No Priors Ep. 48 | With Covariant CEO Peter Chen

(music plays) Hi, listeners. Welcome to another episode of No Priors. This week, I'm joined by Peter Chen, the co-founder and CEO of Covariant, a robotics startup that is developing AI robots. Before he started Covariant, Peter was a research scientist at OpenAI and a researcher at the Berkeley AI Research Lab, where he focused on reinforcement learning, meta-learning, and unsupervised learning. He is a prolific publisher and now a founder. I'm so excited to have you on today to talk about what's, uh, going on in robotics. Welcome, Peter.

Thanks, Sarah. It's great to be here. Um, there, uh, is, there are many exciting reasons to be here. One is I have been a frequent listeners, um, of the podcast, and the second one is just because of the name, like, I just have to be on this show, so it's great to be here.

Right. Let's go establish, uh, some, some priors for everybody, uh, in a very unknown landscape, right? Can we start with just, uh, why you were drawn to robotics and the beginning of your research journey?

Yeah. When I was working on research at both UC Berkeley as part of my PhD, uh, and at OpenAI, there were two topics that were particularly exciting to me. One topic is, like, as you have introduced, unsupervised learning. Like, how can we build models that learn from vast amount of data? And we now more colloquially known this as generative AI because, like, we train this large models on large amount of text, images, videos, uh, and you learn from them in an unsupervised manner. That topic has always been very interesting to me because if you want to train very capable AIs, you want to have a lot of data, uh, and where you can get a lot of data is through this kind of unsupervised dataset. And then the second topic that was really interesting to me was reinforcement learning. Like, it's not just building models that understand, but building models that can make decisions. Um, and reinforcement learning teach these models to make decisions by having them make trials and errors and learn from the better decisions and do less of the worse decisions. And robotics is just a, such a great combination of these fields. Like, in order to build really capable robots, they need to really understand the world in a very, very robust way, and they are not just passive agents that just understand text or what's in an image. They actually need to take actions in the real world, and the consequences do matter. And so we found robotics to be such a great way to both utilize the advances in AI, but also we think of it as a way to also propel AI forward. Like, this is where you get the grounded data. This is where you get that embodied data of not just AI that is trained on browsing the internet, but AI that is trained with physical interactions with the world. And so we also believe robotics would be a key way to advance AI.