![]() “We take very seriously the concerns that it could be used for bad purposes and have worked to avoid that,” Tang said. Results for individuals on whom the decoder had not been trained were unintelligible, and if participants on whom the decoder had been trained later put up resistance - for example, by thinking other thoughts - results were similarly unusable. The paper describes how decoding worked only with cooperative participants who had participated willingly in training the decoder. 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.’”īeginning with an earlier version of the paper that appeared as a preprint online, the researchers addressed questions about potential misuse of the technology. ![]() About half the time, when the decoder has been trained to monitor a participant’s brain activity, the machine produces text that closely (and sometimes precisely) matches the intended meanings of the original words.įor 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, researchers designed it to capture the gist of what is being said or thought, albeit imperfectly. The result is not a word-for-word transcript. “We’re getting the model to decode continuous language for extended periods of time with complicated ideas.” “For a noninvasive method, this is a real leap forward compared to what’s been done before, which is typically single words or short sentences,” Huth said. Later, provided that the participant is open to having their thoughts decoded, their listening to a new story or imagining telling a story allows the machine to generate corresponding text from brain activity alone. Brain activity is measured using an fMRI scanner after extensive training of the decoder, in which the individual listens to hours of podcasts in the scanner. Participants also do not need to use only words from a prescribed list. Unlike other language decoding systems in development, this system does not require subjects to have surgical implants, making the process noninvasive. The work relies in part on a transformer model, similar to the ones that power Open AI’s ChatGPT and Google’s Bard. The study, published in the journal Nature Neuroscience, 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 system developed by researchers at The University of Texas at Austin might help people who are mentally conscious yet unable to physically speak, such as those debilitated by strokes, to communicate intelligibly again. The work relies in part on a transformer model, similar to the ones that power ChatGPTĪ new artificial intelligence system called a semantic decoder can translate a person’s brain activity - while listening to a story or silently imagining telling a story - into a continuous stream of text.
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