Introducing Midcentury. We’re building the data and simulation infra for physical AI. Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos. We’re already supporting frontier labs with: → The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks → Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale

A personal update: I've left Berkeley to lead the development of human potential at Build AI. There are a million things that AI will be able to do in the near future. The most important of which is to create a world in which it allows us to become more human.

I’ve left DeepMind to lead research at Build AI Our first bet: scale monocular vision. We haven’t even scratched the surface :D

The WSJ stopped by to learn more about how our models work. Good write-up to understand broadly what a large action model is, how its different from world models, and why they are promising approaches in robotics. It's becoming increasingly clear that ground-truth action data, like the kind found in games, is a key ingredient to scaling robotics pre-training. Link in comments. Check out the policy waving at @pallipau.