovation hearing entitled "Advances in AI: Are We Ready for a Tech Revolution?"
Image
Click here to watch the video.
Opening Statement
Ranking Member Gerald E. Connolly
Subcommittee on Cybersecurity, Information Technology, and Government Innovation
Hearing on "Advances in AI: Are We Ready for a Tech Revolution?"
March 8, 2023
The Cybersecurity, Information Technology, and Government Innovation Subcommittee has dedicated its first hearing to examining advances in Artificial Intelligence (AI) and its revolutionary impacts on society. This decision reflects our membership's interest and commitment to exploring, understanding, and implementing emerging technologies.
Last Congress, Chairwoman Nancy Mace, Rep. Ro Khanna and I introduced the Quantum Computing Cybersecurity Preparedness Act, which encouraged federal agencies to adopt post-quantum cryptography. I am pleased that the bill was signed into law just a few months ago. I look forward to future bipartisan collaboration as we define the problem sets associated with AI, design solutions, and promote innovation while simultaneously mitigating the dangers and risks inherent to AI technology.
The federal government has a historical, necessary, and appropriate role guiding and investing in research and develop (R&D) for new and emerging technologies. The Defense Advanced Research Projects Agency (DARPA), the well-known research and development agency of the United States Department of Defense, is responsible for the development of myriad emerging technologies.
One of its most famous successes includes the Advanced Research Projects Agency Network (ARPANET), which eventually evolved into the internet we know today. Other innovations include microelectronics, the Global Positioning System (GPS), inferred night imaging, unmanned vehicles, and what eventually became cloud technology. AI will require similar federal investment and engagement.
As stated in the January 2023 final report from the National Artificial Intelligence Research Resource Task Force, "The recent CHIPS and Science Act of 2022 reinforces the importance of democratizing access to a national AI research cyberinfrastructure, via investments that will accelerate development of advanced computing—from next-generation graphics processing units to high-density memory chips—as well as steps to actively engage broad and diverse U.S. talent in frontier science and engineering, including AI," and the report calls for $2.6 billion over the next six years for the purposes of funding national AI research infrastructure.
While government certainly plays a role in R&D, it also has a regulatory role. Congress has the responsibility to foster careful and thoughtful discussions to balance the benefits of innovation with the potential risks of emerging technology.
A recent National Bureau of Economic Research report found that AI could save the U.S. healthcare industry more than $360 billion a year, and be used as a powerful tool to detect health risks. A GAO report also predicts AI could help identify and patch vulnerabilities and defend against cyberattacks, automate arduous tasks, and expand jobs within the tech industry.
As with all technologies, in the wrong hands, however, AI can be abused to hack financial data, steal national intelligence, or create deepfakes—blurring people's abilities to certify reality, and sow further distrust within our democracy.
AI can also cause unintentional harms. GAO found that certain groups, such as workers with no college education, tended to hold jobs susceptible to automation and eventually unemployment.
Another concern relates to machine learning (ML) and data. ML uses data samples to learn and recognize patterns, such as scanning hundreds or thousands of pictures of lungs to better understand pulmonary fibrosis and revolutionize medical care. But what happens if those lung samples only come from a homogenous portion of the population, and that medical breakthrough is inaccurately applied?
When it comes to data, equity is accuracy, and we must ensure data sets include as much and as comprehensive a universe of data as possible.
It is paramount that during this hearing we begin to create a flexible and robust framework, particularly for government's use of AI, to protect democratic values and preemptively address the social, ethical and moral dilemmas that AI raises.
During the 117th Congress, this Committee also voted to pass the AI Training Act (H.R.7683) and the AI in Counterterrorism Oversight Enhancement Act (H.R. 4469) with bipartisan support. This committee is not entirely new to the AI space, and we look forward to continuing efforts to support transformative research.
We also look forward to building on the Biden Administration's efforts such as the National Artificial Intelligence Research Resource Task Force. Just over a month ago, the Task Force released its report to provide a roadmap to stand up a national research infrastructure that would broaden access to the resources essential to artificial intelligence (AI) research and development. I look forward to digging into its suggestions. I am also encouraged by the White House's Blueprint for an AI Bill of Rights to help guide the design, use, and deployment of automated systems to protect the rights of the American public in the age of artificial intelligence.
AI is already integrated within the world around us, and its growing use throughout society will continue to drive technological advancements. America must implement an aggressive research-forward federal AI policy to spur competition with other countries that have already established nation-wide strategies and investment plans, such as China. Additional supporting policy strategies might include promoting open data policies or outcome-based strategies when assessing algorithms.
Finally, and more importantly, our country needs the workforce to properly develop, test, understand, and use AI. This workforce of the future will include technologists who will help govern AI responsibly. I look forward to hearing from our witnesses how to best balance all these priorities and prepare for the benefits and risks of AI.
###
| 2023-03-08T00:00:00 |
2023/03/08
|
http://oversightdemocrats.house.gov/news/press-releases/ranking-member-connolly-s-opening-statement-at-subcommittee-hearing-on
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[
{
"date": "2023/03/08",
"position": 25,
"query": "government AI workforce policy"
}
] |
Peters Convenes Hearing to Examine Potential Risks and ...
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Peters Convenes Hearing to Examine Potential Risks and Opportunities of Artificial Intelligence
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https://www.hsgac.senate.gov
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The hearing examined how artificial intelligence affects our nation's economic competitiveness on a global stage, while ensuring that these technologies are ...
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WASHINGTON, D.C. – U.S. Senator Gary Peters (D-MI), Chairman of the Homeland Security and Governmental Affairs Committee, convened a hearing to assess the potential risks and opportunities related to the use of artificial intelligence (AI). The hearing examined how artificial intelligence affects our nation’s economic competitiveness on a global stage, while ensuring that these technologies are used responsibly and in a way that respects civil rights and civil liberties. Peters also highlighted that the lack of transparency surrounding how these technologies function often undermines public trust in their use. The witnesses discussed how lawmakers can work to support the development of AI to ensure it benefits society and helps industry and government become more effective, while limiting potential harms.
“From the development of lifesaving drugs and advanced manufacturing, to helping businesses and governments better serve the public, to self-driving vehicles that will improve mobility and make our roads safer, artificial intelligence holds great promise. But this rapidly-evolving technology also presents potential risks that could impact our safety, privacy, and our economic and national security. We must ensure that, as use of this technology becomes more widespread, we have the right safeguards in place to ensure it is being used appropriately,” said Senator Peters during his opening statement.
Peters continued: “Adversaries like the Chinese government are racing to be the world leaders in these technologies, and to harness the economic advantages that dominance in artificial intelligence will create. The United States must be at the forefront of developing our own AI systems, and training people how to use them appropriately, to protect our global economic competitiveness.”
To watch video of Senator Peters’ opening remarks, click here. For text of Peters’ opening remarks, click here.
To watch video of Senator Peters’ questions, click here.
Peters and the witnesses discussed how, in some cases, developers of these technologies do not understand how AI algorithms make decisions and the hearing examined the risks posed by this lack of transparency. Lawmakers also heard how algorithmic bias can get into AI systems, and how our nation can ensure we are utilizing fair AI systems. The witnesses also discussed the need to ensure that these technologies are benefitting and not disrupting Americans’ workplaces. The witnesses discussed existing laws and regulatory authorities, as well as needed reforms, that will help ensure that the United States is developing and using responsible AI systems. Finally, with foreign adversaries like the Chinese government prioritizing investments in these technologies, Peters raised the need for Congress to ensure the federal government has the talent, training, and technology needed to maintain the United States’ global leadership on AI.
The hearing builds on Peters’ previous efforts to ensure our nation can use AI effectively and responsibly. His bipartisan bill to create a training program to help federal employees responsible for purchasing and managing AI technologies better understand the capabilities and risks they pose to the American people was signed into law. Peters’ provision to ensure that the use of AI across the federal government is effective, responsible, and accountable by providing resources and guidance to federal agencies was signed into law in 2021. During today’s hearing, the witnesses affirmed the need to quickly implement these laws.
###
| 2023-03-08T00:00:00 |
https://www.hsgac.senate.gov/media/dems/peters-convenes-hearing-to-examine-potential-risks-and-opportunities-of-artificial-intelligence/
|
[
{
"date": "2023/03/08",
"position": 98,
"query": "government AI workforce policy"
}
] |
|
Intelligence Augmentation and Educational Technology
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Intelligence Augmentation and Educational Technology
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https://nextlevellab.gse.harvard.edu
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[] |
We explore ways in which AI is changing how we think about learning and working, and consider how to support workers in developing uniquely human skills.
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Digital tools and artificial intelligence are changing the face of teaching and learning. From computers, intelligent tutoring systems, AI chatbots (software applications used to simulate human-like chat conversations online), and question-answering digital teaching assistants to personalized learning programs using machine learning (a branch of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so) to adapt to each student’s current performance, artificial intelligence (AI) is bringing about a new age of teaching and learning from pre-K through workforce training.
AI is not only impacting the process of teaching and learning; machine learning (ML) in the workplace is also changing what capabilities educators should cultivate and what students need to learn for their future occupation. We explore ways in which AI is changing how we think about learning and working, and consider how to support workers in developing uniquely human skills that will allow them to complement the work of machines rather than be replaced by them.
We also focus on the potential for learning sciences-informed technology in workforce development. Evidence suggests that digital tools are often underutilized or misapplied resources in training and workforce development contexts. When designed effectively, however, these powerful immersive technologies can provide highly contextualized hands-on experiences that offer learners agency, feedback, and opportunities for reflection, taking different perspectives, and practicing adapting knowledge to new scenarios. Our work examines the potential of these new and emerging technologies to improve Next Level Learning, especially focusing on how features of different applications foster different learning outcomes, accounting for differences in learners’ prior experience and skills.
Research Projects:
Publications:
Research Briefs:
Teaching Times Articles:
| 2023-03-08T00:00:00 |
https://nextlevellab.gse.harvard.edu/research/intelligence-augmentation-and-educational-technology/
|
[
{
"date": "2023/03/08",
"position": 4,
"query": "machine learning workforce"
}
] |
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Tech Layoffs Are Allowing Tech Workers to Learn How ...
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The tech layoffs have created the perfect moment for tech workers to get organized and unionize, but getting there isn't easy
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https://www.businessinsider.com
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[
"Paayal Zaveri"
] |
There are unionization efforts like the Alphabet Workers Union, which any type of employee at the tech conglomerate is free join. Over the past several years, ...
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This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
The layoffs that swept Google in January accomplished two big things.
First, it successfully shattered the illusion that tech giants like Google are completely immune to the whims of the economy — despite a famously whimsical corporate culture that encouraged employees to think of working at the search giant as a lifestyle.
Second, the episode drove several hundred Googlers to sign on as members of the small-but-growing Alphabet Workers Union, said Emma Kinema, a lead organizer at Code-CWA, which helps organize the group and others.
As tech giants like Microsoft, Meta, Google, Salesforce, and others continue to cut jobs and slash costs, labor experts say that now is exactly the right moment for tech workers to get organized and demand better treatment from their employers.
"These layoffs are a huge culminating moment where that rose tinted glasses, it's just getting absolutely shattered," Kinema said. "And so in many ways, it really just helps bring about a tipping point in this industry where people are already starting to normalize the idea of organizing."
The tech industry has largely avoided unionization over the last several decades, as high wages and generous benefits have removed the urgency of taking collective action. Now, however, the looming risk of recession is pushing those same workers to claim a certain degree of agency.
Additionally, support for labor unions is the highest it's been in more than half a century, with 71% of American workers saying approve of labor unions, according to a Gallup poll released in August. Still, it's far from certain that we'll see a wave of unionization across Silicon Valley, the way that the United States has seen a massive upswing in the number of Starbucks stores that have formed their own unions.
One challenge is simply that the traditional approach to unionization and collective bargaining may not work in tech, where there's so little precedent and it's not clear the best way to take action. And employees may be more unwilling to stick their necks out in an increasingly-tight job market, potentially slowing the wave of unionization right as experts say it's needed most.
"We are on the brink of a new wave of worker organizing," said Susan J. Schurman, labor professor at Rutgers University. "The question in my mind is what form of organizations will they create? Because many of the types of unions and the structures of unions that we have today simply are not gonna be effective in the we call the knowledge based industries."
Collective action in the tech industry has already been growing
Unionization and collective action in the tech industry is well behind other industries, partly due to the fact that tech is relatively new in the last several decades and that the traditional factors motivating unionization, like wages and benefits, haven't impacted tech workers on a large scale.
Related stories Business Insider tells the innovative stories you want to know Business Insider tells the innovative stories you want to know
However, there's been more collective action in recent years, Schurman and other experts said. There are unionization efforts like the Alphabet Workers Union, which any type of employee at the tech conglomerate is free join.
Over the past several years, we've also seen employees at Salesforce, Google, and others come together to push their company to stop using their technology for things that employees consider unethical.
For example, when Salesforce employees penned a letter to Marc Benioff in 2018 saying the company should not sell to customs and border patrol. Also in 2018, Google employees protested a Pentagon contract called Project Maven, which would have used the company's artificial intelligence technology to analyze drone surveillance footage.
These episodes show a certain appetite for collective action in the tech industry, the experts said, laying a foundation for things to come.
The layoffs are pushing workers to rethink their relationship to their employers
The layoffs of the last several months have recharged employees and their willingness to demand more from their employers, labor organizers said.
That's already been seen by the increased interest the Alphabet Workers Union has seen since the layoffs, said Hayden Lawerence, a software engineer at Google, who is a member of the Alphabet Workers Union.
"It has definitely made people more open to the idea of collective action, he said. "We saw plenty of high performers get laid off. So I think that made it clear to people that their individual performance really isn't enough anymore."
Tech workers have taken inspiration from the service workers at their respective companies, many of whom have unionized in recent years, Code-CWA's Kinema said. There's a growing realization that they're all in the same boat, at equal risk of getting laid off in a downturn, she said.
That's why the Alphabet Workers Union is open to all employees, whether they're full-timers, contractors, or vendors, she said.
"There's something very powerful about having workers from every part and across the spectrum of conditions at Alphabet, all organizing together one way or another," Kinema said.
Despite a change in perspective, there are still hurdles ahead
Despite the change in sentiment, however, at companies without that existing infrastructure, it is hard to organize. The layoffs have made people nervous about job security, and more fearful of retaliation.
It's also more difficult to organize in workplaces that allow for remote work, said a tech employee who has organized their colleagues previously. The employee, who is from a major tech firm that has had layoffs recently, asked not to be named, as they were not authorized to speak with the press.
The irony is, as tech companies push for employees to return to offices, they could also tip them further towards collective action or unions, because employees still largely want flexible work policies, that tech worker said.
Ultimately, the experts say, tech workers just want to have a voice in a fast-changing industry.
"This industry has enjoyed a real facade," such that "if you're fortunate enough to work in this industry, you're treated really well," Kinema said. "I think the layoffs are really a massive shattering of that view."
Got a tip? Contact this reporter via email at [email protected] or Signal at 925-989-8866. (PR pitches by email only, please.)
| 2023-03-08T00:00:00 |
https://www.businessinsider.com/tech-layoffs-allow-workers-learn-how-to-unionize-2023-3
|
[
{
"date": "2023/03/08",
"position": 17,
"query": "AI labor union"
}
] |
|
By Julia Kloiber
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Data Workers' Inquiry
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https://data-workers.org
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[] |
By Julia Kloiber. March 8, 2023. I am standing in the lobby of the labor union ver.di in Berlin, eagerly awaiting a group of 50 content moderators.
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March 8, 2023. I am standing in the lobby of the labor union ver.di in Berlin, eagerly awaiting a group of 50 content moderators. Today marks the first Content Moderators Summit, aimed at networking and developing collective strategies for better working conditions. Many of the moderators will be meeting for the first time.
As I wait, I try to imagine the people who will walk through the door. I know their work is precarious, and they face exploitation and psychological strain every day. In my mind, they appear as an anonymous mass. I wonder if our psyche tricks us into seeing those who suffer injustice as a distant, faceless crowd.
Then, suddenly, they’re before me. Sakine from Iran, who studied philosophy in three countries. Amir, who worked for international aid organizations before coming to Germany. Omar, who grew up in Germany and freelanced before becoming a moderator. Cengiz, who is completing his PhD alongside this job, among many others.
Today, as I write these lines, I know some of them well. We have spent evenings in shisha bars, shared many cups of coffee, exchanged thoughts and experiences. The anonymous mass has transformed into individuals whose stories I know and whom I greatly admire.
Looking back, I wonder what they expected when they walked into the ver.di lobby, on the invitation of the union and three civil society organizations, including my own, SUPERRR Lab. While the roles of unions are clear, the contributions of civil society organizations are often less obvious. What can they achieve in labor struggles and organizing, what is their role?
At SUPERRR, we follow feminist principles and values like care, collaboration, inclusion, power-sharing, and justice. For me, working in a feminist way means to be constantly reassessing our work, structures, and values. I have learned a lot from the content moderators and labor rights activists, which I want to reflect on here.
| 2023-03-08T00:00:00 |
https://data-workers.org/julia/
|
[
{
"date": "2023/03/08",
"position": 38,
"query": "AI labor union"
}
] |
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Industrial Relations Specialist
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Industrial Relations Specialist
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https://www.aihr.com
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[] |
Gen AI Prompt Design for HRMaster Gen AI prompt ... Developing and maintaining positive relationships with employees, management, and labor unions.
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Start by clicking a role on the career map to see all the details
Switch to the career path mode when you're ready to visualize your desired career path (using the toggle above)
| 2023-03-08T00:00:00 |
https://www.aihr.com/hr-career-map/industrial-relations-specialist/
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[
{
"date": "2023/03/08",
"position": 65,
"query": "AI labor union"
}
] |
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AI in Medical Devices and Healthcare
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AI in Medical Devices and Healthcare: Opportunities, Challenges, and What Lies Ahead
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https://www.morganlewis.com
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[] |
In this article, we discuss the existing FDA programs and recently issued guidance impacting AI/ML technologies intended for use in healthcare.
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Insight AI in Medical Devices and Healthcare: Opportunities, Challenges, and What Lies Ahead March 08, 2023
In recent years, the digitalization of the healthcare industry has been accelerated to meet demands for smarter devices and robotics, wearable technology, AI-based data analysis, and enhanced platforms and simulations, among others. This digitization has driven an increased interest in incorporating artificial intelligence (AI) and machine learning (ML) technologies into medical devices.
Over the last decade, the US Food and Drug Administration (FDA) has reviewed and authorized a growing number of devices using its 510(k) clearance, de novo, and approved premarket (PMA) approval processes with AI/ML functionality across many different therapeutic categories—and anticipates this trend to continue. In addition, AI and ML technologies may be used to support the investigation, development, and/or production of medical devices and other FDA-regulated products. When used for medical or other healthcare-related purposes, these technologies are likely subject to FDA regulations, policies, and guidance.
In this article, we discuss the existing FDA programs and recently issued guidance impacting AI/ML technologies intended for use in healthcare, as well as what to expect from the FDA’s fiscal year (FY) 2023 priority list. We also examine the reimbursement framework for AI/ML and some challenges ahead for the medical device industry.
EXISTING FDA PROGRAMS IMPACTING AI/ML TECHNOLOGIES
FDA’s regulation and oversight of AI/ML software continues to grow, as evidenced by the online list compiled and maintained by FDA’s Center for Devices and Radiological Health (CDRH) of medical devices using AI/ML technologies that CDRH has cleared or approved. That list currently includes more than 500 devices, the vast majority of which were cleared via the 510(k) process, along with a few de novo submissions and PMA applications. In terms of an FDA review branch, a significant majority fall under radiology, followed by cardiovascular, hematology, and neurology.
However, FDA/CDRH’s regulatory priorities for AI/ML technologies expand beyond premarket review, and are led by CDRH’s Digital Health Center for Excellence.
Launched in September 2020, the Digital Health Center for Excellence’s main purpose is to foster responsible and high-quality digital health innovation. Its three main goals are to develop and issue guidance documents, increase the number and expertise of the digital health staff, and develop the Software Precertification Pilot.
On January 12, 2021, the FDA released its first Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan that details a multipronged approach to advance FDA oversight of AI/ML-based medical software. This action plan is in response to stakeholder feedback that it received from an April 2019 discussion paper, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning–Based Software as a Medical Device. The five-point SaMD Action Plan outlines how to further develop the proposed regulatory framework, including through draft guidance issued on a predetermined change control plan; harmonize good ML practices to evaluate and improve ML algorithms; foster a patient-centered approach, including device transparency for users; support regulatory sciences; and advance real-world performance monitoring pilots.
In October 2021, the FDA, Health Canada, and the UK Medicines and Healthcare products Regulatory Agency (MHRA) jointly identified 10 guiding principles that can inform the development of “Good Machine Learning Practice” for medical devices and how they can help promote safe, effective, and high-quality use of AI/ML.
Recently Issued Guidance Documents Affecting AI/ML
CDHR remains active in promulgating guidance documents impacting AI/ML technologies, including the following recently issued guidance:
Clinical Decision Support Software – Final Guidance (September 28, 2022) : This long-awaited final guidance represents a significant and more conservative shift from the prior draft guidance issued in September 2019 and could be present challenges for developers of AI/ML technologies. This guidance document describes FDA’s interpretation of the statutory exemption for clinical decision support (CDS) software functions under the Federal Food, Drug, and Cosmetic Act. Software that meets the four criteria set forth in the statute would be exempt from FDA’s medical device regulatory requirements. FDA’s interpretation of these four criteria, as described in the final guidance, will make it more challenging for software developers to fit their software products (including AI/ML software) within the scope of the CDS exemption. Further, unlike the prior draft guidance, the final guidance does not include any proposed enforcement discretion policy for software that does not fully meet all four statutory criteria.
: This long-awaited final guidance represents a significant and more conservative shift from the prior draft guidance issued in September 2019 and could be present challenges for developers of AI/ML technologies. This guidance document describes FDA’s interpretation of the statutory exemption for clinical decision support (CDS) software functions under the Federal Food, Drug, and Cosmetic Act. Software that meets the four criteria set forth in the statute would be exempt from FDA’s medical device regulatory requirements. FDA’s interpretation of these four criteria, as described in the final guidance, will make it more challenging for software developers to fit their software products (including AI/ML software) within the scope of the CDS exemption. Further, unlike the prior draft guidance, the final guidance does not include any proposed enforcement discretion policy for software that does not fully meet all four statutory criteria. Computer Software Assurance for Production and Quality System Software – Draft Guidance (September 28, 2022): This new draft guidance provides recommendations for “computer software assurance” for software and automated systems used for medical device production or quality. The guidance describes “computer software assurance” as a risk-based approach to establish confidence in the automation used for production or quality systems, and identify where additional rigor may be appropriate. The guidance further includes various methods and testing activities to establish “computer software assurance” and ensure compliance with quality system regulation and other regulatory requirements.
This new draft guidance provides recommendations for “computer software assurance” for software and automated systems used for medical device production or quality. The guidance describes “computer software assurance” as a risk-based approach to establish confidence in the automation used for production or quality systems, and identify where additional rigor may be appropriate. The guidance further includes various methods and testing activities to establish “computer software assurance” and ensure compliance with quality system regulation and other regulatory requirements. Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions – Draft Guidance (April 8, 2022): This new draft guidance would replace the 2014 final guidance, Content of Premarket Submissions for Management of Cybersecurity in Medical Devices. This is FDA’s second attempt at a new draft—a prior draft guidance was issued in 2018 and received significant criticism. This new draft guidance includes changes to align with the use of a Secure Product Framework, removal of risk tiers (from the prior draft), replacement of the Cybersecurity Bill of Materials with a Software Bill of Materials, additional clarification regarding premarket submission document requests throughout the draft guidance, and addition of Investigational Device Exemptions to the scope. The guidance also makes clear that cybersecurity is part of FDA’s Quality System Regulation (QSR) design control requirements.
This new draft guidance would replace the 2014 final guidance, Content of Premarket Submissions for Management of Cybersecurity in Medical Devices. This is FDA’s second attempt at a new draft—a prior draft guidance was issued in 2018 and received significant criticism. This new draft guidance includes changes to align with the use of a Secure Product Framework, removal of risk tiers (from the prior draft), replacement of the Cybersecurity Bill of Materials with a Software Bill of Materials, additional clarification regarding premarket submission document requests throughout the draft guidance, and addition of Investigational Device Exemptions to the scope. The guidance also makes clear that cybersecurity is part of FDA’s Quality System Regulation (QSR) design control requirements. Digital Health Technologies (DHTs) for Remote Data Acquisition in Clinical Investigations – Draft Guidance (January 21, 2022): This draft guidance describes considerations when using DHTs in clinical investigations and applies to all types of clinical investigations (whether the investigation is for a drug, biologic, or device product) using a digital health technology for remote data acquisition.
This draft guidance describes considerations when using DHTs in clinical investigations and applies to all types of clinical investigations (whether the investigation is for a drug, biologic, or device product) using a digital health technology for remote data acquisition. Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions – Draft Guidance (December 23, 2021): This new draft guidance sets forth a proposed nine-step process to assess the credibility of computational modeling and simulation (CM&S) used to support a medical device premarket submission. CM&S can be used in a variety of ways in medical device regulatory submissions, such as to support in silico device testing or as-a-device development tools, or within the device itself as software as a medical device (SaMD) or software in a medical device (SiMD).
What’s in Store for 2023?
Toward the end of 2022, the CDRH published its annual list of intended guidance documents for FY 2023 (A-List and B-List). The following priorities from these lists are most likely to impact AI/ML technologies:
A-List
Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (final)
Content of Premarket Submissions for Device Software Functions (final)
Transition Plan for Medical Devices That Fall Within Enforcement Policies Issued During the Coronavirus Disease 2019 (COVID-19) Public Health Emergency (final)
Transition Plan for Medical Devices Issued Emergency Use Authorizations (EUAs) During the Coronavirus Disease 2019 (COVID-19) Public Health Emergency (final)
B-List
Marketing Submission Recommendations for a Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions (draft)
Changes are also on the horizon for AI/ML-enabled devices marketed for pandemic-related uses under an emergency use authorization (EUA) or one of FDA’s many COVID-related guidance documents describing enforcement policies. As noted above, CDRH’s A-list includes finalization of its previously issued draft guidance documents on transition plans for such devices. Under the draft guidance documents, FDA had proposed a three-phase, 180-day transition period for devices covered by either an EUA or a COVID-related enforcement policy. The final guidance documents are expected to issue this year.
REIMBURSEMENT DEVELOPMENTS FOR AI/ML TECHNOLOGIES
Above, we examined how AI/ML technologies may be approved by the FDA. But what are the benefits of developing said technologies if they cannot be implemented into medical devices and sold? How will these AI/ML technologies find their way to day-to-day use in the healthcare industry?
Limited but Growing Opportunities for Direct Reimbursement
The reimbursement framework for AI/ML technologies is not advanced, and there are currently limited opportunities to realize direct reimbursement. One of the biggest impediments lies in the fact that US healthcare reimbursement remains focused on clinicians as the “source” of a reimbursable service. Recognizing that, removing a clinician from a patient care service is in some ways antithetical to the existing payment framework. But, although human clinicians remain a fixture, AI/ML can enhance clinicians’ ability to make faster decisions based on larger sets of patient data collected, see more patients due to efficiencies in appointments and evaluations, and lead to an overall reduction in time and overhead costs.
Indeed, the American Medical Association (AMA) has already developed a conceptual framework for AI/ML in healthcare, releasing its classification system in December 2021 and updating it in August 2022. The AMA recommends classification of AI devices into three overall categories based on the “work performed by the machine” in delivering an overall service: Assistive, Augmentative, and Autonomous.
Minimal Exploration by Federal Health Agencies
There has been some minimal, but growing, exploration of AI/ML reimbursement in federal healthcare programs in recent years. The Centers for Medicare & Medicaid Services (CMS) has been exploring reimbursement for certain limited procedures utilizing AI since 2018, but recent activity demonstrates that the agency’s interest is increasing.
In 2022, CMS continued to explore payment for Current Procedural Terminology Code 92229 (described by AMA as an “autonomous” service) in both the Hospital Outpatient Prospective Payment System (OPPS) and Physician Fee Schedule (PFS), and it requested public comment about software as a service, analytics, and payment for new technologies and clinical software, not only in the context of OPPS payments but as part of future adjustments to the PFS practice expense methodology. Changes to the practice expense methodology could be a game-changer for various technologies that struggle to achieve reimbursement because the expense must be directly incurred by the physician practice.
AI/ML Impact in Value-Based Care
While direct reimbursement of AI/ML technology remains limited, AI can nevertheless be successfully integrated into other existing payment models.
Increased efficiencies and better outcomes that certain AI/ML technologies can foster will ultimately result in greater shared savings opportunities for healthcare providers involved in value-based care models and other alternative payment models.
Private insurers have the flexibility to reimburse for services in a variety of ways, including through pilot programs that may attempt to test the clinical and financial return on investment of AI/ML-enabled services.
State Laws Impacting AI/ML
Irrespective of growing reimbursement opportunities, the use of AI/ML in healthcare settings quickly implicates state rules governing the practice of medicine and other licensed professions. Already, many state medical boards are assessing how the introduction of AI will reshape medical practice.
Boards are considering the impact of telehealth, AI, and the use of other technology on the standard of care and how licensees should responsibly use these tools to furnish healthcare services. For exaample, the Federation of State Medical Boards passed a resolution in 2018 to establish a workgroup on “AI and Its Potential Impact on Patient Safety and Quality of Care in Medical Practice.” Although the working group has not yet issued formal guidance, this highlights professional licensing agencies’ focus in identifying how AI can improve patient safety and care and whether a revised regulatory framework may be necessary to respond to this new reality.
Watch our on-demand Artificial Intelligence Boot Camp session for more information on AI developments in digital health.
| 2023-03-08T00:00:00 |
https://www.morganlewis.com/pubs/2023/03/ai-in-medical-devices-and-healthcare-opportunities-challenges-and-what-lies-ahead
|
[
{
"date": "2023/03/08",
"position": 5,
"query": "AI healthcare"
}
] |
|
Kittl AI | Collaborate with powerful AI companion tools to ...
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Collaborate with powerful AI companion tools to do more
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https://www.kittl.com
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[] |
Kittl Copilot lets you create faster with tools to swap graphics, explore themed packs, generate text, and fine-tune visuals using smart suggestions and natural ...
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Infinite Canvas
Infinite Canvas means you can create unlimited artboards in any size or dimensions in any single project. You can moodboard and design at the same time, and efficiently manage complex projects.
| 2023-03-08T00:00:00 |
https://www.kittl.com/features/ai
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[
{
"date": "2023/03/08",
"position": 8,
"query": "AI graphic design"
}
] |
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Hidden Workers powering AI - Artificial intelligence
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Hidden Workers powering AI
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https://nationalcentreforai.jiscinvolve.org
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[
"Ayesha Fraz",
"Tom Moule",
"Manasa Panikkamparambil Manomohanan"
] |
This blog post focuses on hidden labour involved in AI production. We aim to raise awareness and educate educational institutions about this important issue.
|
Many people are aware of various AI tools and technologies but most of us aren’t aware of invisible workers involved in the production of AI. This blog post focuses on hidden labour involved in AI production. We aim to raise awareness and educate educational institutions about this important issue. Although research in this area is limited, the post highlights the role of hidden workers, who they are, what can be done to improve the situation and make informed decisions about using AI products and services.
However, any AI system requires a significant amount of human effort that is sometimes disregarded. This “hidden work” is crucial to the development and functioning of AI systems but is often unnoticed. The production of AI involves a significant amount of human labour, often called as “Ghost workers “who empower AI, behind-the-scenes. This hidden labour is often overlooked in discussions of AI, but it is essential to understanding the implications of AI on society.
Kate Crawford, in her book “Atlas of AI,” highlights dark side of AI production and suggests AI is neither “artificial” nor “intelligent.” The creation of AI involves a large consumption of resources, such as energy and minerals, and the workers who contribute to its production often have their rights disregarded. Crawford’s book sheds light on the exploitation of workers in the background of AI’s “automation.”
Mary Gray’s book “Ghost Work” focuses on invisible workers behind AI and challenges faced i.e., lack of job security, benefits, fair pay, and recognition. The book highlights need for better treatment, fair work conditions and raises important questions about the ethics of AI and its impact on hidden workers.
How Hidden workers are empowering AI:
To shape and train these applications, digital giants like Amazon, Google, and Facebook, employ an army of invisible labour. But there are others such as Appen, Upwork, CrowdFlower and Microwork. They hire workers for remote work from anywhere in the world to train machine learning models by perform tasks like data labelling, data annotations, transcription etc.
Data labelling:
It is the process of marking or classifying data used to train AI systems. This includes classifying text, transcribing audio, or recognizing objects in photos. For example, in image recognition, data labelling could involve manually labelling each image with information about what’s in it (e.g., “dog”, “car”, “person”). This labelled data is then used to train artificial intelligence algorithms to accurately recognize and categorize images on their own. In essence, data labelling is a crucial step in the development of AI systems as it provides the foundation for the algorithms to learn from.
Autonomous cars , a rapidly growing sector that is expected to be worth $556 billion by 2026. To navigate its driverless vehicles, companies like Tesla require clean and tagged data. This information is obtained via onboard cameras and must be classified and labelled for the automobile to detect items such as people, traffic signs, and other cars.
Data labelling is labour-intensive, time-consuming, and repetitive procedure that needs to be done with great precision. People in low-wage countries who work long hours for low and non-negotiable wages are frequently hired to perform tasks like data labelling. They neither receive contracts nor incentives and are unaware of the usage of their work.
Somehow, we all have likely been participating in data labelling by completing captcha codes, as the responses we provide are used to train machine learning models. It’s possible that you have encountered instances where a website prompts you to enter a series of characters displayed in a distorted image. These are known as captcha codes, which are designed to differentiate human users from automated bots. Your responses, along with others, are utilized as a form of data labelling for machine learning models. This data labelling is important in enhancing the precision of these models across a range of applications, such as image classification and speech recognition.
Who are Hidden Workers?
According to the study these are the individuals who desire employment but are unable to secure suitable job opportunities due to multiple factors such as discrimination, lack of education/ skills, discouragement due to their repeated unsuccessful job search. This includes refugees, people with disabilities, veterans, prisoners, care givers and relocated partners and spouses. The study estimated 27 million hidden workers in US alone by interviewing 8000 hidden workers and over 2250 executives in US, UK, and Germany. These are invisible workers, paid as little as 10 cents- $2 an hour to feed information into computer systems.
Refugees empowering machine learning advances by working for Silicon Valley corporations like Google, Amazon, Facebook from a tent with computers in Dadaab, Kenya, one of the world’s largest refugee camps. They conduct click work, such as video tagging and audio transcription, as one of their limited legitimate job possibilities. However, the task is difficult, paid per piece, and performed in confined, airless environments. The refugees in Lebanon’s Shatila camp are compelled to labour at night labelling footage of large cities for the benefit of foreign capitalists, although the specific goal or beneficiaries of their work are unknown.
M2Work, coordinated by infoDev, is an online program aimed at encouraging poor countries to participate in the production side of digital economy and create jobs. Similarly, Sama, data annotation and labelling platform, teach and train refugees in Kenya, India, and Uganda to accomplish brief data tasks. Sama works with clients such as Walmart, Google, Microsoft and General Motors. Some of its employees in Kenya contributed to filtering toxic content for ChatGPT, which led to wide spread criticism due to the work negatively impacting their mental health, poor working conditions and little to no pay.
Traditionally, prison labour involved physical work but Prisoners in Finland are recruited by Vainu to conduct data labour for struggling startups, with the government getting paid for each assignment done. However, no information is available on what percentage of the cash goes to the convicts. The company claims that the collaboration is a sort of jail rehabilitation that teaches essential skills, but other experts feel that the job training claims are simply hype around AI’s promises.
The Online Labour Observatory platform serve as a centralized hub for researchers and decision-makers. It tracks the real-time number of tasks and projects completed by freelancers on different websites across various countries and job categories. The platform offers valuable information on the gig economy and assists those in decision-making positions to make informed decisions. The Online Labour Observatory provides a comprehensive view of the developments and patterns in online work, and its effect on both workers and the economy.
Despite these known issues this area remains under research. EU- Funded project conduct in-dept study about the working condition of hidden work force involved in the production of AI and its impact on their wellbeing.
How can we make it better?
Offer fair pay, remove inequalities.
Regulate and compensate hidden labour.
Provide proper training and guidelines to labellers to avoid bias in the labelling work.
Diversify labellers to include people from different cultures, backgrounds, and perspectives.
Implement algorithmic fairness techniques, review and audit labelled data to identify and remove bias.
Universities and colleges must be aware of the labour behind AI and consider its impact when using AI services, including examining biases in data used to train AI algorithms and potential consequences in outcomes produced. By approaching AI responsibly, universities and colleges can ensure its benefits are shared and workers are treated fairly. Institutions should thoughtfully consider the ethical implications of AI and ask questions about the workers powering it before making decisions on its use.
Who is developing and creating AI technology?
How is data pre-processed and does it affect accuracy and fairness of AI systems?
Are workers involved in AI production empowered and supported professionally?
Are there concerns about exploitation or human rights violations in AI production process?
How are AI algorithms tested for fairness and bias, and who determines test rigor?
What steps are taken to ensure AI benefits are widely shared and technology is developed responsibly?
Potential impact of AI systems on various demographic groups and how these impacts are monitored and addressed.
Understanding human involvement in AI systems enable individuals to make informed decisions about products and services they use and understand the consequences on society. By promoting critical thinking about the role of humans in AI, we can work towards creating a more equitable and ethical AI industry.
Find out more by visiting our Artificial Intelligence page to view publications and resources, join us for events and discover what AI has to offer through our range of interactive online demos.
For regular updates from the team sign up to our mailing list.
Get in touch with the team directly at [email protected]
| 2023-03-08T00:00:00 |
2023/03/08
|
https://nationalcentreforai.jiscinvolve.org/wp/2023/03/08/hidden-workers-powering-ai/
|
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"date": "2023/03/08",
"position": 6,
"query": "artificial intelligence workers"
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These 20 jobs are the most "exposed" to AI, ChatGPT, ...
|
These 20 jobs are the most "exposed" to AI, ChatGPT, researchers say
|
https://www.cbsnews.com
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"Maybe it will mean automation and maybe workers will be more productive by using these technologies," one researcher said.
|
The rise of AI: Could ChatGPT take your job?
Rapid advances in artificial intelligence-powered tools are quickly making large language models (LLMs) like OpenAI's ChatGPT a can't-live-without resource for many workers.
Executives are relying on the technology to consider different vantage points, real estate pros are using it to write listings and lawyers are using it to generate first drafts of documents like wills and more.
New research examining the effects of language modeling AI like ChatGPT on different occupations and industries finds that certain jobs, like telemarketers and teachers, are more "exposed" to the technology than others, such as psychologists and counselors.
According to the researchers, the term "exposed" has a fairly broad definition, indicating professions may be impacted by AI in a number of ways, ranging from job loss to the inclusion of the technology to assist in some job functions.
Telemarketers topped the list of the 20 most exposed occupations to language modeling AI tools. Postsecondary English language and literature teachers came next, followed by foreign language teachers. Postsecondary teachers of history and other disciplines followed. In fact, teachers claimed nine of the top 11 spots.
These are the 20 most exposed professions:
Telemarketers English language and literature teachers Foreign language and literature teachers History teachers Law teachers Philosophy and religion teachers Sociology teachers Political science teachers Criminal justice and law enforcement teachers Sociologists Social work teachers Psychology teachers Communications teachers Political scientists Cultural studies teachers Arbitrators, mediators and conciliators Judges, magistrate judges and magistrates Geography teachers Library science teachers Clinical, counseling and school psychologists
The legal services industry was found to be the field that's most exposed to language modeling AI tools. Artist and athlete management, travel agent services and grantmaking and giving services were also among the most exposed fields.
Job augmentation or substitution?
AI researchers from Princeton, the University of Pennsylvania and New York University used the AI Occupational Exposure (AIOE) measure to determine jobs' and industries' "exposure" to advances in AI, leaving open the question of whether that exposure leads to augmentation or substitution. They isolated language modeling AI capabilities to hone in on its potential effects on the economy, given ChatGPT's rapid rise in utilization and popularity.
"We went about it by breaking down occupations by the portfolio of abilities that they require. Different jobs have different skills that are important," Manav Raj, one of the researchers, told CBS MoneyWatch. "We took an ability-based perspective and measured exposure to advances in different applications of AI at the ability level."
The researchers took 10 common applications of AI, including image generation like the app DALL-E and language modeling, which powers ChatGPT, and mapped them to different occupations to see how related AI tools' abilities were to various job skills.
"Given the excitement around ChatGPT and language modeling technologies, we thought it would be interesting to isolate their effects," Raj said.
Job roles likely to change
As a researcher at a higher educational institution himself, Raj said professors at the University of Pennsylvania are considering how new AI technologies will affect the jobs they do, including how professors go about creating assignments for students.
"There are a lot of really interesting things you can do with ChatGPT, like use it to create a syllabus or suggest readings," he said.
To him, the overlap makes sense. "The actual abilities educators use on a day-to-day basis involves a lot of content creation and working with language, and that is what these technologies focus on — those abilities," he explained.
In other words, the nature of work is more likely to change in these occupations exposed to AI.
While in some cases AI will replace workers, in others it will complement their skills and free them up to perform higher-level tasks that boost productivity — and companies' bottom line. What remains unknown is what kinds of new jobs will be created as a result of emerging forms of AI.
"It's still unclear exactly what that change will look like," Raj said. "Maybe it will mean automation and maybe workers will be more productive by using these technologies."
| 2023-03-08T00:00:00 |
https://www.cbsnews.com/news/jobs-chatgpt-occupations-industries-exposed-teacher-telemarketer/
|
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"date": "2023/03/08",
"position": 53,
"query": "artificial intelligence workers"
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|
Exploring Generative AI for Business Strategy - IBM
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Exploring Generative AI for Business Strategy
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https://www.ibm.com
|
[] |
Leveraging the IBM Garage methodology, IBM can help business leaders evaluate each generative AI initiative on how risky and how precise the output needs to be.
|
In the first part of this three-part series, generative AI and how it works were described.
IBM Consulting sees tangible business value in augmenting existing enterprise AI deployments with generative AI to improve performance and accelerate time to value. There are four categories of dramatically enhanced capabilities these models deliver:
Summarization as seen in examples like call center interactions, documents such as financial reports, analyst articles, emails, news and media trends.
Semantic search as seen in examples like reviews, knowledge base and product descriptions.
Content creation as seen in examples like technical documentation, user stories, test cases, data, generating images, personalized UI, personas and marketing copy.
Code creation as seen in examples like code co-pilot, pipelines, docker files, terraform scripts, converting user stories to Gherkin format, diagrams as code, architectural artifacts, Threat models and code for applications.
With these improvements, it’s easy to see how every industry can re-imagine their core processes with generative AI.
Leading use cases do more than simply cut costs. They contribute to employee satisfaction, customer trust and business growth. These aren’t forward-looking possibilities because companies are using generative AI today to realize rapid business value including things like improving accuracy and near real-time insights into customer complaints to reduce time-to-insight discovery, reduction in time for internal audits to maintain regulatory compliance and efficiency gains for testing and classification.
While these early cases and the results they’ve delivered are exciting, the work involved in building generative AI solutions must be developed carefully and with critical attention paid to the potential risks involved including:
Bias: As with any AI model, the training data has an impact on the results the model produces. Foundation Models are trained on large portions of data crawled from the internet. Consequently, the biases that inherently exist in internet data are picked up by the trained models and can show up in the results the models produce. While there are ways to mitigate this effect, enterprises need to have governance mechanisms in place to understand and address this risk.
As with any AI model, the training data has an impact on the results the model produces. Foundation Models are trained on large portions of data crawled from the internet. Consequently, the biases that inherently exist in internet data are picked up by the trained models and can show up in the results the models produce. While there are ways to mitigate this effect, enterprises need to have governance mechanisms in place to understand and address this risk. Opacity: Foundation models are also not fully auditable or transparent because of the “self-supervised” nature of the algorithm’s training.
Foundation models are also not fully auditable or transparent because of the “self-supervised” nature of the algorithm’s training. Hallucination: LLMs can produce “hallucinations,” results that satisfy a prompt syntactically but are factually incorrect. Again, enterprises need to have strong governance mechanisms in place to mitigate this risk.
LLMs can produce “hallucinations,” results that satisfy a prompt syntactically but are factually incorrect. Again, enterprises need to have strong governance mechanisms in place to mitigate this risk. Intellectual property: There are unanswered questions concerning the legal implications and who may own the rights to content generated by models that are trained on potentially copywritten material.
There are unanswered questions concerning the legal implications and who may own the rights to content generated by models that are trained on potentially copywritten material. Security: These models are susceptible to data and security risk including prompt injection attacks.
When engaging in generative AI projects, business leaders must ensure that they put in place strong AI Ethics & Governance mechanisms to mitigate against the risks involved. Leveraging the IBM Garage methodology, IBM can help business leaders evaluate each generative AI initiative on how risky and how precise the output needs to be. In the first wave, clients can prioritize internal employee-facing use cases where the output is reviewed by humans and don’t require high degree of precision.
Generative AI and LLMs introduce new hazards into the field of AI, and we do not claim to have all the answers to the questions that these new solutions introduce. IBM Consulting is committed to applying measured introspection during engagements with enterprises, governments and society at large and to ensuring a diverse representation of perspectives as we find answers to those questions.
Learn more about this three-part series by reading the first or third installment, and reach out to an expert for start a conversation about your needs.
| 2023-03-08T00:00:00 |
https://www.ibm.com/think/insights/generative-ai-business-value
|
[
{
"date": "2023/03/08",
"position": 13,
"query": "artificial intelligence business leaders"
}
] |
|
AI and Your Business: How to Unlock the Power of Artificial ...
|
AI and Your Business: How to Unlock the Power of Artificial Intelligence
|
https://www.shiftthework.com
|
[
"Joe Mechlinski"
] |
As an executive leader, you want new ways to stay ahead of the curve and gain a competitive advantage. Artificial intelligence (AI) is one way to do so.
|
As an executive leader, you're always on the lookout for new ways to stay ahead of the curve and gain a competitive advantage. Right now, one of the most exciting and transformative technologies available is artificial intelligence (AI).
AI has been a hot topic lately, with impressive programs such as MidJourney, Stable Diffusion, and ChatGPT gaining attention. ChatGPT, a program from OpenAI, has quickly become the fastest-growing consumer app in history with an estimated 100 million monthly active users. Its ability to generate human-sounding writing in any format and style is impressive.
People are using AI products in various ways – from creating parody videos and generating lyrics to writing news copy and even doing homework.
Microsoft has invested $10 billion into OpenAI and announced an AI-powered Bing homepage, while Google is set to launch its own AI chatbot named Bard.
AI can be broken down into three big areas: artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI).
ANI is focused on specific problems and is good for improving processes and efficiency.
AGI is like the Turing test, where computers can solve more complex problems.
ASI is beyond human comprehension and intelligence.
The median estimate for AGI is around 2040 and it’s around 2060 for ASI. The range of possible outcomes with AGI and ASI is vast.
AI is not a mysterious and complex technology, but rather a tool that can augment human capabilities and automate repetitive tasks. It has the potential to cause both positive and negative disruption in many industries. It is already embedded in our daily lives through technologies such as face recognition, predictive texts on our phones, and recommendations on our smart TVs.
AI can automate many tasks, improve customer experience, and help businesses make better decisions based on data analysis. However, it also poses risks such as job displacement, bias, and ethical concerns.
AI has the ability to find unexpected insights that can make a positive change in people's lives, but it is crucial to address the risks and ethical concerns that come with it.
From chatbots and predictive analytics to natural language processing and machine learning, AI has the potential to transform every aspect of your business from customer service and marketing to operations and finance. But with so many tools and applications out there, it can be hard to know where to start.
That's why we've put together this practical guide to help you unlock the power of AI and start taking advantage of its many benefits.
First, let's start with the basics.
What is AI?
At its core, AI refers to machines that can perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making. While it may sound like something out of a sci-fi movie, AI is already all around us.
Think of voice assistants, recommendation algorithms, fraud detection systems, autonomous vehicles, and the list goes on.
How can you as an executive leader and entrepreneur incorporate AI into your daily work and business?
The good news is that you don't need to be a technical expert or have a huge budget to get started. There are many user-friendly AI platforms and tools available to automate repetitive tasks, improve customer experience, and provide valuable insights based on data analysis.
One great place to start is with all things writing. AI tools can act as your co-pilot or first draft partner for creative writing like copywriting, blogging, content creation, social media posts, proposal writing, video scripts, and email marketing. By using AI to handle the more mundane aspects of content creation, you can free up more time and energy to focus on the bigger picture.
With so many AI tools and applications out there, how can you know which ones to focus on?
Start by joining online communities and groups where people are already leveraging AI, such as Telegram, WhatsApp chats, Facebook groups, or Slack channels. This will help you stay up-to-date on the latest developments and avoid being left behind.
Another key to success is to do your homework and research different AI applications to determine which ones will save you the most time, effort, and money.
Talk to customers and colleagues who have firsthand experience with AI tools and see how they've benefited from them.
Look for companies in your industry that are already using AI and learn from their successes (and failures).
How can AI help me reduce costs and increase efficiency in my business?
One way is predictive maintenance. AI can be used to predict when equipment will need maintenance, reducing downtime and repair costs. For example, the startup Augury provides an AI-powered predictive maintenance platform for industrial companies.
AI is also being used to optimize supply chain operations by reducing inventory costs and improving delivery times. For example, project44 offers an AI-powered platform for supply chain visibility and optimization.
How can AI help me drive a better customer experience?
AI can personalize the customer experience, which can improve engagement and loyalty. For example, Dynamic Yield is an AI-powered personalization platform for e-commerce businesses.
AI chatbots can handle simple customer inquiries, improving response times and reducing customer service costs. For example, Tars integrates AI in its website and WhatsApp chatbot platform for businesses.
How can AI help me create marketing content?
AI can be used to generate text, images, and videos, which reduces the need for manual content creation. For example, Copy.ai’s copywriting platform uses AI to quickly generates fantastic marketing content (like we did for this blog).
Many people use AI to curate content from multiple sources, reducing the need for manual research. For example, Curata is an AI-powered content curation platform for businesses.
How can AI help me make better decisions for planning?
AI can analyze and visualize data, which makes it easier to identify trends and patterns. For example, Tableau is a widely-used AI-powered business intelligence and data visualization platform.
AI is capable of predicting customer behavior and preferences, which can inform marketing, product development, and pricing decisions. For example, DataRobot is an AI-powered predictive analytics platform for businesses.
How can AI help me be even more competitive in the marketplace?
Looking to analyze competitor data? AI is capable of identifying strengths and weaknesses that can inform business strategy. For example, Crayon’s AI-powered competitor analysis platform helps optimize and manage client businesses.
AI can be used to optimize pricing, which helps businesses stay competitive while maximizing profit margins. For example, Revionics’ price optimization platform enables retailers to find pricing clarity, understand data, and enhance strategy.
What are a few of the AI-powered platforms that we use at SHIFT?
Our SHIFT team uses AI in a variety of ways to improve our productivity, efficiency, and customer experience. For example, we use OpenAI to assist with marketing materials, such as email campaigns, landing pages, and social media posts. This not only saves us time and effort but also helps us maintain a consistent brand voice across all channels.
We also use Fireflies.ai for notetaking during meetings and presentations. Fireflies.ai leverages natural language processing to transcribe audio and video recordings into text, which can be searched and shared easily. This allows us to efficiently capture and organize important information, share it with team members, and refer back to it later.
Rev is another valuable AI tool we use to transcribe audio and video recordings. Rev offers accurate and affordable transcriptions in a variety of languages, which saves us time and effort in manually transcribing recordings ourselves. This is particularly helpful for interviews, focus groups, and other research activities where we need to capture a lot of information quickly and accurately.
We use Grammarly to improve our writing. Grammarly’s AI uses natural language processing and machine learning algorithms to analyze and improve the clarity, tone, and grammar of our writing. This helps us to write more effectively and professionally, which is particularly important for our client-facing materials.
MidJourney helps us create compelling images for our marketing materials. MidJourney uses AI to generate high-quality images based on text inputs, which allows us to create unique and eye-catching visuals quickly and easily.
Calendly uses AI to assist with scheduling. It uses natural language processing (NLP) algorithms to understand and interpret the content of the user's calendar, email messages, and other data sources to suggest available meeting times and automate scheduling.
Calendly's AI algorithms can also optimize scheduling based on factors such as meeting duration, time zone differences, and user preferences. Additionally, Calendly uses machine learning algorithms to improve its suggestions over time based on user feedback and usage patterns.
For general queries, we rely on ChatGPT. ChatGPT is a conversational AI model developed by OpenAI that can answer questions and provide information on a wide range of topics. This helps us save time and effort in researching information and allows us to focus on more important tasks.
Finally, we use Latch to measure employee alignment and sentiment. Latch uses AI to analyze employee feedback and identify trends and patterns in employee engagement, satisfaction, and sentiment. This allows us to take proactive steps to address any issues and improve our workplace culture and productivity.
What will you do next?
There are many other AI solutions available for different business needs. It's important to assess your specific business requirements and research the available options to find the best AI solution for you.
The key is to identify the areas where AI can have the most impact and invest in the right tools and applications. It's also important to prioritize ethical and responsible AI practices and stay informed about the latest developments in AI technology and regulation.
AI is not just a buzzword or passing trend. It's a transformative technology that can help organizations become more competitive, efficient, and innovative. By embracing AI and staying ahead of the curve, you can unlock new opportunities and differentiate yourself in the market.
So, what are you waiting for? It's time to embrace your Ironman moment and see what Jarvis can do for your business.
Additional Resources:
| 2023-03-08T00:00:00 |
https://www.shiftthework.com/blog/ai-and-your-business-how-to-unlock-the-power-of-artificial-intelligence
|
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"position": 23,
"query": "artificial intelligence business leaders"
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|
Company \ Anthropic
|
Company \ Anthropic
|
https://www.anthropic.com
|
[] |
Anthropic is a collaborative team of researchers, engineers, policy experts, business leaders ... AI Companies are One Piece of a Big Puzzle. AI has the ...
|
Be good to our users.
At Anthropic, we define “users” broadly. Users are our customers, policy-makers, Ants, and anyone impacted by the technology we build or the actions we take. We cultivate generosity and kindness in all our interactions—with each other, with our users, and with the world at large. Going above and beyond for each other, our customers, and all of the people affected by our technology is meeting expectations.
| 2023-03-08T00:00:00 |
https://www.anthropic.com/company
|
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"date": "2023/09/01",
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The Future of AI in Education Opportunities and Challenges
|
The Future of AI in Education Opportunities and Challenges
|
https://thedatascientist.com
|
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AI has the potential to develop education by providing personalized learning experiences to every student based on their learning styles, interests, and ...
|
In the digital era, education and learning standards have modernized, and students have easier access to research and learning quickly and efficiently. Today, young children between 8 to 15 use smartphones and other apps to work on their educational projects, and digital books, audiobooks, and videos are available on various websites that enable students to gain knowledge of complex ideas and concepts. To gain in-depth knowledge about their project, university students read the blog post, e-mail questions to their teachers, and even have live chat with them via a videoconference. While working in collaboration on a project, students can share their presentations with other group members by applying technology-based tools such as wikis and Google docs, audio-visual aid, and AI tools, making it an ideal environment for pursuing a Data Science course. While working in collaboration on a project, students can share their presentations with other group members by applying technology-based tools such as wikis and Google docs, audio-visual aid, and AI tools.
It has been observed that many schools in the USA have transformed their teaching methods from traditional teaching methods such as using chalkboards for writing. Instead, students are encouraged to learn technological apps to enhance their learning skills. As AI technology continues to develop, many students use different AI tools to work on their assignments. It comprises NLP and machine learning algorithms, ChatGPT, AI presentation makers and an Text Generation program (AITG) that searches the topic of the articles on the web and creates original content based on the student’s requirement.
Significant AI Opportunities That Revolutionized The Education Sector
Personalized Approach To Education
AI has the potential to develop education by providing personalized learning experiences to every student based on their learning styles, interests, and abilities. AI systems adjust educational programs to make them more engaging for students. Consequently, interactive content will bring more effective learning outcomes for students of all ages. AI-powered virtual tutors improve the learning needs of children and provide an interactive learning experience. Additionally, for educational institutions like daycare centers, adopting specialized tools such as daycare bookkeeping software can simplify financial management, allowing educators to focus more on delivering quality care and instruction. Consequently, AI-enhanced assessment tools provide real-time feedback, tracking the progress of students and identifying areas of strength and weakness.
AI help Improve Language Learning
Students can accomplish their time-consuming and hard tasks, assignments, and multiple-choice tests through AI automation. Using chatbots, students can have live talks with teachers and group leaders. AI-powered language learning chatbots allow students to learn foreign languages. For instance, learners will have direct communication with bots. The students will ask questions and customized answers from the bots will simplify the foreign language learning process.
AI emotion recognition technology engages and keeps students enthusiastic about developing their learning trends. The tools boost knowledge and skills through fun and exciting activities. With play and amusement, students improve their social and intellectual skills.
The Duolingo app AI is the biggest language-learning platform. It has made language learning a pleasurable experience for learners. Anyone can use the app to learn a foreign language. Having practical exercises including audio and visual conversations has made language learning easy and convenient for learners. Duolingo ABC for kids and Duolingo English Test have proven methodologies that help teachers to create real-world conversation lessons of any language on the app. The new Duolingo AI system, Birdbrain embedded with learning lessons allow users to know their common language learning errors. BirdBrain’s algorithms ensure a better-personalized experience and enhance the language learning skills of learners.
AI helps students and educators create high-quality, adaptive, and engaging content that fulfills individual learners’ outcomes. For instance, the tools boost the interactive teaching skills of educators. They use AI tools to assess students’ knowledge levels and initiate practical exercises, and assignments that meet each learner’s requirement. The best AI tool that students can employ is Brainly. It is an excellent homework helper. It has significant features to make learning quick, easy, and amusing for students
AI assists teachers in making effective presentations. Employing machine learning tools and NLP technology allow teachers to create outstanding original content and make engaging headlines, and subheadings. If instructors have to write on a topic, they can employ AI tools. Within a few seconds, the AI-powered software searches various websites and gathers relevant information that matches the topic. Hence, they create a well-researched and informative article.
The theFacts101 content technology involving AI algorithms allows teachers to create digital textbooks, guides, and coursework. They can change textbook into smart study guide that explains core concepts clearly. The tools create multiple-choice questions that further improve the academic knowledge of young learners.
Gradescope helps teachers to administer online or in-class assessments of all grades. Employing this AI tool benefits instructors to create lengthy project assignments focusing on problem sets and project goals. Moreover, tutors can generate template assignments that comprise worksheets, quizzes, bubble sheets, and exam sheets.
For content analysis, QuillBot’s AI detector can help ensure the originality of student submissions and avoid plagiarism.
The tool allows teachers to digitally scan students’ work, give online detailed feedback, and send consistent and quick grades to them. The tool comes up as a uniform grader as it assists a tutor to assess the quality of the questions regarding a particular topic. Whether the questions are helpful for students or difficult to comprehend. Consequently, the tutors come to know how students are doing in the class and help monitor their performance.
Hence, using AI tools help educators to focus their time, teaching skills, and energy on learning emerging teaching methodologies and improving student engagement.
AI Help Students improve their visualization
Employing artificial intelligence tools, VR and AR technologies create engaging lessons for learners of every age. The tools create a better understanding of complex data and conceptual subjects. Help students improve their emotional intelligence and awareness. In addition, VR and AR tools increase communication and collaboration skills in young learners.
Genius 3D Learning tools allow students to adopt a more active approach to an interactive visualization-based learning experience. For instance, students can use 3D models to learn complex concepts with ease. Genius 3D Learning process comprises topic overviews of experts, 3D videos, exciting quizzes, theory lessons, imagery, and analytics. Students can practically test these exercises, and use analytics to track their progress. Moreover, it helps students to identify the essential skills they need to learn to boost their visualization skills and enhance their academic goals.
Wolfram Alpha helps students visualize complex mathematical concepts. It generates interactive visualizations of mathematical functions, graphs, and equations that allow students to know abstract mathematical concepts.
Another significant tool is Tableau. A data visualization tool helps students analyze and visualize large amounts of data. It helps students to create interactive dashboards, charts, and graphs to explore data and draw insights
Microsoft Mesh is allow teachers and students to interact with each other in a virtual space. They share ideas, work on projects, and explore new concepts collaboratively. For instance, students can participate in virtual field trips to explore historical sites or natural habitats. Mutually work on 3D models of scientific concepts and work on coding projects in a shared coding environment.
What Are The Challenges Educationalists Face While Adopting AI in Education?
As the significance of using Artificial Intelligence (AI) algorithms and systems in education has eventually increased, educators have consistently adopted AI tools in teaching and learning. Teachers utilize AI algorithms to analyze data on individual students’ learning progress and know about their strengths, weaknesses, and learning style. With AI tools, educators can design their lesson plans and use appropriate resources to create an effective curriculum that fulfills modern teaching standards. Consequently, AI algorithms help teachers to focus on interacting with their students. It helps boost student and teacher relationships. However, corporate training institutions face different challenges in adopting AI for education.
Create an effective public policy on AI for educational foundations
To further streamline your educational journey, consider utilizing AI-powered services that can do my paper for me, providing expert assistance and saving you valuable time for deeper learning experiences. As more technological advancements are taking place, public institutions should provide financial aid to educational foundations aided to help learners develop AI skills. Moreover, public policies should set a policy, allowing local and international organizations to work in partnership. It will help improve AI functions in many educational institutions. As more educational institutes emphasize using AI education tools to transform their teaching and students’ learning, state policies should provide sufficient monetary assistance to academies. They should be provided with funds and resources that provide innovative opportunities for AI in Education.
Governments should also invest in building academic centers of excellence to conduct AI research, gain AI scholarships and prepare AI experts.
Need for basic technological infrastructure in developing countries
The developed countries are unable to employ AI facilities in their educational institutes as they lack basic technological infrastructure such as unavailability of modern electrical equipment, ICT hardware availability, Consistent internet facility, data costs, and deficiency of ICT skills. Therefore, the state should take necessary measures to initiate new strategies that improve AI learning facilities in educational institutes.
Teachers should be empowered with AI training
It implies educators learn AI digital skills and employ them in their instructive methodologies. Teachers work and engage in extensive research and data analytical skills to improve their AI- education systems. The institutes should acquire inventive management skills to manage human and AI resources. Hence, AI tools allow learners to accomplish new skills and competencies.
Government policies should improve data collection and systematization
The state should develop educational system management by providing a quality and comprehensive data system. An inclusive data analytics system will help educational institutions to gain AI- predictive and machine-learning possibilities to transform data collection systems.
Manage ethics and transparency in data collection
It implies the institutes maintain ethical concerns while implementing AI in the education system. A transparent data collection system should ensure the protection of individual data and maintain privacy and ownership of users’ data.
How to Use Artificial Intelligence In Mobile Apps to boost teaching and learning
Personalized learning
AI-powered mobile apps can analyze a student’s learning style, strengths, and weaknesses to create a personalized learning plan. Using a machine learning app, the educators gather data on a student’s response time, accuracy, and preferred learning mode to create a personalized study plan. The app identifies areas where the student is struggling and a comprehensive plan. Students learn at their pace and focus on the areas where they need improvement.
Speech Recognition Technology
It ranges among common Artificial Intelligence technologies for mobile applications. It refers to a voice control system having speech recognition. For instance, they change natural human language and convert it into readable text using artificial intelligence (AI), machine learning (ML), and natural language (NLP) techniques. Today, many mobile app development companies have integrated AI functions into their applications help students to improve their pronunciation and grammar. The app provides feedback to help students improve their speaking skills.
Chatbots
Chatbots integrated into mobile apps provide users with conversational interfaces for various tasks. For instance, Chatbots are used to enhance the user experience of mobile apps by providing instant customer support, answering common questions, and interacting with customers effectively. To create a chatbot for their mobile app, professional mobile app development services can use chatbot platforms, comprising Dialogflow, IBM Watson, Microsoft Bot Framework, and Amazon Lex.
Adaptive learning
With the help of machine learning algorithms, AI-powered mobile apps can analyze students’ performance, and data and provide personalized learning experiences. The AI-powered mobile app tracks a student’s progress and identifies areas where the student is struggling. The app adapts help educators adjust their teaching approach, allowing students to improve their learning experience.
Data Analytics
Mobile apps can analyze student data by integrating AI algorithms to identify trends and patterns in their learning. Analyzing data such as student test scores, homework completion rates, and attendance records. Consequently, AI-powered mobile apps identify areas where students may be struggling and help teachers adjust their teaching methods. Educators can create interactive curricula that effectively meet the needs of each student.
Natural language processing (NLP)
uses artificial intelligence and machine learning, along with computational linguistics, to process text and voice data, and form a response. Moreover, NLP used in mobile applications facilitates natural language interactions between users and apps. It helps to initiate speech-to-text and chatbots. Employing NLP technology in Android, and iOS, mobile companies use NLP for healthcare apps. NLP assists in GPS navigation applications, voice recognition technology, and assist in google translation.
Machine Learning (ML)
Another significant ML is a type of AI that allows machines to learn and improve on their own without being explicitly programmed. In mobile applications, ML can be used to personalize content and provide recommendations based on user behavior and preferences.
Biometrics mobile app
A biometrics mobile app for students helps in biometric authentication that includes facial recognition and fingerprint scanning. : The app tracks using facial recognition or fingerprint scanning to verify students’ presence in the class. Moreover, the app using biometrics allows students to verify their identity before beginning the exam. Hence, it helps maintain exam security.
To recapitulate
Artificial Intelligence (AI) has the potential to transform education in many ways, including personalizing learning experiences, automating administrative tasks, improving accessibility, and providing real-time feedback to both students and teachers. AI helps improve students’ performance by providing real-time feedback to students. Helping teachers adjust their teaching strategies to improve students’ learning outcomes. However, many educational institutions face challenges in effectively integrating AI into their teaching practices. The challenges comprise ethical concerns and the high cost of AI tools that prevents many schools and educational institutions to adopt AI-powered technology in their education system. To further develop the education system, mobile app services have integrated AI technology into their mobile apps. Various apps analyze students’ learning styles, strengths, and weaknesses, provide real-time feedback and create a personalized learning plan. AI-powered mobile apps use speech recognition technology to help students improve their language skills. Hence, AI in mobile apps offers personalized, efficient, and engaging learning experiences to learners.
References:
| 2023-03-09T00:00:00 |
2023/03/09
|
https://thedatascientist.com/the-future-of-ai-in-education-opportunities-and-challenges/
|
[
{
"date": "2023/03/08",
"position": 64,
"query": "artificial intelligence education"
}
] |
Your Journey from AI Novice to AI Educator
|
Your Journey from AI Novice to AI Educator
|
https://www.theaieducator.io
|
[] |
Free resources so you can leverage AI in education with effective strategies, polices and ethical guidelines. Helping you create the future today in your ...
|
86 PAGE BACK-TO-SCHOOL AI GUIDE
I'll send you the bestsellingfor free used by teachers all over the world . You can opt-out at any time with no hard feelings 😉 Here’s ourif you like reading.
| 2023-03-08T00:00:00 |
https://www.theaieducator.io/
|
[
{
"date": "2023/03/08",
"position": 65,
"query": "artificial intelligence education"
}
] |
|
Teaching in the Age of AI
|
Teaching in the Age of AI
|
https://bokcenter.harvard.edu
|
[] |
This section of the website shares information about Harvard-supported AI tools, such as the HUIT AI Sandbox and OpenAI Edu license.
|
This section of the website shares information about Harvard-supported AI tools, such as the HUIT AI Sandbox and OpenAI Edu license. There is also information about adapting teaching for AI's impact, AI course policies for syllabi, and ideas for using AI to enhance learning.
| 2023-03-08T00:00:00 |
https://bokcenter.harvard.edu/artificial-intelligence
|
[
{
"date": "2023/03/08",
"position": 81,
"query": "artificial intelligence education"
}
] |
|
The Future of Education: How AI is Changing the Game
|
The Future of Education: How AI is Changing the Game
|
https://codeeasy.io
|
[
"Digital Marketer At Codeeasy",
"Last Updated On"
] |
Using AI-powered technologies, teachers and educators can design personalised learning experiences for each student according to their needs and learning styles ...
|
The Future of Education: How AI is Changing the Game
Swati Singh Digital Marketer at CodeEasy Last updated on 3/30/2025
Welcome to the era of innovation, where technology reigns supreme and possibilities are limitless! As technology continues to advance at an unprecedented rate, it’s no surprise that the education sector is beginning to feel the impact. Artificial intelligence, or AI, is one area poised to revolutionise how we learn and teach.
But what exactly does this mean for the future of education? Are you curious about how AI and technology might alter education as we currently know it? Believe me, when we say AI in education, we don’t mean that our children are being taught by Robots. But it’s about facilitating our teachers to offer a better learning experience for students. This article will cover how we can leverage AI in the education field.
Key benefits of Artificial Intelligence in Education
The education field is transforming thanks to artificial intelligence (AI). AI offers a wide range of benefits in education that were once thought to be impossible. Shall we get on with some of these key benefits? Here we go!
Personalised learning
First and foremost, AI can significantly enhance the personalisation of learning. Using AI-powered technologies, teachers and educators can design personalised learning experiences for each student according to their needs and learning styles. This means that students can now work at their own pace and focus on the areas where they need the most improvement. AI can also assist in identifying students who may be at risk of falling behind and provide additional support to ensure they stay on track.
Real-time feedback
Gone are the days of relying solely on traditional assessment forms, such as multiple-choice tests that only offer a snapshot of knowledge at a particular moment. With AI, teachers can have their finger on the pulse of their students’ understanding in real time! It has the ability to provide continuous and detailed feedback on a student’s performance. It allows educators to adapt their teaching methods to ensure their students receive the support they need to excel.
Automation of manual tasks for teachers
AI’s benefit in education is not just limited to the students but can also assist teachers in their day-to-day tasks. So, our teachers can say goodbye to mind-numbing admin tasks and say hello to more time, energy, and creativity in the classroom. AI-powered tools can help streamline and automate many tedious tasks that consume a teacher’s time, from grading assignments and tracking attendance to creating lesson plans. AI can free up more time for teachers to focus on what they do best - teaching 👩🏫.
AI-powered virtual assistants (Chatbot)
AI-powered virtual assistants in education are like having a personal tutor that never takes a coffee break. So, if students struggle to understand a tough maths problem or feel lost in a sea of assignments, the virtual assistant has his back. All they need to do is ask an AI-powered virtual assistant for help. They won’t judge you for not knowing the answer and won’t tell anyone you asked. It’s like having a secret study buddy who is always available, and it doesn’t hog all your snacks. Yummy! Isn’t it?😋
Challenges with Artificial Intelligence
As Uncle Ben told Spiderman, “With great power comes great responsibility.” And this applies to AI in education too. Like any other new technology, there are also concerns with AI which must be addressed. For instance, relying entirely on AI may lead to losing the personal touch and human connection in the classroom and a lack of out-of-the-box thinking. Therefore, we must use AI responsibly and ethically, with a keen awareness of its limitations and potential pitfalls. However, despite these challenges, there is no dispute about the potential of AI to transform education. We should embrace the change, stay up-to-date with the latest developments, and explore how we can leverage AI to improve the quality of education for all.
AIs such as ChatGPT will change the education landscape in years to come. It will help our educators offer personalised education and real-time feedback to students. Furthermore, it will enable equal access to educational resources and create an interactive learning experience by providing virtual communication and collaboration channels. However, these technologies are still in their early stages, and their exact impact on education is still being studied. So, let’s embrace the future of education and see where AI takes us!
Meanwhile, Check out our programming facilitation platform! CodeEasy is soon launching their updated version of the platform with AI integration. If you want to try the AI-powered CodeEasy, please register here to have early bird access.
| 2023-03-08T00:00:00 |
https://codeeasy.io/blog/the-future-of-education-how-ai-changes-game
|
[
{
"date": "2023/03/08",
"position": 84,
"query": "artificial intelligence education"
}
] |
|
AI Will Transform Teaching and Learning. Let's Get it Right.
|
AI Will Transform Teaching and Learning. Let’s Get it Right.
|
https://hai.stanford.edu
|
[
"Claire Chen"
] |
Stanford researchers, students, and industry leaders discussed both the potential of AI to transform education for the better and the risks at play.
|
When the Stanford Accelerator for Learning and the Stanford Institute for Human-Centered AI began planning the inaugural AI+Education Summit last year, the public furor around AI had not reached its current level. This was the time before ChatGPT. Even so, intensive research was already underway across Stanford University to understand the vast potential of AI, including generative AI, to transform education as we know it.
By the time the summit was held on Feb. 15, ChatGPT had reached more than 100 million unique users, and 30% of all college students had used it for assignments, making it one of the fastest-ever applications ever adopted overall – and certainly in education settings. Within the education world, teachers and school districts have been wrestling with how to respond to this emerging technology.
The AI+Education Summit explored a central question: How can AI like this and other applications be best used to advance human learning?
“Technology offers the prospect of universal access to increase fundamentally new ways of teaching,” said Graduate School of Education Dean Daniel Schwartz in his opening remarks. “I want to emphasize that a lot of AI is also going to automate really bad ways of teaching. So [we need to] think about it as a way of creating new types of teaching.”
Researchers across Stanford – from education, technology, psychology, business, law, and political science – joined industry leaders like Sal Khan, founder and CEO of Khan Academy, in sharing cutting-edge research and brainstorming ways to unlock the potential of AI in education in an ethical, equitable, and safe manner.
Participants also spent a major portion of the day engaged in small discussion groups in which faculty, students, researchers, staff, and other guests shared their ideas about AI in education. Discussion topics included natural language processing applied to education; developing students’ AI literacy; assisting students with learning differences; informal learning outside of school; fostering creativity; equity and closing achievement gaps; workforce development; and avoiding potential misuses of AI with students and teachers.
Several themes emerged over the course of the day on AI’s potential, as well as its significant risks.
First, a look at AI’s potential:
1. Enhancing personalized support for teachers at scale
Great teachers remain the cornerstone of effective learning. Yet teachers receive limited actionable feedback to improve their practice. AI presents an opportunity to support teachers as they refine their craft at scale through applications such as:
Simulating students: AI language models can serve as practice students for new teachers. Percy Liang, director of the Stanford HAI Center for Research on Foundation Models, said that they are increasingly effective and are now capable of demonstrating confusion and asking adaptive follow-up questions.
Real-time feedback and suggestions: Dora Demszky, assistant professor of education data science, highlighted the ability for AI to provide real-time feedback and suggestions to teachers (e.g., questions to ask the class), creating a bank of live advice based on expert pedagogy.
Post-teaching feedback: Demszky added that AI can produce post-lesson reports that summarize the classroom dynamics. Potential metrics include student speaking time or identification of the questions that triggered the most engagement. Research finds that when students talk more, learning is improved.
Refreshing expertise: Sal Khan, founder of online learning environment Khan Academy, suggested that AI could help teachers stay up-to-date with the latest advancements in their field. For example, a biology teacher would have AI update them on the latest breakthroughs in cancer research, or leverage AI to update their curriculum.
2. Changing what is important for learners
Stanford political science Professor Rob Reich proposed a compelling question: Is generative AI comparable to the calculator in the classroom, or will it be a more detrimental tool? Today, the calculator is ubiquitous in middle and high schools, enabling students to quickly solve complex computations, graph equations, and solve problems. However, it has not resulted in the removal of basic mathematical computation from the curriculum: Students still know how to do long division and calculate exponents without technological assistance. On the other hand, Reich noted, writing is a way of learning how to think. Could outsourcing much of that work to AI harm students’ critical thinking development?
Liang suggested that students must learn about how the world works from first principles – this could be basic addition or sentence structure. However, they no longer need to be fully proficient – in other words, doing all computation by hand or writing all essays without AI support.
In fact, by no longer requiring mastery of proficiency, Demszky argued that AI may actually raise the bar. The models won’t be doing the thinking for the students; rather, students will now have to edit and curate, forcing them to engage deeper than they have previously. In Khan’s view, this allows learners to become architects who are able to pursue something more creative and ambitious.
And Noah Goodman, associate professor of psychology and of computer science, questioned the analogy, saying this tool may be more like the printing press, which led to democratization of knowledge and did not eliminate the need for human writing skills.
3. Enabling learning without fear of judgment
Ran Liu, chief AI scientist at Amira Learning, said that AI has the potential to support learners’ self-confidence. Teachers commonly encourage class participation by insisting that there is no such thing as a stupid question. However, for most students, fear of judgment from their peers holds them back from fully engaging in many contexts. As Liu explained, children who believe themselves to be behind are the least likely to engage in these settings.
Interfaces that leverage AI can offer constructive feedback that does not carry the same stakes or cause the same self-consciousness as a human’s response. Learners are therefore more willing to engage, take risks, and be vulnerable.
One area in which this can be extremely valuable is soft skills. Emma Brunskill, associate professor of computer science, noted that there are an enormous number of soft skills that are really hard to teach effectively, like communication, critical thinking, and problem-solving. With AI, a real-time agent can provide support and feedback, and learners are able to try different tactics as they seek to improve.
4. Improving learning and assessment quality
Bryan Brown, professor of education, said that “what we know about learning is not reflected in how we teach.” For example, teachers know that learning happens through powerful classroom discussions. However, only one student can speak up at a time. AI has the potential to support a single teacher who is trying to generate 35 unique conversations with each student.
This also applies to the workforce. During a roundtable discussion facilitated by Stanford Digital Economy Lab Director Erik Brynjolfsson and Candace Thille, associate professor of education and faculty lead on adult learning at the Stanford Accelerator for Learning, attendees noted that the inability to judge a learner’s skill profile is a leading industry challenge. AI has the potential to quickly determine a learner’s skills, recommend solutions to fill the gaps, and match them with roles that actually require those skills.
Of course, AI is never a panacea. Now a look at AI’s significant risks:
1. Model output does not reflect true cultural diversity
At present, ChatGPT and AI more broadly generates text in language that fails to reflect the diversity of students served by the education system or capture the authentic voice of diverse populations. When the bot was asked to speak in the cadence of the author of The Hate U Give, which features an African American protagonist, ChatGPT simply added “yo” in front of random sentences. As Sarah Levine, assistant professor of education, explained, this overwhelming gap fails to foster an equitable environment of connection and safety for some of America’s most underserved learners.
2. Models do not optimize for student learning
While ChatGPT spits out answers to queries, these responses are not designed to optimize for student learning. As Liang noted, the models are trained to deliver answers as fast as possible, but that is often in conflict with what would be pedagogically sound, whether that’s a more in-depth explanation of key concepts or a framing that is more likely to spark curiosity to learn more.
3. Incorrect responses come in pretty packages
Goodman demonstrated that AI can produce coherent text that is completely erroneous. His lab trained a virtual tutor that was tasked with solving and explaining algebra equations in a chatbot format. The chatbot would produce perfect sentences that exhibited top-quality teaching techniques, such as positive reinforcement, but fail to get to the right mathematical answer.
4. Advances exacerbate a motivation crisis
Chris Piech, assistant professor of computer science, told a story about a student who recently came into his office crying. The student was concerned about the rapid progress of ChatGPT and how this would deter future job prospects after many years of learning how to code. Piech connected the incident to a broader existential motivation crisis, where many students may no longer know what they should be focusing on or don’t see the value of their hard-earned skills.
The full impact of AI in education remains unclear at this juncture, but as all speakers agreed, things are changing, and now is the time to get it right.
Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition. Learn more
| 2023-03-09T00:00:00 |
https://hai.stanford.edu/news/ai-will-transform-teaching-and-learning-lets-get-it-right
|
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"query": "AI education"
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{
"date": "2023/03/09",
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"query": "artificial intelligence education"
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] |
|
Understanding AI at Work
|
IFOW
|
https://www.ifow.org
|
[] |
Algorithmic systems used in recruitment and hiring can automate whom jobs are advertised to and who is hired. This can impact people's access to work in various ...
|
Stay up to date with IFOW research, insights and events.
You can unsubscribe at anytime by clicking the link at the bottom of our emails or by emailing [email protected].
Read our full privacy policy including how your information will be stored by clicking the link below.
| 2023-03-09T00:00:00 |
https://www.ifow.org/toolkit/ai-at-work
|
[
{
"date": "2023/03/09",
"position": 12,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/09",
"position": 73,
"query": "AI skills gap"
},
{
"date": "2023/03/09",
"position": 6,
"query": "future of work AI"
},
{
"date": "2023/03/09",
"position": 19,
"query": "AI labor market trends"
},
{
"date": "2023/03/09",
"position": 16,
"query": "machine learning workforce"
},
{
"date": "2023/03/09",
"position": 2,
"query": "AI labor union"
},
{
"date": "2023/03/09",
"position": 1,
"query": "AI employers"
},
{
"date": "2023/03/09",
"position": 5,
"query": "AI employment"
},
{
"date": "2023/03/09",
"position": 1,
"query": "artificial intelligence workers"
},
{
"date": "2023/03/09",
"position": 4,
"query": "artificial intelligence employers"
},
{
"date": "2023/03/09",
"position": 70,
"query": "artificial intelligence hiring"
}
] |
|
Human weakness 21 jobs Artificial Intelligence is replacing
|
Human weakness 21 jobs Artificial Intelligence is replacing
|
https://www.think-cloud.co.uk
|
[] |
AI is replacing human labour with more efficient and cost-effective computer algorithms, from content creators and data analysts to teachers and librarians.
|
We have always been scared of change, especially regarding technology and automation. However, if used correctly, AI can provide numerous benefits regarding efficiency and job opportunities. Automation can create new roles that don't yet exist and increase productivity across various industries.
Think of AI as the new car when everyone previously used a horse and cart for years. As long as companies use it responsibly, observe the ethical implications and know potential risks, AI can be a powerful tool to help move the world forward. AI is the path forward to our cars’ technological breakthrough - let's think of it as a rocket ship to fly us to greater heights!
While some jobs may become obsolete due to AI, new roles will emerge that require tech-savvy individuals versed in the use of AI. Ultimately, people will not be replaced by artificial intelligence alone - but rather by those trained to utilise it. By understanding AI's potential benefits and risks, organisations can make more informed decisions on the best use of this technology.
The ultimate goal is automation to empower people and help them do better work. Automation should be seen as an opportunity rather than a threat, assisting businesses to become more efficient and providing individuals with new job prospects. With the right strategies, AI can be a powerful tool for organisations, enabling them to reap its many rewards and improve their business practices.
| 2023-03-09T00:00:00 |
https://www.think-cloud.co.uk/blog/is-artificial-intelligence-making-you-obsolete-21-jobs-ai-is-replacing/
|
[
{
"date": "2023/03/09",
"position": 16,
"query": "artificial intelligence employment"
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{
"date": "2023/03/09",
"position": 91,
"query": "AI labor market trends"
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{
"date": "2023/03/09",
"position": 56,
"query": "artificial intelligence workers"
}
] |
|
How to Mitigate Employees' Fear of Being Replaced by AI
|
How to Mitigate Employees' Fear of Being Replaced by AI
|
https://www.lepaya.com
|
[
"Ravianne Van Vliet",
"Hallie Engel",
"September",
"Karolina Fesołowicz",
"April",
"October"
] |
AI is expected to create more jobs than it will destroy, leading to the emergence of new industries and occupations that require skills in data analysis, ...
|
• AI technology is disrupting the workforce, especially in knowledge and creative industries, leading to fears of job redundancy among white-collar employees. • HR leaders need to address employee concerns about AI replacing their jobs by educating, communicating openly, highlighting benefits, organizing feedback sessions, and providing training. • Jobs in customer service, marketing & communications, and graphic design are at risk of being disrupted by AI, while professions requiring emotional intelligence and creativity are less likely to be replaced. • AI is expected to create more jobs than it will destroy, leading to the emergence of new industries and occupations that require skills in data analysis, programming, and machine learning.
When we look back at 2023 in the future, we will probably mark it as the year AI technology deeply disrupted the workforce – especially in the knowledge and creative industry. With the advent of ChatGPT and other types of generative AI, many white-collar employees are nervous they’ll be made redundant. This fear of losing a job to technology is not new – ever since the Industrial Revolution, people have worried about mass unemployment due to machines revolutionizing the work floor. Instead of fueling this sentiment, HR leaders should deal with their people’s concerns. It’s time to flip the script: AI is not a scary science fiction scenario, but it can positively reshape the Future of Work – potentially contributing to happier and more productive employees.
1. Intro: About AI, Homer, and Hollywood
“Technology is the partner of progress.” Although that sounds like something Elon Musk or the late Steve Jobs would say, we’ve had this insight for thousands and thousands of years – just think of the invention of the wheel and the printing press. It was actually Homer who introduced the word ‘automata’ to humankind, and in Greek mythology there a plenty of examples of people envisioning how one day they would be able to imitate, augment and even surpass nature through a concept known as biotechne, which translates as “life crafted through the art of science.” It turns out that even in their time, the ancient Greeks were true ambassadors of techno-positivism.
Is Google Making Us Stupid?
Although technology has brought us an abundance of advancements and innovations that have transformed almost every aspect of our lives today, the fear of automation spans centuries. During the First Industrial Revolution (starting in the mid-1700s), skilled workers were afraid they would lose their jobs to machine manufacturing. When the steam train was introduced, farmers worried their cow’s milk would turn sour: the noise of the steel monster racing through the calm pastures would scare the living daylights out of their animals (it turns out, the cows were fine). More recently, in 2008, a now famous article in the Atlantic titled ‘Is Google Making Us Stupid?’ warned us about the Internet and how it is changing the way we think and process information – not necessarily for the better. Fast forward to today, and 23% of employees working in the software and tech industry are worried about losing their job because of ChatGPT, the game-changing AI model that uses deep learning to perform tasks like coding and computer programming. And they are not the only ones who worry, as this technology will likely disrupt other professions too – like marketing & communications, design, and finance.
We have seen lots of technological advancement in the history of humankind. Each time we worried it would replace human labor, but it turned out fine – human labor has just shifted focus. Technology aided instead of fully replacing the job.
Tobias Hoelzer, AI lead Lepaya
Hooray for Hollywood?
Then, there’s the influence of Hollywood. You only have to say the word ‘artificial intelligence’, and dystopian scenes from movies and series like The Matrix, Blade Runner, and West World come to mind, with self-aware machines and cyborgs ruling a post-apocalyptic world. Though widely inaccurate and far-fetched, these images have helped to popularize the concept of a “robot uprising” in popular culture, with technology taking over and making our jobs redundant. It also influenced people’s perception of artificial intelligence and its practical capabilities in this modern day and age.
You might also like: AI Skills of the Future: Understanding AI and How to Make it Work for You
2. Will AI Take Over The World One Day?
So, should we be afraid of AI? Can artificial intelligence replace human intelligence? Will robots steal our jobs? The short answer is no, at least not in the way that many people imagine. The idea of a global AI takeover might be a common topic in science fiction books and movies, but it is far from a realistic scenario. While AI is certainly becoming more powerful and sophisticated, achieving results that were previously inconceivable, it is still a long way from being able to plan an invasion and take over the world. That’s because AI is only as powerful as the algorithms and data that drive it. While AI can perform specific tasks with incredible accuracy and speed, it cannot have a vision or purpose on its own, nor can it deviate from the predetermined rules it was programmed to follow. As long as there are responsible individuals and organizations developing and monitoring AI systems, there is little risk of AI thinking independently and having a consciousness or moral compass. In fact, AI is applied to far more mundane matters than controlling our lives or threatening the planet. Rather, some might argue that it can open doors to countless opportunities, and its immense potential can even help make the world a better place.
On the other hand, others – like Elon Musk, who actually co-founded the company behind ChatGPT – do warn us about the risks of AI. “I think we need to regulate AI safety, frankly,” Musk said at the World Government Summit in Dubai recently. “It is, I think, actually a bigger risk to society than cars or planes or medicine. We need some kind of, like, regulatory authority or something overseeing AI development,” according to the Tesla founder. “Make sure it’s operating in the public interest.”
3. Fear of Being Replaced by Technology
One thing is for sure: AI has the potential to radically change how businesses operate. This modern-day technology is shaping the Future of Work, and its impact on the workforce is profound. Many industries are now replacing employees with technology, and we’re not just talking about lower-paid jobs that are repetitive, like flipping burgers or picking orders in a warehouse. Even highly skilled professionals aren’t immune to getting replaced by AI, and an increasing number of white-collar employees are nervous about the impact of AI in the workplace – including marketing managers, copywriters, journalists, and Wall Street traders. Headlines warning of the growing influence of AI often don’t help, leading to people walking around with the constant fear of losing their job and all the stress and uncertainty that comes with that concern.
The question of whether AI will replace human workers assumes that AI and humans have the same qualities and abilities — but, in reality, they don’t. AI-based machines are fast, more accurate, and consistently rational, but they aren’t intuitive, emotional, or culturally sensitive. It’s exactly these abilities that humans possess and which make us effective.
Harvard Business Review
4. How HR Leaders Can Deal with Employee Job Fear
HR leaders and people managers have an essential role to play in addressing the fears of AI amongst their workforce because this anxiety is very real – especially among millennials. In fact, 81% worry that AI technology will fully or partially take over their work. Here are some ways to mitigate employees’ fear of losing a job to technology:
Lead by example: one of the first steps to leading your employees through this transformation cycle is to educate yourself and develop a strategic view of how AI can add value to your business. Before talking with your employees about AI, you must fully understand what it encompasses yourself and how it potentially impacts your business and your team. Read about AI in your industry, watch Ted-talks, and take advantage of experts (like software vendors, thought leaders, and tech gurus) that can give you advice and practical tips
one of the first steps to leading your employees through this transformation cycle is to educate yourself and develop a strategic view of how AI can add value to your business. Before talking with your employees about AI, you must fully understand what it encompasses yourself and how it potentially impacts your business and your team. Read about AI in your industry, watch Ted-talks, and take advantage of experts (like software vendors, thought leaders, and tech gurus) that can give you advice and practical tips Communicate openly: to create a technologically advanced and employee-friendly workplace, it’s essential to make your people comfortable with the idea of AI. Explain what it will mean to them in their day-to-day work and how it will affect their activities and responsibilities. Also, be honest: it’s ok to be reluctant about new technologies, but not being able to adapt might have consequences
to create a technologically advanced and employee-friendly workplace, it’s essential to make your people comfortable with the idea of AI. Explain what it will mean to them in their day-to-day work and how it will affect their activities and responsibilities. Also, be honest: it’s ok to be reluctant about new technologies, but not being able to adapt might have consequences Highlight the benefits: explain how technological innovation can help your employees work more efficiently, reduce their workload, and enhance their job performance, freeing them from repetitive tasks to handle more intellectually stimulating projects. Emphasize that technology is meant to level up their work – not replace them
explain how technological innovation can help your employees work more efficiently, reduce their workload, and enhance their job performance, freeing them from repetitive tasks to handle more intellectually stimulating projects. Emphasize that technology is meant to level up their work – not replace them Organize feedback and brainstorming sessions: involve your people in the decision-making process when implementing new technologies. Brainstorm with your team how AI could benefit them professionally and personally, and ask for feedback. If you are mindful that people worry about being replaced by technology, they will feel valued and respected. It also allows them to voice their concerns
involve your people in the decision-making process when implementing new technologies. Brainstorm with your team how AI could benefit them professionally and personally, and ask for feedback. If you are mindful that people worry about being replaced by technology, they will feel valued and respected. It also allows them to voice their concerns Provide training: an important step in demystifying AI and preparing your team for the Future of Work is training and education. Invest in training sessions to help your teams develop the necessary skills to work alongside technology. This can include training on software, hardware, or other technological tools, but even more importantly, behavioral skills. To succeed in today’s fast-paced, digital workplace, employees must learn how to adapt to changes, work well in teams, take ownership, develop a growth mindset, and communicate their ideas genuinely and compellingly. It’s important to remember that, even as technology gets more advanced, there are specific human skills that AI can’t replace, like emotional intelligence and other soft skills – which will become increasingly important in the coming years
5. Jobs That AI Will Disrupt
Automation has already displaced manual labor in for instance the manufacturing industry, but with the advent of ChatGPT and other types of generative AI, other jobs will also be disrupted – particularly white-collar ones. So let’s have a look at three not-so-obvious professions that are at higher risk of being transformed by AI:
Customer service and support: chatbots and virtual assistants are becoming increasingly mainstream in customer service: nothing new so far. But with the rise of conversational AI, not only can chatbots handle customer inquiries and support, they can actually grasp the intent and the sentiment of the client calling or emailing. These bots can adjust themselves to any problem the customer has, respond accordingly, and by doing so personalize the customer journey. Additionally, people no longer have to wait to speak to a support representative, which reduces waiting time and improves their overall experience
chatbots and virtual assistants are becoming increasingly mainstream in customer service: nothing new so far. But with the rise of conversational AI, not only can chatbots handle customer inquiries and support, they can actually grasp the intent and the sentiment of the client calling or emailing. These bots can adjust themselves to any problem the customer has, respond accordingly, and by doing so personalize the customer journey. Additionally, people no longer have to wait to speak to a support representative, which reduces waiting time and improves their overall experience Marketing & Communications: you might think that marketing & communications are typical ‘human’ professions, but AI is already used to automate lead generation, customer segmentation, and predict market trends. Moreover, with the arrival of ChatGPT, AI can develop complete marketing and social media campaigns, writing compelling short copy and advertising texts. This can potentially reshape the role of copywriters in the advertising industry and beyond
you might think that marketing & communications are typical ‘human’ professions, but AI is already used to automate lead generation, customer segmentation, and predict market trends. Moreover, with the arrival of ChatGPT, AI can develop complete marketing and social media campaigns, writing compelling short copy and advertising texts. This can potentially reshape the role of copywriters in the advertising industry and beyond Graphic designers: DALL-E is another AI tool created by the company behind ChatGPT. It can generate and edit images and pieces of art in a matter of seconds and is seen as a potential disruptor of the graphic design industry and the world of visual content creators
6. Jobs That AI Can’t Replace
Yes, AI will disrupt a reasonable number of jobs, but it won’t replace all jobs. There will always be professions that require emotional intelligence and other unique, human skills that machines will never be able to develop. Think: creativity, critical thinking, negotiating, making deep connections with others, teamwork, and thinking outside the box – which is the bedrock of innovation. On the contrary: AI is designed to think inside the box, as it can only work with the flow of data it receives and detect patterns based on that input.
So which jobs are safe from AI? Here are three examples:
Psychiatrists and therapists require emotional intelligence, listening skills, and empathy to connect with their patients on a deep level. They need to truly understand human behavior and provide their patients with personalized treatment. Can you imagine yourself lying on the sofa and chatting with a robot about your fears and problems? We don’t think so
Most of us have played around with AI apps generating fantastic artwork, and the above-mentioned DALL-E can indeed alter the work of the graphic design industry. However, artists, photographers, and designers will never be fully replaced by AI, as human creativity is too complex to replicate. For now, it’s unlikely that AI will create work that is genuinely original and unique
It’s the core job of CEOs and other executives to make strategic decisions and provide vision and leadership to their organizations and people. AI can help them formulate KPIs and be a driving force behind the decision-making process. However, some leadership skills are impossible to be replaced by AI: networking, personal interaction and creative thinking for example
An AI tool like ChatGPT is particularly interesting because it affects white-collar workers, compared to blue-collar workers in the past. ChatGPT will not replace you as a white-collar worker though. A white-collar worker using ChatGPT will replace the ones not using advanced tools.
Tobias Hoelzer, AI lead Lepaya
7. AI and the Future of Jobs
Though many jobs will be reshaped due to technological advancements, the robots aren’t quite taking over yet. But the rise of artificial intelligence? That’s inevitable. It will change the world of work as we know it, and that can be a good thing. Because actually, AI is expected to create more jobs than it will destroy – completely new industries even. There will be new occupations we haven’t yet imagined, which McKinsey says could account for 10% of all jobs created by 2030. The development and implementation of AI require skilled professionals in areas such as data analysis, programming, and machine learning, but also in other fields. Take a look at this overview, compiled by the World Economic Forum:
At Lepaya, we help organizations adapt to the Future of Work by creating a culture of continuous learning. We build stronger teams and inspire future leaders, for instance by offering innovative learning experiences with VR Training and a dedicated AI coach.
TELL ME MORE
| 2023-03-09T00:00:00 |
https://www.lepaya.com/blog/mitigate-employees-fear-of-ai
|
[
{
"date": "2023/03/09",
"position": 22,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/09",
"position": 16,
"query": "automation job displacement"
},
{
"date": "2023/03/09",
"position": 23,
"query": "AI unemployment rate"
},
{
"date": "2023/03/09",
"position": 18,
"query": "AI skills gap"
},
{
"date": "2023/03/09",
"position": 35,
"query": "AI labor market trends"
},
{
"date": "2023/03/09",
"position": 29,
"query": "government AI workforce policy"
},
{
"date": "2023/03/09",
"position": 23,
"query": "machine learning workforce"
},
{
"date": "2023/03/09",
"position": 21,
"query": "artificial intelligence workers"
}
] |
|
WEC adopts Code of Ethical Principles in the use ...
|
WEC adopts Code of Ethical Principles in the use of Artificial Intelligence
|
https://weceurope.org
|
[] |
AI systems used in the recruitment and employment industry should be beneficial for individuals and society as a whole. They should be designed to augment human ...
|
Artificial Intelligence (AI) brings many promises to facilitate the journey to the world of work for both workers and employers: ensuring a better and faster matching between supply and demand of work, grounding labour markets in skills, and contributing to more inclusive labour markets.
Responsible and ethical use of AI is however a prerequisite to ensure that it helps both companies and individuals unlock their potential. That’s why the World Employment Confederation members, as leaders of the HR services industry, agreed a set of principles to guide the deployment of AI in the recruitment and employment industry.
At the core of the principles lies the need to keep the human element centric. AI systems used in the recruitment and employment industry should be beneficial for individuals and society as a whole. They should be designed to augment human capabilities, with clear processes in place to ensure that they remain under human direction and control at all times. Transparency, explainability and traceability should be guaranteed to understand how these systems arrive at their decisions.
Fairness, non-discrimination, diversity, inclusiveness and privacy – principles that WEC members also abide to in their overall practice of HR services – are also principles to be followed to guarantee ethical use of AI in recruitment and employment. While WEC principles state that those deploying AI systems remain at all times responsible and accountable for their use, the industry also believes that clear, transparent, and accountable governance frameworks must be in place. WEC is already involved with relevant stakeholders, such as governments, civil society, and academia, in the decision-making process for such frameworks at both global and European level.
Given the evolving nature of Artificial Intelligence, WEC’s code is a living set of principles that will be adapted over time. As for the principles enshrined in WEC’s overall Code of Conduct, WEC members have a duty to apply those ethical principles in their use of AI.
AI’s potential for better labour markets
AI can ensure a better and faster matching between supply and demand of work by on the one hand, helping companies assess the skills of their existing workforce, understand their in-house talent and how to develop it more effectively, and on the other hand, helping workers understand their own skills profile and develop it in the best way to achieve their career goals.
AI can also contribute to increase diversity, equity, and inclusion. It can help to mitigate the likelihood for unconscious bias in recruitment processes and improve the candidate experience.
| 2023-03-09T00:00:00 |
https://weceurope.org/news-post/wec-adopts-code-of-ethical-principles-in-the-use-of-artificial-intelligence/
|
[
{
"date": "2023/03/09",
"position": 28,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/09",
"position": 73,
"query": "artificial intelligence workers"
}
] |
|
Positive Impacts of AI And Robots on Jobs & Employment
|
Positive Impacts of AI And Robots on Jobs & Employment
|
https://www.onrec.com
|
[
"Stuart Gentle",
"Publisher At Onrec"
] |
Projections speculate that by 2025, AI and robotics will have created over 90 million new jobs. One shortcoming is that they need to gain the creative ability ...
|
As technology advances, people question its impact on jobs and employment. People have wondered how technologies like Artificial intelligence (AI) and robots will revolutionize job search and acquisition in different industries globally.
This concern has become important following the accelerated pace of automation and digitalization in different sectors. In this article, we will discuss some of the positive effects that the rise of AI and robots may bring to the employment landscape.
Tools for Humans
There's a possibility that AI will serve as tools for humans to perform their jobs better. For instance, the sports industry uses AI technologies that assist referees and officials in making the right call during sporting games.
We've seen these applications in the Hawk-Eye technology, used in various sports like crickets, tennis, and many more. In the health sector, AI can assist health professionals in carrying out examinations on intricate body parts, detecting disease faster, and automating specific processes that help improve patient treatment and analyze medical records. In 2019, for example, an ML system was developed by DeepMind Health and the Department of Veterans Affairs (VA) to predict life-threatening Acute kidney injury (AKI), which clinicians find challenging to detect.
There are also industries that are ripe for AI intervention. In the case of online casinos, it's a customer-centric service that aims to provide as wide an offering as possible and make the experience convenient and accessible. This is highlighted by the range of games as there are various options, such as jackpot slots and games including fixed and progressive, as well as Megaways, Jackpot King and other well-known titles. Adopting AI to improve chatbots and automate software development processes is something that could provide immediate returns by improving the customer experience and allowing online casinos to pivot to new trends quickly.
When AI is used as a tool, there's no need to eliminate existing jobs or create new ones. Instead, it aids humans in performing better and more efficiently.
Creating New Jobs
Projections speculate that by 2025, AI and robotics will have created over 90 million new jobs. One shortcoming is that they need to gain the creative ability and imagination that humans possess, making them only capable of performing activities for which they are trained. When training these AI models, a large percentage of human input is often required.
This means that the majority of the changes that will be needed to take AI and robots to their optimal level of performance will be made possible by humans. Existing technology is still undergoing testing and improvements, making them prone to errors. Therefore, the presence of a human factor is required to identify these errors and correct them.
Automating Dangerous Jobs
Robots indeed have the potential to automate certain dangerous jobs. Companies will most likely deploy them to do jobs that may appear risky, monotonous, and physically demanding. This way, they can reduce risks and costs and increase overall efficiency. They also may be used in situations requiring accuracy and precision beyond what humans can attain.
In oil exploration, for example, robots may perform seismic and aerial surveys to ensure the site is safe for exploration. They can be used in the inspection of bridges as opposed to the dangerous alternative of manned bridge inspections. This development will reduce the negative impact of these jobs on the health and safety of the human workforce.
Overall, there are lots of reasons to be optimistic about developments in AI and Robotic technology due to the reasons mentioned above. It's easy to consider the negative effects they could have, but it's hard to predict the future, and technology such as this is only likely to improve our lives.
| 2023-03-09T00:00:00 |
https://www.onrec.com/news/news-archive/positive-impacts-of-ai-and-robots-jobs-employment
|
[
{
"date": "2023/03/09",
"position": 41,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/09",
"position": 54,
"query": "job automation statistics"
},
{
"date": "2023/03/09",
"position": 41,
"query": "AI job creation vs elimination"
}
] |
|
Can We Predict the Jobs and Skills Needed for the AI Era?
|
Can We Predict the Jobs and Skills Needed for the AI Era?
|
https://www.rstreet.org
|
[
"Adam Thierer",
"Logan Seacrest",
"Marc Hyden"
] |
Many academics and policymakers regularly attempt to forecast the jobs and worker skills that will be needed going forward.
|
Because human needs and wants are infinite, and because we go on adapting to meet those needs and wants through persistent experimentation, people find creative and often unexpected ways to create new jobs and skills—or reinvent old ones—over time.
When it comes to preparing workers for the future, the great lesson of history is that policymakers cannot plan for every contingency or easily devise policies or programs to address every potential need. A certain degree of humility remains essential because our “epistemic ignorance,” or hubris concerning the limits of our knowledge, remains a chronic problem.
Show More
The future remains as uncertain as ever, and the relationship between humans and their machine creations continues to be dynamic and unpredictable. Many will cast this inherent uncertainty in a negative light, but it is why we should be optimistic about the future.
| 2023-03-09T00:00:00 |
https://www.rstreet.org/research/can-we-predict-the-jobs-and-skills-needed-for-the-ai-era/
|
[
{
"date": "2023/03/09",
"position": 54,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/09",
"position": 14,
"query": "AI regulation employment"
},
{
"date": "2023/03/09",
"position": 32,
"query": "government AI workforce policy"
},
{
"date": "2023/03/09",
"position": 94,
"query": "artificial intelligence workers"
}
] |
|
The Future of Retail: How Automation is Revolutionizing
|
The Future of Retail: How Automation is Revolutionizing
|
https://www.scripted.com
|
[] |
One of the most significant concerns is the potential for job losses. According to a report by Freethink, 65% of retail jobs could be automated by 2025 due to ...
|
The Future of Retail: How Automation is Revolutionizing the Industry
Embracing the future of retail automation
Photo by PhonlamaiPhoto's Images on Canva
As I strolled into my favorite consumer electronics retailer, I couldn’t help but notice a significant change. There were hardly any sales associates on the floor. Instead, a few self-checkout kiosks had taken their place, effortlessly scanning products and processing payments. Finally, it dawned on me that retail automation is changing the game. With e-commerce rising and customers demanding more convenience, retailers are turning to automation technologies to streamline processes and keep up with customer demand.
Understanding retail automation
So what exactly is retail automation? It refers to business process automation using technology to optimize various retail processes, from inventory to supply chain management. Automation solutions can range from essential inventory software to advanced planning systems that provide data-driven insights for informed decision-making.
One area where automation technology is making a significant impact is inventory management. In the past, retailers relied on manual methods of inventory data that were time-consuming and prone to human error. Now, with real-time tracking provided by automation software, retailers can keep shelves stocked and ensure that the right products are always available.
Another area where automation technology shines is in supply chain and inventory management software. By using automation software, retailers can track product movement throughout the supply chain — from warehouse to store shelves — to identify potential bottlenecks and make data-driven decisions for improved efficiency and reduced costs.
Advantages of retail automation
The advantages of retail automation are numerous and far-reaching. One of the most significant benefits of implementing automation is increased efficiency and productivity in retail operations. By automating time-consuming tasks such as data entry and manual inventory management, retailers can free up valuable time for more meaningful lessons like building customer relationships and providing exceptional customer service. In addition, retailers that embrace automation can increase margins by up to 60%, according to Bizagi.
Photo by anyaberkut on Canva
Another advantage of retail automation is cost savings. Retailers can significantly reduce their operating costs by streamlining processes and reducing the need for manual labor. Additionally, automation helps reduce the cost of human errors, which can be both time-consuming and expensive. According to Detroit Labs, reduced labor costs are one of the top benefits of retail automation software.
Perhaps most importantly, retail must implement automation technology that enhances the overall shopping experience for customers. Through automation technology, retailers can offer a personalized shopping experience to each customer by providing real-time inventory updates, faster checkout processes, and more accurate product recommendations.
By meeting or exceeding customer expectations with these conveniences, retailers can build a loyal customer base that will keep coming back. However, McKinsey reports that retailers must also rethink their operating models across stores, distribution centers, and headquarters to leverage automation technology’s benefits fully.
Challenges and risks of retail automation
As with any technological advancement, some risks and challenges accompany the retail sector and automation. One of the most significant concerns is the potential for job losses. According to a report by Freethink, 65% of retail jobs could be automated by 2025 due to rising costs and wages, tight labor markets, and reduced consumer demand. This can lead to job displacement and unemployment, significantly impacting local economies.
Another concern is the security of sensitive retail business information. With more data being collected and stored, there is an increased risk of cyber-attacks and data breaches. To prevent this from happening, retailers must ensure that their databases are secure by enclosing them within firewalls and keeping all sensitive business information confidential.
Photo by PhonlamaiPhoto's Images on Canva
Moreover, customer backlash is another potential challenge if automation technology is implemented to reduce human interaction and personalized customer service. Customers may have negative experiences with online stores and react negatively to automation if it is relied on too heavily.
For example, customers may feel frustrated or ignored if they need help to reach a human representative to resolve an issue quickly. Therefore, retailers need to maintain a careful balance between the benefits of automation and the need for human interaction to provide an exceptional shopping experience.
While automated systems can help streamline processes, they must maintain the personal touch and individualized attention customers receive from human sales associates. As such, retailers must find the right balance between automation and human interaction to ensure that customers feel valued and appreciated.
This is especially important given that personalized customer service can be a critical factor in customer loyalty and satisfaction. By finding the right balance between automation and personalization of customer experience, retailers can leverage the benefits of technology while still providing high-quality, personalized service to their customers.
Real-life examples of retail automation
Let’s look at some real-life examples of retail automation solutions in action. Amazon Go stores, for example, are fully automated convenience stores that use computer vision and machine learning technology to eliminate the need for checkout lines. Instead, customers can grab the needed products and walk out of the store, and their Amazon account will be charged automatically.
Walmart’s automated warehouse uses robots to move inventory around and fulfill online orders. The robots can pick up and transport products with incredible speed and accuracy, reducing the time and labor required to move products through the supply chain.
Robot-assisted inventory management is becoming increasingly popular in retail businesses, and self-checkout systems are now standard in many retail stores. These systems can improve the efficiency of the checkout process, allowing customers to scan and pay for their products quickly and easily.
Photo by SolStock by Canva
As we move forward, it’s becoming increasingly evident that automation technology will play a pivotal role in the future of the retail industry. Retailers wishing to remain competitive must embrace this change and adapt their hiring strategies by recruiting employees with technological expertise. To stay ahead of the competition, retailers must also be more innovative with their operating models and consider engaging gig workers.
Moreover, advanced analytics and market trends will help retailers make informed decisions about retail marketing based on data-driven insights. By consolidating data from various sales channels through automation technology, retailers can more accurately identify market trends and customer preferences. This information can then be used to create targeted marketing campaigns that improve customer retention.
As automation takes over many manual tasks in retail operations, new customer service roles may emerge shortly. These roles include in-home advisors who provide personalized shopping experiences for customers in their homes or customer retention specialists who create effective customer retention campaigns to increase customer loyalty.
Conclusion
In summary, the rise of retail automation products is changing the game for retailers. Although there may be risks associated with implementing automation technology, the benefits are undeniable. Retailers can achieve tremendous success in the market by optimizing workflows and improving customer satisfaction through automation. So let’s embrace this exciting future of retail automation!
Sources:
https://www.bizagi.com/en/blog/retail-automation-7-ways-your-business-could-benefit
https://www.detroitlabs.com/blog/the-top-5-benefits-of-retail-automation-software/
https://www.mckinsey.com/industries/retail/our-insights/automation-in-retail-an-executive-overview-for-getting-ready
https://www.freethink.com/robots-ai/retail-artificial-intelligence-robots
https://www.dragapp.com/blog/automation-and-personalization-balance-in-customer-support/
https://vispera.co/data-driven-decision-making-in-retail-heres-your-shortcut-to-the-future/
| 2023-03-09T00:00:00 |
https://www.scripted.com/writing-samples/the-future-of-retail-how-automation--152228aa-803a-413e-9480-ee97e83bb6f3
|
[
{
"date": "2023/03/09",
"position": 64,
"query": "automation job displacement"
}
] |
|
Jobs and pay for women haven't changed in 20 years
|
Jobs and pay for women haven't changed much in 20 years, says the ILO
|
https://www.weforum.org
|
[
"This Article Was Originally Published",
"United Nations"
] |
According to the UN labour agency, the global unemployment rate stood at 5.8 ... Day 1 at 'Summer Davos' 2025: AI, trade and the global economy in focus.
|
Women not only get paid less, but also have a harder time finding work than men, a new International Labour Organization report has found.
Almost one in four women, globally, are unable to find a job, compared with 16.6% of men.
Personal and family responsibilities, including unpaid care work are among the reasons why women are disproportionately affected in the search for work.
Significant differences exist between regions, with low and lower-middle income countries seeing far worse gender disparity in income, says the UN agency.
The jobs gap for women is a “stubborn and damaging reality of the global labour market” but it is particularly worrying in developing countries, the International Labour Organization (ILO) said, with almost one in four women unable to find a job, compared with 16.6 per cent of men.
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‘Bleaker picture’
That assessment is based on new data gathered from all people looking for work, as opposed to those registered as unemployed.
“It paints a much bleaker picture of the situation of women in the world of work…(it) shows that women still have a much harder time finding a job than men,” ILO said.
According to the UN agency, 15 per cent of working-age women globally would like to work but do not have a job, compared with 10.5 per cent of men, whereas unemployment levels are very similar for both sexes, “because the criteria used to define unemployment tend to disproportionately exclude women”.
Held back
Personal and family responsibilities, including unpaid care work are among the reasons why women are disproportionately affected in the search for work.
“These activities can prevent them not only from being employed but also from actively searching for employment or being available to work at short notice,” ILO said, adding that these requirements are a pre-requisite for unemployment registration.
Highlighting how the jobs gender gap has remained almost unchanged between 2005 and 2022, ILO pointed out that women continue to be overrepresented in many vulnerable sectors, such as running their households or working for relatives, rather than for themselves.
“This vulnerability, together with lower employment rates, takes a toll on women’s earnings,” the UN agency said. “Globally, for each dollar of labour income men earn, women earned only 51 cents.”
The pace of progress for women in the workplace is slow, further widening the jobs and pay gender gap. Image: United Nations.
Male pay bias
Significant differences exist between regions, with low and lower-middle income countries seeing far worse gender disparity in income, with women earning 33 cents and 29 cents on the dollar, respectively.
In high-income and upper-middle income countries, women’s relative labour income reaches 58 and 56 cents respectively per dollar earned by men.
“Gender imbalances in access to employment and working conditions are greater than previously thought, particularly in the developing world…the pace of progress is disappointingly slow,” said ILO.
According to the UN labour agency, the global unemployment rate stood at 5.8 per cent in 2022, below the average rates in the two decades before the pandemic, and it is projected to remain at that level in 2023.
Discover What's the World Economic Forum doing about the gender gap? Show more The World Economic Forum has been measuring gender gaps since 2006 in the annual Global Gender Gap Report. The Global Gender Gap Report tracks progress towards closing gender gaps on a national level. To turn these insights into concrete action and national progress, we have developed the Gender Parity Accelerator model for public private collaboration. These accelerators have been convened in twelve countries across three regions. Accelerators are established in Argentina, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico and Panama in partnership with the Inter-American Development Bank in Latin America and the Caribbean, Egypt and Jordan in the Middle East and North Africa, and Japan and Kazakhstan in Asia. All Country Accelerators, along with Knowledge Partner countries demonstrating global leadership in closing gender gaps, are part of a wider ecosystem, the Global Learning Network, that facilitates exchange of insights and experiences through the Forum’s platform. Have you read? Global Gender Gap Report 2024 In these countries CEOs and ministers are working together in a three-year time frame on policies that help to further close the economic gender gaps in their countries. This includes extended parental leave, subsidized childcare and making recruitment, retention and promotion practices more gender inclusive. Loading... If you are a business in one of the Gender Parity Accelerator countries you can join the local membership base.
If you are a business or government in a country where we currently do not have a Gender Parity Accelerator you can reach out to us to explore opportunities for setting one up.
| 2023-03-09T00:00:00 |
https://www.weforum.org/stories/2023/03/jobs-and-pay-for-women-havent-changed-much-in-20-years-says-the-ilo/
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"date": "2023/03/09",
"position": 66,
"query": "AI unemployment rate"
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Artificial Intelligence in Business: Applications & Best ...
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Artificial Intelligence in Business: Applications & Best Practices in 2023 – Webskitters
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https://www.webskitters.com
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[
"Atanu Sarkar"
] |
Instead of eliminating jobs, workers are expected to learn and adapt to AI capabilities. ... and assuring learning opportunities for career growth. Some of ...
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The recent hype about ChatGPT everywhere has left us wondering about the endless capabilities of Artificial Intelligence or AI.
It’s an AI-driven natural language processing tool that answers questions, types emails or code, and engages in different human-like conversations.
Even if you haven’t come across ChatGPT yet, we are sure that you are engaging with AI every day multiple times or at least once! It’s only because AI has become prevalent in every aspect of our lives, and we evidently encounter hundreds of examples of it.
Speculating how? Well, the maps we use for navigation, autocorrect options on keyboards or apps like Grammarly, and product recommendations on eCommerce sites– are some live examples of AI used today.
AI allows machines to think, interpret, and act like humans. From smart assistants like Siri or Alexa to Chatbots and face detection, AI is everywhere, and it is developing continuously. Not only AI is helping people become smarter but also transforming how we do business.
Now, this leads us to the big question i.e., what is the role of artificial intelligence in business? This blog answers this and discusses how AI is constantly revolutionizing every sector of business with its human-like intelligence.
Take a read through this comprehensive blog to know how is artificial intelligence used in different fields of business for scalability, growth, decision-making, competitiveness, and many more!
What is Artificial Intelligence?
Before we explore how AI impact businesses, it is important to learn what defines AI.
In simple terms, Artificial Intelligence or AI relates to the field of technology that concerns machines and computer programs to reason, learn, respond, and act like humans.
In other words, it is enabling machines with human intelligence by helping them collect and analyze data. That’s why the term ‘Artificial Intelligence!’
The way AI lets machines analyze data and find solutions exceed the way humans do it. Thanks to it, you can now make informed decisions much faster than before.
Technically, AI is a set of technologies that are based on Machine Learning (ML) and Deep Learning (DL), which makes use of computer sciences and vast datasets for intuitive problem-solving.
It concerns the development of smart machines capable of doing functions that require human intelligence.
Now, to further understand AI and how it is revolutionizing businesses, let’s explore ML and DL.
8 Real-Life Examples of AI-Enabled Products and Services
As AI is enabling machines to learn and make decisions based on data and analytics, it is helping individuals as well as businesses to fast-track their activities in many ways.
Here are a few real-life examples of AI-enabled products and services that will help you understand how.
1. Smartphone applications
Smartphones have become an indispensable part of our lives, and the built-in applications in them, like Siri and Google Assistant, are the most prominent examples of AI. Those are smart virtual assistants that take voice commands and respond with the use of AI.
For instance, if you want to send a message to your Dad that you will be late, just say to Google assistant “Okay, Google! Send Dad a text that I will be late”. The Google assistant in your smartphone will do that straight away.
Besides, there are many apps on mobiles today that use AI and help in functions like battery management, birthday reminders, weather forecasts, event suggestions, and so on.
2. Maps and Navigation
Another obvious example of an AI-based product that we use daily is Google Maps or Apple Maps for navigation. Today, instead of relying on directions from the locals, we can just type the destination and get the most optimal route from our starting point.
What’s even better, we get the route to travel by car, bus and walking. So, you can take an appropriate route suggestion, according to your travel mode.
AI allows the map to provide enhanced navigation with better and more realistic visuals. You also get information road blocks, traffic congestion, street names, house numbers, and so on.
3. Autocorrect or Text Editing Tools
If you have ever heard of or used Grammarly, then you know how AI even works on text editors to correct texts, like checking the spelling and grammar of writing. Even if you use a comma incorrectly, the editor will find the error.
The AI algorithms make use of machine learning, deep learning, and also natural language processing to suggest corrections in a write-up, whether it is a word processor or any texting app.
4. Chatbots
Any on-demand services or product-based apps that we use today are integrated with Chatbots! What are they?
They are simple conversational tools embedded in apps or websites that initiate interactions with customers just like a customer service representative of a company does.
Companies find it tiring to search for customer service representatives who are available 24×7 to respond to the queries, problems, or grievances of the customers.
Besides, it was expensive too! Now, with AI, they have chatbots that answer customers, take data, interpret their concerns, and resolve problems appropriately.
5. Smart cars
Automobile giants like Tesla are continuously looking for ways to include automation in vehicles using AI. It enabled using and sharing of information between the cars to help each other with efficient maneuvering amidst traffic.
The smart cars enabled with AI-based apps provide real-time information on road traffic, landmarks or whereabouts of a place, etc., immediately so that the cars can reach the desired location on time.
6. Music and video streaming
The applications of artificial intelligence are unquestionable in music and video streaming apps including, YouTube, Spotify, Netflix, and so on. The suggested playlists or movie/series recommendations that you get after playing one indicate how AI works!
It interprets your preferences or taste for a particular genre and comes up with more tracks or videos of the same.
7. Security and surveillance
The use of AI is also prevalent in the applications needed for security and surveillance services. Smart devices like intercoms, smart locks, intruder alarms, and smoke detectors are using AI to detect faces, objects, and unusual activities.
These devices are designed with AI to receive inputs, analyze them, and notify if there is any irregular occurrence or an unusual object.
For instance, the smart intercom speaks to the person on the door via video/voice call and automatically unlocks the door after recognizing the voice or face.
In case of an unauthorized visitor, it not only keeps the door locked but also sends an automated alert to the owner.
8. Social Media
Social media is also not much behind when it comes to using AI. It recognizes users’ search intents or preferences and provides users with content according to their preferences.
Twitter is the finest example known! It intelligently identifies hate speech, violence, fake news, and illegal content and blocks them.
It even takes down the account with continuous suspicious activities or sends a warning. Facebook and Instagram also do that.
How Does AI Impact the Working Segment?
With AI applications in business along with various fields of life, many are apprehensive about whether AI will replace humans and force them out of work. With AI automating many business processes, the fear is obvious among the working segment of the population.
However, technology experts are vehemently opposing AI will automate jobs whereas, some are expressing it as a daunting problem. Instead of eliminating jobs, workers are expected to learn and adapt to AI capabilities.
They are needed to support AI tools and make them turnkey solutions for business. It’s also expected that will drive relentless innovation in every sector, which will ultimately create new job opportunities.
Artificial intelligence truly allows humans to build a completely knowledge-based world where they need to leverage automation technologies to fast-track operations and enhance the delivery of services.
Technically, AI is capable of performing tasks only with human domination i.e., you need to apply and support the technology to get results and improve the way business operates.
The debate about whether AI will replace jobs will go on, but here we provided you with some clarity on the same.
The rise of AI will undeniably change how people work and bring a massive transition to the economy. But, it is anticipated to create more opportunities than it replaces.
AI and Its Impact Today on Business
AI is no more a buzzword or a predictable trend in business. It is apparent in every industry and sector, and is benefitting them in hundreds of ways!
From enhancing processes and efficiency to offering better convenience to customers, it is doing everything to help businesses grow.
To understand the role of AI in business, you must view it from the perspective of business capabilities rather than technology.
AI primarily influences business in three vital ways:
Process automation
Data analysis and cognitive insights
Customer engagement
Now, let’s look into each of these briefly to perceive what is AI for business and how it fast transforming the way the business world operates.
1. Process Automation
The common or first impact of AI in business is the automation of tedious physical tasks or data entry activities. Typically, these are all back-office work, including administrative and financial tasks.
It is known as robotic process automation or RPA, which is quite more advanced than earlier process automation tools. That’s because the code on the server called ‘robots’ input data and gather information from other IT systems just like a human.
Some of the other significant processes that AI helps with automation are:
Data transferring from emails or calling systems to the company’s record system.
from emails or calling systems to the company’s record system. Updating records of credit and debit cards into multiple systems after transactions.
of credit and debit cards into multiple systems after transactions. Extracting information for billing and other systems from various document types.
for billing and other systems from various document types. Reading instructional manuals, legal, and contractual information to extract essential information and provisions.
2. Data analysis and cognitive insights
The second crucial impact of AI in the business world is observed in the field of data analysis. The algorithms detect a pattern in huge datasets over time and interpret their trend or pattern. Most companies use machine learning applications to analyze huge volumes of data and predict insights:
What customers are likely to buy?
Identify unusual transactions or frauds.
Analyze warranty data for identifying safety and quality issues in the products (electronics, automobiles, etc.).
Automate targeting with personalized ads.
Providing accurate actuarial modeling to the insurers.
Cognitive insights by AI-based applications are useful for improving the performance of the jobs that machines do, such as programmatic ad buying, high-speed data crunching, and so on, which are typically outside the scope of human capabilities.
3. Customer engagement
AI is progressively used in businesses today to improve customer support services and enhance the overall customer experience.
Thanks to Chatbots and other intelligent agents, companies can now initiate direct interactions with their customers anytime, without the help of any sales agent.
The intelligent Chatbots or agents help them address myriad issues, including custom complaints, grievances, and queries. Interestingly, they do that in the natural language of humans.
However, the use of AI in customer support is not necessarily a threat to customer service executives or sales representatives. It’s because they are designed to handle only a certain range of issues or conversation types. Beyond that, a real executive is required.
In other words, cognitive engagement by Chatbots or AI-based apps are useful to reduce the wait time of customers who usually become impatient when they have an issue or grievance.
Some of the ways AI is enhancing customer engagement are:
24×7 customer support services.
Offering product/service recommendations to companies that help in personalization.
Answering customers on topics, including price, sales offers, company’s customer policy, and so on.
Send personalized messages or notifications to customers (birthdays, anniversaries, new product offers, etc.).
12 Widespread Applications of Artificial Intelligence in Business
The uses or examples of artificial intelligence in business are countless. The use of AI in business is accelerating at the fastest rate, and the scenario in every sector is changing now and then.
According to a report by IBM, the adoption of AI applications in business saw a huge surge in the wake of the COVID-19 pandemic. Facilitating online business operations became a prerequisite for companies to meet the demand of customers.
Most of the advanced tools of AI are accessible to companies today. According to the report, data security, customer support services, and automation of operations are the three primary areas where AI started making a major impact.
But, there are more ways or applications of AI in enterprises or companies. Learn here about them.
1. Automated processes
Enterprises need to perform hundreds of operations daily, most of them being repetitive tasks that are tedious and mundane for the employees.
These include operations like data entry, inventory management, HR and payroll management, etc.
AI-enabled software or applications enable them to automate hundreds of such operations so that employees can emphasize only the critical tasks.
A common example is how the customer service department works these days. Instead of manually responding to every customer with their basic queries, they use AI-powered intelligent Chatbots. Customer support executives take over only the complicated issues that are beyond the scope of Chatbots.
AI also helps in reducing errors in data entries and ascertains accuracy. If AI continues to automate operations in this way, soon enterprises will reach the level of maximum efficiency and productivity.
2. Informed decision-making
AI is performing even better than humans when it comes to decision-making! It’s almost impossible for the workforce to read and interpret a specific dataset from huge chunks of data. AI-empowered apps do that in seconds.
They read and analyze massive data available in the company’s database to identify trends, future scenarios, risks, and challenges.
Accordingly, the AI also suggests an appropriate action plan for the business aligning with the company’s objectives and missions.
We feel AI is more accurate in the case of data interpretation which helps companies in making informed decisions. They extract patterns and consequences from data that humans can never read. So, it’s saving time and efforts of the workforce that they invest in manual decision-making.
3. Improved productivity
This is a noteworthy improvement in business that came after AI successfully automated a huge range of operations. With automated workflows, employees could free their time from easy and repetitive tasks and perform vital operations better.
As a result, they can complete all types of operations faster, together with AI-enabled apps. This enhanced the company’s productivity manifold by increasing the efficiency of the operations.
4. Recruitment and talent acquisition
Recruitment processes involve a line of activities, including posting for jobs, reviewing candidatures, and lining up candidates for interviews.
These are just the initial steps involved in recruitment but there are many more next such as screening the candidates to handling documentation.
AI-powered HR applications eased each step of the recruitment process and helped HR executives select the most suitable candidates fast and efficiently. Here’s a small example of how AI helps. It infers the job description posted and automatically recommends the same to the candidates looking for a similar job.
AI also helps in screening candidates. From the interview performance, qualifications, and skills of the candidates, they summarize and suggest the list of the best candidates suitable for the job role.
5. Customer services
If there’s one field in business that is experiencing progressive transformation due to AI, then it is ‘customer services’.
There are most use cases seen in this field, from Chatbots for instant customer interactions to intelligent systems for identifying the preferences/buying behavior of the customers.
AI helped companies to better serve customers in a personalized way. It is technically not possible for the sales representative to manually collect all the customer information and purchase details to provide personalized recommendations later.
AI does that by drawing conclusions from massive customer data and purchase histories. Using predictive analysis, it suggests products to the companies that customers would need or like to purchase.
On the whole, AI empowered the customer support systems that will help you decide the next course of action and present the best options to your target customers.
6. Product recommendations
Businesses, especially product-based and eCommerce, are using AI to recommend products, i.e., cross-sell and up-sell. It tracks the orders, wish lists, or purchases of buyers and analyzes customer behavior.
It next recommends products based on what they viewed or purchased. Hence, there are maximum chances that customers will buy something that matches their interests or needs.
Another popular example of product recommendations that we come across every day is recommendations of playlists or video content on streaming apps like YouTube, Spotify, Amazon Prime, and Netflix.
7. Fraud and threat identifications
The scope of AI applications in business is truly unimaginable. Who could have thought that AI will prevent fraud and other security threats?
Multiple industries, including healthcare, hospitality, and real estate are using devices embedded with AI technologies such as sensors, motion detectors, and thermometers.
Smart security appliances like cameras and alarms are fed with data to identify problems that are beyond normal. It includes unusual temperatures, smoke/fire danger, suspicious movements, and so on. These helped companies to uplift their security level in every corner.
Another big example of AI saving us from fraud is seen in the financial sector. Many financial companies have AI-powered systems that use ML algorithms to detect fraud or any dubious transactions.
Whenever fraudulent activity is detected, the systems stop transactions and immediately send an alert to the required authority/customer.
8. Streamlining supply chain operations
Companies have been immensely benefitting from AI-based solutions in their supply and delivery functions.
They can predict the price and availability of materials, the distance the materials have to transport through the supply chain, and how fast they can be transported.
All these useful analytical data are helpful for companies to get accurate insights into supply chain operations. It helps them find the optimized way to procure materials and ship the finished products, thereby saving time and costs.
Another basic example of AI use in supply chain operations is the navigation apps that delivery executives use to find the most appropriate routes to their destination.
9. Audience segmentation
AI is enabling brands to hyper-target their customers i.e., reach the right audience at the right time with the right message.
Want to know how? It analyzes the customer data that a business gathers from different sources, including sales and social media platforms. It helps them understand the specific preferences of the customers and segment them into separate groups.
By understanding the needs of each group of customers, you can target your marketing campaigns appropriately to reach a suitable audience. In other words, AI helped increase the effectiveness of the marketing campaigns of companies.
10. Cybersecurity
Cyber security is full of challenges due to the vast threat of attacks across web and software applications. AI helps in vulnerability management and minimizing threats by understanding the network environment.
It also monitors the data center processes to identify threats, prevent them, and provide insights that are valuable for improving the data security level.
Businesses store the maximum amount of data in the cloud and that increases the risk of cyber threats. AI-based cybersecurity systems are now preventing breaches of data in the cloud network system.
Tech giants like Microsoft and Google are hence deploying AI-based cyber threat intelligence like RiskIQ and Mandiant to protect data from threats on the cloud platform.
11. Customer relationship management (CRM)
We already discussed how AI has altered customer support services and improved the satisfaction level of buyers.
So, on an obvious note, it also influences the CRM, an indispensable tool used by businesses today for improving customer experience.
The CRM makes use of the vast customer data collected from Point of Sales, emails, user-generated content in social media, eCommerce, etc., to help businesses in planning the next steps for sales and marketing.
While the integration of AI into the CRM is today, in the infancy stage, you can expect a lot of changes.
Using automated insights and predictive analysis, it helps you in improving the sales cycle, generate leads, curate personalized marketing campaigns, and reduce customer complaints/refund/ product recall.
12. Digital assistants
One of the prominent applications of AI in business is the digital personal assistants that make it easier for companies to perform a wide range of primary tasks, including adding events to the calendar, tracking expenses, filing customer queries, and so on.
Many digital assistants or IPA (Intelligent Personal Assistant) stores customers’ name and contact information but also remembers their buying behavior.
This helps companies to improve their customer satisfaction and loyalty by increasing customer engagement.
Some Prevalent Use Cases of AI in Prominent Industries
With innumerable benefits or uses of artificial intelligence in business, it is tough to imagine a world without it in the future. AI-enabled tools and applications are enhancing the productivity of businesses like never before.
From optimized workflow management to accurate forecasts, responding to customers preventing security threats, AI is changing businesses in many ways. Here are how a few sectors widely benefitted from the intervention of AI.
1. Healthcare
AI is bringing huge changes in diagnostics and treatment procedures, helping physicians and healthcare service providers to improve their services.
AI and ML tools are assisting the providers to analyze the data collected from the patient’s health records. It enables them to evaluate the diseases or conditions of patients faster and provide tailored treatment plans that are more appropriate.
Diagnostic centers are using various AI-enabled machines that examine and screen patients more accurately and provide physicians with precise inferences.
Some of the most common applications of AI in the healthcare sector that are trending at present are:
Medical imaging analysis
Smarter appliances for monitoring patients in ICU and CCU
Monitoring health parameters through smart wearables
Sending earlier warnings for conditions like sepsis by powering predictive analytics
2. Financial services
Today, AI is influencing the finance sector in a major way with most firms using machine learning tools to analyze their cash flows, detect frauds, and improve their credit scores.
It is also allowing financial companies to understand the legitimacy of each transaction by customers, which helps them in vital decision-making regarding loan approvals, portfolio management, trading, and so on.
AI isn’t just helping financial companies at large but also aiding individuals with the management of their finances.
Now, one can check balance, make instant payments, schedule monthly bills, and receive personalized recommendations for investments.
In short, it is helping them to improve their financial portfolio.
Some of the common examples of the use of AI in the financial sector today are:
Assessing customers’ portfolios and providing financial advisory services.
Analyzing patterns from large datasets to make decisions in trading.
Examining customers’ spending patterns to predict their borrowing behavior.
Reducing human errors in feeding huge sets of data into the systems.
Initiating customer interactions and responding to emergency needs of customers.
3. Industrial maintenance
Most industries today are using AI to advance their predictive machine maintenance.
They are using AI-enabled tools to examine the present condition of machines and assets to predict the possible time that they will serve. Subsequently, it helps them to know the appropriate time when the machine needs repair or maintenance.
Most factories are deploying AI with machine learning today to process data, predict plant performance, and enhance equipment reliability. This helps them in avoiding downtime or delays in processes and improves overall productivity.
Overall, AI helps estimate the life cycle of their machinery assets, schedule replacement or new purchases, and optimize their total costs of maintenance.
4. Transportation
While the first instance of AI application in the transportation sector is the self-driving smart cars that provide extraordinary navigation experience, it has many other applications too.
AI traffic management systems emerged lately that are used in smart cities to optimize traffic flow, which helped drivers and pedestrians get an optimum route and save time.
Some other exclusive use cases of AI in transportation that are prominent today are:
Face-scanning at airports
AI ride-hailing services
Delay predictions in flights, trains, etc.
AI-powered parking management
Road condition monitoring
How Artificial Intelligence Turned Useful For Your Business?
The impact of artificial intelligence in business is widely felt as every small or large enterprise has embraced it. It is helping them to make smarter decisions, better engage with customers, and streamline their management.
As a whole, it is a massive technological advancement that aided businesses to accelerate their growth in multiple ways. If you want AI to bring revolutionary improvements to your business, hire dedicated AI developers from one of the best tech firms.
Do you want to dig deeper and learn the uses of artificial intelligence in business operations? Here we have explained how it’s making remarkable advancements in each field of business.
1. Artificial Intelligence in Marketing
AI is helping businesses of all types to enhance their marketing efforts and reach the maximum audience. It is firstly enabling companies to automate their day-to-day marketing tasks such as scheduling emails, predicting and analyzing campaign performance, and tracking social media activities.
Apart from these, a big role that AI plays in marketing is content moderation and creation. It is helping businesses to produce content faster and promote them in the right way.
It is assisting content marketers big time to improve their marketing campaigns and heighten their campaign successes.
A few noteworthy ways AI is aiding businesses in their marketing aspect are:
Ad targeting and performance analysis
Effective AI platforms are helping businesses to create and target ads for the right audience. Most of them, such as the Levity.ai tool analyze the ad performance according to the targeted customer segment and hence, predict their success accurately.
Content creation and moderation
AI-powered tools help in generating content based on keywords, business area, and target audience. It also moderates user-generated content on social media platforms and prioritizes them based on your business.
SEO (Search Engine Optimization)
With the help of Machine Learning algorithms, AI is assisting marketing teams to identify the intent behind buyers’ searches as well as search trends. This enabled them to make better strategies for SEO.
Predictive customer analysis
By analyzing the data of previous purchases and interactions of customers, AI helps to understand your customers better. Subsequently, you can engage in a way to improve your relationships with them.
Image recognition
Many platforms, including Google Lens and FaceApp, can now read digital images, videos, or graphic inputs, and infer meanings from them due to AI.
This is helping marketers to deduce useful insights from thousands of pictures posted daily on social media and know how their products/services are used.
2. Artificial Intelligence in Sales
Most salespeople or executives struggle to meet their monthly or annual targets. To meet their targets, they are keen to adopt any technologies or solutions that look promising. While nothing was really that effective, AI-based solutions worked much better.
The success rate in the sales field grew exponentially. This poses a risk to firms or companies that are not adopting AI tools.
A recent Harvard Business Review revealed how AI has been constantly facilitating sales and assisting businesses to improve their bottom line.
From providing sales managers with self-assessment tools to accurate sales forecasting and augmenting customer engagement, AI has facilitated sales functions in many ways.
Here are some prevalent ways AI is assisting businesses to improve sales.
Targeting well by adapting to the buyers’ needs
Sales teams can quickly collect customer data and process insights to know about the intention and interests of buyers. So, they can adjust their services or modify products according to the buyers’ needs.
Enhanced personalization
AI tools helps the sales team to send personalized notifications or targeted messages to specific buyers after gleaning insights from the huge customer data. This helps them in prioritizing leads and generating more sales.
Access to real-time information or data
AI gives salespeople access to accurate real-time data anytime, which helps them in making informed decisions.
Track progress in sales
AI-enabled tools aid the sales team to monitor their progress across various sales channels by gathering information from multiple sources.
Demand forecasting
Sales teams can now forecast sales precisely with the help of AI-powered tools based on current sales and customer behavior patterns.
3. Artificial Intelligence in Customer Support
When it comes to understanding the relationship between AI and business, customer support services are a primary link.
All thanks to AI, companies can now better interpret customers’ emotions, predict their behavior, and solve their issues instantly. It not only escalated the level of customer experience but also helped enterprises to grow loyalty in their customers.
AI-powered apps like MonkeyLearn and IBM Watson Tone Analyzer are assisting companies in customer sentiment analysis and further improving their customer services.
A great example is how TripAdvisor is using its customer data from feedback and social media interactions to depict customer emotions, future trends, and opportunities for improving its services.
A few prominent ways AI is benefiting businesses by improving their customer services are:
Chatbots engaging in excellent customer interactions
From responding to the customers’ queries faster to reminding customers to order items left in the carts, collecting reviews, and asking them to rate, Chatbots are doing everything!
Enhances lead generation
AI tools facilitate the generation of leads by allowing companies to determine what customers want or what their pain points are.
For instance, AI is useful for companies to analyze the browsing trends of websites to determine what customers are looking for and subsequently, guide them to buy that.
Identify urgency and initiate actions
Using AI, companies are analyzing the interactions with their clients and potential leads to recognize the urgency. AI tools pick critical words like “I’d like to order” and frustrating phrases like “I am highly disappointed”, and “Won’t buy from you anymore” to handle customers’ concerns like a professional sales representative.
Stellar customer care services
With the help of AI-powered bots, companies are facilitating excellent customer care or support services.
An AI bot is available 24X7 to provide resolutions to customer complaints or grievances real quickly. Some of the bots even have multilingual support to help solve issues of global customers.
4. Artificial Intelligence in Operations
To understand why is artificial intelligence important in business, learn how it is transforming every field, including operations management. It is firstly reducing the workload of manual processes with machine learning.
AI-enabled tools do a lot in streamlining business operations. From automating repetitive tasks to eliminating human errors, creating predictive data models to making tasks efficient, they help businesses in their routine operations in many ways.
Workflow automation is a vital improvement AI brings in the aspect of business operations.
It raises the accountability of companies by facilitating centralized communications and allowing employees to access the real-time status of the operations. This reduces downtime and enhances the response time!
ML algorithms are also built to respond to data, and they tend to improve when fed more data.
Thus, when it comes to business operations, the ML algorithms identify patterns from the data and group them into separate clusters. This helps companies to make better decisions whether it’s about processes, product development, supply chain operations, and delivery.
These are the two critical ways AI helps improve operations in business.
Automation of routine operations
AI-enabled devices or smart machines are simplifying processes and doing mundane tasks freeing up personnel for more productive activities.
For instance, the RPA (Robotic Process Automation), a critical component of AI is using software robots or simply ‘bots’ for complete back-office jobs, which include filling in forms, feeding and retrieving data, and forming predictions based on the data.
Predictive analysis of various datasets
This is perhaps the most vital way for AI in business operations that help in determining any anomalies or abnormalities in functions.
So, technically, it helps to avoid any critical situation or catastrophe in your business!
Streamline supply chain operations
A majority of business, including retail manufacturers, uses AI-powered tools to keep track of every aspect of their value chain, starting from manufacturing and distribution to resource deployment and shop floor management.
Inventory management
Using image recognition, an ML ability of AI assists businesses in inventory management. It uploads image data of the products, categories/labels them, and even identifies defective items.
5. Artificial Intelligence in Human Resources
When it’s about using AI for business, organizations are continuously employing AI-based technologies to source the right talents efficiently.
It helps in virtual recruiting (which became common in business after the COVID-19 pandemic) as well as in the usual recruitment processes.
From analyzing the profiles of suitable candidates to finding the best candidates from applications, AI undertakes a whole lot of functions that help Human Resources (HR) managers source talents efficiently.
AI helps HR teams to overcome common challenges of sourcing, which include employee retention, determining wages or compensations according to workforce expectations, and assuring learning opportunities for career growth.
Some of the key ways AI is aiding companies in HR management are:
Sourcing
ML algorithms pick up relevant words from resumes submitted on online job portals to help companies find candidates that are suitable for the jobs. It also recommends that prospective candidates apply for vacant positions according to their job preferences.
Screening and interviewing
AI benefits companies largely during the interview process reminding them about scheduled interviews, providing details of candidates, assessing the skillsets of candidates, and preparing a questionnaire for the HR managers.
Selection
AI-based applications also help HR recruiters to compare candidates based on their interview performance, prepare individualized job offers, and anticipate candidates’ intentions regarding the job offer.
Onboarding
The last step of talent acquisition i.e., the onboarding process also gets easier due to AI.
It helps recruiters ease their onboarding processes which involve loads of documentation, signatures, access and authorization, and orientation.
6. Artificial Intelligence in Accounting
One of the remarkable advantages of AI in business as mentioned earlier is the automation of tasks that results in better accuracy and efficiency of operations.
Accounting is a crucial field in business that involves a lot of repetitive tasks like uploading files, entering data (cash flow, expenses, sales, etc.), classifying data according to correct accounting codes, and so on. Since AI promises to make no errors, it has a demanding role in accounting processes.
AI-empowered tools are using ML to enhance the practices for accounting. It aids in almost all types of accounting processes including the generation of financial statements, payroll, taxation, and auditing by enabling automation.
Some of the prevalent use cases of accounting in business are:
Performing repetitive accounting tasks
AI assists the finance and accounting personnel to avoid tedious tasks like data entry, recording day-to-day transactions and classifying them, reconciling accounts, correlating data from invoices or receipts, evaluating operational and employee expenses, tracking sales, and generating annual reports.
Semi-automation of critical processes
AI-based tools are likely to change the future of critical processes in accounting, such as payroll. With brilliant software applications that are mostly cloud-based, AI is making the tasks of payroll easier, such as checking working hours, making monthly payments, calculating overtime, issuing tax forms, etc.
7. Artificial Intelligence in Business Analytics
Most modern businesses are driven by the huge amount of complex data, which resulted in the adoption of AI-based analytical tools.
They are assisting businesses to have an intuitive risk management model, determine customer engagement, analyze risks in the supply chain, and created targeted campaigns based on customer data.
AI in business analytics is indeed a product of automation of data analysis, which is a usually time-consuming and resource-intensive task. It not just seamlessly analyzes the structured dataset of a business but also the unstructured or raw data using NLP (Natural Language Processing), image/video analytics, and speech analytics.
Some of the commendable ways AI supports business analytics are:
Driving innovation
Analytical tools are performing a comparative analysis of products that will drive innovation and improvements in the existing products.
This helps in creating new products that are better than the rivals and can attain a competitive edge in the industry.
Risk management
No businesses operate without risks or uncertainties. Companies can predict risks and prevent operational disruptions and hence subsequent losses. AI analytical tools perform big data analysis and enhance their risk management model.
So, you can make smarter decisions to avert or minimize risks and save costs.
Customer sentiment analysis
It is an automated AI-enabled process that helps you to discover what customers perceive about your brand, products, or services.
Seamless supply chain management
Supply chain managers and executives use AI tools to extract data about various processes. They use data-driven information to identify challenges and solve them to streamline operations and make their supply chain efficiency.
Challenges to Overcome in AI
While the benefits of AI in business are far-reaching, never overlook the challenges that come across while adopting or implementing AI-driven tools.
That’s why many companies are still reluctant about using AI in their operations.
Here are a few common hurdles that you can come across while adopting AI technology in your business.
1. Lack of awareness
While AI is the recent buzzword everywhere, many businesses, especially small enterprises are still not aware of AI applications in business. For them, identifying the benefits of AI and quantifying them is a major challenge.
When they are fully not aware of the advantages of AI, they become non-interested to learn how is artificial intelligence used in various aspects for improving their business.
2. Inadequate skills
One of the major hurdles to adopting artificial intelligence in business is the lack of skills and knowledge. You need technically competent and knowledgeable professionals who can operate AI-powered tools and get the desired job done.
For instance, AI is now able to interpret a vast amount of sales data and predict trends or patterns in the market. It helps them in discussing future scenarios or outcomes and make decisions based on them.
Thus, your employees must know how to feed the AI-powered tools with the data and make them capable of interpreting data just like they do.
3. Preparing the right dataset
If you are ready to fully explore the capabilities of AI, it is necessary to ensure that you have the right or required data set on your end. The dataset should be properly classified or structured, secured, and accessible.
Unfortunately, many businesses still lack proper resources for data management. They generate clusters of data that are not arranged. So, it becomes impossible for AI algorithms to provide accurate insights or formulate decisions based on disordered datasets.
4. Bias problem
AI systems work based on certain datasets that they are trained for. Those are good data, and hence, artificial intelligence services depend on them. When your company lacks good data, it can fail to implement its AI tools.
Low-quality data produced based on assumptions are never appropriate for AI analysis resulting in anomalies in the predictions or outcomes.
Low-quality data are often developed based on bias. It’s essential to eliminate any such biases and make AI only responsive to unbiased data.
5. Data security
Most AI applications work based on huge volumes of data. Although those chunks of data are useful for business in forecasting situations or decision-making, it also creates data security issues.
The more data you generate for the application of AI in your business, the more users/systems have access to them, and the greater the chances of data security breaches or privacy thefts.
That is why to have AI applications in your business operations, you must have proper data security management.
The more data you generate for the application of AI in your business, the more users/systems have access to them, and the greater the chances of data security breaches or privacy thefts. That is why to have AI applications in your business operations, you must have proper data security management.
What Awaits Us in the Future of AI?
Whatever the present scenario regarding artificial intelligence, it’s obvious that AI is going to rule in the future. As AI is further advancing technology, businesses, and startups are increasingly implementing AI tools to their advantage.
From next-gen consumer experiences to personalized recommendations, solving complex problems to generating the content, it’s doing everything.
AI is explored today in businesses for its unprecedented ability to interpret and analyze datasets and even discover patterns from them. It is assisting them in making smarter decisions, analyzing customer interactions, forecasting sales, predicting market trends, data automation, and many more.
In other words, it is assisting businesses in diverse ways to become competitive in the fast-paced business world. Given this scenario, businesses are likely to use more AI in near future!
While many are apprehensive that AI is going to replace the workforce with its human-like intelligence, it is not the case. AI will be a huge part of our future economy but it will need human intervention to be as effective as possible.
Artificial intelligence is not going to replace humans but is going to augment the intelligence of humans manifold, making them more productive than ever.
Deciding Whether Artificial Intelligence is Right for Your Business!
AI is helpful in plentiful ways for businesses, regardless of the industry they belong to. To leverage the power or usefulness of artificial intelligence in business, you don’t even have to be technically upgraded or an expert.
Thousands of companies, big or small, are today developing their own AI platforms that enable them to analyze data, fast-track processes, reduce repetitive tasks, and make smarter decisions quickly.
In a nutshell, AI is aiding businesses in multiple aspects, from sales and marketing to accounting and data analysis, while reducing the workload of employees. It also helps to avoid errors, prevent crises, and save costs!
If you are thrilled by the all-around advantages of AI in your business, get in touch with Webskitters Technology Solutions Private Limited. We are a pioneering app and software development company in the USA and are leveraging AI technology to build out-of-the-box applications for businesses. Get in touch to know how!
Frequently Asked Questions
1. What are the key attributes of AI?
AI or Artificial Intelligence is a broad-ranging technology concept that comprises five main attributes, which are Data Ingestion, Adaptive, Reactive, Forward-Looking, and Concurrent.
Data ingestion is an attribute because AI deals with chunks of data, whereas AI also needs to adapt to the circumstances constantly due to machine learning which makes it adaptive.
AI tools also are reactive because they need to change according to the environment and situations around them. The forward-looking attribute is also considered for AI because it often predicts results based on possible scenarios.
Lastly, AI tools are powered to handle multiple data, multiple people, and multiple systems at a time and hence, the attribute concurrent is a vital one to consider.
2. What are the most common AI models?
An AI model is a software program designed to perform certain tasks and provide results like identifying some common patterns based on a set of data.
Typically, AI has multiple models but the most common ones known and applied today are Linear regression, Logistic Regression, Deep Neural Networks, Decision Trees, and Support Vector Machines.
3. Which is the most advanced AI?
If you are wondering which is the most advanced AI today, then it is Deep Learning! It is a form of AI in which engineers or programmers train machines or systems by feeding them a vast amount of data.
Machines use the data to solve problems, forecast situations, make decisions, and perform useful tasks.
4. What AI do we use every day?
AI is everywhere! We come across hundreds of examples daily, like voice assistants on phones, digital assistants like Siri/Alexa, Face unlock, Google Maps for navigation, similar product recommendations on shopping apps, reminders for monthly bill payments/billing cycles, and Spam/fraud detection.
5. What are the main problems AI can solve?
Some of the real-life problems as well as business problems that AI is helping to solve are:
Customer issues
Analysis of huge datasets
Demand forecasting
Image/video recognition
Predicting customer behavior or pain points
Accurate diagnosis of diseases
Eliminating hate speech or obscenity in social media
6. What are the latest AI trends?
Most organizations are embracing AI technology and increasingly improving their bottom line in multiple ways. Some of the key AI trends prevalent today and are going to prevail are:
AI for text editing/correction, speech, and image
Generative AI for art and content
Adaptive AI for sharpening customer experiences
AI-powered assistants
Personalized learning
Fraud prevention
7. How AI can help small businesses?
Today, AI and business go hand in hand. It has been assisting businesses to scale and grow exponentially.
Businesses of all sizes and types are benefitting from AI and rather achieving a competitive advantage in the industry. AI is helping small enterprises to grow and take their business to the next level.
Improving customer services Automation of process Boosting cybersecurity Developing better marketing campaigns Nurturing customer relationships
| 2023-05-25T00:00:00 |
2023/05/25
|
https://www.webskitters.com/artificial-intelligence-in-business
|
[
{
"date": "2023/03/09",
"position": 64,
"query": "AI job creation vs elimination"
}
] |
Why Artificial Intelligence Is the Future of Cybersecurity
|
Why Artificial Intelligence Is the Future of Cybersecurity
|
https://www.eccu.edu
|
[
"Ec-Council University"
] |
... and creating innovative methods of identifying cyber threats. This article will explore AI in-depth and its current and potential use cases in cybersecurity.
|
Artificial intelligence (AI) is revolutionizing the world we live in, and one area where its impact is particularly significant is cybersecurity. AI and cybersecurity have been commonly associated, as AI has been utilized in cybersecurity for a while now. However, the advancements in AI technology will lead to more sophisticated applications in digital safety, including new implementations that have not yet been explored.
AI is already being used extensively in cybersecurity, powering antivirus software, detecting hackers using machine learning, and creating innovative methods of identifying cyber threats. This article will explore AI in-depth and its current and potential use cases in cybersecurity.
Some of the current applications of AI in cybersecurity include:
Threat Detection: AI is used to find threats and possible attacks on systems and networks. Machine learning algorithms can look at a lot of data, find patterns, and find outliers that could mean a cyberattack is happening.
AI is used to find threats and possible attacks on systems and networks. Machine learning algorithms can look at a lot of data, find patterns, and find outliers that could mean a cyberattack is happening. Management of Vulnerabilities: AI can make it easier to find bugs in software and systems by automating the process. AI can look at code and find possible security holes. This lets cybersecurity teams fix the problems before they can be used to do harm.
AI can make it easier to find bugs in software and systems by automating the process. AI can look at code and find possible security holes. This lets cybersecurity teams fix the problems before they can be used to do harm. Malware Analysis: AI can be used to look at malware and figure out how it acts. This lets people respond faster and protect themselves better from future attacks. Malware detection tools that use AI can look at how files act and find any strange behavior.
AI can be used to look at malware and figure out how it acts. This lets people respond faster and protect themselves better from future attacks. Malware detection tools that use AI can look at how files act and find any strange behavior. User Authentication: AI can help strengthen user authentication processes by analyzing user behavior, identifying anomalies, and detecting potential attacks. For example, AI can analyze the way a user types or moves their mouse to determine if they are who they claim to be .
AI can help strengthen user authentication processes by analyzing user behavior, identifying anomalies, and detecting potential attacks. For example, AI can analyze the way a user types or moves their mouse to determine if they are who they claim to be Password Management: Artificial intelligence can help keep track of and protect passwords by analyzing password patterns and finding weak passwords that are easy to guess or hack. AI can also tell when a user tries to use a password that has been hacked and tell them to change it.
In the field of cybersecurity, AI is used in many ways to improve threat detection, vulnerability management, malware analysis, user authentication, and password management. In the next few years, the market size for AI in these areas is expected to grow a lot, which shows a growing need for AI-powered cybersecurity solutions.
Future of AI in Cybersecurity: AI’s future in cybersecurity is very exciting, with a lot of room for growth and new ideas. With more use cases and applications, AI’s role in cybersecurity is likely to grow. One potential area where AI could be integrated with other technologies is blockchain and the Internet of Things (IoT).
As AI keeps getting more and more important in cybersecurity, there will be a growing need for people who know how to use AI in cybersecurity. These professionals will need to have a deep understanding of both cybersecurity and AI technologies.
Challenges and concerns with AI in cybersecurity:
Security risks associated with AI systems: AI systems are vulnerable to attacks and, if compromised, can become a liability for organizations.
AI systems are vulnerable to attacks and, if compromised, can become a liability for organizations. Lack of transparency and interpretability: The lack of transparency and interpretability in AI algorithms can make it difficult to understand how they make decisions, making it challenging to identify and address potential issues.
The lack of transparency and interpretability in AI algorithms can make it difficult to understand how they make decisions, making it challenging to identify and address potential issues. Bias in AI algorithms: AI algorithms can be biased due to the data used to train them, which can lead to unintended consequences and discrimination. This means that organizations developing AI systems should put ethical concerns first.
The potential of AI in cybersecurity is significant, and its impact is already being felt. Even though there are problems, it is important to use AI in cybersecurity to better protect against cyber threats better.AI can detect and respond to threats faster and more accurately by analyzing large amounts of data and looking for possible threats.
What’s the potential future of this innovative technology?
A fundamental question in technology is whether a new technology is worth investing in or not. When AI was first introduced in 1956 by John McCarthy from MIT, top engineers were worried that it would lead to the demise of traditional jobs. However, after 50 years of discussions, it has become evident that AI has only contributed to the enhancement of business processes and enabled organizations to recruit better employees with advanced knowledge.
In conclusion, there are challenges and worries that come with using AI in cybersecurity, but there are also big benefits that could come from using AI in cybersecurity. Organizations must put ethical concerns first and deal with problems to make sure that their AI-powered cybersecurity systems are safe, fair, and clear, which will protect them better from cyber threats in the long run.
FAQs:
Can Artificial Intelligence single-handedly eliminate cybercrime?
Artificial Intelligence alone cannot effectively prevent cybercrime, although it can be a valuable resource in combating it. There are various ways in which AI can support cybersecurity, each with its own advantages.
Do you think AI can help protect your company from cyberattacks?
Yes, AI can help protect a company from cyberattacks. AI can be used in various ways to enhance a company’s cybersecurity measures, including:
Threat detection: AI algorithms can be trained to detect anomalous network traffic or user behavior, which may be indicative of a cyberattack. This can help to identify threats more quickly, allowing for a faster response and mitigating potential damage.
AI algorithms can be trained to detect anomalous network traffic or user behavior, which may be indicative of a cyberattack. This can help to identify threats more quickly, allowing for a faster response and mitigating potential damage. Vulnerability management: AI can assist in identifying vulnerabilities within a company’s network or systems that can be exploited by attackers. This can help prioritize patches and updates to prevent potential attacks.
AI can assist in identifying vulnerabilities within a company’s network or systems that can be exploited by attackers. This can help prioritize patches and updates to prevent potential attacks. Access control: AI can be used to monitor and control access to a company’s systems, making sure that only authorized people can get to sensitive data.
AI can be used to monitor and control access to a company’s systems, making sure that only authorized people can get to sensitive data. Response to an incident: AI can be used to automate processes like finding the root cause of an attack, stopping the attack, and fixing damaged systems.
AI can play an important role in enhancing a company’s cybersecurity measures. However, it should be noted that AI is not a silver bullet solution and should be used in conjunction with other security measures and best practices.
Which is harder, AI or cybersecurity?
Both AI and cybersecurity are challenging fields that require a high level of expertise, and it’s difficult to say which is harder as it depends on various factors.
AI is the process of making systems that are smart enough to learn and do things that humans usually do. It takes knowledge of many different fields, like math, computer science, and engineering, and there are a lot of hard problems to solve when making AI systems. These include making algorithms that can learn from a lot of data, figuring out how AI affects ethics and the law and making sure AI systems are safe and secure.
Cybersecurity, on the other hand, is about keeping computer systems and networks safe from theft, damage, and access by people who shouldn’t be there. It requires expertise in computer systems, networks, and programming, and it’s important to stay up-to-date on the latest threats and weaknesses. Cybersecurity experts also need to know how to create and use security measures to protect against these threats, as well as how to find security incidents and deal with them.
AI and cybersecurity are both important fields that are becoming more important as technology changes quickly. Ultimately, both fields require a high level of expertise and are challenging in their own ways.
EC-Council University provides a comprehensive education system focused on cybersecurity. They offer various Cybersecurity programs, including graduate certificate courses, bachelor’s degrees, master’s programs, and non-degree programs, designed to provide individuals with the knowledge and skills necessary to succeed in the cybersecurity industry. EC-Council’s education system covers a wide range of cybersecurity topics, such as ethical hacking, penetration testing, incident response, and network defense, among others. The education system is based on a hands-on, practical approach, allowing students to apply their knowledge to real-world scenarios through virtual labs and simulations. EC-Council’s education system is a comprehensive and practical approach to cybersecurity training that prepares individuals and organizations for the challenges of today’s cybersecurity landscape.
| 2023-03-09T00:00:00 |
2023/03/09
|
https://www.eccu.edu/blog/cybersecurity/artificial-intelligence-the-future-of-cybersecurity/
|
[
{
"date": "2023/03/09",
"position": 80,
"query": "AI job creation vs elimination"
}
] |
Reskilling the Workforce: Benefits and Best Practices
|
Reskilling the Workforce: Benefits and Best Practices
|
https://chronus.com
|
[
"Jennifer Sokolowsky"
] |
Along with clear competitive advantage, reskilling offers many other benefits for businesses, including: Cost savings: When companies reskill existing employees ...
|
In a business world where change is the only constant, reskilling has become more important than ever. Reskilling also helps people adapt to changing demands. Often, workers whose jobs would be eliminated by automation can learn how to use automation technology, such as warehouse workers learning how to work on robots. Sometimes, reskilling may require workers to earn a new degree or certification.
What Is Reskilling?
Reskilling is the process of teaching employees an entirely new set of skills. It is a key tool that organizations can use to adapt to new realities in the marketplace. In all cases, reskilling offers workers the skills they need to move their careers in new and promising directions.
This article offers insights into why reskilling is so critical today, the benefits it brings, and how mentoring can help your company realize your reskilling goals.
The Need for Reskilling
Every industry today faces fast-moving change in business climate, market demands, and the tools and resources needed to keep up. Technology is a major driver, ushering in automation, offering opportunities for organizations to evolve, and profoundly affecting their needs and how they do business. The pandemic, along with the rise of the gig economy and freelancing, have also had a huge impact on the way people work.
By 2025, 85 million jobs may be displaced by a shift in the division of labor between humans and machines, especially in labor-intensive industries such as manufacturing, energy, and transportation. However, 97 million new roles may emerge from this shift, according to the World Economic Forum (WEF).
All of this means that organizations will need far different skills for success than they needed in the past or even in the present. Around 40 percent of workers will require reskilling of up to six months by 2024, the WEF says. Along with clear competitive advantage, reskilling offers many other benefits for businesses, including:
Cost savings: When companies reskill existing employees, they avoid the costs of letting workers go and then hiring and training new workers.
Retention: Reskilling allows companies to keep valuable people even when their current roles no longer meet the company’s needs.
Engagement: Opportunities for reskilling show employees that the company is willing to invest in their future, making them feel valued and boosting engagement and loyalty.
Hiring: Organizations that offer reskilling opportunities demonstrate a commitment to employee growth that attracts quality candidates.
Learning mindset: Businesses that emphasize reskilling instill a company culture where constant learning is the norm.
Reskilling is not just key for business; it’s also critical for employees who want to thrive in the future. Employees who want to fully take advantage of economic opportunity for the length of their careers will need to demonstrate they have the skills that employers demand.
Benefits of Reskilling with Mentoring
Mentoring offers a highly effective way to make sure that employees get the reskilling they need through a people-centric approach. With mentoring, employees gain the advantage of one-on-one guidance to help them navigate roadblocks and have a successful reskilling experience.
Enhances learning and skill acquisition
Mentoring is based on personal relationships, and that means mentors can help their mentees in a customized way. This personalized approach helps mentees receive just the kind of encouragement and teaching that makes the most sense for them. This greatly enhances the learning process and ensures that mentees acquire deep skills more efficiently.
Provides access to industry insights and experiences
One of the biggest advantages of mentorship is that it offers an opportunity for mentors to teach from their own experience and built-up knowledge. Mentors can offer insights based on real-life learnings that mentees would not be able to access in any other way. While this is invaluable information for the mentee, it’s also a big benefit for the organization in terms of capturing institutional knowledge.
Builds employee connectivity
Employees tend to be more engaged when they feel connected to their organization and the people they work with, resulting in greater motivation, productivity, and retention. Mentorships are highly effective at creating these connections. These relationships are formed not only between the mentor and mentee, but throughout the organization because of the networking that is so important for successful mentorships.
Boosts confidence and motivation
Reskilling isn’t easy, but employees with mentors have a cheerleader in their corner to help. Mentors can serve as sounding boards when things get tough, and they provide a big-picture view that can make a real difference. Mentors’ experienced perspective can be the boost that employees need to have faith in themselves and create a vision for their future.
The Role of Mentors in Reskilling
Mentors can play a meaningful role in reskilling by acting as a personal resource for mentees. Along with direct skills training, they can offer mentees friendly advice and support to help reskilling employees move forward in the right direction and stay on track. Long-term mentoring relationships extend these positive interactions over time to enrich the careers of both mentor and mentee and contribute to the cohesiveness of the organization overall.
Effective mentors tend to have some characteristics in common. These include:
Experienced and knowledgeable
Good listening skills
Nonjudgmental
Able to offer constructive feedback
Flexible
Adept at networking
Good mentors are willing and able to spend time helping others, enjoying teaching as well as learning, and have valuable insights to pass on to others.
However, one size does not fit all, and successful mentoring also depends on a good match between mentor and mentee. Organizations need to keep this in mind when designing a mentorship program and take steps to ensure the most effective matching possible. These can include:
Defining the purpose of the mentoring relationship
Determining the type of matching that will best support the desired outcome
Creating criteria and profiles for matching mentors and mentees
Providing training for mentors and mentees
Empowering participants to have choices
Strategies for Reskilling with Mentoring
It takes careful planning to make the most out of reskilling with mentoring. Planners need to think ahead strategically to provide the structure and resources to ensure an effective reskilling program. Here are some actions you should take when designing your program:
Assess your learning needs and goals
Successful reskilling begins with knowing what you want to accomplish. This requires identifying which skills the organization needs to develop within the workforce. These should be prioritized so that the skills that will have the most immediate impact receive the most resources to begin with. As the program develops, resources can be rolled out to lower-priority skills.
Creating a personalized learning plan
Mentors can help personalize learning for each mentee, but it’s important to start with a common foundation of what employees are expected to know at the end of each reskilling program. This includes the skills they should have and the tasks and activities they should be able to perform, based on market challenges and customer needs. As the plan is being designed, you should consult with potential learners. Their feedback at this stage is invaluable in creating a doable, effective learning program.
Establishing regular mentoring sessions
Consistency is key to any kind of learning, and reskilling is no exception. Mentors and mentees should agree on a schedule of regular mentoring sessions. Regular meetings give mentors and mentees opportunities to build a good rapport. The right cadence also offers participants the chance to review/reiterate past learnings while they are relatively fresh, while building knowledge with each session.
Tracking progress and adjusting the plan as needed
The goals for a reskilling program will be set in the design phase; once the program is launched, it’s time to track progress against those objectives. Goals should be SMART: specific, measurable, achievable, relevant, and time-bound. This makes it easy to track results—which should be used to adjust the program over time as needed.
Overcoming Obstacles to Reskilling
Reskilling offers clear benefits, but it’s not always easy to implement. Obstacles for organizations and individuals can include:
Financial barriers
Time constraints
Fear of failure
Lack of support from employers
Leaders should keep these potential roadblocks in mind when they are creating reskilling programs. With the right approach, many of these can be headed off within the structure of the reskilling program before they ever start.
Launch Your Reskilling Program with Chronus Mentoring Software
Reskilling is shaping up as a high-impact strategy for futureproofing in any industry—and the time to begin is now. Chronus mentoring software can help you get a head start on an effective reskilling program with built-in automation that supports your reskilling goals while simplifying administration.
Key features of Chronus Mentoring Software that drive reskilling include:
AI-driven mentor matching that connects participants to the right people and skillsets—based on your organization’s unique needs
Customized guidance for participants at every stage of the process
Tracking tools that allow you to see participant progress and map program impact to key business goals
The ability to insert learning objectives into in-platform mentoring training through the help of Chronus Courses
Taking a modern, technology-driven approach to your mentoring and reskilling programs ensures these key functions can evolve with you as you move into a fast-moving future.
| 2023-03-08T00:00:00 |
2023/03/08
|
https://chronus.com/blog/employee-reskilling
|
[
{
"date": "2023/03/09",
"position": 6,
"query": "reskilling AI automation"
}
] |
About United States Artificial Intelligence Institute | USAII®
|
About United States Artificial Intelligence Institute
|
https://www.usaii.org
|
[
"United States Artificial Intelligence Institute",
"Usaii"
] |
AI skill gap and talent shortage at a global level is a reality, and nobody can deny that. Many organizations are trying to fill these skill gaps at every level ...
|
The United States Artificial Intelligence Institute (USAII®) is an independent, third-party, international certification institute for Artificial Intelligence, Machine Learning, Deep learning, and related industry. It has no interest in the promotion or marketing of its own or any other affiliates. The USAII's Artificial Intelligence certification is designed to deal with the futuristic issues of data-driven decision-making.
Information provided on the official USAII® website is for informational purposes only and does not establish any legal contract between the USAII® and any other person or entity unless otherwise specified. All the information on USAII’s official website is subject to change without any prior notice. Even though every reasonable effort is made to present updated and accurate information, the USAII® makes no guarantees of any kind.
The USAII® website may contain information created and retained by various internal and external sources to the USAII®. These sites are unrestricted forums containing personal opinions and other expressions of the person posting the article. USAII® does not control, monitor, or guarantee the information contained in these websites or the information in links to other external websites, nor does it endorse any views expressed or the products or services provided therein. Under any circumstance, USAII® shall not be liable, directly or indirectly, for any damage or loss caused or allegedly caused by, or related to, or, any such content or services available on or through any such site or resource.
The information shared on the USAII® website w.r.t. names and logos of products and services are their respective owners' trademarks and properties and are on the website for informational purposes only. Individuals or organizations planning to pact with USAII® are presumed to have read and settled to these facts relating to USAII® services, practices, and guidelines. Contact us with your queries at [email protected]. Last updated on March 01, 2024.
| 2023-03-09T00:00:00 |
https://www.usaii.org/about-united-states-artificial-intelligence-institute
|
[
{
"date": "2023/03/09",
"position": 22,
"query": "AI skills gap"
},
{
"date": "2023/03/09",
"position": 57,
"query": "artificial intelligence business leaders"
}
] |
|
New connections: how we're bridging the UK digital skills gap
|
New connections: how we’re bridging the UK digital skills gap
|
https://www.microsoft.com
|
[
"Rebecca King",
"See More Articles This Author",
"Uk Apprentice Lead",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width"
] |
A recent Microsoft study has revealed that 82 percent of UK jobs already require digital skills, and that 69 percent of leaders feel their organisation suffers ...
|
The digital skills gap
Technology advancements are now accelerating faster than our ability to adapt, leaving a huge gap in digital skills. A recent Microsoft study has revealed that 82 percent of UK jobs already require digital skills, and that 69 percent of leaders feel their organisation suffers from a digital skills gap, even though 59 percent of employees believe in the importance of developing their digital skills.
The skills gap is only getting wider. By 2025, there are expected to be three million new tech jobs in the UK, and 60 percent of employers are expecting their reliance on advanced digital skills to grow in the next five years. In effect, organisations hoping to grow and remain competitive in the future need to build a workforce that is equipped to fill these roles and thrive in a digital world of work.
Yet many employees don’t have the skills they need to perform their role currently, let alone in the future.
Demand for digital skills also goes beyond the technology sector. A recent employer survey by the Learning and Work Institute found that the proportion of employers who saw basic digital skills as important for employees was particularly high in certain sectors – including media, marketing, advertising and PR (100 percent), IT and telecoms (99 percent), and finance and accounting (97 percent).
However, even in the industry with the lowest proportion – manufacturing – nearly nine in ten (87 percent) employers said that basic digital skills were important for their workers.
So, what more can businesses and employers do to ensure that we have a steady supply of tech talent joining the sector? And, importantly, how can Microsoft help?
Introducing the Microsoft Connector Community
Whilst many business leaders recognise that their organisation is facing a skills shortage, the challenge often comes in knowing what to do about it.
At Microsoft, we believe that part of the answer to tackling the UK’s digital skills gap is collaboration. Bringing together organisations from across the public and private sector to work collectively and deliver tangible benefits to the prospective careers of young people, while driving growth and innovation across the economy.
The Microsoft Connector Community, part of the Microsoft Apprenticeship Connector, is designed to bridge the digital skills gap. It nurtures a connected community of businesses and organisations that collectively leverage their brands, reputations and resources to address the need for digital skills in the workforce and connect talented individuals to opportunities.
We believe that taking a collaborative approach ensures a steady and well-equipped technology talent pipeline. This is essential to addressing the skills shortages faced by employers today, as well as mitigating future shortages.
I’d like to share my thoughts on how this could work:
1. Creating a connected talent ecosystem: When employers work together to support, empower and inspire young people, rather than bombarding them with too many competing options, we all benefit. By using the Microsoft Apprenticeship Connector to advertise digital vacancies, digital skills bootcamps and opportunities for learning and training, we can create a highly visible and inspiring platform that enables young people to find the opportunities that are right for them. In addition, by pooling vacancies across one platform, with a single access point for roles, we can recycle and share talent. We can also signpost applicants towards other vacancies and employers, even if they have been unsuccessful in applying to a different company.
2. Simplifying the talent pipeline: We can help simplify the technology talent pipeline by reimagining how we advertise digital vacancies and training opportunities. For example, we can reach a larger and more diverse pool of candidates by simplifying the language that we use across our digital vacancies. This might increase engagement with candidates from non-conventional technology and digital backgrounds – allowing us to widen our talent pool and employ people who bring a unique and diverse perspective to the sector.
3. Leveraging expertise and insight: A coalition of like-minded individuals enables us to learn from one another’s expertise and experience, helping us to identify, profile and address the systemic issues that are stifling the UK’s economy. What’s more, the Apprenticeship Connector can also support business leaders and employers to better understand the social impact of the work that they’re doing. With demographic and geographic insight, we can identify and better target under-represented and under-served groups.
Building a Connector Community in Greater Manchester
As part of Microsoft’s UK Get On commitment, we’re committed to helping 30,000 people find jobs in the technology sector in Greater Manchester.
I recently had the pleasure of joining colleagues in the region for an event focused on building a connected talent system, as part of National Apprenticeship Week. After the event, I caught up with Greater Manchester Regional Lead, Marie Hamilton, for her take on how the introduction of Microsoft Connector Community could empower the next stage of the Get On campaign in Greater Manchester, and across the UK. Marie said:
“Greater Manchester is now the fastest growing technology hub in Europe, but to maintain this growth, we need to build a skilled and sustainable digital workforce. Working collectively across sectors and industries, regionally and nationally, allows us to understand and measure the skills gap, as well as enabling us to build a shared plan to tackle it. Our National Apprenticeship Week event demonstrated huge enthusiasm for using the Connector Community model to further the progress made by Get On – as shown by the attendance from not only our largest private sector customers but also across the public sector, academia, central and local government”. Marie Hamilton, Greater Manchester Regional Lead
What’s next?
Over the coming months, Microsoft will be holding a series of regional roundtable events to help businesses and organisations understand more about the Microsoft Apprenticeship Connector, and the Connector Community.
To find out more, please visit the Microsoft Apprenticeship Connector platform.
Microsoft Get On
At Microsoft, we believe everyone deserves access to the skills, knowledge and opportunity needed to achieve more. Through our Get On commitment, we’re helping 1.5 million people build tech careers and connecting 300,000 to tech job opportunities.
You can find out more by visiting our Digital Skills Hub, where you’ll find a host of information and resources designed to provide business leaders and employers with insights on how to close the UK’s skills gap, harness new technology and drive innovation.
You can also explore Microsoft Learn, which has an array of certifications, modules and learning pathways designed to help business leaders to upskill employees, as well as supporting individuals to take control of their careers and build vital digital skills.
Find out more
Microsoft Digital Skills Hub
Microsoft Apprenticeship Network
How Microsoft is connecting jobseekers to employers
About the author
I am the UK Apprentice Lead for Microsoft in the HR team. Working with apprenticeships since 2012, I have also worked with SMEs creating, designing, and delivering large corporate apprenticeship programmes. My role at Microsoft is all about creating and developing an apprenticeship strategy that supports our UK business and delivers an exceptional experience for both our apprentices and our business.
| 2023-03-09T00:00:00 |
2023/03/09
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https://www.microsoft.com/en-gb/industry/blog/education/2023/03/09/new-connections-how-were-bridging-the-uk-digital-skills-gap/
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"date": "2023/03/09",
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"query": "AI skills gap"
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AI Recruitment Market Size, Growth Analysis, 2032
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AI Recruitment Market Size, Growth Analysis, 2032
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https://www.marketresearchfuture.com
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[
"Market Research Future",
"Https",
"Www.Marketresearchfuture.Com"
] |
AI Recruitment Market is expected to reach USD 1053.16 million by 2032, growing at a CAGR of 6.9% during the forecast period 2024-2032.
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AI Recruitment Market Summary As per MRFR Analysis, the AI Recruitment Market was valued at USD 577.7 Million in 2023 and is projected to reach USD 1,053.16 Million by 2032, growing at a CAGR of 6.9% from 2024 to 2032. The market growth is driven by the need for efficient candidate screening, reduced HR spending, and the automation of recruitment processes. AI technologies, including Applicant Tracking Systems and Bots, enhance recruitment efficiency by quickly processing large volumes of applications and improving candidate engagement. Key Market Trends & Highlights Key trends driving the AI Recruitment Market include technological advancements and increased adoption across various sectors. AI recruitment market size in 2023: USD 577.7 Million.
Enterprise segment expected to grow at the fastest CAGR of 9.14% through the forecast period.
Cloud deployment dominates with a market share of 67.2% and a projected CAGR of 6.81%. Market Size & Forecast 2023 Market Size: USD 577.7 Million 2024 Market Size: USD 617.5 Million 2032 Market Size: USD 1,053.16 Million CAGR (2024-2032): 6.9% Largest Regional Market Share in 2022: North America (38.2%). Major Players Key players in the AI Recruitment market include PandoLogic, Inc., Hackajob Ltd., Zoho Corporation, Leoforce, LLC, Harver B.V., Jobvite, Brilent, Inc., Entelo, Inc., and others.
Figure 1: AI Recruitment Market Size, 2023-2032 (USD Billion)
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
AI Recruitment Market Trends
Decreasing overall spending in companies’ HR budgets boosts market growth.
AI has become a part of everyone’s life in one way or another. It can be used in training machines through data labeling and developing AI pattern recognition for autonomous vehicles; AI has continued to match the human brain. Today, for example, with the help of video surveillance and deep learning, security systems worldwide can function better and more effectively.
AI plays a significant role in the overall recruitment process. It helps recruiters scan thousands of CVs using AI technologies, such as the Applicant Tracking System (ATS). In this way, recruiters can save time and fill the positions more quickly.
In many big companies, the recruiting process is automated and uses AI algorithms. These functions include initial screening to onboarding. Other AI tools, such as Bots, also can be said as robots, help set up interviews and easily respond to candidates’ questions. These bots use natural language processing (NLP) and interview analytics to assess candidates' suitability by skimming their soft skills and personality traits.
Figure 2: Average cost cut in HR process using AI, by Regions, 2022 %
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
The above figure shows that in North America, around 40% of the companies use AI in their recruitment process, followed by Europe. While in Asia Pacific, China, Japan, and South Korea are the major countries implementing this type of software in their HR processes. This implementation will be a boon for the SME sector, too, and will be the driving industrial segment for Ai recruitment.
AI Recruitment Market Segment Insight
AI Recruitment Verticals Type Insights
Based on verticals type, the AI Recruitment market segmentation includes Government, IT and Telecommunication, Healthcare, BFSI, Education, Enterprise, and Others. The enterprise segment accounted for the largest share of this market and is expected to grow at the fastest CAGR of 9.14% through the forecast period. The enterprise sector includes manufacturing, trading, and packaging, which heavily utilizes AI for hiring.
Figure 3: AI Recruitment Market, by Verticals, 2022 & 2030 (USD Million)
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
Moreover, the BFSI segment is projected to grow at a CAGR of 6.17% through the forecast period. Due to the financial industry's day to day changing dynamics toward a better hiring process, it is challenging for banks to keep their employees informed about the recent changes in their systems and train each of them. These factors contribute to expanding the BFSI market, which raises the demand for AI in the recruiting sector.
On the other side, AI recruitment technology can benefit any business because it streamlines tasks and reduces costs. One factor driving demand is the requirement to automate the hiring procedure inside a business. Sales will result from adoption in the AI recruitment market.
AI Recruitment by Component Type Insights
The AI recruitment market is segmented into software and services based on component type. The software segment accounted for the largest share and is expected to grow at a CAGR of 6.72% through the forecast period. The AI recruitment market offers tremendous potential for growth. Technology development is a crucial driver of growth. Software is constantly being improved. In software, new features and programs are always being added. In the future, software for AI recruitment will have a lot of new functions. More consumers will visit the market as a result of all these improvements. The automated procedure increases candidate engagement during the hiring process by making the interview scheduling software more user-friendly. The demand for automated procedures expanded due to efficient software and systems implementation, thereby boosting the growth of the worldwide AI recruiting market. However, factors such as threats from open-source recruitment software may hinder the market growth.
However, the services category is also anticipated to grow the fastest during the forecast period. Moreover, this category will show significant growth due to the expanding demand for technological infrastructure and resource development. As they can guarantee high security with their deployment, many large organizations now prioritize the services market area. While the IT administration personnel in the firm control the AI in recruitment, no outside service providers are involved in maintaining companies’ private information. Moreover, businesses that have not yet switched to a cloud platform may find the services market segment particularly advantageous. Throughout the forecast period, artificial intelligence in the recruitment market will increase due to the benefits of the services segment.
February 2023: - VARs, systems integrators, and MSPs who can create high-performance AI, machine learning, and other solutions and services based on the Weka Data Platform are eligible for deal registration, training, certification, and other tools and advantages through the new Weka X Partner Program from WekaIO.
February 2023 - The end-to-end overseas recruiting platform hire EZ said today that its next March 9 product release would include generative AI features. The update will use hireEZ's machine learning data and the Generative Pre-trained Transformer 3 model to help recruiter draught and deliver emails to job candidates more quickly than with conventional methods.
AI Recruitment by Deployment Insights
The AI recruitment market is segmented into cloud and on-premises based on deployment type. The cloud segment dominates this market with a share of 67.2% and is also projected to witness growth opportunities with a CAGR of 6.81% in the upcoming years. Moreover, China, India, and Japan focus more on talent acquisition, intending to find highly qualified candidates whose abilities match the job description. The demand for AI recruiting technology has increased due to rising cloud-based service usage and national government initiatives to further digitization. The Asia Pacific AI Recruitment Market is expanding due to the increased demand for cloud-based AI recruiting solutions from SMEs in the region. The need for AI recruiting technology has increased due to the national government's measures to promote digitalization and the rising use of cloud-based services.
The on-premises category is expected to grow at the fastest CAGR due to in-house implementation by small firms. Occasionally, the pre-trained models or the computational or storage demands of the cloud might be improper or too expensive. An on-premises solution might be more appropriate in those circumstances. Also, a vast ecosystem of tools designed for on-premises environments can operate with enormous amounts of CPU power, which can be costly in the cloud. Some clients believe that doing this on-site is more cost-effective or favor a capital expense model over an operational expense one. Investing in on-premises infrastructure rather than using cloud-based services can make more sense if your company decides to get more involved in this or roll out AI at scale.
AI Recruitment Regional Insights
This market has been segmented by region into North America, Europe, Asia-Pacific, and the rest of the world. The North American region dominated this market with a share of 38.2% in 2022 and is expected to grow at a CAGR of 5.43% through the forecast period. American businesses quickly adopt AI-based machine learning technologies to guarantee the most efficient hiring procedure. Also, the expansion of IT businesses in the United States is fostering market expansion in this area. Innovative technologies like artificial intelligence and machine learning were initially applied in the US. Due to its advanced economic system, robust network infrastructure, and easy access to qualified specialists, China currently dominates the market, followed by Canada.
Furthermore, the Asia-Pacific region is also expected to hold the second-highest position in this market during the forecast period. This region has a large number of software vendors. Also, the variety of job duties fuels the expansion of the AI recruitment industry. Throughout the predicted period, this region will have the second-best profitability rate. Also, there are numerous software vendors in this area. The rise of the AI recruitment sector is also fueled by the diversity of job duties. Over the anticipated period, these areas will have the second-highest profitability rate.
Figure 4: AI RECRUITMENT MARKET, BY REGION, 2022 and 2030 (USD Million)
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
Demand for AI Recruitment is growing in several countries across the North American region.
Also, AI would help recruiters access a broader talent pool through online career boards, social media platforms, and agency databases. In addition, recruiters would have the chance to look at specific profiles, such as former employees of the organization, during screening. These applications of implementing AI will drive artificial intelligence in the recruitment market's growth during the forecast period.
AI Recruitment Key Market Players & Competitive Insights
These markets' leading players will form new collaboration agreements during the anticipated timeframe. That will result in market growth. The major market participants are starting to use AI for hiring in the healthcare sector.
Major AI Recruitment market players include PandoLogic, Inc., Leoforce, LLC, Harver B.V., Hackajob Ltd., Jobvite, Brilent, Inc., Entelo, Inc., and others, who are focusing on developing their business strategies.
Key Companies in the AI Recruitment market include.
PandoLogic, Inc.
Hackajob Ltd.
Zoho Corporation
Leoforce, LLC
Harver B.V
Jobvite
Brilent, Inc.
Entelo, Inc.
SmartRecruiters
CVViZ Softwares Pvt Ltd
HireVue
Talentrecruit
Beamery
TalentMind
Ultimate Software
Automatic Data Processing LCC
Oracle Corporation
Google LLC
SAP SE
Mya Systems Inc.
IBM Corporation
Deloitte
Quantegene
WekaIO
HireEZ
AI Recruitment Industry Developments
In May 2023, With the latest announcement of the enhancement of the relationship between SAP and Microsoft, the vision of an enterprise-ready generative AI that is capable of augmenting worker performance and growth is slowly coming into focus. Enterprises will be able to better retain, reskill and recruit workers to fill skills gaps when SAP SuccessFactors solutions are accompanied by Copilot in Viva Learning also Microsoft 365 Copilot.
In September 2023, Employ Inc. reported that they were acquiring a smaller tech firm that focuses on recruitment analytics for A.I. The purpose is to strengthen the portfolio of Employ Inc. by bringing in top-notch predictive analytics techniques that would allow them to provide actionable insights to clients that would assist them in recruitment.
In July 2023, Workday, Inc. announced the release of a new feature, “Workday Talent Insights,” which has been powered by AI technology. This product applies machine learning models to estimate employee effectiveness and assist organizations with pre-emptive measures for employee retention along with recruitment strategies that streamline the entire hiring process.
In March 2023, Microsoft Corporation expanded its AI capabilities in recruiting by further embedding LinkedIn’s data analytics within Microsoft Teams. This advance has the potential to improve the recruitment process by delivering more extensive candidate profiles and enhancing the interactions of the recruitment team members.
July 2023: Google's usage of AI models from Meta and Anthropic on their platform was expanded with dramatic effect. This partnership focuses on embedding generative AI into tools for HR and recruitment, such as candidate screening tools and interview scheduling tools. By increasing AI model usage, Google is going further to disrupt the conventional nature of HR technology by integrating AI analytics and recruitment tools.
In July 2023, A consortium between SAP SE and IBM was established to enable the integration of AI-based Conversational Cloud AI HR solutions within the modules of SAP’s Success Factors. This collaboration will aim at improving skill assessments in organizations as well as diversity and inclusion analytics in organizations as more AI powered intelligence is applied. HR managers can apply this integration apply sophisticated screening, advanced resume parsing tools and tools related to employee development.
AI Recruitment Market Segmentation
AI Recruitment Component Type Outlook
Software
Services
AI Recruitment Deployment Type Outlook
Cloud
On-Premises
AI Recruitment Verticals Type Outlook
IT & Telecom
Government
BFSI
Healthcare
Education
Enterprises
Others
AI Recruitment Regional Outlook
North America
US
Canada
Europe
Germany
France
UK
Italy
Spain
Rest of Europe
Asia-Pacific
China
Japan
India
Australia
South Korea
Australia
Rest of Asia-Pacific
Rest of the World
Middle East
Africa
Latin America
Report Attribute/Metric Details Market Size 2023 USD 577.7 Million Market Size 2024 USD 617.5 Million Market Size 2032 USD 1,053.16 Million Compound Annual Growth Rate (CAGR) 6.9% (2024-2032) Base Year 2023 Forecast Period 2024-2032 Historical Data 2019 to 2022 Forecast Units Value (USD Million) Report Coverage Revenue Forecast, Competitive Landscape, Growth Factors, and Trends Segments Covered Vertical Type, Deployment Type, Component, and Region Geographies Covered North America, Europe, Asia Pacific, and the Rest of the World Countries Covered The U.S, Canada, Germany, France, the UK, Italy, Spain, China, Japan, India, Australia, South Korea, and Brazil Key Companies Profiled PandoLogic, Inc., Leoforce, LLC, Harver B.V, Hackajob Ltd., Jobvite, Brilent, Inc., Entelo, Inc., SmartRecruiters, CVViZ Softwares Pvt Ltd, HireVue, Talentrecruit, Beamery, TalentMind, Ultimate Software, Automatic Data Processing LCC, Oracle Corporation, IBM Corporation, Google LLC, Zoho Corporation and SAP SE, Mya Systems Inc., Deloitte, Quantegene, WekaIO and HireEZ Key Market Opportunities Big Data promotes better product designs by utilizing technologies at lower costs and with less hardware. Key Market Dynamics Growing usage of advanced technologies Screening high-quality candidates from unlimited databases Improving time and efficiency Decreasing overall spending in companies’ HR budgets
| 2023-03-09T00:00:00 |
https://www.marketresearchfuture.com/reports/ai-recruitment-market-8289
|
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"date": "2023/03/09",
"position": 32,
"query": "AI labor market trends"
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"query": "artificial intelligence hiring"
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What is an AI Product Manager? Definition, role and FAQs
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What is an AI Product Manager? Definition, role and FAQs
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https://airfocus.com
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[] |
Other key responsibilities of an AI product manager include staying up-to-date with the latest AI technologies and industry trends. ... job market. It's ...
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What is an AI product manager?
💬 AI Product Manager definition An AI Product Manager is a relatively new and highly sought-after title given to product managers working on AI projects.
AI Product Managers perform a similar role to traditional product managers but often come from different backgrounds. For example, a traditional PM will usually have a background in UX or marketing. An AI PM will often come from a data processing or statistics background.
eBook All you need to know about product management Read now
What is the role of an AI product manager?
An AI product manager oversees the development and launch of artificial intelligence (AI) products. This involves working closely with cross-functional teams, including engineers, data scientists, designers, marketers, and business stakeholders, to ensure that the AI product meets customer needs and reflects the company’s values.
The role of an AI product manager involves a wide range of responsibilities but generally includes the following:
Additionally, AI PMs are responsible for defining KPIs and measuring the product's success over time.
Other key responsibilities of an AI product manager include staying up-to-date with the latest AI technologies and industry trends. They will identify innovation and product differentiation opportunities while ensuring that the product complies with legal and ethical guidelines.
Overall, the AI product manager plays a critical role in driving the development and success of AI products and requires a combination of technical expertise, business acumen, strong leadership, and communication skills.
What is the average salary of an AI product manager?
As with any role, the salary of an AI product manager will vary based on location, experience, and the local job market. It’s also worth noting that the AI PM role is relatively new, so average wages are still being established.
As of February 2023, the average salary for an AI product manager in the US is around $120,000 annually.
In Europe, an AI PM’s salary is highly dependent on location, with France offering roughly €130,000 per year, while Germany and the UK offer a much lower €67,000 and £56,400, respectively.
eBook All you need to know about product management Read now
What skills does an AI product manager need?
An AI product manager needs to have a deep understanding of artificial intelligence and machine learning. They should know how the product works, what it does, how it improves a customer’s experience, and what role AI will play within the product.
A successful AI product manager also needs to possess various non-technical skills. These include project management skills to manage the development lifecycle of AI products, strong communication skills to effectively communicate with technical and non-technical stakeholders, and leadership skills to lead cross-functional teams and bring AI products to market.
Additionally, data analysis skills are essential to collect and analyze data to make informed product decisions, while strategic thinking is necessary to identify and prioritize opportunities for AI product development. Problem-solving skills are also key to overcoming technical challenges and mitigating risks. User experience design skills are essential to creating user-friendly products that meet customer needs.
Overall, a successful AI product manager should be able to balance technical expertise with business know-how and strong leadership skills to bring successful AI products to market.
How to become an AI product manager?
We expect to see a huge influx of AI Product Manager roles being added to businesses over the next couple of years. As the new “hot” career, it will be tough for applicants to stand out.
The best way to get ahead is to start working towards building up specialty knowledge in artificial intelligence, machine learning, and neural networks. Consider an online training course that can teach you everything you need to know and give you a certification that will impress interviewers.
| 2023-03-09T00:00:00 |
https://airfocus.com/glossary/what-is-ai-product-manager/
|
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"position": 88,
"query": "AI labor market trends"
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Artificial Intelligence Commission Report
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Artificial Intelligence Commission Report
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https://www.uschamber.com
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The U.S. Chamber's AI Commission report highlights the promise of Artificial Intelligence (AI) while calling for a risk-based, regulatory framework.
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The use of artificial intelligence (AI) is expanding rapidly. These technological breakthroughs present both opportunity and potential peril. AI technology offers great hope for increasing economic opportunity, boosting incomes, speeding life science research at reduced costs, and simplifying the lives of consumers. With so much potential for innovation, organizations investing in AI-oriented practices are already ramping up initiatives that boost productivity to remain competitive.
Like most disruptive technologies, these investments can both create and displace jobs. If appropriate and reasonable protections are not put in place, AI could adversely affect privacy and personal liberties or promote bias. Policymakers must debate and resolve the questions emanating from these opportunities and concerns to ensure that AI is used responsibly and ethically.
This debate must answer several core questions: What is the government’s role in promoting the kinds of innovation that allow for learning and adaptation while leveraging core strengths of the American economy in innovation and product development? How might policymakers balance competing interests associated with AI—those of economic, societal, and quality-of-life improvements—against privacy concerns, workforce disruption, and built-in-biases associated with algorithmic decision-making? And how can Washington establish a policy and regulatory environment that will help ensure continued U.S. global AI leadership while navigating its own course between increasing regulations from Europe and competition from China’s broad-based adoption of AI?
Statement on AI Commission Report U.S. Chamber’s AI Commission Report Highlights the Promise of AI While Calling for a Risk-Based, Regulatory Framework
Read more
The United States faces stiff competition from China in AI development. This competition is so fierce that it is unclear which nation will emerge as the global leader, raising significant security concerns for the United States and its allies. Another critical factor that will affect the path forward in the development of AI policy making is how nations historically consider important values, such as personal liberty, free speech, and privacy.
To maintain its competitive advantage, the United States, and like-minded jurisdictions, such as the European Union, need to reach agreement to resolve key legal challenges that currently impede industry growth. At this time, it is unclear if these important allies will collaborate on establishing a common set of rules to address these legal issues or if a more competitive—and potentially damaging—legal environment will emerge internationally.
AI has the capacity to transform our economy, how individuals live and work, and how nations interact with each other. Managing the potential negative impacts of this transition should be at the center of public policy. There is a growing sense that we have a short window of opportunity to address key risks while maximizing the enormous potential benefits of AI.
The time to address these issues is now.
In 2022, the U.S. Chamber of Commerce formed the Commission on AI Competitiveness, Inclusion, and Innovation (“Commission”) to answer the questions central to this debate. The Commission, cochaired by former representatives John Delaney (D-MD) and Mike Ferguson (R-NJ), was tasked with the mission to provide independent, bipartisan recommendations to aid policymakers. Commissioners met over the course of a year with over 87 expert witnesses during five separate field hearings across the country and overseas, while also receiving written feedback from stakeholders answering three separate requests for information posed by the Commission.
The Commission observed six major themes from its fact finding:
Key takeaways
The development of AI and the introduction of AI-based systems are growing exponentially. Over the next 10 to 20 years, virtually every business and government agency will use AI. This will have a profound impact on society, the economy, and national security.
Policy leaders must undertake initiatives to develop thoughtful laws and rules for the development of responsible AI and its ethical deployment.
A failure to regulate AI will harm the economy, potentially diminish individual rights, and constrain the development and introduction of beneficial technologies.
The United States, through its technological advantages, well-developed system of individual rights, advanced legal system, and interlocking alliances with democracies, is uniquely situated to lead this effort.
The United States needs to act to ensure future economic growth, provide for a competitive workforce, maintain a competitive position in a global economy, and provide for our future national security needs.
Policies to promote responsible AI must be a top priority for this and future administrations and Congresses.
Understanding the importance of these findings, the Commission also determined that the following five pillars should be at the core of AI regulatory policy making:
Five pillars of AI regulation
Efficiency
Policymakers must evaluate the applicability of existing laws and regulations. Appropriate enforcement of existing laws and regulations provides regulatory certainty and guidance to stakeholders and would help inform policymakers in developing future laws and regulations. Moreover, lawmakers should focus on filling gaps in existing regulations to accommodate new challenges created by AI usage.
Collegiality
Federal interagency collaboration is vital to developing cohesive regulation of AI across the government. AI use is cross-cutting, complex, and rapidly changing and will require a strategic and coordinated approach among agencies. Therefore, the government will need to draw on expertise from the different agencies, thus allowing sector and agency experts the ability to narrow in on the most important emerging issues in their respective areas.
Neutrality
Laws should be technology neutral and focus on applications and outcomes of AI, not the technologies themselves. Laws regarding AI should be created only as necessary to fill gaps in existing law, protect citizens’ rights, and foster public trust. Rather than trying to develop a onesize-fits-all regulatory framework, this approach to AI regulation allows for the development of flexible, industry-specific guidance and best practices.
Flexibility
Laws and regulations should encourage private sector approaches to risk assessment and innovation. Policymakers should encourage soft law and best practice approaches developed collaboratively by the private sector, technical experts, civil society, and the government. Such non-binding, self-regulatory approaches provide the flexibility of keeping up with rapidly changing technology as opposed to laws that risk becoming outdated quickly.
Proportionality
When policymakers determine that existing laws have gaps, they should attempt to adopt a risk-based approach to AI regulation. This model ensures a balanced and proportionate approach to creating an overall regulatory framework for AI.
Recommendations
Having understood the urgency to develop policies to promote responsible AI and to ensure economic and workforce growth, the Commission used these pillars to develop policy recommendations to put these priorities into action. The Commission recommends areas that policymakers must address, including preparing the workforce through education, bolstering global competitiveness in the areas of intellectual property while shoring up partnerships, and protecting national security.
Preparing the Workforce
Use an Evidence-Based Approach. Policymakers must take action to understand the potential impact of AI on the American workforce by leveraging new data sources and advanced analytics to understand the evolving impact of AI and machine learning on the American public.
Educate the Future Workforce. The United States must increase education around AI in both the K-12 and higher education systems by encouraging policymakers to reform the standard curriculum to better prepare students for developing AI and machine learning systems.
Train and Reskill. The public and private sectors must invest in training and reskilling the future workforce. These investments should be targeted toward programs that help ease worker transitions and improve incentives for businesses to invest in retraining. Policymakers should also leverage community colleges and vocational schools to train workers to perform jobs alongside AI-enabled systems.
Attract High-Skilled Talent. In areas where a worker shortage cannot be addressed through education, training, and reskilling, Congress must act to increase the AI talent pool through targeted refinements to the H-1B visa process to encourage highskilled immigration to the United States.
Bolstering global competitiveness
Shore Up Global Partnerships. U.S. officials must collaborate with key partners and allies to develop more sensible global governance frameworks that advance our common democratic goals and values.
Advance Intellectual Property Protections. Building on the foundation of the current system, policymakers must clarify intellectual property law requirements to ensure adequate protection of AI-enabled intellectual property. Before any change, policymakers must involve relevant stakeholders to consider potential unintended effects.
Provide Necessary Resources. Policymakers should provide additional resources to the U.S. Patent and Trademark Office to support the acquisition of technical expertise, training, and other resources to speed the review of AI- and machine learning– related public patent applications.
Protect Ingenuity. Policymakers should also explore opportunities to grant provisional approvals for submissions under review where appropriate to mitigate the effects of lengthy delays.
Protecting national security
Human Rights. The United States must drive the development and implementation of laws and codes of conduct focused on promoting human rights and innovation.
Establish International Rules of Conduct. As the United States leads in the development of AI-enabled weapons, it should follow and encourage other countries to align with existing international norms and laws.
Systems Validation. The U.S. should invest heavily in new ways of testing, evaluating, verifying, and validating (“TEVV”) military AI and machine learning systems to ensure that they are used safely.
Streamline Procurement. To capitalize on American ingenuity, Congress and the Pentagon must look at streamlining acquisition processes and finding new ways of incorporating industry expertise and experience within the military enterprise.
Work with Allies. The United States should look to open investment opportunities for AI-enabled systems to like-minded countries and allies and vice versa.
These findings and recommendations are not exhaustive, and we welcome the insights of others who may contribute to the AI policy debate. The Commission and individual Commissioners stand ready to collaborate with policymakers to address these issues that are of utmost importance to the United States and the economic wellbeing and safety of the global community.
Artificial Intelligence Commission Report Social Toolkit Join us in driving awareness for the potential of AI systems and the need for smart regulation, investment, and modern policies. Click the link below to share sample social media posts and visuals. Read more
Artificial Intelligence Commission 2023 - Full Report Download
| 2023-03-09T00:00:00 |
2023/03/09
|
https://www.uschamber.com/technology/artificial-intelligence/artificial-intelligence-commission-report
|
[
{
"date": "2023/03/09",
"position": 15,
"query": "AI regulation employment"
},
{
"date": "2023/03/09",
"position": 5,
"query": "government AI workforce policy"
},
{
"date": "2023/03/09",
"position": 41,
"query": "artificial intelligence business leaders"
}
] |
Discrimination and the Human Algorithm, by Mark Lemley
|
Discrimination and the Human Algorithm, by Mark Lemley
|
https://www.yalejreg.com
|
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AIs don't set out to discriminate. They might be programmed to do so, but that is likely to be rare. Rather, if an AI discriminates, it is generally because it ...
|
*This is the third post in a symposium on Orly Lobel’s The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future, selected by The Economist as a best book of 2022. All posts from this symposium can be found here. Further reviews can be found at Science, The Economist, and Kirkus.
Legal scholarship around artificial intelligence (AI) has focused enormous attention on the problem of discrimination by AI. And justly so. Scholars have pointed out that algorithms make categorical judgments about people based on classifications, including race, gender, and other attributes that the law treats as problematic. Indeed, they may have no alternative but to do so. And even when AIs try to avoid racial or gender profiling, they often end up using proxies that are correlated with race, like geography, education, class, or other attributes. The conclusion of this scholarship is relatively straightforward: AI doesn’t solve, and may even worsen, the problem of discrimination, perpetuating it and ensconcing it in a numerical result that may be impossible to dislodge.
But all advantages are comparative. Before we reject the use of AI, we should be careful to avoid a human alternative that is worse. And it may well be. AIs don’t set out to discriminate. They might be programmed to do so, but that is likely to be rare. Rather, if an AI discriminates, it is generally because it is trained on existing data or modeled on behavior or goals set in the human world and that data or behavior turns out to be discriminatory. When Amazon’s hiring AI found that the two best predictors of success at Amazon were being named Jared and playing lacrosse, it wasn’t discriminating; it was reflecting back a history of human hiring practices that had advantaged rich white men. That doesn’t mean we shouldn’t worry about AI discrimination; we should. But discrimination by AIs is almost always a reflection of discrimination by people.
There is one important difference between AIs and people: AIs have to show their work. No judge sentences a criminal defendant unconscious of whether that defendant is a man or a woman. And they undoubtedly take that fact into account. But they don’t talk about gender in explaining why they sentence a man to a longer prison term than they would a woman. The difference is there, but it’s hidden. Nor does the cop who disproportionately stops minority drivers have to explain that he is doing so – perhaps not even to himself. AIs have no such luxury. So it’s not just that AI discrimination tends to reflect human discrimination. It is that the human discrimination is so often hidden. Amazon doesn’t have discriminatory hiring policies, and the people doing the interviewing likely don’t think they are discriminating. But they end up hiring people who look like them, people who are named Jared and play lacrosse. Much of the backlash against discrimination by AIs reflects, I suspect, not a worse record by AIs than humans but the fact that we can actually see what is going on behind the scenes.
Orly Lobel’s book The Equality Machine provides a welcome and much-needed counterweight to the literature demanding that we restrict AIs because they discriminate. She does not deny the problem of discrimination by AIs. Quite the contrary. But she correctly notes that AI offers something human decision-making doesen’t: the chance to be deliberate in designing systems that recognize and confront the imperfections too often hidden in our human society. As she notes, “[t]o embrace digitization as a force for societal good, we don’t need to find it perfect. We only need to be convinced of its potential and ability to do better than our current systems.” Lobel at 5. As Lobel notes, “human decision-making is inherently limited, flawed, and biased.” Lobel at 5. AI offers the potential to design systems that do better than we ourselves have proven able to. AI won’t be perfect. But by making explicit the biases that are too often implicit, we can recognize, confront, and hopefully reduce them. Lobel’s book offers a blueprint for building that brighter future.
Mark Lemley is the William H. Neukom Professor at Stanford Law School and a Partner at Durie Tangri LLP.
| 2023-03-09T00:00:00 |
https://www.yalejreg.com/nc/discrimination-and-the-human-algorithm-by-mark-lemley/
|
[
{
"date": "2023/03/09",
"position": 33,
"query": "AI regulation employment"
}
] |
|
Rules to Survive and Thrive in The Age of AI | Charles N. Cox
|
Rules to Survive and Thrive in The Age of AI
|
https://charlesncox.com
|
[] |
You're faced with a daunting world-shifting technology that every blog, news article and tweet is telling you is coming to take your job. Here are your choices.
|
And you thought last year was uncertain.
127,000 tech layoffs already. The rise of ChatGPT. The massive redoubling of the term “AI” in every second sentence on LinkedIn. Doom-and-gloom articles running rampant including this gem, arguably the most ignorant and smug one I’ve read so far.
I get it. We’re scared. We invented fire and now it’s spreading and we haven’t dreamed up a fire extinguisher yet. theresanaiforthat.com shows 1 to 2 new fully-formed Artificial Intelligence-centric apps being released onto the web every 24 hours.
Do not mistake the zeitgeist - you’re feeling it right. The flywheel of companies going all in on AI is in full unrestricted spin. Forget the Metaverse (the what?), forget Web3, forget whatever-the-heck else came before those two.
Machine learning model-driven computing and working, or commonly, AI, is going to be where the spotlight will sit for a somewhat uncomfortable near future.
AI apps. AI jobs. AI congressional hearings. AI investment portfolios. AI warfare. AI-generated avatars of newscasters, movie stars, priests and politicians. AI memes. AI oddly-specific branded flavors of Mountain Dew.
It could burn out, but let’s assume for the time being it won’t. It’s a tool that has hit the trifecta of general utility: fast, cheap, and just enough quality to fool your boss, teacher, spouse, kids or parents into thinking whatever it made came from you. It’ll stick around.
And everyone’s eventually going to use it or interact with something that came from somebody who used it.
What are you going to do about it?
The Big Picture: You’ve Got Three Choices
You’re faced with a daunting world-shifting technology that every blog, news article and tweet is telling you is coming to take your job.
Here are your choices.
Use it / embrace it Fight it / regulate it Ignore it
You get to do any combination of these 3 you want.
But I don’t think #3 is a good plan if you want to come out on top.
Spoilers - I think the right answer is a combination of 1 and 2. We’ll talk about specifcally how to think about AI in the context of your job in a minute but for everyone out there, at a minimum this is what I’d recommend:
Use and be familiar with AI systems enough to - Know when you’re likely being manipulated
Net decent productivity gains that keep you ahead of the pack at your job
Understand your own level of ethical “red line” you refuse to cross and you believe society should not cross. Use what you learned from 1 to support regulation and collective action against AI overreach Support through financial or signal boosting collective action projects that “fight” AI overreach such as
AI spotting software/debunkers
Misinformation fighting tools/bot banners
Support legislation and legislators who:
Support collectivization/unionization of workers (this’ll be a big thing)
Support restrictions on AI usage where it can negatively affect people of color, LGBTQ people, poor people etc.
Support Universal Basic Income. (Anathema! I can hear the crowd say. Just - think ahead a little bit. Robots, dude.)
To this I’d also add:
“Buy” from companies who are transparent, ethical, and open to change on their use of AI
This includes where you spend your free time, where your eyeballs go, etc. Ad revenue IS revenue, that’s why “buy” is in quotes up there
Be aware of where you’re providing your personal information and where possible use alternatives that treat your data with care.
That’s Nice, What About My Job?
McKinsey has some not-so-nice news for you. There’s a lot of insights here and it’s your call how much to believe, but the headline number is that 1/3rd of the US workforce may need to upskill/reskill and change their career paths by 2030.
Again - we’re going to assume that’s true.
There are three layers to your survival and … thrival(?) in this new world.
Roles that are going to be more at risk or less at risk of overall replacement. Duties in any given role that are going to be more or less at risk of replacement. Tools based on AI that you can use now to automate away low-value tasks.
As before, you get to choose any combination of movement along these three axes you want to survive but here’s my equation:
Learn AI Tools now
now To automate away Duties that can be done by AI
that can be done by AI So that your Role refocuses on valuable work that can’t be done by an AI.
This requires a lot.
It requires you learn and use scary new tools.
It requires you be willing to change your work habits to cede at least some production to an AI.
It requires you take an active role in reshaping your job with the help of your leadership to be future-ready.
There will be protests I’ll hear at each layer. Here are some.
I don’t know what tools I should use!
Here’s a giant list. Here’s a smaller list I made.
Almost all of them have free trials.
Today: Try a tool from either list. Just do it.
Tomorrow: Try another one.
Over the Weekend: Make your own list.
Solved.
My work won’t let me use AI tools!
That’s right, they won’t. Yet. Lawyers are up day and night right now with every C-level exec trying to figure out if letting their workforce use AI is going to destroy their company and for now they’re playing it safe and might be telling you you’re a Bad Person for even thinking about interacting with AI tools.
That’ll change, and faster than you can blink. When one company drops the shield, they all will. That’s how it works. A couple of key cases will get up to the federal level in the US, maybe even to the Supreme Court, and the guidelines will become clearer, companies won’t worry about being the first to get sued for their entire net worth, and off we go.
The answer: Use AI for your side hustle. I know you have one.
Learn at your own risk, your own time, with whatever you’re passionate about.
If you can’t bring it to work now, be ready to bring it to work when they open the floodgates.
Because those gates are going to open.
My boss won’t let me reframe my job!
Bummer. If you can’t have an honest conversation about proactively prepping your role for the future at your current job, get ready to look for a new job. Reframe how you want to interact in the world of AI, write it down. What would you do to solve valuable problems for a company if much of your day-to-day could be automated?
Jobs like this will begin to appear. Get on LinkedIn, get on Glassdoor, get on every site and get looking now, watch for signals. People are joking about the job title AI Prompt Engineer now, but today’s nascent laughable side hustle is tomorrow’s rank and file white collar job.
And that’s the macro picture - no matter what the neo-luddites want to believe about the death of white collar jobs, it ain’t happening. AI isn’t going to destroy knowledge work. It’s going to make knowledge work about a whole new type of knowledge, because AI is a tool, wielded to attain power and wealth, something companies and nations want and will fight for.
The war - whether it’s the political kind or the corporate kind, is going to shift. They’re going to need people to use these tools, build these tools, evolve these tools, deploy these tools, repair these tools, and right after this comes the quantum computing revolution that’ll start the next phase of the war.
This is all to say - those layoffs? They’ll hire ‘em back.
Let’s get learning.
This is Just More Uncertainty
If reading this whole thing has given you hives I don’t blame you.
We weren’t in this pickle a year ago. If it feels like the pace of change is picking up, you’re right.
But tools have always led out in front of our societal understanding and bucketing into “good” and “bad” ideas of behavior, so right now we’re operating without a net. The result? Uncertainty.
Good thing I gave you a guide for how to deal with that last year. I should probably write more often.
I’ll finish here with what I said there.
My rules aren’t meant to control your behavior.
They’re not meant to stop your thinking.
You’re in charge of your own rules.
Read, consider, and apply as fits.
Good luck out there.
| 2023-03-09T00:00:00 |
2023/03/09
|
https://charlesncox.com/survive-and-thrive-in-age-of-ai/
|
[
{
"date": "2023/03/09",
"position": 55,
"query": "AI regulation employment"
}
] |
Government Innovation Showcase Pennsylvania - Event
|
Government Innovation Showcase Pennsylvania - Event
|
https://publicsectornetwork.com
|
[] |
Guide ethical AI implementation within government for increased transparency and accountability. Integrate citizen feedback effectively into policy-making ...
|
Pennsylvania is among the leaders in government innovation, employing technology to raise the quality of public services and make people more involved. It involves updating data systems, AI and IT infrastructure, making digital services better while improving customer experience. They are integrating such advanced technologies as big data into their systems aimed at upgrading government infrastructures to achieve great efficiency levels and accessibility. At its core, this project entails setting ethical standards for managing AI and data; hence Pennsylvania must ensure that these technologies are used responsibly even as they continue expanding their use.
Join us at the Government Innovation Showcase Pennsylvania to explore insights in data management, modernized infrastructure, citizen-centric digital services, digital inclusion, and inter-agency collaboration. This event will highlight Pennsylvania's innovative approaches and provide a platform for sharing ideas with peers across the state.
| 2025-01-20T00:00:00 |
2025/01/20
|
https://publicsectornetwork.com/events/government-innovation-showcase-pennsylvania/
|
[
{
"date": "2023/03/09",
"position": 60,
"query": "government AI workforce policy"
}
] |
Road to GEM | Harvard Kennedy School
|
Road to GEM
|
https://www.hks.harvard.edu
|
[] |
The seminar will explore how AI can shape digital policies aimed at improving societal well-being and economic prosperity in developing nations. We will examine ...
|
Harvard CID’s Road to GEM: Shaping the Future of Global Development
Harvard University’s Center for International Development (CID) is proud to host critical conversations with practitioners, researchers, and global change-makers in the months leading up to the annual Global Empowerment Meeting (GEM).
Engaging Global Leaders Across Sectors
CID's Road to GEM programming brings together influential voices from business, government, public policy, and nonprofits, offering unique opportunities to explore key global challenges and solutions. These discussions set the stage for GEM25, where cutting-edge ideas in development take center stage.
This year, GEM25: Catalyzing AI for Inclusive Change, will delve into artificial intelligence (AI) advancements and their real-world applications across development sectors. From economic growth and governance to healthcare and education, experts will examine how AI is reshaping the future and what it means for emerging economies.
All Road to GEM events are open to the public, inviting a diverse audience to engage in meaningful discussions about the future of international development, innovation, and economic empowerment.
| 2023-03-09T00:00:00 |
https://www.hks.harvard.edu/gem/about/road-gem
|
[
{
"date": "2023/03/09",
"position": 80,
"query": "government AI workforce policy"
}
] |
|
Digital Services for Governments
|
Digital Services for Governments
|
https://www.bcg.com
|
[] |
We help government agencies work in new ways, use data to optimize policy ... GenAI and other AI tools present unique challenges and risks for government ...
|
BCG’s Center for Digital Government is a multidisciplinary team of more than 120 experts who are passionate about digital government trends and creating value for citizens. Using data, technology, and people strategy, we transform organizations and operations, unlocking the governments’ potential. Our independent perspective ensures a strong commitment to finding the right solution to maximize value creation for the government, its citizens, and its employees.
| 2023-03-09T00:00:00 |
https://www.bcg.com/industries/public-sector/digital-government
|
[
{
"date": "2023/03/09",
"position": 81,
"query": "government AI workforce policy"
}
] |
|
Examples of Artificial Intelligence in Healthcare
|
Examples of Artificial Intelligence in Healthcare
|
https://www.medigroup.com
|
[] |
AI is used in healthcare to develop more accurate diagnostic tools and treatment plans. It can automate mundane tasks and provide personalized care to patients.
|
10 Examples of Artificial Intelligence in Healthcare
For a hundred years, Hollywood has been making sci-fi films about Artificial Intelligence (AI) robots causing havoc. The reality of AI is actually much more impressive and useful. It’s revolutionizing modern technology and business with far-reaching positive impacts.
AI is used in healthcare to develop more accurate diagnostic tools and treatment plans. It can automate mundane tasks and provide personalized care to patients.
Here, we explore examples of artificial intelligence in healthcare and how it’s transforming patient care. Keep reading to gain a better understanding of its uses and potential.
Examples of Artificial Intelligence in Healthcare
Below are some categories of healthcare where we’re seeing a lot of AI advancements. In each category, there are several areas where Artificial Intelligence is saving time or money. It’s also proving to be more accurate and catches information that humans may miss.
Diagnostic Tools
There are a number of different diagnostic tools that are used in healthcare, and many of them are now powered by AI. One example is the use of AI to interpret medical images. This can be used for things like CT scans and X-rays, and it can help to identify problems much faster than traditional methods.
AI for diagnostics is also being used to evaluate patient charts and data collected during visits. This can be used to spot trends and patterns that may indicate a particular condition or disease. The information can then be used to make more informed decisions about diagnosis and treatment.
Drug Development
The development of new drugs is a long, complicated, and expensive process. The use of AI in drug development is still in its early stages, but the potential benefits are clear. With AI, new drugs can be developed faster and at lower costs.
The traditional method of drug development involves multiple steps. It includes identifying targets, synthesis of new compounds, testing, and clinical trials in humans. AI is speeding up target identification and compound synthesis.
In addition, AI can be used to analyze data from animal studies and clinical trials more quickly and accurately than traditional methods.
Imaging Software
Imaging software is one of the most commonly used applications of artificial intelligence in healthcare. AI-powered imaging software can help doctors quickly and accurately identify diseases and disorders, as well as help plan treatment strategies.
One example of how AI is being used in imaging software is in the development of 3D medical images. Using data from multiple 2D images, AI algorithms can create realistic 3D images of organs and other structures inside the human body. This can be useful for planning surgeries, as well as for providing patients with a better understanding of their condition.
Another example is in the area of X-ray interpretation. With the help of AI, X-ray images can be automatically interpreted and analyzed for fractures or abnormalities. This can help to speed up the diagnosis process and improve patient care.
Patient Monitoring and Alerts
Patients are always encouraged to take an active role in their wellness and AI can help with that as well. Using AI technology allows for early detection of issues and potential problems or changes in their health even without a visit to the doctor.
Wearable devices like smartwatches and health trackers are good examples of artificial intelligence in healthcare at home. The devices monitor information about heart rates, stress management, and sleep patterns. The information can be used to relay information and detect changes or irregularities.
Doctors and patients can work together to create personalized alert settings based on the issues they’re trying to address. When a change or problem is indicated, AI can notify the patient and schedule appointments with the practitioner’s office. It can also advise the doctor about potential treatment courses based on the data and calculations.
Virtual Assistants
Virtual assistants are computer programs that can perform tasks such as making appointments, providing information about medical conditions, and even giving reminders about taking medications.
Google Now and Alexa are examples of virtual assistants that many people have access to. These programs can provide information about nearby hospitals, pharmacies, and doctors’ offices. They also:
Find medical information online about symptoms and diseases
Offer advice on implementing treatments
Suggest doctors in the area
Keep track of medications and their schedules
Provide reminders for medications and appointments
Virtual assistants provide patients with convenient access to information and care with fewer in-person visits. This can help to save time and money for both patients and healthcare providers.
Medical practitioners’ offices use virtual assistants as well. In this capacity, the AI streamlines a multitude of tasks:
Send reminders and confirmations to patients
Maintain office schedules
Handle most incoming patient inquiries
Manage automated billing and payments
Access the patient’s digital file for visits
Alert the doctor to any new changes in the patient’s files
Send prescriptions to the pharmacy
Using virtual assistants for some of the tasks in the doctor’s offices saves valuable time for staff to focus on things that need
Closing Thoughts
As you can see, there are many examples of artificial intelligence in healthcare. AI is revolutionizing the medical industry for care providers and patients.
It’s providing doctors with new tools to diagnose and treat diseases. It’s analyzing images of the human body to look for signs of disease and creating personalized treatment plans. It’s helping develop new drugs and making it possible for patients to get access to better care.
AI is also helping to make the medical industry more efficient. This allows human doctors and nurses to focus on the patient.
Contact MediGroup to learn about solutions that are right for your office.
| 2023-03-09T00:00:00 |
2023/03/09
|
https://www.medigroup.com/blog/10-examples-of-artificial-intelligence-in-healthcare/
|
[
{
"date": "2023/03/09",
"position": 4,
"query": "AI healthcare"
}
] |
Data and AI for Leaders - Alliance Manchester Business School
|
Data and AI for Leaders
|
https://www.alliancembs.manchester.ac.uk
|
[
"Alliance Manchester Business School"
] |
On this four-day Data & AI course, uncover new opportunities for your organisation and develop skills needed to make impactful, data-driven decisions.
|
Built with the aim of connecting theory to practice, this course will help you gain valuable industry insights from world-leading experts through a variety of interactive teaching and learning methods.
Delivered face-to-face at Alliance Manchester Business School’s Executive Education Centre, you will take part in collaborative workshops, group discussions and activities, and hear from a selection of inspiring guest speakers including Vlad Jiman, Group Head of Data at Policy Expert, and Joe Loxham, Software Engineer at Peak.
You will also have the opportunity to explore Alliance Manchester Business School’s state of the art Data Visualisation Laboratory and experiment with your own data sets.
Designed to break down access barriers and reduce complex language surrounding data and AI, this course uses case studies, prepared models, and dashboards to emphasise the practicality of AI models and business applications.
We aim for several stand-out learning experiences, including:
Applied Learning - A learning environment created by the course facilitator that draws on the experience and input of your course peers.
Knowledge Transfer - A steady stream of feedback from participating peers with diverse business backgrounds on, for instance, how they have implemented data and AI within their organisation.
Reflection - A rare time-out to contemplate your own opportunities and challenges while engaging with our world-class faculty.
Network Expansion - An expanding professional network drawn from your peers.
Personal Growth - An expanding self-awareness of your own competencies and how they can be honed.
Behavioural Change - An adoption of new rationales and mindsets that improve decision making.
Facilitated Challenge - A regular raising of difficult questions by the course facilitator designed to take you away from your work environment into new work-territory.
As part of this course, you will gain access to our comprehensive virtual learning environment before, during and after your time with us. Here, you will find course materials, including pre-work, slides, case studies, and recommended readings, all conveniently available to aid your learning.
At the end of the course, you continue to have access to this online learning area and the University of Manchester online library for up to one year, supporting you to embed your new toolkit of skills and knowledge back into your workplace.
| 2023-03-09T00:00:00 |
https://www.alliancembs.manchester.ac.uk/study/executive-education/short-business-courses/data-and-ai-for-leaders/
|
[
{
"date": "2023/03/09",
"position": 36,
"query": "artificial intelligence business leaders"
}
] |
|
Artificial Intelligence (AI) at Grammarly
|
Artificial Intelligence (AI) at Grammarly
|
https://www.grammarly.com
|
[] |
We build AI that transforms how people communicate, making writing faster, clearer, and more impactful—no matter where you work or write ...
|
Innovation doesn’t need to come at the cost of ethics or safety. Explore the framework that guides our AI development and deployment to keep human oversight, data privacy, and user safety at the forefront of every decision.
| 2023-03-09T00:00:00 |
https://www.grammarly.com/ai
|
[
{
"date": "2023/03/09",
"position": 89,
"query": "artificial intelligence business leaders"
}
] |
|
The Pros and Cons of AI in the Workplace
|
The Pros and Cons of AI in the Workplace: How AI is Transforming Jobs and Industries
|
https://trimzoinfotech.com
|
[] |
With the increasing adoption of AI, there are concerns about job displacement and the need for retraining and reskilling workers for new roles. This can be ...
|
AI can improve efficiency, accuracy, and speed in many industries, such as healthcare, finance, and logistics. It can also automate repetitive and tedious tasks, freeing up time for humans to focus on more creative and complex work.
Artificial Intelligence (AI) has the potential to transform various industries by improving efficiency, accuracy, and speed. In healthcare, AI-powered technologies can help doctors diagnose diseases accurately and provide personalized treatments to patients. In finance, AI algorithms can analyze financial data, identify trends and patterns, and automate repetitive tasks such as data entry and processing. In logistics, AI can optimize the supply chain by analyzing shipping data to identify the most efficient routes and transportation methods. Moreover, AI can automate repetitive and tedious tasks, enabling human workers to focus on more creative and complex work that requires critical thinking and problem-solving skills. This can lead to increased job satisfaction and a more productive workforce. However, there are also concerns about the impact of AI on employment, as some jobs may become redundant due to automation. As such, businesses and governments need to consider reskilling and retraining programs for workers to adapt to the changing job market.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://trimzoinfotech.com/the-pros-and-cons-of-ai-in-the-workplace-how-ai-is-transforming-jobs-and-industries/
|
[
{
"date": "2023/03/10",
"position": 60,
"query": "automation job displacement"
},
{
"date": "2023/03/10",
"position": 83,
"query": "artificial intelligence workers"
}
] |
Robot Policies in Asia
|
Robot Policies in Asia
|
https://equalocean.com
|
[
"Author",
"Qinqie He"
] |
While automation can drive economic growth and create new employment opportunities, it can also lead to job displacement and widen the gap between the rich and ...
|
Over the past few years, Countries worldwide have been showing growing interest in robotics and automation and investing in robotics to support developments in industry and society. We at EqualOcean tracked the exact targets of robotics research funding programs (R&D) officially driven by governments in Asia.
China's Robot Policies
China is a country that has been making significant investments in the robotics industry in recent years. In 2015, the Chinese government launched the "Made in China 2025" initiative, which focuses on developing the robotics industry. The initiative aims to make China a world leader in advanced manufacturing, and robotics is seen as a key technology to achieve this goal.
The Chinese government has also established the "China Robot Industry Alliance" (CRIA) to support the development of the robotics industry in the country. The alliance supports robotics research and development and organizes robotics competitions and events to promote the technology.
The Chinese government has recognized the potential impact of automation on the workforce. In 2017, the government launched the "New Generation Artificial Intelligence Development Plan" to provide training and education programs to prepare workers for the changes brought about by automation.
Japan's Robot Policies
Japan is a country that has been at the forefront of robotics technology for decades. The Japanese government has been investing in robotics research and development since the 1980s, and today, the country is a world leader in the field.
The Japanese government has established the "Robot Revolution Realization Council" to support the development of the robotics industry in the country. The council brings together government, industry, and academia to promote the development and use of robotics in various industries.
The Japanese government has also recognized the potential impact of automation on the workforce. In 2015, the government launched the "Work Style Reform" initiative to promote a more flexible and productive work environment. The initiative includes a focus on using robotics and automation to increase productivity and reduce the workload of workers.
South Korea's Robot Policies
South Korea is a country that has been making significant investments in the robotics industry in recent years. In 2014, the South Korean government launched the "Creative Economy" initiative, which focuses on the development of the robotics industry. The initiative aims to promote using robotics in various industries to drive economic growth and create new employment opportunities.
The South Korean government has also established the "Korea Institute for Robot Industry Advancement" (KIRIA) to support the development of the robotics industry in the country. The institute supports robotics research and development and organizes robotics competitions and events to promote the technology.
The South Korean government has recognized the potential impact of automation on the workforce. In 2017, the government launched the "Human-Centered Fourth Industrial Revolution" initiative, which focuses on training and education programs to prepare workers for the changes brought about by automation.
Singapore's Robot Policies
Singapore is a small city-state that has made significant investments in the robotics industry recently. The Singaporean government has recognized the potential of robotics to drive economic growth and has taken steps to support the sector's development.
In 2016, the Singaporean government launched the "Industry Transformation Map" (ITM), which focuses on the development of the robotics industry. The ITM aims to promote robotics in various industries, including manufacturing, healthcare, and logistics.
The Singaporean government has also established the "Advanced Remanufacturing and Technology Centre" (ARTC) to support the development of the robotics industry in the country. The center supports robotics research and development and organizes robotics competitions and events to promote the technology.
The Singaporean government has recognized the potential impact of automation on the workforce. In 2018, the government launched the "SkillsFuture" initiative to provide training and education programs to prepare workers for the changes brought about by automation. The initiative aims to equip workers with the skills they need to stay relevant and competitive.
It is worth noting that the development of robotics technology and the policies surrounding it is challenging. One major challenge is the ethical implications of robotics and automation. As robots and automation become more prevalent in various industries, there is a need to address the ethical issues surrounding their use. These issues include the potential loss of jobs and the ethical implications of using robots in healthcare and the military.
Another challenge is the potential for increased economic inequality due to automation. While automation can drive economic growth and create new employment opportunities, it can also lead to job displacement and widen the gap between the rich and the poor. Governments need to address these challenges and ensure that the benefits of automation are shared equitably among all members of society.
While there are challenges to address, such as the ethical implications of robotics and the potential for increased economic inequality, the use of robotics and automation can drive economic growth and create new opportunities for employment.
| 2023-03-10T00:00:00 |
https://equalocean.com/analysis/2023031019532
|
[
{
"date": "2023/03/10",
"position": 99,
"query": "automation job displacement"
}
] |
|
What AI Says We Should Know about AI
|
What AI Says We Should Know about AI
|
https://tracom.com
|
[
"Elena Essex",
"About Elena Essex",
"Related Posts"
] |
The fear of job displacement, loss of privacy, and the ethical implications of AI are some of the key factors contributing to this anxiety.
|
Combatting Negativity About the Unknown
Gallup and others have recently published studies showing that workplace stress is at an all-time high and employee engagement near an all-time low. One of the topics currently affecting stress is the potential impact of Artificial Intelligence (AI) on the workforce. So I asked ChatGPT, a tool for accessing AI, to write a five paragraph blog about the anxiety people feel about AI.
Here’s what ChatGPT had to say:
Anxiety surrounding AI is a common sentiment among many individuals today. As AI technology continues to advance at an unprecedented rate, people are understandably concerned about its potential impact on various aspects of their lives. The fear of job displacement, loss of privacy, and the ethical implications of AI are some of the key factors contributing to this anxiety.
One of the primary sources of anxiety is the fear of job displacement. With the increasing automation of tasks and the rise of AI-powered systems, many individuals worry that their jobs may become obsolete. This fear is not unfounded, as certain industries have already witnessed significant changes due to AI integration. However, it is important to note that AI also has the potential to create new job opportunities and enhance productivity in various sectors.
Another aspect that contributes to anxiety is the concern over privacy. As AI systems collect and analyze vast amounts of data, there is a legitimate fear that personal information may be misused or exploited. The potential for surveillance and the unauthorized use of data raise valid concerns about privacy infringement. It is crucial for AI developers and policymakers to prioritize data protection and establish robust regulations to address these concerns.
Ethical considerations surrounding AI also contribute to anxiety. The ability of AI systems to make autonomous decisions raises questions about accountability and fairness. Concerns about biased algorithms, lack of transparency, and potential misuse of AI technology are valid and require careful attention. It is essential for AI developers and researchers to prioritize ethical frameworks and ensure that AI systems are designed with human values and societal well-being in mind.
While anxiety about AI is understandable, it is important to approach the topic with a balanced perspective. AI has the potential to revolutionize various industries, improve efficiency, and enhance our daily lives. By addressing the concerns surrounding job displacement, privacy, and ethics, we can work towards a future where AI is harnessed responsibly and for the benefit of humanity. Open dialogue, collaboration, and proactive measures are key to alleviating anxiety and ensuring that AI technology is developed and deployed in a manner that aligns with our values and aspirations.
Pretty good blog right? The writing is clear and the key points understandable. Most people probably wouldn’t be able to tell that this was AI generated. But is that a good thing or bad thing? Some fear AI because it’s “going to take over” or “we’re going to lose our jobs to robots.” As someone who writes blogs, I can understand this fear.
What is really manifesting here is not a fear of AI, but a fear of the unknown. More specifically, it’s our own Negativity Bias kicking in because of the unknown. Our brain interprets that ambiguity as a threat and we stress out. It’s completely natural and it’s the result of thousands of years of cognitive development.
Fortunately, people can learn techniques to head off or reverse the Negativity Bias. TRACOM’s Adaptive Mindset for Resilience assessment identifies how the Negativity Bias affects each learner and they learn specific ways to counteract it. People with these skills don’t let change or ambiguity dominate their thinking. They recognize the situation and move forward productively.
So whether you’re worried about the impact of AI, a pending corporate merger or the quarterly financial numbers, you can benefit from resilience training.
| 2023-10-03T00:00:00 |
2023/10/03
|
https://tracom.com/blog/what-ai-says-we-should-know-about-ai
|
[
{
"date": "2023/03/10",
"position": 100,
"query": "automation job displacement"
}
] |
AI could lead to a new era of work-life balance
|
AI could Lead to a New Era of Work-Life Balance
|
https://analyticsindiamag.com
|
[
"Purnesh Gali",
"Purnesh Gali Quit His Job As The Head Of Analytics At A Multi-Billion-Dollar Enterprise To Start Actalyst",
"An Ai Startup Reimagining Manufacturing Ai"
] |
Marc Andreessen recently discussed why AI won't lead to unemployment. Andreessen's view is that technology adoption is more challenging in regulated industries ...
|
Allowing people to pursue their hobbies and interests.
The traditional wisdom that jobs that need creativity, emotional intelligence, and critical thinking is less likely to be automated is false. In fact, these jobs will be disrupted first. Nearly three years ago, I predicted that the technology sector will be among the first to feel the impact of AI. Here is an excerpt:
“. . . we are going to compete with AI for many of the current jobs. Just to be clear, I am not talking about blue collar jobs. . . but jobs like Data Scientists and Software Engineers. . .”
What does it mean that these skills are not uniquely human and cannot be replicated by machines? What does it mean to different industries?
Technology impacts the technology industry first
Marc Andreessen recently discussed why AI won’t lead to unemployment. Andreessen’s view is that technology adoption is more challenging in regulated industries driving prices higher (as shown by the red line in the graph below), while prices tend to decrease in unregulated Industries (represented by the blue line in the graph below). While regulated sectors grow as a percentage of GDP and less regulated end up shrinking, it follows that AI won’t lead to unemployment.
High prices in red categories is a very complex and highly nuanced topic, and I am by no means an expert to debate this. What is clear from the above graph, however, is that unregulated sectors can drive costs down through competition and the use of technology. The technology sector is unregulated and highly competitive, and tends to feel the impact of any technological progress first.
The above data shows that AI is impacting one of the most complex jobs in technology: Developers.
While I am not an expert to discuss the global impact of AI, I can speak to its influence on the technology industry. In fact, if there’s one buzzword that’s been consistently echoing in the industry for the last decade or so, it’s “developers, developers, developers,” as famously—or infamously—proclaimed by Steve Ballmer.
GitHub Copilot, an AI tool that can write code, is an example of how AI is going after developers. Currently, it may not be a magic wand to convert a bad programmer into a good one, but it can vastly increase the productivity of a skilled one. So, for now, it’s not like it will perform miracles but it sure can help the experts work their magic better!
The latest data reveals that Copilot can boost developer productivity by over 50%. We can debate about the degree of productivity improvement but if you use Copilot, you will not be able to deny its potential. Other studies have arrived at similar conclusions, though the degree of productivity varies.
Credits: github.blog
What about the impact of technology on other industries?
While Andreessen’s chart paints a compelling picture of the impact of technology on regulated industries, there’s a whole other side of the story that often goes unnoticed. Take process manufacturing, for instance—an industry that has several layers between them and their customers and is relatively less regulated. Is such an industry impacted by advances in technology?
I looked at one specific industry to get a sense: Primary Metals. This US industry had nearly 400,000 employees in 2012 and by 2022, that number had decreased by almost 50,000 to 350,000.
Despite this, the unemployment rate within this industry hit a record low in 2022. At the same time, this industry’s contribution to US GDP nearly doubled. While correlation doesn’t always equal causation, I can’t help but feel that technology played a pivotal role in this situation.
What is the significance of this?
When developers are becoming more productive and manufacturing is becoming more efficient, what does it imply? The popular way to view it is as the elimination of one job profile. However, what if there is a different narrative? With less work, does it mean more free time? Does it mean the elusive four-day work week could finally become a reality because of AI?
The extent of technology’s impact on labour is a very complex topic and I don’t claim to understand it in its entirety. But, I do believe AI could redefine the work-life balance for good and free up time so we pursue hobbies and interests, or just binge-watch Netflix!
| 2023-03-10T00:00:00 |
2023/03/10
|
https://analyticsindiamag.com/ai-features/ai-could-lead-to-a-new-era-of-work-life-balance/
|
[
{
"date": "2023/03/10",
"position": 28,
"query": "AI unemployment rate"
}
] |
Equitable Growth's Jobs Day Graphs: February 2023 ...
|
Equitable Growth’s Jobs Day Graphs: February 2023 Report Edition
|
https://equitablegrowth.org
|
[
"Carmen Sanchezcumming",
"Authors",
"Kathryn Zickuhr",
"Carmen Sanchez Cumming",
"Michael Linden",
"David S. Mitchell",
"Shayna Strom",
"Hiba Haroon",
"Shaun Harrison"
] |
The unemployment rate increased to 3.6 percent in February, and is highest for Black workers (5.7 percent) and Latino workers (5.3 percent), compared to ...
|
On March 10, the U.S. Bureau of Labor Statistics released new data on the U.S. labor market during the month of February. Below are five graphs compiled by Equitable Growth staff highlighting important trends in the data.
The employment rate for prime-age workers increased from 80.2 percent in January to 80.5 percent in February as total nonfarm employment rose by 311,000.
The unemployment rate increased to 3.6 percent in February, and is highest for Black workers (5.7 percent) and Latino workers (5.3 percent), compared to White workers (3.2 percent) and Asian American workers (3.4 percent).
Nominal wage growth (not accounting for inflation) did not change dramatically in February, with average hourly earnings growth at 4.62 percent over the past 12 months.
Employment in many sectors, including construction, retail, and educational services is now back to or surpassing pre-pandemic levels. Employment in leisure and hospitality has not quite recovered, but added 105,000 jobs in February.
The share of unemployed workers who are unemployed due to job loss increased to 32.2 percent, and the share on temporary layoffs rose to 13.6 percent; the share who left their jobs (14.8 percent), are reentering the labor force (30.8 percent), or are new entrants (8.6 percent) declined.
| 2023-03-10T00:00:00 |
2023/03/10
|
https://equitablegrowth.org/equitable-growths-jobs-day-graphs-february-2023-report-edition/
|
[
{
"date": "2023/03/10",
"position": 89,
"query": "AI unemployment rate"
}
] |
Business Intelligence Automation - Data & Analytics
|
Business Intelligence Automation
|
https://www.bluwave.net
|
[
"Kyle Johnson"
] |
Business intelligence automation tools have myriad benefits that can positively impact your business. Our experts discuss what they are.
|
Business intelligence continues to be among the most high-demand services in the Business Builders’ Network.
One aspect of BI&A that’s popular is automation.
The founding partner of one of our BluWave service providers says BI automation is essential to modernizing data analysis.
“A lot of times the process involves people pulling data into spreadsheets manually, analyzing, cleaning, doing stuff with the data and then giving it to their bosses or whoever downstream needs them,” says the partner, Mike Datus*. “That’s usually a very error-prone process because it’s done by humans.”
BI automation can change all that, and make life much easier for both the analysts as well as those downstream superiors.
Let’s talk in more detail about BI automation tools, their benefits as well as potential drawbacks.
READ MORE: What is Business Intelligence & Analytics?
What is Business Intelligence Automation?
Business intelligence automation is the process of consolidating and streamlining your company’s data into a single warehouse that can be accessed in real-time.
Automation provides instantaneous insights that forgo manual input and data manipulation to give team members actionable, consistent information to drive their day-to-day decisions.
Put another way, it helps you automate business processes.
Companies that are older, or perhaps resource-challenged, can benefit greatly from automating their data collection and analysis.
Another data firm’s founding partner, who we’ll call Steve Holms*, puts it this way:
“Holding larger data sets and integrating more data sources to do analysis across several different places makes it a lot easier to analyze.”
Business Process Automation Benefits
It’s no surprise that business intelligence tools are in such high demand. We have seen countless PE firms and other companies streamline processes and improve real-time decision-making because of them.
Here are just a few of the reasons why you should consider implementing or upgrading your automation efforts.
Save Time
Not only will you complete key tasks sooner, but you’ll be able to make important decisions faster, too.
“You’re talking about orders of seconds instead of hours or days, right? And then that’s huge,” Datus says. “With one of our clients, we built a platform, so instead of waiting a week, the CFO now had a live dashboard in board meetings. So when he was asked a question, he didn’t have to say, ‘I’ll get back to you next week.’ He literally just popped up his dashboard, did a quick filter, and had the answer.”
Our service providers often see situations where top executives need different versions of the same report depending on who they’re working with or what meeting they’re in at a given moment.
This often meant one-off iterations of the same data sets that take might not be available the same day, or even week.
“If the analyst has to go back, they have to go back and pull the data again, do the analysis, run it through, right? That’s another runtime,” says Holms, who noted that those iterations add up.
Another time-saving scenario is if an analyst leaves the company, is on vacation or has an emergency. Data analysis doesn’t stop as soon as that key player becomes unavailable.
“You only have to program it once, and you’re done,” Holms says. “It’s all in the database, and they don’t have to email anybody in case they didn’t get the report.”
READ MORE: Data Warehouse Types: How To Choose the Right One
Scalability
Have you ever tried to access a report so robust that you thought your computer might break down? You’re not alone.
Another benefit of business intelligence automation is the ability to scale.
“Sometimes your data’s so large, it’s hard for Excel to even open, right?” Holms says. “How does sales correlate with product performance, correlate with manufacturing, correlate with this? —putting it in one place makes things a lot easier to expand.”
Save Money
There are multiple ways BI automation can save your company money:
You may be able to reduce headcount on your analytics team and reinvest those savings elsewhere
The time you do save – as mentioned earlier – is time for which you’re no longer paying
The data itself could unveil inefficiencies in your business that are ripe for improvement
Manual intervention is expensive. By cutting out intermediaries, and empowering decision-makers more quickly, they can use expertise that no program can account for to make impactful decisions
Consistency
Humans are much more error-prone than machines. Especially well-designed and well-programmed machines.
While you wouldn’t want to automate a process so heavily that it’s no longer monitored, the correct calibration can set your team much more at ease.
“You’re building good processes to make sure it’s consistent. It’s done by computers, so once you do it once it’s pretty robust, unless the data itself changes or the business changes,” Holms says. “Sometimes you just get errors that are difficult to detect. And if you want to go back to see what were my numbers last week or two weeks ago or three months ago, you have to go into your email inbox and search for the report.”
With BI automation, you can leave the inbox behind and find everything you need in your dashboard.
“It’s all in the database,” Holms says, “and they don’t have to email anybody in case they didn’t get the report.”
Dynamic Reports
As we already hinted at above, automated dashboards and visualizations are essentially living, breathing databases.
Instead of plugging new information into a spreadsheet every time you want to update a report, it’s available instantaneously. Not only that. Since it’s connected to the source, you don’t have to input the data at all.
“Once you have it all there getting updated predictably, you can create these really rich charts and graphs, because with these tools you can get these visuals that aren’t static,” Datus says. “The real-time dashboards update as the data comes into the system. So if you want to see one chart or the set of 20 charts for last week just for finance, you can click a few things, and you can get that report.”
Risks of Automation
While automation can be valuable to a business, it doesn’t come without some potential downside. With the right help, though, we believe all of these can be overcome.
Job Loss
Automation may replace human workers and lead to job losses – at least in the short term.
A benefit of this, though, is that it frees those some people up to learn and use new skills that are equally valuable to the business. Money saved on one area of human capital can be reinvested in your talent.
System Failures
Automated systems can experience technical issues, thereby disrupting business operations. You would hope that this is the exception and not the norm, but even so, manual intervention may be required to fix the issues.
Expert service providers, however, are familiar with the most common vulnerabilities, and will know how to not only fix them, but also proactively prevent them.
READ MORE: What is Technical Debt in Due Diligence?
Lack of Flexibility
Automated systems are designed to handle repetitive, routine tasks in a predetermined manner. They may lack the flexibility to adapt to unexpected situations or changes.
This is quickly changing, though, with the implementation of more and more AI tools that can often course-correct much faster than humans.
This perceived “risk” is quickly becoming a moot point in many senses.
Cost
Implementing and maintaining automated systems can be expensive. This is most likely to be an issue for very small businesses that have less to automate and can handle all their data by traditional means.
Large companies with more robust budgets will probably find that the investment is well worth it in the long run. This includes private equity firms, their portcos, and private and public companies of all shapes and sizes.
While automation involves these and other risks, it’s an increasingly valuable and in-demand facet of business intelligence. Based on the feedback we receive from our clients and expert service providers, we wouldn’t shy away from exploring how your business can benefit from automation.
BI Automation Tools
Now that you have considered the pros and cons of BI Automation, it’s time to look at the tools at your disposal. While all of these can have a significant impact on your business, you want to make sure you’re using the right ones.
Let’s get familiar with a few of the high-level categories, as well as some specific business automation technologies within them. That way, when our research and operations team connects you to a tailor-made, niche-specific firm to set up your BI automation, you’ll have an idea of what you’re looking for.
Dashboards
BI automation dashboards display key performance indicators, data points and other important metrics in an easy-to-understand format. They provide a 360-degree view of performance using charts, graphs and other visuals.
They offer a quick-glance overview of your organization’s most important metrics, allowing users to quickly identify areas of strong or weak performance, spot emerging trends and gain data-driven insights. Some examples include Power BI, Tableau and Qlik Sense.
READ MORE: Platform Modernization: App, Software Upgrade
Common metrics used to evaluate business performance are cash flow, customer satisfaction and website traffic. Others include sales revenue and customer loyalty.
When you work with an experienced data analytics firm, they’ll be able to match your business needs to the right tools.
Visualizations
BI automation visualizations enable end users to execute automated workflows based on insights within a report. The workflows can be data-contextual, meaning they can change based on filters.
They are often used to connect multiple data sources, create interactive dashboards and charts, provide real-time visualizations and alerts and utilize natural language processing.
Power Automate visual, DataBox, Datapine, Domo and IBM Cognos Analytics are a few of these tools. They can be used to connect to Excel spreadsheets, SQL databases, social media platforms and more.
Predictive Analytics
This type of BI automation tool leverages artificial intelligence and machine learning to automatically generate and apply predictive models based on data insights. Predictive models are employed to forecast what may occur in the future dependent on historical and current data.
These are often used to predict things like customer churn, sales revenue and product demand. They’re especially utilized in the healthcare, finance and marketing industries.
Some of the more popular tools include RapidMiner, Alteryx, SAS Visual Analytics, KNIME, and SAP Analytics Cloud.
Data Mining
Data mining techniques to extract valuable insights from large data sets for making more informed decisions. It’s a branch of data science that searches for patterns, anomalies and correlations in using statistics, artificial intelligence and machine learning.
READ MORE: How To Extract Data from ERP Systems
It’s often used to solve customer segmentation, fraud detection and market basket analysis. Many of the tools listed in the sections above can also be used for these tasks.
If a lot of this sounds new to you and your team, that’s OK. In fact, Holms says that even a well-composed manual report can be a great launching point for BI automation.
“I would say even if you have an Excel report and it’s a good Excel report, you’re already ahead of the game,” he says.
If you don’t know where to start, set up a scoping call with our research and operations team. We’ll connect you to world-class firms like Datus’s, Holms’, or other PE-grade service providers that can serve your exact needs for your particular industry.
*Privacy is important to us. While the source and company name have been changed, these are real quotations from a real service provider in the BluWave Business Builders’ Network.
| 2023-03-10T00:00:00 |
https://www.bluwave.net/business-intelligence-automation-data/
|
[
{
"date": "2023/03/10",
"position": 57,
"query": "job automation statistics"
}
] |
|
U.S. Manufacturing Employment Declined in February
|
Manufacturing Hiring Trends in February 2023
|
https://www.industryselect.com
|
[] |
... data provided from the Labor Department ... If you're looking to help manufacturers with their staffing, workforce or automation ...
|
Posted by IndustrySelect on Friday, March 10, 2023
U.S. manufacturing employment declined in February for the first time in two years, according to the latest jobs report from the Labor Department issued March 10th.
U.S. Manufacturing Job Gains and Losses by Subsector
According to the latest data, U.S. manufacturers shed 4,000 net jobs in February 2023, marking the first time the sector has registered a net employment decline since March 2021.
Meanwhile, the U.S. economy added 311,000 jobs overall in January, and the unemployment rate rose to 3.6%.
Additionally, the latest Job Openings and Labor Turnover survey finds the number of job openings in manufacturing has increased, with the Labor Department recording 791,000 unfilled positions for the sector in January (January JOLTS data is reported in March). This is compared to 730,000 open positions recorded for December and 914,000 unfilled positions reported one year ago.
Unfilled positions in manufacturing and in the U.S. economy at large have remained elevated since the pandemic. As an example, open positions in the industrial sector totaled 432,000 in March 2019—a little less than half of what they are today.
If you’re looking to help manufacturers with their staffing, workforce or automation needs, an IndustrySelect subscription can help you find new customers in the industrial world. Click here to try out a free demo.
Despite last month’s losses, there were some notable manufacturing industries that added jobs. In this post, we’ll explore the most recent manufacturing numbers from the Labor Department, providing gains and losses by specific subsector.
The Manufacturing Sectors That Added Jobs in February 2023
Non-durable goods manufacturing accounted for all of the sector’s losses in February, losing 4,000 jobs altogether, while durable goods manufacturing posted no change in employment.
Computer and electronic product manufacturing led job gains in February, adding 2,800 positions.
This was followed by chemical manufacturing, which added 2,500. Other sectors that added jobs included navigational, measuring, electromedical and control instruments (+1,500); nonmetallic minerals manufacturing (+1,500); transportation equipment (+1,300) and petroleum and coal products (+1,000).
The Manufacturing Industries That Lost Jobs in February 2023
U.S. manufacturing jobs losses in February 2023 were sharpest in plastics and rubber manufacturing, down 4,700 jobs, followed by furniture manufacturing, which shed 2,800 jobs.
Other manufacturing industries losing jobs included textile mills (-1,700 jobs), apparel manufacturing (-1,300 jobs), paper mills (-1,100); and fabricated metals (-1,100).
Helping Manufacturers With Staffing Needs
To get a hands-on look at how an IndustrySelect subscription can help you reach manufacturers like these, visit our website and try out a free demo.
Powered by MNI’s hand-verified industrial information, IndustrySelect provides instant access to 460,000 industrial business profiles, including one million executives. Types of executive decision-makers include those in Human Resources, Health & Safety, Research & Development and more.
| 2023-03-10T00:00:00 |
https://www.industryselect.com/blog/manufacturing-hiring-trends-in-february-2023
|
[
{
"date": "2023/03/10",
"position": 93,
"query": "job automation statistics"
}
] |
|
35+ AI Recruitment Statistics Shows You How AI Has ...
|
35+ AI Recruitment Statistics Shows You How AI Has Revolutionized Hiring
|
https://bloggingx.com
|
[
"Akshay Hallur",
"After Dropping Out Of University In",
"I Pursued My Passion For Content Creation",
"Built A Successful Career. I Bring A Unique Set Of Interdisciplinary Skills To The Table",
"Including Content Creation",
"Blogging",
"Youtube",
"Systems Design",
"Especially Notion",
"Course Design"
] |
HR teams have a pretty tough job to do, from writing job descriptions and sieving through hundreds or thousands of candidates to scheduling interviews and ...
|
HR teams have a pretty tough job to do, from writing job descriptions and sieving through hundreds or thousands of candidates to scheduling interviews and having to consider loads of important factors.
It’s a completely tiring process, and there’s no guarantee they don’t miss crucial information because, well, they’re humans. As a survey by Deloitte revealed, recruitment teams have to deal with loads of challenges, especially finding qualified talents.
However, HR and others in charge of recruitment can have a much-improved experience and get better results by embracing artificial intelligence.
AI technologies can help businesses accommodate more talent, select the most qualified candidates, and handle all the lifting so recruiting teams can focus on other aspects of the job that matter to boost productivity.
Below, you’ll find AI recruitment statistics showing how AI has revolutionized the recruiting process, its many benefits, and everything in between.
The need for AI in recruitment
As humans, it’s pretty normal to stick to things we’re already familiar with in place of new things we’ve barely had many experiences with. Hence, it’s understandable that many are less receptive to new ideas such as artificial intelligence.
That is why you may want to ask, why AI? Why not stick with the traditional recruitment methods that have always worked? The first response I’d give is that conventional methods haven’t always worked and for my second response, let’s look at the stats below.
As mentioned in the introduction above, recruitment teams find little of what they do easily and need help to do better.
The survey from Deloitte states that recruitment teams encounter different challenges, such as finding qualified hires (61%), identifying full-time talent with the right skills (37%), and finding qualified entry-level hires (34%).
Recruiters spend 13 hours a week sourcing candidates for a single role.
80% of recruiters say employing to fill roles in their businesses is more challenging due to the skills gap.
The average number of applications received per job position rose between 2012 and 2022 by 39%. Meanwhile, the quality of 72% of employment applications is considered low to average.
Employers recognize the many challenges in recruiting and, therefore, the need for intelligent tools and AI to help ease the processes.
71% of recruitment teams say they need intelligent tools to manage data.
92% of HR professionals think it will be great to use AI for talent acquisition.
The current state of AI in recruitment
The current state of artificial intelligence shows great promise for what’s to come in the field. It shows how successful the efforts that have been put into are and tells you the future will be massive as far as the efforts are upheld.
The following statistics explain what the current state is.
30% of organizations use AI for their recruitment campaigns.
65% of recruiters already use AI for recruitment.
Close to 60% of participants in a survey say their organizations are already using AI to manage talent.
These survey respondents confirm their organizations use artificial intelligence for experience personalization, candidate-to-role matching, upskilling and reskilling, customer service, and mapping career paths.
Currently, there are over 100 startups working to create more AI tech solutions for human resources managers.
35% of talent professionals and hiring managers say their hiring processes are affected most by AI.
Benefits of AI in recruitment
As expected, AI has been immensely beneficial for organizations and their recruitment teams, and the stats below will cast more light on this fact.
Around 85% of recruiters say AI is useful and will be in charge of some aspects of hiring.
Around 67% of HR executives believe AI is advantageous for different parts of recruitment processes. 58% of recruiters reveal that AI has been the most useful for sourcing candidates. 56% also say AI is most beneficial for screening candidates, while 55% say it is most helpful for nurturing candidates. The Job Seeker Nation Survey of 2020 revealed that AI delivers a positive experience for candidates. Around 89% of respondents believe AI could be beneficial for candidates during the application process.
These statistics reveal that artificial intelligence isn’t just great for recruitment, but it can offer solutions for several aspects as far as recruiting is concerned.
42% of recruiters think that AI will be useful to help them become more strategic.
Around 79% of recruiters and 43% of candidates think AI will continue to advance and be effective in hiring and firing.
67% of recruitment managers in a LinkedIn survey say AI helps them save time.
94% of recruiters say AI has improved their hiring process.
36% of recruiters say AI improves their jobs.
66% of executives believe AI can enhance HR values.
44% of respondents say AI will help recruiters save time.
80% of executives say AI helps them boost performance and productivity.
38% of organizations are focusing their AI efforts on talent acquisition.
45% of respondents believe that using AI in HR processes helps the company with scalability and driving business impact.
Companies that use AI for recruitment are able to cut their cost-per-screen expenses down to 75% and reduce turnover by 35%.
This is possible because companies and their recruitment teams can leverage AI to automate processes.
68% of recruiters believe AI in recruitment will help eliminate unintentional bias.
The future of AI in recruitment
As stated earlier, the development of AI for recruitment is on an excellent trajectory. The future may see the introduction of better AI solutions for recruitment departments. At least, that’s what these stats are pointing at.
Predictions say that AI for the recruitment industry is expected to grow to USD 890.51 million. For context, it was USD 610.3 million in 2021, and the CAGR is around 6.5% for the period in focus. Human resources departments are expected to introduce more AI tools to their talent management tech stack between 2021 and 2025. Around 77% of human resource professionals think recruitment processes will soon be handled completely by AI without human interference. 54% of recruiters say integral roles within the HR department will feel the impact of AI. 60% of HR executives think AI can promote equity and inclusion among employees.
More people are accepting AI in recruiting, but they want a human in the loop to double-check things just in case.
If a human is contributing to the decision-making process, 75% of people feel comfortable letting AI decide the fate of their applications.
Only around 13% of respondents worry that AI may present certain dangers in recruitment.
A fairly small number (13%) believe there are adverse effects that may arise from utilizing AI for recruitment processes. Some of the dangers mentioned include overlooking atypical characteristics, algorithmic bias, and manipulation by candidates.
However, the belief that AI poses a danger to recruitment is more prevalent among older respondents over 40 years.
Challenges of AI in recruitment
As you must have guessed already, AI in recruitment isn’t quite where it should be. The need for improved AI solutions means people still have reservations, as the following stats show.
35% of respondents think that AI may cause recruiters to overlook candidates with unique talents, and 26% believe it is harmful to the HR industry. Only 31% of respondents would let AI decide whether they get hired. 25% think it is completely unfair to let AI make the decision, but many (75%) will be receptive to the idea if a human is involved. Only 56% of job applicants believe AI is less biased than humans. 90% of people believe job applicants can manipulate AI.
Many people still think that job applicants can find ways to manipulate AI to certain degrees. There has always been the question of what happens when people find ways around AI, and according to this survey, only 9% think AI is immune to manipulation.
46% think job applicants can manipulate AI easily, 36% think they will take advantage of AI to some extent, and 44% are convinced AI will certainly be influenced.
Around 7% of job applicants would not like to be onboarded by artificial intelligence.
While the number is a relatively small one, it shows the reluctance that people still have towards embracing artificial intelligence as far as the recruitment process is concerned.
Conclusion
Artificial intelligence plays a significant role in recruitment processes, as shown by these statistics. While switching from traditional recruitment methods to AI technology can take a while, data shows it has more benefits in the long run.
As it is with adopting different technological changes, it will be helpful to ease it in to assist your staff members with getting used to it. Don’t worry. You can always refer back to this list of statistics as motivation to put in the work and expect the good things to come after.
Source:
| 2023-03-10T00:00:00 |
2023/03/10
|
https://bloggingx.com/ai-recruitment-statistics/
|
[
{
"date": "2023/03/10",
"position": 93,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/10",
"position": 17,
"query": "artificial intelligence hiring"
}
] |
Towards a well-rounded AI Skills Strategy for Europe - Arisa
|
Towards a well-rounded AI Skills Strategy for Europe
|
https://aiskills.eu
|
[] |
AI skills gap analysis. The Needs Analysis aims to demonstrate the gap between present and future demand for AI skills, as well as the current supply.
|
We are finalising the AI Skills Needs Analysis (to be released in early 2023) — a stepping stone to building a coherent and well-rounded strategy for AI skills development in the EU. The Needs Analysis helps to identify the critical skills gap between the market needs and the educational offerings.
Developing a framework for an AI Skills Strategy
Having a complete overview of the supply and demand levels of AI skills in EU countries is essential to develop solutions to bridge the current and emerging skills gap. What kind of AI knowledge and skills does the market need to seize new business opportunities? What do people need to understand about AI to be able to use this technology in business and policy contexts? How can we ensure that privacy, bias, and trust are at the centre of AI-based solutions?
At this stage, we are examining data collected from companies, policymakers, learning providers, and educational institutions. The multifaced analysis will provide skills intelligence insights into the current and future workforce requirements for AI-related professions.
Upskilling and reskilling priorities for “near-AI” talents
While analysing the input gathered, a particular emphasis is put on “near-AI” talents. “Near-AI” talents are professionals and prospects who can rapidly take up AI-related roles, with limited efforts in training or education. The “near-AI” talents have the potential to double the current size of the AI-related workforce in the EU.
To attract “near-AI” talents into the field of AI, it is important to explain the possibility of a fast transition into an AI-related role. It is possible thanks to short-term learning programmes, as well as micro-credentials. The recognition of learning outcomes of short-term programmes can accelerate the transition of professionals and prospects into a new role, thus facilitating the upskilling or reskilling of individuals.
An overview of the AI Skills Needs Analysis
ARISA Figure: Overview of the AI Skills Needs Analysis
The AI Skills Needs Analysis has five constitutive elements:
| 2023-01-11T00:00:00 |
2023/01/11
|
https://aiskills.eu/news/towards-a-well-rounded-ai-skills-strategy-for-europe/
|
[
{
"date": "2023/03/10",
"position": 6,
"query": "AI skills gap"
},
{
"date": "2023/03/10",
"position": 81,
"query": "AI labor market trends"
}
] |
The Role Of Generative AI And Large Language Models in ...
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The Role Of Generative AI And Large Language Models in HR
|
https://joshbersin.com
|
[] |
Generative AI can quickly do salary benchmarking, assess pay levels across millions of open jobs, and analyze external and labor market data to help understand ...
|
Human Resources is one of the most complex, imperfect areas of business. Virtually every decision we make about people (who to hire, who to promote, how much to pay someone, how to develop someone) is based on judgment, experience, personal bias, and some amount of data. And since well over 50% of all corporate spending is on salaries (United States payroll is around $15 Trillion), these “judgmental decisions” cost companies a lot of money.
And in my world, where we deeply study every part of management, leadership, and HR, we often try to correlate various “HR practices” against outcomes to figure out what works. Much of our business is based on this work, and we regularly “re-run” most of our analysis every few years as culture, the labor market, and technology changes.
Right now, for example, we know that workplace stress, pay equity, and career growth are among the most important drivers of employee satisfaction and workforce productivity. Only a few years ago it was all about fancy benefits, bonuses, and grandiose titles.
So what I’m essentially saying is that much of HR is based on organizational psychology, many forms of social science research, and never-ending effort to experiment, learn from others, and figure out what works. And it’s difficult, imperfect, and always subject to debate.
The Underlying Data Set In HR Is Textual
While this massive effort has been going on, most of the “hard science” in HR and management has been focused on numbers. We ask people to take tests, we look at people’s “performance ratings” and grade point averages (which are extremely subjective), and we ask people for surveys, feedback, and lots of data to make decisions. And then we correlate business results (sales, profit, market share) against various people metrics, and think “we have the answer.”
For recruiting and selection we look at experience, job-related tests, and opinions and scores from interviewers. Theoretically if we get enough of this data we can make better and better hiring decisions. And the precise same thing happens when we look at who to promote, who to demote, and who should make it to the very top ranks of the company.
The whole premise of promotion is based on old ideas of “promotability” or “potential” rated against “current job performance” (the 9-box grid). That approach, which sounds quantitative, is filled with bias, so we have to “infer” who has high potential from various assessments, observations, and inputs. Again, when we get lots of data (looking at the background and behaviors of many high performers), we can improve the science of promotion. But for the most part this is based on judgement.
The core “science” of HR is often rooted in Industrial Psychology, which is a fascinating domain which studies attributes, behaviors, and psychology at work. And as much as I admire and follow much of this science, most companies don’t use it very much. There is a billion dollar industry of “validated pre-hire assessments” and they are extremely useful. But for many jobs they are misleading and companies have to validate these tests so they don’t get sued for discrimination.
So if you want to really do a “big data” analysis of your workforce’s skills, experience, and suitability for different work, you’re dealing with mountains of “anecdotal data,” much of which is encoded in biographies, work output, company leadership frameworks, assessments, and lots of communications. And of course there are performance appraisals, business results, and more.
Consider the two most common parts of HR: a job requisition (job posting) and a job description. Both these artifacts are “thrown together” by hiring managers or HR professionals, often based on what people think a job is like, a set of company standards, and some “technical skills” we know this person will use. As we all know, these artifacts don’t really predict who will succeed, because so much of “success” is based on ambition, learning agility, culture fit, and alignment with purpose.
In other words, this is one of the most complicated and fascinating “mixed data” problems in the world, and making decisions a few percent better can drive billions of dollars of business value.
How Generative AI and Large Language Models Can Help
Given the complex, important, and messy business we are in, how can Generative AI and Large Language Models help? Well while it’s still early days, let me venture the idea that this new branch of AI has the potential to totally reinvent how much of HR works. And in this disruptive change we will see new platforms, new vendors, and new ways of running our companies.
(For those of you who don’t know what Generative AI and Large Language Models are, let me simply say these AI systems can index, categorize, and cluster billions of “tokens” which include words, phrases, numbers, and even code, to find patterns and predictions you can query. And through English language interface (and other language as well) they can analyze, summarize, and infer meaning from all this mess. Read about the statistics behind it here.)
Let me rattle off a few of the huge use-cases we’ve uncovered in the last few months:
1/ Creating content for job descriptions, competency guides, learning outlines, and onboarding and transition tools.
I’ve always felt that the best way to “describe a job” is to watch what people are doing. If you actually observe, capture, and analyze a few months of work, you could literally “write the job description” based on the actual work. Well Generative AI can do this.
You could use Generative AI to look at “the sales operation in your company” and analyze all the biographies, work histories, sales tools, and various sales materials in your sales organization. And it could likely describe “what sales people in your company do” and help you write realistic job reqs based on real roles.
Then if you want to know how to train sales people, you could ask it “tell me what the top performers do vs. the low performers.” And it would find things you may not know. And then you could ask the Generative AI machine to “read all our sales and product training” and “give me an outline of what people need to learn and know.” It could then build you tests, online learning guides, and eventually become the “sales coach” for your company. (This is essentially what Salesforce Einstein GPT is trying to do – you don’t need to buy this from Salesforce by the way, you can do it yourself.)
Then you could ask the tool “who are our top accounts measured by total revenue and total profit” and if it has access to financial data it could answer that too. So not only could it help you improve and rewrite all your job descriptions, it could help you “define the success criteria,” help you “evaluate who is performing well and why,” and then build the killer “sales training materials” you know are badly needed.
Now replicate this idea in manufacturing, marketing, finance, logistics, and even HR.
I’m sure it won’t be perfect at all this, but in a short period of time you’ll learn things you didn’t know and I would not be surprised to see these types of apps come “out of the box” within a year.
2/ Create skills models, experience models, and candidate profiles for recruiting
The second, and probably biggest spending area for improvement is recruiting. You all know how hard it is to find, assess, and select the “right person” for a job. Well right now everyone is gaga about “skills-based hiring.” But what does that really mean? Does it mean this person has passed a test in some tool or programming language? Does it mean they’ve done it 100 times before? Or does it mean they worked in a company that was really good at this so they probably learned a lot about it there?
See, it’s complicated. Supposed you could crawl millions of employee profiles and then look at the “work they did” (ie. scan Github, articles written, legal briefs, etc) and then decide “how good” this person is at this job? That would be almost impossible to do manually, but Generative AI can do this. And it can do much more.
Suppose it looks at this person’s biography and work history and then compares it to other candidates. It could probably tell you which has higher education, which has better spelling, and what other personal characteristics vary. One of the second-generation talent intelligence vendors we’re working with now has a tool that can show you “the leadership profile of company A” compared to “the leadership profile of company B” simply by scanning, analyzing, and deeply understanding the different experiences, language, education, and credentials of leaders from these two companies. Not a bad way to do your competitive analysis or recruiting eh?
I know L&D vendors who have already used ChatGPT to build lesson plans, learning objectives, and skills assessments from existing content. This kind of analysis applied to billions of job candidates can start to show recruiters who the “adjacent skilled” professionals are who could take that hard to fill job. They may have “related experience” that is 100% perfect for the job you’re filling. This is already happening, and it’s going to get better.
And by the way, with tuning these models can remove gender bias, age bias, racial bias, and more. So not only are they potentially more useful, they’re actually likely to be “safer” as well.
3/ Analyze and improve pay, salary benchmarks and rewards
A third massive challenge in HR is “how much to pay people” and “what benefits to provide.” And this is a very tricky subject. More than 95% of companies have pay equity problems already (our new research details this whole area) and as inflation goes up and salaries keep varying based on demand, HR departments are always struggling to keep up.
Generative AI can quickly do salary benchmarking, assess pay levels across millions of open jobs, and analyze external and labor market data to help understand competitive pay, rewards, incentives, and other benefit programs. Most companies try to do this by hiring expensive consultants: these consultants should soon come armed with AI-enabled tools, and then you’ll be able to get the tools yourself. I know of at least five vendors leaning into this today, and it is likely to make all these decisions better.
The whole issue of pay equity is a mess to fix as well. While some AI vendors are starting to focus here, we know from our research that most companies have 5-15% of their total aggregate payroll in some from of “inequitable pay” distribution. People get raises for political reasons and then over time we end up with highly paid, highly tenured people far overpaid based on their market salary or competition with others. I know software engineers who make $500K or more just because they hired into a “hot company at a hot time.” Suddenly a few months later they’re making 1.5-2X more than their peers. Companies hate trying to solve these problems.
4/ Performance management and feedback
One of the most difficult and often despised part of HR is performance management, performance appraisal, and development planning. While there are hundreds of fantastic books and models to define this process, it often comes down to personal judgement. And in most cases the manager gives an appraisal without doing a comprehensive look at an employee’s entire year of work. Imagine if the Generative AI system indexed a year of an employee’s work, hours worked, meetings, and other production and helped managers assess what happened?
Imagine then if the Generative AI took this work effort and perhaps compared it to similar roles to show the manager where this employee was outperforming and perhaps underperforming? I know the technology can do this to some degree today: I recently asked Bing Chat to tell me how Microsoft’s financial performance varied from 2021 to 2022 and it did a pretty good job. Many of the new models of Generative AI can “learn skills” from this kind analysis and these “skills” can be saved and shared with others. And this leads me to the next use-case: Coaching and Development.
5/ Coaching and leadership development
As most of us know, the most valuable assistance we have in our careers is a “coach.” A Coach is someone who watches what we do, knows how it should be done, and gives us developmental feedback. They coach may or may not be an “experts” (many coaching models are built around the idea of “coach as psychologist”) so the coach may simply be observing us and giving us badly needed support. They may interview our peers and help us see blind spots and understand challenging situations.
Well today this market is explosive. Vendors like BetterUp, CoachHub, Torch, SoundingBoard, Skillsoft, and many others have created nearly a $billion dollar market for “coaching on demand.” Well what if this coaching came from an intelligent bot? Medical providers have built these systems for suicide prevention, medical intervention, and other support needs and they work quite well. In the business world this is an enormous area of “low hanging fruit.”
Imagine, for example, if I have to lay someone off. I could easily ask the ChatBot (which may have access to many guides, books, and videos from our company and experts): “how should I approach the layoff conversation?” Or “what is the best way to coach someone who keeps coming late to meetings? Or even “how can I have greater impact on my team” or even “how can I make my meetings more effective?”
These types of questions have been asked millions of time by millions of leaders, so there are well honed answers, suggestions, and tips for all of them. And most companies have license to leadership development content, compliance content, and all sorts of “difficult conversation” content now. The Generative AI system can easily find this, interpret it, and make it easy for managers to use.
And it will get better. Imagine, as I described above, if you put your own particular leadership model and approach to management into this system. You would get “the Starbucks store manager coach” or “the Fiat Chrysler manufacturing leader coach” and so on. My friends in the leadership development and coaching industry are probably excited (and nervous as well). This is coming fast.
6/ Individual Coaching, Mental Health, and Wellbeing
Perhaps one of the biggest successes in Generational AI has been the success of tools like “Woebot” which help treat mental health, stress, and suicide. This tool, which was launched in 2017, has reduced stress, anxiety, and suicide with almost twice the effectiveness of therapy. How could it be so good? By using the feedback loops in Generative AI (with human training), the system can quickly identify a user who is considering suicide and just by listening, help them relax and move forward.
I strongly recommend the story in the New Yorker this week (Can AI Treat Mental Illness) which convincingly explains how this technology has become so successful. These tools were not trained for work-related stress, but the problem is very similar. Over the last five years the Workplace Wellbeing market has grown to over $50 Billion in size and our research on The Healthy Organization found that the typical solutions (EAP programs, online coaches, training, mindfulness) have less impact than we expected. Witness the fact that most statistics on workplace mental health show that it continues to be a problem, even after billions of dollars have been invested.
This particular use case, which every company needs, could end up being pretty important. So we can expect healthcare providers, insurance companies, and forward-thinking vendors like Ginger.io (who now owns Headspace) to jump into this market.
7/ HR self service and knowledge management
The final use-case I will mention is self-service and knowledge management, perhaps the “lowest hanging fruit” of all. We all have thousands of documents, compliance books, diversity guidelines, safety rules, process maps, and help systems to aid employees in selecting benefits, understanding company policies, and even just resetting our password. And things like “figuring out what button to push in Workday or SAP” goes into this category as well.
All this complicated “knowledge enablement” and self-service stuff is perfect for Generative AI. Microsoft’s new Power Platform interface to OpenAI lets companies embed workflows into the system, so you could tell the chatbot “please apply for family leave and ask my manager for approval” or “please put a case into IT for me to upgrade my laptop.” And the use cases will go wild. Many of you who work in HR operations, call centers, and service delivery centers will be investing in this almost immediately. And that means every HR Tech vendor from Oracle to Workday to SAP, ServiceNow and ADP will embed this technology into its platforms.
Bottom Line: This Technology Will Make Work Better
Let me make one final point. Despite the fears and inflammatory headlines you read in the New York Times (the NYT seems particularly unhappy about this technology), I want you to remember that this technology will be a massive step forward in business. Last week I published an article by two MIT PhD students who analyzed the use of ChatGPT on 400+ business professionals and the productivity improvements were stunning. This will start to happen in all these other areas as well.
I would remind you to consider Generative AI a tool, not a living person. Just as Microsoft Excel was groundbreaking in the early 1980s (and there were fears of it putting accountants out of business), so this system will become an essential business tool as well. We all have to learn how to use it.
Will it be perfect? Of course not. But today, as I touch on above, we make thousands of critical decisions with poor data, uneducated judgement, and often just not enough internal research. I believe Generative AI and all its variations will be a total gamechanger in HR. And for everything we do just a little bit better, our employees end up with a better work experience and our companies perform at a higher level.
Stay tuned, there is much more to come.
How To Learn More: Come To Irresistible 2023
How can you learn more? Here are a few big resources.
First, come to Irresistible 2023 – our big annual conference held at USC on June 20-22. We are bringing in several famous experts in Generative AI for HR to that event and we are going to have a big discussion about how it will impact HR. I promise you this will be well worth your time.
Second, if you’re a geek like me, read Steven Wolfram’s article “What is ChatGPT and How Does it Work.” And I will post some other cool articles below.
Finally, if you are doing something exciting and want to share, please let us know. We are talking with hundreds of companies about these tools and I promise we will share more as time goes on. I will be presenting some of our newest research at the HR TechFest Singapore in mid-May and then much more at the Irresistible Conference in June.
Onward and upward!
Additional Resources
New MIT Research Shows Spectacular Increase In White Collar Productivity From ChatGPT
Microsoft Launches OpenAI CoPilots For Dynamics Apps And The Enterprise.
Fighting ‘Woke AI,’ Musk Recruits Team to Develop OpenAI Rival
Mark Zuckerberg announces Meta’s new large language model as A.I. race heats up
Understanding Chat-GPT, And Why It’s Even Bigger Than You Think (*updated)
What Is A Large Language Model (LLM) Anyway? (good overview)
Why Microsoft’s Investment in OpenAI Threatens Google (Fortune)
Listen to Satya Nadella Describe Microsoft’s View of OpenAI
Something Bothering You? Tell It to Woebot.
| 2023-03-10T00:00:00 |
2023/03/10
|
https://joshbersin.com/2023/03/the-role-of-generative-ai-and-large-language-models-in-hr/
|
[
{
"date": "2023/03/10",
"position": 24,
"query": "AI labor market trends"
},
{
"date": "2023/03/10",
"position": 79,
"query": "artificial intelligence hiring"
}
] |
Staff Augmentation Trends to Watch for in 2023
|
Staff Augmentation Trends to Watch for in 2023
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https://remotoworkforce.com
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[] |
From predictive analytics capabilities to automated recruitment processes, AI could streamline hiring and onboarding processes while increasing accuracy and ...
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As an entrepreneur, start-up, recruiter, or corporate employee looking to stay on top of the game and ensure your business can react quickly to changes in staffing needs, it’s time for you to familiarize yourself with the major trends that are evolving across industries when it comes to the future of staff augmentation.
Let’s take a look at what staff augmentation looks like now and what experts anticipate will be on everyone’s recruitment radar come 2023.
Automation Trends: How Artificial Intelligence Could Make Staff Augmentation More Precise
Automation and artificial intelligence (AI) have the potential to revolutionize many aspects of modern industry, with talent management, staff augmentation, and remote work being no exception.
AI-augmented staffing solutions can provide more personalized and specific accommodations than ever before, allowing businesses across a variety of industries to maximize the efficiency of their workforce.
From predictive analytics capabilities to automated recruitment processes, AI could streamline hiring and onboarding processes while increasing accuracy and reducing costs.
Plus, when integrated with skill-tracking applications, AI can ensure that employees have the right skillset at the right time for any task, making staff augmentation better than ever.
Balancing Security and Efficiency in a Global Market: What’s at Stake for Your Company
The world of global business seems to get more competitive with each passing month; it’s a wild, unforgiving landscape that requires both strength and technique to succeed.
With the stakes so high, company-wide security has never been more important. After all, if your data falls into the wrong hands, everything you’ve built can tumble like a poorly-constructed house of cards. This is why security and efficiency matter to staffing augmentation processes in 2023.
So how does one strike the perfect balance between cybersecurity and efficiency with a contingent workforce? It’s no small feat, for sure! It requires dedication to best practices, technological know-how, and sharp strategic decision-making.
Your winning equation may look something different from someone else’s. However, as long as it allows you to operate securely while still taking advantage of opportunities within an ever-evolving global market, then you can weather whatever storm comes your way.
The Most In-demand Soft Skills for Remote Staff Augmentation
In 2023, working remotely will continue to present a world of opportunities for professionals. However, it requires special skills to excel in this environment. As more companies embrace remote staff augmentation services to increase their staffing diversity and inclusion, candidates with strong, soft skills are in high demand.
Collaboration and communication are essential components of these soft skills, which include problem-solving, leadership, critical thinking, and creativity. These attributes go beyond technical abilities and enable remote workers to focus on their tasks without compromising the expectations of their roles.
As these skills translate well into virtual office environments, they are the most sought-after attributes for anyone aiming for a successful career in remote staff augmentation. Human resources must prioritize seeking out candidates who possess these valuable soft skills to ensure success within their remote teams.
If you’re interested in gaining a more profound comprehension of soft skills, then be sure to watch this TED talk! It’s an incredible opportunity to further your understanding and take knowledge and application of these valuable skills to the next level.
Exploring the Benefits of Increasing Your Team with Remote Staff Augmentation
If you are looking for a way to extend your team without hassles, then staff augmentation is the way to go. You can also take advantage of the gig economy to quickly scale up your team size and adjust it according to market demand in 2023.
Here are five benefits of staff augmentation for your team:
Access to specialized skills and talent : By using remote staff augmentation, you can tap into a wider pool of skilled professionals who may not be available in your local area. This allows you to access expertise that may be difficult or expensive to find otherwise.
More flexibility in utilization : With remote staff augmentation, you have more control over how and when you utilize the services of your augmented team members. You can scale up or down quickly depending on demand or project requirements without the hassle of full-time hiring.
Cost management : Remote staff augmentation can help reduce costs associated with recruiting, onboarding, and maintaining a full-time workforce. This is especially important for small businesses or startups that need to manage their resources efficiently.
Quick scalability : The growing tech economy offers an abundance of skilled professionals that can be quickly onboarded and integrated into your team as needed, allowing you to easily scale up your team size according to market demand.
Improved productivity : With access to specialized skills and more flexible utilization, remote staff augmentation can lead to improved productivity and faster project completion times.
Challenges Ahead: Overcoming Common Hurdles When Working With Remote Talent
Working with remote talent as a part of a business strategy can be challenging, although thankfully, there are steps you can take to make the most of this type of arrangement.
Here are six tips to overcome challenges related to remote work and staff augmentation in 2023.
Set clear expectations and goals
Before bringing on external talent, make sure you have a clear understanding of what you want to achieve and communicate it effectively to the remote team. This will help everyone stay on track and aligned with the project’s objectives. Project management skills are crucial when it comes to doing this.
Establish open communication
To ensure effective collaboration and workforce planning and management, establish an open line of communication between all parties involved. This can include regular check-ins, video conferences, or messaging apps that allow for instant communication.
Build trust
Trust is essential when working with remote talent acquisition and employee retention. Ensure that all parties understand each other’s roles and responsibilities and follow through on commitments made. This will help build trust in the working relationship.
Follow up regularly
Timely follow-up is important to keep everyone accountable and ensure objectives are met within the agreed timeline. Regularly checking in can also help address any issues or concerns before they become bigger problems.
Prioritize transparency
Transparency should be a top priority when working remotely. Be honest about any changes in timelines or project scope, and encourage open dialogue among team members to prevent misunderstandings from arising.
Training and development
Training and professional career development can be a challenge when it comes to remote work in 2023. Remote workers may not have the same access to training and development opportunities as their office-based counterparts, which can hinder their professional growth and development.
To overcome this challenge, you can offer online training programs accessible to all employees regardless of location. MOOCs can be ideal options for you if you’re contemplating reskilling or upskilling your remote workforce.
Takeaway
Automation might feel like a double-edged sword for your business: it can bring efficiency and security but also require staff augmentation. However, when you take the time to properly investigate all of the options, the benefits of hiring beyond your walls can be even more in-depth than you expected.
Remote staff augmentation requires not only deeper, more in-demand soft skills but also brings a wealth of diverse cultures and experiences to the team. With careful preparation and thoughtful research into your specific needs and nuanced industry demands, increasing your external talent capabilities can easily give you an edge over the competition.
Companies with remote staff augmentation are poised to showcase innovation and creativity that help them run smoother in the ever-evolving global market while streamlining their projects and maintaining security protocols.
| 2023-03-10T00:00:00 |
https://remotoworkforce.com/staff-augmentation-trends-to-watch-for-in-2023/
|
[
{
"date": "2023/03/10",
"position": 30,
"query": "AI labor market trends"
}
] |
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