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Computer Beats Go Champion
Koreantoast writes: Go (weiqi), the ancient Chinese board game, has long been held up as one of the more difficult, unconquered challenges facing AI scientists... until now. Google DeepMind researchers, led by David Silver and Demis Hassabis, developed a new algorithm called AlphaGo, enabling the computer to soundly defeat European Go champion Fan Hui in back-to-back games, five to zero. Played on a 19x19 board, Go players have more than 300 possible moves per turn to consider, creating a huge number of potential scenarios and a tremendous computational challenge. All is not lost for humanity yet: DeepMind is scheduled to face off in March with Lee Sedol, considered one of the best Go players in recent history, in a match compared to the Kasparov-Deep Blue duels of previous decades.
When I first read the headline, I pictured a robot's arms flailing about, whacking its human competitor upside the noggin. "So, A.I. finally got the emotion thing down."
Table-ized A.I.
What makes this especially interesting, is the victory was not achieved with the sort of brute-force approach used by Deep Blue in chess. This used a deep neural net, and algorithms similar to how we believe that humans think. Last time I heard about this, they could consistently beat humans on a 9x9 board, and were working on 13x13. I was surprised to hear that can already win on a full sized 19x19 board. I thought that was still a few years away. This is amazing progress.
Come on, Siri would be quite impressive if you showed it to a researcher from the 1950's. Same with the IBM chess win. Our expectations have simply increased and/or it "looks simple" after you see how its done.
Table-ized A.I.
I've read the paper.
It doesn't quite use a "brute-force" approach, but it certainly does use significant, and intelligently designed, Monte Carlo searches which are informed by well-trained neural networks. The neural-network alone approach, without any Monte Carlo search during play, is not as strong, though it does appear to equal a state of the art conventional Go program. See Figure 4b.
And the training of the neural networks and construction of their training sets certainly did need quite a bit of 'brute force' as well as 'efficiently wielded force in large quantity'.
No, this is not an accurate understanding of Go strategy or how it is played at the highest level.
In fact, if the game is played in the way you describe, previous computer algorithms were quite good at analyzing the local interactions of pieces, yet were roundly defeated even by top-level amateurs with handicaps. The reason is that at more sophisticated levels of play, one's skill level is correlated with how one perceives and evaluates the entire board. There is a sort of "gestalt" of Go that good players seem to grasp in ways that are very difficult to objectively describe, and sometimes a stone placement can seem arbitrary but become pivotal many, many moves later. This is reflective of a deep and global strategy that computer algorithms--at least until now, it seems--have had tremendous difficulty in emulating.
Wake me when he beats Lee Sedol. There is a signficant difference in skill level between a European champion and someone like Lee Sedol.
The point is to program a computer that thinks like a computer. The expectation is that once we discover AI, we will understand how humans think. There is a name for this: strong AI.
The first computers were very cool, and could calculate faster than people. We have a name for things that are cool, and do intellectual tasks faster than humans: it's weak AI.
In this case, the computer is still using the MonteCarlo approach to finding a move.....which is roughly "choose a bunch of moves at random and choose the best one." It's one way to prune the tree, and it is surprisingly effective in the case of Go. But it's not how humans think.
"First they came for the slanderers and i said nothing."
There is a name for this "not AI" comment: The AI effect. Basically, whatever can be done with a machine is automatically considered "not AI", because it's no longer magical, just engineering.
https://en.wikipedia.org/wiki/...
what a name. like: Tiger Woods. sorry he got beat up. hope Computer is convicted.
Using well known and solid techniques along with vast computing power, Google has finally broken into the majors of Go. The next question is whether a home computer can run the neural network now that it's been trained;.. or do the CPU and RAM requirements still place this level of play into the corporate-only bracket.
Once we can run our own purpose-designed expert systems on commodity hardware, that's when the social change AI will bring will be nigh. Whether it's beneficial to everyone, to just the 1%, or whether everything goes tits-up I have no clue. But we aren't there yet, because things like this are still hugely expensive to train and operate. A government can make a single person fly with a jetpack, but that has had zero impact on our daily lives. Social change rarely occurs until revolutionary capabilities are available to the masses (or at least the small business owners).
"I will trust Google to 'do no evil' until the founders no longer run it." Hello Alphabet.
As wickerprints pointed out, this is completely false. A good move in a local position in Go may not be the best move overall and may, in fact, be a bad move when other areas on the board are taken into consideration. If a computer program split the board into smaller and smaller sections it could very easily get confused by a good player. Also, the number of moves possible at any given time in Go is exponentially higher than in Chess; you can brute force every possible path in Chess, you can't (not yet) in Go. It wasn't that it was more popular; it was much easier.
-SaNo
This doesn't brute force the board combinations completely; they used a Monte Carlo algorithm and a neural net to intelligently limit potential moves. Chess is brute forced; this is not.
-SaNo
So intelligence is 'doing things the way humans do'? There can't be any other type of intelligence?
If a problem requires intelligence to solve, then any solution to that problem on a computer is artificial intelligence regardless of what 'parlour tricks' are used. And yeah, humans are really good at pattern recognition while computers are really good at arithmetic so I would expect artificial intelligence to differ significantly from human intelligence.
PS: This AI evaluates significantly fewer moves than deep-blue. A brute force search of go is woefully ineffective no matter how much processing power you have.
What makes this especially interesting, is the victory was not achieved with the sort of brute-force approach used by Deep Blue in chess. This used a deep neural net, and algorithms similar to how we believe that humans think.
Mainly due to the different goals of Chess and Go. In Chess you can have as much material and positional advantage as you want but it's worthless if your opponent can mate, which means you have to calculate the ways that could happen. A blunder and a sacrifice might look the same unless you look deep enough. In Go there is from what I've understood just stones, it's not like cornering one king stone turns the game. It seems their key break-through was being able to evaluate the position and find winning patterns and disclose losing ones without actually playing them out, which you can do because unlike chess there's no way throwing away all your stones can lead to victory.
Live today, because you never know what tomorrow brings
In this case, the computer is still using the MonteCarlo approach to finding a move.....which is roughly "choose a bunch of moves at random and choose the best one." It's one way to prune the tree, and it is surprisingly effective in the case of Go. But it's not how humans think.
We don't know that. It certainly isn't how players usually characterize what they consciously do. However, it is certainly possible dozens or even hundreds of possible arbitrary moves are tested and discarded unconsciously, and a few "good" moves, selected by various heuristics such as studied joseki, are bubbled up for conscious consideration. We only ever hear about the conscious portion, and even then it's jumbled. It's hard to know what's getting them to that point, and once past it even good players can't always describe what makes a move good or problematic. They don't even know what they're doing, necessarily. We only know the outcome of that process.
~Anguirel (lit. Living Star-Iron)
QA: The art of telling someone that their baby is ugly without getting punched.
We don't know that.
That is exactly the problem.
"First they came for the slanderers and i said nothing."
Nope. It isn't AI. Chess playing computers aren't AI either. Neither is Siri. It isn't magic either, just engineering.
This isn't AI. And yes, AI means human style of intelligence. This is just a algorithm and some clever programming.
The most a computer will ever be is an algorithm with some clever programming. Are you saying that AI is impossible.
Take go for example. Let's say hypothetically that I develop this mega-awesome heuristic for evaluating go positions. So good that without search (1 ply) I can play a mean game. That heuristic evaluation function is either: me encoding knowledge about how to play the game into programming or it is 'learned' through random manipulation of data on a computer which is rewarded when it wins games.
Is either of these intelligent?
1) Trained a deep neural net to predict a human player's move. Correct predictions were 57% based off 30 million samples. The previous best was 44%.
2) Create a second deep neural net to determine the value of a board, meaning if you're winning or losing.
3) Use the two networks as the heuristics in a tree search.
4) Let the computer play itself to get better (basic reinforcement learning).
5) Have excellent hardware to run the tree search during a real game.
This is all standard AI stuff. Here's a quote from the article: "AlphaGo is a step toward an “enlightened” AI because, as Schaeffer says, AlphaGo is a first example of an AI with “general intelligence.”". Just disgusting. This AI has no general intelligence. The AI can't do anything but play Go. You'd have to retrain the nets for anything else. A general AI wouldn't need retraining, or at least no more than we do. They used a general AI algorithm combined with another general AI algorithm. You could take their combined algorithm and apply it to other things, but that does not equate to general intelligence.
Oooo, I'll make an AI with super intelligence by using three neural networks. 1 to predict moves, 1 to evaluate the current state, and the 3rd to estimate the value of 10-20 moves in the future. The 3rd will help in pruning more branches of the tree thus letting the AI search deeper and make better movies. Send me research money and fame!
I googled Fan Hui: one source says he's 8 dan amateur, another that he's 2 dan pro. That's only a little bit better than go programs have been for several years, and much weaker than the best professional players. If he's a top player in Europe, that mostly says that go isn't played at a very high level in Europe. I think that the progress that has been made on go software is really great, but the claim to have beat a 'go champion' seems a bit of a spin.
let's play global thermonuclear war
If the computer could beat a 2-dan professional, then it's clearly even smarter than SHODAN!
The most a computer will ever be is an algorithm with some clever programming. Are you saying that AI is impossible.
It all depends how meta you want to go, I would say that as a minimum an AI should be able to come up with its own algorithms and solution strategy. Like if you hand it a book of chess rules it should be able to work out by itself that an opening book is useful, maybe an end-game database, maybe some brute force search, some positional analysis, monte carlo searches, neural nets, whatever. Not just finding the right parameters/weights or crunching through someone else's algorithm, it has to be able to fundamentally alter the way it plays chess. Just like we don't learn everything from our parents/teachers, sometimes we find new and better ways of doing things of our own creation.
Of course you might say that's an algorithm too, but I have no idea how an algorithm-generating algorithm would look like. Like if you tell it to "make money" it might start looking for work or selling drugs or counterfeiting or to become a chess world champion for the prize money and teach itself chess. And unlike chess it has to deal with unclear rules and incomplete information and perhaps even conflicting goals, since you probably don't want it to become a hit man even though that could be lucrative. I do come up with ideas myself but I'd have a very hard time explaining how or why I came up with them, I don't know what creativity is but an AI needs it. I just hope that doesn't extend to goals too, because Skynet.
Live today, because you never know what tomorrow brings
Almost certainly this has never been true. Chess may be better known in Europe and America - approaching a billion people, being generous - but Go has been by a margin the most popular board game in China, Japan the Koreas, Taiwan and other oriental countries, currently totalling over 1.5 times as many people. With the thousand-plus year time advantage Go has over Chess, I really doubt your assertion has ever been true.
But WTF - I've been proselytising for Go since 13 years before I got a phone line, let alone dial-up internet access. What would I know?
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
Good luck with that. I mean, people have only been trying that for ...
The first computer-Go programmer I met and played against was in 1984. He was trying to do that, on IIRC a BBC Micro with 128kb of RAM.
I've been following the subject since then. Shockingly, you describe EXACTLY the process that most people have tried to implement. It was only about a decade ago that their progress crept ahead of my playing level. But since I only manage a half-dozen games a year on average, I've maintained the same strength for over 20 years.
Going up to the point that they can beat PROFESSIONAL PLAYERS, consistently ... Those people are 15 to 20 stones stronger than I am. That's somewhat comparable to comparing my 30 years of playing to someone who is literall picking up the stones for the first time in their life.
This is a huge advance, even if it is only optimising multiple moderate-depth playing engines.
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
Videos are available.
a,e,i,o,u and sometimes w and y (at be if of up cwm by)
I've never seen a discussion of AI that REQUIRED them to mimic humans. In fact, the unintelligent neural network approach to antenna design (artificial, with NO implication of intelligent search for a solution) produces designs explicable to physics, which work (your cell phone may well rely on one), but no-one knows how they get from this design to that. Their non-intelligence is different to human intelligence, but in some ways stronger.
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
You are correct, and the three other clowns that responded to you don't understand your post.
In Go, symmetrical board states are identical, and thus, for the beginning several dozen moves of a game, the effective search space is much, much, smaller than a naive approach considering all 361 positions.
The popularity of Chess over Go in the west is absolutely why it was the focus of early publicity stunts and man-vs-machine matches. The corporations building and programming the machines were all western and focused on western markets, and the computational effort to solve Chess was very much aligned to the growth in speed and capacity of the computers they were trying to sell.
The 19x19 board in Go makes it a much wider tree to search, but it is a finite tree. Chess is an infinitely deep tree with very long potential loops. (Though some tournament rules declare a draw if a given position is repeated a certain number of times.) They require different strategies for solving efficiently. Naive Go algorithms will beat any human player on any size board given enough time and enough storage.
Poker is a game of incomplete knowledge - you don't know what cards are in the other players hands.
Go is a game of complete knowledge. As is chess. And draughts.
The two classes are completely different.
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
If by enough storage you mean more bits than there are atoms in the universe, and by time you mean longer than the life of our solar system, you are correct. However, constructing and powering your computer does not seem a trivial task.
I have some involvement in this field and I can't think of a single time I've seen the term AI in a book or research paper. The only time I ever see anyone use it is in the media or various futurists. Usually people just name their specific subfield or name the types or algorithms in which they specialize. "I specialize in unstable learners" or "I specialize in transfer learning" but I've never heard someone say "I specialize in AI."
I think naming neural networks as they did was probably a very bad idea. The mathematics have very little to do with actual neurons in the brain but the name leads you to believe differently.
"There is no such thing as AI currently."
The fact that it's currently pretty stupid doesn't make it Artificial Intelligence any less.
It's not AI like you see threatening the world in movies. No one has ever made a general artificial intelligence, and this particular example does not bring us any closer to it. It is, however, one more thing that computers shall forever be better than humans at.
Don't waste your vote! Vote for whoever you want, unless you live in a swing state it won't matter anyways
Chess is brute forced; this is not.
Oddly enough, I'm currently in an AI class and brought up Go just yesterday... The improved algorithm and neural net is one thing, but I wouldn't be surprised if they still tossed more computing power at it than Deep Blue.
I don't read AC A human right
But even in Go there is capture and situation reversals are possible, so a sacrifice might still be confused with a bad move.
That's because AI has a real definition: "computers that think like humans."
If you use a trick to solve the problem, then good job, but it's not AI.
"First they came for the slanderers and i said nothing."
That's because AI has a real definition: "computers that think like humans."
I dispute that. What a computer that definitely thinks, but not like a human? What if we could develop a computer that thought like a dolphin? Would that not be AI?
systemd is Roko's Basilisk.
Computers have beaten higher-ranked players (Catalin Taranu, 5-p) on the 9x9 board. Computer go is nowhere near computer chess where humans cannot stand a chance against the top engines like Komodo, which is rated over 3300 ELO.
I cannot help but notice that Google are advertising their AI system, after IBM pushed Watson for years, and Microsoft have recently open-sourced their system:
https://github.com/Microsoft/CNTK/
I am curious though about the result against a 9-dan pro, and what will such a player say about the way the engine plays.
Just before /. wets itself with all this comps won a go game - there is still a small difference between 2d player from France and a professional players taking part in professional leagues in Japan, China and Korea. It is indeed a respectful result beating 2p professional. I wonder how that performs with people on the top of the league tho.
As someone put it, a real AI would spend much of its time wondering whether to kill itself.
Ex Machina was quite nice actually, for the whole question of how to test whether a thing is sentient.
But I'd guess that the "brain-machine" is what produces/structures any phenomena/data, like being able to recognise a tree amongst all the patterns of colour, or the right moves in a game, whereas sentience is that which experiences that data — so artificial intelligence can be any clever data processing, sufficiently clever to impress us, like a computer being able to say "that's a tree" when its camera is pointed at a tree, whereas artificial sentience would be when that machine actually becomes sentient... actually starts experiencing what it is processing, and who knows if that's even possible.
[Cum grano salis alert: extremely amateur go player here. ]
Also, Go often has many local "battles" going on simultaneously. If you've figured out that you are losing in one corner, you switch to a different corner. If the opponent finishes you in the first corner, that gives you one or more free moves in the new corner. So, the opponent will generally follow you to the new corner. What you can then try to do is build from the new position towards the old in the hope of rescuing that position. But this means that there can easily be 50 moves between having effectively lost a position, and the material disadvantage from the position being finished.
Well chess is giant in India, Europe, NA, and generally more popular in SA.
And at least in Japan, while Go is popular, in all my years living there I've only ever met shogi players (and the Chinese I have interacted with quite regularly don't play at all).
But oddly every Chinese person I know has played chess.
Without a doubt, both in absolute and computer professional relative terms, chess is more popular.
Really it's more likely brute force is easy to program compared to neural networks and in general, the advancement in chess was from position evaluation which is markedly easier than Go (because you can simulate chess to a deep enough level to see if a sacrifice of piece or position is worth it as long as you prune your tree even marginally intelligently). As a player, I find both games awesome (though I am terrible at Go), but I think it is pretty obvious position evaluation is easier in chess.
Of course you cannot "solve" poker or other games with randomness or hidden info in the sense of guaranteeing a win.
But that doesn't mean that such games cannot be solved in a more general optimization sense, e.g. maximizing your probability of winning a single match, or maximizing your expected monetary gain in poker.
E.g. consider a typical late endgame situation in Backgammon. You cannot "solve" it in the sense of guaranteeing a win. But clearly there is a "best move" in the sense of maximizing your probability of winning. And analogously backtracking to earlier moves.
We can't (and the existing AI Chess players don't try to) brute force every move in Chess. The compute resource needed would be quite extreme, and if you could do it at all, you'd _solve_ Chess and we'd be able to say confidently "White always wins" or, perhaps as likely "It's a draw" when players know what they're doing. For example we have solved Tic-Tac-Toe and Connect Four, in both those games the first player wins if they play perfectly, regardless of what their opponent does.
AI Chess players begin with an opening "book" which is a collection of sets of early moves players have found to be strong. Human players typically learn to play Chess openings this way too, but from an actual written books as learning that some openings are weak the hard way can be disheartening and also misleading. Once book moves are exhausted, or if the opponent deviates from the book and plays in an unorthodox style, the AI has a function that assesses arbitrary positions and ranks them as more or less desirable. For example, a position where you have lost a pawn is generally worse than one where you haven't, but one where you threaten the opponent's King with a Rook may be worth the loss of the pawn. It can "brute force" lots of possible plays and responses, to see which ones consistently end in good positions and favour those, but by no means enough to foresee the end of the game from the beginning.
Towards the end, what we _have_ brute forced is the endgame tablebases. These tell us how in practice a win can be forced by one side, or a draw reached, from particular positions, usually with only a handful of pieces remaining such as a King and two Bishops versus a King with one Bishop and a Knight. But there is no prospect of the tablebases eventually expanding to cover the whole game. They're already _enormous_ and they cover only an infinitesimal part of the game.
Anyway, while brute forcing Chess is implausible and will probably never happen, brute forcing Go isn't merely implausible, it would require far more compute and storage than could conceivably exist in our universe. It is _literally impossible_. Go is being attacked by AIs in the same way humans do it, by intuition. It's just that we're scared to label what the machines do as "intuition", or the same, to label human intuition as just a neutral network blindly associating certain patterns with success despite not having any communicable "understanding" of what it has done.
I think that you are underestimating the breakthrough here. Previous Go programs were not even close; there were fundamental flaws and inability to solve the problem. If this program can beat someone of this rank on a 19x19 board then even if they lose to other ones later this year, they will beat them handily next year. It's all over but the crying.
The more people I meet, the better I like my dog.
That's because AI has a real definition: "computers that think like humans." If you use a trick to solve the problem, then good job, but it's not AI.
Except that it is beginning to look like human intelligence is also just 'tricks'. Your brain takes short-cuts, makes assumptions, 'fills things in' both perceptually and conceptually, and forms a consciousness that is largely made up from evolutionary history and previous memories. Yes, it's wet and it evolved, but it's just a bundle of ad hoc solutions that combine to form your mind.
The more people I meet, the better I like my dog.
What makes this especially interesting, is the victory was not achieved with the sort of brute-force approach used by Deep Blue in chess.
Wake me when a computer can beat a human champion while using 100 w of power or less - about the equivalent power consumption of a human. Actually, the brain uses about 20% of this but lets be generous.
From some sort of objective measure of the underlying OS, perhaps you are right. However, as a person that runs Linux on my workstations / servers, I cannot use it on my desktop because I need Outlook and MS Office. That's not a technical requirement, but it's the world that people actually live in and need to be productive. No, Thunderbird doesn't cut it; no, LibreOffice does not cut it. Plus, I can't play Eve Online in Linux. So, I use Windows.
The more people I meet, the better I like my dog.
The underlying algorithm has learned to play many games. That's actually the more important part of this breakthrough. Yeah, yeah, you're going to say that it doesn't count because the exact same trained system doesn't play all the games simultaneously. That's coming.
The more people I meet, the better I like my dog.
I never claimed that Go was popular in the west. Or even known. Unusually I had heard about it before I came to university - that was very unusual. But then I hit the proselytization trail for the next 3 years.
Significantly, I had to do far less explanation amongst the computer science students, because it was already quite well known as a really challenging case for information processing. For the corresponding case of chess, there were already commercial machines available that could take people up to something like a national-team grade player, and the topic was seen as dead from a theoretical point of view having moved to engineering. No wonderfully new insights seemed necessary to beat the rest of the world, just bigger, faster machines and/ or bigger faster ending databases.
So 30 years ago, there was a generation of CS students who were looking for AI challenges, and Go was on the radar. That it has stood as a challenge for 30 years after the fall of chess suggests that it is a genuinely harder problem.
There is also a point that John Tromp hinted at in his "we've counted L19" post of just a couple of days ago : 21x21 go has been an occasional pastime for regular players since ... well at least 1965, since it was mentioned in a text book then. But I think that it has been experimented with back into the Middle Ages. (Sensei's Library has a discussion of 21x21, about how the balance of influence against territory may be different, and the corner-vs-centre balance is changed too, but few people have the experience to really say (a few thousand games played and recorded). But the same rules will work perfectly on a 21x21, making John's technique for analysing 19x19 applicable there, but it would just be a harder problem. So there is an open-ended range of exercising benchmark possibilities there.
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
Like if you hand it a book of chess rules it should be able to work out by itself that an opening book is useful, maybe an end-game database, maybe some brute force search, some positional analysis, monte carlo searches, neural nets, whatever.
Did you figure these things out on your own, or read them somewhere, or someone told you?
For anyone who wants to enjoy playing chess, I don't think memorizing a book full of openings is going to be "obvious."
Unity? Screw that: XFCE. Slashdot Beta? Screw that: SoylentNews. Australis? Screw that: Pale Moon. UX developers DIAF
You read...the...paper?!
I don't know how they do things wherever you come from, but this is Slashdot.
Next time, just read the headline and skim the summary, then spout off whatever pops into your head.
Informed commentary, sheesh!
And the training of the neural networks and construction of their training sets certainly did need quite a bit of 'brute force' as well as 'efficiently wielded force in large quantity'.
To be fair, it'd take a fair bit of brute force training for a human to beat Fan Hui too - you aren't exactly going to rock up, read a pamphlet explaining the rules and win 5-0 on your first ever attempt at the game.
I'm surprised that you didn't meet any Go players in Japan. You may not have known that some people you knew were players, but that's a different point. When I had a weekend in Seoul, I was keeping my eyes open for the characters for a "go club", but I didn't find one. It's difficult to find things in a culture you don't know.
Entirely accepted. This hasn't been a secret in Go-programming circles for years. Over a decade (I used to hang out in the appropriate USENET group).
For the audience ... to a first approximation, each move in a tree of Chess positions (when you're looking forward, to evaluate a proposed move) has about 8 legal possibilities. So to look 10 moves ahead, brute-force evaluation requires around 8^10 branches to your tree - 1,073,741,824 a billion or so. Trim it a bit (say, to 7 legal moves per position) and you're only looking at 282,475,249 possibilities. Those ranges are broadly similar through the mid-game of chess.
Correspondingly for Go, if we go to mid-game (a corner play in each corner, plus 10 other mid-game moves ; symmetry has gone ; we're at move n=50), each move has around (361-n)*(361+(n+1))*(361+(n+2))*(361+(n+3))*(361+(n+4))*(361+(n+5))*(361+(n+6))*(361+(n+7))*(361+(n+8))*(361+(n+9)) moves. I make that 7,078,156,841,415,990,000,000,000 moves to evaluate for a 19x19 board (with no easy way apart from applying a "legality engine" to every move).
That's a fair difference in the brute force level needed.
A "legality engine" is non-trivial - unless you impose "superko" (thou shall never repeat a board position). Which is why "superko" is tremendously popular in theoretical evaluations.
FYI, if you move to 21x21, the corresponding number of moves for "mid-game" will be about 10 times higher. But the "mid-game" will be about a third longer, for a total of x30. (Tromp says about x25 for two stones ; seems reasonable, given that I haven't read his working on this).
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
tic-tac-toe is trivial to solve (I did so as a child -- there aren't very many moves) and, yes, the first player wins. Every time. Unless they're you, I guess.
To paraphrase Dijkstra, would you consider "strong swimming", i.e., swim like a fish, to be a requirement for understanding how to navigate an artifact under water? And is a submarine merely a "weak swimmer", even though it outperforms all known types of fish in speed?
To respond to Dijkstra, some people are interested in the question of whether computers can think, even if he isn't.
"First they came for the slanderers and i said nothing."
We're very far from any computer program that looks like a human
"First they came for the slanderers and i said nothing."
What if we could develop a computer that thought like a dolphin? Would that not be AI?
That would be cool, too.
Right now humans and dolphins are closer to each other than computers.
"First they came for the slanderers and i said nothing."
> Calculating all of the possible moves then picking the best one is hard to call intelligent. The book on AI we use at our university disagrees with you.
That's because most AI researchers are quite happy to call anything involving a machine doing something "artificial intelligence" even if it's just the speaking clock.
To have a right to do a thing is not at all the same as to be right in doing it
Recreating human intelligence is already trivial
Well it certainly didn't work in your case.
To have a right to do a thing is not at all the same as to be right in doing it
Eliza gave mostly fake, canned responses with the user's key-words sprinkled in. It was not practical.
I will agree that Siri's technology existed in labs by the late 70's, for the most part, but moving from lab/garage/experiment to common use quite often takes a couple a decades. Same with TV, cars, transistor radios, and others.
You can ask Siri about type-of-food restaurant locations, current weather, appointments, and most of the other typical smart-phone services, all in a little box. It's not as flexible as a human would be, but the criteria is "impressive", not "as good as a human".
Table-ized A.I.
Correction, it probably should be "from a little box" instead of "in a little box". Smart-phones often rely on servers they network with for many types of queries.
Table-ized A.I.
This is one of the worst comments ever. While it's true that it's failings belong to the developers, so do all of it's successes.
It's asinine to believe a computer or algorithm has abilities or brilliance that are not the creator's, in the same way it would be asinine to praise an automobile itself.
This is a fallacy. With a completely *prescriptive* algorithm where the learning itself is baked into the code you might be right, but with more modern "ai-like" algorithms, it is often the algorithm + training and not the developer that is responsible for the abilities/brilliance of the machine.
That's like a parent taking all the credit/blame for the brilliance of their offspring when mostly all they did was pass down the "algorithm". The teachers, peers, and the rest of the environment are likely deserve the as much or more credit than the parent (except in the case if they were home-schooled with no contact with friends/peers until they were adults). Of course that doesn't prevent many parents from taking all the credit/blame for their offspring, but it doesn't mean it's justified.
tic-tac-toe is trivial to solve (I did so as a child -- there aren't very many moves) and, yes, the first player wins. Every time. Unless they're you, I guess.
The only winning move is not to play...
Please watch wargames scene...
| This is a huge advance, even if it is only optimising multiple moderate-depth playing engines.
It isn't doing that.
They trained some strong neural networks to first predict move probabilities from 30 million expert moves and positions. That was just the start. Then they used 'reinforcement learning' where they played games against itself and propagated back the final outcome (game won/lost) all the way through the net space to improve the learning to the correct outcome (game won vs lost) vs matching expert's moves.
Then they used samples from those (huge sampling space), taking one single position from synthesized games, to train a one-step position evaluator deep network. Along the way they trained a simpler (1000 x faster) policy network to evaluate potential moves.
And then finally the actual algorithm mixed stochastic monte carlo simulations using the fast policy network (playing a subset of moves to game completion) with the trained deep value network. On a decent sized cluster with GPU acceleration.
Fixed moderate depth engines (as in chess) are a major generation behind even for conventional Go engines---there was a generational shift to the Monte Carlo deep tree searches some years ago, and the new method uses that approach partially, augmented by big deep learning networks.
It's a huge achievement, but it was also a huge amount of work.
I know nothing about Go, some stuff about machine learning, and I did download and read the full-text paper.
Hmm... You'd not praise a car for being a good car? I'm not sure that I understand why you'd think the creation was not deserving praise. Much like you praise good art, the art itself, yet still have praise for the artist, so too can you praise (or damn) a device for its inherent qualities. It's not like there is a value of praise where there's none left for the creator or user of a tool. I dare say, we humans praise a whole host of tools all the time. Everything from programming languages to operating systems to browsers are praised or damned here, on this site, on a daily basis.
I don't think that qualifies as asinine to praise a tool for having the virtues that make it a good tool. It's not like you can't also praise the tool's creator.
"So long and thanks for all the fish."
From the announcement's lack of associated bullshit, I take it that deGroot has been thoroughly walked past. Good. In the same way that he walked over other people's work after misappropriating it.
Whichever way it works, it's an improvement to previous programmes. Unfortunately it remains computer-Go, so I'm unlikely to ever sit down against it. I've tried various computer-Go and computer-mediated-Go options. I prefer the clock of shell on wood.
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
people typically learn object recognition with far fewer examples than a computer requires using a deep learning approach.
Single Sample Face Recognition using Deep Learning Autoencoders.
IMHO deep learning is just the latest fad, popular with AI programmers who otherwise don't have a clue.
How many world class Go players have you defeated?
You're right; I apologize. Chess isn't completely brute force but from the Go page on wikipedia:
"There is significant strategy involved in the game, and the number of possible games is vast (10^761 compared, for example, to the estimated 10^120 possible in chess)"
And from the Computer Chess page:
"The Nalimov tablebases, which use state-of-the-art compression techniques, require 7.05 GB of hard disk space for all five-piece endings. To cover all the six-piece endings requires approximately 1.2 TB. It is estimated that seven-piece tablebases will require between 50 and 200 TB of storage space"
Which is just not doable in Go; regardless of the fact that in endgame the match is usually already over or will only change the score by a few points.
-SaNo
That's because most AI researchers are quite happy to call anything involving a machine doing something "artificial intelligence" even if it's just the speaking clock.
That's because most researchers have to deal with undergraduates, so they've had to relax their definition of "intelligence".