Speaker: Dan Yamins
Assistant Professor of Psychology and Computer Science
Stanford University
Hosted by: John Lafferty
In-person event with remote access option via Panopto
A Fruitful Reciprocity: The Neuroscience-AI Connection
Abstract: The emerging field of NeuroAI has leveraged techniques from artificial intelligence to analyze large-scale brain data. In this talk, I will show that the connection between neuroscience and AI can be fruitful in both directions. Towards “AI driving neuroscience”, I will discuss recent advances in self-supervised learning with deep recurrent networks that yield a developmentally-plausible model of the primate visual system. In the direction of “neuroscience guiding AI”, I will present a novel cognitively-grounded computational theory of perception that generates powerful new learning algorithms for real-world scene understanding. Taken together, these ideas illustrate how neural networks optimized to solve cognitively-informed tasks provide a unified framework for both understanding the brain and improving AI.
Bio: Dan Yamins is a computational neuroscientist at Stanford University, where he’s an assistant professor of Psychology and Computer Science, and a faculty scholar at the Wu Tsai Neurosciences Institute. Dan works on science and technology challenges at the intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis.
The brain is the embodiment of the most beautiful algorithms ever written. His research group, the Stanford NeuroAILab, seeks to “reverse engineer” these algorithms, both to learn both about how our minds work and build more effective artificial intelligence systems. Website: http://stanford.edu/~yamins/
Monday, April 17, 2023
3:30pm – Pre-talk meet and greet teatime – Dana House, 24 Hillhouse Avenue
4:00 – 5:00 pm – Talk – In-Person seminars will be held at Mason Lab 211 with virtual participation (on campus only):
(https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa)
