We Don't Know What We're Doing

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No one yet knows how ChatGPT and its artificial-intelligence cousins will transform the world, and one reason is that no one really knows what goes on inside them. Some of these systems' abilities go far beyond what they were trained to do—and even their inventors are baffled as to why. A growing number of tests suggest these AI systems develop internal models of the real world, much as our own brain does, although the machines' technique is different. They do not, like most of the characters in this fresh and at times brilliant collection, know what they are doing. But their author certainly does. Sarah Gilmartin In the final story, Nos Da (the Welsh for "good night"), Morris envisions an alternative world where a father watches his family through a camera as he contemplates his mediocre relationship with his new girlfriend. Information is dispensed incrementally, building suspense to a heart-breaking realisation. In this strange new world of deathday parties and Memory Tapes, these characters have little left but their past. In Clap Hands the gruelling life as a single mother of Amy, a nursery worker, is poignantly depicted. After her husband remortgages their home and absconds to Australia to find himself, Amy is caught between the demands of her ailing mother and her three children. She emerges as a modern heroine, refusing to take out her loss on the next generation. It is certainly much more than a stochastic parrot, and it certainly builds some representation of the world—although I do not think that it is quite like how humans build an internal world model,” says Yoshua Bengio, an AI researcher at the University of Montreal.

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MyHome.ie (Opens in new window) • Top 1000 • The Gloss (Opens in new window) • Recruit Ireland (Opens in new window) • Irish Times Training (Opens in new window) The power of great literature lies in its ability to reflect society. Writing about John McGahern, the American author John Updike called it “that tonic gift, the sense of truth – the sense of transparency that permits us to see imaginary lives more clearly than we see our own”. His Welsh heritage comes through in his stories with names such as Rhys, Rhian, Bethan and Gareth. Repeated locations – Caerphilly Castle, Morrisons, the ironically titled Castle View housing development – give the feel of an interlinked collection. Recurring themes have the same effect: absent parents, traumatic pasts, depression and mental illness, double lives. Although LLMs have enough blind spots not to qualify as artificial general intelligence, or AGI—the term for a machine that attains the resourcefulness of animal brains—these emergent abilities suggest to some researchers that tech companies are closer to AGI than even optimists had guessed. “They're indirect evidence that we are probably not that far off from AGI,” Goertzel said in March at a conference on deep learning at Florida Atlantic University. OpenAI's plug-ins have given ChatGPT a modular architecture a little like that of the human brain. “Combining GPT-4 [the latest version of the LLM that powers ChatGPT] with various plug-ins might be a route toward a humanlike specialization of function,” says M.I.T. researcher Anna Ivanova.Beautifully written in unshowy prose, the 10 stories in this short collection are also very funny. A newly married teacher in Castle View wonders why his wife no longer desires him: “He goes to google ‘why does my girlfriend not get aroused?’ but halfway through typing, Google auto-suggests ‘why does my girlfriend hate me?’.” Scientific American is part of Springer Nature, which owns or has commercial relations with thousands of scientific publications (many of them can be found at www.springernature.com/us). Scientific American maintains a strict policy of editorial independence in reporting developments in science to our readers.

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At a conference at New York University in March, philosopher Raphaël Millière of Columbia University offered yet another jaw-dropping example of what LLMs can do. The models had already demonstrated the ability to write computer code, which is impressive but not too surprising because there is so much code out there on the Internet to mimic. Millière went a step further and showed that GPT can execute code, too, however. The philosopher typed in a program to calculate the 83rd number in the Fibonacci sequence. “It's multistep reasoning of a very high degree,” he says. And the bot nailed it. When Millière asked directly for the 83rd Fibonacci number, however, GPT got it wrong, which suggests the system wasn't just parroting the Internet. Rather it was performing its own calculations to reach the correct answer. That GPT and other AI systems perform tasks they were not trained to do, giving them “emergent abilities,” has surprised even researchers who have been generally skeptical about the hype over LLMs. “I don't know how they're doing it or if they could do it more generally the way humans do—but they've challenged my views,” says Melanie Mitchell, an AI researcher at the Santa Fe Institute. There is no end to education. It is not that you read a book, pass an examination, and finish with education. The whole of life, from the moment you are born to the moment you die, is a process of learning. Thanks for reading Scientific American. Create your free account or Sign in to continue. Create Account The tonic comes in large doses in Thomas Morris's debut short-story collection, We Don't Know What We're Doing, set primarily in the Welsh town of Caerphilly. The troubled, centreless narrators drift through the everyday in search of meaning. They are mostly young – teenagers to twentysomethings – and their listlessness says much about a society that has lost direction. As editor of the Stinging Fly magazine and last year's Dubliners 100 anthology (Tramp Press), Morris may be known to Irish readers already. A native of Caerphilly, he studied at Trinity College and lives in Dublin.In addition to extracting the underlying meaning of language, LLMs can learn on the fly. In the AI field, the term “learning” is usually reserved for the computationally intensive process in which developers expose the neural network to gigabytes of data and tweak its internal connections. By the time you type a query into ChatGPT, the network should be fixed; unlike humans, it should not continue to learn. So it came as a surprise that LLMs do, in fact, learn from their users' prompts—an ability known as in-context learning. “It's a different sort of learning that wasn't really understood to exist before,” says Ben Goertzel, founder of AI company SingularityNET. Contrasting nicely with the anxieties of the youngsters, 78-year-old Jimmy, in Strange Traffic, is on the lookout for his third wife: "He was in good enough shape to last another ten years, and where was the point in going lonely all that time?" Another type of in-context learning happens via “chain of thought” prompting, which means asking the network to spell out each step of its reasoning—a tactic that makes it do better at logic or arithmetic problems requiring multiple steps. (But one thing that made Millière's example so surprising is that the network found the Fibonacci number without any such coaching.)