Physical Intelligence, every mention
21 scenes · ← back to Physical Intelligence
tap a year for its mentions
every year anyone Quan Vuong 19Chelsea Finn 7Garry Tan 6Harj Taggar 3Diana Hu 1Charu Thomas 1Bob McGrew 1
Verbatim, from the transcripts: the passages where Physical Intelligence comes up
World Models, JEPA And The Path To Sample-Efficient RL · Y Combinator
- ▶ 46:05 unnamed speaker So if you're, if you're like a humanoid company, like figure or pie or whatever, again, same S T A R setup.
The GPT Moment for Robotics Is Here · Y Combinator
- ▶ 0:49 Garry Tan He's one of the co-founders of Physical Intelligence, which we think might be the robotics AI lab that brings about the GPT-I moment for all of robotics.
- ▶ 2:54 Diana Hu Walk us through the seminal papers that a lot of the team of PI Robotics published that gave you the inkling that the GPT-I moment is near, and that started in twenty-twenty-four.
- ▶ 12:19 Quan Vuong So I think we were doing kind of like an inventory of robot in the company. 2 times in the scene
- ▶ 15:44 Quan Vuong The context is that Pi 4 times in the scene
- ▶ 17:18 Garry Tan And I think they were very inspired by physical intelligence's first demos with, um, with laundry folding.
- ▶ 18:50 Quan Vuong When we first published PI Zero, people thought of us as the laundry company. 4 times in the scene
- ▶ 23:52 Quan Vuong People are often really surprised when I tell them that almost all of the robot evaluation that we run at Pi today, including the really 2 times in the scene
- ▶ 28:09 Quan Vuong Like, I intentionally don't ask them this question to understand whether it's possible for an organization like Pi to parachute into their existing system and to work really closely with them on the thing that actually matters to get the… 3 times in the scene
- ▶ 29:39 Garry Tan And load pie, and you're off and running in like a day. 3 times in the scene
- ▶ 36:31 Quan Vuong You know, for Pi, if we talk about why Pi is going to fail, it's probably going to be because the problem is just way too hard.
- ▶ 38:50 Harj Taggar Like, um, how did the company get started? 3 times in the scene
- ▶ 41:49 Quan Vuong Um, one of the really surprising thing that we learned when we started the company is that the infrastructure for supporting large-scale 3 times in the scene
- ▶ 48:10 Garry Tan Thank you for making physical intelligence.
Chelsea Finn: Building Robots That Can Do Anything · Y Combinator
- ▶ 0:57 Chelsea Finn I co-founded a company called Physical Intelligence that's trying to solve this problem.
- ▶ 3:30 Chelsea Finn And so at physical intelligence, we've been, um, this is an example of a data episode
- ▶ 5:35 Chelsea Finn I was personally actually working quite a bit, um, on this laundry folding robot along with, uh, Michael and Suraj, uh, and of course supported, uh, and with contributions from the whole physical intelligence team. 2 times in the scene
- ▶ 11:03 Chelsea Finn Now, at this point, we were still training models largely, um, kind of, we were pre-training, ah, and fine-tuning only on laundry data, and we weren't leveraging, ah, kind of, pre-trained models in the community, and there were some folks…
- ▶ 31:08 Chelsea Finn I'd also like to mention that at physical intelligence, we're hiring a number of roles. 2 times in the scene
- ▶ 39:07 Charu Thomas Um, when you think about how software and hardware have, are going to continue to evolve, what are the biggest opportunities for builders today for your vision of physical intelligence?
Bob McGrew: AI Agents And The Path To AGI · Y Combinator
- ▶ 28:44 Bob McGrew Um, but if you look at companies like skilled AI or physical intelligence,