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In February Amigobulls reported that Alphabet Inc-A (NASDAQ:GOOGL) claimed a major Artificial Intelligence (AI) breakthrough with a software program, dubbed AlphaGo, which taught itself to beat a top human player of the board game Go.

Go is much more complex than chess and, until recently, no machine had beaten a human Go champion. In fact, 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. But AlphaGo achieved this milestone AI challenge much sooner, beating European Go champion Fan Hui in October 2015. The announcement was delayed until the publication of a research article titled “Mastering the game of Go with deep neural networks and tree search” in Nature.

AlphaGo combines Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and further improves its performance by learning from games played against itself.

AlphaGo was developed by Google DeepMind, a British AI company founded in 2010 as DeepMind Technologies and acquired by Google in 2014. Google DeepMind has created neural networks that learn how to play games in a similar fashion to humans and appears to mimic key cognitive aspects of the human brain.

Google announced that “AlphaGo’s next challenge will be to play the top Go player in the world over the last decade, Lee Sedol.” The match eventually took place at the Four Seasons Hotel in Seoul, South Korea, between 9 and 15 March, has been video-streamed live and watched by tens of thousands of viewers worldwide. The result: AlphaGo won four games to one.

Go and AI experts have started analyzing the match. In particular, AlphaGo’s move 37 in the second game, which is attracting a lot of attention, is considered beautiful, effective – and inhuman. “It’s not a human move. I’ve never seen a human play this move,” said Fan Hui. The move was initially questioned but then acclaimed by experts. Based on its knowledge base derived from both games played by human experts and games played against itself, AlphaGo “knew” that a human wouldn’t play that particular move, but also that the move is ultimately effective.