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Demis Hassabis· @demishassabis · X·· 2026-05-02精选AI 评分67
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DeepMind创始人Demis Hassabis在访谈中回顾了从国际象棋神童到获得诺贝尔化学奖的生涯,并探讨了实现AGI仍需解决的关键挑战,如记忆、推理与持续学习。他分析了小模型能力提升、多模态Gemini设计以及推理成本下降的趋势,强调AI已成为科学研究的强大工具,从蛋白质预测扩展到虚拟细胞研究。Hassabis还为创业者提供了前瞻性建议,指出在AGI到来前应关注的方向及AI驱动科学发现的潜力。

推荐理由

Demis 这次没聊虚的,直接把 AGI 的瓶颈摊开讲——记忆、推理、持续学习,每个点都是创业者的机会清单,做 Agents 的可以反复看。

正文 · 原文

Really enjoyed this conversation - thanks again @garrytan for hosting!

引用Y Combinator@ycombinator
Demis Hassabis (@demishassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads @GoogleDeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's @garrytan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives
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来源:Demis Hassabis · x.com