A 'Brief' History of Game AI Up To AlphaGo, Part 1

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This is the first part of'A Brief History of Game AI Up to AlphaGo'. Part 2 is here and part 3 is here. In this part, we shall cover the birth of AI and the very first game-playing AI programs to run on digital computers. On March 9th of 2016, a historic milestone for AI was reached when the Google-engineered program AlphaGo defeated the world-class Go champion Lee Sedol. Go is a two-player strategy board game like Chess, but the larger number of possible moves and difficulty of evaluation make Go the harder problem for AI.



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.