<\/p>\n
\u201cIt\u2019s just totally miraculous,\u201d says Frank. \u201cIf you train GPT-2 on 30 million words, you get a nonsense generator; you don\u2019t get a kid.\u201d\u00a0<\/p>\n
Exactly how babies pull this off is a mystery. Researchers know a lot about what kids learn and how they use language at different stages in development, but there\u2019s still a lot we don\u2019t know. Perhaps the most enduring question is why babies can learn language at all. The syntax of human language\u2014the rules for combining words into sentences\u2014includes recursive, nested structures that allow us to express virtually infinite ideas with a finite lexicon of words and pieces of words. This seems like something that should be a problem for babies. They only splash about in the shallows of a fathomless ocean of language. And yet, somehow, that\u2019s enough. From a drop, they infer the depths.<\/p>\n
One solution, put forward in the 1950s by the MIT linguist Noam Chomsky, is that babies are born with hardwired knowledge of grammar. Chomsky was reacting to a rival view, championed by the psychologist B.F. Skinner, that language acquisition is entirely environmental. Skinner thought language was learned through conditioning and reinforcement, the way a dog figures out how to sit or shake for treats. Chomsky countered by citing the \u201cpoverty of the stimulus\u201d\u2014the idea that language, especially syntax, is too complex and children\u2019s exposure to it too \u201cimpoverished\u201d for them to learn entirely from experience. \u201cHis signature argument was, essentially, that language cannot be learned on the basis purely of statistics,\u201d says Richard Futrell, a linguist and cognitive scientist at the University of California, Irvine. Instead, Chomsky posited that language is based on a set of logical rules and argued that children needed innate knowledge of those rules to deduce the grammar of their language from scraps of speech.<\/p>\n
\n\u201cIt\u2019s just totally miraculous \u2026 If you train GPT-2 on 30 million words, you get a nonsense generator; you don\u2019t get a kid.\u201d<\/strong><\/p>\n
Michael C. Frank, cognitive scientist, Stanford University<\/cite><\/p><\/blockquote>\n
The Chomskyan view of language dominated linguistics in the US for decades under the moniker of generative grammar. And it was a major influence on computer science in the 1950s and \u201960s, when AI was enjoying its first boom time and the lines between linguistics and natural-language processing dissolved in a flood of military funding; the Pentagon wanted computers that could understand English and translate Russian.\u00a0<\/p>\n
Despite early successes of simple neural networks, which learn to recognize and reproduce statistical patterns, AI researchers in the United States largely adopted a rule-based framework influenced by Chomsky\u2019s theories. They tried to teach language to computers by explicitly coding the rules into programs\u2014think less immersion experience, more grammar class. This approach, part of a broader trend called symbolic AI, prevailed for decades. It also largely failed to produce models actually capable of handling human language at scale. Interest in natural-\u00adlanguage processing chilled in the \u201cAI winter\u201d that began in the 1970s.\u00a0<\/p>\n
In the aftermath, neural networks started to make a comeback. But it wasn\u2019t until the 2010s, when computer hardware was getting cheap and capable and the internet was getting big, that their performance began turning heads. By 2018 and 2019, the models BERT and GPT-2, which were built on a new architecture\u2014the transformer\u2014and trained on billions of tokens, made it clear to insiders that learning from a massive glut of data could work for language. In 2022, with the breakout success of OpenAI\u2019s chatbot ChatGPT, it was clear to everyone.<\/p>\n
LLMs are not brains. What they are is powerful statistical learners\u2014na\u00efve pattern-learning machines without any of the evolved biological quirks folded into the human cortex. In other words, they are exactly the kind of thing a generative linguist two decades ago would have thought could not<\/em> learn language. And yet here they were, writing believable sonnets and passing grammar tests.<\/p>\n
\u201cNo matter how skeptical you are about AI, the thing that everyone has been really impressed with is: These things learn syntax,\u201d says Alison Gopnik, a developmental psychologist at the University of California, Berkeley. \u201cI didn\u2019t think that was going to turn out to be true. And I think most people didn\u2019t think that you could just look at the statistics of a large sample of language and figure out grammar.\u201d<\/p>\n<\/p><\/div>\n
#Kids #outlearn #AIand #dont<\/p>\n","protected":false},"excerpt":{"rendered":"
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