AI researchers debate how close we are to recursive self-improvement
New episode with John Schulman, Beren Millidge and Charlie O’Neill.
I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next.
Watch on YouTube; listen on Apple Podcasts or Spotify.
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Timestamps
(00:00:00) – Steelmanning the case against RSI
(00:18:39) – What’s driving the Chinese labs’ progress
(00:28:06) – How will automated AI researchers be trained
(00:33:51) – Will long-horizon RL elicit AGI?
(00:45:24) – The sim-to-real gap
(01:00:33) – How much progress is explained by data?
(01:18:03) – Why is RL working so well?
(01:24:54) – Move 37 and entropy collapse
(01:28:32) – Rapid-fire timelines
Transcript
00:00:00 – Steelmanning the case against RSI
Dwarkesh Patel
Today, I’m chatting with three of my AI researcher friends from whom I learn a lot every time we talk. They also happen to be at somewhat open-ish labs and companies, so you guys can actually say things on the record. I’m joined by Beren Millidge, who is the CTO of Zyphra, which is developing open source models. John Schulman is the chief scientist at Thinking Machines, previously a co-founder of OpenAI, and led the RLHF work that led to ChatGPT. And Charlie O’Neill is head of model training at Baseten.
The first question I have: If we’re in 2036 and we don’t have billions of crazy superintelligences running around that have radically transformed the world, what is the most likely reason that doesn’t end up being the case? Other than ex
