At the heart of the program is a group of software neurons that are connected together to form an artificial neural network. For each turn of the game, the network looks at the positions of the pieces on the Go board and calculates which moves might be made next and probability of them leading to a win. After each game, it updates its neural network, making it stronger player for the next bout. Though far better than previous versions, AlphaGo Zero is a simpler program and mastered the game faster despite training on less data and running on a smaller computer. Given more time, it could have learned the rules for itself too, Silver said.
What is AI?
Artificial Intelligence has various definitions, but in general it means a program that uses data to build a model of some aspect of the world. This model is then used to make informed decisions and predictions about future events. The technology is used widely, to provide speech and face recognition, language translation, and personal recommendations on music, film and shopping sites. In the future, it could deliver driverless cars, smart personal assistants, and intelligent energy grids. AI has the potential to make organisations more effective and efficient, but the technology raises serious issues of ethics, governance, privacy and law.
Writing in the journal
Nature, the researchers describe how AlphaGo Zero started off terribly, progressed to the level of a naive amateur, and ultimately deployed highly strategic moves used by grandmasters, all in a matter of days. It discovered one common play, called a joseki, in the first 10 hours. Other moves, with names such as small avalanche and knights move pincer soon followed. After three days, the program had discovered brand new moves that human experts are now studying. Intriguingly, the program grasped some advanced moves long before it discovered simpler ones, such as a pattern called a ladder that human Go players tend to grasp early on.
AlphaGo Zero starts with no knowledge, but progressively gets stronger and stronger as it learns the game of Go. Credit: DeepMind
It discovers some best plays, josekis, and then it goes beyond those plays and finds something even better, said Hassabis. You can see it rediscovering thousands of years of human knowledge.
Eleni Vasilaki, professor of computational neuroscience at Sheffield University, said it was an impressive feat. This may very well imply that by not involving a human expert in its training, AlphaGo discovers better moves that surpass human intelligence on this specific game, she said. But she pointed out that, while computers are beating humans at games that involve complex calculations and precision, they are far from even matching humans at other tasks. AI fails in tasks that are surprisingly easy for humans, she said. Just look at the performance of a humanoid robot in everyday tasks such as walking, running and kicking a ball.
Tom Mitchell, a computer scientist at Carnegie Mellon University in Pittsburgh called AlphaGo Zero an outstanding engineering accomplishment. He added: It closes the book on whether humans are ever going to catch up with computers at Go. I guess the answer is no. But it opens a new book, which is where computers teach humans how to play Go better than they used to.
David Silver describes how the AI program AlphaGo Zero learns to play Go. Credit: DeepMind
The idea was welcomed by Andy Okun, president of the American Go Association: I dont know if morale will suffer from computers being strong, but it actually may be kind of fun to explore the game with neural-network software, since its not winning by out-reading us, but by seeing patterns and shapes more deeply.
While AlphaGo Zero is a step towards a general-purpose AI, it can only work on problems that can be perfectly simulated in a computer, making tasks such as driving a car out of the question. AIs that match humans at a huge range of tasks are still a long way off, Hassabis said. More realistic in the next decade is the use of AI to help humans discover new drugs and materials, and crack mysteries in particle physics. I hope that these kinds of algorithms and future versions of AlphaGo-inspired things will be routinely working with us as scientific experts and medical experts on advancing the frontier of science and medicine, Hassabis said.