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Hub AI
AlphaGo versus Lee Sedol AI simulator
(@AlphaGo versus Lee Sedol_simulator)
Hub AI
AlphaGo versus Lee Sedol AI simulator
(@AlphaGo versus Lee Sedol_simulator)
AlphaGo versus Lee Sedol
AlphaGo versus Lee Sedol, also known as the DeepMind Challenge Match, was a five-game Go match between top Go player Lee Sedol and AlphaGo, a computer Go program developed by DeepMind, played in Seoul, South Korea between the 9th and 15th of March 2016. AlphaGo won all but the fourth game; all games were won by resignation. The match has been compared with the historic chess match between Deep Blue and Garry Kasparov in 1997.
The winner of the match was slated to win $1 million. Since AlphaGo won, Google DeepMind stated that the prize would be donated to charities, including UNICEF, and Go organisations. Lee received $170,000 ($150,000 for participating in the five games and an additional $20,000 for winning one game).
After the match, the Korea Baduk Association awarded AlphaGo the highest Go grandmaster rank – an "honorary 9 dan". It was given in recognition of AlphaGo's "sincere efforts" to master Go. This match was chosen by Science as one of the runners-up for Breakthrough of the Year, on 22 December 2016.
Go is a complex board game that requires intuition, creative and strategic thinking. It has long been considered a difficult challenge in the field of artificial intelligence (AI). It is considerably more difficult to design strong computer players for than chess. Many in artificial intelligence consider Go to require more elements that mimic human thought than chess. Mathematician I. J. Good wrote in 1965:
Go on a computer? – In order to program a computer to play a reasonable game of Go, rather than merely a legal game – it is necessary to formalise the principles of good strategy, or to design a learning program. The principles are more qualitative and mysterious than in chess, and depend more on judgement. So, I think it will be even more difficult to program a computer to play a reasonable game of Go than of chess.
Prior to 2015, the best Go programs only managed to reach amateur dan level. On the small 9×9 board, the computer fared better, and some programs managed to win a fraction of their 9×9 games against professional players. Before AlphaGo, some researchers had claimed that computers would never defeat top humans at Go. Elon Musk, an early investor of Deepmind, said in 2016 that experts in the field thought AI was 10 years away from achieving a victory against a top professional Go player.
The match AlphaGo versus Lee Sedol is comparable to the 1997 chess match when Garry Kasparov lost to IBM computer Deep Blue. Kasparov's loss to Deep Blue is considered the moment a computer became better than humans at chess.
AlphaGo is significantly different from previous AI efforts. Instead of using probability algorithms hard-coded by human programmers, AlphaGo uses neural networks to estimate its probability of winning. AlphaGo accesses and analyses the entire online library of Go, including all matches, players, analytics, literature, and games played by AlphaGo against itself and other players. Once set up, AlphaGo is independent of the developer team and evaluates the best pathway to solving Go (i.e., winning the game). By using neural networks and Monte Carlo tree search, AlphaGo calculates colossal numbers of likely and unlikely probabilities many moves into the future [citation needed].
AlphaGo versus Lee Sedol
AlphaGo versus Lee Sedol, also known as the DeepMind Challenge Match, was a five-game Go match between top Go player Lee Sedol and AlphaGo, a computer Go program developed by DeepMind, played in Seoul, South Korea between the 9th and 15th of March 2016. AlphaGo won all but the fourth game; all games were won by resignation. The match has been compared with the historic chess match between Deep Blue and Garry Kasparov in 1997.
The winner of the match was slated to win $1 million. Since AlphaGo won, Google DeepMind stated that the prize would be donated to charities, including UNICEF, and Go organisations. Lee received $170,000 ($150,000 for participating in the five games and an additional $20,000 for winning one game).
After the match, the Korea Baduk Association awarded AlphaGo the highest Go grandmaster rank – an "honorary 9 dan". It was given in recognition of AlphaGo's "sincere efforts" to master Go. This match was chosen by Science as one of the runners-up for Breakthrough of the Year, on 22 December 2016.
Go is a complex board game that requires intuition, creative and strategic thinking. It has long been considered a difficult challenge in the field of artificial intelligence (AI). It is considerably more difficult to design strong computer players for than chess. Many in artificial intelligence consider Go to require more elements that mimic human thought than chess. Mathematician I. J. Good wrote in 1965:
Go on a computer? – In order to program a computer to play a reasonable game of Go, rather than merely a legal game – it is necessary to formalise the principles of good strategy, or to design a learning program. The principles are more qualitative and mysterious than in chess, and depend more on judgement. So, I think it will be even more difficult to program a computer to play a reasonable game of Go than of chess.
Prior to 2015, the best Go programs only managed to reach amateur dan level. On the small 9×9 board, the computer fared better, and some programs managed to win a fraction of their 9×9 games against professional players. Before AlphaGo, some researchers had claimed that computers would never defeat top humans at Go. Elon Musk, an early investor of Deepmind, said in 2016 that experts in the field thought AI was 10 years away from achieving a victory against a top professional Go player.
The match AlphaGo versus Lee Sedol is comparable to the 1997 chess match when Garry Kasparov lost to IBM computer Deep Blue. Kasparov's loss to Deep Blue is considered the moment a computer became better than humans at chess.
AlphaGo is significantly different from previous AI efforts. Instead of using probability algorithms hard-coded by human programmers, AlphaGo uses neural networks to estimate its probability of winning. AlphaGo accesses and analyses the entire online library of Go, including all matches, players, analytics, literature, and games played by AlphaGo against itself and other players. Once set up, AlphaGo is independent of the developer team and evaluates the best pathway to solving Go (i.e., winning the game). By using neural networks and Monte Carlo tree search, AlphaGo calculates colossal numbers of likely and unlikely probabilities many moves into the future [citation needed].