Sergey Levine on the bottlenecks to generalization in RL and picking good research problems

Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In this episode, we talked about the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems. Blog: generallyintelligent.com/podcast/2023-03-01-podcast-episode-28-sergey-levine/ Spotify: open.spotify.com/episode/25aWr3OsE3fVNEw6cEhoB4 RSS: anchor.fm/s/42cab330/podcast/rss About Generally Intelligent We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one. We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research. Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research. LinkedIn: linkedin.com/company/generallyintelligent/ Twitter: @genintelligent Spotify: Generally Intelligent