Hassabis 点出了当前大语言模型的核心天花板——文本无法替代真实体验，做 AI 研究或关注 AGI 路径的人值得细读，看完会对世界模型的价值有更深理解。
DeepMind 创始人 Demis Hassabis 指出当前 AI 的局限：语言可以描述世界，但无法包含世界。语言模型从文本中意外学到了大量现实结构，但文本只是经验的压缩残渣，而非经验本身。世界由需要亲身经历、触摸、预测、违反和修复的约束构成，而非仅由可命名的事实组成。Hassabis 认为世界模型旨在学习物理现实的隐藏语法——物体如何持续、力如何展开、空间如何变化、行动如何产生反馈。他强调，智能不仅是回答得好，更是知道如果你移动、伸手、推、闻、滑倒或失败，接下来会发生什么。
Demis Hassabis on the limit in today’s AI: languag…
Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion".
Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect.
But text is still a compressed residue of experience, not experience itself.
A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them.
The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired.
That is why world models matter.
They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback.
Language models can often reason about the world because people have written so much about it.
World models try to learn what the world is like before it becomes words.
The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed.
A mind trained only on descriptions may become brilliant at explanation.
A mind trained on experience may become better at consequence.
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Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)