AlphaGo Zero is also set to give back to the community DeepMind's project has shaken up. New ideas from its predecessors like that jaw-dropping move against Lee Sedol have invigorated the game.

Go experts were puzzled by AlphaGo's moves until they realised that where a human might play to win by a wide margin, the AI didn't care if it won by a single point.

Most famously, this includes AlphaGo, a program revealed in 2017 that taught itself to play the ancient board game Go to a grandmaster level.Go is too subtle and instinctive to be tamed using ...

AlphaGo started its training by analyzing the still pictures of boards with stones positioned in various locations, drawn from a database containing 30 million positions from 160,000 different ...

AlphaGo's makers don't plan to sell it commercially just yet. But already, it's challenging 2,500 years of traditional Go strategies and thought.

Google's AlphaGo already beat us puny humans to become the best at the Chinese board game of Go. Now, it's done with humans altogether. DeepMind, the Alphabet subsidiary behind the artificial ...

AlphaGo didn’t just beat Fan Hui—it beat him soundly in every match of a five-game series. The news rippled through the Go world. It was widely believed that an AI strong enough to beat a ...

Documentarian Greg Kohs follows the team of programmers trying to build an algorithm for the world's most complicated game in 'AlphaGo.' By THR Staff One more milestone in humanity’s path to a ...

AlphaGo’s approach, on the other hand, is, potentially, a game changer. It can master games by adjusting as it goes. Unlike chess, where each move affords about 40 options, the ancient board ...

But though AlphaGo's success is impressive, ultimately it is winning in a game, and that can only be extrapolated so far when it comes to real world problems.

AlphaGo rose to prominence a little over a year ago when it unexpectedly defeated legendary player Lee Se-dol 4-1 in a match held in Seoul. Most computer scientists expected the feat of beating a ...

So AlphaGo, and AI in general, is data inefficient in terms of learning. You may think this is a moot point because an AI system is capable of playing millions of games.