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曲线拟合的局限性

📅 2026-04-06 00:21 François Chollet 人工智能 2 分鐘 2379 字 評分: 81
AI 哲学 曲线拟合 因果推理
📌 一句话摘要 这是一条后续论点,指出曲线拟合无法复制科学发现所需的因果推理能力。 📝 详细摘要 作为上一条推文的续篇,此推文指出,虽然曲线拟合可以近似现有的知识,但它缺乏实现真正科学创新所需的“极端泛化”能力,从而强调了统计近似与因果推理之间的区别。 📊 文章信息 AI 评分:81 来源:François Chollet(@fchollet) 作者:François Chollet 分类:人工智能 语言:英文 阅读时间:1 分钟 字数:97 标签: AI 哲学, 曲线拟合, 因果推理 阅读推文
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The Limits of Curve Fitting

The Limits of Curve Fitting

![Image 2: François Chollet](https://www.bestblogs.dev/en/tweets?sourceId=SOURCE_fa42b4ed) ### François Chollet

@fchollet

You can fit a curve to our knowledge of physics, but you can't fit a curve that can do the above.

Apr 5, 2026, 4:21 PM View on X

7 Replies

1 Retweets

60 Likes

6,683 Views ![Image 3: François Chollet](https://www.bestblogs.dev/en/tweets?sourceid=fa42b4ed) François Chollet @fchollet

One Sentence Summary

A follow-up argument stating that curve fitting cannot replicate the causal reasoning required for scientific discovery.

Summary

As a continuation of the previous thread, this tweet asserts that while curve fitting can approximate existing knowledge, it lacks the capability to perform the 'extreme generalization' necessary for true scientific innovation, reinforcing the distinction between statistical approximation and causal reasoning.

AI Score

81

Influence Score 12

Published At Today

Language

English

Tags

AI Philosophy

Curve Fitting

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The Limits of Curve Fitting | BestBlogs.dev

查看原文 → 發佈: 2026-04-06 00:21:18 收錄: 2026-04-06 04:00:51

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