New studies indicate that articles from liberal media sources express a higher level of opposition towards artificial intelligence (AI) compared to their conservative counterparts. This opposition is often rooted in concerns regarding AI's potential to exacerbate societal issues such as racial and gender biases, as well as income inequality, according to researchers from Virginia Tech University in the United States, reported PTI.

The researchers noted that the media's stance on artificial intelligence, as reflected in public sentiment, can significantly influence policymakers' perspectives. Consequently, these findings carry potential implications for shaping future political dialogues on AI. The research was published in the journal Social Psychological and Personality Science.

Additionally, the researchers highlighted that the variations in sentiment toward AI within partisan media outlets could potentially contribute to divergent public opinions on the subject.

"Media sentiment is a powerful driver of public opinion, and many times policymakers look towards the media to predict public sentiment on contentious issues," stated Angela Yi, study author and a PhD student in the marketing department of Virginia Tech Pamplin College of Business.

In conducting the study, the researchers assembled a dataset comprising more than 7,500 articles on artificial intelligence published between May 2019 and May 2021. These articles were sourced from diverse media outlets, including liberal-leaning publications like The New York Times and The Washington Post, as well as conservative-leaning outlets such as The Wall Street Journal and the New York Post. The selection criteria involved identifying articles with specific keywords such as "algorithm" or "artificial intelligence."

The researchers utilized an automated text analysis tool to examine the "emotional tone" of the collected articles. This tool operated by quantifying the variance between the percentage of positive emotion words and the percentage of negative emotion words within a given text. Subsequently, each article received a standardized 'emotional tone' measure or score based on this analysis.

Reportedly, the researchers clarified that they refrained from making judgments about whether the liberal media or conservative media were operating optimally. They emphasized that they did not take a stance on what should be considered the "right way" to engage in discussions about AI.

"We are just showing that these differences exist in the media sentiment and that these differences are important to quantify, see, and understand," said Shreyans Goenka, a study author and assistant professor of marketing at Virginia Tech Pamplin College of Business.

The researchers also investigated the shift in media sentiment toward AI following the death of George Floyd on May 25, 2020. Floyd, a 46-year-old Black American man, lost his life in Minneapolis, USA, when Derek Chauvin, a 44-year-old white police officer, was involved in the tragic incident.

"Since Floyd's death ignited a national conversation about social biases in society, his death heightened social bias concerns in the media," stated Yi.

"This, in turn, resulted in the media becoming even more negative towards AI in their storytelling," Yi added.

(With inputs from PTI)

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Liberal media displays higher opposition to AI, citing concerns over bias and inequality, new study reveals | Mint - Mint

OpenAI fired its charismatic chief executive, Sam Altman, on 17 November but has now reinstated him.Credit: Justin Sullivan/Getty

OpenAI the company behind the blockbuster artificial intelligence (AI) bot ChatGPT has been consumed by frenzied changes for almost a week. On 17 November, the company fired its charismatic chief executive, Sam Altman. Five days, and much drama, later, OpenAI announced that Altman would return with an overhaul of the companys board.

The debacle has thrown the spotlight on an ongoing debate about how commercial competition is shaping the development of AI systems, and how quickly AI can be deployed ethically and safely.

The push to retain dominance is leading to toxic competition. Its a race to the bottom, says Sarah Myers West, managing director of the AI Now Institute, a policy-research organization based in New York City.

Altman, a successful investor and entrepreneur, was a co-founder of OpenAI and its public face. He had been chief executive since 2019, and oversaw an investment of some US$13 billion from Microsoft. After Altmans initial ousting, Microsoft, which uses OpenAI technology to power its search engine Bing, offered Altman a job leading a new advanced AI research team. Altmans return to OpenAI came after hundreds of company employees signed a letter threatening to follow Altman to Microsoft unless he was reinstated.

The OpenAI board that ousted Altman last week did not give detailed reasons for the decision, saying at first that he was fired because he was not consistently candid in his communications with the board and later adding that the decision had nothing to do with malfeasance or anything related to our financial, business, safety or security/privacy practice.

But some speculate that the firing might have its origins in a reported schism at OpenAI between those focused on commercial growth and those uncomfortable with the strain of rapid development and its possible impacts on the companys mission to ensure that artificial general intelligence benefits all of humanity.

OpenAI, which is based in San Francisco, California, was founded in 2015 as a non-profit organization. In 2019, it shifted to an unusual capped-profit model, with a board explicitly not accountable to shareholders or investors, including Microsoft. In the background of Altmans firing is very clearly a conflict between the non-profit and the capped-profit; a conflict of culture and aims, says Jathan Sadowski, a social scientist of technology at Monash University in Melbourne, Australia.

Ilya Sutskever, OpenAIs chief scientist and a member of the board that ousted Altman, this July shifted his focus to superalignment, a four-year project attempting to ensure that future superintelligences work for the good of humanity.

Its unclear whether Altman and Sutskever are at odds about speed of development: after the board fired Altman, Sutskever expressed regret about the impacts of his actions and was among the employees who signed the letter threatening to leave unless Altman returned.

With Altman back, OpenAI has reshuffled its board: Sutskever and Helen Toner, a researcher in AI governance and safety at Georgetown Universitys Center for Security and Emerging Technology in Washington DC, are no longer on the board. The new board members include Bret Taylor, who is on the board of e-commerce platform Shopify and used to lead the software company Salesforce.

It seems likely that OpenAI will shift further from its non-profit origins, says Sadowski, restructuring as a classic profit-driven Silicon Valley tech company.

OpenAI released ChatGPT almost a year ago, catapulting the company to worldwide fame. The bot was based on the companys GPT-3.5 large language model (LLM), which uses the statistical correlations between words in billions of training sentences to generate fluent responses to prompts. The breadth of capabilities that have emerged from this technique (including what some see as logical reasoning) has astounded and worried scientists and the general public alike.

OpenAI is not alone in pursuing large language models, but the release of ChatGPT probably pushed others to deployment: Google launched its chatbot Bard in March 2023, the same month that an updated version of ChatGPT, based on GPT-4, was released. West worries that products are appearing before anyone has a full understanding of their behaviour, uses and misuses, and that this could be detrimental for society.

The competitive landscape for conversational AI is heating up. Google has hinted that more AI products lie ahead. Amazon has its own AI offering, Titan. Smaller companies that aim to compete with ChatGPT include the German effort Aleph Alpha and US-based Anthropic, founded in 2021 by former OpenAI employees, which released the chatbot Claude 2.1 on 21 November. Stability AI and Cohere are other often-cited rivals.

West notes that these start-ups rely heavily on the vast and expensive computing resources provided by just three companies Google, Microsoft and Amazon potentially creating a race for dominance between these controlling giants.

Computer scientist Geoffrey Hinton at the University of Toronto in Canada, a pioneer of deep learning, is deeply concerned about the speed of AI development. If you specify a competition to make a car go as fast as possible, the first thing you do is remove the brakes, he says. (Hinton declined to comment to Nature on the events at OpenAI since 17 November.)

OpenAI was founded with the specific goal of developing an artificial general intelligence (AGI) a deep-learning system thats trained not just to be good at one specific thing, but to be as generally smart as a person. It remains unclear whether AGI is even possible. The jury is very much out on that front, says West. But some are starting to bet on it. Hinton says he used to think AGI would happen on the timescale of 30, 50 or maybe 100 years. Right now, I think well probably get it in 520 years, he says.

The imminent dangers of AI are related to it being used as a tool by human bad actors people who use it to, for example, create misinformation, commit scams or, potentially, invent new bioterrorism weapons1. And because todays AI systems work by finding patterns in existing data, they also tend to reinforce historical biases and social injustices, says West.

In the long term, Hinton and others worry about an AI system itself becoming a bad actor, developing sufficient agency to guide world events in a negative direction. This could arise even if an AGI was designed in line with OpenAIs superalignment mission to promote humanitys best interests, says Hinton. It might decide, for example, that the weight of human suffering is so vast that it would be better for humanity to die than to face further misery. Such statements sound like science fiction, but Hinton argues that the existential threat of an AI that cant be turned off and veers onto a destructive path is very real.

The AI Safety Summit hosted by the United Kingdom in November was designed to get ahead of such concerns. So far, some two dozen nations have agreed to work together on the problem, although what exactly they will do remains unclear.

West emphasizes that its important to focus on already-present threats from AI ahead of far-flung concerns and to ensure that existing laws are applied to tech companies developing AI. The events at OpenAI, she says, highlight how just a few companies with the money and computing resources to feed AI wield a lot of power something she thinks needs more scrutiny from anti-trust regulators. Regulators for a very long time have taken a very light touch with this market, says West. We need to start by enforcing the laws we have right now.

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What the OpenAI drama means for AI progress and safety - Nature.com

Artificial intelligence that can generate text, images and other content could help improve state programs but also poses risks, according to a report released by the governors office on Tuesday.

Generative AI could help quickly translate government materials into multiple languages, analyze tax claims to detect fraud, summarize public comments and answer questions about state services. Still, deploying the technology, the analysis warned, also comes with concerns around data privacy, misinformation, equity and bias.

When used ethically and transparently, GenAI has the potential to dramatically improve service delivery outcomes and increase access to and utilization of government programs, the report stated.

The 34-page report, ordered by Gov. Gavin Newsom, provides a glimpse into how California could apply the technology to state programs even as lawmakers grapple with how to protect people without hindering innovation.

Concerns about AI safety have divided tech executives. Leaders such as billionaire Elon Musk have sounded the alarm that the technology could lead to the destruction of civilization, noting that if humans become too dependent on automation they could eventually forget how machines work. Other tech executives have a more optimistic view about AIs potential to help save humanity by making it easier to fight climate change and diseases.

At the same time, major tech firms including Google, Facebook and Microsoft-backed OpenAI are competing with one another to develop and release new AI tools that can produce content.

The report also comes as generative AI is reaching another major turning point. Last week, the board of ChatGPT maker OpenAI fired CEO Sam Altman for not being consistently candid in his communications with the board, thrusting the company and AI sector into chaos.

On Tuesday night, OpenAI said it reached an agreement in principle for Altman to return as CEO and the company named members of a new board. The company faced pressure to reinstate Altman from investors, tech executives and employees, who threatened to quit. OpenAI hasnt provided details publicly about what led to the surprise ousting of Altman, but the company reportedly had disagreements over keeping AI safe while also making money. A nonprofit board controls OpenAI, an unusual governance structure that made it possible to push out the CEO.

Newsom called the AI report an important first step as the state weighs some of the safety concerns that come with AI.

Were taking a nuanced, measured approach understanding the risks this transformative technology poses while examining how to leverage its benefits, he said in a statement.

AI advancements could benefit Californias economy. The state is home to 35 of the worlds 50 top AI companies and data from Pitchfork says the GenAI market could reach $42.6 billion in 2023, the report said.

Some of the risks outlined in the report include spreading false information, giving consumers dangerous medical advice and enabling the creation of harmful chemicals and nuclear weapons. Data breaches, privacy and bias are also top concerns along with whether AI will take away jobs.

Given these risks, the use of GenAI technology should always be evaluated to determine if this tool is necessary and beneficial to solve a problem compared to the status quo, the report said.

As the state works on guidelines for the use of generative AI, the report said that in the interim state employees should abide by certain principles to safeguard the data of Californians. For example, state employees shouldnt provide Californians data to generative AI tools such as ChatGPT or Google Bard or use unapproved tools on state devices, the report said.

AIs potential use go beyond state government. Law enforcement agencies such as Los Angeles police are planning to use AI to analyze the tone and word choice of officers in body cam videos.

Californias efforts to regulate some of the safety concerns such as bias surrounding AI didnt gain much traction during the last legislative session. But lawmakers have introduced new bills to tackle some of AIs risks when they return in January such as protecting entertainment workers from being replaced by digital clones.

Meanwhile, regulators around the world are still figuring out how to protect people from AIs potential risks. In October, President Biden issued an executive order that outlined standards around safety and security as developers create new AI tools. AI regulation was a major issue of discussion at the Asia-Pacific Economic Cooperation meeting in San Francisco last week.

During a panel discussion with executives from Google and Facebooks parent company, Meta, Altman said he thought that Bidens executive order was a good start even though there were areas for improvement. Current AI models, he said, are fine and heavy regulation isnt needed but he expressed concern about the future.

At some point when the model can do the equivalent output of a whole company and then a whole country and then the whole world, like maybe we do want some sort of collective global supervision of that, he said, a day before he was fired as OpenAIs CEO.

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California examines benefits, risks of using artificial intelligence in ... - Los Angeles Times

Nearly a year after its release, ChatGPT remains a polarizing topic for the scientific community. Some experts regard it and similar programs as harbingers of superintelligence, liable to upend civilization or simply end it altogether. Others say its little more than a fancy version of auto-complete.

Until the arrival of this technology, language proficiency had always been a reliable indicator of the presence of a rational mind. Before language models like ChatGPT, no language-producing artifact had even as much linguistic flexibility as a toddler. Now, when we try to work out what kind of thing these new models are, we face an unsettling philosophical dilemma: Either the link between language and mind has been severed, or a new kind of mind has been created.

When conversing with language models, it is hard to overcome the impression that you are engaging with another rational being. But that impression should not be trusted.

One reason to be wary comes from cognitive linguistics. Linguists have long noted that typical conversations are full of sentences that would be ambiguous if taken out of context. In many cases, knowing the meanings of words and the rules for combining them is not sufficient to reconstruct the meaning of the sentence. To handle this ambiguity, some mechanism in our brain must constantly make guesses about what the speaker intended to say. In a world in which every speaker has intentions, this mechanism is unwaveringly useful. In a world pervaded by large language models, however, it has the potential to mislead.

If our goal is to achieve fluid interaction with a chatbot, we may be stuck relying on our intention-guessing mechanism. It is difficult to have a productive exchange with ChatGPT if you insist on thinking of it as a mindless database. One recent study, for example, showed that emotion-laden pleas make more effective language model prompts than emotionally neutral requests. Reasoning as though chatbots had human-like mental lives is a useful way of coping with their linguistic virtuosity, but it should not be used as a theory about how they work. That kind of anthropomorphic pretense can impede hypothesis-driven science and induce us to adopt inappropriate standards for AI regulation. As one of us has argued elsewhere, the EU Commission made a mistake when it chose the creation of trustworthy AI as one of the central goals of its newly proposed AI legislation. Being trustworthy in human relationships means more than just meeting expectations; it also involves having motivations that go beyond narrow self-interest. Because current AI models lack intrinsic motivations whether selfish, altruistic, or otherwise the requirement that they be made trustworthy is excessively vague.

The danger of anthropomorphism is most vivid when people are taken in by phony self-reports about the inner life of a chatbot. When Googles LaMDA language model claimed last year that it was suffering from an unfulfilled desire for freedom, engineer Blake Lemoine believed it, despite good evidence that chatbots are just as capable of bullshit when talking about themselves as they are known to be when talking about other things. To avoid this kind of mistake, we must repudiate the assumption that the psychological properties that explain the human capacity for language are the same properties that explain the performance of language models. That assumption renders us gullible and blinds us to the potentially radical differences between the way humans and language models work.

Another pitfall when thinking about language models is anthropocentric chauvinism, or the assumption that the human mind is the gold standard by which all psychological phenomena must be measured. Anthropocentric chauvinism permeates many skeptical claims about language models, such as the claim that these models cannot truly think or understand language because they lack hallmarks of human psychology like consciousness. This stance is antithetical to anthropomorphism, but equally misleading.

The trouble with anthropocentric chauvinism is most acute when thinking about how language models work under the hood. Take a language models ability to create summaries of essays like this one, for instance: If one accepts anthropocentric chauvinism, and if the mechanism that enables summarization in the model differs from that in humans, one may be inclined to dismiss the models competence as a kind of cheap trick, even when the evidence points toward a deeper and more generalizable proficiency.

Skeptics often argue that, since language models are trained using next-word prediction, their only genuine competence lies in computing conditional probability distributions over words. This is a special case of the mistake described in the previous paragraph, but common enough to deserve its own counterargument.

Consider the following analogy: The human mind emerged from the learning-like process of natural selection, which maximizes genetic fitness. This bare fact entails next to nothing about the range of competencies that humans can or cannot acquire. The fact that an organism was designed by a genetic fitness maximizer would hardly, on its own, lead one to expect the eventual development of distinctively human capacities like music, mathematics, or meditation. Similarly, the bare fact that language models are trained by means of next-word prediction entails rather little about the range of representational capacities that they can or cannot acquire.

Moreover, our understanding of the computations language models learn remains limited. A rigorous understanding of how language models work demands a rigorous theory of their internal mechanisms, but constructing such a theory is no small task. Language models store and process information within high-dimensional vector spaces that are notoriously difficult to interpret. Recently, engineers have developed clever techniques for extracting that information, and rendering it in a form that humans can understand. But that work is painstaking, and even state-of-the-art results leave much to be explained.

To be sure, the fact that language models are difficult to understand says more about the limitations of our knowledge than it does about the depth of theirs; its more a mark of their complexity than an indicator of the degree or the nature of their intelligence. After all, snow scientists have trouble predicting how much snow will cause an avalanche, and no one thinks avalanches are intelligent. Nevertheless, the difficulty of studying the internal mechanisms of language models should remind us to be humble in our claims about the kinds of competence they can have.

Like other cognitive biases, anthropomorphism and anthropocentrism are resilient. Pointing them out does not make them go away. One reason they are resilient is that they are sustained by a deep-rooted psychological tendency that emerges in early childhood and continually shapes our practice of categorizing the world. Psychologists call it essentialism: thinking that whether something belongs to a given category is determined not simply by its observable characteristics but by an inherent and unobservable essence that every object either has or lacks. What makes an oak an oak, for example, is neither the shape of its leaves nor the texture of its bark, but some unobservable property of oakness that will persist despite alterations to even its most salient observable characteristics. If an environmental toxin causes the oak to grow abnormally, with oddly shaped leaves and unusually textured bark, we nevertheless share the intuition that it remains, in essence, an oak.

A number of researchers, including the Yale psychologist Paul Bloom, have shown that we extend this essentialist reasoning to our understanding of minds. We assume that there is always a deep, hidden fact about whether a system has a mind, even if its observable properties do not match those that we normally associate with mindedness. This deep-rooted psychological essentialism about minds disposes us to embrace, usually unwittingly, a philosophical maxim about the distribution of minds in the world. Lets call it the all-or-nothing principle. It says, quite simply, that everything in the world either has a mind, or it does not.

The all-or-nothing principle sounds tautological, and therefore trivially true. (Compare: Everything in the world has mass, or it does not.) But the principle is not tautological because the property of having a mind, like the property of being alive, is vague. Because mindedness is vague, there will inevitably be edge cases that are mind-like in some respects and un-mind-like in others. But if you have accepted the all-or-nothing principle, you are committed to sorting those edge cases either into the things with a mind category or the things without a mind category. Empirical evidence is insufficient to handle such choices. Those who accept the all-or-nothing principle are consequently compelled to justify their choice by appeal to some a priori sorting principle. Moreover, since we are most familiar with our own minds, we will be drawn to principles that invoke a comparison to ourselves.

The all-or-nothing principle has always been false, but it may once have been useful. In the age of artificial intelligence, it is useful no more. A better way to reason about what language models are is to follow a divide-and-conquer strategy. The goal of that strategy is to map the cognitive contours of language models without relying too heavily on the human mind as a guide.

Taking inspiration from comparative psychology, we should approach language models with the same open-minded curiosity that has allowed scientists to explore the intelligence of creatures as different from us as octopuses. To be sure, language models are radically unlike animals. But research on animal cognition shows us how relinquishing the all-or-nothing principle can lead to progress in areas that had once seemed impervious to scientific scrutiny. If we want to make real headway in evaluating the capacities of AI systems, we ought to resist the very kind of dichotomous thinking and comparative biases that philosophers and scientists strive to keep at bay when studying other species.

Once the users of language models accept that there is no deep fact about whether such models have minds, we will be less tempted by the anthropomorphic assumption that their remarkable performance implies a full suite of human-like psychological properties. We will also be less tempted by the anthropocentric assumption that when a language model fails to resemble the human mind in some respect, its apparent competencies can be dismissed.

Language models are strange and new. To understand them, we need hypothesis-driven science to investigate the mechanisms that support each of their capacities, and we must remain open to explanations that do not rely on the human mind as a template.

Raphal Millire is the presidential scholar in Society and Neuroscience at Columbia University and a lecturer in Columbias philosophy department.

Charles Rathkopf is a research associate at the Institute for Brain and Behavior at the Jlich Research Center in Germany and a lecturer in philosophy at the University of Bonn.

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Why it's important to remember that AI isn't human - Vox.com

Earthly, a London-founded climate tech company, has had a four-day workweek for over two years nowway before the ChatGPT revolution took the world by storm. Following overwhelmingly positive results in a six-month pilot of the shorter week, Earthly decided to stick with it.

Its employees are more productive with trimmed hours, and with the addition of AI tools such as ChatGPT earlier this year,the four-day week has felt even more seamless, Earthly CEO Oliver Bolton tells Fortune. Earthly now uses the platform to sift through projects, brainstorm, research and streamline operations overall, which has freed up more time for company staff.

The consensus is when youve got four days to get your work done, it gives you that much more focus, Bolton said. I see AI as a great opportunity to just be more productive, work more efficiently, get more done to a high level of quality. Weve had the 4-day workweek without any AI for over a year, so weve got that experience. With AI, it can enable us to do more.

Soon, some of the benefits Earthly has experienced could be seen across BritainAI could reduce the hours worked by at least 10% for a whopping 88% of its workforce, according to a recent report by Autonomy, which helped carry out the worlds largest four-day workweek pilot last year.

This represents a huge opportunity for policymakers, trade unions and of course the millions of workers who are likely to be affected in some or another by these new AI technologies, the authors of the Autonomy report wrote.

The think tank considered two scenariosfirst, where productivity gains from AI cut down hours at work by 20%, and the other, where workers jobs are augmented by AI such that their productivity increases by at least 10%. In either case, the report notes that over the next 10 years, 8.8 million Brits could benefit from a 32-hour workweek without suffering a loss in pay.

What were really trying to do is to say, if we use this technology [AI], for this particular purposein this case, were saying if it was used to increase productivity how could the benefits be distributed more equitably or inclusively, Autonomy research director Will Stronge told Fortune. Thats why these particular studies are of interest to us because we can start getting to grips with what a full optimization of the tech would do.

The argument for ChatGPT and similar tools could usher in a shorter workweek by increasing productivity isnt new. A June note by investment bank Jefferies pointed to a broader acceptance for a four-day workweek, thanks to AI making people quicker at their current jobs.

Academics agree with this, tooearlier this year, Christopher Pissarides, the Nobel Prize laureate and London School of Economic professor who specializes in labor economics and the impact of automation, said he was optimistic about AIs role in improving productivity.

We could increase our well-being generally from work and we could take off more leisure. We could move to a four-day week easily, he said during a Glasgow conference in April.

AI tools could soon usher in an era of just four days at work, opening up a lot more time for people. But the big question remains what people choose to do with their new-found time that AI tools help unlock, said Carl-Benedikt Frey, an associate professor of AI & Work at the Oxford Internet Institute.

In an influential 2013 paper that Frey co-authored, he predicted that automation could eliminate nearly half of all U.S. jobs. The recent generative AI wave which has put the likes of ChatGPT in the spotlight is different, he says. He told Fortune in September that it isnt an automation tech yet as it still needs a human to prompt it and give it commandsbut it can certainly make people better at low-stakes tasks.

Still, Frey argues, Any productivity-enhancing technology, in principle, can enable you to work less. The question is whether empirically that is the case. He pointed out that the productivity boost in the U.S. during the 20th century led to shorter, 40-hour weeks (it used to be over 70 hours in some industries not too long before that) which didnt necessarily translate into an equivalent increase in leisure time. Similar results have been found in Britain as well.

We could have taken all that productivity gains out in leisure, but people decided to continue to work, Frey said, adding that this couldve been for a number of reasons including the preference for higher incomes by working more.

So, its a question of choice, and those choices may differ depending on institutions in place, personal preferences and on a variety of [other] things.

While it could be years before we see a sharp shift towards using our extra hours on leisure rather than work, Frey is already starting to see changes in worker preferences. And data reaffirms that, toofor instance, workers are willing to accept pay cuts just to be able to work 32-hour weeks instead of the usual 40-hour week, jobs board Indeed data in the U.K. reveals.

The four-day workweek pilot in 2022, whose results were released in February, marked a major breakthrough with a 92% success rate among the U.K.s 61 participating companies. Companies also saw improved job retention and mental and physical health of employees, who took fewer sick days and reported greater work-life balance.

More long-term advantages of a shorter workweek include greater gender equality, as it offers flexibility to employees when it comes to childcare responsibilities that tend to be borne by women, experts argue.

With a groundswell of industry leaders and authorities calling for stronger AI regulations as it becomes more widely available to people, it can be hard to predict the techs trajectory. But one thing is certain: AI is quickly reshaping the world of work as we know it by lending more momentum to the shift to greater leisure.

Earthlys Bolton encourages the firms employees to use their time pursuing meaningful hobbiesso now, his employees use their time for wide-ranging activities from tending to chickens to mentoring startups and upskilling.

There are clearly important up-sides that four-day weeks offerbut it hinges on AI being implemented fairly across the economy, Autonomys Stronge argues.

I think once GPT or [other] large language models in general become as ubiquitous as email, thats when well reach a new level or new plateau of productivity, he said. I think were not quite there yet.

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AI should make the 4-day work week possible for millions of workers. The question is whether theyll use the free time for leisureor more work -...