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Google's AlphaGo AI Beats Lee Se-dol Again, Wins Go Series 4-1 (theverge.com)
An anonymous reader quotes an article at The Verge about Korean grandmaster's fifth and final game with Google's AlphaGo AI: After suffering its first defeat in the Google DeepMind Challenge Match on Sunday, the Go-playing AI AlphaGo has beaten world-class player Lee Se-dol for a fourth time to win the five-game series 4-1 overall. The final game proved to be a close one, with both sides fighting hard and going deep into overtime. The win came after a "bad mistake" made early in the game, according to DeepMind founder Demis Hassabis, leaving AlphaGo "trying hard to claw it back."
Nobody is trying to make a general intelligence because nobody wants it. What is wanted is domain specific algorithms that are very good at what they do.
Although, it seems that the tech is quite general and learned to play multiple Atari games without having to be tuned for each.
It's a huge surprise.
Your comment is a perfect example of the AI Effect:
https://en.wikipedia.org/wiki/AI_effect
Sorry, but I'm a mathematician. Check my comment history, I'm the first to disparage any kind of "AI" (which just means human-programmed heuristic most of the time), especially that which just does brute-force search of possibilities. That's NOT AI. Almost every "game AI" isn't AI. Not even close.
However, in uni, one of my lecturers was studying Go as one of his prime areas of research, and I've seen - and checked - some of the numbers here.
You have no idea what this machine has just done. It's leapt forward some 10-20 years in terms of computer Go-playing capability in one fell swoop. The numbers involved in Go are so huge that brute-force search, even for a limited number of moves, is absolutely impossible in the times given.
And it isn't being given programmed hints, because Go is just too complex a game for that beyond amateur play. There's a handful of hard-and-fast rules of what's a stupid move and what's not and everything else interacts SO MUCH with the rest of the board and future plays that it's almost impossible to even tell who's winning most of the time!
As such, this system, no matter the power behind it, is doing something that dumb, brute-force, play-the-game AI written by world-experts in Go, AI, and game theory wasn't expected to be able to achieve within the next decade. And it primarily gets there because it learns from information fed to it.
At that point, although it's only limited to Go, the engine is proving itself capable of - almost - a kind of intuition, insight and "feel" for the positions rather than anything to do with numbers and scoring and weighting and pre-written rules. Now, that's a vastly overblown explanation, still. The computer isn't "feeling" anything. But whatever it's emulation and use of such, it's leaps-and-bounds ahead of its competitors.
This is why it makes BBC News, Slashdot and every other media outlet. It's not just winning by brute force. It's doing something else. It's spotting patterns in data it's never been exposed to before. It's able to hypothesise and learn from mistakes on board layouts that maybe NO HUMAN HAS EVER SEEN BEFORE OR WILL AGAIN (that's how large some of the numbers of possibilities get!).
Even a pack of cards, with 52! = 8x10^67 potential arrangements of a shuffled deck:
http://www.murderousmaths.co.u...
Pales in comparison to the number of possible Go positions (2x10^170) and the ways that you can move from one to another (~ 1 x 10^768). And that's just on a standard 19x19 board (something almost unplayable for a computer just a decade again).
This thing isn't calculating. It's gaining insight from historical observation and applying that to self-similar situations that nobody has ever been able to analyse, nor which it could ever analyse fully in the time given. That's the start of "true" AI. It's only a start, but it's quite seriously ground-breaking in that ability.
And once you start down that route, there's nothing stopping AlphaGo quickly learning every similar game, then dis-similar games, then other games, then other things entirely, using the same kinds of system underneath.
Honestly, there's a reason that game theorists and AI-experts are making a fuss about this.
I've been following the matches with the same expectation and anger I felt in 1997 during the Kasparov & Deep Blue rematch. The final result has been similar, and although it has been well reasoned that chess and go are pretty different games and Deep Blue and AlphaGo are pretty different machines, the bittersweet sensation is identical. I had a naive hope in the human superiority just for a little more time. I was pretty sad after the final game: Lee Sedol seemed really disappointed and sad himself. I can't imagine the pressure he's felt throughout the event, and his face -that's my impression- seemed to tell us "I've failed you all". He later told in the press conference that he felt he could have done more in the games -I'm sure he'd like to play more games to test himself again- and I wonder what could have happened if the matches would have been played without general knowledge. Feeling that kind of coverage must have been really stressful. If you ever read this, Mr. Sedol, thank you. And please, don't ever feel disappointed, you've done a fantastic job.
It won't turn out that Se-Dol has quite a few other skills. That's the problem. There's too much focus on brainpower to solve these highly restricted set of problems. That's the issue. What makes real creativity is not a mind like Lee Se Dol (with respect). It's the people that are capable of inveting somethng truly original. AI can't do that .. yet. That's true creativity, and it's not something Lee Se dol has, or something that you find very easily in Asia, generally.
How would Einstein do against Lee Se Dol? Not ver y good..
Who would you put your money on to contribute to meaningful, original science? Einstein .. every time.
Even just this last week, another of Einstein's theories has been verified - gravitational waves, as shown by the LIGO istrument. He has more "creativity" than the sum totoal of all these Go players.
Playing a game, and doing it well, requires real creativity. Arguably a lot more than science, actually. When you study science, all you're doing is discovering information already out there - water had its properties and was built by molecules long before it was classified as H2O, and nothing changed after. Doing well at Go can't be calculated cold and hard - much of it is subjective, and that's what makes this discovery so important. The computer didn't win by just repeating the same patterns or evaluations over and over, but actually learned from each game and was able to apply that to the future. That's the start of self learning AI.
Like a ton of people in the world (the majority most likely), you apply the no true scotsman argument to this debate. It's not real AI until it learns strategies not programmed into it? Oh wait, no, it's not true AI until it creates its own strategies? Oh wait, no, it's not a true AI until it can do this to something other than Go? What next, it has to socialize and disobey? The approach this machine used was incredible, and the insight was extremely important - being able to learn by studying a history of decisions, that's something that lays the groundwork for every future AI project from here on out.
This represents a massive step forward in artificial intelligence, by leaps and bounds, and the sad part is, you don't even know it.
"Set a man a fire, he'll be warm for the rest of the night. Set a man afire, he'll be warm for the rest of his life."
He's likely to be remembered as the last human being to beat a Go AI on tournaments.
Move 78, in particular, was so good that his partners and commentators in China have already called it "the hand of God", but it really was one of those things which happens once in a blue moon, even for a player like Sedol.