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 game of go and chess are highly discrete. One slightly wrong move and the game is lost. Still AlphaZero does very well. Even move proposals from a ResNet (without search) beats very good amateur go players.
These games involve a mixture of pattern recognition (what a player would call 'intuition') and explicit reasoning. It's not all or nothing. The better you are at pattern recognition, the less you need to rely on reasoning, and inversely.
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AlphaZero illustrates this trade-off well: the better the convnet the lesser the need to rely on MCTS. It also really shows how explicit reasoning (e.g. MCTS) enables much greater experience-efficiency in achieving high skill (i.e. greater intelligence): (cont)
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playing purely based on pattern recognition (intuition) requires an insane amount of training data (a dense sampling of the manifold). That's not how humans play: for best efficiency, we rely on an interconnected *mix* of intuition and explicit reasoning & planning.
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