#331 Sergey Levine: The Robot Revolution Nobody Is Talking About

A lot of startups these days say they're building foundation models for robots. What does that actually mean for a non technicalist? I think every time that people try to take robots out of the factory and into open world environments, they very quickly realize that in the real world, there's a huge range of things that can happen. But since then, there's been this explosion of humanoids, and everyone's talking about humanoids. I mean, did I get it wrong? Do you think that humanoids are much closer to being in the world? Most AI is just speech to text plus a language model. It's full for reading transcripts, not understanding conversations. Velma for Modulate, an AI built on ensemble listening model architecture, specializes in audio analysis. It orchestrates hundreds of smaller sub models purpose built to understand the nuances of voice like tone, timing, stress, and intent. Perfect for fraud defense, deep fake detection, agent attrition prevention, or customer service moderation. Check out the live Velma preview at preview.modulate.ai. That's preview.modulate.ai. To see how the model breaks down audio providing time stamped explainable signals. Stop transcribing. Start listening with modulate.ai. My name is Sergei Levin. I'm one of the founders of physical intelligence. I'm also a professor at UC Berkeley. And, what I work on these days is, algorithms for, reinforcement learning for optimal decision making as well as, applications of robotics. And something that I've been very interested in lately in particular is robotic foundation models. These are general purpose models that control any robot in principle to perform any task. And I think we've seen some, like, pretty sure traumatic transformations in the last few years in the capabilities of these kind of generalist robotic systems where we can use very diverse data sources for many different robotic platforms performing a wide range of different tasks and acquire a kind of general physical understanding from these datasets that then make it much more feasible to rapidly acquire effective and robust and highly generalizable robotic skills. So this is something that I've been very interested in the last few years, and I think it's an area where we see a lot of progress. Yeah. And a lot of startups these days say they're building foundation models, for robots. What does that actually mean for a nontechnical listener? Yeah. This is a it's actually a surprisingly nuanced question because after the success of Chad GPT, you know, the term foundation model became, obviously, very much a buzzword. So Yep. It you know, it's in some cases, it's almost synonymous to saying, like, you know, I have a good model. It's a foundational model. But I think that insofar as there's a consistent definition, it's something like this that, the principle behind language models, vision language models, things like this, is that you can use very large and diverse data sources that are not necessarily of extremely high quality. Like, it might be just data harvested from the web. And …