# Yann LeCun 谈 LLM 智能瓶颈：语言是世界的降维描述，感官数据远超文本

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-06 14:04
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmr8tky2p00irsllsksd6rt8o
- 原文链接：https://x.com/rohanpaul_ai/status/2074011687024517141

## AI 摘要

Yann LeCun 在 Bloomberg 采访中指出，LLM 的智能受限于语言本身：语言是对世界的近似降维描述，LLM 只能处理离散符号序列。最大 LLM 预训练约 20 万亿词（30 万亿 token），但相当于 4 岁儿童 4 年通过视觉获取的数据量，文本需 40 万年才能读完。感官输入数据远超语言，孩子无需大量阅读就能理解物理世界，而 LLM 因缺乏感官反馈无法建立真实世界模型，体现 Moravec 悖论。另引用 Anthropic CEO Dario Amodei 观点：1 亿词上下文窗口已可实现，推理支持是唯一瓶颈，AI 模型可在上下文窗口内学习而不改变权重。

## 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...