The no free lunch theorems basically say that if you are unlucky enough with your prior, and the problem to be solved is maximally general, then you can’t improve on your efficiency beyond random sampling/brute force search, which requires you to examine every input, and thus you can’t get away with algorithms that don’t require you to examine all inputs like in brute-force search.
It’s closer to a maximal inefficiency for intelligence/inapproximability result for intelligence than an impossibility result, which is still very important.
Only if you can’t examine all of the inputs.
The no free lunch theorems basically say that if you are unlucky enough with your prior, and the problem to be solved is maximally general, then you can’t improve on your efficiency beyond random sampling/brute force search, which requires you to examine every input, and thus you can’t get away with algorithms that don’t require you to examine all inputs like in brute-force search.
It’s closer to a maximal inefficiency for intelligence/inapproximability result for intelligence than an impossibility result, which is still very important.
I think Searle would disagree. But I also think this entire thought experiment is dumb.