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Mark Lee wins 3rd Cotsen title

Sunday October 22, 2017

Sweeping all five games, Mark Lee (left, with Eric Cotsen) won his third consecutive Cotsen Open title October 22 at the Korean Culture Center in Los 2017.10.22_cotsen-MarkLee-awardAngeles. All five wins were by resignation, including the exciting Round 4 game Sunday morning against Mateusz Surma. The European professional fell behind early but made skillful use of two weak groups to generate a thrilling game for viewers watching live on KGS as Surma chased a one-eyed dragon across the board. “Mateusz’ attack was a bit stronger than I expected,” said Lee. “I met him four years ago when we studied at the same school and he’s improved a lot since then.” See below for the game record, with commentary and variations by Lee.
Lee donated his $1,000 prize to the American Go Foundation, “to support its work training a new generation of go players.”

Winner’s Report:
Open: Mark Lee (5-0), Aaron Ye (4-1), Andrew Lu (4-1), Xiaocheng Hu (4-1), Mateusz Surma (3-2), Daniel Liu (3-2)
2-4d: Tyler Oyakawa (5-0), Jinming Pan (4-1), Pei Guo (4-1)
1d-2k: Kim In (4-1), Jay Chan (4-1), Irving Lai (4-1)
3k-7k: Matthew Hecht (4-1), Barnett Yang (4-1), Jonathan Zhang (4-1)
8k-19k: Heung Suh (5-0), Choashane Chang (4-1), Zongren Huang (4-1)
20-30k: Xiang Cai (5-0), Kyungsoo Lim (4-1), Alex Ledante
Club prizes: 1st: Santa Monica, 2nd: Orange County; 3rd: San Diego
- report/photo by Chris Garlock



Seattle Go Center Celebrates 22nd Anniversary

Saturday October 21, 2017

Playing Beat the KyuNick presenting Beat the Kyu A rousing game of “Beat the Kyu”, led by famous YouTube instructor Nick Sibicky, was the highlight of the 22nd Anniversary Party at the Seattle Go Center on Sunday October 15. Sibicky teaches at the Go Center on Monday nights.  In “Beat the Kyu,” the group reviews a kyu level game together. After each move is made, members have the opportunity to score points by finding a better move than the kyu player did.   Sibicky brought his charming son Beckett along, who provided additional commentary.  About 30 people came to the party, which also included good food, wine and beer, and casual games.  Report /photos by Brian Allen.


AlphaGo-AlphaGo Game 12: An interesting opening and an unusual collapse

Saturday October 21, 2017

“In this game we start with a variation of the mini Chinese opening, so we’re going to have another of these 3-3 invasions,” says Michael 2017.10.20_ag-12Redmond 9p in his game commentary on AlphaGo-AlphaGo Game 12. “It’s a really interesting opening, and it’s one of the games that ends in a collapse, so it’s an unusual game in that way.”

Click here for Redmond’s video commentary, hosted by the AGA E-Journal’s Chris Garlock.

The Game 12 video is produced by Michael Wanek and Andrew Jackson. The sgf file was created by Redmond, with editing and transcription by Garlock and Myron Souris.



Self-taught AlphaGo Zero bests all previous versions in record time

Wednesday October 18, 2017

AlphaGo Zero, the latest version of the go-playing AlphaGo AI, defeated all previous versions of AlphaGo in just forty days. More importantly, AlphaGo Zero was taught go’s rules, but given no additional instructions, instead learning the best moves by playing 2017.10.18_alphago-zero-saran-poroongmillions of games against itself. Details of the new program were published Wednesday in the journal Nature.

“AlphaGo Zero independently found, used and occasionally transcended many established sequences of moves used by human players,” write the AGA’s Andy Okun and Andrew Jackson in an accompanying article. “In particular, the AI’s opening choices and end-game methods have converged on ours — seeing it arrive at our sequences from first principles suggests that we haven’t been on entirely the wrong track. On the other hand, some of its middle-game judgements are truly mysterious and give observing human players the feeling that they are seeing a strong human play, rather than watching a computer calculate.”

The ability to self-train without human input is a crucial step towards the dream of creating a general AI that can tackle any task, reports Nature. In the nearer-term, though, it could enable programs to take on scientific challenges such as protein folding or materials research, said DeepMind chief executive Demis Hassabis at a press briefing. “We’re quite excited because we think this is now good enough to make some real progress on some real problems.”

Prof Satinder Singh, a computer scientist at Michigan University, who reviewed the findings for the journal said: “The AI massively outperforms the already superhuman AlphaGo and, in my view, is one of the biggest advances, in terms of applications, for the field of reinforcement learning so far.”
- Chris Garlock; Image credit: Saran_Poroong Getty Images

Read more…
DeepMind’s Go-playing AI doesn’t need human help to beat us anymore
AlphaGo Zero: Google DeepMind supercomputer learns 3,000 years of human knowledge in 40 days
DeepMind has a bigger plan for its newest Go-playing AI
AI versus AI: Self-Taught AlphaGo Zero Vanquishes Its Predecessor
AlphaGo Zero Goes From Rank Beginner to Grandmaster in Three Days—Without Any Help
‘It’s able to create knowledge itself’: Google unveils AI that learns on its own
DeepMind AlphaGo Zero learns on its own without meatbag intervention
This more powerful version of AlphaGo learns on its own


Problem of the Week

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