Scientists have created a neural network with the human-like ability to make generalizations about language1. The artificial intelligence (AI) system performs about as well as humans at folding newly learned words into an existing vocabulary and using them in fresh contexts, which is a key aspect of human cognition known as systematic generalization. The researchers gave the same task to the AI model that underlies the chatbot ChatGPT, and found that it performs much worse on such a test than either the new neural net or people, despite the chatbot’s uncanny ability to converse in a human-like manner. The work, published on 25 October in Nature, could lead to machines that interact with people more naturally than do even the best AI systems today. Although systems based on large language models, such as ChatGPT, are adept at conversation in many contexts, they display glaring gaps and inconsistencies in others. The neural network’s human-like performance suggests there has been a “breakthrough in the ability to train networks to be systematic”, says Paul Smolensky, a cognitive scientist who specializes in language at Johns Hopkins University in Baltimore, Maryland. Systematic generalization is demonstrated by people’s ability to effortlessly use newly acquired words in new settings. For example, once someone has grasped the meaning of the word ‘photobomb’, they will be able to use it in a variety of situations, such as ‘photobomb twice’ or ‘photobomb during a Zoom call’. Similarly, someone who understands the sentence ‘the cat chases the dog’ will also understand ‘the dog chases the cat’ without much extra thought.
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