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> If the problem space is without clear definitions and unambiguous axioms then non deep-net alternatives fall apart.

I'm not sure deep-nets are the key here. I see the key as being lots of data and using statistical modeling. Instead of trying to fit what's happening into nice and clean black-and-white categories.

Btw, I don't even think Gofai is all that good at domains with clear definitions and unambiguous axioms: it took neural nets to beat the best people at the very clearly defined game of Go. And neural net approaches have also soundly beaten the best traditional chess engines. (Traditional chess engines have caught up a lot since then. Competition is good for development, of course.)

I suspect part of the problem for Gofai is that all the techniques that work are re-labelled to be just 'normal algorithms', like A* or dynamic programming etc, and no longer bear the (Gof) AI label.

(Tangent: that's very similar to philosophy. Where every time we turn anything into a proper science, we relabel it from 'natural philosophy' to something like 'physics'. John von Neumann was one of these recent geniuses who liberated large swaths of knowledge from the dark kludges of the philosophy ghetto.)




I very much agree about the A* idea, but this idea

> Tangent: that's very similar to philosophy.

doesn't click with me. Maybe, could your elaborate a bit, or provide an example, please?




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