1 hr 45 min

Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms Generally Intelligent

    • Technology

Ben Eysenbach is a PhD student from CMU and a student researcher at Google Brain. He is co-advised by Sergey Levine and Ruslan Salakhutdinov and his research focuses on developing RL algorithms that get state-of-the-art performance while being more simple, scalable, and robust. Recent problems he’s tackled include long horizon reasoning, exploration, and representation learning. In this episode, we discuss designing simpler and more principled RL algorithms, and much more.


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.  



Learn more about us

Website: https://generallyintelligent.com/

LinkedIn: linkedin.com/company/generallyintelligent/ 

Twitter: @genintelligent

Ben Eysenbach is a PhD student from CMU and a student researcher at Google Brain. He is co-advised by Sergey Levine and Ruslan Salakhutdinov and his research focuses on developing RL algorithms that get state-of-the-art performance while being more simple, scalable, and robust. Recent problems he’s tackled include long horizon reasoning, exploration, and representation learning. In this episode, we discuss designing simpler and more principled RL algorithms, and much more.


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.  



Learn more about us

Website: https://generallyintelligent.com/

LinkedIn: linkedin.com/company/generallyintelligent/ 

Twitter: @genintelligent

1 hr 45 min

Top Podcasts In Technology

Lex Fridman Podcast
Lex Fridman
Go Time: Golang, Software Engineering
Changelog Media
Dwarkesh Podcast
Dwarkesh Patel
Today in iOS  - The Unofficial iPhone, iPad, and Apple Watch Podcast
Rob @ podCast411 and Part of the podcast411network
How AI Is Built 🛠
Nicolay Gerold
Software Misadventures
Ronak Nathani, Guang Yang