Go is a topological counting game that has been challenging for a computer to play due to its global phase-transition like game dynamics, requiring accurate reads of position early in the game. When the AlphaGo paper (preprint) first came out in January purporting a new system that plays Go at a human-professional level, I didn’t have a chance to read it thoroughly, partly because the paper is badly written (yes, I said it), and partly because it was a lot of information. Now that the DeepMind Challenge Match against Lee Sedol is happening, with a surprise nearly every game, I thought to give it another go (no pun intended).

The main objective is to extract from the paper four key pieces of information: (1) How did computers play Go before, and what was the state-of-the-art before AlphaGo? (2) What is the novelty of AlphaGo from which it derives its improved performance? (3) How is AlphaGo actually implemented? (4) What are the future and the limitations of this approach?
(Read the article)

Comments(0)