书籍、文章、播客都只是大脑的训练语料。 大模型不因为读完了整个互联网而变得聪明，而是因为在海量token中涌现了注意力的远程依赖。
人也一样：一本书翻到第三页就放下，没关系。
但如果那三页，让你大脑里两个从未对话的区域突然通了电。
这就是一次有效的参数更新，也就是我们的学习收获。
6 Replies
11 Retweets
76 Likes
16.4K Views 
One Sentence Summary
By drawing an analogy between LLM attention mechanisms and human learning, the author proposes that the essence of effective learning is to create connections between brain regions that have never communicated before.
Summary
The author presents a profound analogy: LLMs don't become intelligent by reading the entire internet, but because long-range dependencies in attention emerge from massive tokens. Extending to human learning, the author argues that the quantity of books read matters less than whether certain pages can suddenly create connections between two brain regions that have never communicated. This constitutes an effective parameter update—the learning gain. This insightful analogy cleverly links AI training mechanisms with human cognitive learning, offering a new criterion for judging learning effectiveness.
AI Score
88
Influence Score 20
Published At Yesterday
Language
Chinese
Tags
LLM
Attention Mechanism
Learning Cognition
Parameter Update
AI Analogy