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.
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.
There have been a couple of brief discussions of this in the Open Thread, but it seems likely to generate more so here's a place for it.
The original paper in Nature about AlphaGo.
Google Asia Pacific blog, where results will be posted. DeepMind's YouTube channel, where the games are being live-streamed.
Discussion on Hacker News after AlphaGo's win of the first game.