Y Combinator Startup Podcast
00:39:05 · Chelsea Finn, S1
So the last thing that I'll mention shamelessly is that, we are hiring at Physical Intelligence. So if you're excited about some of the stuff that I talked about, we encourage you to take a look at some of the open roles and apply. And yeah, definitely have time for questions and happy to get all your thoughts. Thanks. [00:39:24] S1: Okay, so the first question is, how far away are we from a ChatGPT moment for robotics and what will that look like? [00:39:31] S0: So I'll start with the second part actually, which is that I'm not sure it will really look like the ChatGPT moment that we saw in language models, which is that with something like ChatGPT, we saw like, it passed like what, a million new users in five days. I think that the distribution channel for physical models is gonna be slower, unfortunately, because you actually need a physical robot there. Mean, I think that we've seen for something like Waymo, it's actually been incredible to see the rollout that they've had, but it still takes time, to actually deploy things on physical devices. So I don't know if we'll have a single moment that has the distribution that ChatGPT had. At the same time, in terms of the capabilities of these models, I think that we are really starting to get to the point where these models are actually useful in the real world. And I think that getting to the kind of the capabilities of ChatGPT, I I think is, yeah, very much on the horizon in the next few years. Cool.
00:39:31 · Chelsea Finn, S1
[00:39:31] S0: So I'll start with the second part actually, which is that I'm not sure it will really look like the ChatGPT moment that we saw in language models, which is that with something like ChatGPT, we saw like, it passed like what, a million new users in five days. I think that the distribution channel for physical models is gonna be slower, unfortunately, because you actually need a physical robot there. Mean, I think that we've seen for something like Waymo, it's actually been incredible to see the rollout that they've had, but it still takes time, to actually deploy things on physical devices. So I don't know if we'll have a single moment that has the distribution that ChatGPT had. At the same time, in terms of the capabilities of these models, I think that we are really starting to get to the point where these models are actually useful in the real world. And I think that getting to the kind of the capabilities of ChatGPT, I I think is, yeah, very much on the horizon in the next few years. Cool. [00:40:31] S1: The second question is when should a small team switch from scaling per site models to a generalist policy and what does that transition actually look like? What signals tell you it's time?
00:40:42 · Chelsea Finn
[00:40:42] S0: So that's a good question. I think that the... I mean, at the very least, I actually think that just starting with the generalist policy and then fine tuning it, even like right off the bat, can be really effective. And we've, And fortunately, a lot of generalist policies, like they're actually really powerful generalist policies that are open source. And so the Pi0 and Pi0.5 models are open source, for example, and we've seen a lot of people, get a lot of use out of those models already. And we're also working with a lot of partners like the tractor company, like Ultra, like Weave, to develop to take our most recent models and get even more juice out of them, even more powerful models for their own applications. So even right off the bat, I think that you can use them. The only scenario in which I actually wouldn't use them is if you're really in a really constrained environment. I've talked to some folks working on surgical robots that are in an Operating Room in the basement with no internet connection and a really bad GPU. And so sometimes it's just really hard to use a larger model, but you still can't do local inference on a workstation with these models. And so I think that, yeah, right away, just taking Pi0.5 or your favorite model and fine tuning it is the way to go. And I think that we'll see lots of these small companies. And I think there's, like, yeah, so much work to do in terms of actually getting these robots to work, with this technology in the real world. Great.
00:42:12 · S1
[00:42:12] S1: The next question is, given how fast robotics is moving in industry, what are the real advantages and drawbacks of doing a PhD today, especially for someone who wants to go into industry afterwards?
00:42:24 · Chelsea Finn
[00:42:24] S0: So I was not planning to do a PhD. I was always planning to go straight to industry. My parents are engineers and worked in industry. And I kind of thought that the way to have impact was to go to a company so forth. And my dad even told me that he wouldn't hire someone with a PhD. And so I was like, maybe I shouldn't get a PhD if I wouldn't be able to get a job. There's also, he's in a different field as well, civil engineering. And so the, but at the same time, I think that like a PhD is an incredible opportunity and I love my PhD. And obviously it depends a lot on the advisor, depends a lot on like what you would be doing and so forth. But I think that PhD is an incredible opportunity to first learn a lot about how to handle uncertainty, how to pick good problems to work on. And I think that basically, in research, no one kind of even gives you the problem to work on. You have to pick the problem, and you don't know for the problem that you picked if it is achievable to make progress on that problem in a six month time span, two year time span, ten year time span. And so you learn about how to deal with that uncertainty. And then the... So that's really useful. And then it also, it also is an opportunity to, like, do amazing research, and do whatever... In many cases, do it, like, have a lot of freedom to work on the research that you find most exciting. And so I think that today is still, yeah, an amazing opportunity to do to work, to learn about uncertainty. I think that learning about uncertainty is really useful in the startup environments, in being at the frontier of AI because we don't know now, like no one knows what the best route is to make these models more and more powerful. At the same time, there's also a lot of incredible opportunities in industry. I think that the first in terms of what goes into developing, like everything that I showed, it's not just the research. There is a whole like software stack that needs to run on the robot, needs to run reliably. There's obviously everything on the hardware side. And then there's also even the machine learning infrastructure, the data infrastructure, and all that. And so even with like... You don't need a PhD necessarily to do a lot of that engineering work. And then on the research side, there's often opportunities to get involved as well.
00:44:44 · Chelsea Finn, S1
And a lot of research is engineering these days as well. And so I think it kinda depends on... Yeah, it's a very personal decision and what you want. Even today, I think I probably, like, kind of retrospectively would want to do a PhD just to learn about how to handle uncertainty to do research, because I really love being at the frontier and thinking about challenging problems. But there's also a lot of really amazing opportunities in both paths. Okay. [00:45:11] S1: The next question is large language models learn from the internet, but robots don't really have an internet scale dataset of physical experience. What's the robotics equivalent and how do we get it?