Go Grandmaster Reflects on Losing Series to Google's Computer
It's not easy losing to a computer.
Lee Sedol, the Go world champion, lost the final game today in a best of five series against Google's artificially intelligent AlphaGo computer. The final score: Machine 4, Human 1.
"Today I wanted to bring the match to a successful conclusion. Personally, I am regretful about the result, but would like to express my gratitude to everyone who supported and encouraged me throughout the match," Sedol said at a post-game press conference.
Aside from bragging rights, a $1 million prize was also at stake in the competition, which AlphaGo's team will donate to charity.
"I have questioned at some points in my life whether I truly enjoy the game of Go, but I admit that I enjoyed all five games against AlphaGo," Sedol said. "After my experience with AlphaGo, I have come to question the classical beliefs a little bit, so I have more study to do."
Go, a board game that was played in ancient China, pits two players against each other. The players take turns placing black or white stones on a grid, with the object of dominating the board by surrounding the other player's pieces. The stones can't be moved unless they are surrounded or are captured by the other player.
While computers can now compete at the grand master level in chess, teaching a machine to win at Go has until now presented a unique challenge since the game has trillions of possible moves. It's estimated there are 10 to the power of 700 ways a game of Go could be played. By comparison, chess has around 10 to the power of 60 possibilities, according to Google.
AlphaGo's victory shows how stunningly smart computers are becoming but also the potential artificial intelligence can have in helping to solve real-world problems -- from robotics to climate modelling and disease analysis.
"We are thrilled to have achieved this milestone, which has been a lifelong dream of mine," Demis Hassabis, CEO of Google's DeepMind said after the match. "Our hope is that in the future, we can apply these techniques to other challenges — from instant translation to smartphone assistants to advances in health care."
The team was able to perfect AlphaGo by setting up two neural networks. One network predicts the next move while the other predicts the outcome. The more games it played against human experts and between its neural networks, the smarter the program has become, according to Google.
Google said in January its program won 99.8 percent of games against other programs designed to play Go -- giving it a nearly perfect record.
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