28 ms·

AlphaGo beats the world champion Lee Sedol in first of five matches

• GraffitiTim 11y ago
  A historic moment here, folks. Incredible, and in my opinion a little terrifying.
  • singhrac 11y ago
    Not that terrifying? It's a very special purpose-built tool. We'll still always have Calvinball! https://xkcd.com/1002/ https://xkcd.com/1002/
    • cromwellian 11y ago
      The way AlphaGo plays is more general purpose than other game playing systems. It doesn't have any heuristics, rules, or game rule books programmed into it, it learned to play Go like you would. The same technique could be applied to other areas, the same way DeepMind originally got super-human level Atari 2600 game performance purely by having it watch pixels.
      • singhrac 11y ago
        Unless I'm wrong, the AlphaGo paper doesn't mention transfer learning at all. Yes, the methods are new and exciting, but I mean there are hand-crafted features in the MCTS.
      • deleted 11y ago
        [deleted]
      • skybrian 11y ago
        Unlike with the Atari games, AlphaGo learned from a large database of human games. It'll be interesting to see how much that matters when they try it again without it.
      • cromwellian 11y ago
        What I mean is that it doesn't have particular heuristics programmed into it by observing chess experts. It seems to me that the general technique of having policy and value networks combined with monte carlo search scales to other domains and doesn't require the amount of expertise that it took to get Deep Blue to a winning level. Another way to look at is, is how fast they were able to make it a lot better in a few months.
    • oneeyedpigeon 11y ago
      I went looking for that xkcd to post an "obligatory xkcd needs updating" comment, but you beat me to it! What surprises me about it is that connect four was only solved in 1995; that seems relatively late for a 6x7 grid with only 7 possible moves per turn.
  • nbaksalyar 11y ago
    What's more terrifying is that AlphaGo can learn even further, from these and other matches. Just hard to imagine what lies ahead.
    • visarga 11y ago
      AlphaGo playing itself would be like playing Lee Sedol or better, so it has itself as a great training companion.
    • taneq 11y ago
      The Singolarity is here!
• scott_hardy 11y ago
  What an amazing game to watch. Congratulations to the AlphaGo team, and good luck to both players in the next four games!
• Aissen 11y ago
  Just for context, this is the first of a five-game match. Next one tomorrow at the same time! (6am CEST, 8pm PT).
  • moonshinefe 11y ago
    Thank you. The title on HN here didn't imply it was a 5 game series at all, nor did the tweet it linked to. It's a cool win but despite the way the titles are being presented, this isn't over yet.
• pushrax 11y ago
  That sequence on the right side was excellent, I am so impressed with the level of play.
• sethbannon 11y ago
  I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing computer in history. Kasparov drew that match, but it was clear it was the last stand of homo sapiens in the man vs machine chess battle. Back then, people took solace in the game of Go. Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Tonight, that happened. Google's DeepMind AlphaGo defeated the world Go champion Lee Sedol. An amazing testament to humanity's ability to continuously innovate at a continuously surprising pace. It's important to remember, this isn't really man vs machine, as we humans programmed the algorithms and built the computers they run on. It's really all just circuitous man vs man. Excited for the next "impossible" things we'll see in our lifetimes.
  • esturk 11y ago
    I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.
    • taneq 11y ago
      It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.
      • mjn 11y ago
        > What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. I don't think that's quite true as a description of what we knew about computer Go previously, though it depends on what precisely you mean. Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated). So we've had systems that can in principle play to any arbitrary strength, if you can throw enough computing power at them. Though you might legitimately argue: by "in principle" do you mean some truly absurd amount, like more computing power than could conceivably fit in the universe? The answer to that is also no; scaling trends have been such that people expected computer Go to beat humans anywhere from, well, around now [1], to 5 to 10 years from now [2]. The two achievements of the team here, at least as I see them, are: 1) they managed to actually throw orders of magnitude more computing power at it than other recent systems have used, in part by making use of GPUs, which the other strong computer-Go systems don't use (the AlphaGo cluster as reported in the Nature paper uses 1202 CPUs and 176 GPUs), and 2) improved the scaling curve by algorithmic improvements over vanilla MCTS (the main subject of their Nature paper). Those are important achievements, but I think not philosophical ones, in the sense of figuring out how to solve something that we previously didn't know how to solve even given arbitrary computing power. While I don't agree with everything in it, I also found this recent blog post / paper on the subject interesting: http://www.milesbrundage.com/blog-posts/alphago-and-ai-progress http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... [1] A 2007 survey article suggested that mastering Go within 10 years was probably feasible; not certain, but something that the author wouldn't bet against. I think that was at least a somewhat widely held view as of 2007. http://spectrum.ieee.org/computing/software/cracking-go http://spectrum.ieee.org/computing/software/cracking-go [2] A 2012 interview though that mastering Go would need a mixture of inevitable scaling improvements plus probably one significant new algorithmic idea, also a reasonably widely held view as of 2012. https://gogameguru.com/computer-go-demystified-interview-martin-mueller/ https://gogameguru.com/computer-go-demystified-interview-mar...
• dwaltrip 11y ago
  This is my generation's Gary Kasparov vs. Deep Blue. In many ways, it is more significant. Several top commentators were saying how AlphaGo has improved noticeably since October. AlphaGo's victory tonight marks the moment that go is no longer a human dominated contest. It was a very exciting game, incredible level of play. I really enjoyed watching it live with the expert commentary. I recommend the AGA youtube channel for those who know how to play. They had a 9p commenting at a higher level than the deepmind channel (which seemed geared towards those who aren't as familiar).
  • 21 11y ago
    I know absolutely nothing about Go, and I enjoyed the deepmind channel and found the commenting very good. I was actually thinking about playing a game with another total noob, just for fun, since the rules can be explained in 1 minute (unlike chess).
    • yiyus 11y ago
      I totally recommend it. Learning Go is a beautiful experience. It is indeed very interesting to play against another new player just to see what you come up with, then do some reading and solve some basic problems (it may even be a good idea to have a look at the easier problems before playing your first game), play more games, read more advanced books, join KGS... It is a very nice rabbit hole to fall into.
    • blue1 11y ago
      The rules can be explained in 1 minute, but the game takes some time to just start making sense. I suggest starting on a 9x9 or 13x13 board. The regular 19x19 has too much strategic depth and noobs feel lost on it.
      • exDM69 11y ago
        Many Go players suggest starting with Atari Go (aka Capture Go). It has the same rules as Go, but the starting position is predetermined and the winner is the player to capture the first stone. You only need to play a few rounds of Atari Go, say 30 minutes to an hour to get a grasp of the capturing rules and then you can move to a 9x9 or 13x13. I'd go straight for the 13x13 because it's not that much bigger but it has much more depth into it without being overwhelming. And many Go boards have 19x19 on the other side and 13x13 on the other. https://en.wikipedia.org/wiki/Capture_Go https://en.wikipedia.org/wiki/Capture_Go
• tarvaina 11y ago
  The YouTube video: https://www.youtube.com/watch?v=vFr3K2DORc8 https://www.youtube.com/watch?v=vFr3K2DORc8
  • acid__ 11y ago
    Is this video laggy and constantly showing "The match will start in -0- seconds" for anyone else?
    • mijoharas 11y ago
      Yes, apparently it gets better towards the end, but I have had to give up watching the start because it is too annoying.
• conanbatt 11y ago
  This not only shows the insane advances in computer AI, but an incredible advancement between the Fan Hui games and this one. Im still going through the kifu to get a sense of how could it have improved so much in only 6 months.
  • ktRolster 11y ago
    I feel like it started being more aggressive, playing more fighting moves....whereas in the last match it was playing mostly a defensive game (I'm not an expert by any stretch of the imagination, though).
    • apetresc 11y ago
      It's exactly the opposite – this last game was much more aggressive on both sides than the Fan Hui match.
• 21 11y ago
  The thing that was supposed to take at least 10 years happened. Only last month people were still saying that no way AlphaGo will beat the champion and that it will be crushed. Today everybody will have seen it coming and say that it was normal. Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out.
  • simonh 11y ago
    Highly optimised single-function algorithms like this are impressive stuff and can lead to useful tools, but that's it. This gets us no closer to strong AI than a tic tac toe program. Until we have systems that can tackle a wide range of fundamentally different problems and independently adapt strategies for dealing with one class of problems to deal with other classes of problems, systems like Alphago will remain one trick wonders with little relevance to 'true' AI. Edit: I do understand that the techniques used to implement Alphago can be used to implement other single-function solvers. That doesn't make it a general purpose strong AI.
    • lololomg 11y ago
      'True AI will always be defined as anything a computer can not yet do'
      • ubernostrum 11y ago
        Sort-of repeating a comment I made last time AlphaGo came up: As far as I know there is nothing particularly novel about AlphaGo, in the sense that if we stuck an AI researcher from ten years ago in a time machine to today, the researcher would not be astonished by the brilliant new techniques and ideas behind AlphaGo; rather, the time-traveling researcher would probably categorize AlphaGo as the result of ten years' incremental refinement of already-known techniques, and of ten years' worth of hardware development coupled with a company able to devote the resources to building it. So if what we had ten years ago wasn't generally considered "true AI", what about AlphaGo causes it to deserve that title, given that it really seems to be just "the same as we already had, refined a bit and running on better hardware"?
• rybosome 11y ago
  What an incredible moment - I'm so happy to have experienced this live. As noted in the Nature paper, the most incredible thing about this is that the AI was not built specifically to play Go as Deep Blue was. Vast quantities of labelled Go data were provided, but the architecture was very general and could be applied to other tasks. I absolutely cannot wait to see advancements in practical, applied AI that come from this research.
  • ktRolster 11y ago
    Here's the Nature article: http://www.nature.com/news/google-ai-algorithm-masters-ancient-game-of-go-1.19234 http://www.nature.com/news/google-ai-algorithm-masters-ancie... (it has a link to the free paper, as well) The position evaluation heuristic was developed using machine learning, but it was also combined with more 'traditional' algorithms (meaning the monte-carlo algorithm). So it was built specifically to play go (in the same way deep blue used tree searching specifically to play chess.....though tree searching is applicable in other domains).
• jonbaer 11y ago
  "AlphaGos Elo when it beat Fan Hui was 3140 using 1202 CPUs and 176 GPUs. Lee Sedol has an equivalent Elo to 3515 on the same scale (Elos on different scales aren't directly comparable). For each doubling of computer resources AlphaGo gains about 60 points of Elo."
  • taneq 11y ago
    So has AlphaGo raised its level so far just by continuing with the games against itself? Or did they just throw their entire server farm at it? (Or both, probably.)
    • jonbaer 11y ago
      I would bet a mix of both. What will be more interesting is if it ends at 5-0 if we will see AlphaGo vs. Darkforest (Facebook's engine) soon after.
      • anonaoeu 11y ago
        Facebook's Go engine is not remotely close to competing with AlphaGo. In fact, it's about comparable to the pre-existing top Go programs; the only newsworthy thing about it is that it's from Facebook and it uses neural nets.
        • PascalsMugger 11y ago
          Since AlphaGo's architecture was published in nature, wouldn't it be somewhat straightforward to reproduce something similar to it given the resources of Facebook and an already existing team of people tackling Go? I would expect other Go software to quickly follow in AlphaGo's footsteps and then build on it, perhaps surpass it by adding some different optimizations.
    • Teodolfo 11y ago
      Demis said it used roughly the same hardware resources as against Fan Hui?
• EGreg 11y ago
  Does this mean in the next few decades, computers will make better sex partners and companions than any human?
  • visarga 11y ago
    I am sure they will. People will have to rival perfect mannered AIs for other people's feels.
  • Eliezer 11y ago
    Worrying about the effect of strong AI on sexual relationships is like worrying about the effect on US-Chinese trade patterns if the Moon crashes into the Earth.
    • jholman 11y ago
      And yet, I feel that US-Chinese trade patterns could directly affect my life.
    • ue_ 11y ago
      Who says? I mean, computers can be used for multiple purposes, and although some applications of AI seem more "noble" or "intellectual" than others, the pursuit of knowledge and sexual relationships are both sense pleasures that we indulge in to make ourselves feel good. On a very large scale, one is hardly more noble than the other.
    • gjm11 11y ago
      You're right. You should get in touch with the author of this http://lesswrong.com/lw/xu/failed_utopia_42/ http://lesswrong.com/lw/xu/failed_utopia_42/ and tell him :-).
    • koder2016 11y ago
      ...or like worrying about being on board of a heavier than air aircraft (surely that's impossible).
  • mcintyre1994 11y ago
    Maybe someone can hack a dating/popular fetish forum or one of the many IM services used heavily for that sort of thing and we can get a dataset to teach computers to sext!
• tvvocold 11y ago
  Poll: https://news.ycombinator.com/item?id=11250806 https://news.ycombinator.com/item?id=11250806
• bencoder 11y ago
  I was really expecting Lee Sedol to win here. I'm very excited, and congratulations to the DeepMind team, but I'm a bit sad about the result, as a go player and as a human.
  • visarga 11y ago
    But now every amateur will have access to unlimited play against Lee Sedol-level opponents.
    • spacehome 11y ago
      Yea, let me just go home and grab my hundreds of GPUs and CPUs.
      • eru 11y ago
        Renting them in the cloud for a single game should actually not be all that expensive. Around 100 USD per game perhaps? And the price is only going to fall. An Amateur can learn plenty from slightly weaker version on less hardware already.
        • goblinking 11y ago
          Lee Se-dol as a Service!
  • studentrob 11y ago
    If it's any consolation, there are still tons of things humans are far better at than machines.
    • atemerev 11y ago
      The only remaining are language-related. Natural languages are the next focal point of AI research.
      • 11y ago
• kul 11y ago
  Here's one: how long until a computer can beat a human assisted by a computer?
  • visarga 11y ago
    Will humans be able to keep up with the depth of analysis these AIs will have, or will it become a problem for the AI to dumb down its thinking in order for us to grasp it? More generally, scientists using AI for research will probably have to do research on the research, to understand what the AI discoveries mean. Maybe they mean something we can't grasp at all, in which case they go completely over our heads, like ants trying to learn about the finer points of financial markets. We will probably have to learn new concepts and even new languages designed by the AI to convey the meaning.
    • d0m 11y ago
      I wouldn't say "dumb down" but it definitely needs to explain why it took some lines of reasoning. With deep learning, you need to rebuild the whole system with different test-cases to change a minor behavior.. but imagine if we could just say "Why did you do that? XYZ. And adjust it: "Oh, gotcha. You can't because of ABC", and then the AI has that problem solved. I guess that would be the next step in AI. I think it's called symbolic reasoning. Here's a very good article: http://dustycloud.org/blog/sussman-on-ai/ http://dustycloud.org/blog/sussman-on-ai/ (A conversation with Sussman on AI and asynchronous programming)
  • cing 11y ago
    Ah yes, I believe in the machine learning community that ensemble learning technique is called "meat bagging"
• typeformer 11y ago
  Lee Sedol should have played that top left 3,3 move earlier (at least before white covered it) WTF. Humanity is not longer at the top of the intelligence pyramid...
• panic 11y ago
  It'll be interesting to see what new things we learn about Go itself from DeepMind. The game is very deep, and apparently we haven't found the bottom yet!
  • visarga 11y ago
    Instead of the prize money, if I were Lee Sedol I'd request unlimited play time against the latest AlphaGo.
• clickok 11y ago
  I posted in the earlier thread because this one wasn't up yet[1]. Some quick observations 1. AlphaGo underwent a substantial amount of improvement since October, apparently. The idea that it could go from mid-level professional to world class in a matter of months is kinda shocking. Once you find an approach that works, progress is fairly rapid. 2. I don't play Go, and so it was perhaps unsurprising that I didn't really appreciate the intricacies of the match, but even being familiar with deep reinforcement learning didn't help either. You can write a program that will crush humans at chess with tree-search + position evaluation in a weekend, and maybe build some intuition for how your agent "thinks" from that, plus maybe playing a few games. Can you get that same level of insight into how AlphaGo makes its decisions? Even evaluating the forward prop of the value network for a single move is likely to require a substantial amount of time if you did it by hand. 3. These sorts of results are amazing, but expect more of the same, more often, over the coming years. More people are getting into machine learning, better algorithms are being developed, and now that "deep learning research" constitutes a market segment for GPU manufacturers, the complexity of the networks we can implement and the datasets we can tackle will expand significantly. 4. It's still early in the series, but I can imagine it's an amazing feeling for David Silver of DeepMind. I read Hamid Maei's thesis from 2009 a while back, and some of the results presented mentioned Silver's implementation of the algorithms for use in Go[2]. Seven years between trying some things and seeing how well they work and beating one of the best human Go players. Surreal stuff. --- 1. https://news.ycombinator.com/reply?id=11251526&goto=item%3Fid%3D11250748 https://news.ycombinator.com/reply?id=11251526&goto=item%3Fi... 2. https://webdocs.cs.ualberta.ca/~sutton/papers/maei-thesis-2011.pdf https://webdocs.cs.ualberta.ca/~sutton/papers/maei-thesis-20... (pages 49-51 or so) 3. Since I'm linking papers, why not peruse the one in Nature that describes AlphaGo? http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html http://www.nature.com/nature/journal/v529/n7587/full/nature1...
  • Mangalor 11y ago
    Since the European match went 5-0, how do we know the bot wasn't just as good months ago?
    • z0r 11y ago
• tunesmith 11y ago
  I watched the commentary that Michael Redmond gave (9-dan-professional) and he didn't point out one obvious mistake that Lee Sedol made the entire match. Just really high quality play by AlphaGo. Really amazing moment to see Lee Sedol resign by putting one of his opponent's stones on the board.
  • mathgenius 11y ago
    Yeah according to Redmond, it seemed that AlphaGo made a few "mistakes" whereas Sedol made none. And yet AlphaGo came out substantially ahead. So I'm not sure what that means. Perhaps we need to see more in-depth analysis of the moves, but it seems that AlphaGo just out-calculated Sedol.
    • reacweb 11y ago
      maybe the mistakes were unrecognized brilliant moves (tesujis).
      • jboggan 11y ago
        I would love to see an expert analysis of the game, but there were definitely a few moves where AlphaGo was "pushing from behind" that were probably not what an expert human would play.
        • eru 11y ago
          I think that's the point of reacweb: alphago might already be beyond expert human level understanding. TD-Gammon was at that point for a while in the early 90s, but the experts caught up, and this changed the generally accepted Backgammon strategies.
    • nkurz 11y ago
      I wonder if their move selection algorithm takes into account the "surprise" factor: given two moves that are almost equal in strength when analyzed to a depth of N, chose the one that looks worst at N-1. That is, if all else is equal, assume that you can search deeper than your human opponent, and lay traps accordingly.
• dropdatabase 11y ago
  I don't think a computer could ever beat me at Calvinball
• bane 11y ago
  It's almost kind of bad timing in the U.S., what with one of the most insane primary seasons in our history -- this will probably not make the news at all let alone the front page like Kasparov's and Magnus's games did.
• nefitty 11y ago
  What do you guys think of the future progress on the game Go? Will our only chance against AI be to team up with an AI to beat the lone AI? Like in this article about centaur chess players: http://www.wired.co.uk/magazine/archive/2014/12/features/brain-power/page/2 http://www.wired.co.uk/magazine/archive/2014/12/features/bra... (2014) It all sounds very Gundam Wing to me.
• ankurdhama 11y ago
  What this actually means is that "the approach" AlphaGo team developed to "computationally" play Go, which is an computationally intractable problem, will be very useful in other computationally intractable problems. The media is going to get crazy without understanding what actually happened. If you are going very hysteric over this and thinking that robots are going to take over then please try this:- Before the start of the game add/remove/update any rules of the game and tell both the players - the human and computer - at the start of the game about new rules and lets see who wins.
• pvinis 11y ago
  i would like to see the same match, but switched placed. alphago plays itself, this time as black, to kind of see the choices it would make, and if they would align with lee's.
• hrnnnnnn 11y ago
  We still have Arimaa. It's designed specifically to make it difficult for computers to play. http://arimaa.com/arimaa/ http://arimaa.com/arimaa/
  • simonbw 11y ago
    A computer won the Arimaa challenge last year. https://en.wikipedia.org/wiki/Arimaa https://en.wikipedia.org/wiki/Arimaa
    • faizshah 11y ago
      I guess the only thing left is to design a game where you can change the rules of the game as a turn.
      • Scarblac 11y ago
        Calvinball.
      • eru 11y ago
        https://en.wikipedia.org/wiki/Nomic https://en.wikipedia.org/wiki/Nomic
      • aurelianito 11y ago
        Congratulations! You reinvented Nomic. https://en.m.wikipedia.org/wiki/Nomic https://en.m.wikipedia.org/wiki/Nomic
      • ucho 11y ago
        Might not work for long. There are already contests for best generic solutions[0] and it seems like quite popular topic in machine learning. [0] https://en.wikipedia.org/wiki/General_game_playing https://en.wikipedia.org/wiki/General_game_playing
    • hrnnnnnn 11y ago
      Well, that's that then :| Or maybe this will spur the human players to improve :)
• kowdermeister 11y ago
  I'm truly amazed also, I'm not surprised or shocked. Once I knew that the previous master was beaten, I knew it's just a matter of time to see the #1 player topped. What would be shocking is to find out that a famous writer, musician or scientist is in fact, just an alias for an advanced AI system :) It needs a little trick, because people should be tricked into believing that there's a real person behind the name. Oh wait, I just remembered that there's a (mediocre) movie made on the subject: S1m0ne ( http://www.imdb.com/title/tt0258153/ http://www.imdb.com/title/tt0258153/ ) Are you saying it won't happen? Think of the guys saying the same of go :)
  • pgeorgi 11y ago
    > What would be shocking is to find out that a famous writer, musician or scientist is in fact, just an alias for an advanced AI system :) so, Milli VanAIlli? (https://en.wikipedia.org/wiki/Milli_Vanilli https://en.wikipedia.org/wiki/Milli_Vanilli)
    • kowdermeister 11y ago
      Nice catch, that would be the perfect working title for the project :)
• nopinsight 11y ago
  Deep Blue: Massive search + Hand-coded search heuristics + Hand-coded board position evaluation heuristics [1] AlphaGo: Search via simulations (Monte Carlo Tree Search) + Learned search heuristics (policy networks) + Learned patterns (value networks) [2] Human strongholds seem to be our ability to learn search heuristics and complex patterns. We can perform some simulations but not nearly as extensively as what machines are capable of. The reason Kasparov could hold himself against Deep Blue 200,000,000-per-second search performance during their first match was probably due to his much superior search heuristics to drastically focus on better paths and better evaluation of complex positions. The patterns in chess, however, may not be complex enough that better evaluation function gives very much benefits. More importantly, its branching factor after using heuristics is low enough such that massive search will yield substantial advantage. In Go, patterns are much more complex than chess with many simultaneous battlegrounds that can potentially be connected. Go’s Branching factor is also multiple-times higher than Chess’, rendering massive search without good guidance powerless. These in turn raise the value of learned patterns. Google stated that its learned policy networks is so strong “that raw neural networks (immediately, without any tree search at all) can defeat state-of-the-art Go programs that build enormous search trees”. This is equivalent to Kasparov using learned patterns to hold himself against massive search in Deep Blue (in their first match) and a key reason Go professionals can still beat other Go programs. AlphaGo demonstrates that combining algorithms that mimic human abilities with powerful machines can surpass expert humans in very complex tasks. The big questions we should strive to answer before it is too late are: 1) What trump cards humans still hold against computer algorithms and massively parallel machines? 2) What to do when a few more breakthroughs have enabled machines to surpass us in all relevant tasks? Note: It is not entirely clear from the IBM article that the search heuristics is hand-coded, but it seems likely from the prevalent AI technique at the time. [1] https://www.research.ibm.com/deepblue/meet/html/d.3.2.html https://www.research.ibm.com/deepblue/meet/html/d.3.2.html [2] http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html http://googleresearch.blogspot.com/2016/01/alphago-mastering...
  • _xander 11y ago
    Strong AI is not necessarily a bad thing. Instead of worrying about questions 1 & 2, we could be thinking less about constraint and competition with AI and more about cooperation and goal-orientation: e.g. the work of Yudkowsky (https://intelligence.org/files/CFAI.pdf https://intelligence.org/files/CFAI.pdf) or some of the thoughts provided by Nick Bostrom http://nickbostrom.com/ http://nickbostrom.com/. Goal-orientation is preferable to capability constraint because the potential benefits are far larger. Tl;dr: I, for one, welcome our robot overlords (so long as they don't behave like our robot overlords).
• narrator 11y ago
  The funny thing about AI at this scale is we don't really know why the computer does what it does. It's more of a inductive extrapolation that we can verify that a technique works for a small problem, so we'll throw a whole bunch of GPU power and data at it and it SHOULD work for a big problem. How it actually works is fuzzy though as there's just a couple of gigabytes of floats representing weights in neural networks. No human can look at that and say: "Oh! I see why it made that move". It's so much data that it becomes kind of nebulous what the AI is doing.
  • gearhart 11y ago
    It certainly seems incomprehensible now, but that isn't necessarily true in neural networks - the amazing thing about some of the experiments using the results of intermediate layers in image recognition is that they seem to be building up higher and higher order "understanding" as you get to deeper and deeper layers which correlates directly with how a human might explain their strategy. You can imagine a means of interpreting intermediate layers of alphago's weighting function similar to the second image in [1] (not the best example, I apologise) that would produce images or other abstract representations of the strategy that layer was encoding, similar to how a human might classify moves or patterns into categories. [1] http://cs231n.github.io/convolutional-networks/ http://cs231n.github.io/convolutional-networks/
    • narrator 11y ago
      There's a name for this phenomenon: "Subsystem inscrutablity". See this presentation on Alpha Go linked to from here: http://nextbigfuture.com/2016/03/what-is-different-about-alphago-versus.html?m=1 http://nextbigfuture.com/2016/03/what-is-different-about-alp...
  • panic 11y ago
    The neural net guides the search for the right move, but the program ultimately considers many possible sequences and picks the one it thinks is best. The program could justify its choice in the same way human players do: by playing out alternative choices and showing how those choices would have put it into a worse position.
• moonshinefe 11y ago
  Can someone explain why this is more impressive than a computer beating top chess players over a decade ago? I'm not very familiar with Go, and while there were far more squares on a Go board, it seems less sophisticated than chess to me. Maybe Go has way more moves possible and emergent strategies or something I'm not taking into account.
  • fcmk 11y ago
    What do you mean by sophistication in this context?
    • moonshinefe 11y ago
      In chess there are various pieces with differing powers, with Go it seems like they all have similar powers. Again, I don't really know the game and might be wrong, but that was my impression.
      • eru 11y ago
        Actually, that's the draw of Go over Chess for me. The different powers in chess seems arbitrary. We can easily imagine aliens coming up with Go by convergent evolution, but not Chess. Too many free parameters. Yes, the fascination lies in the strategies that emerge from the simple base.
      • kqr 11y ago
        One thing that's cool about Go is that the pieces get their value and power not from the rules, but from how they are used. In an actual game of Go, you will find groups of ten pieces that are casually thrown away, and you will find single pieces that the whole game revolve around. In the rulebook, they are the same piece, but in actual effort expended to save/attack them, you'll see they're valued vastly differently. This happens in chess too, of course, but in Go their value is decided only based on how they are used. The sophistication is about the same, but the rules are simpler.
        • 11y ago
• Radim 11y ago
  Beating humans in Go is, in itself, not all that exciting. Go bots have been beating strong humans for quite some time now (just not the very top humans). There are other implications that make this AlphaGo progress super exciting though. Go captures strategic elements that go well beyond the microcosm of one nerdy board game. That's the real reason Go has been around for >2,000 years, and why this AI progress is relevant, despite its limited "game domain". I wrote about it here, from my perspective of an avid Go player & machine learning professional [1]. [1] http://rare-technologies.com/go_games_life/ http://rare-technologies.com/go_games_life/
  • hendekagon 11y ago
    Yes. This is the point. To what extent can AlphaGo transfer what it has learned in Go to other domains. It was a very smart move to train their first AI on Go!
  • habosa 11y ago
    I disagree with this due to the rate of AlphaGo's progress. Consider CrazyStone which was the previous state of the art in Go computers. That program reached 5dan after many years of development and has not shown any signs of being able to reach Lee Sedol level (9dan). In October of this year AlphaGo beat a 5dan player, bringing it into the range of CrazyStone. Only ~6 months later it beats a 9dan player which means it is now ~400 Elo higher. This means the new version would be predicted to beat the old version ~99% of the time. Such incredible consistent progress of a problem considered somewhat intractable is notable and exciting. Imagine where this machine will be in 6 more months.
    • habosa 11y ago
      Edit: Fan Hui was only 2dan so this is even more insane.
      • Radim 11y ago
        Yes, AlphaGo's progress is amazing. I don't think there's any disagreement there :) But I don't think you know much about Go, if you can say Fan Hui is "just" 2 dan professional. What do you reckon the strength difference is between 2p and 9p? Nitpick: while AlphaGo today is certainly stronger than AlphaGo last October, it doesn't follow in any way from the fact that both programs beat their respective opponents. A > B, C > D, D > B, therefore C > A? By "400 ELO", no less?
• deleted 11y ago
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• thomasahle 11y ago
  Giant spoiler! Does Hacker News have any policy against these things?
• bwang29 11y ago
  I was just thinking, does AlphaGo's game strategy also emulate some sort of psychological strategies used by real human, such as bullying, confusing or making fun of its opponent when it sees fit.
• codecamper 11y ago
  A human was beaten with some thousands of CPUS & GPUS. On a calorie level, the human is still more efficient. On a time to learn these skills... going from zero (computer rolls off assembly line) to mastery, the computer wins. Actually maybe the computer wins even on the caloric level, if you consider all the energy that was required to get the human to that point (and all the humans that didn't get to that point, but tried).
  • ragebol 11y ago
    But the computer certainly does not win on the amount of training samples required. The human is at the same level as the computer now for Go, but the computer has had much more training samples as Lee Sedol could process in his lifetime. The next step is to reduce the training time/samples for the computer to get the same performance.
    • jules 11y ago
      That's silly. Why would you want to put human limitations on the computer? We don't artificially put computer limitations on the human.
      • ragebol 11y ago
        I don't want to put a limit on the computer, not at all. But I do think humans have an edge on computer because at least for now, humans can learn the same skills from less samples (at least in this example: Go) Of course, if there are many samples, the computer can go through those faster, but if there are no samples already and the computer has to learn example by example as humans do as well, humans may still have an advantage. Of course, this advantage will diminish as well as AI advances.
        • jules 11y ago
          How do you count examples? The computer can generate its own examples by playing against itself. So in theory it needs 0 examples. This is not a useful metric at all.
• georgehaake 11y ago
  I have read a fair amount about how it was written without much detail. Anyone know what it was written in?
• ccvannorman 11y ago
  reference: SGF file on OGS: https://online-go.com/demo/114161 https://online-go.com/demo/114161 To my untrained eye, AlphaGo was already way ahead by move 29 in the match tonight with black having a weak group in the upper side, while black wasted a lot of moves on the right side as white kept pushing (Q13, Q12), which white erased later because those pushes were 4th line for black and the area was too big too control. Black never had a chance to recover this bad fight. After those reductions and invasion on right side white came back to the 3-3 at C17 which feels like solidified the win. Some people are asking what was the losing move for Lee Sedol? I wanted to joke and say "the first one.." but maybe R8 was too conservative being away from the urgent upper side where white started all the damage.
• couchand 11y ago
  When there's a computer that can beat the world champion at both go and chess with no modifications, then I'll be scared.
  • picozeta 11y ago
    You just take your top-notch Go/Chess engines and detect the game in the initial step.
• mrdrozdov 11y ago
  How much did this match cost the AlphaGo team? (From a computing resources perspective)
• supergirl 11y ago
  after so much press about this, it would be funny if overall the human wins
• cgearhart 11y ago
  I was really hoping to see a more technical discussion than what I found here in the comments. It's too bad that such a cool accomplishment gets reduced to arguments about the implications for an AI apocalypse and "moving the goalposts". This isn't strong AI, and it was at least believed to be possible (albeit incredibly difficult), but it is still a remarkable achievement. To my mind, this is a really significant achievement not because a computer was able to beat a person at Go, but because the DeepMind team was able to show that deep learning could be used successfully on a complex task that requires more than an effective feature detector, and that it could be done without having all of the training data in advance. Learning how to search the board as part of the training is brilliant. The next step is extending the technique to domains that are not easily searchable (fortunately for DeepMind, Google might know a thing or two about that), and to extend it to problems where the domain of optimal solutions is less continuous.
  • j2kun 11y ago
    > without having all of the training data in advance What? They certainly trained the algorithm on a huge database of professional go games. It's even in the abstract. [1] [1]: http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html http://www.nature.com/nature/journal/v529/n7587/full/nature1...
    • spot 11y ago
      they were amateur expert games from the KGS server.
      • hyperpape 11y ago
        I believe the October match used amateur games, but for this match, they added a professional database.
    • cgearhart 11y ago
      > What? Exactly They used the game database to learn the value network, then reinforcement learning of the policy network was performed on self-play games. I.e., the machine learned to play from existing data, then played against itself to learn the search heuristics (the policy network) without the need for expert data.
• ausjke 11y ago
  No surprise at all, human brain is an organ with limited neurons, and computer doubles its performance very 18 months. In fact not just the chess, I would say that AI will beat human all around at unlimited ratio in the future, when they learned how to improve themselves especially.
• deleted 11y ago
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• devy 11y ago
  I had a feeling that AlphaGo would beat Lee Sedol yesterday after watching Fan Hui's interview [1]. According to Hui's recall, the defeat all came down to these things: the state of the mind, confidence and human error. The gaming psychology is a big part of the game, without the feelings of fear of being defeated and almost never making mistakes like humans do, machine intelligence beating human at the highest level of competitive sports/games is inevitable. However, to truly master to game of Go, which in ancient Chinese society, it's more of an philosophy or art form than a competitive sport, there is still a long way to go. There were a ton of details Hui cannot speak of due to the non-disclosure agreement he signed with DeepMind, but those were the gist of the interview. In the end, AlphaGo match is 'a win for humanity', as ​Eric Schmidt put it. [2] [1] http://synchuman.baijia.baidu.com/article/344562 http://synchuman.baijia.baidu.com/article/344562 (In Chinese) Google Translate: https://translate.google.com/translate?hl=en&sl=zh-CN&tl=en&u=http%3A%2F%2Fsynchuman.baijia.baidu.com%2Farticle%2F344562 https://translate.google.com/translate?hl=en&sl=zh-CN&tl=en&... [2] http://www.zdnet.com/article/alphago-match-a-win-for-humanity-eric-schmidt/ http://www.zdnet.com/article/alphago-match-a-win-for-humanit...
• arao 11y ago
  Lee is not the best player NOW.
• mark_l_watson 11y ago
  I just wrote a blogg about this. I was up to 1am this morning watching the game live. I became interested in AI in the 1970s and the game of Go was considered to be a benchmark for AI systems. I wrote a commercial Go playing program for the Apple II that did not play a very good game by human standards but did play legally and understood some common patterns. At about the same time I was fortunate enough to get to play both the woman's world Go champion and the national champion of South Korea in exhibition games. I am a Go enthusiast! The game played last night was a real fight in three areas of the board and in Go local fights affect the global position. AlphaGo played really well and world champion (sort of) Lee Sedol resigned near the end of the game. I used to work with Shane Legg, a cofounder off DeepMind. Congratulations to everyone involved.
• agentultra 11y ago
  Isn't this jumping the shark a bit? It's a 5-game match. The first was really, really close.
  • Matetricks 11y ago
    It's worth mentioning that Lee Sedol mentioned in an interview that even if he loses a single game against AlphaGo, he will have lost the match. He was expecting to win all 5 games.
    • agentultra 11y ago
      I hope that doesn't shake his determination and ability to concentrate. He could still win.
  • zeven7 11y ago
    As a ~1 dan amateur, whether the game was "really, really close" was not clear to me. For one of my games, yeah it was close, but professional play is on such another level I'm not sure how close this game should be considered. I watched two 9d pro commentaries, Redmond's and Kim Myungwan's. Redmond was obviously being charitable in saying the game was close near the end. Myungwan said the victory was apparent several moves before the resignation, and Myungwan also said AlphaGo was clearly stronger than himself. Either way, even if this game should be considered close, it's still not clear if AlphaGo was holding back in order to hold a secure win. It's possible it can play at a higher level, but it wasn't needed. We can't really know AlphaGo's strength until (if) it is beaten. The following matches will be very interesting.
  • Florin_Andrei 11y ago
    Yes, but it's still a historic first. It's the first time a machine wins a game against a top human player.
  • slm_HN 11y ago
    Jumping the shark and jumping the gun are two different things. You want the second one.
• andrepd 11y ago
  He has lost 1 game of a 5 game match, on a handicap. Hardly a defeat.
  • kllrnohj 11y ago
    > on a handicap What handicap? There was no handicap?
  • fogleman 11y ago
    What handicap?
    • andrepd 11y ago
      I misread it, he had only the usual first move advantage handicap.
    • deleted 11y ago
      [deleted]
  • tunesmith 11y ago
    There was no handicap - 7.5 komi is traditional to counteract the benefit of moving first.
• vancan1ty 11y ago
  Does Lee Sedol have access to AlphaGo training games and/or matches?
• terryf 11y ago
  Extremely interesting news and kind of sad as a human being :) I don't really know that much about AI, but hopefully some experts can tell me - how different are the networks that play go vs chess for example? Or recognise images vs play go? What I mean is - if you train a network to play go and recognise images at the same time, will the current techniques of reinforcement learning/deep learning work or are the techniques not sufficient at the moment? If that works, then it really does seem like a big step towards AGI.
  • GolDDranks 11y ago
    This is basically a combination. A "traditional" chess program would use a tree search, but trees get quickly ot of hand since they grow exponentially. The trick is to prune them, and they trained a network to do that. It selects just the moves that look good to it. (It has some level of randomness to it, too) After reaching deep enough in the search tree, they use another network to evaluate who's winning. Usually this is hard to do in Go, and that's why the second network is quite novel and helpful. So, they use a combination of techniques. And they're doing well at it.
    • terryf 11y ago
      right, yes, but my question was meant to be a bit more general - this and various other results have shown that it is possible to train a deep net to do a specific task very successfully - my question was if it's possible to train it to do two or more tasks as successfully or will the network then have to be exponentially larger. I suppose there is no known way to "combine" trained networks together.
      • andreyk 11y ago
        The standard is to have each neural net be trained just for one task, as you say; there may be research into multi-skill neural nets but I have yet to see any. AlphaGo in particular is extremely specialized to Go, even in terms of how the algorithm is implemented.
• geebee 11y ago
  Terrific accomplishment. Just a question to throw out there - does anyone feel like statements like this one "But the game [go] is far more complex than chess, and playing it requires a high level of feeling and intuition about an opponent’s next moves." … seem to show a lack of understanding of both go and chess? I understand there may be some cross-sports trash talking, but chess, played at a high level by humans, relies on these things as well. The more structured nature of chess means that it is (or at least was) more amenable to analysis by brute force computer algorithm, but no human evaluates and scores hundreds of millions of positions while playing chess or go. Eh, the mainstream media is going to say this regardless, and I suppose it's just unrealistic to expect them to draw a distinction between complex for humans and amenable to brute force computation but statements like this always seemed to show a remarkable lack of awareness of how people actually play these games (though I am not an especially skilled chess or go player).
  • jscahefer 11y ago
    think of it this way: The search space for chess is much smaller, so we can lean very heavily on brute forcing in our A.I. implementations. The search space for Go is much larger, so while brute force searches are critical in tight fighting, and in endgame play, something more has to happen to play go well in the middle game. Chess fell to a much earlier generation of A.I. While Go held out until A.I. as a field had advanced as well several generations/decades as well.
    • geebee 11y ago
      Agreed. The part I object to is the unqualified statement that go requires a high degree of intuition, whereas chess doesn't. As humans play the game, I think it's safe to say that this is generally inaccurate. Both games, for humans, rely very heavily a high degree of intuition. I would tend to agree that there is something interesting and new at work here, though, in that computers didn't get better than humans at go simply by applying the same brute force algorithm, just with more processing power. It does suggest that at least some of what we previously thought required "intuition" can be modeled through a random forest (I think that's what they're using, if not RF, then some other combination of ML).
• cm2012 11y ago
  After Go, the next AI challenge they're looking at is Starcraft: https://twitter.com/deeplearning4j/status/706541229543071745 https://twitter.com/deeplearning4j/status/706541229543071745
  • zouhair 11y ago
    Good luck with that.
    • LockeWatts 11y ago
      Starcraft in many ways is a much easier game for an AI to beat top pros at than Go.
      • cm2012 11y ago
        They say Starcraft is still 5-10 years out for AI to beat pros: http://www.newyorker.com/tech/elements/deepmind-artificial-intelligence-video-games http://www.newyorker.com/tech/elements/deepmind-artificial-i... (ctrl+f for Starcraft at the bottom of this article)
        • LockeWatts 11y ago
          That's great and all, but it's already been done by much more simplistic algorithms by abusing the mechanical nature of the game. Some units are balanced by the fact that no human can manipulate them to their full potential. Once you remove that restriction, the AI can abuse the speed of execution, acting as a force multiplier that will cover any strategic lackings. If they want DeepMind to really "play" Starcraft in the traditional sense, i.e. make it win based on decision making and reasoning about the game, then they'll need to artificially rate limit its APM.
  • Cookingboy 11y ago
    If that's true...they really just hate the Koreans LOL
  • sago 11y ago
• imh 11y ago
  I want to scratch my itch and play some go. I suck, and playing against other players online I get destroyed so quickly I feel like I'm ruining their fun. Where can I find a fun bot with variable difficulty?
• chimtim 11y ago
  AlphaGo can be beaten. It uses reinforcement learning so it will perform the set of moves that in the past led to its win. So predictable. Sedol just needs to take control and make it play in a predictable fashion. Also, perhaps play obscure moves that AlphaGo wouldn't have trained on. Perhaps next year's Go winner will have a PhD in computer science.
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• socrates2016 11y ago
  I think it will be very interesting if Lee Sedol can win one. Humans have different blueprints and environments. Who is to say a human can't become better?
• bitmapbrother 11y ago
  Some people were downplaying the victory of AlphaGo over the European champion because he was only a 2p player. I wonder what they have to say now.
  • z0r 11y ago
    The last victory was significant, but this victory was far more significant. The professional dan scale isn't exactly linear and the ranks can't simply be compared numerically even when they are granted by the same organization - and Korea, China and Japan all have at least one organization of professional go players that each maintain their own rankings. Sedol is a current top player who has won many, _many_ titles and Fan Hui is 10 years out of regular professional play and doesn't have a title to his name. What people were saying before is still true today. All of the reporting has suffered from the usual problems of describing something specialized to the general public, and all the typical inaccuracies of such journalism (compounded by Google's PR department being the source of some of it). Congratulations to the team at Deepmind, and I'm wishing good luck for Sedol in the remaining matches - if he wins we would certainly get to see a second series rematch some months down the line, and that would be very exciting for go fans everywhere.
• jorgecurio 11y ago
  man I am fired up to watch tonights game...like I am fired up for UFC there should be like a North American Go Nationals or something like that televised on twitch Anyone putting money down on Sedol? He said it will be either 5-0 or 4-1 in his favor.
• joe563323 11y ago
  Learning from experience goes both to the program and to the champion. Does this mean if the champion keeps playing with the machine several times, he has a chance of winning?
• rwayasi 11y ago
  2-0 to AlphaGo! Check out our prediction of DeepMindAI AlphaGo vs Lee Sedol superforcasting http://blog.theasi.co/predicting-alphago-vs-lee-sedol/ http://blog.theasi.co/predicting-alphago-vs-lee-sedol/
• randomgyatwork 11y ago
  AI is good for rules based systems, but most of the worlds problems that need to be solved don't have rules in the same way a board game does. Sure it's cool that a computer beat a human at a board game, but thats like celebrating a penguin being better at fishing than a person with bare hand