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AlphaGo shows its true strength in 3rd victory against Lee Sedol

• guelo 11y ago
  I don't understand how this is considered fair. AlphaGo has been trained on a database that includes every recorded game Sedol has ever played while Sedol is seeing AlphaGo's play style for the first time. Sedol should have been allowed to play against AlphaGo for a few months before the match so he could study its style.
  • krackers 11y ago
    One member of the deepmind team responded to this very question during the interview at the beginning of part 3. He said that the training data set size is much, much larger than the number of lee sedol games. It is like a drop in the ocean and not enough to significantly influence the resulting policy network.
    • gus_massa 11y ago
      Perhaps the computer didn't know that it was playing against Lee Sedol, but from Wikipedia "As of February 2016, he ranks second in international titles (18), behind only Lee Chang-ho (21)." I don't know the details of the algorithm and perhaps it doesn't give more explicit weight to his games. But I wouldn't be surprised if after some iterations the algorithm decided to give more implicit weight to the games of the world leaders.
      • sdenton4 11y ago
        Well, I'm not even sure if they attached individual identities to the games fed in as features...
        • arcticfox 11y ago
          The implication is that because Sedol wins more tournaments than almost anyone else, if you feed "Tournament winner" or "ELO" etc. as a feature, his games will be much more weighted than others. So even if AlphaGo isn't explicitly designed to play against his style, it's implicitly trained in it. But it's pretty subtle and I'm guessing that the volume of high-level tournaments overwhelms any effect, as the AlphaGo team said.
      • lambda 11y ago
        They mentioned what the training input to AlphaGo was in the paper; a database of a few hundred thousand games from dan ranked players on KGS. This means mostly amateur players, though there are a few professionals who play on KGS as well. However, this only gets you so far, and the training set is fairly small compared to what you want to really train both the policy and value networks well. So then they had it play millions of games against different versions of itself, training both a new policy network and the value network based on that. It's unlikely that Lee Sedol's games made much if any impact on AlphaGo's training. It was bootstrapped off of high-level amateur, and some casual pro, play, but from then on it just trained against itself.
  • yeukhon 11y ago
    I did not downvote you, FYI. But I think a computer as a training buddy is indeed a good idea to improve Lee's skill. I don't know how Lee feel right now, he's defeated, there must be an enormous pressure inside of him, but I think he will appreciate the challenge because he needs more challenge! He has played against top players all over the world now for his 20+ years since earning 1 dan rank. Also, I think the computer play against itself and many random moves. Unlike human players, one would expect computers to play unconventionally since the computer can predict so far ahead about the probability of maximizing winning.
    • fma 11y ago
      I wouldn't say Lee is unchallenged. Ke Jie defeated Lee just before Lee played AlphaGo. But I would say he might feel like crap having lost his title to a 19 year old, and now losing to a machine all in the same week.
      • yeukhon 11y ago
        Yes, I am aware of that, but I think he will appreciate more new opponents.
  • clarle 11y ago
    This is more than fair. Go AIs weren't expected to reach this level for at least another 10 years. Before AlphaGo, Zen and Crazy Stone (the previous Go AIs) could only play against top-level professionals with a significant 4-5 stone starting handicap, and this was less than 3 years ago. A 4-5 stone handicap is basically taking control of half the board before the game has even started. It really shows how the neural network approach made a huge difference in such a short time.
    • emcq 11y ago
      Part of this timing jump is Google throwing hardware at the problem with a large 280 GPU + 1920 CPU cluster. I would venture this is almost 100x bigger than most of the Go AI hardware we've seen to date. The nature paper suggests without this cluster it would be playing competitively with other single workstation Go AI, but nowhere near top level players.
      • papapra 11y ago
        Apparently it is still strong on a single machine https://twitter.com/demishassabis/status/708489093676568576 https://twitter.com/demishassabis/status/708489093676568576
      • jonknee 11y ago
        FWIW Deep Blue was also a Supercomputer at the time it beat Kasparov. The phone in your pocket can now beat a Grandmaster.
        • andromeduck 11y ago
          Yeah but Moore's law is dead now so that really doesn't say much anymore.
          • codehotter 11y ago
            Don't underestimate algorithmic improvements. Today's chess engines running on DeepBlue hardware outperform DeepBlue running on today's hardware. Modern chess engines are built on a testing infrastructure that makes it possible to measure how each potential change affects the playing strength. This "Testing revolution" has brought massive improvements in playing strength. For AlphaGo, it's probably the training that requires the most computational resources. The 'distilled knowledge' could perhaps run on a desktop PC. The program would search fewer variations and would be weaker, but if AlphaGo improves further, that version might still be stronger than any human.
  • zeroxfe 11y ago
    It's probably not that different from some new hotshot kid that has no recorded history of playing, but got really good playing on the street and studying the masters. Kid gets discovered by some credible major promoter who challenges Sedol to a $1M game.
  • randomguy7788 11y ago
    sedol's game actually has zero bias over how alphago plays. the team lead for alphago himself said that its like a drop in the ocean/bucket for alphago.
  • asdfologist 11y ago
    It's actually entirely possible that if the program were unsupervised, i.e. had to learn Go "from scratch" without relying on any human games, it would be even stronger than it is now.
    • arcanus 11y ago
      I've very, very suspicious of this statement. Unsupervised learning is generally speaking not as advanced as supervised. While unsupervised learning might be the holy grail, this victory is really about deep learning, which is an advanced supervised learning technique.
      • asdfologist 11y ago
        But in this case, the training data consists of human games, which are flawed. Supervising with flawed data can have unpredictable results.
        • pixl97 11y ago
          That depends on the reasons that humans make flaws. If human flaws are mostly errors related to failures in our meat (stress, lack of focus, jittery nerves) that keep us from looking in depth then the algorithm will easily use the good points in each game and with its superior ability to look deep into the future the results are predictable that the machine will win every time.
        • seanwilson 11y ago
          I think flawed is a strong word. It's likely humans are stuck in locally optimised strategies though.
      • moultano 11y ago
        Even more importantly, the policy network that chooses which move to explore must choose human like moves in order to function correctly because it must choose to explore the correct moves of Alphago's opponent.
  • plank 11y ago
    So what? I could watch every game Roger Federer played in any tennis tournament and still lose all sets to love. It used to be that computers could only do combinatorics better, but that where 'intuition' played a strong part, there was still hope for us humans... Well, guess I will have to start playing Calvinball...
    • philovivero 11y ago
      Okay, I have $10k says Plank beats the computer in Calvinball until at least 2020. At that point, I'm only willing to bet $1k/game.
    • fma 11y ago
      There's a difference between having skills and having knowledge. In your case you have knowledge but lack skills. When Federer was losing to Nadal he changed his practice and game to counter Nadal's style. He took knowledge and applied it to his skills. When Federer came out with SABR people were like wtf. Now they know of it and put more umph on their second serve. See. Knowledge + skills
    • fma 11y ago
      Also assuming you are a tennis player...if you do not study your opponents' shots during warm up, you are doing it wrong. The way the would play against a lefty is different than a righty. Someone with lots of topspin vs someone who hits flat, a pusher vs power hitter, etc.
  • _yosefk 11y ago
    As it happened in chess, machines will beat humans in Go no matter how much knowledge about their inner workings will be provided to the human. (I've watched this story unfold in chess and the various hopes of how humans are still somehow better. $1K sez Go is exactly the same story. Can't beat a machine in a formal universe with a defined goal.)
    • pixl97 11y ago
      >Can't beat a machine in a formal universe with a defined goal. That's why the next goal AI developers are pushing towards is incomplete information games.
  • hyperpape 11y ago
    I don't think this is unfair, but I think other people are replying to a suggestion that AlphaGo might be optimized to beat Lee that I don't see in the parent comment. What does seem right is that whatever strategic insights are in Lee's play are reflected in current games--his and the younger generation who came up in his shadow. Whatever strategic novelties shape AlphaGo, they are totally new to Lee. I don't think that would make the difference: 1) there's no trick to be learned and 2) the same thing happens with human players to some extent--when Lee Changho appeared on the international go scene, his style was misunderstood and underestimated, even when his games were public. However, it is true that there is a real asymmetry--AlphaGo may not know Lee from other players, but it has had the opportunity to "study" the best games of the current players, and no one outside of DeepMind has had an opportunity to study its games.
  • 21 11y ago
    Lee Sedol didn't complain about this. In fact, even a few days before the match Lee Sedol was predicting a 5-0 win for him. If someone is clearly better than you, you can study it's style all you want, it won't make a difference. You probably wouldn't even understand it's style.
    • andromeduck 11y ago
      But that wasn't based on the performance of AG but sumlsr AIs
    • fma 11y ago
      That's a very amateurish and closed minded comment. There's a reason professional sports team and players study the styles of their opponent. Teams employ statisticians for this exact role. Before games, teams study the style of their next opponent. After every after game teams go and study the recordings of their last game.
      • 21 11y ago
        Which is why I qualified my comment with "clearly better". You can study Usain Bolt's running style all you want, it will still leave you in the dust. Studying makes sense only when the difference is small.
  • est 11y ago
    > Sedol should have been allowed to play against AlphaGo for a few months It's not whether AI can outperform human, it's a matter of when.
• greenyoda 11y ago
  The main discussion of the third game can be found here: https://news.ycombinator.com/item?id=11271816 https://news.ycombinator.com/item?id=11271816 However, this article contains much more analysis of the game than previously posted articles, and is worth reading.
• atrudeau 11y ago
  I felt that Lee thought global sometimes, especially when he had time on the clock, but his mind could only take so much thinking, and time was catching up, so for a great many number of moves he thought locally. AlphaGo doesn't tire and thinks globally on every move. We saw that time and time again. Lee playing locally and AlphaGo surprising everyone with an unexpected move somewhere else on the board. Obviously it is better to be globally optimal than locally optimal.
  • highwind 11y ago
    Doesn't principle of optimality state that if a solution is optimal than any of its sub problem must be optimal as well?
    • wickawic 11y ago
      Instead of downvoting this comment, let me provide a counter example. Imagine the knapsack problem, where you can lift 10kg and you have a 10kg gold bar and 5kg cinderblocks. In this case solving 2 5kg knapsack problems will give you two cinderblocks, which is obviously not optimal. I don't know a lot about the strategy of go, but it seems to me that any play that doesn't take into account the entire state of the board is allowing for the same class of sub-optimal behavior as the example above.
      • felixgallo 11y ago
        the guy you're responding to is correct: https://en.wikipedia.org/wiki/Bellman_equation#Bellman.27s_Principle_of_Optimality https://en.wikipedia.org/wiki/Bellman_equation#Bellman.27s_P... and your attempted counterexample makes no sense (as the 'two cinderblocks' solution is not a subproblem of the optimal solution).
        • skybrian 11y ago
          Under this definition of "subproblem" the different parts of a Go board are not subproblems either. If the moves on different parts of a Go board didn't influence each other and could be solved separately via dynamic programming, it would be a much easier game (and probably uninteresting).
• mchahn 11y ago
  It was fascinating when the author experienced a weird bit of nausea when thinking about the implications of AI. I'm not sure how I feel about it yet. It does scare me a bit.
  • agumonkey 11y ago
    Think about that we're constantly pushing toward our own replacement.
    • catshirt 11y ago
      considering the possibility of augmentation, supplantation seems like somewhat of an assumption.
    • mdpopescu 11y ago
      When a student surpasses the teacher, that's supposed to be a good thing.
      • hanniabu 11y ago
        That's not really relatable because a student can only replace one teacher. When it comes to software, it's more so like saying that one student can replace all the teachers. We're seeing this now with self driving vehicles and the trucking industry. Once the software gets good enough and it's approved, there'll be a massive push for it's adoption and I'm sure that within roughly 3 years, about 70% of truckers will be replaced from that single breakthrough. Then comes taxi drives, maybe pilots and train operators in another 8 years or so. It'll be a cascade of lay-offs and that's just with this technology. So while it's a good thing that a student surpasses the teacher, this isn't exactly that same scenario.
        • z1mm32m4n 11y ago
          Sure, but this phenomenon is not new. It's called creative destruction: innovators discover a market advantage, creating new industries while destroying the old.
• 71817188 11y ago
  Did Lee Sedol have a reasonable idea of AlphaGo's recent strength? To put it more harshly, did DeepMind misrepresent the true strength of the program? I find it very hard to find information about this online.
  • lukaslalinsky 11y ago
    They challenged a top player for $1M. Doesn't that imply they think it's stronger than any human player?
    • gus_massa 11y ago
      I think they expected that the computer to play a good game, but probably they were not sure about the outcome. Anyway, Google has Billons in the bank, so if they were defeated they would not miss that single Million too much.
    • wolfgke 11y ago
      This can also imply that for Google it is worth $1M to know where AlphaGo still has weaknesses.
      • iainmerrick 11y ago
        Don't be silly, a Go AI has very little direct value for Google's business. But the publicity is great, easily worth $1M. More important is keeping the DeepMind team busy and engaged -- the real value is in spinning that know-how off into other projects.
        • wolfgke 11y ago
          > Don't be silly, a Go AI has very little direct value for Google's business. On the other hand: Knowing what makes some problems much easier for humans than for computers can have direct value on Google's business. Go was a hot candidate for a game with this property.
    • Bud 11y ago
• petegrif 11y ago
  Holy crap. It's a total beatdown.
  • jacquesm 11y ago
    So far, but yes, it looks bleak (or good, depending on which side you're rooting for).
• sdenton4 11y ago
  It would be interesting to know AlphaGo's estimated probability of winning as the game progressed; presumably we can see directly how worried it was at any given point in the game. And thus get another sense of whether it ever really felt threatened by Sedol. At this point, the match is won, but games 4 and 5 will commence. The question shifts from whether AlphaGo is better than humanity's best, to whether humanity can even have a chance of beating AlphaGo in a single game. And so far, it sounds like the answer is likely to be a resounding no.
  • teraflop 11y ago
    This isn't exactly what you were asking for, but here are some graphs from a different Go AI (Facebook's "darkforest") showing its estimated win probabilities: http://zhuanlan.zhihu.com/yuandong/20639694 http://zhuanlan.zhihu.com/yuandong/20639694
    • sdenton4 11y ago
      Neat! My understanding is that AlphaGo is a stronger player, and so will likely have different estimations than the darkforest. But this is still really cool.
      • daveguy 11y ago
        Looked up the strength (of dark forest) and it is claimed to be mid level amateur similar to crazystone and zen. So, the close measurements probably aren't too precise (when they are both around 50%), but the break away is probably about right.
    • EGreg 11y ago
      Can anyone with Go knowledge comment on this?
      • deleted 11y ago
        [deleted]
      • 11y ago
• raverbashing 11y ago
  This is different from DeepBlue and Garry Kasparov Chess may have a big search space, but it is "well behaved". Moves might be out of the ordinary, but not too much (weird moves that still help to win the match are rare) AlphaGo might also have learned from past matches, but that doesn't give all the answers The black-box aspect, the fact that you can't understand what it is thinking is curious, to say the least
  • asdfologist 11y ago
    I don't know if that's true. "Computer moves", referring to weird moves that computers play that often turn out to be very good, tend to get mentioned a lot in chess. And you might hear about them less and less, because many of them have been absorbed into the human chess repertoire.
    • jdietrich 11y ago
      Also notable is the verb "spacebar", meaning "to play the same move an AI would". The chess world provides an interesting microcosm of how humans adapt to a world dominated by AI.
  • seanwilson 11y ago
    > The black-box aspect, the fact that you can't understand what it is thinking is curious, to say the least How true is this? Presumably you could ask AlphaGo to give you a list of the top 10 best moves it considered and perhaps, given a certain move, what it sees the board looking like 10 moves ahead. If you're watching the commentary, one of the commentators (Michael Redmond) will try to explain the outcome of certain moves and this would seem to be equivalent although I imagine he could explain this in terms of the endgame better.
• clickok 11y ago
  How strong is AlphaGo? DeepMind doesn't generally comment on these sorts of things, but I think they knew it would be strong, just not how strong. It is hard to accurately gauge the playing strength of such a program for the following reasons: * Go is not solved, it doesn't really even admit a good heuristic for close games. In chess, given enough time, we can do a pretty good job of searching the possible outcomes from a given position, and even if we can't evaluate all the possible endgames, evaluation functions exist that give us an idea of which side is ahead when we terminate the search. Go has a much higher branching factor (more moves available at each turn, making the search more expensive) and short of a catastrophic blunder, it's hard to quantify who is ahead at each point in time[1]. So we can't (in general) quantify optimal play, and therefore cannot quantify how much AlphaGo (or anyone else) deviates from it. * One known aspect of programs using Monte Carlo Tree Search is that they play to win, and are willing to sacrifice margin of victory to maintain or increase their odds of winning. According to some people I've talked to, this can be suboptimal, but there are methods of addressing this[2]. Note that you can't just change the objective function from "winning" to "win by as much as possible" without potentially reducing AlphaGo's strength. * The value function learned by a deep net is hard to interpret, partly because it encodes information about the possible futures arising from a position, and partly because it involves a tremendous number of calculations to compute. We do not know what it is representing at the intermediate levels-- techniques that aim to visualize or cluster unit activations can provide a bit of insight, but there's always the possibility that we're interpreting the patterns incorrectly because we're trying to fit it into the framework of "what would a human think?". Further, the representation is somewhat monolithic-- we can't tweak the value of one thing without changing the values of others. In chess engines, we might modify the material value of, say, a knight, without affecting the value of a rook. In a convolutional net, if we adjust the value of one position, it will tend to affect the value of many others. * We can attempt to quantify its strength by comparing it to other programs, but AlphaGo has already crushed other programs (99% win rate), so all that it tells us is that it is stronger than those programs. Essentially, without the ability to perform a significant amount of searching, or gauge strength via margin-of-victory, or to examine the program from other angles, it's hard to gauge just how good the program is. The only thing we can do is throw skilful opponents at it, and see if it fails eventually[3]. In its current incarnation, though, it seems like its playing strength is just going to be "stronger than you". --- 1. Hence the need for Deep Reinforcement Learning-- we learn how valuable each position is based on the results of the positions that it can lead to. 2. By modifying the search to maintain a given margin once it has been attained-- but I do not work at DeepMind, nor am I an expert on MCTS like some of my colleagues, so I don't know if this could adversely affect AlphaGo's overall strength. 3. We might be able to get a better idea by having extremely strong players play against weaker versions of AlphaGo (ones with less computing power) and then sort of telescope upwards once a good baseline has been set, but it remains to be seen if the current level of invincibility is due to it not having been around long enough for its weaknesses to become apparent[4]. 4. How would an AI researcher play against it? I've talked to a few people, and the answers have been: (a) play the game to the conclusion, don't resign; (b) attempt to take the game off-the-rails into parts of the state-space it hasn't really explored before (although this is risky because the human player is more likely to make mistakes in these cases as well), and (c) let it get ahead somewhat so that it "relaxes" (see the points about margin), allowing you to catch up and overtake it.
• eagsalazar2 11y ago
  I'm curious if AlphaGo is simply winning by virtue of having more computational capability (and therefore can never be defeated consistently by humans) or if in its training it actually discovered and is now deploying interesting new tactics that humans, studying these games, can uncover and therefore use to defeat AlphaGo in the future.
  • hodwik 11y ago
    Sedol said that it was doing tactics he had never seen before -- like the now infamous move 37 that caused Sedol to get up and walk out of the room.
    • cjbprime 11y ago
      Apocryphal, but romantic -- he left the room ten seconds earlier.
      • hodwik 11y ago
        Is that right? From the Google stream you couldn't actually see him leave, that's just what the commentators said.
        • cjbprime 11y ago
          Yeah, reddit /r/baduk had a thread about it including video from a different angle. The commentator only noticed that the move had been played and he was gone, didn't get the ordering correct.
    • partycoder 11y ago
      move 37 was a black move (lee sedol)
      • platz 11y ago
        "move 37", the infamous "shoulder hit", is a reference to what happened in Game 2, in which AlphaGo was black.
• nzonbi 11y ago
  This is awesome, finally AlphaGo has beaten Lee Sedol. Congratulations to the team at DeepMind and Google. But I will go straight to the really interesting point: My completely baseless intuition tells me that we will have full AGI (artificial general intelligence) in 5-10 years. Maybe not very powerful at first, but a least working. Real general intelligence. I say this as a programmer with superficial knowledge on ML (machine learning). All that is needed is to figure a way, to make neural networks handle the flow of thoughts of human consciousness. Reasoning, short/long term memory, basic controlling "emotions", etc. Then to assemble this central thought unit, in a system with various neural networks to handle more specialized tasks: Vision, natural language processing, audition, motion,etc. The task is of course extremely difficult, but I have the feeling that it will be possible to overcome. Once this have been achieved, it is downhill from there. The singularity. In my opinion, we must start to consider how this tremendous breakthrough will affect the human race. Our lives, economy, culture, etc. Awesome things we will be witnessing in the coming times. It would be great if it were possible to make public, the progress made researching specifically AGI. I wonder how big of an effort is currently being done. This would be possibly the biggest discovery ever for humanity. It deserves a fantastic effort.
  • bgar 11y ago
    I don't know if you're serious, but how can you make such grand predictions, considering you yourself admit to having only superficial knowledge of machine learning?
    • nzonbi 11y ago
      It is just a guess. Human thought, although highly complex, is in my opinion something actually feasible to be simulated. If I had a large research budget under my control, I would be betting most of it on AGI.
    • ilaksh 11y ago
      No one can really predict the future. But given the number of times AI pessimists have been proven wrong in recent years and the massive increase in AI investment, my best guess is that truly human-level artificial general intelligence is in fact around the corner. Considering what a large impact this will have, we must take that possibility seriously. Not to do so would be foolish. AIs can play chess, recognize speech consistently, drive cars, win at Jeopardy and now Go. Deep Mind has some general capabilities. We must anticipate programs with even more general intelligence.
• smitherfield 11y ago
  I just thought of an interesting (and somewhat more "real-world") task for AI research: Can an AI outperform a human at play-calling in [American] football?
  • cortesoft 11y ago
    The problem is that it REALLY hard to determine who "wins" at play calling.
  • rcthompson 11y ago
    Considering that plays are often called or changed by quarterbacks on the field right before the snap based on what the QB sees from the opposing team, I think it would be difficult to provide all that input to a computer AI.
    • jonknee 11y ago
      A neural net would be able to read the opposing defense more quickly and accurately than any human, but it would not be a fair fight (the human doesn't get a bird's eye view of things!).
      • tolas 11y ago
        It just doesn't have as big of an impact though. Each team has a very limited set of plays it's actually able to call and execute. Maybe 300 max (as a guess). Many times much less than that. So there's really not much room for an AI to see huge improvements via play calling. It's really all about execution on the field and the "feel" of the game.
  • Harimwakairi 11y ago
    Yahoo's fantasy football software creates millions of machine-written summaries of their fantasy football matches after each week of play. They're several paragraphs long and point out interesting things about the particulars of the teams' performance. I would imagine that given the "All-22" camera footage from a football game, it would be possible to construct a piece of software to monitor what was going on (visually identify players by name/number, determine who has the ball, etc.) and then convert that model to a stream of English text. Making it nuanced and entertaining, on the other hand, would be a challenge. :)
• 131hn 11y ago
  Can alphago be defeated ?
  • iainmerrick 11y ago
    Deep Blue can be defeated at chess now.
  • tim333 11y ago
    Facebook are working on a rival AI.
• partycoder 11y ago
  Skeptics have said that achieving this milestone would not happen within this decade, our lifetimes or even ever. It happened yesterday. It took Lee Sedol many decades of his life to train to achieve this level. And his ability to pass on his skills is limited. Now that a computer has achieved this level, the state of the neural network behind it can be serialized and ran into an unlimited number of computers and have millions of systems that are more proficient at Go than the best player in the world. People have said that achieving the cognitive level of the human brain requires to match its computational power. But if you take out all the parasympathetic and motor boilerplate, what is actually left for mental tasks is much less from that, most of that power is not even recruited for higher level mental tasks. That lowers the bar for strong AI. Then, strong AI can be immortal, and never physically deteriorate from aging. Strong AI can multiply infinitely and communicate at a rate that would be equivalent to writing millions of books in a second. It could transfer all its knowledge in seconds. It can also recursively improve itself. This advantage will lower the bar for strong AI even more.
  • doktrin 11y ago
    > Skeptics have said that achieving this milestone would not happen within this decade, our lifetimes or even ever. Did any credible skeptics actually claim this would never happen? If so, I'd love to hear their reasoning.
    • partycoder 11y ago
      Enough people is skeptical about AI to make it a problem. Now, being more specific about Go: - Lee Sedol himself was confident he would win. - Ke Jie said he would win against AlphaGo. I think he would lose too.
      • the_af 11y ago
        Though it's true many people were skeptical about an AI winning at Go played at this level, I think people are mostly skeptical about general AI, not about algorithms that win at playing board games. It's different to be skeptical about specialized AI than about general AI.
• the_af 11y ago
  I understand very little of Go beyond its basic rules and having played only a couple of matches with a friend who knows as little as I do... I reviewed the match, and it seems that very quickly White (AlphaGo) went aggressive, and Black was trying to contain it. I suppose a human player can commit serious mistakes by playing too aggressively from the start, right? But the summary of the game speculates move 31 might have been the losing move (maybe implying the game until that moment wasn't going so bad for Lee Sedol?), while to my untrained eyes it looks as if White was constantly on the offensive from the start, and Black was playing almost exclusively to contain it. Or am I reading this wrong?
  • partycoder 11y ago
    In game 3, when Lee Sedol played on the left side, a capture race started. AlphaGo made his dragon look vulnerable to be captured. Lee Sedol attacked it, and AlphaGo defended it enough so it would not die, but making a huge territory on the bottom part of the board. When AlphaGo's dragon lived, Lee Sedol realized AlphaGo was ahead in score, and needed to invade the largest AlphaGo's territory in the bottom. This invasion was not feasible, and Lee Sedol was relying on a long shot strategy involving kos. This idea was finally refuted by AlphaGo, leaving him with no feasible ko threats, forcing Lee Sedol to resign.
  • autarch 11y ago
    I think that most high-level Go players (including AlphaGo) play moves that serve multiple purposes simultaneously. The ideal move reinforces your own position while forcing your opponent to defend their own. White certainly played plenty of moves that required black to respond, but black also did the same. For example, the sequence beginning at 77 is black getting a foothold inside an area that white might've hoped to claim later. Then at move 115 black attempts an invasion into a very strong white area, ultimately failing to escape or live, leading to resignation. That all said, I wouldn't say that either player was particularly aggressive. I've watched plenty of human games where one player or the other very intentionally picks an all-out fight. If you're really good at reading ahead locally and you think your opponent is not, this make sense. This is especially true if your large-scale game play is not as good as your tactical play. This is also common in high handicap games. If white can't slowly eke out an advantage early on, the only remaining tactic might be to go for a huge fight and see what happens.
• lilcarlyung 11y ago
  Well, at least we still got poker. Until AI knows how bluff or how to call out bluffers.
  • VikingCoder 11y ago
    Hmmm... http://www.latimes.com/nation/great-reads/la-na-c1-claudico-poker-20150521-story.html http://www.latimes.com/nation/great-reads/la-na-c1-claudico-... http://spectrum.ieee.org/tech-talk/computing/software/computer-battles-top-human-poker-players http://spectrum.ieee.org/tech-talk/computing/software/comput... http://www.npr.org/sections/alltechconsidered/2015/01/08/375736513/look-out-this-poker-playing-computer-is-unbeatable http://www.npr.org/sections/alltechconsidered/2015/01/08/375...
• Sir_Cmpwn 11y ago
  What are some good resources to learn Go?
  • dyoo1979 11y ago
    There's a good interactive tutorial at http://playgo.to/iwtg/en/ http://playgo.to/iwtg/en/ The community has also collected a lot of good Go resources at Sensei's Library: http://senseis.xmp.net/ http://senseis.xmp.net/ Good luck!
    • Sir_Cmpwn 11y ago
      Thanks!
• andrewflnr 11y ago
  What I found especially chilling was how the AI took its time when it thought it was ahead, and played exactly as aggressively as needed to win. It seems like we're seeing the full benefit of detaching logic from human emotional drives like pride, loss of nerve, etc. Which at first sight looks awesome, but in a way it's deeply terrifying, because if such a machine is ever given power in the real world, we would really like it to have some regard for human emotion. More than anything else I've seen, this makes me feel in my gut like I'm seeing my replacement.
  • jtolmar 11y ago
    On the perhaps more reassuring side, it's possible for there to be an AI that takes into account human emotion, but does not have any of its own. Given power in the real world, it would do a better job of making people happy than a human with all their biases. It wouldn't assume that other people must like things it like, for example. (Of course, making an AI that understands emotion isn't easy. And happiness by itself is an insufficient goal if we're building ourselves a benevolent overlord.)
    • armitron 11y ago
      I view all such arguments about "friendly AI", "emotion", having machine intelligence "understand us" as wishful thinking at best and laughable delusions otherwise. I think the writing on the wall is clear, to anyone who cares to take a look. The moment we create an artificial intelligence capable of self-improvement and let it loose, we will have fulfilled our function (others say destiny) and will therefore be obsolete in every sense of the word. What will happen to humanity after that point is irrelevant. You don't see humans trying to keep bacteria "in the loop", why expect otherwise from our artificial progeny?
      • jtolmar 11y ago
        You're anthropomorphising the machine. An AI will do what it's programmed to do, not develop free will and decide on its own destiny. Correctly programming something that takes into account all of the nuances of the human condition might be impossible, and the results of mistakes are uniformly terrifying, but the result is entirely in the hands of humans (and human mistakes).
• bobbles 11y ago
  So what happens if we play AlphaGo vs AlphaGo?
  • tim333 11y ago
    They did loads of that in the cloud during training.
• mingodad 11y ago
  Out of curiosity how much electric power is alphago burning while playing ? Does someone know it ?
  • hutzlibu 11y ago
    Much more than then "stupid" human used, I guess. So for now, we are at least much more energy-efficient ...
    • tim333 11y ago
      Apparently 1920 cpu's so maybe 200 kW?
• naveen99 11y ago
  Seems like just yesterday (actually seven months ago), I was saying dbn's aren't good at go yet in an argument: https://news.ycombinator.com/item?id=10028908 https://news.ycombinator.com/item?id=10028908 i guess it's time to play with dbn's again. At least I have accumulated a few more Titan x cards in the mean time.
• hyperpallium 11y ago
  Huh, Go doesn't require intelligence after all.
• botw 11y ago
  The actual match is largely effected by psycology at the time of game. The match is somewhat unfair to human player. AlphaGo and its team know all about each human player and preference. But human player knows little about AlphaGo. Also it is AlphaGo team who decides whom to choose to play against AlphaGo, not the other round. In the sense of psycology and , the game is greatly favor to AlphaGo right now. This is the difference with the match between IBM DeepBlue and the chess world champion 10 years ago. It should be that any player(human or AI) can be allowed to enter the game, with or without a fee.