当人工智能自我提升 | 理查德·索cher（递归）
When AI Improves Itself | Richard Socher (Recursive)
The MAD Podcast with Matt TurckPodcast2026-09-10
当人工智能开始自我改进，并将这种智能转向科学时，会发生什么？Recursive的AI先驱研究员、CEO和联合创始人Richard Socher与Matt Turck一起探讨他新书《Eureka Machine》背后的愿景。他们讨论了为什么科学进步可能正在放缓，大型语言模型如何学习蛋白质和生物学的隐藏语言，以及模拟、验证和自主实验如何解锁超越人类的人工智能能力。对话涵盖了递归自我改进、AI药物发现和癌症研究、幻觉作为创造力、虚拟细胞、自动驾驶实验室、代理集群、AI经济学家、Recursive的计划，以及构建一个永不停止学习的人工智能科学家所需的计算和数据——这个人工智能科学家最终可能发现人类无法发现的事物。（00:00）简介：自我改进的AI（00:55）为什么科学进步正在放缓（03:08）人类知识的迷宫（05:59）AI能否将科学重新组合？（07:57）LLMs如何学习生物学和蛋白质（10:56）下一个预测作为世界模型（16:44）AI能否产生真正原创的想法？（17:32）模拟、验证和超越人类的人工智能（22:18）递归自我改进的道路（24:49）为什么AI幻觉可以推动发现（27:42）从阅读生物学到编写生物学（31:31）AI能否加速药物发现？（33:31）AI能否帮助治愈癌症？（38:03）生物学、能源和材料领域的AI突破（40:19）某些社会会拒绝AI吗？（45:07）构建AI经济学家（52:22）科学数据瓶颈（53:41）《Eureka Machine》的四个支柱（55:01）教AI现实世界的规则（57:44）模拟和虚拟细胞（1:00:40）自动驾驶机器人实验室（1:02:51）代理集群和开放式发现（1:04:30）计算瓶颈（1:05:44）Recursive内部（1:07:33）Recursive将首先构建什么（1:10:10）我们如何定义智能？（1:11:32）智能能走多远？
原文
What happens when AI begins improving itself, and then turns that intelligence toward science? Richard Socher, pioneering AI researcher and CEO and co-founder of Recursive, joins Matt Turck to explore the vision behind his new book, The Eureka Machine. They discuss why scientific progress may be slowing, how large language models can learn the hidden languages of proteins and biology, and why simulations, verifiers and autonomous experiments could unlock superhuman AI capabilities. The conversation covers recursive self-improvement, AI drug discovery and cancer research, hallucination as creativity, virtual cells, self-driving laboratories, agent swarms, the AI Economist, Recursive’s plans, and the compute and data needed to build an AI scientist that never stops learning—and may eventually discover what humans cannot.(00:00) Intro: AI That Improves Itself(00:55) Why Scientific Progress Is Slowing(03:08) The Labyrinth of Human Knowledge(05:59) Can AI Put Science Back Together?(07:57) How LLMs Learn Biology and Proteins(10:56) Next-Token Prediction as a World Model(16:44) Can AI Generate Truly Original Ideas?(17:32) Simulations, Verifiers and Superhuman AI(22:18) The Path to Recursive Self-Improvement(24:49) Why AI Hallucinations Can Drive Discovery(27:42) From Reading Biology to Writing It(31:31) Can AI Accelerate Drug Discovery?(33:31) Will AI Help Cure Cancer?(38:03) AI Breakthroughs in Biology, Energy and Materials(40:19) Will Some Societies Reject AI?(45:07) Building the AI Economist(52:22) The Scientific Data Bottleneck(53:41) The Four Pillars of the Eureka Machine(55:01) Teaching AI the Rules of Reality(57:44) Simulations and Virtual Cells(1:00:40) Self-Driving Robotic Laboratories(1:02:51) Agent Swarms and Open-Ended Discovery(1:04:30) The Compute Bottleneck(1:05:44) Inside Recursive(1:07:33) What Recursive Will Build First(1:10:10) How Do We Define Intelligence?(1:11:32) How Far Can Intelligence Go?