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Why football, not chess, is the true final frontier for robotic artificial intelligence

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First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.


Why football, not chess, is the true final frontier for robotic artificial intelligence

#artificialintelligence

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.


Why football, not chess, is the true final frontier for robotic artificial intelligence

#artificialintelligence

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.


How an artificial intelligence learnt to play

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Go looks simple, deceptively so. The Chinese board game is played on a board with a grid of 19x19 lines. The object is for two players to alternately place black and white markers on vacant intersections of those lines. And now, this nearly 3,000-year-old board game is a frontier of Artificial Intelligence development. At the time of writing, Google's DeepMind AI's AlphaGo program has played four games of a five game series against Go world champion, South Korea's Lee se-Dol.


Artificial intelligence marches on The Japan Times

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Google's artificial intelligence program AlphaGo's overwhelming win over South Korean go grandmaster Lee Sedol in a five-game tournament this month has shown that machine intelligence is rapidly evolving and underlined the possibility that it will catch up with and eventually surpass human intelligence. The time has come for us to think how best to use AI in ways that will contribute to -- and not detract from -- our well-being. In the tournament held in Seoul, the program built by a Google subsidiary DeepMind defeated Lee, a 33-year-old 9-dan professional go player with 18 world titles, in a 4-1 victory. Google had chosen Lee as an opponent in view of his impressive records, considering him as the world's strongest player of the board game. The outcome has stunned go players, professional programmers and the public alike -- given that experts had previously expected it would take more than 10 years for an AI program to beat a world-class professional go player.