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When we asked\u00a0GPT-3<\/a>, an extremely powerful and popular artificial intelligence language system, whether you\u2019d be more likely to use a paper map or a stone to fan life into coals for a barbecue, it preferred the stone.<\/p>\n To smooth your wrinkled skirt, would you grab a warm thermos or a hairpin? GPT-3 suggested the hairpin.<\/p>\n And if you need to cover your hair for work in a fast-food restaurant, which would work better, a paper sandwich wrapper or a hamburger bun? GPT-3 went for the bun.<\/p>\n Why does GPT-3 make those choices when most people choose the alternative? Because GPT-3 does not understand language the way humans do.<\/p>\n One of us is a\u00a0psychology researcher<\/a>\u00a0who over 20 years ago presented a series of scenarios like those above to\u00a0test the understanding of a computer model of language<\/a>\u00a0from that time. The model did not accurately choose between using rocks and maps to fan coals, whereas humans did so easily.<\/p>\n The other of us is a\u00a0doctoral student in cognitive science<\/a>\u00a0who was part of a team of researchers that more recently\u00a0used the same scenarios to test GPT-3<\/a>. Although GPT-3 did better than the older model, it was significantly worse than humans. It got the three scenarios mentioned above completely wrong.<\/p>\n GPT-3, the engine that powered the initial release of ChatGPT, learns about language by noting, from a trillion instances, which words tend to follow which other words. The strong statistical regularities in language sequences allow GPT-3 to learn a lot about language. And that sequential knowledge often allows ChatGPT to produce reasonable sentences, essays, poems and computer code.<\/p>\n Although GPT-3 is extremely good at\u00a0learning the rules of what follows what<\/a>\u00a0in human language, it doesn\u2019t have the foggiest idea what any of those words mean to a human being. And how could it?<\/p>\n Humans are biological entities that evolved with bodies that need to operate in the physical and social worlds to get things done. Language is a tool that helps people do that. GPT-3 is an artificial software system that predicts the next word. It does not need to get anything done with those predictions in the real world.<\/p>\n The meaning of a word or sentence is\u00a0intimately related to the human body<\/a>: people\u2019s abilities to act, to perceive and to have emotions. Human cognition is empowered by being embodied. People\u2019s understanding of a term like \u201cpaper sandwich wrapper,\u201d for example, includes the wrapper\u2019s appearance, its feel, its weight, and, consequently, how we can use it: for wrapping a sandwich. People\u2019s understanding also includes how someone can use it for myriad other opportunities it affords, such as scrunching it into a ball for a game of hoops, or covering one\u2019s hair.<\/p>\n All of these uses arise because of the nature of human bodies and needs: People have hands that can fold paper, a head of hair that is about the same size as a sandwich wrapper, and a need to be employed and thus follow rules like covering hair. That is, people understand how to make use of stuff in ways that are\u00a0not captured in language-use statistics<\/a>.Bodiless words<\/strong><\/h4>\n
I am, therefore I understand<\/strong><\/h4>\n
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