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Google's AlphaGo Beats Lee Se-dol In the First Match (theverge.com)
New submitter Fref writes with news from The Verge that "A huge milestone has just been reached in the field of artificial intelligence: AlphaGo, the program developed by Google's DeepMind unit, has defeated legendary Go player Lee Se-dol in the first of five historic matches being held in Seoul, South Korea. Lee resigned after about three and a half hours, with 28 minutes and 28 seconds remaining on his clock. "
Lee will face off against AlphaGo again tomorrow and on Saturday, Sunday, and Tuesday. Also at the New York Times. Science magazine says the loss may be less significant than it seems at first.
Lee will face off against AlphaGo again tomorrow and on Saturday, Sunday, and Tuesday. Also at the New York Times. Science magazine says the loss may be less significant than it seems at first.
go uses sgf
https://gogameguru.com/i/2016/03/Lee-Sedol-vs-AlphaGo-20160309.sgf
Since I doubt that most people unfamiliar with go have a way to view an sgf file here is link to the gogameguru article about the game. At the bottom there is a javascript applet you can use to play through the game.
https://gogameguru.com/alphago-defeats-lee-sedol-game-1/
Here's a talk by deepmind about this AI https://www.youtube.com/watch?...
AlphaGo did beat the European champion 5 out of 5. So Lee will have to step up his game a bit.
The European master was 2 Dan. Lee is 8 Dan (or 9, but the last one is honorary, so it doesn't count for comparing strengths). In the world of Go, that difference is rather staggering. It's like the difference between a chess ELO rating of 2100 and 2600. Someone consistently beating the lower ranked player may not have a chance against the higher rated player.
If Lee were to play the European master, he'd be expected to trounce him too. Quite thoroughly.
What counts in AlphaGo's favor here is that it has had over a year to improve. That it did win the first game says a lot more about its strength than any play against much lower ranked players.
I am still waiting for a computer who can recognize my bags at a conveyor belt at least as efficiently as me
I've worked with industrial vision devices in the past and trust me, you could set up a machine to recognize luggage as efficiently as a human being today if you wanted to. In fact, it will do better.
The only thing surprising about the Go event is that it did not happen like ten, or even twenty, years ago. You may be impressed, but I find this most underwhelming.
That's likely because you don't understand what it involves. Go is unlike chess in the sense that just throwing raw computing power at the problem won't help you at all; for a "small" 13x13 there are over 10^300 valid game trees to compute, and the number gets exponentially worse once the board increases in size. For reference, the estimated number of atoms in the universe is 10^130.
Google's AlphaGo engine is an actual machine-learning AI which had to be trained plays the game much like a regular person would - Myungwan Kim actually remarked that it feels like playing against a human being. Having a competitive Go engine today is a major milestone, make no mistake about it.
It requires more than skill. Pruning such a massive game tree is no minor feat - in fact, we don't know how to do it even today. All Go engines are based on some form of adaptive AI.
Again, chess is waaaaay easier in comparison. Pretty much all chess engines work the same way: they start with precomputed moves from an opening book and then move to what's an essentially brute force approach where the engine tries positions, assigns them scores and then picks the highest score available. How these positions are scored / discarded is what separates them, but the base procedure is unchanged. This is also what leads to what chess players call "computer moves" - most chess engines will favor unassuming, conservative moves yielding small positional advantages instead of, well, more "human", intelligent ones. Picking up pivotal moments from classic games (move 17 on Fischer-Byrne, for example) and feeding them to top-rated chess engines is an enlightening exercise.
This is all but impossible to perform in Go with even modest board sizes. The game complexity, given its simple rules, is just staggering.
It's not just that it won a game. It won the game AS WHITE! I haven't been seeing this pointed out elsewhere...
The other human professionals who read out this game to score it said that the win was beyond the Komi (the 7.5 points granted to white due its not going first). IOW, AlphaGo beat TWO levels of disadvantage; playing as white and achieving more than the balance granted by komi.
I think Lee Sedol will have a much more difficult time in the next game as AlphaGo will have black (first move). And while komi is supposed to make up for the first move advantage, it doesn't take away the feeling from the _human_ playing white that they play the entire game attempting to come from behind.