This week on No Priors, Elad speaks with Chelsea Finn, cofounder of Physical Intelligence and currently Associate Professor at Stanford, leading the Intelligence through Learning and Interaction Lab. They dive into how robots learn, the challenges of training AI models for the physical world, and the importance of diverse data in reaching generalizable intelligence. Chelsea explains the evolving landscape of open-source vs. closed-source robotics and where AI models are likely to have the biggest impact first. They also compare the development of robotics to self-driving cars, explore the future of humanoid and non-humanoid robots, and discuss what’s still missing for AI to function effectively in the real world. If you’re curious about the next phase of AI beyond the digital space, this episode is a must-listen.
Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ChelseaFinn
Show Notes:
0:00 Introduction
0:31 Chelsea’s background in robotics
3:10 Physical Intelligence
5:13 Defining their approach and model architecture
7:39 Reaching generalizability and diversifying robot data
9:46 Open source vs. closed source
12:32 Where will PI’s models integrate first?
14:34 Humanoid as a form factor
16:28 Embodied intelligence
17:36 Key turning points in robotics progress
20:05 Hierarchical interactive robot and decision-making
22:21 Choosing data inputs
26:25 Self driving vs robotics market
28:37 Advice to robotics founders
29:24 Observational data and data generation
31:57 Future robotic forms
