# Yann LeCun 谈 LLM 真实世界智能的局限

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-06 14:03
- AIHOT 分数：45
- AIHOT 链接：https://aihot.virxact.com/items/cmr8tky2p00issllsyquuahe8
- 原文链接：https://x.com/rohanpaul_ai/status/2074011224677953993

## AI 摘要

Yann LeCun 在 Bloomberg 采访中指出，语言只是对世界的粗略简化描述，LLM 仅能处理离散符号，而现实世界远比语言复杂。最大 LLM 预训练于约 20 万亿词（30 万亿 token），相当于 4 岁孩子四年视觉接收的数据量，但读完这些文本需 40 万年。孩子通过视觉、触觉、运动获得密集反馈，LLM 虽能流畅谈论物理，却对玻璃的脆弱度无原生感知。Moravec's paradox 揭示这一反转。引用 Anthropic CEO Dario Amodei：1 亿词上下文窗口已可实现，推理支持是唯一瓶颈，且模型可在上下文窗口中“学习”而不改变权重。

## 正文

During a Bloomberg interview, Yann LeCun (@ylecun ) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg interview.

"Language is a very approximate, reduced, quantized, and simplified description of the world, and LLMs can only deal with discrete sequences of symbols. The world is much more complicated than language.

The biggest LLMs are pre-trained on the totality of all the publicly available text on the internet. That’s about 20 trillion words, or 30 trillion tokens.

A token is about 3 bytes. So total 10¹⁴ bytes of text.

This is the amount of data a four-year-old has seen through vision during four years. Now, the text, though, would take 400,000 years to read?

So, there is enormously more data from sensory input, like vision, touch, and everything else, than there could ever be through language."

A child does not need 400,000 years of reading to understand cups, doors, balance, faces, falls, or heat, because the body is already collecting dense feedback from vision, touch, motion, and consequence.

Text strips most of that away.

It turns a living scene into symbols, then asks the model to infer the missing world from traces left by people describing it.

That is why an LLM can sound fluent about physics and still have no native sense of how fragile glass feels in a hand.

Moravec’s paradox names this reversal: the things humans find intellectual can be easier for machines than the things toddlers do without applause.

The hard part is not producing an answer, but building a model of the world that survives contact with weight, friction, surprise, and failure.

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Link to the full video on Bloomberg's site. Link in comment.

### 引用推文

> Rohan Paul："100 million words context window is already possible, which is roughly what a human hears in a lifetime. Inference support is the only bottleneck to achieve it...