Recursive Self-Improvement and the Future of AI

The fundamental idea here is to build AI that improves itself. And I think we're getting there. >> For the first time, we could swap the optimizer as the optimiz. And this leads to a reinvestment of cognitive improvement into itself. >> You can think of RSI at many different levels of the stack, prompts, harness,

weights, and you can go all the way to basically chips. Our goal here is to optimize all the levels of the stack. Our approach to RSI is that we have a multi- aent system working together that's been recursively improved. >> I'm so impressed by your belief that like this idea of a model improving

itself, >> but it's not a belief. >> It is a dramatic statement, which is I think it's the first time I've heard you say it. >> Because we have been so thorough, we have not released things until we're super sure it's correct. >> Do you envision a system where the amount of energy that goes into it

somehow decreases over time relative to the improvement? We can't share too much, but one of the very important things that we've tried to do. >> Welcome both of you uh for joining. We're going to be talking about everybody's favorite topic, which is RSI, recursive self-improvement. Maybe to start, it'd be great to just

introduce yourselves and your backgrounds. Then we'll talk a little bit about what you're working on now, and then we'll dig into the the the content. >> Yeah, I I I think of [clears throat] myself as like um a physicist of some kind. I I studied that. Then I did the PhD in reinforcement learning at the MIT

Media Lab. I really like building things and I'm very excited to be building um a cool startup make a maker with Owen and Grace. My name is Owen. Um I was uh first studying math and then one day I realized that all mathematicians are proving theorems and I was taking a class called formal language and logic