Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: What is the good argument against? [00:00:00] S1: My best attempt at steelmanning this is that if you want something reliable in a really complicated open world setting, then you can't afford not to use what you already know about the physical world. And we've got textbooks full of this stuff, so why don't we just plug in what we know from the textbooks? [00:00:00] S0: What is compositional learning? Can you describe that? [00:00:00] S1: One of my students, he had this idea where he asked a language model to provide a recipe for how to make a sandwich in International Phonetic Alphabet. International phonetic alphabet is these symbols that they use in a dictionary to explain how to pronounce a word. And it's very peculiar because it only ever appears for individual words in a dictionary. You never see freeform text written in International Phonetic Alphabet. But if you ask a good language model, it will write paragraphs in IPA for you. And that is compositional generalization. It means that you have never seen this particular language, this particular alphabet used to write paragraphs, but you understand paragraphs, you understand that it's compositional with different alphabets, so you can solve the problem. You can imagine the same thing coming up in robotics, that you've learned a repertoire of skills, and now you can combine and mix those skills and apply them to solve new problems. [00:00:00] S0: It makes me wonder what the last type of tasks you think will be possible for a robotic system to achieve.
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: It makes me wonder what the last type of tasks you think will be possible for a robotic system to achieve. [00:00:00] S1: I think changing a child's diaper will be really, really hard. This really is just Moravec's paradox all over again, that people are extremely good at certain things. We're very good at physical things. We're also very good at interacting with other people. And that makes sense. We have to be. That's a lot of our existence. So things that involve behaviors, interacting with other people, where you have to help somebody – I think that's a lot harder than people appreciate. Elderly care, taking care of small children. I think those things are going to be hard, and they're probably going to be harder than people think. [00:00:00] S0: And the stakes are very high. [00:00:00] S1: It's not just that. The stakes are high in many places. It's just that it's probably the pinnacle of something that fools us into thinking that it's easier than it really is. We are so evolved for interacting with people and doing things physically. If you're helping somebody get up the stairs or get out of bed, you don't have to think very carefully about how you're going to do that. So I think it's really the pinnacle of Moravec's paradox.
· Sergey Levine, Patrick O'Shaughnessy
[00:00:00] S1: It's not just that. The stakes are high in many places. It's just that it's probably the pinnacle of something that fools us into thinking that it's easier than it really is. We are so evolved for interacting with people and doing things physically. If you're helping somebody get up the stairs or get out of bed, you don't have to think very carefully about how you're going to do that. So I think it's really the pinnacle of Moravec's paradox. [00:00:00] S0: If I think about an LLM as a brain, and now it's effectively studied everything – I don't know how else to put it. And then I think about a robotics model's brain instead. What are the dark parts of the brain? What has it not been able to study? What are the areas that have just been really difficult that matter but have been hard for us to get into?
· Sergey Levine
[00:00:00] S1: One of the things that people are remarkably good at is using physical analogies to understand other situations. I don't know whether this is something that LLMs can or can't do, but it is something that people use a lot. They use it in everyday life, and they also use it for very sophisticated problems. So for example you could say, that company has a lot of momentum. That's a physical analogy. You know exactly what it means. I don't have to explain that statement to you. But if you actually think about that, this is quite a complex thing. There's a lot riding on that word momentum. There is an interview with Richard Feynman where he talks about teaching, but he talks about analogies that he makes in regard to subatomic particles. And he says, we use the word spin. The thing is not really spinning – it's not like a spinning top. But all those analogies help us make sense of it. And not just in a way that allows explaining concepts, but it actually leads to conclusions. It actually leads to inferences, and those inferences actually make sense. We're so primed to interact with the physical world, so primed to have physical intelligence that you can use it in everyday speech by saying that company has a lot of momentum, and you can use it when advancing fundamental theoretical physics. That's kind of remarkable. I don't know if LLMs can do that. Maybe they can, but I think that really understanding physical interactions, causal structures, all that kind of stuff – there is something special about that. And it's clearly something that people get a lot of mileage out of.
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: I'd love to talk about the role of researchers and the actual people doing the research. In LLM world, it's fairly shocking how few people are, at the global scale, responsible for basically all the progress in LLMs. Someone like Ilya is an example. What is that like in robotics? How many people in the world are truly impacting this trajectory? And then I want to ask what good research means. [00:00:00] S1: I think those questions are often very hard to answer about science because I think that we sometimes have a tendency – especially when we look at history – to underline particular milestones. And certainly in machine learning, this is the case. AlexNet was a big step forward. That's true. But I think it's also important to remember that these advances, they happen because lots of people are trying lots of things, and even some of the failures are actually very instructive. I complained before a little bit in a low-key way about the controversy around end-to-end robotic learning, but I don't know if robotic learning would have advanced the same way if it were not for the controversy, so to speak. It is true that you can look through the list of successes and mark them down like, "Oh, these folks have a history of repeatedly hitting home runs." But I think in reality, in the scientific community, it's not just the home runs that are responsible for progress. And even some of the failures and even some of the bad ideas are very instructive in pushing towards the good ideas.
· Sergey Levine, Patrick O'Shaughnessy
[00:00:00] S1: I think those questions are often very hard to answer about science because I think that we sometimes have a tendency – especially when we look at history – to underline particular milestones. And certainly in machine learning, this is the case. AlexNet was a big step forward. That's true. But I think it's also important to remember that these advances, they happen because lots of people are trying lots of things, and even some of the failures are actually very instructive. I complained before a little bit in a low-key way about the controversy around end-to-end robotic learning, but I don't know if robotic learning would have advanced the same way if it were not for the controversy, so to speak. It is true that you can look through the list of successes and mark them down like, "Oh, these folks have a history of repeatedly hitting home runs." But I think in reality, in the scientific community, it's not just the home runs that are responsible for progress. And even some of the failures and even some of the bad ideas are very instructive in pushing towards the good ideas. [00:00:00] S0: It's fascinating to think about. The example you gave before is so interesting, where the research insight was like, just give it some coaching, and it gets better. It seems like that sort of insight can be very powerful and high leverage, which makes me wonder, what have you learned about what makes for a great researcher?