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My (long) commentary here: https://www.facebook.com/yudkowsky/posts/10154018209759228 https://www.facebook.com/yudkowsky/posts/10154018209759228 Sample: At t

by Eliezer 11y ago

My (long) commentary here: https://www.facebook.com/yudkowsky/posts/10154018209759228 https://www.facebook.com/yudkowsky/posts/10154018209759228 Sample: At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol's probability of victory diminishes. The 8p and 9p pros who analyzed games 1 and 2 and thought the flow of a seemingly Sedol-favoring game 'eventually' shifted to AlphaGo later, may simply have failed to read the board's true state. The reality may be a slow, steady diminishment of Sedol's win probability as the game goes on and Sedol makes subtly imperfect moves that humans think result in even-looking boards... The case of AlphaGo is a helpful concrete illustration of these concepts [from AI alignment theory]... Edge instantiation. Extremely optimized strategies often look to us like 'weird' edges of the possibility space, and may throw away what we think of as 'typical' features of a solution. In many different kinds of optimization problem, the maximizing solution will lie at a vertex of the possibility space (a corner, an edge-case). In the case of AlphaGo, an extremely optimized strategy seems to have thrown away the 'typical' production of a visible point lead that characterizes human play...

taneq 11y ago
This is reminding me more and more of the central theme of the book Echopraxia, which was that when dealing with superhuman intelligences, you are by definition not smart enough to even know whether you're winning.
- studentrob 11y ago
  Hmm maybe but aren't humans capable of learning too? One could make the same statement of two humans of different ranks playing each other. In some cases, the more highly ranked player might be the only one who knows who's winning. This doesn't mean that the lesser ranked player can't improve over time. It just means that at that moment, that player is inexperienced or less skilled, which we already knew.
  - Radim 11y ago
    Alas, time is not on meat's side in this one.
    - studentrob 11y ago
      I'll grant that for this very constrained problem ;-) I respect your work a lot. I've studied and used ML myself, including gensim, in industry. I've given nowhere near your level of contribution to society / the field. My opinion is true AI is quite a ways off. I haven't read anything from a ML researcher that says it isn't. Perhaps you weren't saying true AI is nearer.
      - quanticle 11y ago
        I find it funny that whenever there's a case of computers being able to do something that they couldn't before, whether it's drive or beat humans at Go, the goalpost on what is "true AI" shifts to be something that computers can't do yet. So let me ask you this. What would you consider to be "true AI"? At what point are you willing to say, "Okay, that's it, computers are just plain smarter than we are?" Because, frankly, it seems to me that that day is getting closer and closer. Saying that AIs can't be smarter than humans because they don't think and act like humans is like saying that airplanes don't "truly" fly because they don't flap their wings.
        eddyb 11y ago
        Accelerando[0] starts with this quote, which I really like: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." – Edsger W. Dijkstra [0] http://www.antipope.org/charlie/blog-static/fiction/accelerando/accelerando-intro.html http://www.antipope.org/charlie/blog-static/fiction/accelera... (readable online at http://www.antipope.org/charlie/blog-static/fiction/accelerando/accelerando.html http://www.antipope.org/charlie/blog-static/fiction/accelera...)
        hythloday 11y ago
        We know that true human intelligence includes being able to help machines think better. So it seems reasonable to state that true machine intelligence includes being able to help humans think better. Other things that humans can do that machines can't yet: * change other human's minds * contribute to the state of the art of human knowledge * determine the difference between a human and a machine.
- jules 11y ago
  When I play chess against a computer I can see very easily that I'm not winning.
  - GolDDranks 11y ago
    But that is most likely be because of the structure of Chess. The evaluation of single board position is more "linear" and certainly easier than in Go. If the utility function is about winning, going though easily-evalutable board positions might be the straightforward route. If the utility function is to win while minimizing your estimate of of you losing, you might see different results. Plus if you know that you are playing against a stronger opponent, your prior might bias your perception of the board situation.
    - pmoriarty 11y ago
      It would be interesting to see how computers fare against humans on a 19x19 chess board, and how easily a human could evaluate such a board.
  - jacquesm 11y ago
    The question is not if you can see that or not. The question is if you can see that earlier than the computer can see that it is winning.
    - jules 11y ago
      If that is what is meant then you can't see that you're losing against any player that's any stronger than yourself by definition, because if you could see it earlier you wouldn't have made that move.
      - mastazi 11y ago
        Which is exactly what the parent meant
      - hyperpape 11y ago
        As a club level go player (4 kyu AGA) I disagree. When you play against a much stronger human, you very quickly see moves that frustrate your plans, and find yourself struggling to find moves that work. It's common for the opening to pass by without feeling behind--Go doesn't have set openings to the same extent as chess, but if you play joseki, you might have an opening where the weaker player feels ok. Even that is not guaranteed, but once you start fighting, you will quickly feel the strength difference. Of course you can play a move that looks good when you play it, but against a much stronger player, it doesn't take long for it to look bad.
- monk_e_boy 11y ago
  Have you read Existence by David Brin, it has a lot of great AI stuff in it.
studentrob 11y ago
I wonder if he could identify those long-term-thinking moves by playing a version of AlphaGo on a smaller board, then gradually increasing the size of the board over successive games once he is able to consistently win or tie at a given board size. I also wonder if when Alpha Go plays itself, does it always win when it is black or white, does it always tie, or is it a mix? So much to learn from this.
- oneeyedpigeon 11y ago
  I was thinking the opposite. AI has already been beating humans on smaller boards, so maybe the only chance we have is to start playing go on bigger and bigger boards.
  - studentrob 11y ago
    Huh, interesting. I'm not well-versed in Go at all. It would be interesting if AlphaGo's maneuvers remain opaque to humans for an extended period of time. Can anyone at this point say with confidence that its strategies will indefinitely remain unknown?
    - Eliezer 11y ago
      If this plays out anything like it did in chess, in a few years we'll have very strong machine players that we can use to figure out with high confidence the quality of both players' moves.
  - tunichtgut 11y ago
    Obviously the chances for humans to win, increase with the board size. Given the computational power (=number of cpu/gpu) is fixed and the time for each move to be done is fixed. Perhaps a future "dr. evil / skynet" A.I. will first try to conquer the microchip production plants to increase its computational power and memory. goodbye taiwan, goodbye south korea, goodbye usa...
- nathan_f77 11y ago
  There is typically a point advantage of around 7 points when you get the chance to play the first move. In professional matches, white will be given a 6.5 point bonus to compensate for this. So yes I think it's fair to assume that black usually wins. Actually, AlphaGo could probably be used to calculate the perfect number of points, because it has changed over the years. In China they have a 7.5 point bonus for white.
deleted 11y ago
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RikNieu 11y ago
Isn't this what the machines learned in the Matrix?
alexlikeits1999 11y ago
Regarding the expectation that it would have gone 5-0 or 4-1 to either one, you'd have to have equally predicted a similarly one-sided result in the Deep Blue v Kasparov match. Rate of improvement might end up being completely domain-dependent, and hard to predict a-priori how it will turn out.
YeGoblynQueenne 11y ago
>> At this point it seems likely that Sedol is actually far outclassed by a superhuman player. I still don't agree that this is the case, and I don't care what a thousand Google-hyped press releases say, beating the best human player in anything is not "superhuman" and "superhuman" performance has not been achieved by anything yet. [1] Why do I think so? Two reasons. One, because you can be entirely human and still beat all other humans, without fail for a very long time. Long "winning streaks" in professional sports are very well documented. For instance Rocky Marciano went entirely undefeated in his whole heavyweight boxing career. In chess, Mikhail Tal went undefeated for 95 games. And so on, so forth. Of course most humans' winning streaks end eventually. That's because our performance degrades over time. When a computer wins against the best player, it keeps on winning, and in fact it actually gets even better over time. Still, and this is number two: in most instances where a computer does better than a human, we don't claim superhuman performance. Automatic calculators, going back to mechanical calculators, have been better than humans at arithmetic for a very, very long time. I would wager that nobody discusses pocket calculators as exhibiting "superhuman" performance. You only hear this sort of claim when it comes to Deep Blue, AlphaGo or Watson. So maybe we need a better definition of what it means to be "superhuman" that covers both pocket calculators and AlphaGo. Without one, I don't accept that the performance of AlphaGo can be said to be superhuman, unless pocket calculators' performance is also celebrated as superhuman. _______________________ [1] I'm perfectly willing to go even further than that and say that we can't make machines that have superhuman intelligence and that even if we did, we wouldn't be able to recognise them (this last bit is similar to what the GP says).
- m0g 11y ago
  Your pocket calculator is superhuman at arithmetic. Alphago is superhuman at go. It's not a big deal.
  - YeGoblynQueenne 11y ago
    My pocket calculator is faster than a human and has better memory. I don't know that this means the same thing as "superhuman in arithmetic". I can concede that it means superhuman in speed and memory, but, arithmetic? I don't think so. What it really does is move bits around registers. We are the ones interpreting those as numbers and the results of arithmetic operations. AlphaGo is rather different in that it actually has a representation of the game of Go and it knows how to play. I don't doubt at all that it's intelligent, in the restricted domain it operates in. But I do doubt that it's possible for an intelligence built by humans to be "superhuman" and I don't see how your one-liner addresses that.
pacala 11y ago
> The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points [snip] I wonder how many handicap stones do Lee Sedol, or Ke Jie for that matter, need to have a shot at winning even one game.
- ceejayoz 11y ago
  Now that's a really interesting question. Give AlphaGo progressively worse losing positions and see if it can rescue what the human masters would consider a disaster.
omphalos 11y ago
> the stark degree to which "AlphaGo output that move because the board will later be in a winning state" sometimes doesn't correlate with conventional Go goals like taking territory or being up on points Minor quibbles: 1) taking territory or being up on points are the same thing, and 2) in go there are many other things to optimize for besides territory/points - influence, shape, efficiency, and sente being examples of this.
drited 11y ago
How do we know that it's not just that the AI dominates the later stages where the number of permutations has reduced to the point where AI can calculate optimal moves? In other words, how do we know that the computer has an edge on the early stages and isn't just breaking even until the later stages with less permutations arrive, at which point it dominates because there are still too many for humans to assess?
- hasenj 11y ago
  At least in the case of AlphaGo it was pretty clear that it was dominating the board early on.
bratfarrar 11y ago
The implication is that humans have been trapped in a local maxima in terms of strategy and board reading. The question then becomes whether we can use this to bootstrap ourselves.
Praxilla 11y ago
I agree with you as this makes perfect sense. I'm new to the game Go but have been riveted by these games and the commentary. Here's where I think you're right. It seems from the commentary during the matches that the pro player thought the game to be close...on every match. Then the tides turned...or so we thought...and the game always swayed in AlphaGo's favor. I like how they were saying that the computer changed up the game style as well. All of this is very interesting, and I see that they have games 4 and 5 listed however I wonder if they are going to go thru with them and play them as scheduled. Maybe to see if AlphaGo can sweep the series? Or show it wasn't a fluke of any kind? I hope they play the last two...just to see how AlphaGo plays them. :D
- daveguy 11y ago
  They will play all 5 games in the match. The result can end up 3-2 4-1 or 5-0. Based on the past 3 which ended as sound defeats for Lee with different play styles and approaches (aggressive, calm and a mix typical of his usual game) I expect the final result will be 5-0 for AlphaGo.
deleted 11y ago
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contraban 11y ago
Or its just playing along until the state is complex enough, when it outperforms Seoul simply in terms of memory. I'd call that mid-term, my offer as a compromise ;)
dfan 11y ago
> For all we know from what we've seen, AlphaGo could win even if Sedol were allowed a one-stone handicap. What do you mean by a one-stone handicap? Just no komi? > In the case of AlphaGo, an extremely optimized strategy seems to have thrown away the 'typical' production of a visible point lead that characterizes human play. Maximizing win-probability in Go, at this level of play against a human 9p, is not strongly correlated with what a human can see as visible extra territory - so that gets thrown out even though it was previously associated with 'trying to win' in human play. Pros have been aware for centuries that "visible extra territory" is a poor indicator of win probability. They use the term "thickness" (in English) to denote the positive potential resulting from a strong, safe, influential position, independent of current territory. Quite often a pro game will be a battle of territory vs thickness.
ionforce 11y ago
> AI alignment theory What is this?
- sbierwagen 11y ago
  The problem of making sure an AI does what you want it to. http://lesswrong.com/lw/n4l/safety_engineering_target_selection_and_alignment/ http://lesswrong.com/lw/n4l/safety_engineering_target_select...
YeGoblynQueenne 11y ago
>> But Go is rich enough to demonstrate strong cognitive uncontainability on a small scale. In a rich and complicated domain whose rules aren't fully known, we should expect even more magic from superhuman reasoning - solutions that are better than the best solution we could imagine, operating by causal pathways we wouldn't be able to foresee even if we were told the AI's exact actions. Hang on. Where are we going to find this magic agent that can do "even more" in "a rich and complicated domain whose rules aren't fully known" (the real world, as opposed to a Go board) than in a game of Go? What are we going to train such a learner with, if we ourselves don't fully know the rules of the domain, as you point out? Even if a learner somehow magically found a superhuman path to perfect reasoning which is unavailable to us, entirely by accident and purely on its own, why would we select it from other trained learners to keep and foster further, if we think it's actually pretty dumb, rather than magically smart? You're saying at some point that a fantastic paperclip maximiser might achieve superhuman intelligence and then lie in waiting, poised to turn us all into paperclips only when it knew it was safe to make its move, basically. But, how is it going to become that smart in the first place? It has to be smart enough to know that it must bide its time, but dumb enough that its time hasn't come yet. Sounds like a bit of an impossible double-bind there. Just because sufficiently advanced AI may look like magic at first, it doesn't mean we should start all believing in magic because it may just be sufficiently advanced AI in disguise.
- hacker42 11y ago
  > What are we going to train such a learner with, if we ourselves don't fully know the rules of the domain, as you point out? This does not make any sense if you assume general intelligence. A physicist who makes a discovery also was not trained to know about this new law or rule beforehand (how could he?).
- nevada_smith 11y ago
  This is not the final frontier, far from it, in truth. Go is very much like Chess, with an overshadowing branching factor. AI is yet to conquer Bridge. The first time we see a bot false-card will be the day when AI arrives. What we see now however, is still just searches and pattern matches...
YeGoblynQueenne 11y ago
>> AlphaGo's core is built around a similar machine learning technology to Deepmind's Atari-playing system - the single, untweaked program that was able to learn superhuman play on dozens of different Atari games just by looking at the pixels, without specialization for each particular game. Woa there. The Deepmind Atari-playing AI was too specialised for each particular game. It had a reward function that translated the score for it. It couldn't learn the importance of the game score on its own, just from "looking at the pixels" and it couldn't learn the significance of the score display and how it changed as a result of its actions on its own. All this had to be hand-coded. And if I've seen a hint that this means it couldn't really-really generalise to other games, like Deepmind claimed, then that's this bit that you report yourself: >> Deepmind ... did reuse the widely known core insight of Monte Carlo Tree Search. That is no mere detail. That is the crux of the matter, right there. Deepmind used their architecture to improve MCTS far enough that it could beat Lee Se-Dol. They didn't just add MCTS to their already general-game playing system. Because the original Atari-playing AI was completely useless for playing Go, a game that doesn't have a score and looks nothing like an Atari game. So it wasn't very general at all, despite Google's and DeepMind's claims to the contrary.
- moyix 11y ago
  I think you're wrong here. From the Nature paper describing AlphaGo: "We also tested against the strongest open-source Go program, Pachi, a sophisticated Monte Carlo search program, ranked at 2 amateur dan on KGS, that executes 100,000 simulations per move. Using no search at all, the RL policy network won 85% of games against Pachi." AlphaGo does use MCTS, but it seems that most of its improvements are actually coming from the deep reinforcement learning approach.
  - YeGoblynQueenne 11y ago
    I don't know whether this is true. If AlphaGo could do just as well without search, then why did it use search at all? But in any case, I'm not necessarily disputing that. I'm particularly refuting the claim that the AlphaGo architecture is identical to the one that learned to play Atari games and that Deepmind have advertised as a general game-playing agent. My comment here is specifically in reply to the GP who repeated this claim, but I'll dig up the relevant link if you're interested.
joelthelion 11y ago
> the maximizing solution will lie at a vertex of the possibility space Keep in mind that as well as AlphaGo plays, it's still extremely far from playing optimal moves.
skybrian 11y ago
On the other hand, in many ways AlphaGo plays much like a human professional. The openings and fights were not radically different. The professional commentators did not have a hard time explaining why most moves were good. The structure of the board was understandable. Some moves were "forced" moves that were predictable and many others were good moves that a human would play. The human player isn't efficient enough to win, but attacks and defenses still mostly work as intended. This suggests that Go space was already very well understood and there aren't radically different play styles we've somehow overlooked. AI is not a magic wand that radically changes how the game works. However there may be some bias due to AlphaGo having trained on human games and playing against a human. The real proof will happen when they redo the retraining from scratch.
- adrianN 11y ago
  AlphaGo learned the game from humans by looking at recorded games. It's not suprising that its style mimicks its master's.
rwallace 11y ago
The problem with 'flag and footprints' demonstrations is precisely the same as their attraction - they are, in essence (even though that was not the intent), selected to be as misleading as possible about progress in solving the real problems. We put a man on the moon, which encouraged decades of optimism about the near-future colonization of space... unfortunately it turns out we are nowhere near solving the hard problems associated with being able to live in space. We spelled out the letters IBM in xenon atoms, five atoms tall, which encouraged optimism about near-future molecular manufacturing... unfortunately it turns out we are nowhere near solving the hard problems associated with making useful things with atomic precision. We achieve superhuman performance in simple games, which encourages what should be optimism that becomes reflected by the distorting mirror of the zeitgeist of the times into pessimism, about near-future AI... unfortunately it turns out we are nowhere near solving the hard problems associated with making our tools something better than depressingly dumb. In each case, the flurry of optimism turns as the decades pass into confusion, anger, soul-searching about why the expected follow-on progress has failed to materialize, fading slowly into despair, then into nothing as the optimists die of old age, one by one, and the rate of technological progress becomes slower with every passing decade. For this conversation is necessarily happening at a tech level that allows it, but that provides no guarantee whatsoever that the species that reached tech level X will proceed to tech level X+1. Moore's law has officially expired, and that was the last major area where we were making rapid sustained progress. It's hard to say we shouldn't do 'flag and footprints' demonstrations; am I really willing to bite the bullet and say we shouldn't have put a man on the moon? I don't know the answer to that. I am, however, convinced that we should remember not to take them too seriously.
- nostrademons 11y ago
  The "flag and footprints" demonstrations serve essentially as prototypes. They demonstrate that a certain tech level is possible. But as anyone who's built a prototype knows, when the prototype works you are maybe 5% of the way there. We put a man on the moon in 1969, and we are just now getting privately-funded spaceflight and satellites. We made the first computers in 1945; we got them on every desk in the 1980s. The first steamboat was built in 1783; the transatlantic shipping industry didn't transition to steam until the 1840s & 1850s. Probably some kid who's watching AlphaGo's game today is the person who, late in life, invents the first strong AI.
bricemo 11y ago
I think this is one of the most fascinating (and disturbing) facts about AI: they seek to solve the problem they are given without being dependent on convention. They are completely alien in this regard, so many of their choices don't make any sense to us. This results in huge possibility but also the huge dangers that people worry about.

by Eliezer 11y ago

taneq 11y ago

This is reminding me more and more of the central theme of the book Echopraxia, which was that when dealing with superhuman intelligences, you are by definition not smart enough to even know whether you're winning.

studentrob 11y ago
Hmm maybe but aren't humans capable of learning too? One could make the same statement of two humans of different ranks playing each other. In some cases, the more highly ranked player might be the only one who knows who's winning. This doesn't mean that the lesser ranked player can't improve over time. It just means that at that moment, that player is inexperienced or less skilled, which we already knew.
- Radim 11y ago
  Alas, time is not on meat's side in this one.
  - studentrob 11y ago
    I'll grant that for this very constrained problem ;-) I respect your work a lot. I've studied and used ML myself, including gensim, in industry. I've given nowhere near your level of contribution to society / the field. My opinion is true AI is quite a ways off. I haven't read anything from a ML researcher that says it isn't. Perhaps you weren't saying true AI is nearer.
    - quanticle 11y ago
      I find it funny that whenever there's a case of computers being able to do something that they couldn't before, whether it's drive or beat humans at Go, the goalpost on what is "true AI" shifts to be something that computers can't do yet. So let me ask you this. What would you consider to be "true AI"? At what point are you willing to say, "Okay, that's it, computers are just plain smarter than we are?" Because, frankly, it seems to me that that day is getting closer and closer. Saying that AIs can't be smarter than humans because they don't think and act like humans is like saying that airplanes don't "truly" fly because they don't flap their wings.
      - eddyb 11y ago
        Accelerando[0] starts with this quote, which I really like: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." – Edsger W. Dijkstra [0] http://www.antipope.org/charlie/blog-static/fiction/accelerando/accelerando-intro.html http://www.antipope.org/charlie/blog-static/fiction/accelera... (readable online at http://www.antipope.org/charlie/blog-static/fiction/accelerando/accelerando.html http://www.antipope.org/charlie/blog-static/fiction/accelera...)
      - hythloday 11y ago
        We know that true human intelligence includes being able to help machines think better. So it seems reasonable to state that true machine intelligence includes being able to help humans think better. Other things that humans can do that machines can't yet: * change other human's minds * contribute to the state of the art of human knowledge * determine the difference between a human and a machine.
jules 11y ago
When I play chess against a computer I can see very easily that I'm not winning.
- GolDDranks 11y ago
  But that is most likely be because of the structure of Chess. The evaluation of single board position is more "linear" and certainly easier than in Go. If the utility function is about winning, going though easily-evalutable board positions might be the straightforward route. If the utility function is to win while minimizing your estimate of of you losing, you might see different results. Plus if you know that you are playing against a stronger opponent, your prior might bias your perception of the board situation.
  - pmoriarty 11y ago
    It would be interesting to see how computers fare against humans on a 19x19 chess board, and how easily a human could evaluate such a board.
- jacquesm 11y ago
  The question is not if you can see that or not. The question is if you can see that earlier than the computer can see that it is winning.
  - jules 11y ago
    If that is what is meant then you can't see that you're losing against any player that's any stronger than yourself by definition, because if you could see it earlier you wouldn't have made that move.
    - mastazi 11y ago
      Which is exactly what the parent meant
    - hyperpape 11y ago
      As a club level go player (4 kyu AGA) I disagree. When you play against a much stronger human, you very quickly see moves that frustrate your plans, and find yourself struggling to find moves that work. It's common for the opening to pass by without feeling behind--Go doesn't have set openings to the same extent as chess, but if you play joseki, you might have an opening where the weaker player feels ok. Even that is not guaranteed, but once you start fighting, you will quickly feel the strength difference. Of course you can play a move that looks good when you play it, but against a much stronger player, it doesn't take long for it to look bad.
monk_e_boy 11y ago
Have you read Existence by David Brin, it has a lot of great AI stuff in it.

studentrob 11y ago

I wonder if he could identify those long-term-thinking moves by playing a version of AlphaGo on a smaller board, then gradually increasing the size of the board over successive games once he is able to consistently win or tie at a given board size. I also wonder if when Alpha Go plays itself, does it always win when it is black or white, does it always tie, or is it a mix? So much to learn from this.

oneeyedpigeon 11y ago
I was thinking the opposite. AI has already been beating humans on smaller boards, so maybe the only chance we have is to start playing go on bigger and bigger boards.
- studentrob 11y ago
  Huh, interesting. I'm not well-versed in Go at all. It would be interesting if AlphaGo's maneuvers remain opaque to humans for an extended period of time. Can anyone at this point say with confidence that its strategies will indefinitely remain unknown?
  - Eliezer 11y ago
    If this plays out anything like it did in chess, in a few years we'll have very strong machine players that we can use to figure out with high confidence the quality of both players' moves.
- tunichtgut 11y ago
  Obviously the chances for humans to win, increase with the board size. Given the computational power (=number of cpu/gpu) is fixed and the time for each move to be done is fixed. Perhaps a future "dr. evil / skynet" A.I. will first try to conquer the microchip production plants to increase its computational power and memory. goodbye taiwan, goodbye south korea, goodbye usa...
nathan_f77 11y ago
There is typically a point advantage of around 7 points when you get the chance to play the first move. In professional matches, white will be given a 6.5 point bonus to compensate for this. So yes I think it's fair to assume that black usually wins. Actually, AlphaGo could probably be used to calculate the perfect number of points, because it has changed over the years. In China they have a 7.5 point bonus for white.

deleted 11y ago

[deleted]

RikNieu 11y ago

Isn't this what the machines learned in the Matrix?

alexlikeits1999 11y ago

Regarding the expectation that it would have gone 5-0 or 4-1 to either one, you'd have to have equally predicted a similarly one-sided result in the Deep Blue v Kasparov match. Rate of improvement might end up being completely domain-dependent, and hard to predict a-priori how it will turn out.

YeGoblynQueenne 11y ago

>> At this point it seems likely that Sedol is actually far outclassed by a superhuman player. I still don't agree that this is the case, and I don't care what a thousand Google-hyped press releases say, beating the best human player in anything is not "superhuman" and "superhuman" performance has not been achieved by anything yet. [1] Why do I think so? Two reasons. One, because you can be entirely human and still beat all other humans, without fail for a very long time. Long "winning streaks" in professional sports are very well documented. For instance Rocky Marciano went entirely undefeated in his whole heavyweight boxing career. In chess, Mikhail Tal went undefeated for 95 games. And so on, so forth. Of course most humans' winning streaks end eventually. That's because our performance degrades over time. When a computer wins against the best player, it keeps on winning, and in fact it actually gets even better over time. Still, and this is number two: in most instances where a computer does better than a human, we don't claim superhuman performance. Automatic calculators, going back to mechanical calculators, have been better than humans at arithmetic for a very, very long time. I would wager that nobody discusses pocket calculators as exhibiting "superhuman" performance. You only hear this sort of claim when it comes to Deep Blue, AlphaGo or Watson. So maybe we need a better definition of what it means to be "superhuman" that covers both pocket calculators and AlphaGo. Without one, I don't accept that the performance of AlphaGo can be said to be superhuman, unless pocket calculators' performance is also celebrated as superhuman. _______________________ [1] I'm perfectly willing to go even further than that and say that we can't make machines that have superhuman intelligence and that even if we did, we wouldn't be able to recognise them (this last bit is similar to what the GP says).

m0g 11y ago
Your pocket calculator is superhuman at arithmetic. Alphago is superhuman at go. It's not a big deal.
- YeGoblynQueenne 11y ago
  My pocket calculator is faster than a human and has better memory. I don't know that this means the same thing as "superhuman in arithmetic". I can concede that it means superhuman in speed and memory, but, arithmetic? I don't think so. What it really does is move bits around registers. We are the ones interpreting those as numbers and the results of arithmetic operations. AlphaGo is rather different in that it actually has a representation of the game of Go and it knows how to play. I don't doubt at all that it's intelligent, in the restricted domain it operates in. But I do doubt that it's possible for an intelligence built by humans to be "superhuman" and I don't see how your one-liner addresses that.

pacala 11y ago

> The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points [snip] I wonder how many handicap stones do Lee Sedol, or Ke Jie for that matter, need to have a shot at winning even one game.

ceejayoz 11y ago
Now that's a really interesting question. Give AlphaGo progressively worse losing positions and see if it can rescue what the human masters would consider a disaster.

omphalos 11y ago

> the stark degree to which "AlphaGo output that move because the board will later be in a winning state" sometimes doesn't correlate with conventional Go goals like taking territory or being up on points Minor quibbles: 1) taking territory or being up on points are the same thing, and 2) in go there are many other things to optimize for besides territory/points - influence, shape, efficiency, and sente being examples of this.

drited 11y ago

How do we know that it's not just that the AI dominates the later stages where the number of permutations has reduced to the point where AI can calculate optimal moves? In other words, how do we know that the computer has an edge on the early stages and isn't just breaking even until the later stages with less permutations arrive, at which point it dominates because there are still too many for humans to assess?

hasenj 11y ago
At least in the case of AlphaGo it was pretty clear that it was dominating the board early on.

bratfarrar 11y ago

The implication is that humans have been trapped in a local maxima in terms of strategy and board reading. The question then becomes whether we can use this to bootstrap ourselves.

Praxilla 11y ago

I agree with you as this makes perfect sense. I'm new to the game Go but have been riveted by these games and the commentary. Here's where I think you're right. It seems from the commentary during the matches that the pro player thought the game to be close...on every match. Then the tides turned...or so we thought...and the game always swayed in AlphaGo's favor. I like how they were saying that the computer changed up the game style as well. All of this is very interesting, and I see that they have games 4 and 5 listed however I wonder if they are going to go thru with them and play them as scheduled. Maybe to see if AlphaGo can sweep the series? Or show it wasn't a fluke of any kind? I hope they play the last two...just to see how AlphaGo plays them. :D

daveguy 11y ago
They will play all 5 games in the match. The result can end up 3-2 4-1 or 5-0. Based on the past 3 which ended as sound defeats for Lee with different play styles and approaches (aggressive, calm and a mix typical of his usual game) I expect the final result will be 5-0 for AlphaGo.

deleted 11y ago

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contraban 11y ago

Or its just playing along until the state is complex enough, when it outperforms Seoul simply in terms of memory. I'd call that mid-term, my offer as a compromise ;)

dfan 11y ago

> For all we know from what we've seen, AlphaGo could win even if Sedol were allowed a one-stone handicap. What do you mean by a one-stone handicap? Just no komi? > In the case of AlphaGo, an extremely optimized strategy seems to have thrown away the 'typical' production of a visible point lead that characterizes human play. Maximizing win-probability in Go, at this level of play against a human 9p, is not strongly correlated with what a human can see as visible extra territory - so that gets thrown out even though it was previously associated with 'trying to win' in human play. Pros have been aware for centuries that "visible extra territory" is a poor indicator of win probability. They use the term "thickness" (in English) to denote the positive potential resulting from a strong, safe, influential position, independent of current territory. Quite often a pro game will be a battle of territory vs thickness.

ionforce 11y ago

> AI alignment theory What is this?

sbierwagen 11y ago
The problem of making sure an AI does what you want it to. http://lesswrong.com/lw/n4l/safety_engineering_target_selection_and_alignment/ http://lesswrong.com/lw/n4l/safety_engineering_target_select...

YeGoblynQueenne 11y ago

>> But Go is rich enough to demonstrate strong cognitive uncontainability on a small scale. In a rich and complicated domain whose rules aren't fully known, we should expect even more magic from superhuman reasoning - solutions that are better than the best solution we could imagine, operating by causal pathways we wouldn't be able to foresee even if we were told the AI's exact actions. Hang on. Where are we going to find this magic agent that can do "even more" in "a rich and complicated domain whose rules aren't fully known" (the real world, as opposed to a Go board) than in a game of Go? What are we going to train such a learner with, if we ourselves don't fully know the rules of the domain, as you point out? Even if a learner somehow magically found a superhuman path to perfect reasoning which is unavailable to us, entirely by accident and purely on its own, why would we select it from other trained learners to keep and foster further, if we think it's actually pretty dumb, rather than magically smart? You're saying at some point that a fantastic paperclip maximiser might achieve superhuman intelligence and then lie in waiting, poised to turn us all into paperclips only when it knew it was safe to make its move, basically. But, how is it going to become that smart in the first place? It has to be smart enough to know that it must bide its time, but dumb enough that its time hasn't come yet. Sounds like a bit of an impossible double-bind there. Just because sufficiently advanced AI may look like magic at first, it doesn't mean we should start all believing in magic because it may just be sufficiently advanced AI in disguise.

hacker42 11y ago
> What are we going to train such a learner with, if we ourselves don't fully know the rules of the domain, as you point out? This does not make any sense if you assume general intelligence. A physicist who makes a discovery also was not trained to know about this new law or rule beforehand (how could he?).
nevada_smith 11y ago
This is not the final frontier, far from it, in truth. Go is very much like Chess, with an overshadowing branching factor. AI is yet to conquer Bridge. The first time we see a bot false-card will be the day when AI arrives. What we see now however, is still just searches and pattern matches...

YeGoblynQueenne 11y ago

>> AlphaGo's core is built around a similar machine learning technology to Deepmind's Atari-playing system - the single, untweaked program that was able to learn superhuman play on dozens of different Atari games just by looking at the pixels, without specialization for each particular game. Woa there. The Deepmind Atari-playing AI was too specialised for each particular game. It had a reward function that translated the score for it. It couldn't learn the importance of the game score on its own, just from "looking at the pixels" and it couldn't learn the significance of the score display and how it changed as a result of its actions on its own. All this had to be hand-coded. And if I've seen a hint that this means it couldn't really-really generalise to other games, like Deepmind claimed, then that's this bit that you report yourself: >> Deepmind ... did reuse the widely known core insight of Monte Carlo Tree Search. That is no mere detail. That is the crux of the matter, right there. Deepmind used their architecture to improve MCTS far enough that it could beat Lee Se-Dol. They didn't just add MCTS to their already general-game playing system. Because the original Atari-playing AI was completely useless for playing Go, a game that doesn't have a score and looks nothing like an Atari game. So it wasn't very general at all, despite Google's and DeepMind's claims to the contrary.

moyix 11y ago
I think you're wrong here. From the Nature paper describing AlphaGo: "We also tested against the strongest open-source Go program, Pachi, a sophisticated Monte Carlo search program, ranked at 2 amateur dan on KGS, that executes 100,000 simulations per move. Using no search at all, the RL policy network won 85% of games against Pachi." AlphaGo does use MCTS, but it seems that most of its improvements are actually coming from the deep reinforcement learning approach.
- YeGoblynQueenne 11y ago
  I don't know whether this is true. If AlphaGo could do just as well without search, then why did it use search at all? But in any case, I'm not necessarily disputing that. I'm particularly refuting the claim that the AlphaGo architecture is identical to the one that learned to play Atari games and that Deepmind have advertised as a general game-playing agent. My comment here is specifically in reply to the GP who repeated this claim, but I'll dig up the relevant link if you're interested.

joelthelion 11y ago

> the maximizing solution will lie at a vertex of the possibility space Keep in mind that as well as AlphaGo plays, it's still extremely far from playing optimal moves.

skybrian 11y ago

On the other hand, in many ways AlphaGo plays much like a human professional. The openings and fights were not radically different. The professional commentators did not have a hard time explaining why most moves were good. The structure of the board was understandable. Some moves were "forced" moves that were predictable and many others were good moves that a human would play. The human player isn't efficient enough to win, but attacks and defenses still mostly work as intended. This suggests that Go space was already very well understood and there aren't radically different play styles we've somehow overlooked. AI is not a magic wand that radically changes how the game works. However there may be some bias due to AlphaGo having trained on human games and playing against a human. The real proof will happen when they redo the retraining from scratch.

adrianN 11y ago
AlphaGo learned the game from humans by looking at recorded games. It's not suprising that its style mimicks its master's.

rwallace 11y ago

The problem with 'flag and footprints' demonstrations is precisely the same as their attraction - they are, in essence (even though that was not the intent), selected to be as misleading as possible about progress in solving the real problems. We put a man on the moon, which encouraged decades of optimism about the near-future colonization of space... unfortunately it turns out we are nowhere near solving the hard problems associated with being able to live in space. We spelled out the letters IBM in xenon atoms, five atoms tall, which encouraged optimism about near-future molecular manufacturing... unfortunately it turns out we are nowhere near solving the hard problems associated with making useful things with atomic precision. We achieve superhuman performance in simple games, which encourages what should be optimism that becomes reflected by the distorting mirror of the zeitgeist of the times into pessimism, about near-future AI... unfortunately it turns out we are nowhere near solving the hard problems associated with making our tools something better than depressingly dumb. In each case, the flurry of optimism turns as the decades pass into confusion, anger, soul-searching about why the expected follow-on progress has failed to materialize, fading slowly into despair, then into nothing as the optimists die of old age, one by one, and the rate of technological progress becomes slower with every passing decade. For this conversation is necessarily happening at a tech level that allows it, but that provides no guarantee whatsoever that the species that reached tech level X will proceed to tech level X+1. Moore's law has officially expired, and that was the last major area where we were making rapid sustained progress. It's hard to say we shouldn't do 'flag and footprints' demonstrations; am I really willing to bite the bullet and say we shouldn't have put a man on the moon? I don't know the answer to that. I am, however, convinced that we should remember not to take them too seriously.

nostrademons 11y ago
The "flag and footprints" demonstrations serve essentially as prototypes. They demonstrate that a certain tech level is possible. But as anyone who's built a prototype knows, when the prototype works you are maybe 5% of the way there. We put a man on the moon in 1969, and we are just now getting privately-funded spaceflight and satellites. We made the first computers in 1945; we got them on every desk in the 1980s. The first steamboat was built in 1783; the transatlantic shipping industry didn't transition to steam until the 1840s & 1850s. Probably some kid who's watching AlphaGo's game today is the person who, late in life, invents the first strong AI.

bricemo 11y ago

I think this is one of the most fascinating (and disturbing) facts about AI: they seek to solve the problem they are given without being dependent on convention. They are completely alien in this regard, so many of their choices don't make any sense to us. This results in huge possibility but also the huge dangers that people worry about.