As with Go, we are excited about AlphaZero’s creative response to chess, which has been a grand challenge for artificial intelligence since the dawn of the computing age with early pioneers including Babbage, Turing, Shannon, and von Neumann all trying their hand at designing chess programs. But AlphaZero is about more than chess, shogi or Go. To create intelligent systems capable of solving a wide range of real-world problems we need them to be flexible and generalise to new situations. While there has been some progress towards this goal, it remains a major challenge in AI research with systems capable of mastering specific skills to a very high standard, but often failing when presented with even slightly modified tasks.
AlphaZero’s ability to master three different complex games – and potentially any perfect information game – is an important step towards overcoming this problem. It demonstrates that a single algorithm can learn how to discover new knowledge in a range of settings. And, while it is still early days, AlphaZero’s creative insights coupled with the encouraging results we see in other projects such as AlphaFold, give us confidence in our mission to create general purpose learning systems that will one day help us find novel solutions to some of the most important and complex scientific problems.
This work was done by David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis.