Earlier quoted context omitted.
They might be trying to talk about Rice's theorem? https://en.wikipedia.org/wiki/Rice%27s_theorem Formally, any non-trivial semantic property of a Turing machine is undecidable. Semantic here (roughly) means "behavioral" questions of the turing machine. E.g. if you only look at the "language" it defines (viewing it as a black box), then it is undecidable to answer any question about that language (including things li…
Rice's theorem is a close corollary, but I did mean the halting problem. Pointing to the halting problem was a bit of a throwaway quip because the "general shape" of it is an easy smell test for whether something is likely to be possible: If you have access to run a transform on data, you can use it to train a model that acts as a detector of whether that transform has been applied to the data. When you have a detect…
This still seems too handwavy. For example, you're implying that the transform is one that can be learned by gradient descent. As a trivial counterexample, you can't train a model to detect valid (text, SHA) pairs.
This particular one doesn't seem to be a problem for your argument, but I still think your argument generally does not hold.