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AlphaGo Beats Lee Sedol in Final Game

• johnloeber 11y ago
  This was probably the closest game in the series. Livestream: https://www.youtube.com/watch?v=mzpW10DPHeQ https://www.youtube.com/watch?v=mzpW10DPHeQ A few months back, the expert consensus was that we were many years away from an AI playing Go at the 9-dan level. Now it seems that we've already surpassed that point. What this underscores, if anything, is the accelerating pace of technological growth, for better or for worse. In game four, we saw Lee Sedol make a brilliant play, and AlphaGo make a critical mistake (typical of monte carlo-trained algorithms) following it. There's no doubt that with further refinement, we'll soon see AI play Go at a level well beyond human: games one through three already featured extraordinarily strong (and innovative) play on part of AlphaGo. Previous Discussions: Game 4: https://news.ycombinator.com/item?id=11276798 https://news.ycombinator.com/item?id=11276798 Game 3: https://news.ycombinator.com/item?id=11271816 https://news.ycombinator.com/item?id=11271816 Game 2: https://news.ycombinator.com/item?id=11257928 https://news.ycombinator.com/item?id=11257928 Game 1: https://news.ycombinator.com/item?id=11250871 https://news.ycombinator.com/item?id=11250871
  • wheresmypasswd 11y ago
    > What this underscores, if anything, is the accelerating pace of technological growth, for better or for worse. While growth may be accelerating, this is simply the result of one big paradigm shift in deep learning/NNs. Once we've learned to milk it for all its worth, we'll have to wait for the next epiphany.
    • hmate9 11y ago
      Milking neural networks out completely is pretty much AI as depicted in the movies. If we can milk it completely there probably isn't a need for the next epiphany.
      • swombat 11y ago
        Or, at the very least, the next epiphany need not be human-designed. Just train a neural network in the art of creating AI paradigms and implementations that can do general purpose AI. Once that's "milked", the era of human technological evolution is finished.
        • foota 11y ago
          That sounds like a bad idea.
          • swombat 11y ago
            It's the core idea of AI, the primary reason why it is suspected that developing strong AI will inevitably lead to the end of the human era of evolution.
          • deleted 11y ago
            [deleted]
        • YeGoblynQueenne 11y ago
          I don't want to be mean, but that's like saying you'll train a magic neural net with the mystical flavour of unicorn tears and then the era of making rainbows out of them will be finished. Or something. I mean, come on- "the art of creating AI paradigms"? What is that even? You're going to find data on this, where, and train on it, how, exactly? Sorry to take this out on you but the level of hand-waving and magical thinking is reaching critical mass lately, and it's starting to obscure the significance of the AlphaGo achievement. Edit: not to mention, the crazy hype surrounding ANNs in the popular press (not least because it's the subject of SF stories, like someone notes above) risks killing nascent ideas and technologies that may well have the potential to be the next big breakthrough. If we end up to the point where everyone thinks all our AI problems are solved, if we just throw a few more neural layers to them, then we're in trouble. Hint: because they're not.
          • swombat 11y ago
    • HaikuEU 11y ago
      Could you care to explain what was that big paradigm shift ?
      • bla2 11y ago
        This four-part series explains it well: http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning/ http://www.andreykurenkov.com/writing/a-brief-history-of-neu...
    • JulianMorrison 11y ago
      But that's what technological growth is. A series of epiphanies, building on what came before.
      • msbarnett 11y ago
        Yes, but an epiphany is not evidence of an accelerating rate of epiphanies, nor evidence that such epiphanies will continue apace into the future.
        • JulianMorrison 11y ago
          You can look at the past for that, although obviously it doesn't predict the future. But it ought to be a priori obvious, at least, that the more you know (as a species), the more surface area of knowledge you have to synthesize into an extending step beyond the known.
          • ska 11y ago
            You could look at the past, but that isn't what the claim did. In fact looking at the rate of change in applications over an "epiphany" period is probably the least useful estimate of progress & rate of change in progress.
  • seanwilson 11y ago
    > In game four, we saw Lee Sedol make a brilliant play, and AlphaGo make a critical mistake (typical of monte carlo-trained algorithms) following it. Can you explain why this is typical? What can be done against this to strengthen the algorithm?
    • rymate1234 11y ago
      I can't remember where I read this, but one theory was that the move Lee Sedol made was thought to be unlikely by AlphaGo, and so didn't explore down that path. When Lee Sedol made the move, the AI was in unknown territory as it hadn't explored down that avenue.
      • seanwilson 11y ago
        > When Lee Sedol made the move, the AI was in unknown territory as it hadn't explored down that avenue. Sounds similar to what a human would do then: you wouldn't spend much time simulating in your head what would happen if your opponent made a very atypical move or a move that would seem very bad at first thought.
        • kqr 11y ago
          That's exactly it. The difference, as far as I have understood it, is that there was a similar move that is typical, but in that particular situation, pretty simple reasoning (of the highly abstract "if this then that so this must lead to that" sense) leads a human to conclude that this version of the move is superior. So while atypical in the sense of "occurring infrequently", it was not a difficult move to find for a player of that level – all the pro commentators saw it pretty much right away. This might be the one weakness of AlphaGo, which is interesting.
      • dfan 11y ago
        David Silver said at the beginning of the broadcast of game 5 that AlphaGo's policy network had given Lee Sedol's move 78 only a 1 in 10,000 chance of occurring.
    • 11y ago
  • aquadrop 11y ago
    > There's no doubt that with further refinement, we'll soon see AI play Go at a level well beyond human Will we though? AlphaGo trains on human games, so can it go well beyond that level? Will it train on its own games?
    • krig 11y ago
      It is already mainly training by playing against itself: https://googleblog.blogspot.se/2016/01/alphago-machine-learning-game-go.html https://googleblog.blogspot.se/2016/01/alphago-machine-learn... > To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and adjusting the connections using a trial-and-error process known as reinforcement learning.
      • aquadrop 11y ago
        It's still based on human games. It plays itself but the way it plays was inherited from human. I wonder if there is some fundamental barrier to what you can reach with reinforcement depending on your base.
        • relic 11y ago
          It is based on human games until it can explore well enough to sufficiently break away from local optimums.
        • aflinik 11y ago
          Having it learn on human games was just a way of speeding up the initialization process before running reinforcement learning, it didn't limit the state tree that was being searched later on.
    • deleted 11y ago
      [deleted]
    • StreamBright 11y ago
      It already went beyond human level, look for Go players commenting on the game, citing that they would have never thought about steps that the AI made. In a sense it brought new strategies to the table that humans can learn and apply in human vs human games.
  • cgearhart 11y ago
    >A few months back, the expert consensus was that we were many years away from an AI playing Go at the 9-dan level. These kinds of predictions are almost always useless. You can always find people who say it'll take n years before x happens, but no one can predict which approaches will work, and how much improvement they'll confer. > What this underscores, if anything, is the accelerating pace of technological growth, for better or for worse. What? This is a non-sequitur. Continued advancement doesn't mean that it is accelerating, and even if this does represent an unexpected achievement that doesn't mean that future development will maintain that pace. Appreciate it for what it is - an historic achievement for AI & ML - and stop trying to attach broader significance to it.
    • lefnire 11y ago
      You sound like the kinda person who says "AI will never drive," "AI will never play Go." True there's a lot of hype, which ML experts are concerned may lead to another burst & winter. On the flip-side there's a lot of curmudgeonly nay-sayers such as yourself at which ML experts roll their eyes and forge ahead. What I find is both extremes don't understand ML, they're just repeating their peers. ML is big, and it's gonna do big things. Not "only Go", not "take over the world"; somewhere in between.
      • cgearhart 11y ago
        I'm actually very optimistic about the state of AI and ML lately. The difference is that I don't anthropomorphize the machines or ascribe human values to their behavior. I absolutely believe AI will drive (and save lives); I have always believed that AI will play Go; I believe that AI will grow to match and surpass humans in many things we assume that only humans can do. Humans aren't perfect, but that doesn't mean that machines who outperform us are perfect either. AlphaGo plays Go. It probably doesn't play Go like a human (because a human probably can't do what it does), but that's OK because it also appears to be better than humans. AlphaGo is interesting not because it has done something impossible, but because it has proven possible a few novel ideas that could find other interesting applications, and adds another notch to the belt of a few other tried and tested techniques.
    • 11y ago
  • NotUsingLinux 11y ago
    this reads as the professional go world couldn't wait for this (Alphago) to arrive to find new ways of play new moves.
  • gambler 11y ago
    There's no doubt that with further refinement, we'll soon see AI play Go at a level well beyond human No doubt? Seriously? What kind of knowledge do you have to make such statements? There are plenty of examples where technology has rapidly advanced to some remarkable level, but then almost completely plateaued. For example, space travel or Tesla's work on applications of electromagnetism. Heck, even other areas of AI research. I really don't see why people here readily assume that this particular approach to computers playing Go is easily improvable. Neither do I see why everyone assumes there will be no discoveries of anti-AI strategies that will work well against it. With neural networks involved, it's hard to say. And all we have so far is information about about, what, 15 games? Some of which were won by people. Mind you, those people never played AlphaGo before, while the bot benefited from a myriad of training samples, as well as from Go expertise of some of its creators. I'm also tired of all the statements about "accelerating progress". It's not like all the AI research of the past was useless until DNNs came along. That's the narrative I often get from the media, but it misrepresents the history of the field. There was no shortage of working ML/AI algorithms in the past decades. The main problem was always at applying them to real-world things in useful ways. And in that sense, AlphaGo isn't much different from Deep Blue. One big shift in the field is that these days a lot of AI research is done by corporations rather than universities. Corporations are much better at selling whatever they do as "useful", which isn't such a good thing in the long run. We're redefining progress as we go and moving goalposts for every new development.
    • tim333 11y ago
      Well, little doubt. When did any technology suddenly stop improving when it reached human levels?
    • YeGoblynQueenne 11y ago
      >> Corporations are much better at selling whatever they do as "useful", which isn't such a good thing in the long run. Yep. There's a grave risk that funding to AI research ends up being slashed just as badly as in the last AI winter, if people start thinking that Google has eaten AI researchers' lunch with its networks and there's no point in trying anything else. Incidentally, Google would be the first to pay the price of that, since they rely on a steady stream of PhDs to do the real research for them but now I'm just being mean. The point is, we overhype the goose that lays the golden eggs, we run out of eggs.
  • mcv 11y ago
    > There's no doubt that with further refinement, we'll soon see AI play Go at a level well beyond human Why is there no doubt? I strongly doubt there even exists a go level that's well beyond human. There is hypothetical perfect play of course, but there is absolutely no way to guarantee perfect play. And while I have no way to judge, I've heard that 9p players may not be all that far removed from perfect play. One legendary player once boasted that if he had black (no komi, I assume), he would beat God (who of course plays perfect go). There is of course no way to know if that's true or gross overconfidence, but it's certainly possible that there's not all that much room left beyond the level of 9p players. AlphaGo will no doubt improve, and reduce the number of slips like his move 79 in the 4th game, but it's never going to be perfect, and there's always the chance that it will miss an unexpected threat.
    • baddox 11y ago
      You could always argue what "a level well beyond humans" means, but I'd say if a computer can consistently dominate the best human players that would count.
    • TwoBit 11y ago
      Given the crushing that AlphaGo did, I don't believe your statement about humans having near perfect play.
      • goldbrick 11y ago
        Can you quantify "crushing"?
        • eru 11y ago
          4 - 1.
      • mcv 11y ago
        Not all humans, obviously, but 9p players really are far, far better than other players. And there's another 9p who has won 8 out of 10 matches against Lee Sedol, so there's nothing superhuman about a 4-1 result at that level. I'm really just objecting to the description of this as "beyond human". Yes, it's good, and it's many orders of magnitude beyond my level, but so are Lee Sedol and other 9p players.
  • ChuckMcM 11y ago
    In an odd way, it makes me more optimistic about fusion power plants in my lifetime. The reality is that we work on these advances but are terrible at predicting when we will achieve them, and then one day we find we have arrived. That AlphaGo can play at this level suggests that similar techniques could help other parts of the infrastructure (like air traffic control) and that would also positively impact the quality of life for a many air passengers every year.
    • eru 11y ago
      Fusion power is neat, but not really necessary. Fusion would have similar political problems to fission; and the economics aren't much improved either. Perhaps if we ever ran out of fissionable material, fusion would become economic.
      • lyle_nel 11y ago
        What political problems might that be?
        • eru 11y ago
          All the anti-nucular protestors? Or eg whatever made Angela Merkel turn off the German reactors after a reactor of a very different design in a very different set of circumstances in Japan broke. Fusion is just yet another nuclear reactor design as far as politics might be concerned.
          • lyle_nel 11y ago
            Ah is see. Although I would not put it past people to protest nuclear fusion, it would be strange indeed, since nuclear fusion does not produce the same kind of radioactive waste(shorter half-life) as alternative nuclear technologies.
  • colllectorof 11y ago
    A few months back, the expert consensus was that we were many years away from an AI playing Go at the 9-dan level. Any sources for this statement? I've seen it repeated over and over again, but without any specific examples of who those experts were or what they said.
• Wildgoose 11y ago
  Four components: Learning (viewing millions of professional game moves). Experience (playing different versions of itself) Intuition (ability to accurately estimate the value of a board) Imagination (evaluating a series of "what if?" scenarios using Monte Carlo Tree Search) I think the significant thing about AlphaGo is that apart from some hand-crafting in the Monte Carlo Tree Search routines, this is all general purpose programming. It may only be baby-steps, but it does feel like a genuine step towards true (general) AI.
  • awwducks 11y ago
    > Learning (viewing millions of professional game moves) According to the last press conference, it was apparently strong amateur games from the internet that it used to train with. Afterwards, it just played itself, as you mentioned.
    • thomasahle 11y ago
      Yes, that was surprising to me as well. It seems unfair to not give it access to the thousands of years of knowledge in the go community, though even more impressive that it still plays so well.
      • visarga 11y ago
        I think self play games would be an even better source for learning because they are at 9p level, not amateur.
  • _yosefk 11y ago
    I dunno, neural nets don't do that well on chess, for instance, at least not the chess engine that was recently published (it reached an IM level which is really bad compared to top-notch chess-engines.) That convolutional neural nets work for Go better than for chess is intuitively unsurprising because of the different game rules. IMO it's at least as likely that this is a step towards a better Go engine but not towards "true AI." (People say Go is much harder than chess, but this is misleading. Both games are finite trees that are too large to exhaustively search for any existing physical entity we know of. Which tree is larger is irrelevant in a game of two players none of whom can search the entire tree; both players essentially rely on heuristics. Machines beat people earlier in chess, hence it was assumed that "chess is easier for machines" and "Go is harder", but a conclusion of that sort can always be reversed by further research; eventually, it is IMO likely that machines will be impossible for humans to beat at both games, and generally in any kind of board game, given enough research. But IMO no board game is very much like "real life" where our own intelligence operates, and I think people do not have a great intuition of which game is more like "real life" compared to other games - instead, that game which is most popular among the group of people in question and is not "solved" yet is considered the hallmark of intelligence (and here the process through which Go aficionados progress as machines get better is very much like the process chess aficionados went through a decade plus ago.) Then once a game is "solved", the goalpost moves to the next and the "solved" game is officially declared unrelated to "real intelligence", this part happens when a credit bubble pops and AI breakthroughs get peddled less as a result. Personally, "the" test of intelligence is still the Turing test, or if I can't get that, some variant such as automated translation that you can't tell from good human translation. This of course is "unfair" to machines, in that they've been better at multiplying numbers since the 40s and that ought to count for something, too; the reason I like the Turing test is that a machine passing it seems very likely to be almost strictly smarter than me, that is, being as good or better than me at almost everything.)
• awwducks 11y ago
  My rough summary of the match, informed by the various commentators and random news stories. Game 1: Lee Sedol does not know what to expect. He plays testing moves early and gets punished, losing the game decisively. Game 2: Lee Sedol calms down and plays as if he is playing a strong opponent. He plays strong moves waiting for AlphaGo to make a mistake. AlphaGo responds calmly keeping a lead throughout the game. Game 3: Lee Sedol plans a strategy to attack white from the start, but fails. He valiantly plays to the end, creating an interesting position after the game was decided deep in AlphaGo's territory. Game 4: Lee Sedol focuses on territory early on, deciding to replicate his late game invasion from the previous game, but on a larger scale earlier in the game. He wins this game with a brilliant play at move 78. Game 5: The prevailing opinion ahead of the game was that AlphaGo was weak at attacking groups. Lee Sedol crafted an excellent early game to try to exploit that weakness. Tweet from Hassabis midgame [0]: #AlphaGo made a bad mistake early in the game (it didnt know a known tesuji) but now it is trying hard to claw it back... nail-biting. After a back and forth late middlegame, Myungwan Kim 9p felt there were many missed chances that caused Lee Sedol to ultimately lose the game by resignation in the late endgame behind a few points. Ultimately, this match was a momentous occasion for both the AI and the go community. My big curiosity is how much more AlphaGo can improve. Did Lee Sedol find fundamental weaknesses that will continue to crop up regardless of how many CPUs you throw at it? How would AlphaGo fare against opponents with different styles? Perhaps Park Jungwhan, a player with a stronger opening game. Or perhaps Ke Jie, the top ranked player in the world [1], given that they'd have access to the game records of Lee Sedol? I also wonder if the quick succession of these games on an almost back-to-back game schedule played a role in Lee Sedol's loss. Myungwan Kim felt that if Lee Sedol were to play AlphaGo once more, the game would be a coinflip since AlphaGo is likely stronger, but would never fix its weakness between games. [0]: https://twitter.com/demishassabis/status/709635140020871168 https://twitter.com/demishassabis/status/709635140020871168 [1]: http://www.goratings.org/ http://www.goratings.org/
  • jacobolus 11y ago
    Lee Sedol was also coming directly from playing a tournament against human players. It’s not clear how much he prepared for the Alphago match. I’d be very curious to see a game between Lee Sedol and Alphago where each was given 4–5 hours of play time, instead of 2 hours each. I suspect Lee Sedol would get more benefit from spending a longer time reading into moves than Alphago could get. Or even a game where the overtime periods were extended to 4–5 minutes. This last game, Lee spent the whole late middlegame and endgame playing in his 1 minute overtime periods, which doesn’t give much time to carefully compare very complex alternatives.
• thatsadude 11y ago
  The game is amazingly close though.
  • the_mitsuhiko 11y ago
    I wish they counted it. Was curious to see how that works.
    • awwducks 11y ago
      Here's a tutorial. http://playgo.to/iwtg/en/count.html http://playgo.to/iwtg/en/count.html The whole series isn't bad if you're interested in other aspects of the game.
      • the_mitsuhiko 11y ago
        They would have counted with the Chinese method which is different.
    • spade 11y ago
      This video compares the Chinese counting method vs. the Japanese/Korean counting method: https://www.youtube.com/watch?v=d4OqfDA0-hE https://www.youtube.com/watch?v=d4OqfDA0-hE
• krig 11y ago
  Really interesting and close match, it was great listening to the expert player analyse the game and having the final score be uncertain until very late in the game. I found the discussion around weaknesses in the Monte Carlo tree search algorithm interesting. It sounds like the opinion from the expert is that there are some inherent weaknesses in how MCTS tends to play moves against theoretical moves from the opponent that don't make sense; ie. that AlphaGo sees a potential win that would only happen if the human player made very bad moves. It's fascinating that the seeming weakness in AlphaGo would come from the algorithmic part of the AI and not the neural net. Could it be that as the neural net becomes stronger and stronger at the game, eventually the algorithmic part of it would become less useful to it? If that's the case, it really feels like this could be the path to truly general AI.
  • GolDDranks 11y ago
    I think the "weakness" isn't that much of a weakness in the sense, that it's still playing optimally given it's search space – but it doesn't know how to overplay to make a comeback. (Overplay is a non-optimal play that is intended to be confusing for the opponent. AlphaGo doesn't regard it's opponent in any way, or assess what might be confusing for him, it just plays moves that it thinks are optimal.) A (min-max, alpha-beta-pruning) tree search is the optimal way to determine your best move if you can afford to search the situation space globally. However, as that's clearly impossible, there's clever ways to reduce the search space. Random pruning, as a "normal" monte carlo search would do, or semi-random pruning with a neural network estimating the situations, like AlphaGo does. However, as the search space is now non-global, it might exclude the optimal solution. And thus, the min-max-assumption doesn't hold: your opponent might come up with moves that you didn't think of, screwing your calculations off. If your opponent is a god ( = can afford global search space), or at least has a search space that is a strict superset of yours, it's "game over, man". But: if your opponent isn't a god, it's likely that his search space is NOT the same as yours. And you can exploit the fact. If you have any idea what kind of search space your opponent has, you can come up with moves, that he couldn't have included in his tree search, and bet on that his/her "hidden" moves aren't better than yours. Currently AlphaGo doesn't do that. It behaves like it'd be playing against... well, against another AlphaGo.
• malanj 11y ago
  After the first 3 games I thought that AlphaGo was far beyond human level, but it's a harder call to make now. It seems very unlikely that an AI would be very close to exactly matching a human, one would expect it to be much stronger or much weaker. Perhaps humans are closer to the "Perfect Game" than we think? http://hikago.wikia.com/wiki/Hand_of_God http://hikago.wikia.com/wiki/Hand_of_God The top players estimate they would need a 4 stone advantage to win a perfect player.
  • semiel 11y ago
    I think there's something analogous to the anthropic principle happening here. This match is happening now because Google realized we are in the moment when Go AIs are passing the top players in skill. Two years ago, Lee Sedol would have won easily. Two years from now, AlphaGo will win easily. In either case, the match wouldn't have happened at all, or wouldn't receive the same amount of attention.
    • Houshalter 11y ago
      That assumes a continuous function of Go AI ability. It's not continuous, as alphaGo proves. AlphaGo was a huge leap in ability from the best previous AIs. It was a totally new method, and one given Google scale resources at that.
      • pdpi 11y ago
        Right, but alphago was the closest thing we have to the transition - yesterday ais were too weak, today they need to try them against world champs to validate their work
      • semiel 11y ago
        AlphaGo isn't a fixed strength, though. It's substantially stronger now than it was in October. Obviously it's not a completely continuous function, but I think it's close enough to cause this effect.
      • deleted 11y ago
        [deleted]
• tunesmith 11y ago
  AlphaGo was strong enough to survive a mistake, not knowing a known tesuji, and still claw back to win by a couple of points. I wonder wonder that means in terms of handicap, maybe it is a stone stronger than Sedol?
• ioncube 11y ago
  Anybody knows the music playing in the background? -EDIT- Thats what Shazaam was able to recognize Hit Me! - Dreamliner - https://soundcloud.com/hit-me-music-production/dreamliner https://soundcloud.com/hit-me-music-production/dreamliner
  • zeeZ 11y ago
    There's also Somnium - Bright Future somewhere in there.
    • zeeZ 11y ago
      Coldplay - Adventure Of A Lifetime Also in the mix, just came up in shuffle.
• tunesmith 11y ago
  By the way, for those who want to learn by themselves, there are a lot of ways to play Go against a computer in a way that is friendly for beginners. My rough journey so far - on a Mac, but much of this can be done on Linux - I started out playing 9x9 games against Gnugo, giving myself as much handicap as possible (without it resigning immediately), and then removing stones as I improve. I got to the point where I could sometimes beat 9x9 when I started with two extra stones, and then I started with 19x19. Took me a while to win 19x19 with 9 stones, but then I won by learning a bit more about extending on hane. Then you can improve from there. After that point, you can also switch to fuego or pachi, which are stronger by default. The end result is it really is easy and possible to learn a ton just by playing against software, tracking your ability throughout, just by picking programs with different strength and handicap levels. I've also enjoyed using GoGui to pit two computer programs against each other and watch how they play with various handicaps. Then there's all the puzzles - goproblems.com, smartgo, etc. Finally, there are plenty of ebooks you can buy through smartgo books. This doesn't get into playing against humans on the various servers, but there's plenty of information about that online.
  • i_don_t_know 11y ago
    Which program do you use on the Mac? A long time ago I've used Goban[1] and I enjoyed it very much, but it's not available here in the App Store and apparently it doesn't fully work yet on El Capitan. (I don't know if it's not available right now because of the El Capitan problems or for some other reason.) What are some good go programs for the iPhone, both for playing and for learning/improving? [1] http://www.sente.ch/?p=1206&lang=en http://www.sente.ch/?p=1206&lang=en
    • tunesmith 11y ago
      Goban is working for me on El Capitan, but I installed it before upgrading. There's also the older free version which might still be up on a webpage somewhere. But the better option is that I was able to get GoGui working - I did have to manually build/compile it myself but there is a way to build it so that it creates a real OS X Application. It's quite good, you can set any board position and then tell a computer program to respond from that point. http://gogui.sourceforge.net http://gogui.sourceforge.net EDIT: For the iPhone I like SmartGo Kifu for playing games. 'Tsumego Pro' and 'GoProblems' for puzzles (they're adaptive) and 'Go Books' by smartgo for ebooks.
• mikhail-g-kan 11y ago
  Yann Lecun commented AlphaGo regarding General AI: https://www.facebook.com/yann.lecun/posts/10153426023477143 https://www.facebook.com/yann.lecun/posts/10153426023477143 in short - we are at least 1 big step before creating human-level AI
• pervycreeper 11y ago
  Could anyone suggest a good introduction to the rules and basic strategies of this game?
  • kqr 11y ago
    https://online-go.com/learn-to-play-go https://online-go.com/learn-to-play-go May need an account, but registration takes 5 seconds (you just type in your desired username and password). It puts you through 10 simple lessons and then lets you play a really easy computer opponent.
    • pervycreeper 11y ago
      Thanks.
  • monjedetonsura 11y ago
    http://playgo.to/iwtg/en/ http://playgo.to/iwtg/en/
  • vinchuco 11y ago
    Someone made an extensive comment above full of resources
• partycoder 11y ago
  It was very close though. Lee Sedol resigned in the yose after noticing he was behind by no more than a moku.
• trott 11y ago
  I tried to learn Go a decade ago. After spending some time on it, I came to the conclusion that it's just not an enjoyable game for me. Here's why: As you can see in this match, games are often won and lost by just a few points (1% of the whole territory). So, not only do you have to count territory precisely at the end, but throughout the game, and this isn't easy to do in your head. Maybe if you are an autistic accountant, that's fun, but not for me. If I have to play a strategic board game, it will be good old chess. And now that computers are finally beating people at both, there is no longer any need to look at Go as some kind of mythical last refuge of humanity.
  • argonaut 11y ago
    I read some online comments saying, at the amateur level, both sides make so many mistakes anyways that the gap is usually quite large (10+ points after komi), which seems reasonable.
    • zeven7 11y ago
      It's true. I'm an amateur ~1 dan, and games rarely come down to 1-2 points. It does happen, but way less often than some giant group dying and one side resigning in the middle of the game. If a game ever comes within a few points, neither me nor my opponent is really sure who won until we actually count to determine the winner at the end of the game, because usually neither of us is an autistic accountant. That kind of close record keeping throughout the game is necessary in top level pro games, but not in amateur games.
  • lmm 11y ago
    > So, not only do you have to count territory precisely at the end, but throughout the game, and this isn't easy to do in your head. You don't need to count - you can just play to take as many points as you can. > Maybe if you are an autistic accountant, that's fun, but not for me. If I have to play a strategic board game, it will be good old chess. I find it's the opposite. In chess you have to play with constant vigilance, because a single blunder decides the game - even at grandmaster level, something like 60% of games are decided by blunders. In go you can play much more casually, you can take some risks, because a mistake costs one or two points but it doesn't snowball much. So not every move has to be perfect; it's much more possible to recover from mistakes.
• xefer 11y ago
  I'm curious, if these machines can consistently beat 9-dan players like this, is there talk of creating a 10-dan level?
  • xzephyr 11y ago
    You can just have the ELO score, which is the basis for assigning dan levels: http://www.goratings.org/ http://www.goratings.org/
• haffi112 11y ago
  Next challenge: Can AlphaGo beat 10 or 100 humans playing together against it?
• typon 11y ago
  Does anyone else find it funny that the Game 4 in which Lee Sedol won got the most upvotes on Hacker News? We're still firmly with team human it seems :P
  • madez 11y ago
    I think your reasoning is flawed. Imagine Lee Sedol won the first three matches and AlphaGo the fourth. I think that news would have more upvotes than the former three matches. The outcome was a surprise and therefore gathered more attention.
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• tetraodonpuffer 11y ago
  note that form whomever is interested reddit will do an AMA with all the various pro commentators this Saturday it seems, check r/baduk for more information https://www.reddit.com/r/baduk/comments/4ai8e8/what_do_i_need_to_know_about_setting_up_the_ama_w/ https://www.reddit.com/r/baduk/comments/4ai8e8/what_do_i_nee...
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• brador 11y ago
  I question how much of it's success is down to the AI/understanding the game/outplaying the opponent vs. simple culled brute force. Especially when they can throw Google level computing power at it and they have mentioned using heat maps and looking at move sets. It could be argued that it's only AI when it understand the game rules and plays to them without iterating random choices until it finds a hit. Machine learning would be between the two, but still not what many would consider true AI.
  • return0 11y ago
    It's not a conscious entity, but it's a network good at what it's trained for. Since the network is frozen, it doesn't really learn anything or trying to "outplay" anyone (let alone it doesn't know there's an opponent). While it's impressively crafted machine learning, we shouldn't rush to call it AI. It seems like it's showing the right direction though, and, who knowns, the "intelligent conscious agent" part may one day be built on top of this, with the same building blocks.
    • visarga 11y ago
      If we made a Turing test based off Go, then would a Go player be able to tell when playing against a human or when playing an AI? That's why I think AlphaGo does manifests consciousness, in its play. It is not conscious of what we are conscious about, but rather limited to the domain of Go play. It might even have developed concepts about the game that are completely alien to us, maybe untranslatable to us.
      • return0 11y ago
        I think it would be a stretch to say that now. The neural net acts like a big lookup table atm , even if its states contain high level concepts. However, consciousness is a human concept, so the system should be coupled to another, expressive system that we could relate to, and communicate with. Thats why the turing test is proposed through language communication and not some other limited vocabulary.
  • visarga 11y ago
• zubspace 11y ago
  Google improved the outcome by putting in large amounts of processing power. What happens, if humans would do the same? Instead of just Lee Sedol, how about putting the top 10 Go players in a room vs. AlphaGo? Would the chance to win increase? Maybe we find out, that 3 top go players vs. AI is the optimal way and adding more humans decreases the odds to win the match? This would lead to following question: Why does AI improve, if we add more processing power while adding more human brainpower decreases their overall power? Maybe we find out, that 3 good developers working on a project are optimal and more decrease the chance of success?
  • SixSigma 11y ago
    With multiple humans, the co-ordination and communication would dominate. Do you think the humans would win at Twitch Plays AlphaGo ?
    • crazysim 11y ago
      Knowing Google, it would be a good promotion to have a Youtube Plays AlphaGo.
  • vinchuco 11y ago
    It may come down to limits in https://en.m.wikipedia.org/wiki/Network_performance https://en.m.wikipedia.org/wiki/Network_performance humans have as well as the speed at which consensus is reached.
  • jessaustin 11y ago
    Ultimately it may be one or more humans networked in some fashion with computers that proves most effective at tasks such as this.
  • programmer_dude 11y ago
    In one word: Bandwidth.
• conanbatt 11y ago
  One interesting thing happened to me: I got to the game before knowing which side was which color or the result, and I could tell which one was the computer, an exercise I hadn't tried with the previous games.
• skarist 11y ago
  Great game and amazing series/match. This last one was absolutely nail biting. My hat off to the AlphaGo team and to Mr. Lee Sedol. Sedol showed incredible fighting spirit and stamina. Just imagine sitting through a 5 hour game like that last one, with full concentration all the time. And seeing the expression of exhaustion and disappointment on Sedol's face after last moves and his resignation. Phew... I bet that he came in rather confident into this last game, after beating AlphaGo in the fourth, figuring he had found a weakness. And he seemed to have a rather good start, securing a decent territory in the lower right corner. We can all marvel at the machine/software the DeepMind team has built, but still I feel that the real marvel is the human brain. Will we learn anything from this series, about how it functions and evaluates game positions in a stratetgic games? The classic problem/mystery is how extremely good the human brain is at pruning game-trees. Whole branches are thrown out in split seconds and probably never explored. Currently taking a watt-for-watt comparison there is no question about whose "hardware" is superior -> Lee Sedol's brain. But I guess the DeepMind team and the community will take plenty of lessons from this and in a few years span, Lee Sedol's phone will beat him 100% of the time. At least I wouldn't be willing to bet against it, even though we are hitting the roof in Moore's law.
  • eggie 11y ago
    I would love to compare the energy requirements of the AlphaGo and Mr. Sedol. I imagine there are many orders of magnitude in difference between them. Perhaps the most fair comparison would be between a computer that uses no more energy than a human does. Or, to let the human work with a computer provided they do not use more total energy to play the game.
    • frabcus 11y ago
      Nice question! To make it fair, do you include the energy used to train it? From scratch, or from the amateur human game data? Likewise, do you include the energy used to evolve the human brain?
      • eggie 11y ago
        > Likewise, do you include the energy used to evolve the human brain? I was thinking of this in a limited, human-promoting sense. We shouldn't lose sight of our own special powers just because a computer the size of a house can outsmart us in a specialized domain :)
• guilhas 11y ago
  Shouldn't Lee have had the opportunity to train 1 year against this AI before the competition? training (Human vs Human) not the same as (Human vs Algorithm) or (Algorithm vs Algorithm)
• vbezhenar 11y ago
  What I'm finding terrifying is that we compare best players of entire human population to machine. Even if machine is only barely on the same level, honestly, 99.9% of people probably wouldn't stood a chance against it, no matter how hard they would be trying. Those professional players are best of best. Now compare that level of AI with average person. Go game might not be directly applicable to our lives, but it's only a demonstration. And it's replicable as easy as copy'n'paste, compare that to amount of time, money and efforts required to grow and train a human. That future where not only drivers and factory workers are replaced by robots, but anyone who's not doing extremely intellectual work, is getting closer. Factory robot is not that cheap, it requires manipulators, repairs. But cheap office work does not require anything physical, it's replicable extremely fast and gonna cost very low. It's exciting and terrifying feature. It's not going to look well with current capitalistic economical model.
• nichochar 11y ago
  Guys, this is fantastic, but lets not forget: What "shows how capable the human brain actually is," is: 1) The human brain invented Go to begin with 2) The long and celebrated history of Go 3) The human brain made DeepMind 4) The human brain finds value and beauty in all of this, which no machine would
  • ASalazarMX 11y ago
    We will always win at love and free will /s
    • gboudrias 11y ago
      Until robosexuality I guess.
    • cLeEOGPw 11y ago
      Robots will never feel emotions, that's what separates us from animals and robots. /s
• nickjj 11y ago
  Is it just me or is it really lame that Google isn't going to pay Lee? From the article: > Lee was competing for a $1 million prize put up by Google, but DeepMind's victory means it will be donated to charity. So Lee provides Google knowledge that only he is capable of providing due to his extreme skill in the game and Google won't even pay him?
  • asherkin 11y ago
    He's getting $170,000, $150k for playing the 5 matches, and $20k for winning match 4.
    • nickjj 11y ago
      Ah. I wish they included that in the article. I'm all for open source and unleashing Skynet, but I was concerned someone as important and skilled as Lee will be to improving AI was going to go unpaid.
  • tyrust 11y ago
    Unless the agreement was "we'll pay you to play us", this seems like a fine result.
  • JibberMeTimbers 11y ago
    I can't find any sources but supposedly he got 150k to participate and 20k for each win he got.
• anocendi 11y ago
  When it comes to competitive gaming, Koreans were Gods, and now AI has come to top them. Before we realize it, we will hear Google's StarCraft 2/3 bot beating the Korean World Champion.
• wowzer 11y ago
  While what the AlphaGo team has accomplished is nothing short of amazing, I'm not sure if everyone's thinking about this in the right context. While playing there's a "super computer" behind the scenes with these specs 1,920 CPUs and 280 GPUs [0]. Then consider all the machines used to train this neural net. I'd say Sedol's brain is pretty freaking powerful. Also, with that much computing power I would expect AlphaGo to win with the right team and the right approach to solving the problem. It would be very interesting to change the rules and limit the processing power of the computer playing a human. [0] https://en.wikipedia.org/wiki/AlphaGo https://en.wikipedia.org/wiki/AlphaGo
  • chillacy 11y ago
    You can make AlphaGo stronger by adding machines, you can't make Sedol stronger by gluing more gray matter in his brain (yet I guess). So yea, efficiency is nice but so is scalability, and that's why this is exciting. Also in a few years AlphaGo could be running on your cellphone. The chess AI Stockfish runs on an iphone today, and cellphones from what I could find online, uses less power than the brain (brain is roughly 20W, iphone has 1.4Wh battery which even if stockfish drains in 1 hour, is still 1W of power consumption) Give it a few years and we'll all be saying "of course computers can play Go, but at least they can't <Insert task humans are still good at>"
  • xzephyr 11y ago
    These CPU/GPUs are doing very inefficient digital simulations of analogue processes. If anything, human brain has significantly more 'processing power' than Alpha Go when you restrict the computation to the activation of linear threshold units.
• dopamean 11y ago
  I've gone from thinking "it will be impressive if AlphaGo wins a game in this series" to "wow, it's pretty impressive that Sedol took a game off AlphaGo." Craziness.
• vermontdevil 11y ago
  Ken Jennings just welcomed Lee Sedol to the "Human Loser Club" http://www.slate.com/articles/technology/technology/2016/03/google_s_alphago_defeated_go_champion_lee_sedol_ken_jennings_explains_what.html http://www.slate.com/articles/technology/technology/2016/03/... Pretty good article here.
  • laxatives 11y ago
    Haven't read/heard about Ken Jennings in awhile, but he's a great writer.
  • scott_s 11y ago
    Jennings is a surprisingly good and humorous writer. (I say "surprising" because there is no reason to expect that someone so good at Jeopardy would also be so good at expressing himself with such charming self-deprecation.)
• marcell 11y ago
  I've heard that in Chess, there are specific strategies that are effective against computers, but not against humans. Is this the case, and is it possible to do anything similar in Go?
  • sobellian 11y ago
    This was the case many years ago because of limited search depth and simple evaluation heuristics. For example, chess engines used to be known to be overly attached to material; piece sacrifices for long-term compensation used to confuse them. Those problems have since gone away. Anti-chess-computer tactics are dead. A human would be very happy to play a modern chess engine to a draw, and winning is not even in the discussion.
• yangtheman 11y ago
  Would it be conceivable that similar type of AI can deploy and manage unmanned military vehicles, e.g. unmanned drones and tanks, and monitor battle progress (assuming that the other side is managed by human)? It wouldn't necessarily be turn-based, but constantly evaluating its moves against changing environment outside its control and reach its objective? I think such future is conceivable and scary at the same time.
• goquestion 11y ago
  What's needed to use programs like AlphaGo to enhance human enjoyment of Go (and other games like chess where i have more experience)? I'm more interested in this than in the "man vs. machine" narrative. Ideally we could take AlphaGo and produce an algo that can smoothly vary its playing proficiency as a human opponent increases in skill. The problem I've seen in chess computers is that setting them to "amateur" results in 3-4 grandmaster-perfect moves followed by a colossal blunder to enable the human opponent to catch up. Ideally you could use a computer opponent as an always-available, continuously adapting challenger to train hard against all the time.
  • bnewbold 11y ago
    Nothing! This is already very much a "thing" in the Go community. Bots like GNU Go, CrazyStone, and Zen are all running under multiple accounts on most of the popular online Go servers (KGS, etc). There are enough bots of differing age and ability that one can either hop up a chain of different bots or try to configure a strong bot into a weak configuration. The GNU Go bot is also downloadable free software and is frequently integrated into, eg, mobile apps (it is, however, old and not as strong as other bots, I think around 6k level). The game of Go also has a wonderful and essential handicap system to allow players (human or bots) of differing abilities (within a reasonable range; eg not possible for a novice to play an even game against Lee Sedol even with 9 stones). As far as I can tell the vast majority of amateur players play against bots online and review games to improve their skills. It would be nice if it was easier to select a bot with a given skill rating, but you can figure this out pretty easily by playing some games or reading up on bots. Playing against a skilled human who cares about your advancement is still the best way to advance though, in my opinion. Getting good feedback on your mistakes and style of play is extremely helpful.
• eatbitseveryday 11y ago
  I wonder what factors of game play create different advantages. For example, if per-turn time limits are below some threshold, whether humans would be at an advantage. It would certainly make for an interesting game.
• ep103 11y ago
  Does anyone have any suggested ways to learn the algorithmic techniques alphago uses? I've heard monte carlo tree search, and neural nets both mentioned
• atrudeau 11y ago
  Has anyone found the 15 minute summary for match 5? They had them for the first 4 matches. Really great stuff.
• atrudeau 11y ago
  Has anyone found the 15 minute summary for match 5? They had them for the first 4 matches. Really great stuff.
• sebkomianos 11y ago
  Excuse my most probably naive approach at this but aren't such games very unfair at this point? AlphaGo has been trained with data provided by humans while Lee Sedol played against an AI opponent of such calibre for the first time, no? Is it a false assumption that with the right training humans will take the upper hand again?