The folks at Google have created a program that can compete at the elite level in Go. If you aren’t familiar with Go, it has a level of complexity greater than chess:
The search space in Go is vast — more than a googol times larger than chess (a number greater than there are atoms in the universe!). As a result, traditional “brute force” AI methods — which construct a search tree over all possible sequences of moves — don’t have a chance in Go. To date, computers have played Go only as well as amateurs. Experts predicted it would be at least another 10 years until a computer could beat one of the world’s elite group of Go professionals.
That prediction was less than 2 years ago, so a bit off.
So how strong is AlphaGo? To answer this question, we played a tournament between AlphaGo and the best of the rest – the top Go programs at the forefront of A.I. research. Using a single machine, AlphaGo won all but one of its 500 games against these programs. In fact, AlphaGo even beat those programs after giving them 4 free moves headstart at the beginning of each game. A high-performance version of AlphaGo, distributed across many machines, was even stronger.
So on to the next challenge:
It seemed that AlphaGo was ready for a greater challenge. So we invited the reigning 3-time European Go champion Fan Hui — an elite professional player who has devoted his life to Go since the age of 12 — to our London office for a challenge match. The match was played behind closed doors between October 5-9 last year. AlphaGo won by 5 games to 0 — the first time a computer program has ever beaten a professional Go player.
Next AlphaGo will play the top Go player in the world. But regardless of the outcome, this is a really big deal that will span far beyond gaming.
We are thrilled to have mastered Go and thus achieved one of the grand challenges of AI. However, the most significant aspect of all this for us is that AlphaGo isn’t just an ‘expert’ system built with hand-crafted rules, but instead uses general machine learning techniques to allow it to improve itself, just by watching and playing games. While games are the perfect platform for developing and testing AI algorithms quickly and efficiently, ultimately we want to apply these techniques to important real-world problems. Because the methods we have used are general purpose, our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis.