|DeepMind’s New AI Taught Itself to be the World’s Greatest Go Player|
The AlphaGo AI that grabbed headlines last year after beating a master of the board game Go has just been trounced 100-0 by an updated version. And unlike its predecessor, the new system taught itself from first principles paving the way for AI that can think for itself.
When chess fell to AI in the 1990s, computer scientists looking for a new challenge turned to the millennia-old Chinese game Go, which despite its simpler rules has many more possible moves and often requires players to rely on instinct.
It was predicted it would be decades before an AI could beat a human master, but last year a program called AlphaGo developed by Google’s DeepMind subsidiary beat 18-time world champion Lee Sedol 4–1 in a series of matches in South Korea.
It was a watershed moment for AI research that showcased the power of the “reinforcement learning” approach championed by DeepMind. Not only did the system win, it also played some surprising yet highly effective moves that went against centuries of accumulated wisdom about how the game works.
Now, just a year later, DeepMind has unveiled a new version of the program called AlphaGo Zero in a paper in Nature that outperforms the version that beat Sedol on every metric. In just three days and 4.9 million training games, it reached the same level that took its predecessor several months and 30 million training games to achieve. It also did this on just four of Google’s tensor processing units—specialized chips for training neural networks—compared to 48 for AlphaGo.
The most striking departure from the previous system is the simplicity of the inputs. AlphaGo learned the basics of Go by analyzing thousands of games between human players, before honing its skills by playing itself millions of times. In contrast, AlphaGo Zero started with nothing more than the rules of the game and learned entirely from playing games against itself starting with completely random moves.
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