AI 检测是否会像停机问题一样成为根本不可解的问题？

Could AI detection eventually become a fundamentally unsolvable problem, similar to the halting problem? [D]

AI 导读

有用户在 r/MachineLearning 提出讨论：随着 AI 模型不断进步，AI 生成内容与人类内容可能变得完全无法区分，届时检测的瓶颈将不再是检测器不够强，而是内容本身已不含足以区分的信号，即触及数学层面的根本限制而非技术限制。发帖者认为这不等同于停机问题式的不可判定性，并询问是否存在对应的既有理论框架。

正文

In theoretical computer science, we have the halting problem, which proves that certain problems cannot be solved by any general algorithm.
This made me wonder about something similar in machine learning.
Today, we can often distinguish AI-generated content from human-created content, at least statistically. But as AI models improve, could we eventually reach a point where the two become completely indistinguishable?
Not just because our detectors aren’t advanced enough, but because there is no longer enough information in the content itself to tell them apart, regardless of how powerful the detector is.
In other words, could AI detection eventually hit a fundamental mathematical limit rather than just a technological one?
I know this wouldn’t be the same kind of undecidability as the halting problem, but I’m curious whether there’s an established theoretical framework for this.
What do you think?

submitted by /u/Hope999991
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