Excerpted and adapted from a report in The New York Times
“Last year, (AlphaGo) was still quite humanlike when it played,” said Ke Jie 9P after the first match against the go-playing AI Tuesday. “But this year, it became like a god of Go.”
“AlphaGo is improving too fast,” Ke said in a news conference after the game. “AlphaGo is like a different player this year compared to last 
year.”
Mr. Ke, who smiled and shook his head as AlphaGo finished out the game, said afterward that his was a “bitter smile.” After he finishes this week’s match, he said, he would focus more on playing against human opponents, noting that the gap between humans and computers was becoming too great. He would treat the software more as a teacher, he said, to get inspiration and new ideas about moves.
Chinese officials perhaps unwittingly demonstrated their conflicted feelings at the victory by software backed by a company from the United States, as they cut off live streams of the contest within the mainland even as the official news media promoted the promise of artificial intelligence.
Excerpted from Wired
This week’s match is AlphaGo’s first public appearance with its new architecture, which allows the machine to learn the game almost entirely from play against itself, relying less on data generated by humans. In theory, this means DeepMind’s technology can more easily learn any task.
Underpinned by machine learning techniques that are already reinventing everything from internet services to healthcare to robotics, AlphaGo is a proxy for the future of artificial intelligence.
This was underlined as the first game began and (DeepMind CEO Demis) Hassabis (in photo) revealed that AlphaGo’s new architecture was better suited to tasks outside the world of games. Among other things, he said, the system could help accelerate the progress of scientific research and significantly improve the efficiency of national power grids.
DeepMind Match 1 wrap up
“There was a cut that quite shocked me,” said Ke Jie, “because it was a move that would never happen in a human-to-human Go match. But, afterwards I analyzed the move and I found that it was very good. It is one move with two or even more purposes. We call it one stone, two birds.”
“Ke Jie started with moves that he had learned from the Master series of games earlier this year, adding those new moves to his repertoire,” said Michael Redmond 9P. “Ke Jie used the lower board invasion point similar to AlphaGo in the Masters games, and this was a move that was unheard of before then. Although this was one of the most difficult moves for us to understand, in the last month or players have been making their own translations and interpretations of it.”
“Every move AlphaGo plays is surprising and is out of our imagination,” said Stephanie Yin 1P. “Those moves completely overthrow the basic knowledge of Go. AlphaGo is now a teacher for all of us.”
photos: (top) courtesy China Stringer Network, via Reuters (middle) Noah Sheldon/Wired (bottom) DeepMind
Ke Jie: AlphaGo “like a god of Go”
Tuesday May 23, 2017
Excerpted and adapted from a report in The New York Times
“Last year, (AlphaGo) was still quite humanlike when it played,” said Ke Jie 9P after the first match against the go-playing AI Tuesday. “But this year, it became like a god of Go.”
year.”
“AlphaGo is improving too fast,” Ke said in a news conference after the game. “AlphaGo is like a different player this year compared to last
Mr. Ke, who smiled and shook his head as AlphaGo finished out the game, said afterward that his was a “bitter smile.” After he finishes this week’s match, he said, he would focus more on playing against human opponents, noting that the gap between humans and computers was becoming too great. He would treat the software more as a teacher, he said, to get inspiration and new ideas about moves.
Chinese officials perhaps unwittingly demonstrated their conflicted feelings at the victory by software backed by a company from the United States, as they cut off live streams of the contest within the mainland even as the official news media promoted the promise of artificial intelligence.
Excerpted from Wired
This week’s match is AlphaGo’s first public appearance with its new architecture, which allows the machine to learn the game almost entirely from play against itself, relying less on data generated by humans. In theory, this means DeepMind’s technology can more easily learn any task.
Underpinned by machine learning techniques that are already reinventing everything from internet services to healthcare to robotics, AlphaGo is a proxy for the future of artificial intelligence.
This was underlined as the first game began and (DeepMind CEO Demis) Hassabis (in photo) revealed that AlphaGo’s new architecture was better suited to tasks outside the world of games. Among other things, he said, the system could help accelerate the progress of scientific research and significantly improve the efficiency of national power grids.
DeepMind Match 1 wrap up
“There was a cut that quite shocked me,” said Ke Jie, “because it was a move that would never happen in a human-to-human Go match. But, afterwards I analyzed the move and I found that it was very good. It is one move with two or even more purposes. We call it one stone, two birds.”
“Ke Jie started with moves that he had learned from the Master series of games earlier this year, adding those new moves to his repertoire,” said Michael Redmond 9P. “Ke Jie used the lower board invasion point similar to AlphaGo in the Masters games, and this was a move that was unheard of before then. Although this was one of the most difficult moves for us to understand, in the last month or players have been making their own translations and interpretations of it.”
“Every move AlphaGo plays is surprising and is out of our imagination,” said Stephanie Yin 1P. “Those moves completely overthrow the basic knowledge of Go. AlphaGo is now a teacher for all of us.”
photos: (top) courtesy China Stringer Network, via Reuters (middle) Noah Sheldon/Wired (bottom) DeepMind