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Human Go Champion 'Speechless' After 2nd Loss To Machine (phys.org)
Reader chasm22 points to a Phys.org report about the second straight loss of Lee Sedol to AlphaGo, the program developed by Google's DeepMind unit. The human Go champion, Sedol found himself "speechless" after the showdown on Thursday. The human versus machine face-off lasted more than four hours, which to Sedol's credit is a slight improvement over his previous match, which had ended with him resigning nearly half an hour remaining on the clock.
"It was a clear loss on my part," Sedol said at a press conference on Thursday. "From the beginning there was no moment I thought I was leading." Demis Hassabis, who heads Google's DeepMind, said, "Because the number of possible Go board positions exceeds the number of atoms in the universe, top players rely heavily on their intuition."
Sedol will battle Google's AlphaGo again on Saturday, Sunday, and Tuesday.
This is nothing like the "regular" AI used on modern chess engines - those are useless for the essentially infinite game tree possibilities like the ones presented in Go. AlpaGo decides moves using a machine learning neural network and then selects the best one using classic heuristics.
Search space, basically, and the amount of moves you have to inspect before selecting a best one. Chess has about 10^120 possible moves, but you can reduce this using opening books and heuristics to a sensible number which still lets you pick very strong moves. At that point it is just a matter of throwing CPU power at the problem.
Go is a completely different beast though. A "small" 13x13 board has 10^170 valid moves, and the count for a 21x21 board is well over 10^210. So for even small, beginner-level sized boards no amount of CPU power, now or in the future, is bound to help you solve the problem. Go is interesting because every engine out there uses some form of adaptative AI - AlphaGo uses a machine learning neural net which had to be trained like a human would, but with over 30 million recorded moves.
Sedol will battle Google's AlphaGo again on Saturday, Sunday, and Tuesday.
Note that for many people in the western hemisphere, the days are actually Friday, Saturday, and Monday.
Live streams are here.
Li will eventually learn to manipulate the machine; it's *very* intelligent, but not creative or insightful.
Not creative? That's your opinion. Here are what other people (including serious Go professionals) think...
"AlphaGo met Lee’s solid, prudent play with a creativity and flexibility that surprised professional commentators" - https://gogameguru.com/alphago...
An Youngil (8d) wrote of AlphaGo playing Black: "Black 13 was creative ... Black 37 was a rare and intriguing shoulder hit ... Black 151, 157 and 159 were brilliant moves, and the game was practically decided by 165 ... AlphaGo’s style of play in the opening seems creative! Black 13, 15, 29 and 37 were very unusual moves." -- https://gogameguru.com/alphago...
Redmond (9d) wrote "I was impressed with AlphaGo’s play. There was a great beauty to the opening ... It was a beautiful, innovative game. ... AlphaGo started with some very unusual looking moves ... It played this shoulder-hit here which was a very innovative move ... I really liked the way it played in the opening, because I wasn't so impressed about the orthodox October games, but now it's playing a much more interesting exciting game." -- http://googleasiapacific.blogs...
Anders Kierulf (3d, creator of SmartGo) wrote: "The peep at move 15: This is usually played much later in the game, and never without first extending on the bottom. AlphaGo don’t care. It adds 29 later, and makes the whole thing work with the creative shoulder hit of 37 ... AlphaGo don’t care, it just builds up its framework, and then shows a lot of flexibility in where it ends up with territory." -- http://www.smartgo.com/blog/al...
Maybe you start with a philosophical axiom that "a computer can by definition never be considered creative". That's fair enough, but it's not the way that the Go playing community use the word "creative".
The designers at DeepMind didn't expect it to lose a couple of matches and the version of AlphaGo was finalized before the start of the match and thus couldn't 'learn the kinds of moves that a really top go player made'. So you are wrong on all accounts.
They had a good idea of the playing strength when the offered the challenge, and the playing strength was probably such that they expected at minimum a 3:2 win for the bot.