Yesterday, the AlphaGo program beat South Korea’s 9-dan Go player Lee Sedol in a live-streamed Go match.
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Computers conquered chess in 1997 in a match between IBM’s Deep Blue and chess champion Garry Kasparov, leaving Go as “the only game left above chess” Demis Hassabis, Google DeepMind’s CEO, said before the game.
AlphaGo and Lee will play three more games between now and March 15th, and the pair will complete all three remaining games even if Lee loses the next game, and therefore the competition. AlphaGo first attracted world’s attention when it defeated the European champion previous year. I admit it was a very clear loss on my part”, Lee told reporters after the match, adding he had found “no weakness” in AlphaGo’s performance during today’s match.
This is not the first time Google’s AlphaGo has won over a champion.
“AlphaGo is comparable to professional Go players”, a commentator admired.
AlphaGo’s creators have described Go as the “Mt Everest” of AI, citing the complexity of the game, which requires a degree of creativity and intuition to prevail over an opponent.
The games will be even (no handicap), with 1 million United States dollars (3.4 Billion UGX) in prize money for the victor.
Go is an incredibly hard game for a computer program to master because of the vast number of possible moves, and the success of AlphaGo is a major landmark in the development of AI.
Artificial intelligence programs have long sought to master popular games and defeat human players. The game rewards patience and balance over aggression and greed; the balance of influence and territory may shift many times in the course of a game, and a strong player must be prepared to be flexible but resolute.
As impressive as AlphaGo’s victories have been so far – and as decisive – we’re still in the very early days of AI, and the most impressive examples of AI thus far have been hyperfocused.
DeepMind team built “reinforcement learning” into AlphaGo, meaning the machine plays against itself and adjusts its own neural networks based on trial-and-error.
Back in the 1990s, software programs became adept at classic board games like backgammon.
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In recent years, scientists have made strides in getting computers to think and learn in ways more similar to people, with the eventual goal that AI will one day assist humans in advanced fields, such as healthcare and scientific research.
