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A Computer that is Almost Human

What AlphaGo Means for Transportation

2/15/2016 by Parth Raval

On January 27th, 2016, something remarkable happened in Google’s London office – a computer beat a human player at the game Go. One of the world’s oldest board games, Go is primarily played in East Asia, with over 40 million players worldwide. The rules are simple: two players take turns placing stones on a 19 x 19 grid, trying to cover more territory than the other. There are many strategies to capture and contain an opponent’s stones and gain an advantage.  This combination of simplicity and intricacy has made Go popular for over 2,500 years.

The fact that a computer was able to beat a human player at a game may not come as a surprise. After all, computers have been able to defeat humans in a range of games for years. In 1996, a computer beat Gary Kasparov, considered by many to be the greatest chess player of all time. Computers have successfully learned how to play Atari games just by reading the pixels on the screen. What does it matter if one more game is added to that list?

It matters because Go is one of the world’s most complex games – far more complex than chess. Computers learn how to play games by following patterns and predetermining outcomes. There are more possible Go games than there are atoms in the universe. Until now, this number was thought to be too big for a computer to handle. That was until Google’s AlphaGo program beat European Go champion Fan Hui, 5 games to 0. AlphaGo is programmed to learn using trial-and-error. It uses reinforcement learning, which takes human psychology principles and applies them to machines.

AlphaGo is a major advance in artificial intelligence. A computer with AlphaGo’s capabilities was thought to be at least ten years away. Though exciting, AlphaGo may seem to occupy a world far removed from that of fleet management.  The truth is that software that can learn complex tasks, as AlphaGo can, will be the foundation of transportation’s future. Drivers and dispatchers will work with vehicles that can perform many tasks of their own, including caravan driving and navigation. A major concern with current autonomous vehicle technology is that machines cannot replace a human’s instantaneous decision-making or intuition. It is likely that they never will – but they will get close. AlphaGo’s advanced abilities to predict human behavior may do much to close the gap between fleet managers and the future.

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