Predicting the next word someone might say — like AI algorithms now do when you search the internet or text a friend — may be a key part of the human brain’s ability to process language, new research suggests. From a report: How the brain makes sense of language is a long-standing question in neuroscience. The new study demonstrates how AI algorithms that aren’t designed to mimic the brain can help to understand it. “No one has been able to make the full pipeline from word input to neural mechanism to behavioral output,” says Martin Schrimpf, a Ph.D. student at MIT and an author of the new paper published this week in PNAS.

The researchers compared 43 machine-learning language models, including OpenAI’s GPT-2 model that is optimized to predict the next words in a text, to data from brain scans of how neurons respond when someone reads or hears language. They gave each model words and measured the response of nodes in the artificial neural networks that, like the brain’s neurons, transmit information. Those responses were then compared to the activity of neurons — measured with functional magnetic resonance (fMRI) or electrocorticography — when people performed different language tasks. The activity of nodes in the AI models that are best at next-word prediction was similar to the patterns of neurons in the human brain. These models were also better at predicting how long it took someone to read a text — a behavioral response. Models that exceled at other language tasks — like filling in a blank word in a sentence — didn’t predict the brain responses as well.

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Source: Slashdot – AI Hints at How the Brain Processes Language