In my view, all results should be expressible in the form: because the real world data exhibits empirical structure X, algorithm Y succeeds in describing/predicting it.
Even if I know the exact probability distribution over images, there is an algorithmic problem (namely, how to do the inference), so your view is definitely at least a little too extreme.
In fact, this algorithmic difficulty is an issue that many researchers are currently grappling with, so in practice you really shouldn’t expect all results to be making a novel statement about how the world works. Applying this standard to current research would stall progress in the directions I (and I think most serious AI researchers) currently believe are most important to actually reaching AI, especially human-comprehensible AI which might possibly be friendly.
Maybe we are wrong, but the argument you gave, and your implications about how NFL should be applied, are not really relevant to that question.
Even if I know the exact probability distribution over images, there is an algorithmic problem (namely, how to do the inference), so your view is definitely at least a little too extreme.
I don’t dispute that the algorithmic problem is interesting and important. I only claim that the empirical question is equally important.
Applying this standard to current research would stall progress in the directions I (and I think most serious AI researchers) currently believe are most important to actually reaching AI
What you’re really saying is that you think a certain direction of research will be fruitful. That’s fine. I disagree, but I doubt we can resolve the debate. Let’s compare notes again in 2031.
Even if I know the exact probability distribution over images, there is an algorithmic problem (namely, how to do the inference), so your view is definitely at least a little too extreme.
In fact, this algorithmic difficulty is an issue that many researchers are currently grappling with, so in practice you really shouldn’t expect all results to be making a novel statement about how the world works. Applying this standard to current research would stall progress in the directions I (and I think most serious AI researchers) currently believe are most important to actually reaching AI, especially human-comprehensible AI which might possibly be friendly.
Maybe we are wrong, but the argument you gave, and your implications about how NFL should be applied, are not really relevant to that question.
I don’t dispute that the algorithmic problem is interesting and important. I only claim that the empirical question is equally important.
What you’re really saying is that you think a certain direction of research will be fruitful. That’s fine. I disagree, but I doubt we can resolve the debate. Let’s compare notes again in 2031.