00:38:40 · Aditya Agarwal, Sergey Levine
[00:38:40] S1: Yeah. It is always surprising to me how that meta observation ends up being just a key part of, like, great great companies early on, which is that it's not that everyone is a generalist. It's that everyone actually shows up with, like, something that they're world class at, but then they don't constrain themselves to just the thing that they are world class at. There's both a curiosity, but then also just willingness to kind of go out and learn about the whole stack. And it's a weird mixture, and you have to strike a balance because... You know? But I think great early... Great founding teams often are able to find a great equilibrium on that. [00:39:17] S0: Yeah. One one thing that, like, really stands out to me in my memory, which kind of made me think like, oh, man, I'm really happy about how this company functions, is that we have a a person on our hardware team who works on, you know, very low level hardware stuff, like, you know, actuator sensors, that sort of thing. And he was giving a talk, kind of like a lunch tech talk type type of thing. And I could see that a disproportionate number of the people that showed up to the talk are the ones that are furthest away from the hardware, like, you know, the ML infrastructure, like folks that are, you know, doing the really esoteric reinforcement learning algorithms. Like, those are the people that most wanted to learn about this thing because they have this burning desire to understand the entire stack.
00:39:17 · Sergey Levine, Aditya Agarwal
[00:39:17] S0: Yeah. One one thing that, like, really stands out to me in my memory, which kind of made me think like, oh, man, I'm really happy about how this company functions, is that we have a a person on our hardware team who works on, you know, very low level hardware stuff, like, you know, actuator sensors, that sort of thing. And he was giving a talk, kind of like a lunch tech talk type type of thing. And I could see that a disproportionate number of the people that showed up to the talk are the ones that are furthest away from the hardware, like, you know, the ML infrastructure, like folks that are, you know, doing the really esoteric reinforcement learning algorithms. Like, those are the people that most wanted to learn about this thing because they have this burning desire to understand the entire stack. [00:39:52] S1: That's cool. I'm gonna give you some rapid fire questions. What are the tasks that surprised you most when a robot did it? [00:39:58] S0: Using a plastic bag to pick up simulated dog poop. [00:40:04] S1: That's good. Humanoid or wheelbase with two arms for the first form factor for the home? [00:40:11] S0: Wheelbase because then it's not gonna fall down and injure your dog. [00:40:16] S1: Or child. But, yes. One paper from the last year that is not yours that the room should read. There
00:40:16 · Aditya Agarwal, Sergey Levine
[00:40:16] S1: Or child. But, yes. One paper from the last year that is not yours that the room should read. There [00:40:27] S0: is a paper called Neural Thickets from Esolo's group. I unfortunately forget the name of the first author, which has a really interesting insight that as models get bigger, random perturbations to their weights are actually still very good, whereas if they're small, random are not good, and this can be seen as an explanation for why RL works that is like sort of a slightly subversive explanation. Maybe RL works because it just adds random noise rather than doing anything smart. [00:40:53] S1: And this is not specific to robotics, right, if [00:40:55] S0: I remember correctly? Not It's specific to robotics, but I I I think that it will... It's very interesting to me because I care a lot about reinforcement learning because I think it's essential to make robots work. So anything that sort of tells us more about why reinforcement learning works, even if it's a little subversive, is very valuable to me. Yeah. That was a good paper. [00:41:11] S1: One researcher under 30 this room should be paying attention to. It's a tricky one. That's an ageist question. Yeah. One Ageist question. One early career researcher. [00:41:24] S0: See, the the trouble is that since I'm a professor, I kinda have to, like, name one of my students. [00:41:29] S1: Go for it. Go for it. This is why, you know, you get the stage of exam. [00:41:31] S0: I I have a compulsion that [00:41:34] S1: I have work with.
00:41:34 · Aditya Agarwal, Sergey Levine
[00:41:34] S1: I have work with. [00:41:35] S0: The people I'm responsible for. I actually think that my former student, Abhishek Gupta, who's now at University of Washington, is doing some really fantastic and creative work. And I think that, you know, in academic research, we should really be paying a lot of attention to folks that are unafraid to step outside of the mainstream. So if you haven't seen any of Abhishek's research, I would encourage you to check it out. His work on universal world models is particularly interesting. [00:42:01] S1: I think that's all we have time for. Thank you all for coming. Sergey, that was awesome. I feel as though that we end up getting a wide variety of talks here, but I feel as though the compression of intelligence in that one hour was very high. So thank you. Thank you for the time, and you're always welcome back to SPCO. Thank you so much. Thank you.