If you spend any time chatting with a modern AI chatbot, you've probably been amazed at just how human it sounds, how much it feels like you're talking to a real person. Much ink has been spilled explaining how these systems are not actually conversing, not actually understanding — they're statistical algorithms trained to predict the next likely word. 

But today on the show, let's flip our perspective on this. What if instead of thinking about how these algorithms are not like the human brain, we talked about how similar they are? What if we could use these large language models to help us understand how our own brains process language to extract meaning? 

There's no one better positioned to take us through this than returning guest Laura Gwilliams, a faculty scholar at the Wu Tsai Neurosciences Institute and Stanford Data Science Institute, and a member of the department of psychology here at Stanford.

Learn more:

Gwilliams' Laboratory of Speech Neuroscience

Fireside chat on AI and Neuroscience at Wu Tsai Neuro's 2024 Symposium (video)

The co-evolution of neuroscience and AI (Wu Tsai Neuro, 2024)

How we understand each other (From Our Neurons to Yours, 2023)

Q&A: On the frontiers of speech science (Wu Tsai Neuro, 2023)

Computational Architecture of Speech Comprehension in the Human Brain (Annual Review of Linguistics, 2025)

Hierarchical dynamic coding coordinates speech comprehension in the human brain (PMC Preprint, 2025)

Behind the Scenes segment:

By re-creating neural pathway in dish, Sergiu Pasca's research may speed pain treatment (Stanford Medicine, 2025)

Bridging nature and nurture: The brain's flexible foundation from birth (Wu Tsai Neuro, 2025)

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Episode Credits

This episode was produced by Michael Osborne at 14th Street Studios, with sound design by Morgan Honaker. Our logo is by Aimee Garza. The show is hosted by Nicholas Weiler at Stanford's

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