American Go E-Journal » 2017 » August

By Michael Bacon

Enjoyed the coverage of the Go Congress immensely! Could not help but poke a few of my chess friends in the eye while contrasting all the coverage it received with all the coverage the recent US Open did not receive on the organ of US chess, the USCF webpage. I’ve also been transfixed by Michael Redmond’s videos. The man is a national treasure!

Former World Human Chess Champion Gary Kasparov, who will always be remembered as the human who lost to a ‘machine,’ in his apologia for having lost to the computer chess ‘engine’ called ‘Deep Blue’ — not for having turned Kasparov a deep shade of blue, and a whiter shade of pale, I might add — writes about go in ‘Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins‘:
“The nineteen-by-nineteen Go board with its 361 black and white stones is too big of a matrix to crack by brute force, too subtle to be decided by the tactical blunders that define human losses to computers at chess. In that 1990 article on Go as a new target for AI, a team of Go programmers said they were roughly twenty years behind chess. This turned out to be remarkably accurate. In 2016, nineteen years after my loss to Deep Blue, the Google-backed AI project DeepMind and its Go-playing offshoot AlphaGo defeated the world’s top Go player, Lee Sedol. More importantly, and also as predicted, the methods used to create AlphaGo were more interesting as an AI project than anything had produced the top chess machines. It uses machine learning and nural networks to teach itself how to play better, as well as other sophisticated techniques beyond the usual alpha-beta search. Deep Blue was the end; AlphaGo is the beginning.” (pgs. 74-75)

Please note the author capitalizes “Go,” but not “chess.” I find that curious as I have always capitalized “Chess.” (note: the EJ does not capitalize go, consistent with AP style) In addition, Lee Sedol, as all go players know, was not the “…world’s top Go player,” when he lost to the computer program known as AlphaGo.

We move along to page 104 where one finds this:
“The machine-learning approach might have eventually worked with chess, and some attempts have been made. Google’s AlphaGo uses these techniques extensively with a database of around thirty million moves. As predicted, rules and brute force alone weren’t enough to beat the top Go players. But by 1989, Deep Thought had made it quite clear that such experimental techniques weren’t necessary to be good enough at chess to challenge the world’s best players.”

Finally, on page 121, Kasparov, or his co-author Mig Greengard, writes this paragraph:
“More success was had with another method for allowing machines to extend their thinking into the hypothetical outside of the direct search tree. Monte Carlo tree search simulates entire games played out from positions in the search and records them as wins, draws, or losses. It stores the results and uses them to decide which positions to play out next, over and over. Playing out millions of “games within the game” like this was not particularly effective or necessary for chess, but it turned out to be essential in Go and other games where accurate evaluation is very difficult for machines. The Monte Carlo method doesn’t require evaluation knowledge or hand-crafted rules; it just keeps track of the numbers and moves toward the better ones.”

While reading I continually thought of former World Human Chess Champion Emanuel Lasker’s famous quote, “If there are sentient beings on other planets, then they play Go.”

Not chess; go!