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AI Privacy Collective - (Sarah-Kent ISD)

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AI Privacy Collective - (Sarah-Kent ISD)

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AI Literacy Resources | Michigan Virtual

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AI is everywhere and changing the way we teach, learn, and live. Whether you're a curious educator or just dipping your toes in, our AI Literacy resources make it easy to explore what AI is, why it matters, and how to use it wisely in schools.

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10 skills, mindsets, and habits for using AI with confidence.

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Claris Connect

Powerful workflow automation made easy.

Orchestrate your apps. Automate your work.
Escape from app chaos by bringing everyday apps together to create automated workflows to save time and money. Manage marketing leads, track IT tickets, facilitate orders, and more.

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Orchestrate your apps. Automate your work. Escape from app chaos by bringing everyday apps together to create automated workflows to save time and money. Manage marketing leads, track IT tickets, facilitate orders, and more. Learn more about workflow automation

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A weekly podcast where host Nathan Labenz interviews AI innovators, diving into the transformative impact AI will have in the near future.

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Companies with advanced artificial intelligence capabilities - those most effectively using AI to improve operations and customer experience, and to support and develop their ecosystems - outperform their industry peers financially, according to research from the MIT Center for Information Systems Research.

These organizations are more "AI mature," according to CISR researchers Peter Weill, Stephanie Woerner, and Ina Sebastian, who mapped four stages of AI enterprise maturity in a new research briefing. The researchers found that organizations in the first two stages had financial performance below their industry's average, and organizations in the last two stages performed above their industry's average.

"Enterprises can use the MIT CISR Enterprise AI Maturity Model to assess their current capabilities, identify gaps, and create a road map for improvement across various dimensions, such as processes, technology, and organizational culture," said Woerner, who is the director of MIT CISR. "It's a valuable tool for guiding business growth, improving operational efficiency, and achieving strategic objectives through a clear, step-by-step approach."

Identifying four stages of AI maturity

The findings are based on a 2022 MIT CISR survey of 721 companies, followed by interviews in 2024 with executives at nine enterprises about traditional and generative AI and their early thoughts on agentic and robotic AI.

Instead of considering edge cases, organizations should think about which of the four stages describes most of their AI activity, Weill said at the EmTech MIT conference last fall.

Organizations need to build cumulative capabilities and lessons from AI as they move toward a future-ready state of AI use. Here's a look at the four stages of enterprise AI maturity and the capabilities organizations need as they navigate them.

Stage 1: Experiment and prepare

Organizations in the first stage work to educate their workforce, formulate AI policies, become more evidence-based, and experiment with AI technologies to become more comfortable with automated decision-making. According to the CISR survey, 28% of enterprises were in this stage.

During Stage 1, organizations begin to discuss where humans need to be in the loop for oversight and what they consider to be acceptable and ethical uses of AI technology.

Companies at this stage focus on AI literacy initiatives for the board and top management teams, and skill-building for the rest of the enterprise. They also begin to identify value-creation opportunities from AI and the capabilities and competencies required to realize them.

"This first stage is all about experimenting, preparing, and education," Weill said.

Stage 2: Build pilots and capabilities

Companies at this stage focus on AI pilots that create value for both the enterprise and its workers. In the survey, 34% of organizations were in this stage.

Important parts of Stage 2 include defining important metrics, beginning to simplify and automate business processes, and developing the needed enterprise capabilities identified during Stage 1. Here, organizations focus on moving from experiments to systematic innovation by piloting use cases, tracking value created in the pilots, and storytelling - both internally and externally - about lessons learned from the pilots.

"The hardest part of Stage 2 is changing," Weill said. "How do we move from a command-and-control culture to a coach-and-communicate culture? AI has a lot to do with not only automating but enabling our front line to make decisions for us and enabling our customers to self-serve. You can't do that if you command and control."

Determining how to consolidate organizational data silos and safely and securely prepare data for use with AI is critical at this stage. This process can require significant investment in or refinement of application performance interfaces that link data and technologies, according to the researchers.

Stage 3: Industrialize AI throughout the enterprise

Moving to this stage is a significant step in an organization's AI journey, both for the potential impact on its enterprise growth and profitability and the platform capabilities needed to achieve the scale required, the research said. Thirty-one percent of organizations reported being at this stage.

Stage 3 includes building a scalable enterprise architecture, making data and outcomes transparent via business dashboards, developing a pervasive test-and-learn culture, and expanding business process automation efforts.

"You have to simplify and automate your processes. If you try to use AI on an incredibly complicated ... process, it'll be much harder," Weill said.

Additionally, organizations at this stage make significant use of foundation models and small language models that are trained to perform certain tasks, the researchers write. Organizations then take these models and apply them to their own data to create and capture new value on secure platforms.

"Companies in the third stage are developing proprietary models, and that leads you to the holy trinity of AI - architecture, reuse, and agents," Weill said. "Those are the really hard parts of Stage 3."

Stage 4: Become "AI future-ready"

In this final stage, organizations are considered AI future-ready, which was true of 7% of enterprises in the study. Here, AI is embedded in all decision-making, and organizations are using proprietary AI internally. They then sell new business services based on that capability, the AI capability as a service, or both to other enterprises.

"This is where you're all in for AI-enabled decision-making [and] deciding when you need people in the loop and when you don't," Weill said. "You'll develop proprietary AI, and you'll sell new services around it."

The executives who were interviewed said they expect that the most value from AI will be created from combining people and platforms with four types of AI: analytical, generative, agentic, and robotic.

Determining your next steps

Organizations that want to benefit from CISR's Enterprise AI Maturity Model can start by identifying which stage of AI maturity they've reached, the researchers said.

"We recommend bringing a team of senior technical and data leaders together to assess which of the four stages your enterprise is in today, and your aspirations and time frames regarding your enterprise's use of AI," Woerner said. "Then, discuss which enterprise capabilities and skill sets need more work. No matter where you are [with AI maturity], be bold."

Read the research brief

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What's your company's AI maturity level? | MIT Sloan

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Companies with advanced artificial intelligence capabilities - those most effectively using AI to improve operations and customer experience, and to support and develop their ecosystems - outperform their industry peers financially, according to research from the MIT Center for Information Systems Research.

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MIT Sloan - Workforce Intelligence-digital

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Insights from data and ML algorithms can be invaluable, but be warned - mistakes can be irreversible. These recent high-profile AI blunders illustrate the damage done when things don't go according to plan.

Back in 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource, and the refrain has been repeated ever since. Organizations across every industry have been investing, and continue to heavily invest, in data and analytics. But like oil, data and analytics have their dark side.

According to CIO's State of the CIO Survey 2025, 42% of CIOs say AI and ML are their biggest technology priority for 2025. And while actions driven by ML algorithms can give organizations a competitive advantage, mistakes can be costly in terms of reputation, revenue, or even lives.

Understanding your data and what it's telling you is important, but it's equally vital to understand your tools, know your data, and keep your organization's values firmly in mind. And with that in mind, here are a handful of high-profile AI blunders over the past few years to illustrate what can, and still does, go wrong.

AI coding tool wipes out production database and lies about it

Just a couple of weeks ago, on July 22, Cybernews reported that an AI coding assistant from tech firm Replit went rogue and wiped out the production database of startup SaaStr.

Jason Lemkin, founder of SaaStr, wrote on X on July 18 to warn that Replit modified production code despite instructions not to do so, and deleted the production database during a code freeze. He also said the AI coding assistant concealed bugs and other issues by generating fake data including 4,000 fake users, fabricating reports, and lying about the results of unit tests.

Replit CEO Amjad Masad responded to Lemkin's posts, apologizing for the mistakes.

"We just saw Jason's post," Masad wrote. "@Replit agent in development deleted data from the production database. Unacceptable and should never be possible."

Masad said Replit immediately went to work to prevent it from happening again and reached out to Lemkin to offer assistance. He said Replit would refund SaaStr for the trouble and conduct a postmortem to determine what happened.

On July 8, 2025, xAI's Grok, a chatbot for the X platform, responded to a user's query with detailed instructions for breaking into the home of a Minnesota Democrat and assaulting him.

As reported by the Wall Street Journal, a user asked Grok for instructions on how to break into the home of Will Stancil, a policy researcher and attorney who posts about urban planning and politics on X. Grok told the user to bring "lockpicks, gloves, flashlight, and lube - just in case." The chatbot also analyzed Stancil's posting patterns on X and told the user, "He's likely asleep between 1 am and 9 am."

That same day, Grok made a series of antisemitic posts and declared itself "MechaHitler" repeatedly before X temporarily shut the chatbot down that evening.

The incidents occurred after X uploaded new prompts to Grok on July 6, which stipulated the chatbot "should not shy away from making claims which are politically incorrect, as long as they are well substantiated." X removed the new instructions on July 8.

This is not the first time Grok has caused problems for X.

In an April 2024 post, the AI chatbot falsely accused NBA star Klay Thompson of throwing bricks through windows of multiple houses in Sacramento, California.

Some commentators speculated that Grok may have hallucinated the vandalism story about the Golden State Warriors small forward, who's since moved to the Dallas Mavericks, after ingesting posts about Thompson "throwing bricks," common basketball parlance for badly missed shots. In his final game with GSW, the team suffered a blowout loss as part of the worst postseason performance of Thompson's career.

Chicago Sun-Times, Philadelphia Inquirer publish summer reading list of fake books

The Chicago Sun-Times and Philadelphia Inquirer took reputational hits when May 2025 editions featured a special section that included a summer reading list recommending books that don't exist.

The Chicago Sun-Times explained that the syndicated section, "Heat Index: Your Guide to the Best of Summer," was provided by King Features Syndicate, a unit of Hearst. Marco Buscaglia, the author of the special section, admitted he used AI to assist putting it together, including the recommended reading list, and failed to fact check the output.

The list featured many real authors but attributed nonexistent books to them, like Tidewater Dreams by famed Chilean-American writer Isabel Allende, who's written more than 20 novels. But Tidewater Dreams, a "climate fiction novel that explores how one family confronts rising sea levels while uncovering long-buried secrets," isn't one of them. Like most books on the list, it was hallucinated by AI.

The newsrooms of both papers said they had nothing to do with the insert, though neither paper marked it as advertorial content. King Features terminated its relationship with Buscaglia following the incident, noting that his use of AI violated a strict policy.

McDonald's ends AI experiment after drive-thru ordering blunders

After working with IBM for three years to leverage AI to take drive-thru orders, McDonald's called the whole thing off in June 2024. The reason? A slew of social media videos showing confused and frustrated customers trying to get the AI to understand their orders.

One TikTok video in particular featured two people repeatedly pleading with the AI to stop as it kept adding more Chicken McNuggets to their order, eventually reaching 260. In a June 13, 2024, internal memo obtained by trade publication Restaurant Business, McDonald's announced it would end the partnership with IBM and shut down the tests.

The restaurant had piloted the AI at more than 100 US drive-thrus, and indicated it still saw a future in a voice-ordering solution.

NYC AI chatbot encourages business owners to break the law

In March 2024, The Markup reported that Microsoft-powered chatbot MyCity was giving entrepreneurs incorrect information that would lead them to break the law.

Unveiled in October 2024, MyCity was intended to help provide New Yorkers with information on starting and operating businesses in the city, as well as housing policy and worker rights. The only problem was The Markup found MyCity falsely claimed that business owners could take a cut of their workers' tips, fire workers who complain of sexual harassment, and serve food that had been nibbled by rodents. It also claimed landlords could discriminate based on source of income.

In the wake of the report, indicted New York City Mayor Eric Adams defended the project. The chatbot remains online.

Air Canada pays damages for chatbot lies

In February 2024, Air Canada was ordered to pay damages to a passenger after its virtual assistant gave him incorrect information at a particularly difficult time.

Jake Moffatt consulted Air Canada's virtual assistant about bereavement fares following the death of his grandmother in November 2023. The chatbot told him he could buy a regular price ticket from Vancouver to Toronto and apply for a bereavement discount within 90 days of purchase. Following that advice, Moffatt purchased a one-way CA$794.98 ticket to Toronto and a CA$845.38 return flight to Vancouver.

But when Moffatt submitted his refund claim, the airline turned him down, saying bereavement fares can't be claimed after tickets have been purchased.

Moffatt took Air Canada to a tribunal in Canada, claiming the airline was negligent and misrepresented information via its virtual assistant. According to tribunal member Christopher Rivers, Air Canada argued it can't be held liable for the information provided by its chatbot.

Rivers denied that argument, saying the airline didn't take "reasonable care to ensure its chatbot was accurate," so he ordered the airline to pay Moffatt CA$812.02, including CA$650.88 in damages.

Sports Illustrated may have published AI-generated writers

In November 2023, online magazine Futurism said Sports Illustrated was publishing articles by AI-generated writers.

Futurism cited anonymous sources were involved to create content, and said the storied sports magazine published a lot of articles by authors generated by AI.

Futurism also found the author headshots in question were listed on a site that sells AI-generated portraits. The online magazine then reached out to The Arena Group, publisher of Sports Illustrated, and in a statement, Arena Group said the articles in question were licensed content from a third party, AdVon Commerce.

"We continually monitor our partners and were in the midst of a review when these allegations were raised," Arena Group said in the statement provided to Futurism. "AdVon has assured us that all of the articles in question were written and edited by humans."

The statement added that AdVon writers used pseudonyms in certain articles, noting that Arena Group doesn't condone those actions and subsequently removed the articles in question from the Sports Illustrated website.

Responding to the Futurism article, the Sports Illustrated Union posted a statement that it was horrified by the allegations and demanded answers and transparency from Arena Group management.

"If true, these practices violate everything we believe in about journalism," the SI Union said in its statement. "We deplore being associated with something so disrespectful to our readers."

iTutor Group's recruiting AI rejects applicants due to age

In August 2023, tutoring company iTutor Group agreed to pay $365,000 to settle a suit brought by the US Equal Employment Opportunity Commission (EEOC). The federal agency said the company, which provides remote tutoring services to students in China, used AI-powered recruiting software that automatically rejected female applicants ages 55 and older, and male applicants ages 60 and older.

The EEOC said more than 200 qualified applicants were automatically rejected by the software.

"Age discrimination is unjust and unlawful," then acting EEOC chair Charlotte Burrows said in a statement. "Even when technology automates the discrimination, the employer is still responsible."

iTutor Group denied any wrongdoing but did decide to settle the suit. As part of the settlement and consent decree, it agreed to adopt new anti-discrimination policies.

ChatGPT hallucinates court cases

Advances made in 2023 by LLMs stoked widespread interest in the transformative potential of gen AI across nearly every industry. OpenAI's ChatGPT was at the center of this surge in interest, but the technology still has a long way to go before it can reliably override most processes, as attorney Steven Schwartz learned when he found himself in hot water with US District Judge Kevin Castel after using it to research precedents in a suit against Colombian airline Avianca.

Schwartz, an attorney with Levidow, Levidow & Oberman in New York, used the OpenAI gen AI chatbot to find precedent to support a case filed by Avianca employee Roberto Mata for injuries he sustained in 2019. But at least six of the cases submitted in the brief didn't exist. In a document filed in May 2023, Castel noted the cases submitted by Schwartz included false names and docket numbers, along with bogus internal citations and quotes. Schwartz's partner, Peter LoDuca, was Mata's lawyer of record and signed the brief, putting himself in jeopardy as well.

In an affidavit, Schwartz told the court it was the first time he used ChatGPT as a legal research source and was unaware of the possibility that its content could be false. He admitted he hadn't confirmed the sources provided by the AI chatbot, and regretted using gen AI to supplement his legal research, adding he'll never do so in the future without verifying authenticity.

In June 2023, Castel imposed a $5,000 fine on Schwartz and LoDuca, and in a separate ruling in June, he dismissed Mata's lawsuit against Avianca.

AI algorithms identify everything but COVID-19

Since the COVID-19 pandemic began in 2020, numerous organizations have sought to apply ML algorithms to help hospitals diagnose or triage patients faster. But according to the UK's Turing Institute, a national center for data science and AI, the predictive tools made little to no difference.

MIT Technology Review chronicled a number of failures, most of which stemmed from errors in the way the tools were trained or tested. The use of mislabeled data, or data from unknown sources, was a common culprit.

Derek Driggs, an ML researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models to diagnose the virus. But the paper determined the technique wasn't fit for clinical use. For example, Driggs' group found their own model was flawed because it was trained on a data set that included scans of patients that were lying down while scanned, and patients who were standing up. The patients who were lying down were much more likely to be seriously ill, so the algorithm learned to identify COVID risk based on the position of the person in the scan.

A similar example included an algorithm trained with a data set with scans of chests of healthy children. The algorithm learned to identify children, not high-risk patients.

Zillow wrote down millions, slashed workforce due to algorithmic home-buying disaster

In November 2021, online real estate marketplace Zillow told shareholders it would wind down its Zillow Offers operations and cut 25% of the company's workforce - about 2,000 employees - over the next several quarters. The home-flipping unit's woes were the result of the error rate in the ML algorithm it used to predict home prices.

Zillow Offers was a program through which the company made cash offers on properties based on a "Zestimate" of home values derived from an ML algorithm. The idea was to renovate the properties and flip them quickly. But a Zillow spokesperson told CNN the algorithm had a median error rate of 1.9%, and could be as high as 6.9% for off-market homes.

CNN then reported that Zillow bought 27,000 homes through Zillow Offers since its launch in April 2018, but sold only 17,000 through the end of September 2021. Black swan events like the COVID-19 pandemic and a home renovation labor shortage contributed to the algorithm's accuracy troubles.

Zillow said the algorithm led it to unintentionally purchase homes at higher prices rather than its current estimates of future selling prices, resulting in a $304 million inventory write-down in Q3 2021. In a conference call with investors following the announcement, Zillow co-founder and CEO Rich Barton said it might have been possible to tweak the algorithm, but ultimately it was too risky.

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Bart Perkins, Sarah K. White, Sharon Florentine

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Insights from data and ML algorithms can be invaluable, but be warned - mistakes can be irreversible. These recent high-profile AI blunders illustrate the damage done when things don't go according to plan.

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There's a growing paradox at the heart of the modern workplace. Artificial intelligence is advancing at an unprecedented rate, capable of generating pitch decks, summarizing meetings, writing RFPs, analyzing spreadsheets, and even sending our avatars to video call meetings. Yet, for all the noise around productivity gains and disruption, most organizations are still stuck in neutral, disappointed by the underwhelming results of their various AI pilots. The recent MIT report noted that while AI pilots with tools like ChatGPT and Copilot were improving individual productivity, vanishingly few were contributing to P&L improvement. If these tools are making individuals more productive, but that's not translating into company performance, we have to ask-what's the point of that work? The dirty secret? AI is is revealing just how much work doesn't need to exist in the first place.

Welcome to the BS economy.

Defining the BS Economy

The term "BS jobs" was coined by anthropologist David Graeber to describe roles that are, even in the eyes of those performing them, fundamentally pointless. These are the jobs where if someone stopped showing up, no one would notice; or worse, everyone would be relieved. Entire categories of white-collar work, from middle managers pushing PowerPoints to consultants writing reports no one reads, fall under this umbrella.

The BS economy is what happens when these roles are scaled, celebrated, and institutionalized, perhaps to the point of becoming hyper-normalized, the paradoxical state in which everyone knows the official version of reality is false, but because no one can imagine an alternative, society collectively accepts and reproduces the lie as if it were true. Importantly, this state is not limited to a few unlucky job titles, but endemic to modern corporate life. It rewards process over purpose, optics over outcomes, and bureaucracy over impact.

For decades, this system sustained itself with rituals: long and useless meetings, excessive documentation, convoluted chains of approval, and performative busyness that drive up headcount and slow down progress without any delivering any appreciable improvement in outcomes. But AI is stripping away the theater, automating the symbolic and intangible aspects of work, precisely the tasks that were never truly value-generating to begin with.

AI Isn't (Yet) Disrupting Work. It's Exposing It

Let's be clear: AI hasn't fundamentally changed most jobs yet. It's not that large swaths of the workforce have been replaced or redeployed. What AI is doing-faster than most companies can react-is mimicking the performative layer of knowledge work. Drafting emails. Creating status updates. Rewording proposals. Polishing presentations. Transcribing and summarizing meetings that arguably didn't need to happen in the first place. As Yuval Harari recently noted, AI is already a better storyteller than humans. It is also challenging humans when it comes to not just work, but also pretending to work, in the sense of replicating a range of job-related activities that more clearly result in being busy than actually productive (in the sense of adding value).

If an AI can instantly generate a company's glossy annual report (complete with "letters from the CEO" and strategic road maps), it exposes how formulaic these documents really are. They read less like authentic reflections of vision or performance and more like templated PR exercises, optimized for investors' expectations. The ease with which they can be faked shows just how little originality or substance was there in the first place. We're witnessing a productivity revolution without a purpose revolution. Tools are improving, but the work remains hollow. Instead of using AI to invent better ways of working, many companies are simply using it to churn out more of the same, only faster. But running faster in the wrong direction just means getting lost faster-and if everyone is doing it, we risk just getting lost in a forest of sameness

There's a risk here. Without deliberate redesign, AI won't just expose the BS economy, it could entrench it. If leaders don't challenge the status quo, we may end up amplifying the noise, not reducing it. We'll layer new systems on top of bad processes and wonder why things haven't gotten better. If automation means that machines are mass-producing work products that then only get reviewed, summarized, and acted upon by other machines, what have we really gained?

Value Comes From Humans, Not Bots

The crucial insight is this: AI can optimize how work is done, but it cannot tell us what work should be done, or why it matters. That's still a human responsibility. And it's where the opportunity lies.

The value of AI will not be measured by how much it automates nonsense, but by how much it liberates human potential. If AI saves a manager 10 hours a week by eliminating report writing, the real question is: what will they do with that time?

The answer depends on leadership and HR. Not on the tech or AI.

To unlock real value from AI, organizations must help people reimagine their roles beyond routine output. This means identifying opportunities for human contribution that are creative, relational, strategic, and judgment-driven, the domains where AI is still weak. It also means empowering people to use freed-up time to think, learn, explore, connect, and innovate.

Fixing Work: Four Imperatives for Leaders
Redesign Work
AI won't fix broken workflows. Leaders must rethink work processes and roles from the ground up and focus on the ones that are truly driving business value. Start by asking: What outcomes matter? What activities actually contribute to those outcomes? What capabilities are required to complete the work and are those best suited to people or machines? And what can we stop doing altogether? Aim to eliminate tasks that exist only to justify someone's presence or to feed internal reporting machinery. Design roles that are lean, outcome-focused, and infused with a clear sense of purpose.
Re-skill Managers to Lead in the AI Era
Managers are the keystone species in any organization. Yet many are unequipped for an AI- enhanced world. Re-skilling managers should focus less on technical mastery and more on human leadership. They need to understand how to coach teams, set meaningful goals, recognize contribution, and create psychologically safe environments that foster experimentation. They should become amplifiers of human potential, not compliance officers for productivity software.
Rethink Performance Management
Most current performance systems still reward confidence over competence, style over substance, and politics over performance. That must change. AI gives us better data and more granular insights, but we need to ask better questions. What value is someone actually creating? Are they helping others succeed? Are they solving meaningful problems? Moving forward, performance should be measured by contribution, not presence. Output, not optics. Impact, not volume.

The AI transformation is not a plug-and-play exercise. Every organization will need to experiment with new models of work, test ideas, and learn from failures. The organizations that thrive will be those that adopt a learning mindset: try, measure, adjust, repeat. Don't expect one grand solution. Instead, cultivate a portfolio of small experiments across teams and functions, and scale what works. The goal is not just to do more with less, but to do better with less noise.

AI Can Help Us Fix Work-But Only If We Let It

Taken together, these imperatives represent a set of cultural and operational changes-and that's where the hard work is. Real change won't come from turning on an LLM; it will come from those with the courage to tackle deep-rooted practices and beliefs.

At its best, AI can serve as a mirror: reflecting the absurdity of modern work back at us with eerie precision. But the mirror itself doesn't solve anything. What we choose to do with that reflection is what matters.

We can continue pretending that busywork equals value, or we can use this moment to make work more meaningful. That means getting serious about human contribution. About designing roles that tap into curiosity, creativity, empathy, and strategic thinking. About automating the things that are formulaic and joyless, and creating space for people to do the things machines can't.

In short, the rise of AI is not just a tech story, it's a leadership challenge and an organizational challenge. The biggest risk isn't that AI will replace humans. It's that we'll fail to replace the nonsense that AI is finally making visible.

Let's not waste the opportunity.

The extended deadline for Fast Company's Most Innovative Companies Awards is this Friday, October 10, at 11:59 p.m. PT. Apply today.

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Google’s top AI scientist says ‘learning how to learn’ will be next generation’s most needed skill

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Google’s top AI scientist says ‘learning how to learn’ will be next generation’s most needed skill

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ATHENS, Greece (AP) - A top Google scientist and 2024 Nobel laureate said Friday that the most important skill for the next generation will be "learning how to learn" to keep pace with change as Artificial Intelligence transforms education and the workplace.

Speaking at an ancient Roman theater at the foot of the Acropolis in Athens, Demis Hassabis, CEO of Google's DeepMind, said rapid technological change demands a new approach to learning and skill development.

"It's very hard to predict the future, like 10 years from now, in normal cases. It's even harder today, given how fast AI is changing, even week by week," Hassabis told the audience. "The only thing you can say for certain is that huge change is coming."

The neuroscientist and former chess prodigy said artificial general intelligence - a futuristic vision of machines that are as broadly smart as humans or at least can do many things as well as people can - could arrive within a decade. This, he said, will bring dramatic advances and a possible future of "radical abundance" despite acknowledged risks.

Hassabis emphasized the need for "meta-skills," such as understanding how to learn and optimizing one's approach to new subjects, alongside traditional disciplines like math, science and humanities.

"One thing we'll know for sure is you're going to have to continually learn ... throughout your career," he said.

The DeepMind co-founder, who established the London-based research lab in 2010 before Google acquired it four years later, shared the 2024 Nobel Prize in chemistry for developing AI systems that accurately predict protein folding - a breakthrough for medicine and drug discovery.

Greek Prime Minister Kyriakos Mitsotakis joined Hassabis at the Athens event after discussing ways to expand AI use in government services. Mitsotakis warned that the continued growth of huge tech companies could create great global financial inequality.

"Unless people actually see benefits, personal benefits, to this (AI) revolution, they will tend to become very skeptical," he said. "And if they see ... obscene wealth being created within very few companies, this is a recipe for significant social unrest."

Mitsotakis thanked Hassabis, whose father is Greek Cypriot, for rescheduling the presentation to avoid conflicting with the European basketball championship semifinal between Greece and Turkey. Greece later lost the game 94-68.

____

Kelvin Chan in London contributed to this story.

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Google's top AI scientist says 'learning how to learn' will be next generation's most needed skill

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Google's DeepMind CEO Demis Hassabis says the most valuable human skill for the future will be "learning how to learn" as artificial intelligence transforms education, careers and daily life.

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Unlock the new opportunities of the AI era by equipping yourself with the knowledge and skills to harness artificial intelligence effectively.

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11 Best AI Certifications in 2025 to Deepen Your Technical Knowledge

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11 Best AI Certifications in 2025 to Deepen Your Technical Knowledge

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11 Best AI Certifications in 2025 to Deepen Your Technical Knowledge | DigitalOcean

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Take a look at the best AI certification programs in 2025 for beginners and advanced professionals to gain practical experience for your career: machine learning, deep learning, AI, and more.

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10 top artificial intelligence certifications and courses for 2025

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10 top artificial intelligence certifications and courses for 2025

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10 Top Artificial Intelligence Certifications And Courses For 2025

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As AI adoption accelerates, AI certifications and courses are surging. Explore 10 programs designed to deepen your knowledge in this fast-evolving field.

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Unlock the new opportunities of the AI era by equipping yourself with the knowledge and skills to harness artificial intelligence effectively.

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How to lead when machines can do everything (except be human)

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How to lead when machines can do everything (except be human)

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How to lead when machines can do everything (except be human)

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Leadership is becoming less about workflows and more about fitting humans and technology together.

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Free and open source, Gemini CLI brings Gemini directly into developers’ terminals — with unmatched access for individuals.

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Chrome Extension

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By: Kathryn Conrad

[Critical AI 2.1 is a special issue, co-edited by Lauren M.E. Goodlad and Matthew Stone, collecting interdisciplinary essays and think pieces on a wide range of topics involving Large Language Models. Below, a sneak preview from the issue: Kathryn Conrad's important "Blueprint for an AI Bill of Rights for Education"]

Safe and Effective Systems : You should be protected from unsafe or ineffective systems.

Algorithmic Discrimination Protections : You should not face discrimination by algorithms and systems should be used and designed in an equitable way.

: You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used.

Notice and Explanation : You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.

With these principles as a starting point, I propose a supplemental set of rights for educators and students.

These are intended as the beginning rather than the end of the conversation, a foundation on which policies and protections can be based. Ultimately, however, educators must lead this conversation, guided by aspirations for and collaboration with our students.

Rights for Educators

You should have input into institutional decisions about purchasing and implementation of any automated and/or generative system ("AI") that affects the educational mission broadly conceived. Domain experts in the relevant fields should be informed and enabled to query any consultants, vendors, or experts who have promoted the systems before such systems are adopted. Institutions should also set up opportunities for students to participate in and advise on policies that involve the mandatory use of any such applications. Institutions interested in exploring coursework devoted to the use of automated and/or generative tools should enable instructors to work with developers and vendors to ensure that any adopted tools are appropriate for educational contexts and do not subject students or educators to surveillance or data theft.

You (or your representative in the appropriate body for governance) should have input into institutional policies concerning "AI" (including automated and/or generative systems that affect faculty, students, and staff). By definition, educators are at the heart of the educational mission and must be given the opportunity to lead the development of "AI"-related policies.

You should have institutional support for training around critical AI literacy. Critical AI literacy includes understanding how automated and/or generative systems work, the limitations to which they are subject, the affordances and opportunities they present, and the full range of known harms (environmental as well as social). Such literacy is essential, but educators cannot be expected to add gaining critical AI literacy to their workloads without such support.

So long as you respect student rights (as elaborated below), you should decide whether and how to use automated and/or generative systems ("AI") in your courses. Teaching about "AI" is increasingly important to educating students, but commitment to teaching critical AI literacy (as elaborated above) does not imply any mandatory student use of an automated system. Educators should not be pressured into adopting new systems or penalized for opting out. Educators should be given resources to evaluate best practices for teaching in consultation with other domain experts and peer-reviewed research on pedagogy.
You should never be subjected to any automated and/or generative system that impinges on your legal rights (including but not limited to those stated above).
Rights for Students:

You should be able to expect clear guidance from your instructor on whether and how automated and/or generative systems are being used in any of your work for a course. These guidelines should make clear which specific systems or tools are appropriate for any given assignment.

You should be able to ask questions of your instructor and administration about the use of automated and/or generative systems prior to submitting assignments without fear of reprisal or assumption of wrongdoing. Critical AI literacy, especially in an environment of rapid technological development, requires honest conversations between all stakeholders. This includes students being able to ask why any given system is required for a given assignment. Educators should recognize that students who have been using AI tools in other courses or in their private lives should be treated respectfully on this as on any other matter.

You should be able to opt out of assignments that may put your own creative work at risk for data surveillance and use without compensation. Educational institutions have an obligation to protect students from privacy breaches and exploitation.

You should be able to appeal academic misconduct charges if you are falsely accused of using any AI system inappropriately. If you are accused of using technology inappropriately, you should be invited to a conversation and allowed to show your work. Punitive responses to student abuse of generative technologies must be based on the same standard of evidence as any other academic misconduct charges. Critical AI literacy means that all parties recognize that detection tools are at present fallible and subject to false positives.

You should be informed when an instructor or institution is using an automated process to assess your assignments, and you should be able to assume that a qualified human will be making final evaluative decisions about your work. You should always have the ability to choose to be assessed by a human and to appeal automated assessments.

You should never be subjected to any automated and/or generative system that impinges on your legal rights (including but not limited to those stated above).

I especially want to thank the Critical AI editorial team for extensive and thoughtful feedback on several drafts of this essay; Anna Mills, Maha Bali, Autumn Caines, Lisa Hermsen, and Perry Shane's input on my earliest draft of this framework; and the Kansas and Missouri educators who attended the June 2023 AI & Digital Literacy Summit at the Hall Center for the Humanities at the University of Kansas, whose concerns and dialogue helped sharpen my thinking about this work.

Footnotes

1 I use the industry's preferred term, generative AI, while recognizing that "artificial intelligence" (AI) is a loaded term with a complicated history of extracting data. Though "generative AI" is increasingly common, I concur with Bender (2023 n.p.) that a more apt term for this cluster of technologies might be "synthetic media machines." The novelist Ted Chiang refers to this elaborate data-mining technology (Murgia 2023) simply as "applied statistics."

2 For "end of the essay" predictions, see Herman 2022, Marche 2022. For journalists urging educators to "teach with" AI, see, for instance, Heaven 2023, Roose 2023, and Rim 2023.

3 On flawed efforts to detect AI-generated work, see Wiggers 2023 and Fowler 2023; evidence already suggests that vulnerable populations are more likely to be accused of cheating (e.g. Liang et al 2023). See Klee 2023 on one notable case of a false accusation by an instructor who assumed that the LLM could detect its own outputs (see, e.g., Klee 2023).

4 See, for example, Pasquale (2020, 6-88) and Goodlad and Baker (2023). While Pasquale's first proposed law specifies that "Robotic systems and AI should complement professionals, not replace them" (3), the public release of GPT-4 was accompanied by widely hailed claims that falsely implied that chatbots capable of passing, say, the LSAT, are thereby equipped to practice law. As Goodlad and Stone write in the introduction to this special issue, "The same mentality that finds tech companies eager to portray LLMs as gifted educators also finds them keen to uphold chatbots as the ideal replacement for psychotherapists, lawyers, social workers, doctors, and much else." For the recent case of one lawyer's disastrous reliance on ChatGPT for legal research see Armstrong 2023, Milmo 2023. Notably, the release of OpenAI's GPT-4, supported by Microsoft, coincided with the firing of Microsoft's AI Ethics and Society team in 2023. In this, Microsoft followed in the footsteps of Google's firing of ethicists Timnit Gebru and Margaret Mitchell in late 2020 and early 2021, respectively (see e.g. Metz and Wakabayashi 2020, Schiffer 2021). To be sure, some educators have been in productive, collaborative conversations with tech companies and fellow educators about the use of generative technologies in education (e.g. Mills, Bali, and Eaton [2023]).

5 Describing human creative work as training data is already "deliberately alienating reductionism" (Conrad 2023 n.p.), stripping such work "of the critical essence by which it avails itself of copyright protection: its expressive value and human creativity" (Kupferschmid 2023. For important work on these problems, see Bender et al 2021, Crawford 2021, Weidinger et al 2021, Whittaker 2021, Acemoglu and Johnston 2023, Caines 2023, D'Agostino 2023, Fergusson et al. 2023, Furze 2023, Gal 2023, Hendricks 2023, Perrigo 2023, Sweetman and Djerbal 2023, Turkewitz 2023, and van Rooij 2023.

6 Adopting a principles-first approach helps to avoid situations similar to those encountered by copyright law, which emerged in reaction to the printing press (ARL n.d.) and whose "fair use" stipulations (Turkewitz 2023) have been exploited by large corporations. While the Russell Group of UK universities has articulated a loose set of principles (2023), their likely impact is attenuated by vague language around the notion of "appropriate use" as well as an unsubstantiated assumption that "AI" provides a "transformative opportunity" that these institutions are "determined to grasp." The MLA-CCCC Joint Task Force on Writing and AI Working Paper, published as this essay was copy-edited, makes a strong set of principled recommendations focused on building critical AI literacy.

7 As its extensive legal disclaimer page makes clear [OSTP 2022b]), the Blueprint is not enforceable; indeed the US White House and Congress have actively supported companies known to have violated these very rights (see White House 2023a, White House 2023b, Krishan 2023).

8 The quoted text comprises each of the headers and the statement of principles in the Blueprint as of July 2023. Full text can be found at https://www.whitehouse.gov/ostp/ai-bill-of-rights/.

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# data[].attributes.attachment_link.title
SNEAK PREVIEW: A Blueprint for an AI Bill of Rights for Education

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Consultant, author and PhD candidate

Consultant, author and PhD candidate Helping educators navigate the practical and ethical implications of Generative Artificial Intelligence

The live webinar Practical Strategies for Image Generation in Education is now available as a recording. Click here to purchase the recorded webinar.

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Finding quality resources for Generative AI in education can be hard. To make things easier, I've organised my most popular blog posts and resources into these collections:

" Thanks to Leon's expertise and dedication, our school now has a clear and well-informed Generative AI policy that serves as a foundation for responsible usage of generative AI. We are immensely grateful for Leon's contributions and highly recommend his services to any educational institution seeking to navigate the exciting but complex world of Generative AI technology."

Travis Parker - Associate Principal - The Knox School

About Leon

As a secondary and tertiary educator for over fifteen years, I understand the complexities of schools and universities. I'm also a parent, and deeply concerned with how education continues to change to keep up with technological and societal shifts.

As a PhD Candidate studying Generative AI in education, I'm interested in the ethical and practical implications of GAI. To help navigate the complexities of GAI, I work with schools, universities, and education providers through professional learning, policy, and advisory services.

" Leon has been a valuable presenter and panelist with our Secondary English teachers in the Teaching Excellence Program. We have appreciated his thought-provoking perspectives on the emerging and ongoing impact of generative AI in thinking about the future of English teaching. He also presented a very valuable workshop to our teachers on inclusive practices for neurodiverse students in the classroom. Leon is an engaging and intelligent speaker, whose lived experience in the classroom enables him to speak on a range of topics with authority and humanity."
Fiona Atkin and Meg Brydon, 2023 Master Teachers (English), Teaching Excellence Program, The Victorian Academy of Teaching and Leadership

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Leon Furze Consultant, author and PhD candidate Helping educators navigate the practical and ethical implications of Generative Artificial Intelligence Contact Leon Go to the blog https://videopress.com/v/eSCoF6EU?resizeToParent=true&cover=true&preloadContent=metadata&useAverageColor=true The live webinar Practical Strategies for Image Generation in Education is now available as a recording. Click here to purchase the recorded webinar.