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Google's AlphaGo AI Defeats the World's Best Human Go Player (engadget.com)
It isn't looking good for humanity. Google's AI AlphaGo on Tuesday defeated Ke Jie, the world's number one Go player, in the first game of a three-part match. The new win comes a year after AlphaGo beat Korean legend Lee Se-dol 4-1 in one of the most potent demonstrations of the power of AI to date. Adding insult to the injury, AlphaGo scored the victory over humanity's best candidate in China, the place where the abstract and intuitive board game was born. Engadget adds: After the match, Google's DeepMind CEO Demis Hassabis explained that this was how AlphaGo was programmed: to maximise its winning chances, rather than the winning margin. This latest iteration of the AI player, nicknamed Master, apparently uses 10 times less computational power than its predecessor that beat Lee Sedol, working from a single PC connected to Google's cloud server. [...] The AI player picked up a 10-15 point lead early on, which limited the possibilities for Jie to respond. Jie was occasionally winning during the flow of the match, but AlphaGo would soon reclaim the lead, ensuring that his human opponent had limited options to win as the game progressed.
Slightly modify one rule in the game and the AI would be immediately and convincingly trounced.
Playing games is not AI. A game has strict rules. These are easy problems for computers to solve. Computers love strict rules. It isn't intelligence. And don't say "well you cannot do a depth first traversal of same Go states becuase it is so huuuuge". That doesn't make any difference: just use a different algorithm. It still isn't AI.
Mathematically, which is harder to solve for, Go or Chess? Is this some sort of diversity thing that they've started using Go over Chess?
Adding insult to the injury, AlphaGo scored the victory over humanity's best candidate in China,
There is no insult to losing in China. The appropriate response is, "Thank you for allowing me to win."
The real question is: when two identically trained systems compete against each other, what are the underlying mechanisms of competition leading to one winning?
AlphaGo at its core is an MCTS
For such a thing, one needs (I think) to do some unexpected moves to constantly force machine into sparsely probed regions.
And, during discovery stage, one needs doing it "off-line" to avoid google's retraining. Thankfully, space is big enough to ensure that google can be forced quickly enough into deep woods.
For a match like this - one needs to use different precalculated prologs for all games (won or lost).
It's more like hacking than playing...
How is it at hungry hungry hippos?
It isn't looking good for humanity.
Purpose built machines have been able to, or be used to out do humans for a very long time. A lever can be used to lift more weight than a person alone can. But we're not being ruled by sticks. Cranes can lift even more.
Cars are used to move people further and faster than they could on their own. Computers can do many more calculations per second. These things make life better for humanity as a whole.
Unless AlphaGo figures out a way to keep a person from unplugging it, I'm guessing that humanity will be just fine.
Checkers was, at least as of a few years ago, actually the most complex game to be completely solved..
Spoiler: it's a draw.
Is AlphaGo programmed in Go?
Why doesn't Google actually apply this to solve some REAL world problems, huh?
Like figuring out how to end war, share resources and be peaceful? Or whom to vote for as president?
That's right - Google is bringing useless things to the table.
My problem with these things is that they are purpose built by a team of engineers, and updated massively in between matches. It's valid to say "AI did X", but given how the AI is redesigned, tweaked, and special cased for each new opponent, it's much more accurate to say "a massive well funded team of specialists beat this one old guy at his life's work", because the AI that is developed will never be made available in the manner that an AI or other fixed product would be. If the top guy is allowed to play against the AI a thousand times, will the AI still win?
In other words: is it the AI doing the work, or is it all the tweaks and redesigns that happen between matches, done by the combination of game specialists and engineers, that have rigged up some kinda trick that holds out for a few matches?
Someone call Boston Dynamics.
Fuck.
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something actually useful? Like compete against the world's best cellular biologist to create a cure for cancer?
I'll be impressed when a computer wins Hungry Hippos, that game is obviously rigged towards anyone younger than 7. My kid beats me in it all the time.
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Who the hell downvoted the parent?!!
What the fuck is going on??
Go is, as you mention, even bigger. So it was solved by brute force, much more powerful computers, and improved pruning algorithms.
No, that's not at all how Alpha Go works.
...Go have no strict rules,
Go most certainly does have strict rules. You can only play one stone in a turn, for example; you can't play another stone until your opponent plays; and you're not allowed to just pick up all the stones that you don't like and throw them into the trash.
yes there are some basic rules describing player moves however just applying them doesn't play a good game.
Sure. In chess, too: it's easy to learn how to move the pieces. Moving the pieces to win a game is hard. Saying that "following the rules doesn't play a good game" is not a subset of saying "Go doesn't have strict rules."
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Why doesn't Google actually apply this to solve some REAL world problems, huh?
Possibly they have. The problem they seem to be solving is "how to we get Google to take over the internet, and from there insert itself into every crevice of human endeavor?" And it seems to be pretty good at solving that problem.
Wow, I didn't think anybody still remembered "Game of Life".
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this should not be a surprise
Seriously here, I thought that already happened. Hum.
I just wonder what would happen if the next tournament play with a board that is double the x,y size
A 38x38 positions board
Would Alpha Go maintain the lead ?
Maybe we adapt faster ?
Any GO expert care to explain why this is feasible or silly ?
It was already lost in the 1950s, when the original researchers tried to make an end-run around perception, which required a density of compute Not Available Soon.
You can't not deliver on your founding conceit for six decades and then expect no social erosion of your Tokamak grandeur. Of course, they made lots of progress, but hardly any of this happened under their original Tokamak brand.
Those who still care about the One True Meaning usually trot out the term AGI (artificial general intelligence).
The next rung up the ladder from AGI is MHHAGI (muhaha artificial general intelligence). At this point, it's clear to everyone that we're finally and truly talking about penises as endowed by God himself, so King Arthur's quest for the master term usually ends here.
Any GO expert care to explain why this is feasible or silly ?
It is silly. If you train a neural net to differentiate a photo of a dog from a photo of a cat, it can learn to do that. But it is then silly to expect it to recognize a picture of, say, a horse. That is NOT what it was trained to do.
Likewise, Alpha-Go was specifically trained to play on a 19x19 board. Any other size, such as 18x18, would not even be recognized as valid input.
On the other hand, if you trained it on variable sized boards, then it could adapt to that.
Here is an actual example: Deepmind trained a NN to play a wide variety of video games. When it was introduced to a new video game, it could used its existing training to play and master the new game much faster than even the best humans.
Go is played on 9x9, 13x13, and 19x19 boards. On the smaller boards, tactics (joseki) is more important. On bigger boards, strategy (fuseki) is more important, and apparently innocuous early moves can have far reaching effects much later in the game. On a 38x38 board, strategy would likely be even more important, and winning the game would require a profoundly different style of play. My gut feeling is that an AI, trained by playing against itself, could master that new style much faster than a human.
It seems to be about goalposts and definitions. One could have the same discussion with "Does peg legged Pete have an artificial leg?" Some would say "Yeah, sure. Artificial leg." Others would say" No way. It's just a piece of wood, driftwood even, that he uses to hobble around on. An artificial leg is something else" and they would keep saying that even after we'd have the "six-million dollar man" legs. Same with artificial life. Is it life, but not really, just a good approximation because it is "artificial". Or is it "life" that did not arrise through the ongoing natural evolution process. When talking A.I, we're not talking artificial humans or even "general artifical intelligence" (whatever that would be) but something that gives the impression that the thing on the other side (of the screen, the board, the table, the whatever) is intelligent. For the champion Go player, if he was to play against AlphaGo without knowing so, he would not be able to tell if he was playing a human or a machine. So, AlphaGo, in that incarnation ( it can learned to a totally different skillset) is real A.I for Go in a sort of successfull but limited Turing test. Same for Big Blue for an even more limited chess Turin test. Now that we know Big Blue can beat world champions and AlphaGo can beat human Go champions, we kinda say, meh, yeah, we know it's better than humans but it's not real A.I. Things will progress piecemeal in this fashion untill we have natural conversations with our digital assistants (on phones? Tablets? Robots?) knowing that they are not real humans but acting as if they were. And still people will say "Yeah, but that's not real A.I"
"uses 10 times less computational power than its predecessor that beat Lee Sedol, working from a single PC connected to Google's cloud server"
That's more likely to mean that it uses 10 times MORE computing power, it just lets other systems do it.
... is that AlphaGo won by half a point. That's the smallest possible victory margin, and it's a common new go player mistake to aggressively grab more territory than you need to win, with the whole thing blowing in your face later on because you're overextended. AlphaGo "saw" it was winning early enough and stuck to the low risk strategy to ensure it did just that. Pretty cool game to watch in 2x if you've the time.
I just wonder what would happen if the next tournament play with a board that is double the x,y size
A 38x38 positions board
Would Alpha Go maintain the lead ?
Maybe we adapt faster ?
Any GO expert care to explain why this is feasible or silly ?
I'm not an expert on this particular system but my understanding is that generally this system doesn't learn "on the fly". Rather there's a seperate system that trains the neural net and one that uses the net to play.
So it doesn't make sense to test the adaptability of the game playing system in that way as that isn't the system that can learn, and involving a human player in the learning system would be really time consuming and wouldn't be likely to demonstrate anything interesting (I don't think anyone thinks humans take more games to reach a given level of skill than the AI).
It's clear that, for national security reasons, this technology should be trained and deployed to assist with foreign relations. Particularly, since it should theoretically be a master of game theory, it should be trained on a set of prior foreign relations incidents. In order to deal with North Korea and other rogue nations, it must be taught brinksmanship. In order for this to be effective and to prevent the enemy from calling our bluff, it must be given direct control of our nuclear arsenal. The only question left is whether to call it 'Joshua' or 'Skynet'. /s
Corruption is convincing someone that the selfless ideal is the same as their selfish ideal.
All I have been able to glean so far is that the rewritten version uses around 10% of the computing power (both to train its neural networks, and during actual play) to achieve much improved play compared with the original AlphaGo used to beat Lee Sedol. Thus far, although promised, the architecture behind the rewritten version is unpublished. Later this week, some insight is going to be provided.
The old version was based on a combination of techniques (primarily multiple neural networks, combined with Monte Carlo techniques). The interesting thing about the way it operated was that it could tell you which move was likely best, but could not explain why. The same is actually true of human Go players. While locally best moves can be identified, the human selects the globally best move based to a large extent on feel. The game is too complex (both for humans and AIs) to use calculation on a board wide basis. Both the old and new AlphaGo systems appear to demonstrate characteristics we would refer to as "intuition" and "creativity" if seen in humans. How similar is it to human instinct and creativity? We really do not know.
I am extremely interested in learning how the rewritten system works. I think the twinning matches (between two teams each with one human expert and AlphaGo collaborating with each other) will also be extremely significant. The short to medium term promise of AI involves humans and AIs working together. As the AIs become increasingly complex, and the manner in which it comes up with recommendations ever harder to comprehend, this is a critical challenge to be addressed.
We had a shot, but then Marion Tinsley died. (Dam, a weakness of these temporary carbon units)
After that, no human had a chance at checkers.
It depends how exactly it was trained. Most likely it was trained on a fixed-size board, so the architecture of the neural net wouldn't match a bigger board without modification. That's not necessarily true though, there are neural net designs that can take flexible sized input. Alpha Go could certainly be trained to do it.
The question is interesting. Given a human and computer that had never played on a larger board, which would do better? Go is a game that is fairly local (unlike chess) so it doesn't change radically if you make the board bigger, which means a lot of your strategy can be transferred... for both the computer and the human.
It's worth keeping in mind that although Alpha Go has played more games (against itself) than any human has, it has played for less clock time than any human champion has.
First off, the board would have to be an odd number of lines. That said, AlphaGo learns at a running pace, while humans learn at a walking pace. Indeed, I think a program like AlphaGo could master its strategy faster than humans could no matter what the board size is.
John Brunner made me curious as he is one of my favorite writers.
The story however is from Fritz Leiber.
Oops!!
Shows I should never trust my memory, and should always look things up even if I think I know them. You're right, the John Brunner chess story was the novel "Squares of the City."
I stand corrected. Fritz Leiber it is.
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