Earlier quoted context omitted.
The problem is the author's giving a misleading picture of the problem space with those examples. Tasks like optimizing whole programs or running a theorem prover are difficult/impossible tasks to do perfectly. We don't have a solution verifier that we can plug into the "free" brute force framework. With theorem provers, even when restricted to fixed finite (non-trivial) lengths, I don't think we have one that always…
> The intuition that such a thing is "too powerful" is actually a pretty bad intuition here. I still disagree. Just focus on theorem proving and not the examples that are too simple. If the halting problem could be solved, we'd be able to magically solve all these "impossible" problems. But our intuition is that just doesn't make sense, it's "too good to be true", "the universe is just more complex than that", etc. T…
This example is a little flawed, but go with me. Imagine someone was making an argument that an algorithm is too powerful because it can solve really hard problems, then list a bunch of problems that need ridiculous amounts of compute time to solve, but half their examples are NP-hard and half their examples are P.
The intuition that says "wow, that problem is very difficult to solve, so I'm very skeptical of a solution" is wrong. Because that intuition applies to both the NP examples and the P examples. That intuition is too simplistic and overgeneral.
You need an intuition that is right with both classes of problem. It has to say "no" to one class and "yes" to another. Ignoring the wrong examples is not how you evaluate an intuition.