Yann LeCun(@ylecun)
1. I never said LLMs were not useful. They are, particularly with all the bells and whistles that ar...
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TL;DR · AI 摘要
Yann LeCun强调了大语言模型(LLMs)的实用性,但也指出它们缺乏对物理世界的理解,无法预见行动后果,这限制了其在构建机器人丰富未来中的作用。
核心要点
- LLMs是有用的,尤其是在添加了许多附加功能后。
- 未来的机器人需要能够理解物理世界并预见行动后果的AI。
- 仅依靠LLM不足以实现非反乌托邦式的未来社会。
结构提纲
按章节快速跳转。
- §引言
Yann LeCun回应关于LLMs实用性的讨论,并提出对未来AI能力的看法。
LeCun承认LLMs的价值,特别是在不断加入新特性的情况下。
强调了AI需要具备理解和预测物理世界的能力以支持机器人技术的发展。
思维导图
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查看大纲文本(无障碍 / 无 JS 友好)
- Yann LeCun on LLMs and AI Future
金句 / Highlights
值得收藏与分享的关键句。
I never said LLMs were not useful. They are, particularly with all the bells and whistles that are being added to them.
A robot-rich future can't be built with AIs that don't understand the physical world and don't anticipate the consequences of their actions.
The future in the cartoon looks pretty dystopian TBH, but even a non-dystopian version will require world models and zero-shot planning abilities.
#AI#LLM#机器人
打开原文- A robot-rich future can't be built with AIs that don't understand the physical world and don't anticipate the consequences of their actions." / X
Yann LeCun on X: "@ben_j_todd 1. I never said LLMs were not useful. They are, particularly with all the bells and whistles that are being added to them. I use them. 2. A robot-rich future can't be built with AIs that don't understand the physical world and don't anticipate the consequences of their actions." / X
Don’t miss what’s happening

- I never said LLMs were not useful. They are, particularly with all the bells and whistles that are being added to them. I use them. 2. A robot-rich future can't be built with AIs that don't understand the physical world and don't anticipate the consequences of their actions. And LLMs really don't. 3. The future in the cartoon looks pretty dystopian TBH, but even a non-dystopian version will require world models and zero-shot planning abilities. 4. I rarely wear a suit and absolutely never wear a tie. 5. I would never ever place a coffee mug on top of a piece equipment. 6. I hope I'll look this young in 2032.
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