Go at Facebook
by Anders Kierulf
2015-11-25
Before I left Microsoft in 1999 to pursue SmartGo full-time, I tried to convince them to start a computer Go project. No luck. It appears my timing was off by a decade: Microsoft released Path of Go for Xbox in 2010. Now Facebook and Google are getting into the game. Will they have a chance against the top programs?
We don’t know much about Google yet, just a hint from Demis Hassabis about a surprise from their DeepMind project. But researchers at Facebook (Yuandong Tian and Yan Zhu) have written a paper with more details.
Facebook researchers built a Deep Convolution Neural Net (DCNN) that they trained with games from KGS and GoGoD to predict good moves to play. They’re achieving an impressive 1 dan in ranked games on KGS, which beats any previous pattern-based approach. However, when they combine their approach with Monte Carlo Tree Search (MCTS), the results are a mixed bag. Evaluating a position with a DCNN is computationally expensive (their paper mentions about 3,000 MCTS playouts for every DCNN board evaluation); it’s not clear that using a neural net to generate moves for tree search is the best use of computing power.
Neural nets can be a powerful solution for pattern recognition problems, and many such problems are relevant to Facebook, so I understand why they have chosen to focus on improved move prediction using DCNN. My guess is that Google will take a similar approach. However, pattern recognition works well for shallow problems; Go is a deep problem, lookahead is essential.
As long as Facebook and Google stick with trying to find general solutions to general problems, I don’t think top Go programs like Zen and Crazy Stone have anything to worry about. But once these giants decide to beat the strongest human players and are willing to focus on Go-specific solutions, it will get interesting.