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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.


Conquering More Than Games: The Next Level of AI Observer

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Future historians of technology may look back at one week this March as a tipping point of a new era, and it all started with smooth black-and-white stones on a simple wooden board. It was a five-game match of Go, the ancient Chinese board game, pitting top-ranked world champion Lee Se-dol against an artificial intelligence system called AlphaGo from Google's DeepMind. Although Lee confidently predicted a shutout victory over AlphaGo, the system beat him a resounding 4-1. Games were live-streamed around the world, with a monumental ending reminiscent of Garry Kasparov's 1997 defeat against Deep Blue. But this AI victory goes far beyond the basic mathematical win that Deep Blue achieved.


Artificial intelligence: Use it well for a society where humans can thrive

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The recent feat achieved by AlphaGo was a marvel of striking progress in artificial intelligence (AI) technology. AlphaGo, an AI-based computer program developed by a British corporation under the umbrella of Google Inc. of the United States, has won against the world's top Go player, South Korea's Lee Se Dol, 4-1. Previously, AI programs had defeated skilled human players in the fields of chess and shogi. However, it was said that it would take 10 years to see an AI system win against human players in the world of Go. It was cited as a high hurdle that the surface of a Go board is broad, and that there are an immeasurable number of choices for moves to be made in playing a match.