DL is applicable when you're doing *pattern recognition*: when you have data that lies on a smooth manifold, along which samples can be interpolated. And you're going to need a dense sampling of your manifold as training data in order to fit a parametric approximation of it
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Generalization in deep learning is interpolation along a latent manifold (or rather a learned approximation of it). It has little to do with your model itself and everything to do with the natural organization of your data
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Differentiability & minibatch SGD are the strengths of DL: besides making the learning practically tractable, the smoothness & continuity of the function & the incrementality of its fitting work great to learn to approximate latent manifold. But its strengths are also its limits
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The whole setup breaks down when you are no longer doing pattern recognition -- when you no longer have a latent manifold (any kind of discrete problem) or no longer have a dense sampling of it. Or when your manifold changes over time.
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This is indeed, and unfortunately, a sign of the current status of ML as a research field. I frankly can't imagine how to employ deep learning for ARC (at least in a way that makes sense and that is coherent with the expectations of your paper "On the measure of Intelligence").
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It might be a good thing. Sometimes we can see unexpected surprises from being “foolish”
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This is not about "following the rules" or breaking them, this is about understanding what you are doing. Being clueless is not the same as being creative.
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From what little knowledge of ML/DL I have, I don't think this problem is completely solvable by computers. But yes I understand what real intelligence is like.
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The ARC competition is a great way to realize how impressive the human brain is and how little we know about intelligence.
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