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That is, if they can achieve fish-level cognition.
That is how NYU philosophy professor David Chalmers on Monday threaded the needle of an extremely controversial topic.
Chalmers's talk, titled, "Could a large language model be conscious?" was the Nov. 28th opening keynote of the 36th annual Neural Information Processing Systems conference, commonly known as NeurIPS, the most prestigious AI conference in the world, taking place this week in New Orleans.
A he tweeted, "It may be that today's large neural networks are slightly conscious."
This summer, Google researcher Blake Lemoine caused even more controversy with his contention that the LaMDA language program was sentient.
Those controversies "piqued my curiosity," said Chalmers. He was in no rush to take sides. Having played around with LaMDA, he said, "I didn't have any, suddenly, hallelujahs where I detected sentience."
Instead, Chalmers decided to approach the whole matter as a formal inquiry in scholarly fashion. "You know, what actually is or might be the evidence in favor of consciousness in a large language model, and what might be the evidence against this?" he put to the audience.
(Chalmers said he considers the two terms "conscious" and "sentient" to be "roughly equivalent," at least for the purpose of scientific and philosophical exploration.)
Chalmers's inquiry was also, he said, a project to find possible paths to how one could make a conscious or sentient AI program. "I really want to think of this also as a constructive project," he told the audience, "one that might ultimately lead to a potential roadmap to consciousness in AI systems."
He said, "My questions will be questions like, Well, first, are current large language models plausibly conscious? But maybe even more important, Could future large language models and extensions thereof be conscious?"
Also: generalist program Gato, "is at least some initial reason to take the hypothesis seriously," of sentience.
"I don't want to overstate things," said Chalmers. "I don't think there's remotely conclusive evidence that current large language models are conscious; still, their impressive general abilities give at least some limited initial support, just at least for taking the hypothesis seriously."
Also: just written a book on virtual worlds, offered, "I think this kind of work in virtual environments is very exciting for issues tied to consciousness."
Virtual worlds are important, he noted, because they may help to produce "world models," and those might rebut the most serious criticisms against sentience.
Chalmers cited the criticisms of scholars such as Timnit Gebru and Emily Bender that language models are just "stochastic parrots," regurgitating training data; and of Gary Marcus, who says the programs just do statistical text processing.
In response to those critiques, said Chalmers, "There's this challenge, I think, to turn those objections into a challenge, to build extended language models with robust world models and self models."
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"It may well turn out that the best way to minimize, say, loss through prediction error during training will involve highly novel processes, post-training, such as, for example, world models," said Chalmers. "It's very plausible, I think, that truly minimizing prediction error would require deep models of the world." There is some evidence, he said, that current large language models are already producing such world models, though it's not certain.
Summing up, Chalmers told the audience that "where current large language models are concerned, I'd say none of the reasons for denying consciousness in current large language models are totally conclusive, but I think some of them are reasonably strong."
In response to criticisms of large language models, Chalmers argued the statistical loss function employed by such models may already be developing world models. "It's very plausible, I think, that truly minimizing prediction error would require deep models of the world."
"I think maybe somewhere under 10% would be a reasonable probability of current language models" having consciousness, he said.
But Chalmers noted rapid progress in things such as LLM+ programs, with a combination of sensing and acting and world models.
"Maybe in 10 years we'll have virtual perception, language, action, unified agents with all these features, perhaps exceeding, say, the capacities of something like a fish," he mused. While a fish-level intelligent program wouldn't necessarily be conscious, "there would be a decent chance of it.
"I'd be, like, 50/50 that we can get to systems with these capacities, and 50/50 that if we have systems of those capacities, they'd be conscious," he said. "That might warrant greater than 20% probability that we may have consciousness in some of these systems in a decade or two."
If in that next decade, or whenever, it appears possible to answer the challenge, said Chalmers, then the discipline would have to grapple with the ethical implications. "The ethical challenge is, should we create conscience?" said Chalmers.
Today's large large language models such as GPT-3, he noted, have all kinds of ethical issues already.
"If you see conscious A.I. coming somewhere down the line, then that's going to raise a whole new important group of extremely snarly ethical challenges with, you know, the potential for new forms of injustice."