Last week, I was at Web Summit in Lisbon where I met a number of interesting startups, most of which were using A.I. in one way or another. There was certainly a lot of enthusiasm about generative A.I.—systems that can take a simple prompt and then use that example or instruction to create, depending on the system, images, video, music, or long passages of coherent text. Many of these A.I. systems have at their core a large language model that has been trained on vast amounts of human-written text taken from the Internet.
The impressive output of these generative networks has renewed rumblings that artificial general intelligence—the kind of A.I. that would be able to perform most economically-useful tasks as well or better than we can—may be close at hand.
But just how smart are these large language models? On the last day of the conference, I interviewed legendary linguist Noam Chomsky, now 93 years old, and Gary Marcus, an emeritus professor of cognitive science at New York University who has spent much of the past decade highlighting the limits of deep learning. Both were distinctly unimpressed with today’s cutting edge A.I.
Chomsky’s big disappointment is that these large language models don’t tell us anything at all about how the human brain works. Chomsky has devoted much of his life to advancing the theory that there is a universal grammar, or at least a set of structural concepts, that underpin all human languages, and that this grammar is somehow hard-wired into the brain. Chomsky thinks this explains why human infants can master language so easily—whereas today’s computer systems need to be fed what Chomsky rightly calls “astronomical amounts of data” and even then still don’t actually understand language at all. They merely predict the most statistically likely association of words, or in the case of text-to-image generation A.I., words and images. (In recent years, cognitive science has moved away from Chomsky’s idea of a universal grammar. But we are still grappling with what exactly it is that makes humans such efficient language learners. It is clearly something. We don’t know what, and definitely can’t replicate it in software or silicon.)
Chomsky did allow that while, in his view, large language models were mostly worthless as objects of scientific interest, they might still be useful. He compared them to snowplows and said he had no objection to people using a snowplow to clear the streets after a blizzard rather than doing so by hand. That’s an important reminder for business: software can still be very useful—and make you a lot of money—even if it doesn’t function at all like a human brain would.
Marcus, on the other hand, was even less certain of how useful large language models would prove to be. His reason is that large language models are superficially good enough that they can fool us into thinking they possess human-like capabilities—and yet they can then fail in completely unexpected ways. “We put together meanings from the order of words,” he said. “These systems don’t understand the relation between the orders of words and their underlying meanings.”
He pointed to recent work he and collaborators had done looking at DALL-E, the text-to-image generator created by OpenAI. DALL-E does not understand a key grammatical concept called compositionality. Prompt DALL-E to produce an image of a red cube atop a blue cube and it is almost as likely to produce images in which the red cube is next to the blue cube or even where the blue cube is sitting on top of a red cube. Ask DALL-E to create images of a woman chasing a man, and at least some of the images are likely to depict a man chasing a woman.
He also cited other recent research that shows that most state-of-the-art computer vision models that are trained to describe complex scenes and videos, fail at simple physical reasoning tests that cognitive scientists have shown infants can easily pass. These have do with understanding object continuity (that occluded objects are usually still there), solidity (that most types of objects are solid and cannot pass through one another), and gravity (that dropped objects tend to fall).
Marcus said his biggest concerns were three-fold. One is that large language models have ingested a tremendous amount of human bias from the data on which they’ve been trained and will produce racist, sexist and otherwise biased content, maybe in ways we don’t fully understand. (GPT-3, for instance, was more likely to associate Muslim and Islam with violence.) Another is that these language models would supercharge misinformation. “The amount of misinformation that troll farms are going to be able to produce and the cost of that is really going to be devastating to the democratic process,” he said.
Finally, he worried about opportunity cost. The billions of dollars and vast intellectual talents wasted on pure deep learning approaches, he said, was concerning. Those resources, he said, might have been better spent researching human cognition to unlock the secrets of human intelligence, so that we might hope to one day replicate those digitally. (Marcus has long been an advocate of hybrid approaches that use deep learning for perception and older, more hard-coded symbolic A.I. approaches for reasoning. He is also a critic of deep learning’s obsession with learning everything from scratch, believing that most biological systems have powerful innate capabilities, or at least architectures that predispose them to very efficient mastery of critical tasks.)
It was a cautionary note that will probably be worth remembering in the coming months as the hype around generative A.I. looks likely to grow and as more companies rush to incorporate elements of large language models A.I. into processes and products.
And now here’s the rest of this week’s A.I. news.
Jeremy Kahn
@jeremyakahn
jeremy.kahn@ampressman
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