AlphaGo in a Personal Computer?
A Google Artificial Intelligence (AI) program recently defeated a world-renowned master Go player Lee Sedol three times, in Go matches which has been known as an extremely complex game (Refer to the news article). According to the Nature article written by David Silver et al. (2016), AlphaGo defines not only value networks evaluating board positions but also policy networks choosing moves, and both networks are trained through supervised learning. The AlphaGo uses a distributed system consisting of 1,202 CPUs and 176 GPUs, which couldn't be easily implemented in a small lab. My interest is whether it is feasible to have similar performance with less system resources (for example, even in a personal computer)? I have no idea, but it might be feasible if both networks could be trained continuously using a concept of Collective Intelligence based on Web Cloud (such as Wikipedia). If you are a computer scientist who are interested in how the AlphaGo works, please visit the repository of source codes for AlphaGo.