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MrMind comments on AlphaGo versus Lee Sedol - Less Wrong
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I'm quite interested in how many of the methods employed in this AI can be applied to more general strategic problems.
From talking to a friend who did quite a bit of work in machine composition, he was of the opinion that tools for handling strategy tasks like go would also apply strongly to many design tasks like composing good music.
AlphaGo has convolutional neural network, supervised learning, self-generated supervised learning, a mix-up strategy between Monte Carlo rollouts and goal function estimation.
All these strategies are apted to go because it is a spatial game with a very well defined strategy function.
While I do see CNN and supervised learning well worth of being used for music, it is much more difficult to come up with something that resembles the third step in AlphaGo: generating millions of random 'games' (simphonies) with their own label (good music/bad music) to train an 'intuitive' network.
Adversarial generative networks give you a good objective if you want to take a purely supervised approach.
A spotify like service could be used to label the quality.
Alternatively it would also be nice to have music that's trained for specific goals like helping people concentrate while they work or reducing stress.