With just three seconds of brain activity data, an AI developed by MetaAI can guess what you heard with surprising, but limited, accuracy.
Most existing technologies to help such patients communicate require risky brain surgeries to implant electrodes. This new approach “could provide a viable path to help patients with communication deficits … without the use of invasive methods,” says neuroscientist Jean-Rémi King, a Meta AI researcher currently at the École Normale Supérieure in Paris.
The team applied an AI with this language model to databases from four institutions that included brain activity from 169 volunteers. In these databases, participants listened to various stories and sentences from, for example, Ernest Hemingway’swhile the people’s brains were scanned using either magnetoencephalography or electroencephalography. Those techniques measure the magnetic or electrical component of brain signals.
Using magnetoencephalography, or MEG, the correct answer was in the AI’s top 10 guesses up to 73 percent of the time, the researchers found. With electroencephalography, that value dropped to no more than 30 percent. “[That MEG] performance is very good,” Di Liberto says, but he’s less optimistic about its practical use. “What can we do with it? Nothing. Absolutely nothing.”
It’s also important to understand what “decoding” really means in this study, says Jonathan Brennan, a linguist at the University of Michigan in Ann Arbor. The word is often used to describe the process of deciphering information directly from a source — in this case, speech from brain activity. But the AI could do this only because it was provided a finite list of possible correct answers to make its guesses.
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