“Players often choose moves, Hassabis said, because they “felt right”—which is not a way a computer program acts. DeepMind’s solution was to build two neural networks—two computer systems that are modeled after the human brain, and can be trained on large data sets to perform certain tasks based on the knowledge it’s accrued. One of the networks, called a “value network” evaluates the computer’s positions on the board, and the other, a “policy network” decides where to move. Instead of evaluating every possible move, it selects a few moves that it senses to likely be potentially good moves.
“It was one of the most exciting moments in my career,” Chouard said at a press briefing Jan. 25. But Chouard said the event prompted mixed feelings in him. While the technical achievement was worth celebrating, “one couldn’t help but root for the poor human being getting beaten,” he said.
The ancient Chinese game of Go is one of the last games where the best human players can still beat the best artificial intelligence players. Last year, the Facebook AI Research team started creating an AI that can learn to play Go.Scientists have been trying to teach computers to win at Go for 20 years. We’re getting close, and in the past six months we’ve built an AI that can make moves in as fast as 0.1 seconds and still be as good as previous systems that took years to build. Our AI combines a search-based approach that models every possible move as the game progresses along with a pattern matching system built by our computer vision team.The researcher who works on this, Yuandong Tian, sits about 20 feet from my desk. I love having our AI team right near me so I can learn from what they’re working on.You can learn more about this research here: http://arxiv.org/abs/1511.06410
Posted by Mark Zuckerberg on Tuesday, January 26, 2016
AlphaGo had a 99.8% win rate against other Go programs, as well as beating Hui. And it will likely only get stronger with more training. “Humans have weaknesses. They get tired when they play a very long match. They can play mistakes,” Hassabis said. “Humans have a limitation in terms of the actual number of Go games that they’re able to process in a lifetime. AlphaGo can play through millions of games every single day.”
Google is by no means the only company or research institution that has been working on solving the Go problem. Prior to today, scientists had only been able to create systems that could beat a human with a few moves’ head start. And in December, a prominent AI researcher, Rémi Coulom—who’s spent years trying to crack the game, and is even cited in DeepMind’s research paper—told Wired that he believed someone would crack the game in the next ten years. A little over a month later, Google has done just that. And, not to be upstaged by Google, Facebook CEO, Mark Zuckerberg posted this morning that his company’s AI researchers are also pretty close to beating the game.” said qz.com
“Given enough training and processing, David Silver, a researcher at DeepMind, said in the video that he thinks it’s conceivable that AlphaGo could play the game at a level that no human could ever attain. Hassabis said that games like Go represent perfect stepping stones for researchers to hit on the pathway to potentially creating something that could be considered a true artificial intelligence system. AlphaGo is slated to face off against the reigning world champion of Go, Lee Sedol, in Korea in March.
As Google has cracked what has long been held as one of the “grand challenges” in AI research, giving credence to the idea that true, general-purpose, artificial intelligence may be possible.” said qz.com
“AlphaGo has finally reached a professional level in Go,” DeepMind said in its research paper, “providing hope that human-level performance can now be achieved in other seemingly intractable artificial intelligence domains.”