Sergey Levine - Building LLMs for the Physical World - [Invest Like the Best, EP.465]
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Patrick O'Shaughnessy is the CEO of PositiveSum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of PositiveSum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of PositiveSum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.
My guest today is Sergei Levine, one of the co founders and researchers at Physical Intelligence. As a disclaimer, I'm an investor in physical intelligence because I believe it's one of the most important companies tackling the problem of robotics. As you hear us discuss today, robotics has what I would call a scarecrow problem. All of these amazing physical devices are becoming ever more possible in all sorts of cool permutations, but what they all really need is an intelligence, a brain, and that is what they're developing at physical intelligence. They're trying to develop foundation models
Fundamentally, the goal of physical intelligence is to develop robotic foundation models that can control basically anybody's system to do any task. Broadly speaking, you could imagine that in the same way that a language model is kind of rapidly evolving towards a system that can do any task that can be expressed in language, what we would like is to build a new class of models that can do any task that can be done by a physical actuated device. Part of the thesis of this company is that we believe that doing it at the full level of generality might actually in the long run be easier than try
That may not be obvious why you would make that bet versus a robot that just does your dishes or something. What are the key trade offs to understand and why make the decision that he made? In the world
of natural language, we saw that there were a lot of efforts to develop domain specific solutions that tackled specific problems. Somebody would spend a lot of time thinking about how like English differs from French and then build a machine translation system. The reason that language models took over for all of those different application domains is because they can leverage much broader sources of data. It's not even as simple as saying like, we have this data for this application, this data for this application, but like merge everything. It's actually more than that. It's when you can lev
What is the hardest part about building in this way for you when you see other approaches that are more maybe legible to the average person? Oh, there's a robot moving around doing this one specific thing. It looks a certain way. What's the hardest part about this approach as you're doing it?
I think this has actually been kind of an issue in my whole career because when you work on robotic learning, the more general the more that this becomes important is that effective robotic learning, effective generalization isn't actually the optimal way to have like a really exciting demo
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