The powerful new generation of AI tools that has come out over the past few years — DALL-E, ChatGPT, Claude, Gemini, and the rest — have blown away our old ideas about what AI can do and raised questions about what it means for computers to start acting... intelligent?

This week, we ask what the rise of these systems might teach us about our own biological intelligence — and vice versa. What does modern neuroscience have to say about how AI could become as flexible, efficient, and resilient as the human brain.

Few people are better positioned to speak to the intersection of neuroscience and AI than today's guest: Surya Ganguli.

Ganguli's lab produced some of the first diffusion models — which are at the foundation of today's AI revolution — and is now working to understand how complex emergent properties arise from biological and artificial neural networks.

Ganguli is a member of the Neuroscience Theory Center at the Wu Tsai Neurosciences Institute, a Senior Fellow at Stanford's Institute for Human-Centered Artificial Intelligence (HAI), and an associate professor in Stanford's Department of Applied Physics.

Further Reading

• Interpreting the retinal neural code for natural scenes: From computations to neurons (Neuron, 2023)
• Beyond neural scaling laws: beating power law scaling via data pruning (arXiv, 2023)
• Cortical layer-specific critical dynamics triggering perception (Science, 2019)
  • Stanford team stimulates neurons to induce particular perceptions in mice's minds (Stanford Medicine, 2019)
• What DALL-E reveals about human creativity (Wu Tsai Neurosciences Institute, 2023)

Visit us!
Want to learn more about AI and Neuroscience? Join us at Wu Tsai Neuro's annual symposium on October 17, 2024, which will showcase the frontiers of biological and artificial intelligence research. (More details coming soon!)

Episode credits
This episode was produced by Michael Osborne at 14th Street Studios, with production assistance by Morgan Honaker. Our logo is by Aimee Garza. The show is hosted by Nicholas Weiler at Stanford's Wu Tsai Neurosciences Institute.

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