Foreword

For us, the members of the AlphaGo team, the AlphaGo story was the adventure of a lifetime. It began, as many great adventures do, with a small step—training a simple convolutional neural network on records of Go games played by strong human players. This led to pivotal breakthroughs in the recent development of machine learning, as well as a series of unforgettable events, including matches against the formidable Go professionals Fan Hui, Lee Sedol, and Ke Jie. We’re proud to see the lasting impact of these matches on the way Go is played around the world, as well as their role in making more people aware of, and interested in, the field of artificial intelligence.

But why, you might ask, should we care about games? Just as children use games to learn about aspects of the real world, so researchers in machine learning use them to train artificial software agents. In this vein, the AlphaGo project is part of DeepMind’s strategy to use games as simulated microcosms of the real world. This helps us study artificial intelligence and train learning agents with the goal of one day building general purpose learning systems capable of solving the world’s most complex problems.