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AlphaGo beats Lee Sedol again in match 2 of 5
- github-cat 11y agoShould we be worried about the win of AlphaGo? http://www.pixelstech.net/topic/141-Should-we-be-worried-about-the-win-of-AlphaGo http://www.pixelstech.net/topic/141-Should-we-be-worried-abo...
- grouma 11y agoExciting match with top notch commentary. I'm rooting for a sweep of the series.
- pushrax 11y agoIf AlphaGo wins all 5 matches, what do you think DeepMind will do with it? My intuition is that they won't continue development, and instead focus on other applications. Great game btw, a pleasure to watch.
- makoz 11y agoI think people in the Go community wants to see AlphaGo play against the top ranked player. Otherwise I hope they'd at the very least release it as an AI for people to play against.
- Confusion 11y agoI thought Lee Sedol was the top ranked player?
- pushrax 11y agoKe Jie is 8-2 against Sedol.
- bnegreve 11y agoEven if he is now, there is a probability that people will learn how to beat AlphaGo. In a sense this is unfair: alphaGo was trained with a lot of human data, but AFAIK Lee Sedol is playing AI for the first time.
- Confusion 11y agoThe probability that any human player will learn to beat state of the art chess engines is zero (at least until we have humans with biologically or electronically augmented brains). There was a very small time window in which an expert player could beat chess engines by 'adapting their style'. Do you have reason to believe Go will be any different?
- sgw928 11y agoLee is widely considered the greatest player of the last decade, winning 18 world titles, but has been surpassed in the last few years by younger players, with this unofficial ELO rating system ranking him at No.4: http://www.goratings.org/ http://www.goratings.org/. Go is really a young man's game right now.
- jules 11y agoThey might continue for a while. They probably have a lot of ideas they still want to try out. Plus they haven't played against Ke Jie yet.
- deleted 11y ago[deleted]
- frgewut 11y agoThere's still Computer vs Computer, we also might see Freestyle Go (just as in Freestyle Chess). I would assume the development will continue.
- albertzeyer 11y agoStarCraft is their next challenge. :)
- lmm 11y agoThey'll keep using the technology. They probably won't want to play Go again - they'll have little to gain and everything to lose - and I suspect most of the possible audience will lose interest.
- eru 11y agoI'd bet they'll probably continue playing. They are not using any special purpose hardware, and they already have the software. However, they won't make it such a big PR focus. (And even if they stop, other people will implement the same ideas.)
- Jach 11y agoPutting on a conspiracy hat for pretend, the whole thing is clearly all a big PR scheme to get Google the brand better name recognition and improve their position in Asian markets.
- lmm 11y agoThat's not a conspiracy theory - I think everyone agrees this is as much a publicity stunt as a true test. Unless you're suggesting they bribed Sodol to lose?
- hasenj 11y agoI would want to see a longer game where a team of professionals play against AlphaGo, just to test its limits.
- espadrine 11y ago> Demis Hassabis, Google DeepMind's CEO, has expressed the willingness to pick Ke as AlphaGo's next target. http://www.shanghaidaily.com/national/AlphaGo-cant-beat-me-says-Chinese-Go-grandmaster-Ke-Jie/shdaily.shtml http://www.shanghaidaily.com/national/AlphaGo-cant-beat-me-s... Ke Jie believes he is slightly better than AlphaGo given the play he has seen, although he thinks AlphaGo will outpower him in a few months. The belief that every good player that has so far played against AlphaGo would win highlights a common misunderstanding of the nature of the human mind. There seems to be in fact a very small difference between the aptitudes of the average players and those of the top players, such that it is very easy for a machine to go from average to superhuman. Getting to average is by far the harder part.
- saulrh 11y ago> There seems to be in fact a very small difference between the aptitudes of the average players and those of the top players This is an interesting observation across many domains that involve "intelligence". It's actually pretty easy to see why, if you reconsider the scales for "good" vs "bad": The "bad" end of the scale is not at "the village idiot". The "bad" end of the scale is at "a rock". So, yes, once you get from "rock" to "village idiot", "superhuman" is a comparatively tiny step away. http://lesswrong.com/lw/ql/my_childhood_role_model/ http://lesswrong.com/lw/ql/my_childhood_role_model/
- eruditely 11y agoOh come on Lee Seedol we believe in you man, you might crack under pressure, it's cool. Bring it home for us meatbags will you? HK-47 why T_T.
- ljk 11y agoWow you're fast! good to know they'll play all 5 games no matter what the result is though People seem to think Lee knew he lost and was just playing to learn more. Hope he learned enough to take the overlord down in the next three games
- pavpanchekha 11y agoWhat's even more exciting is that there weren't direct mistakes by Lee Sedol in this game, like there were in Game 1. So does that mean that AlphaGo is just playing on a level beyond him?
- rurban 11y agoLooks like so. He miscalculated the center, while it looked he got the corners. But AlphaGo didn't care and continued pressure
- rurban 11y agoThat might be the only strategy to learn from AlphaGo: If she makes an unexpected bold move, like she did in the center a couple of times, she's onto something and you certainly miscalculated that area. Don't gamble, defend that.
- mixedmath 11y agoThis game was largely played extremely well by both sides. There were a a few peculiar-seeming moves made by AlphaGo that the commentator found very atypical. These moves ended up playing a very important role in the end game. I should also say that it's somewhat clear that Sedol made one suboptimal move, and AlphaGo capitalized on it. Interestingly, the English commentator made the same mistake as he was predicting lines of play. This involved play in the center of the board, in a very complicated position. Prior to this set of moves, the game was almost a tie. Afterwards, it was very heavily in AlphaGo's favor.
- dwaltrip 11y agoCenter evaluation is hard for humans, but not for bots, it seems.
- thomasahle 11y agoIt was until a few days ago :-)
- visarga 11y agoEndgame is also a strong point of AlphaGo.
- ljk 11y ago> These moves ended up playing a very important role in the end game. Does this mean AlphaGo was playing at a higher level or is it just a coincident?
- thomasahle 11y agoThe 9p commentator at the AGA channel said you should judge genius versus madness on whether alphago won. And it did.
- zamalek 11y agoThe "mad" moves could also be throwing the human off. I wonder how Sedol would fare given a year or so of practice against AlphaGo.
- 0x777 11y agoLee Sedol seemed to be doing well before he went into extra time (as far as I could follow from the commentators). How is it ensured that this is a fair game given the time constraints? I'm guessing adding more computing power to the AlphaGo program should definitely help it in this regard.
- zodiac 11y ago> How is it ensured that this is a fair game given the time constraints? Both players get the same time controls, seems fair to me But if you're saying humans might fare better against computers in a game with a longer time control, I suspect that's true
- 0x777 11y agoWith longer time controls, wouldn't the human element of 'coming under pressure' has little effect?
- GolDDranks 11y agoActually, Fan Hui 2p, the European master was able to win agains AlphaGo in an unofficial "Speed Go" (30 seconds per move) tournament. It seems that additional time might actually work _against_ humans and for AlphaGo.
- kqr 11y agoThat said, Fan Hui did better against AlphaGo in the inofficial blitz games they played!
- jacquesm 11y agoTime is not a good measure when competing with parallel hardware. Joules would be a much better one.
- hughperkins 11y agoThis is very insightful. Id like to mod you up ten times if i could :)
- studentrob 11y agoThat was entertaining and I don't even really know the game. Props to Google for making this available live on a solid feed. I wonder if Lee Sedol will have an interest in studying deep learning after this =)
- Dawny33 11y agoWow! Monte Carlo Search learning into play in this match. Especially when AlphaGo capitalized on just one suboptimal move of Lee Sedol.
- jknz 11y agoThe next person that will beat alphaGo may not be a top go player. In particular, I'm wondering if a computer scientist with access to the alphaGo source code and all the weights of the network could trick alphaGo in order to win games automatically (cf. the papers that show a neural net can be tricked to classify a plane as any other class). If a human with the knowledge of the source code and the weights can do this, it is scary. Imagine a similar algorithm runs your car. An attacker that knows the source code and the weights may trick the algorithm to send your car in a wall!
- axelfreeman 11y agoYou cannot do something unpredictable in a game with strict rules and borders. You had to trick this algorithm the billion times it trained for this game.
- raverbashing 11y agoI think this is less relevant Tricking the NN works in a no noise situation, also an image has many more parameters than a Go board
- Jach 11y agoI had this conversation earlier with a friend wondering if any of Sedol's Korean pro buddies have noticed any systematic biases that could be exploited. I think it would be possible to make the neural net relatively useless by playing strange sequences that hack the weights, but you're still left with a monte-carlo-tree-search bot which alone depending on its implementation is between 2d and 6d amateur (so on the far weaker side of the professional scale). Whether you could make those strange sequences to trick the neural net while also not dying to MCTS, I'm skeptical. I'm kind of hoping for an unconventional opening in game #3. Come on tengen or 5,5... Additionally I think that might be one way of weakening the bot in that it will find its net for suggesting candidate moves less useful and lean more on unguided MCTS, but that's just a wild guess.
- kqr 11y agoI'm fairly certain that among the many thousands of high-level games from KGS AlphaGo was trained on, several opened tengen and 5,5.
- IvyMike 11y agoI sense a change in the announcer's attitude towards AlphaGo. Yesterday there were a few strange moves from AlphaGo that were called mistakes; today, similar moves were called "interesting".
- 2bitencryption 11y agofun fact: when TD-Gammon hit the backgammon scene in the 90's, it didn't just defeat the top level human pros, it shattered the whole metagame and changed how humans play the game. It could be that human vs human go will look very different in the future due to what AlphaGo has learned and can teach us.
- mathgenius 11y agoAlphaGo maximises the probability of winning, and not the margin by which it does. So those "mistakes" yesterday turned out to be fortifying moves because AlphaGo was confident of a win. And similarly today the weird moves were interesting because they perhaps indicated that AlphaGo thought it was ahead.
- taneq 11y agoYeah, that explanation from the DeepMind team member today put a whole new spin on some of the 'odd' late game moves. It doesn't 'care' about about margins so it will shore up its odds of a win in preference to increasing the margin if it wins.
- kmeanscluster 11y agoGET REKT HUMANS
- Jerry2 11y agoDoes anyone know what DeepMind's software stack looks like? Just based on past work of some of the people working there, I'm guessing most of the code is C++ with some Lua. Anyone know for sure?
- kozikow 11y agoFor the most significant part, deep learning, deep mind uses Torch (http://torch.ch/ http://torch.ch/), although they are slowly moving to Tensorflow.
- bronz 11y agoI am so glad that I got to see this live. These matches will be historic.
- bradley_long 11y agoHuman can become tired, emotional and nervous. However, a computer/ software would not have these problems. Especially for Lee, the whole world is looking at him. An "ordinary" human like me won't be able to make the right decisions under this pressure. A great respect to Lee and the Developers of AlphaGo. Good Game!
- ludamad 11y agoAt the same time, only now are people seeing how its hard for people to play Go against computers. Until AlphaGo I didn't even think computers were close.
- bradley_long 11y agoI know how hard it is. I still cannot beat my 15 year-old PDA when playing chess with it. The intelligence of Lee is far higher than I can interpret.
- mikepurvis 11y agoGo is much harder than chess. The tree of available moves is massive and difficult to prune, as it's hard to get a reasonable heuristic about the "strength" of a given Go position.
- hasenj 11y agoActually computers were already in the top 10 percentile or so. I mean top go playing bots ranked something like 5dan amateur which is very hard to achieve, so it could already defeat most human players.
- jonknee 11y agoFrom being in the top 10% to beating the top player is a gigantic difference. Until AlphaGo computers were indeed "not close" to besting humans at Go.
- javajosh 11y ago
- brianchu 11y agoI'm totally uninformed about Go, but by now it seems that unless you're clearly in the lead by the end of the midgame, AlphaGo is going to win, simply because at that point its Monte Carlo Tree Search is going to our-compute you in examining all the tactical variations in the endgame. Lee Sedol really seemed to be under a lot of time pressure at the end. EDIT: clarified to what I originally meant: "end of midgame"
- deleted 11y ago[deleted]
- PakG1 11y agoIt really depends on how good AlphaGo is at avoiding mistakes. If you get critical points on the board, it's possible to create a resilient structure that is defensible against comebacks. But when I play, there are times when I can make an overwhelming comeback because my opponent missed one critical move. I've likewise had it done to me. This is of course probably less common at higher and higher levels of play. I'm reminded of the time when Deep Blue made a buggy move that totally threw Kasparov off because it was so counterintuitive. But it turned out to be a real mistake. If Kasparov had kept his composure, who knows what could have happened? http://www.wired.com/2012/09/deep-blue-computer-bug/ http://www.wired.com/2012/09/deep-blue-computer-bug/ Of course, as been stated here, AlphaGo doesn't have the issue of having to keep composure, while Lee does have that issue and pressure. But who's to say that AlphaGo won't make a similar significant mistake, and that Lee won't be able to capitalize on it to make a comeback in that particular game? edit: admittedly, as pshc as noted, the search space probably grows smaller as the game continues. edit 2: suddenly, his comment disappeared for some reason?
- kqr 11y agoIn the mid-game – and hell, even in the early end-game – the search space is still too big to practically exhaust the useful branches with MCTS. Keep in mind "mid-game" in Go means there's still over 100 moves left in the game – well longer than a full chess game. I'm not saying you're wrong, I'm just saying I don't agree with your motivation.
- nandemo 11y ago
- sams99 11y agoThe thing I find amazing about this is how soon this has happened. We all were expecting this to eventually happen but if you asked anyone who played go and was across the computer go scene when it would happen, say a year ago, they would say it was "10 years out". AlphaGo is one incredible feat of engineering.
- pgodzin 11y agoIs it best of 5 or are they definitely playing 5 matches?
- i_don_t_know 11y agoIs there a complete recording of the commentary? They had one for game 1. The current live stream only goes back two hours and doesn't include the beginning of the game. I'm looking at the DeepMind channel on Youtube: https://www.youtube.com/channel/UCP7jMXSY2xbc3KCAE0MHQ-A https://www.youtube.com/channel/UCP7jMXSY2xbc3KCAE0MHQ-A
- forgotpwtomain 11y agoI tried to watch commentary on the DeepMind channel for game #1 and found it to be very un-informative: I recommend the AGA channel (although it's been linked a few times): https://www.youtube.com/watch?v=6ZugVil2v4w https://www.youtube.com/watch?v=6ZugVil2v4w, still waiting on the game 2 static video.
- sago 11y agoYes, the AGA version was better. Unfortunately they aren't broadcasting for the next game on Friday. At least that's what they said.
- kqr 11y agoThere's a good reason for that: there is no game on Friday. Next game is Saturday, and they may or may not broadcast that. Andrew is ready, but he needs a pro or otherwise really skilled player to provide high-level commentary. Hyungwan who did it the last two games will not be available.
- sago 11y agoFriday US time, yes. ;)
- igravious 11y agoI disagree. Last night I found that on the DeepMind channel they were explaining the game a lot for beginners. This was a bit tedious for me but then this is a historic event so there'll be noobs tuning in so I can understand that, and I appreciate that the TV people are thinking of them. Also, the AGA channel you just seem to have a split screen showing two people with headphones on talking over Skype? (Granted, one of them is Myungwan Kim, 9p, a super likeable guy - but Michael Redmond, 9p, is also super likeable.) The DeepMind channel switches between the game board and the large commentary board, with occasional shots of Lee Sedol and occasional shots of the DeepMind terminal. Michael Redmond was reading out lots of variations and repeatedly trying to count the board and evaluate the position. I think he was trying to be as informative as he could. Occasionally he would get lost in thought as he calculated owing to the complexity. You could call it many things but uninformative is not one of them. edit: added pro commentators names...
- LaFolle 11y agoThis is superb awesome!!! In future, it will be interesting to see AlphaGo playing against itself!
- skc 11y agoI find it very interesting that to a layperson, the idea of a computer being able to beat a human at a logic game is pretty much expected and uninteresting. You try and share this story with a non-technical person and they will likely say "Well, duh..it's a computer".
- KennyCason 11y agoI encountered this many times over the past couple of days haha. I then have to explain that previous AIs, like those in chess largely used brute force computation to simulate each move, while these AIs "actually learn, similar to our brain". Probably not the most scientific explanation, but I feel your sentiment.
- Scea91 11y agoThere is still tree search going on. However I don't know the details about it. Is it that the deep net works mainly as an evaluation function of the current position? I guess it does more than that right?
- umanwizard 11y agoThere's also tree search in my brain when I play chess or go. (Consciously!)
- Terribledactyl 11y agoThey are using Monte Carlo Methods for looking around, more like a tiny sampling, it's an incomprehensibly large space. They have both value and policy (deep) neural nets. Train one to get the sense of good/bad individual board states (value) and one to get the sense of good/bad trajectories (policy, evaluates chains of moves). Chess has a fairly straight forward ranking of pieces, fairly well established ranking of piece power. A knight is more or less always a knight. Go, you kinda make up your "pieces" from scratch and there isn't any single common ranking. This makes the problem of even evaluating the board difficult (value), which is a sub step of making your higher plans (policy). The output of the two dNN help cut off, or prevent the sampling of the bad game states and encourage looking into the fruitful areas. Here is a (edit: not old) new paper, https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf
- deleted 11y ago[deleted]
- bronz 11y agoWho was the GO professional commentator? He was consistently predicting the moves of both Sedol and alphago. I was extremely impressed.
- igravious 11y agoMichael Redmond, 9p Closing in on nearly 600 career championship wins. https://en.wikipedia.org/wiki/Michael_Redmond_%28Go_player%29 https://en.wikipedia.org/wiki/Michael_Redmond_%28Go_player%2... http://gobase.org/information/players/?pp=Michael%20Redmond http://gobase.org/information/players/?pp=Michael%20Redmond
- ramidarigaz 11y agoMichael Redmond. I thought he was excellent too, and I really liked his willingness to jump into hypothetical situations and play it out. Really, really good commentator.
- sgw928 11y agoAs the only 9p player in the western world, Michael Redmond is of course very impressive. Chris Garlock, on the other hand, doesn't add that much value to the broadcast. Maybe somebody will start a "Left Commentator" meme, just like Left Shark.
- jules 11y agoIt would be great if his co-commentator was a computer scientist who is knowledgeable about AlphaGo's algorithm.
- octatoan 11y agoIndeed, I wish someone could talk about how the value/policy thing works.
- taneq 11y agoAs I understand it, the value network takes the place of the heuristic for scoring a given board layout, and the policy network takes the place of the heuristic for ordering moves from most to least promising. When searching the game tree, at each ply the most promising N moves are examined (as determined by the policy network) and leaves of the game tree are scored by the value network.
- Jach 11y agoI may be glad no one took my bet offer of me paying $19 if AlphaGo won 3/5 vs them paying $1 otherwise... I had a prediction at 90% confidence that nothing would show up before the end of this year that would be capable of beating the top players (though since I first heard about MCTS's success the idea of coupling it with deep learning seemed obvious, so I had an unfortunately non-recorded prediction that if a company ever bothered to devote about 8-12 months of research and manpower into combining those two algorithms with a very custom supercomputer or tons of GPUs then they would have something that could beat the best), then AlphaGo was announced. But the top pros weren't too impressed with its defeat of Fan Hui, and Ke Jie estimated something like "less than 5%" chance of it beating Sedol so I updated to 5% for this match of it winning 3/5... Tonight's game was beautiful. Last night's was a fighting game way too high level for me to really grasp (I have no idea how to play like that, all those straight and thin groups would make me nervous). I'm expecting Sedol to win Friday since I imagine he's going to have a great study session today, but I'm no longer confident he'll win the last two.. Still rooting for him though. :) (I also want to see AlphaGo play Ke Jie (ed: sounds like from the other submission on Ke's thoughts that may happen if Sedol is soundly defeated), and for kicks play Fan Hui again and see whether it now crushes weaker pros or is strangely biased to adopt a style just slightly stronger than who it's facing.)
- Smaug123 11y agoRe last paragraph: "just slightly stronger" is expected. AlphaGo is designed to maximise its probability of winning, not its margin of victory. You can expect solid not-very-flashy plays that definitely maintain an advantage, rather than even slightly risky plays that probably increase the advantage.
- eru 11y agoAlphaGo has been playing Fan Hui every so often---he's hired as a consultant after all. That greatly polished his skills. Not sure if any of the games he played are available, though.
- davelondon 11y agoLet's compare Go and Chess. We all know that Go is more complex that Chess, but how much more? There's 10^50 atoms in the planet Earth. That's a lot. Let's put a chess board in each of them. We'll count each possible permutation of each of the chess boards as a separate position. That's a lot, right? There's 10^50 atoms, and 10^40 positions in each chess board so that gives us 10^90 total positions. That's a lot of positions, but we're not quite there yet. What we do now is we shrink this planet Earth full of chess board atoms down to the size of an atom itself, and make a whole universe out of these atoms. So each atom in the universe is a planet Earth, and each atom in this planet Earth is a separate chess board. There's 10^80 atoms in the universe, and 10^90 positions in each of these atoms. That makes 10^170 positions in total, which is the same as a single Go board. Chess positions: 10^40 (https://en.wikipedia.org/wiki/Shannon_number https://en.wikipedia.org/wiki/Shannon_number) Go positions: 10^170 (https://en.wikipedia.org/wiki/Go_and_mathematics https://en.wikipedia.org/wiki/Go_and_mathematics) Atoms in the universe: 10^80 (https://en.wikipedia.org/wiki/Observable_universe#Matter_content https://en.wikipedia.org/wiki/Observable_universe#Matter_con...) Atoms in the world: 10^50 (http://education.jlab.org/qa/mathatom_05.html http://education.jlab.org/qa/mathatom_05.html)
- justsaysmthng 11y agoWhy am I feeling a bit scared of all this ?
- __john 11y agoThe oldest and strongest emotion of mankind is fear, and the oldest and strongest kind of fear is fear of the unknown. -- H.P. Lovecraft
- ekianjo 11y agoI am not sure that calculating the raw number of positions is a good indication of complexity at a given point. What if most positions are obviously junk in go while they are more difficult to assess in chess? Not saying this is the case in this particular example but thats a possibility in theory.
- pmontra 11y agoDoes anybody know how many CPUs and GPUs they're using this time? It was 1200+ and 700+ in October against Fan Hui. It would be interesting to know if AlphaGo became better only because the extra learning or also because of extra hardware. I googled for that and didn't find anything but I could have missed the right source.
- spdy 11y agoMust be amazing seeing how the program you helped to create beat the best player in one of the most complex games on this planet. This is a milestone in modern informatics.
- vedaprodarte 11y agoA question: Should we take it as "a computer beating a human" or "developers beating a Go player"? I had this discussion with my friends and we have opposite opinions.
- florianletsch 11y agoIt should be "a computer beating the human" in my opinion. It's just like parents bathing in the success of their offspring. Yes, they can be proud, but no, they didn't achieve the "win" themselves.
- EliRivers 11y agoIf the developers behind AlphaGo did the same calculations with pen and paper as they sat across from the human player, would that be them achieving the win themselves? If so, why does having an automated piece of paper change that? "It's just like parents bathing in the success of their offspring." There is no offspring here. There is a set of calculations, written by some people.
- Smaug123 11y agoIf a parent watches their child win a game, would that be them achieving the win themselves? If so, why does having nine months of creation time and ten years of training time change that?
- EliRivers 11y agoThere is no child here. There is a deterministic set of calculations. The developers created a deterministic set of calculations, and then executed those calculations. AlphaGo is not a child. AlphaGo is a set of deterministic calculations, defined and created by some people. Your question about humans and their children is irrelevant. There is no child here. In this flawed analogy, the "parents" are the developers, and the "child" is a set of calculations. If someone wrote down a set of calculations, and then executed those calculations and won, is it right to say that the person who wrote down those calculations won? I suggest that it is.
- Cookingboy 11y agoSomeone somewhere asked why a lot of people in the Go community is taking this in a somewhat hard way, here is my hypothesis: Go, unlike Chess, has deep mytho attached to it. Throughout the history of many Asian countries it's seen as the ultimate abstract strategy game that deeply relies on players' intuition, personality, worldview. The best players are not described as "smart", they are described as "wise". I think there is even an ancient story about an entire diplomatic exchange being brokered over a single Go game. Throughout history, Go has become more than just a board game, it has become a medium where the sagacious ones use to reflect their world views, discuss their philosophy, and communicate their beliefs. So instead of a logic game, it's almost seen and treated as an art form. And now an AI without emotion, philosophy or personality just comes in and brushes all of that aside and turns Go into a simple game of mathematics. It's a little hard to accept for some people. Now imagine the winning author of the next Hugo Award turns out to be an AI, how unsettling would that be.
- xiaq 11y agoArt is, mostly, domain of knowledge not yet claimed by science. When a technique turns art into science, it means that humans are ready to tackle even harder problems. Think about medicine before and after microscopes. Holy art turned into boring science: huge win for human race. If an AI won the next Hugo award, I would be rejoiced. It wouldn't mean the end of literature at all; it would mean that humans are ready to produce an even higher form of literature.
- imaginenore 11y agoWe already have a successful AI classical music writer https://en.m.wikipedia.org/wiki/Emily_Howell https://en.m.wikipedia.org/wiki/Emily_Howell We have all kinds of visual art made by computers and AI - prom painting from photos to abstract art to 3D renders. We have computers writing poems and haiku. The only thing that's missing is the conceptual creation, which, let's be honest, most human artists struggle at as well. So writing and interesting story is not yet in the AI's domain.
- 11y ago
- openaccount 11y agoMeanwhile 'Google Translate' translates texts terribly bad. Why don't they work on important tasks?
- ceejayoz 11y agoThe AI learning techniques they're developing here are likely directly applicable to stuff like Google Translate in the long run.
- skarist 11y agoI predict 5-0 for DeepMind. Now, Lee has a broken self-confidence to battle (crucial for a human player), something that will not and can not trouble the DeepMind team.
- ChuckMcM 11y agoAnd today you lost your bet :-)
- rurban 11y agoWhat I really liked about those games so far, and Michael Redmond commentary, is that AlphaGo not only beat Lee Sedol twice, but also Redmond. He is playing the same style as Sedol, he constantly predicts Sedol's moves and he is as surprised and does the same miscalculations as Sedol. He really needs some time to find out when he made a mistake, the same mistake Sedol was eventually doing. This is high class commentary. Even if they have no Computer screen to clear the screen after some variations. He remembers all the stones and immediately clears his own moves, amazing. I'm not sure if a better device would actually help.
- eru 11y ago> He remembers all the stones and immediately clears his own moves, amazing. It looks impressive, but I think any strong amateur can do that.
- masklinn 11y ago> He remembers all the stones and immediately clears his own moves IIRC that's basic expectation of any Go player at a non-trivial level, starting from the mid-high amateur ranks. It's completely expected that both players and observers can record the game on a kifu or replay and discuss it immediately after they finish.
- cjbprime 11y ago> He remembers all the stones and immediately clears his own moves, amazing. Chess grandmasters can often play simultaneously and blindfolded against 20 different opponents (and win all but one or two games). This is nowhere near the extent of Redmond's memory strength. :)
- dingo_bat 11y agoI watched both days' commentary and does it have a lot of cuts and skips? Or is something wrong with my internet?
- drjesusphd 11y agoSo is this it then as far as games go? Does anyone know of any efforts to develop a more "human-friendly" complete information game than go?
- dfan 11y agoThe game of Arimaa was created in 2002 specifically to be a complete-information game that computers couldn't beat humans at. As of 2015, the best player in the world is a computer.
- drjesusphd 11y agoWhoa. Well, I guess that's a losing battle.
- oneeyedpigeon 11y agoWhat I find fascinating - and I guess this really highlights that I have no idea whatsoever how AlphaGo works - is that at the start of game 2, AlphaGo plays P4, then Lee Sedol plays D16. To a layman, this looks like it would be a very, very common opening. Moreover, it's symmetrical - I'm not sure how that affects things, but my naive intuition is that it makes the game state less complex. Nonetheless, AlphaGo takes a minute and a half to play its next move. Can anyone explain what on earth is going on during those 90 seconds?
- edraferi 11y agoMy guess is it's doing the same thing it does every move: look at the state of the board and calculate the next move. A person might memorize standard openings and play them by rote. The computer _could_ do that, but it would just be a computational short cut. In a sense, the machine is re-discovering the classic opening.
- versteegen 11y ago90 seconds per move was normal for AlphaGo, wasn't it? I didn't pay attention to how its time per move varies, but it's a question I would love to ask the team (although, maybe it's explained in the paper?) You have to realise that it's not using an opening book, it's computing the best response from scratch.
- ElisaJezz 11y agoThat's really interesting I mean how could AlphaGo beats on this time. hah
- blahblah12 11y agohttp://alphagochat.herokuapp.com http://alphagochat.herokuapp.com Slack channel for discussion if anyone's interested. We're using it for commentary while the games go on. Was created by AGA people.
- karussell 11y agoWasn't reading the whole thread, but was it possible for Lee Sedol to play against the final AlphaGo before? Although AlphaGo seems to be a huge achievement I would find the lack of training before a bit unfair as AlphaGo was probably able to play lots of Games from Sedol before.
- TheArcane 11y agoI wonder how long until AI starts writing bestselling novels.
- axelfreeman 11y agoI don't get the mystery of this. This algorithm is complex. SURE! But deep learning is very fast training / repeatition of a game (or some other goal) while saving the good or bad results. Predict user moves. Find good positions/patterns. Or did i miss some here? https://web.archive.org/web/20160128151110/https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf https://web.archive.org/web/20160128151110/https://storage.g...
- fma 11y agoYeah...AlphaGo has played more games in the last few months than a human will in their life time. I'd be interested in how strong it would be if given the same constraints as human learning (playing thousands of games, rather than millions).
- fhe 11y agoAs someone who studied AI in college and am a reasonably good amateur player, I have been following the matches between Lee and AlphaGo. AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. I've been giving it some thought. When I was learning to play Go as a teenager in China, I followed a fairly standard, classical learning path. First I learned the rules, then progressively I learn the more abstract theories and tactics. Many of these theories, as I see them now, draw analogies from the physical world, and are used as tools to hide the underlying complexity (chunking), and enable the players to think at a higher level. For example, we're taught of considering connected stones as one unit, and give this one unit attributes like dead, alive, strong, weak, projecting influence in the surrounding areas. In other words, much like a standalone army unit. These abstractions all made a lot of sense, and feels natural, and certainly helps game play -- no player can consider the dozens (sometimes over 100) stones all as individuals and come up with a coherent game play. Chunking is such a natural and useful way of thinking. But watching AlphaGo, I am not sure that's how it thinks of the game. Maybe it simply doesn't do chunking at all, or maybe it does chunking its own way, not influenced by the physical world as we humans invariably do. AlphaGo's moves are sometimes strange, and couldn't be explained by the way humans chunk the game. It's both exciting and eerie. It's like another intelligent species opening up a new way of looking at the world (at least for this very specific domain). and much to our surprise, it's a new way that's more powerful than ours.
- megalodon 11y agoI think an important point was brought up by the Google engineer in the beginning of the game: Humans usually consider moves that put them ahead by a greater margin and base their strategies on that, while computers don't have that bias.
- thomasahle 11y agoWas that in the official livestream, or is there an interview somewhere, where things like these are discussed?
- conanbatt 11y agoWhat is interesting to me is that the computer makes clear mistakes when its on the lead. Since it might find the chances to win equally among different scoring results, it often picks a weaker one. This has a powerful consequence: we have not seen AlphaGo pushed to the limit, he is lowering the distances as if it were playing a teaching game. Lee Sedol I think came to this conclusion, and the only human strategy left is to take a lead big enough to maintain the rest of the game. And that might be the last strategy to play to show the computer is already unbeatable, because it will be pushed to its limits to win a game and it might overcome humans.
- taneq 11y agoI don't see how you can term them as "clear mistakes" when the game is playing at a higher level than any of us meatbags. In this case (according to DeepMind) it's no different to a racing driver backing off the pace in the last few laps if they have a big lead - it's better to guarantee a win than to win by a large margin.
- conanbatt 11y agoYou might be confusing correctness with relevance. You can pass at the last move and lose 1 point, but if your lead is 5, its the same win-result but not the same count-result. Well, that would be a mistake by all accounts, even if AlphaGo made it. Some of the moves AlphaGo played both in this game and the previous one are very definite mistakes, but they were irrelevant to the difference in the game.
- astrofinch 11y agoSo given that this victory seems to be happening a decade or so before experts predicted, how likely are we to see similar acceleration in reaching other AI milestones? (Especially given that AlphaGo is using the same algorithm that won the Atari games, so it has the potential to be very general in its application)
- Jach 11y agoYou probably saw this, but linking anyway: https://news.ycombinator.com/item?id=10983539 https://news.ycombinator.com/item?id=10983539 The general point is that it's more evidence improvement can come in discontinuous leaps, it doesn't have to be some smooth (even if accelerating) incremental process. So timeline predictions should probably be wide, with closer-to-present lower bounds (especially when successful generalizable techniques become public). I don't think this initial view would have changed much this week unless Sedol just totally crushed the bot, perhaps suggesting there's still something more. Personally I think the approach of combining deep learning with MCTS to beat Go was obvious to anyone sort of familiar with each thing, and with a good funded team could be done in a year or less, but a lot of 'experts' were ignorant of one or more of the areas. The uncertainty should have revolved around when some group would get around to writing the software and scaling up with powerful hardware. Implementation details, the theory work was already known. My own lesson from this is that if all that is needed is tough engineering work (but not really new theory) that work could literally arrive in a week instead of the x+delay time it might take from scratch, because some group could already have been in the process for about x time that isn't public knowledge. AlphaGo kind of came out of nowhere; there were early signs with papers on using deep learning techniques, but I don't remember any public commitments to much. That just indicates companies are still quite capable of doing secret projects. If they had a secret new theory, too, it could be even more amazing. I know of one startup in particular that's been securely at the top of its niche because internally they have secret CS research unknown to academia. The OpenAI initiative may be useful from the perspective that if they've shared what probably shouldn't be shared, at least we can suspect something is imminent and try to plan a last minute stand of getting it right first, vs someone like Google doing all the research in secret and then bam, unleashing UFAI.
- dynjo 11y ago"By the 4th game, AlphaGo apparently became self-aware and the fate of mankind was sealed..."
- PascalsMugger 11y agoMankind was harvested... every man, woman, and child forced to play Go for the rest of their lives.
- hutzlibu 11y agoDoes anyone know of a site/video, where I can just see the game moves without commentary and thinking pauses?
- thomasahle 11y agoAt the end of this article: https://gogameguru.com/alphago-races-ahead-2-0-lee-sedol/ https://gogameguru.com/alphago-races-ahead-2-0-lee-sedol/
- hutzlibu 11y agoalso thx
- jpmattia 11y agoGame 1: https://online-go.com/demo/114754 https://online-go.com/demo/114754 Game 2: https://online-go.com/demo/114161 https://online-go.com/demo/114161
- dineshp2 11y agoMost people other than the researchers and hackers, really did not understand what AI was capable of doing. The very idea of AI seemed too abstract to comprehend(I consider myself guilty). But AlphaGo showed us what AI is really capable of doing in an eerie sort of way and I think interest in AI will soon become mainstream which is a good thing for the development of AI. Now it's at least easier to comprehend the context of all those doomsday warnings about AI destroying humanity which I never took seriously.
- typon 11y agoI've had this thought watching this play out over the past few months. You have this deeply mystical, zen-like game of ancient China which represents the philosophy of the East and it's pitted against this pure capitalist, cold and calculating (literally) machine. You can hold out for a few thousand years, but eventually the uncontrollable and amoral technological imperative will catch on and crush you. It's kind of poetic and sad. It feels like technology will render everything un-sacred eventually.
- aaimnr 11y agoOn the other hand one could say that it's a pretty shallow understanding of Zen, given that Buddhism assumes that everything in the mind - being a part of nature - is fully conditioned (deterministic), as opposed to Christian view of a human as a holy, God-like creature, distinctly different than the rest of nature.
- typon 11y agoI agree. I think the same concept applies in Daoism. Sort of giving yourself up to the flow of the universe. That's why I only said "zen-like".
- blacktulip 11y agoDoes anyone notice the lack of ko[1] in the games? In all 7 public games (5 with Fan and 2 with Lee) there isn't any ko. This is unusual. If we still can't see ko fights in the following 3 games...I would suspect that AlphaGo isn't able to handle ko well enough yet, and Google asked Lee and Fan to not initialize ko fights in the games. [1] https://en.wikipedia.org/wiki/Ko_fight https://en.wikipedia.org/wiki/Ko_fight
- w0bb13 11y agoI can't believe they would be told not to, or that Lee would abide by it in this match! Now, I'm not good enough to evaluate if this seems likely, but I could understand that AlphaGo (especially if it was winning) avoided Kos, since they are high variance situations.
- lukev 11y agoIsn't it more likely that the opposite is true, and Ko is the kind of thing where it is possible for a computer to play flawlessly? I am very very very far from a Go expert, but Ko did always seem to be "first person to make a mistake looses". If that's true, I'm sure Sedol deliberately stayed away from it as an area where the computer is likely to be particularly strong.
- Tistel 11y agodoes anyone know anything about the implementation (language etc)?
- Tistel 11y agodoes anyone know anything about the implementation (language etc)?
- pkaye 11y agoLet's say AlphaGo can beat all the best human Go players. Then what will the next more difficult game for computers to compete against humans and win?
- throwaway3301 11y agoPoker remains unsolved (except for 2-player "Heads-up" limit hold'em[1]). Poker has many different variations and a player's strategy can change significantly based on many factors. The University of Alberta Computer Poker Research Group has already used counterfactual regret minimization in their Cepheus AI. I wonder if they could work with DeepMind to apply some of the techniques used in AlphaGo to poker. [1]: http://science.sciencemag.org/content/347/6218/145 http://science.sciencemag.org/content/347/6218/145
- hutzlibu 11y agoTotal War
- awl130 11y agodo you think lee sedol should change his goal from trying to win all remaining three games to winning just one? in other words, sacrifice the next two games to learn about alphago and then try to win the final game.
- myohan 11y agoI would like to see another experiment where Lee is aided by a computer and plays against AlphaGo and see who wins...some believe that human intuition working with a mediocre computer is much more powerful than a supercomputer by itself.
- NiftyFifty 11y agoSounds like recursively prime pussy.
- EGreg 11y agoSomeone here said an interesting thing. Perhaps the next AI challenge would be to see whether AI running on weaker machines can beat AI of yesterday on stronger machines. And this test can be automated to find even better algorithms. Like can Rybka running on an iPhone today beat Fritz running on a distributed supercomputer? Or thinking for 2 seconds rather than 2 minutes, on the same computer? There is something unnerving about a computer that can answer in 0.01 seconds and still have the move be better than any human would come up with in an hour. At that point a robot playing simulatenous bullet chess would wipe the floor with a row of grandmasters, beating them all without exception.
- jonbarker 11y agoAI enthusiast and amateur player here: Michael Redmond made a great point yesterday, if the algorithm is only interested in maximizing probability of win and ignoring margin of victory, shouldn't there be some override for weak moves played when the lead is sufficient? AlphaGo played some weak moves when it perceived it was sufficiently ahead yesterday in the end game. A truly intelligent opponent will play strong moves even when sufficiently ahead, no?
- patejam 11y agoI think the idea is that AlphaGo decided that the "strong" moves, while it may have increased its lead, would have been more risky than moves that just fortified its current lead.
- jsf666 11y agoI think the point is the seemingly stronger move (giving a bigger margin) can have follow ups that can lead to a lower chance of winning for the AI evaluation
- w8rbt 11y agoWould it be possible to play in a random/unpredictable fashion and win a game of go? If so, that may be one approach to beating the computer.
- mhagiwara 11y agoI always wanted to learn to play Go and one of the reasons was because it was the only game where computers hadn't defeated human - well, it is no longer the case and I kind of lost motivation to learn it. I wonder what would be interesting games (intellectual sports) where computers have yet to defeat humans that you would probably be interested in learning?
- vancan1ty 11y agoDid Lee Sedol have access to a dataset of AlphaGo games in preparation for this match series? I wonder if it would help him if he could study the computers moves and strategies in other matches.
- jasonjei 11y agoIsn't it kind of interesting that Google is pushing the lead for these projects? It reminds me when IBM took on the gusto of developing Chess AI when they had strong technical superiority. It's almost as if Google is taking the mantle from IBM to develop these renaissance projects.
- toolslive 11y agoThese matches are not really fair: the AI team can "prepare" and examine the human's previous games, find weaknesses, aso, while the human doesn't really have anything to guide his/her preparations.
- sushirain 11y agoI don't think that AlphaGo was trained more on Lee Sedol's games, than on others' games. The team said that they can't find computer weaknesses until AlphaGo plays against top caliber.
- lottin 11y agoFrom what I gather, if you have a computer powerful enough, you can solve any game by simply applying Game Theory, as long as you can assign a numerical value to the possible outcomes.
- mark_l_watson 11y agoI wonder how this will affect future human play. About 30 years ago my brother and I started playing a simple African stone game Kala. We each won about half the games until I coded up a brute force search to play against. Given a game tree to the end, the program made the weirdest looking opening move, when playing first. I started making that move and forever after won. The situation with Go is different. (I wrote the Go program Honninbo Warrior in the 1970s, so I am a Go player and used to be a Go programmer.) Still, I bet the AlphaGo, and future versions, will strongly impact human play. Maybe it was my imagination, but it sometimes looked like Lee Sedol looked happy + interested even late in the two games when he knew he was losing.
- chm 11y agoThis will be buried by now but: What happens if the Go master tries to deceive the oponent? As in purposefully play a counter-intuitive position, or even "try to lose"? Will the AI's response be confused as it is expecting rational moves from its oponent?
- spyckie2 11y agoNo. There is a scoring heuristic that AI programs use to determine if they are winning or not - ie: +2 stone ahead, -2 stones behind, etc. (Note: I don't actually know what the real heuristic is, I'm making it up). A computer will have no real reaction to a "try to lose" or counter intuitive move; it would merely recalculate the heuristic score and doing exactly what it has been doing all along - finding the next move that maximizes that score.
- danielrm26 11y agoWhat I find fascinating about this is that the system was programmed by people who were presumably not as good at Go as Lee Sedol. So if the first comment in this thread (about how it's a completely non-human approach) is true, it's really interesting that humans can enable computers to come up with non-human ways of solving complex problems. Seems like a big part of this story, if I'm not being completely dumb here.
- andreyk 11y agoVery impressive. Since there is a ton of hype about this and many media stories (at least NYTimes, with no citation at all) saying that this came 'a decade early', I think its worth looking over Yann LeCun retrospective on research in this area (https://www.facebook.com/yann.lecun/posts/10153340479982143 https://www.facebook.com/yann.lecun/posts/10153340479982143). Clearly he was saying all this to preface the results of Facebook research in comparison to Google's, but I still think it is a very good overview of the history and shows the ideas did not come about suddenly. Quoting a few key things since the whole things is very long: "The idea of using ConvNet for Go playing goes back a long time. Back in 1994, Nicol Schraudolph and his collaborators published a paper at NIPS that combined ConvNets with reinforcement learning to play Go. But the techniques weren't as well understood as they are now, and the computers of the time limited the size and complexity of the ConvNet that could be trained. More recently Chris Maddison, a PhD student at the University of Toronto, published a paper with researchers at Google and DeepMind at ICLR 2015 showing that a large ConvNet trained with a database of recorded games could do a pretty good job at predicting moves. The work published at ICML from Amos Storkey's group at University of Edinburgh also shows similar results. Many researchers started to believe that perhaps deep learning and ConvNets could really make an impact on computer Go. ... Clearly, the quality of the tactics could be improved by combining a ConvNet with the kind of tree search methods that had made the success of the best current Go bots. Over the last 5 years, computer Go made a lot of progress through Monte Carlo Tree Search. MCTS is a kind of “randomized” version of the tree search methods that are used in computer chess programs. MCTS was first proposed by a team of French researchers from INRIA. It was soon picked up by many of the best computer Go teams and quickly became the standard method around which the top Go bots were built. But building an MCTS-based Go bots requires quite a bit of input from expert Go players. That's where deep learning comes in. ... A good next step is to combine ConvNets and MCTS with reinforcement learning, as pioneered by Nicol Schraudolph's work. The advantage of using reinforcement learning is that the machine can train itself by playing many games against copies of itself. This idea goes back to Gerry Tesauro's “NeuroGammon,” a computer backgammon player that combined neural nets and reinforcement learning that beat the backgammon world champion in the early 1990s. We know that several teams across the world are actively working on such systems. Ours is still in development. ... This is an exciting time to be working on AI."
- bennyg 11y agoI wonder how "smart" the AI can become once Lee Sedol starts pattern matching and playing against its moves better.
- dvcrn 11y agoI think this is really fascinating but also scary. Imagine you are the best in the world in something. That is your thing and no one else can do it better than you. Then suddenly a computer comes along and takes that title from you. But it takes it in such a way that you are never in your life able to re-take it because of how the AI works. A game will likely just be the first field. My girlfriend is working in translation and interpretation which is another area already in the crosshair of neural networks. AIs will step by step become more efficient than people and that is terrifying.
- naveen99 11y agoWell, the nice thing with go is the handicap system. I wonder how many stone handicap the human champion needs to beat alpha go, and watch that number increase over time. I wonder if chess could use a handicap system to keep things interesting.
- kerkeslager 11y agoAs a programmer and a go player, I knew this day would come, but I'm a bit disappointed that this is how it happened, for two reasons: 1. As the game of go progresses, the number of reasonable moves decreases, so that as the game progresses, players on average play closer and closer to optimally. By the end of the game, even weak amateurs can calculate the optimal move. Logically, I would guess that stronger players are able to play optimally earlier than weak ones. Lee Sedol is known for his strong middle and endgame, often falling behind early on and making it up late in the game. He is so strong at this that he has driven an entire generation of go players to developing very strong endgame. But AlphaGo, running Monte Carlo simulations, almost certainly can brute force the game earlier than Lee Sedol can. Lee Sedol is playing AlphaGo on its own turf. A player known for their opening prowess, such as Kobayashi Koichi in his heyday, might have had an advantage that Lee Sedol doesn't. (Note: I'm not strong enough to analyze Lee Sedol or Kobayashi Koichi's play styles; I'm repeating what I've heard from professionals.) 2. I hoped that when an AI beat a pro at go, it would be with a more adaptive algorithm, one not specifically designed to play go. If my understanding of AlphaGo is correct, it's basically just Monte Carlo: the advances made were primarily in improving the scoring function to be more accurate earlier, and the tree pruning function, both of which are go-specific. It's not really a new way of thinking about go (at least, since Monte Carlo was first applied to go). It's just an old way optimized. The AI can't, for example, explain its moves, or apply what it learned from learning go to another game. It's certainly a milestone in Go AI, and I don't want to downplay what an achievement this is for the AlphaGo developers, but I also don't think this is the progress toward a more generalized AI that I hoped would be the first to beat a professional.
- versteegen 11y ago> I hoped that when an AI beat a pro at go, it would be with a more adaptive algorithm, one not specifically designed to play go. The particular algorithm used by AlphaGo is of course specific to Go (the neural network inputs have a number of hand-crafted features), but the overall structure of the algorithm - MCTS, deep neural nets, reinforcement learning - is very general. So there's two ways to look at it. One is that what you wanted has actually transpired. The other is that what you asked for is completely unreasonable. I think it highly unlikely that an algorithm not specialised to Go will ever be able to beat all specialist Go playing programs. AlphaGo can't explain the outputs of its two NNs, but it can still explain its moves by showing which variations it thinks are likely.
- rwayasi 11y ago2-0 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/