00:18:14 · Aditya Agarwal
[00:18:14] S1: You know, one of the kind of the classic question that I get from people outside of Silicon Valley is, why are AI researchers so highly paid? And the answer is pretty simple. There's just not that many of them, Right? Like, relative to kind of like the impact of these foundation models is the basic supply demand problem. So I'm curious how many great robotics researchers do you think there are in the world right now that could have that level of attention to detail? And, you know, could like... Is it like a few 100? If there's just not that many people who have done this. Right?
00:18:47 · Sergey Levine, Aditya Agarwal
[00:18:47] S0: It... It's a tricky question because I think there's a big difference between the... You know, saying, like, how many people have a robotics PhD from a top university university and, like, a publication record and the willingness to get stuff to actually work and have that attention to detail as opposed to how many people Okay. Yeah, have the the mindset necessary to get this right. And there's a big difference. Like, you know, there are certainly people that have strong publication record, PhD from top school, but just don't have quite the mindset to go to work. And on the flip side, there are people that have the right mindset, don't have the credentials, but have the ability to get there, you know, in the right environment. So it's a very hard question to answer, and, you know, probably the main lesson for me is, like, not to prejudge these things and to really pay attention to how somebody approaches the problem, what is their, you know, their actual thought process, and what is their, I guess, like, life philosophy for lack of another word. [00:19:39] S1: So I guess maybe to to kinda put you on the spot here, like, do you think it is possible to get hired at Physical Intelligence as a robotics researcher without having a great publication record, but having a great mindset? It's like, listen. I I am a great software engineer. I care about this. I can learn. I have attention to detail. I've kinda done, you know, kind of at least the basics of how all these models work. Do you think that's, like, do you think that's a possibility?
00:19:39 · Aditya Agarwal, Sergey Levine
[00:19:39] S1: So I guess maybe to to kinda put you on the spot here, like, do you think it is possible to get hired at Physical Intelligence as a robotics researcher without having a great publication record, but having a great mindset? It's like, listen. I I am a great software engineer. I care about this. I can learn. I have attention to detail. I've kinda done, you know, kind of at least the basics of how all these models work. Do you think that's, like, do you think that's a possibility? [00:20:02] S0: Yeah. Absolutely. I think the biggest challenge with that is is the the signaling aspect of it. Right? That in any such situation, if you want a job, the other party needs to have some way to evaluate [00:20:14] S1: Yes.
00:20:14 · Aditya Agarwal, Sergey Levine
[00:20:14] S1: Yes. [00:20:15] S0: Yes. And that's very, very hard. And, you know, I I I wish there there was a way to to do that better, but, you know, certainly, the most important thing is not the the the PhD or the credentials. It's the right mindset, the willingness to learn things, to admit that there's some things that you don't know, and most importantly in robotics, the willingness to have the attention to detail. And the attention to detail at at many levels in the stack, it's like easy to say, but it's hard to do because people, especially smart people with an engineering mindset, often have a natural tendency to specialize. And that is a bit antithetical to having that attention to detail to realize that, like, the reason your model is not running is not because you don't have the right learning rate. It's not because you didn't use the right dataset. It's because you ran on a robot where the fingers have a little gap between them when you command them to close fully because somebody didn't attach the connector correctly. So you have to go there, plug in the connector properly, test it yourself, and then go back to training your models.
00:21:10 · Aditya Agarwal, Sergey Levine
[00:21:10] S1: Maybe that's a good segue. Right? Kind of the common concern slash complaint that you hear about robotics is that the training data is too high dimensional to kind of be able to collect the amount of data that you need in sufficient kind of quantity to be able to train these models. And you're already kind of essentially starting from on the back foot because you don't have kind of you know, existing repository of data much like, you know, we had for text. So help us kind of shape this. Right? Like, what what are your latest views on either the scarcity or perhaps actually, like, you know, relative abundance of data for robotics. Right? Like, how should one think about kind of that general thought? [00:21:58] S0: So I'll I'll give maybe, like, a frustratingly big picture answer to this. [00:22:02] S1: I'll just Go from big... Yeah. Please.
00:22:03 · Sergey Levine
[00:22:03] S0: The the universe is really big, and almost none of the universe is inside computers. Almost none of it is on the Internet. The entirety of the universe, effectively up to a rounding error, is the physical world, which means that if you want a machine that understands the universe, that understands physical phenomena, it has to be a robot. There's no other way to get that information. You will not find it on the Internet because it is not there, because the Internet is very small and the universe is very big. Now, if we bring that big picture principle down to something a little bit more concrete, insofar as robots become useful, insofar as anybody wants a robot around them, well, there's probably gonna be lots of robots. Like, you know, cars are useful, and there's lots of cars. Robots become useful, there'll be lots of robots. When there are lots of robots, they are all collecting data. They are trying things, succeeding, failing Sure. Recording what's going on around them. So even if we step back from the... From how big the universe is and just consider, you know, economically relevant work that somebody wants done, if some fraction of that is done by robots, the data coming from robots, dwarfs everything else. Like, it's not even a close call. It's it's just enormous. Now, you can backpedal from that even more. You could say, well, okay, maybe they're not so useful. There's billions of them, but there's only nearly thousands. And you can do that calculation. You still end up with robots at any kind of commercial scale producing enormous amounts of data. So now that's that's very, very good news. That means that the sort of asymptotic picture for this Yeah. Looks extremely good.