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The big question is: What will the courts think?

These are some of the most closely watched legal brawls of the moment. For Silicon Valley, the dawn of the AI age has been a spiritual revival; after a decade of increasing public wariness about techs influence on the world, the roaring enthusiasm for tools like ChatGPT has created a new boom. Call it the Second Age of Move Fast and Break Things. Theres plenty of hype, and eye-popping valuations. (OpenAIs current reported value, for example, is $80 billion.) But its distinct from the recent hype cycles around the metaverse and crypto in that generative AI is actually useful. Its still a gold rush, for sure. This time, though, the hills arent hollow, and the industry knows it. These lawsuits, which allege that OpenAI, Meta, Stability AI, and other companies broke the law when they built their tools, threaten the steamroller momentum of the generative AI movement. The stakes are sky-high.

The outcomes could help entrench the industry as we know itor force it to make radical changes. And while a security guard might not have recognized Butterick, the legal teams at AI companies certainly know him by now. Their futures could depend on how well, or poorly, he makes his cases.

Butterick grew up in New Hampshire. He was a strong student, good enough to get into Harvard in the late 80s. When he was there, though, he felt alienated from his more conventionally ambitious classmates. They were already thinking about things like law school. He was drawn to a more esoteric world. Tucked in the basement of his dormitory in Cambridge, Massachusetts, a long-running printing press called Bow & Arrow Press operated a workshop, giving students a unique opportunity to learn traditional printing techniques. It was a cozy, beloved hangout, with whitewashed, poster-covered walls, machinery that looked ancient, and an atmosphere that attracted offbeat aesthetes. When Butterick found it, his life changed.

He became obsessed with typography. He started working in font design when he was still in school. People in my life thought it was a ridiculous thing to do, he says. He loved playing with the old tools, but even more than that, he loved thinking about new ways to create beautiful typefaces. After he graduated in 1992, he had his own ambitions: Hed heard there were exciting things happening in the tech world in San Francisco, and it seemed like the perfect place for a guy who wanted to bring typography into the computer age. Two years later, he moved west.

Turns out, lawyers love fonts.

Like so many young Ivy Leaguers who show up in the Bay Area hoping to make a name for themselves in tech, Butterick decided he might as well try his hand at a startup. My dotcom adventure, he calls it, sounding half-embarrassed. He founded a web design company, Atomic Vision. By the time he was 28, he had around 20 employees. But he didnt love managing people. When an opportunity to sell the company came in 1999, he took it.

Flush with cash and unsure what to do next, Butterick figured hed follow in the footsteps of countless other young adults who dont know what they want out of life: He went to grad school. He enrolled at UCLA to get a law degree. After graduating, he started a website called Typography for Lawyers. It was meant to be a nerdy sideline, he says. But it snowballed. Turns out, lawyers love fonts. He turned the website into a shockingly popular book of the same name, which he published in 2010. Courts and private firms across the country started using his typefaces. After adopting his Equity font, a Fifth Circuit judge praised it as a fully-loaded F-150 compared to the Buick that was Times New Roman. The stuff of finicky opinion-readers dreams, the judge wrote.

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Meet the Lawyer Leading the Human Resistance Against AI - WIRED

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 -...

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A good working definition of applied AI is: the acquisition, manipulation, and exploitation of knowledge by systems whose behaviours may change on the basis of experience, or which are not constrained to be predictable or deterministic.

That (applied) AI knowledge can be:

Hybrid approaches, e.g. expert-validated machine learning, work well. Some large pre-trained models use this approach to a surprising degree.

Complex ecosystems of software systems can exhibit emergent behaviour, or intelligence. Just as ant colonies exhibit more intelligence than individual ants, AI-behaviour can emerge from complex ordinary software systems.

Until recently, once an AI technique was established, it was no longer perceived as AI; knowing how the rabbit is pulled out of the hat destroys the magic. This was the de-facto moving goal-posts definition of AI: that which a computer cant do.

AI used to be wide but shallow: horizontally applicable, but not powerful, such as a 1990s multi-lingual summariser which, though effective, had little idea of what it was writing. Alternatively AI could be deep but narrow: powerful only on tightly related problems.

The art of the computer scientist is explored in Professor Wirths influential eponymous book, Algorithms + Data Structure = Programs. But some AI systems are now either creating algorithms and data structures, or acting as if they have:

GLLMs have changed perceptions: AI can at last do things again, and AI systems which invent programs (self programming computers?) are both wide and deep. Some even give an appearance of edging up from machine intelligence towards sentience; should accidental or deliberate machine sentience arrive, we wont necessarily understand or even recognise it.

With greater public understanding of AI capabilities, the label AI is less frequently used simply to glamourise mundane software though it remains a popular buzz-word, replacing the meaningless big data.

AI discussions often conflate its three depths. Overloaded terms help marketing, but hinder understanding: deep learning means a neural net with more than three levels, but is often misunderstood as profound learning.

When systems make decisions, explainability becomes important when welfare is at stake. Explainability is the AI equivalence of human accountability. Arguably there is a need to make GLLMs explainable. Unfortunately, by their very black-box (neural net) nature they are not. Powerful AI (which learns its own knowledge representations and reasoning techniques) might be necessarily intrinsically opaque with unexplainable decisions.

Misunderstanding AI characteristics can lead people to try regulating AI techniques but it is only the system effect that might be regulated, not the means used to achieve it. A wrongly declined mortgage has equal impact whether due to a requirements mistake, biased dataset, database error, bug, incorrect algorithm, or misapplied AI technique. Regulating AI as if it were just clever software would impinge on the fundamental characteristics from which its capability flows, and inhibit its benefits. A reasonable requirement would be that any system, not just AI, which impinges on welfare must be able to explain its decisions.

As a colleague observed, defining AI is like defining time: we all think we know what it means, but it is actually hard to pin down. Just as our understanding of time changes appropriately enough, with time so AI itself may cause us to change our definition of AI.

Andrew Lea (FBCS), with the connivance of the BCS AI interest group - based on his four decades of applying AI in commerce, industry, aerospace and fraud detection - explores why AI is so hard to define. He has been fascinated by AI ever since reading Natural Sciences at Cambridge and studying Computing at London University.

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Why is AI hard to define? | BCS - BCS