Keynote speech III: Computer go research - The challenges ahead

Abstract

With the success of Monte Carlo Tree Search, the game of Go has become a focus of games research. Recently, deep convolutional neural networks have achieved human-level performance in predicting master moves. Even before that, machine learning techniques have been used very successfully as an automated way to improve the domain knowledge in Go programs. Go programs have now reached a level close to top amateur players. In order to challenge professional level players, we must combine the three pillars of modern Go programs — search, knowledge, and simulation — in a high performance system, possibly running on massively parallel hardware. This talk will summarize recent progress in this exciting field, and outline a research strategy for boosting the performance of Go programs to the next level.