# Google 论文：AI 的工程严谨过剩，科学严谨不足

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-21 03:29
- AIHOT 分数：35
- AIHOT 链接：https://aihot.virxact.com/items/cmrtn23gv0fvwbihz9nb5dl8x
- 原文链接：https://x.com/rohanpaul_ai/status/2079287653594546228

## AI 摘要

Google 新论文指出，AI 领域的主要问题不是严谨过多或过少，而是工程严谨过剩、科学与哲学严谨不足。论文定义了三种严谨：概念严谨（如“智能”是否指单一特质）、知识严谨和现实世界性能严谨。这种失衡导致 AI 能力快速提升，但失败预测能力却在恶化。

## 正文

A very interesting Google paper.

The main problem with AI is not too little or too much rigor. It has too much engineering rigor， and not enough scientific and philosophical rigor.

It's proven that engineering can work before science can explain it. But AI's rigour isn't balanced， so it's hard to know when it'll fail.

Modern AI is quite demanding on performance but not so confident on explanation and prediction.

The paper identifies three types of rigor： clear ideas， reliable knowledge and reliable performance in the real world.

Conceptual rigor asks whether terms like "smarts" and "understanding" refer to one trait or a cluster of related skills.

They discuss this framework with respect to debates about intelligence， reproducibility， prediction， explanation， benchmarks and already deployed systems.

For science to be rigorous， the results must be able to hold up under new conditions， predict what will happen in the future and explain why something worked or didn't work.

Benchmarks， post-training， tools， monitoring， safety checks： they can all make systems better even without a full theory. This is engineering rigor.

This is why capabilities are rapidly advancing while failure predictions are worsening.

---

- arxiv. org/abs/2607.03634

Title： "The Role of Rigor in AI"