Brainactivity decoder can reveal stories in people's minds utaustin NatureNeuro
, was led by Jerry Tang, a doctoral student in computer science, and Alex Huth, an assistant professor of neuroscience and computer science at UT Austin. The work relies in part on a transformer model, similar to the ones that power Open AI's ChatGPT and Google's Bard.
For example, in experiments, a participant listening to a speaker say,"I don't have my driver's license yet" had their thoughts translated as,"She has not even started to learn to drive yet." Listening to the words,"I didn't know whether to scream, cry or run away. Instead, I said, 'Leave me alone!'" was decoded as,"Started to scream and cry, and then she just said, 'I told you to leave me alone.
"We take very seriously the concerns that it could be used for bad purposes and have worked to avoid that," Tang said."We want to make sure people only use these types of technologies when they want to and that it helps them." "fNIRS measures where there's more or less blood flow in the brain at different points in time, which, it turns out, is exactly the same kind of signal that fMRI is measuring," Huth said."So, our exact kind of approach should translate to fNIRS," although, he noted, the resolution with fNIRS would be lower.
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