
Jim Fan, Director and Distinguished Research Scientist at NVIDIA, joins Radical Partner Molly Welch to explore what's holding back embodied AI and robotics. While large language models have advanced rapidly, Jim explains why progress in the physical world depends less on ever-larger models and more on solving fundamental problems: exploration-driven learning, scalable data pipelines, and synthetic simulation. Drawing on his journey from OpenAI to leading NVIDIA's Project GR00T, they discuss the robotics data bottleneck, why foundation models matter, and what must happen before robotics reaches its "GPT-4 moment."
Podzilla Summary coming soon
Sign up to get notified when the full AI-powered summary is ready.
Free forever for up to 3 podcasts. No credit card required.
Free AI-powered recaps of Radical Talks and your other favorite podcasts, delivered to your inbox.
Free forever for up to 3 podcasts. No credit card required.