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AI can help scientists write code, analyze data, and draft papers. But can it generate the ideas that lead to new discoveries? @RichardSocher, former chief scientist at Salesforce, founder of @youdotcom, and co-founder of @Recursive_SI, joins us to explore the move from AI assisting researchers to AI conducting research. Starting with AI improving AI, we examine what it takes to propose ideas, run experiments, and validate results, and how those capabilities could eventually accelerate discovery across biology, chemistry, and physics.
The conversation covers Recursive’s reported results in model training and GPU kernel optimization, why fast verification matters, and how reward hacking complicates the search for real improvements. We also discuss the data and experimental infrastructure needed for broader scientific discovery, whether commercial incentives can keep increasingly capable systems aligned, and Richard’s vision for a “Eureka machine.”
🗒️ Full show notes: twimlai.com/go/783.
📖 CHAPTERS
===============================
00:00 - Intro - Richard Socher and the Eureka Machine
03:11 - AI for Science Examples across Physics, Chemistry, and Biology
05:47 - Organoids, Virtual Cells, and Drug Repurposing Case Studies
10:16 - Evaluating AI for Science Timelines, Hype, and Physical Bottlenecks
13:47 - Four Pillars of the Eureka Machine and AI Scientist Agents
16:37 - Open-Endedness and Environments for Scientific Ideation
21:04 - Guided Search, Reward Engineering, and Reward Hacking
26:02 - Can AI Truly Innovate? Recursive Self-Improvement in Practice
29:25 - Recursive’s Reported Results on NanoChat, NanoGPT, and CUDA Kernels
33:02 - Verification Speed, Long-Horizon Attribution, and System Generalizability
37:49 - Scaling Compute, Biological Data, and Mitigating Reward Hacks
40:11 - Commercial Incentives and AI Alignment
41:46 - AI Pacing Debates and Regulating Applications vs. Intelligence
44:29 - Dual-Use Science Risks, Accountability, and the Case for Progress
The conversation covers Recursive’s reported results in model training and GPU kernel optimization, why fast verification matters, and how reward hacking complicates the search for real improvements. We also discuss the data and experimental infrastructure needed for broader scientific discovery, whether commercial incentives can keep increasingly capable systems aligned, and Richard’s vision for a “Eureka machine.”
🗒️ Full show notes: twimlai.com/go/783.
📖 CHAPTERS
===============================
00:00 - Intro - Richard Socher and the Eureka Machine
03:11 - AI for Science Examples across Physics, Chemistry, and Biology
05:47 - Organoids, Virtual Cells, and Drug Repurposing Case Studies
10:16 - Evaluating AI for Science Timelines, Hype, and Physical Bottlenecks
13:47 - Four Pillars of the Eureka Machine and AI Scientist Agents
16:37 - Open-Endedness and Environments for Scientific Ideation
21:04 - Guided Search, Reward Engineering, and Reward Hacking
26:02 - Can AI Truly Innovate? Recursive Self-Improvement in Practice
29:25 - Recursive’s Reported Results on NanoChat, NanoGPT, and CUDA Kernels
33:02 - Verification Speed, Long-Horizon Attribution, and System Generalizability
37:49 - Scaling Compute, Biological Data, and Mitigating Reward Hacks
40:11 - Commercial Incentives and AI Alignment
41:46 - AI Pacing Debates and Regulating Applications vs. Intelligence
44:29 - Dual-Use Science Risks, Accountability, and the Case for Progress
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