What happens when AI starts building itself? | TechCrunch

"Our unique approach is to use open-endedness to get to recursive self-improvement, which no one has yet achieved. It's an elusive goal for a lot of people. A lot of people already assume it happens when you just do auto-research. You know, you can take AI and ask it to make some other thing better, which could be a machine learning system, or just a letter that you write, or, you know, whatever it might be, right? But that's not recursive self-improvement. That's just improvement."

"Our main focus, is to build truly recursive, self-improving superintelligence at scale, which means that the entire process of ideation, implementation and validation of research ideas would be automatic."

"First [it would automate] AI research ideas, eventually any kind of research ideas, even eventually in the physical domains."

Recursive Superintelligence, a San Francisco startup, emerged from stealth with $650 million in funding and is led by Richard Socher alongside prominent AI researchers. The goal is a recursively self-improving AI model that can identify its own weaknesses and redesign itself to fix them without human involvement. The approach emphasizes open-endedness to reach recursive self-improvement, which is distinguished from ordinary improvement tasks like asking an AI to make something better. The plan is to automate the full research loop, including ideation, implementation, and validation, at scale. The automation is intended to extend from AI research ideas to broader research domains, potentially including physical domains.

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