One interesting thing about the ARC competition is that it serves to highlight how people who use deep learning often have little idea of what deep learning actually does, and when they should be using it or not
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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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Many thanks for your insights
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Knowing deep learning fits manifolds is like knowing software manipulates bits: correct but not that helpful. My favorite example is machine translation: would you have predicted it was a good manifold fitting problem?
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