Greta Tuckute

In their most sophisticated forms, large language models can serve as a proxy for how the human brain processes information like sounds and words. As a PhD candidate at MIT, Greta Tuckute is pushing that relationship one step further by using language models like GPT to help build better cochlear implants and brain-machine interfaces. 

Tuckute, 29, says neuroscientists know which parts of the brain are used for different language tasks, but the specifics aren’t yet clear. That means the devices we create to help people with impairments, though often miraculous, still have much room for improvement. Cochlear implants, for example, require extensive training once placed in the ear to make the proper brain-interface connections. “My work is focused on obtaining a more precise understanding of these brain regions that are involved,” she says. 

To get there, Tuckute is building more accurate models of the brain using neural networks. In one study, she and her team measured the brain activity of people as they read 1,000 sentences. Tuckute then built GPT-based models that could predict which language-processing parts of the brain would be most stimulated by particular sentences. With this information, she identified sentences that, when read, either intensify or reduce neural activity, serving as a non-invasive way of controlling brain activity. 

Discovering this relationship—where a language model can help identify how to non-invasively activate certain parts of the brain—could lead to better devices to treat impairments, Tuckute says. The research has sparked a host of new directions for her, such as how to build more of what she calls “biologically plausible” language models, or AI models that more closely imitate brain functions like predicting which words will come next in a sentence.