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As 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 unu

by fhe 11y ago

As 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 ago
I 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 ago
  Was that in the official livestream, or is there an interview somewhere, where things like these are discussed?
  - megalodon 11y ago
    Here: https://www.youtube.com/watch?v=l-GsfyVCBu0&t=2511 https://www.youtube.com/watch?v=l-GsfyVCBu0&t=2511
- jtolmar 11y ago
  Building on that, I suspect that if AlphaGo thinks it has a 100% chance of winning with any of several moves, it has no way of distinguishing between them and chooses effectively at random. The longer that goes on - and once it hits 100% chance of winning, it will be that way for the rest of the game - the more chances it has to pick bad moves. As long as the move isn't bad enough to ruin its 100% chance of winning, it can't tell the difference between that and a good move. (This also applies without a 100% chance of winning, as long as its chances of winning hover near the highest percent it's able to distinguish.)
  - nbouscal 11y ago
    I doubt the value network ever outputs a literal 100% chance of winning, it would at most be a lot of nines. Even if it did output an actual 100% chance, AlphaGo would still end up picking moves favored by the policy network, so it would probably just revert to playing like it predicts a human pro would.
    - jtolmar 11y ago
      Once it gets to enough nines, its monte carlo trees will run out of sample resolution. If it can resolve to three nines, then a 99.93% win branch has a 70% chance of being reported as 99.9% and a 30% chance of being reported as 100%. When all the branches here get rolled up, they report some average around 99.93% but not necessarily exactly it. This propagates upwards in the tree, adding more meaningless digits. Adding the evaluation network in increases the number of decimals, but doesn't really change the effect. It's similar to how ray tracing renderers start to return weird speckle patterns when the room is dark enough. And the policy network chooses branches to investigate, not which one to choose. It adds sample resolution to places pros might play, but doesn't add to the estimated probability of winning. Edit: Actually, since places pros might play have higher sample resolution, they're less random. So worse moves get worse evaluation, and a higher chance of leading the pack. This might actually bias AlphaGo to play some pretty bad moves - but, again, this is all assuming it's going to win anyway.
ig1 11y ago
AlphaGo is essentially built on the work that IBM did on TD-Gammon (a reinforcement learning backgammon player) in the 90s. Pretty much the same thing happened with TD-Gammon with it playing unconventional moves, in the longer term humans ended up adopting some of TD-Gammon's tactics once they understood how they played out, it wouldn't be surprising to see the same happen with Go.
- bdalgaard 11y ago
  From my understanding, computers have also had this affect on chess. The play styles of younger champions has evolved to the point where unpredictability is actually part of the strategy. I'm not a chess expert by any means, but this quote by Viswanathan Anand (former World Chess Champion) describes it. “Top competitors who once relied on particular styles of play are now forced to mix up their strategies, for fear that powerful analysis engines will be used to reveal fatal weaknesses in favoured openings....Anything unusual that you can produce has quadruple, quintuple the value, precisely because your opponent is likely to do the predictable stuff, which is on a computer” [1] [1] http://www.businessinsider.com/anand-on-how-computers-have-changed-chess-2013-11 http://www.businessinsider.com/anand-on-how-computers-have-c...
  - slm_HN 11y ago
    >powerful analysis engines will be used to reveal fatal weaknesses in favoured openings... Anand isn't really talking about strategy here, he's just talking about choice of opening. Players with narrow opening repertoires, like Fischer, have always been easier to prepare for than players who play a wide variety of openings. As far as actual changes to strategy, the most obvious one is that computers tend to value material more highly than humans. So a computer will take a risky pawn if it looks sound, while a human will see that taking the pawn is very complicated and prefer a simpler move.
  - oli5679 11y ago
    Computers and the internet have changed chess in several ways: (1) Online game databases have made it easier for players to track developments in opening theory and prepare to play specific opponents (2) Chess engines add to this be used to search for antidotes to complicated opening systems (3) Young players have greater access to high-quality sparring partners - either engines or fellow humans on online servers. This has lead to the best players becoming younger, and players playing more varied and less 'sharp' openings.
astrofinch 11y ago
There's also the fact that some of the unexpected moves were apparently more about solidifying against a loss than increasing the magnitude of a win. Which has its own kind of eerie implication: since AIs (like all computer programs) do what you say, not what you mean, the "intelligent species" can sometimes work really intelligently towards a goal that wasn't quite what you had in mind. (Gets especially interesting for any AlphaHuman/AlphaCEO/AlphaPresident successors that are given goals more complicated & nuanced than "maximize Go win probability regardless of ending score". BTW, if you haven't already read the Wait But Why series on the future of AI, I recommend it: http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html http://waitbutwhy.com/2015/01/artificial-intelligence-revolu...)
- colanderman 11y ago
  About a year ago I wrote an AI to play the board game "Hive" (shares some similarities with chess). Because I scored all wins equally, it behaved almost exactly like this. It would simply try to minimize my advantage while always keeping open the possibility for it to win, almost like a cat toying with prey. It never actually would make the winning move – however obvious – until it had no other options! I fixed this behavior by scoring earlier wins higher than later wins. Now it will actually finish games (and win), but almost invariably its edge is very small, no matter how well or poorly I play. Because of the new win scoring, it willingly sacrifices its own advantage if it means securing a win even one turn earlier. (And since scoring is symmetrical, this has the added advantage of working to delay any win it sees for me, thus increasing the possibility of me making a mistake!) I suppose I could try modifying the scoring rules again, to weight them by positional advantage. A "show off" mode if you like :) And again, with the flip side of working to create the least humiliating losses for itself.
  - reacweb 11y ago
    In go, the purpose is to have more territory than the opponent. There is no point in humiliating the opponent by having a big advantage. I think the aim of the strange moves was to increase the confidence of the program in its advance, not to increase the advance.
ap22213 11y ago
Sometimes optimal solutions don't make sense to the human mind because they're not intuitive. For instance, I developed a system that used machine learning and linear solver models to spit out a series of actions to take in response to some events. The actions were to be acted on by humans who were experts in the field. In fact, they were the ones from whom we inferred the relevant initial heuristics. Everyday, I would get a support call from one of the users. They'd be like, 'this output is completely wrong. You have a bug in your code.' I'd then have to spend several hours walking through each of the actions with them and recording the results. In every case, the machine would produce recommended actions that were optimal. However, they were rarely intuitive. In the end, it took months of this back and forth until the experts began to trust the machine outputs. This is the frightening thing about AI - not only can an AI outperform experts, but it often makes decisions that are incomprehensible.
- caskance 11y ago
  The only frightening part of your story is the insecurity of the human experts.
  - ghaff 11y ago
    Or, maybe, there could have been bugs in the code. If I'm an expert in some domain and a computer is telling me to do something completely different ("Trust me--just drive over the river!") I'm certainly going to question the result.
  - grayclhn 11y ago
    Not really. The alternative is like driving your car into a lake because the GPS told you to.
- sigmar 11y ago
  What you said about the expert calling something a bug reminded me of how the commentator in the first game would see a move by alphaGo and say that it was wrong. He did this multiple times for alphaGo but never once questioned the human's move. Yet even with all those "wrong" moves alphaGo won. Didn't watch the second game, so not sure if he kept doing that.
1024core 11y ago
The analogy I can come up with, based on your post, is of something like addition. We don't know how we add numbers in our heads; but we somehow do it. Some people can do it very, very quickly[1], but won't be able to explain how they did it. On the other hand: a computer doesn't look at digits and numbers; it just looks at bits and shifts them around as appropriate. [1] https://en.wikipedia.org/wiki/Shakuntala_Devi https://en.wikipedia.org/wiki/Shakuntala_Devi
teekert 11y ago
I like your statement: "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." I think this will the theme of our future interactions with AIs. We simply can't imagine in advance how they will see and interact with the world. There will be many surprises.
- keitmo 11y ago
  That quote reminds me of "The Two Faces of Tomorrow" by James P. Hogan. One of the subplots is that humans can communicate because of shared experience. We all must eat, sleep, breathe, seek shelter, etc. Communication with an alien or artificial intelligence may be difficult or even impossible without this shared framework.
poppingtonic 11y ago
There's an interesting angle to this phrase "intelligent species opening up a new way of looking at the world", which is that we (humans) designed go as a game - a subset of the real world we interact with. Go is "reality" to alphago. The superset of all possible sense data it could have, in principle. Whatever "chunks" AlphaGo uses, if it does use them, all of its policies are built only from subsets of the sense data that is the interactions (self-plays) and inferences from past games. There's nothing outside the game to bring into its decision process. With humans, however, our policies are noisy and are rife with what, for lack of a better term, I would call leaky abstractions.
- greg_data 11y ago
  that's an absolutely fascinating way to think about it.
- mattmcknight 11y ago
  I think it's more metaphor than leaky abstraction in this case, except to the extent that metaphor is mapping an abstraction of a domain we are trying to understand to an abstraction of one we are better able to understand.
forgotpwtomain 11y ago
> 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. I have been watching Myungwan Kim's commentary for the games - and it seems notable that a few moves he finds very peculiar immediately when they are made, he will later point out to as achieving very good results some 20 moves later. So it also seems quite possible that AlphaGo is actually reading this far ahead, to find those peculiar moves achieve better results than from the more standard approaches. Whether these constitute a 'new way' or not I think depends highly on whether these kind of moves can fit into some general heuristics useful for considering positions, or whether the ability to make them is limited to intelligence's with extremely high computational power for reading ahead.
- statsaresimple 11y ago
  It seems that you are trying to create a new word that describe this new way of looking at the world. If human are able to decode the information contained in those unexpected moves, perhaps by creating a new heuristic, that could be viewed as a way of understanding the features the machine use internally, that is reading the machine brain. If human are able to decode that information creating new heuristics we could say that we are in a new state in IA in which learning among different intelligent species should be studied.
- bpicolo 11y ago
  I would imagine it's absolutely thinking that far ahead. That said, it can't possibly search every possible solution, just needs to find an adequate one
- lpage 11y ago
  > he will later point out to as achieving very good results some 20 moves later This. It's a fairly common feature of any AI that uses some form of tree search/minimax, and the effect is very pronounced in chess. Even the best human players can only think 6-8 plies into the feature versus ~18 for a computer. What we can (could?) do is apply smarter evaluation functions to the board states resulting from candidate plays and stop considering moves that look problematic earlier in the search (game tree pruning). AI tends to use very simple evaluation functions that can be computed quickly. They do so given that 1) it allows for deeper search, and a weak heuristic evaluated far in the future often beats a strong one evaluated a few plies prior and 2) for some games (like Go) it's really hard to codify the "intuitions" that human players speak of. Because search based AI considers board states __very__ far in the future, the results are often completely counterintuitive in a game with an established theory of play. Those theories are born of humans, for humans. The introduction of MCTS some years back was the first leap towards a human level Go AI (incidentally, MCTS is more human-like than exhaustive tree search in that it prunes aggressively by making early judgement calls as to what merits further consideration). AlphaGo's use of deep policy and evaluation networks to score the board is very cool, and the next step in that journey. What's interesting to me is that, unlike chess AI, AlphaGo might actually advance the human theory of Go. It's possible that these "strange moves" will lead to some very interesting insights if DeepMind traces them through the eval and policy networks and manages to back out a more general theory of play.
Ensorceled 11y ago
> 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. Could AlphaGO be winning in a way similar to left handed fencers having an advantage over right handers by wrong footing them rather than simply being better? Would giving Lee more chance to see this style give him a chance to catch up?
- sdenton4 11y ago
  Seems unlikely. Training was partly from human games, and partly from self play; if there's some new, off book heuristics at play, there's no way to know that humans would respond poorly to them. Though I suppose it's possible it would notice that humans do poorly on simply off book moves generally.
  - Ensorceled 11y ago
    Why does this seem unlikely? Humans do poorly with "off book" moves in general in sports and other games; it's why new styles of play or management work really well until others get used to them. Why would it be unlikely in Go?
- fma 11y ago
  I'm not a Go player but play other competitive sports. Humans have a herd mentality...as Op mentioned there's certain styles of playing...which has their own strengths and weaknesses. Sometimes people will not examine other styles that may have better strengths and just focus on the exist one. Then comes along someone who 'thinks outside the box' with a new style and revolutionize the playing field. Think Bruce Lee and the creation of Jeet Kune Do. Before him everyone concentrated on improving one style by following it classically, rather than just thinking of 'how do I defeat someone'. IMHO Lee is the best at the current style of Go. AlphaGO is the best at playing Go. Maybe humans can devise a better style and defeat AlphaGo, but I'm sure AlphaGo can adapt easily if another style exists.
  - 11y ago
makmanalp 11y ago
I wonder if this is similar to how musket battles were fought in the american civil war era, with soldiers lining up across each other in a battlefield and taking turns shooting at each other. I hear they did this because the rifles were very inaccurate so it made sense to use a bunch of them at the same time as an area-effect weapon, in effect like a gigantic shotgun. Until someone got better weapons and suddenly the "rules" of the battlefield that dictated standing in lines across each other made no sense to follow anymore because the original principles that dictated those rules to be good were not valid anymore.
anantzoid 11y ago
> It's like another intelligent species opening up a new way of looking at the world. And this is just the beginning with AlphaGo. As we keep on training Deep Learning systems for other domains, we'll realise how differently they approach problems and solve them. It'll, in turn, help us in adapting these different perspectives and applying them to solve other problems as well.
statsaresimple 11y ago
A great breakthrough could be to decode the information contained in the feature space of the nn or the rnn. A topological language in which shapes and chains are explained by analogies with real world situations and actions. Being able to share our vision and communicate our intentions (the weight given to the distinct features and the links among the several layers of the nn - the overall plan) should transform the concept of AI into one of CAI communication between intelligent agents to create a synergistic approach).
astazangasta 11y ago
>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. It's not like this at all; let's not do this sort of thing. Humans are inveterate myth makers (viz. your description of how people conceive the Go board as army units), and our impositions on the world are easily confused for reality. In this case, there's no "intelligent species" at work other than humans. We made this, and it is not an intelligence, it is a series of mathematical optimization functions. We have been doing this for decades, and these systems, while sophisticated, are mathematical toys that we have applied. We built and trained this thing to do exactly this. As a student of AI you know that convolutional neural networks are black boxes and are hard to interpret. A different choice of machine would have yielded more insight about how it is operating (for example, decision trees are easier to interpret). The inscrutability of the system is not a product of its complexity; even a simple neural network is hard to understand. This, actually, is my primary objection to using CNNs as the basic unit of machine learning - they don't help US learn, they require us to put our faith in machines that are trained to operate in ways that are resistant to inspection. In the future I hope that this research will move more towards models that provide interpretable results, so they ARE actually a tool for improved understanding.
- statsaresimple 11y ago
  As a follow up to your idea, we should explore two paths: first create the most powerful AI, second create subsystems devised to be interpretable. The powerful method could be used to train the interpretable method, that is we need an interpreter to translate from machine AI to human AI, and interpretable systems provide a middle ground.
  - astazangasta 11y ago
    I think training one function to approximate another function wouldn't help much; we'd inevitably lose the subtleties of the higher-order function and any insights that come with it. If we could train a decision tree to do what a CNN does and then interpret the outcome, why not use decision trees in the first place? I think the answer must be in figuring out how to decompose the black box of a CNN - it is, after all, just a set of simple algebraic operations at work, and we should be able to get something out of inspection. I have to imagine Hinton et al. have done work in this regard, but this is far afield for me, so if it exists I don't know it.
hitekker 11y ago
I want to thank you for this comment. It's this kind of subtle, low-key, informed speculation that generates good, hard sci-fi concepts, which are absolutely relevant to my WIP novel. "oh what if the machine suddenly came alive!?" has been done 1000 times. But such concepts like: a computer can detect and act patterns which we cannot, in ways that are almost, if not possibly intelligence, are magnitudes more believable, and therefore, compelling. Thanks! :-)
- Jobbers 11y ago
  Is it about a tyrannical super-AI that maintains power over the human race by strategically releasing butterflies into the wild at specific times and locations?
  - Anderkent 11y ago
    Why would it bother if it can just convince people to do the thing it wants done by talking to them?
  - deleted 11y ago
    [deleted]
  - Falcon9 11y ago
    It is now.
  - hitekker 11y ago
    Actually, it's a Soviet knock-off of a PDP-10. Constructed in 1970's India, the machine has a 12mhz clock rate, a 4M of RAM, and a directive to bring about "World Peace". Of course, those fools underestimated it. They should have known better...
Mizza 11y ago
Can you give an example of an "unusual" move? I'm a (very) novice Go player, and I think it'd be really interesting to see some specific commentary on how the machine is playing the game.
z0r 11y ago
I think AlphaGo is playing very natural go! The 5th move shoulder hit that is the subject of so much commentary would fit into the theory of go that players like Takemiya espouse. It has chosen to emphasize influence and speed and has not been afraid to give solid territory early in the games so far. It's very exciting play but not inhuman play, and if professionals are allowed to train with AlphaGo it will surely usher in the next decade's style of play. Don't forget that the game has changed every 10 years for the past 100 years, it should not be surprising that it is continuing to change now!
- zeven7 11y ago
  It didn't look like a Takemiya-style move to me. Takemiya tends to play for a huge moyo in the center. AlphaGo had no such moyo. It wasn't only a strange move; it was also a strange time to play it, and it definitely went against conventional wisdom.
  - z0r 11y ago
    The result of the shoulder hit coordinated with black's bottom formation, and the extension on the 4th line that threatened to cut white's stones off was flexible and could have easily formed an impressive moyo on the bottom. It did not play out that way, but I think that black's strategy was as cosmic as anything Takemiya might have played. His games did not always end with a giant moyo, he was also very flexible. I hope to see written reactions from professional players, and maybe Takemiya will give AlphaGo's style his endorsement :) Some examples of 5th line early shoulder hits in recent professional play - these situations are not the same as the one seen in today's game, but something like a 5th line shoulder hit is always going to be highly contextual and creative. http://ps.waltheri.net/database/game/26929/ http://ps.waltheri.net/database/game/26929/ (move 23) http://ps.waltheri.net/database/game/69545/ http://ps.waltheri.net/database/game/69545/ (move 22) http://ps.waltheri.net/database/game/71408/ http://ps.waltheri.net/database/game/71408/ (move 22) http://ps.waltheri.net/database/game/4663/ http://ps.waltheri.net/database/game/4663/ (move 9)
    - zeven7 11y ago
jonahx 11y ago
> 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. The excellent point you're making applies in general to nearly every type of human thinking. The way we think about other people, our intuitions about probabilities, our predictions about politics, and so on -- all are based on our peculiarly effective, yet woefully approximate, analogy based reasoning. It shouldn't be surprising in the least when commonly accepted "expert" heuristics are proved wrong by AIs that actually search the space of possibilities with orders of magnitude more depth than we can. What's surprising -- and I think still a mystery -- is how human heuristics are able to perform so well to begin with. I'm not a Go player, but I saw this same phenomenon as poker bots have surpassed humans in ability. As with AlphaGo, they make plays that fly in the face of years of "expert" wisdom. Of course, as with any revolutionary thinking, some of the new strategies are "obvious" in hindsight, and experts now use them. Others seem to require the computational precision of a computer to be effective in practice, and so can't be co-opted. That is, we can't extract a new human-compatible "heuristic" from them -- the complexity is just irreducible.
- netheril96 11y ago
  > all are based on our peculiarly effective They are peculiarly effective only because of lack of comparison. Humans have been the most intelligent species on this planet for millennia, where no other species come even close. We don't know how ineffective those strategies are seen by a more advanced species. Well, until now.
  - jonahx 11y ago
    This is a good point. I was coming from the point of view that we've had powerful computers for a while, and yet humans were still dominating them, at least until recently, in games like Go, poker, and many visual and language tasks. Of course, the counterpoint could be that it's only the case because humans, with their laughable reasoning abilities, are the ones programming those computers.
jdietrich 11y ago
The same thing happened in chess. Computers play in a very "computerish" way that was initially mocked, but became hugely influential on how humans play chess. Computer analysis opened up new approaches to the game. http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/ http://www.nybooks.com/articles/2010/02/11/the-chess-master-...
atom-morgan 11y ago
As a competitive speedcuber (Rubik's Cubes) this makes sense. If I watch a fellow cuber solve a cube, I understand their process even if it's a different method than the one I'd use. But a robot solving it? To my brain it looks like random turns until...oh shit it's finished.
- semi-extrinsic 11y ago
  Have you ever managed to learn the human Thistlethwaite algo? It basically lets you solve the cube like a robot would. I'm pretty rusty at cubing nw, but I always wanted to learn it.
  - atom-morgan 11y ago
    I have not. It's just not something I'm very interested in.
veeragoni 11y ago
so, is it not possible to get the log of its thinking and take a look at why it took certain step later?!
- larakerns 11y ago
  It might look something like attention detailed in Show, Attend, Tell: http://arxiv.org/abs/1502.03044 http://arxiv.org/abs/1502.03044 Which attempts to visualize machine areas of attention that look like: http://www.wildml.com/wp-content/uploads/2015/12/Screen-Shot-2015-12-30-at-1.42.58-PM-1024x762.png http://www.wildml.com/wp-content/uploads/2015/12/Screen-Shot...
mtgx 11y ago
I believe that when Google talked last year about DeepMind playing those 70's Atari games, it also surprised the team with some of the tricks that it learned to be more effective in the game. So this is quite interesting stuff.
phantarch 11y ago
Your metaphor about army units has got me thinking: When are we going to see the next generation of AlphaGo, but applied to a real world army?
return0 11y ago
> It's like another intelligent species opening up a new way of looking at the world .. that we'll be probably unable to comprehend ourselves.
deleted 11y ago
[deleted]
nihilnegativum 11y ago
Abstraction is the domain we need to research before we understand intelligence in general, the ways our abstraction is determined by nature and more importantly the ways that will become possible when we surpass it.

by fhe 11y ago

megalodon 11y ago

I 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 ago
Was that in the official livestream, or is there an interview somewhere, where things like these are discussed?
- megalodon 11y ago
  Here: https://www.youtube.com/watch?v=l-GsfyVCBu0&t=2511 https://www.youtube.com/watch?v=l-GsfyVCBu0&t=2511
jtolmar 11y ago
Building on that, I suspect that if AlphaGo thinks it has a 100% chance of winning with any of several moves, it has no way of distinguishing between them and chooses effectively at random. The longer that goes on - and once it hits 100% chance of winning, it will be that way for the rest of the game - the more chances it has to pick bad moves. As long as the move isn't bad enough to ruin its 100% chance of winning, it can't tell the difference between that and a good move. (This also applies without a 100% chance of winning, as long as its chances of winning hover near the highest percent it's able to distinguish.)
- nbouscal 11y ago
  I doubt the value network ever outputs a literal 100% chance of winning, it would at most be a lot of nines. Even if it did output an actual 100% chance, AlphaGo would still end up picking moves favored by the policy network, so it would probably just revert to playing like it predicts a human pro would.
  - jtolmar 11y ago
    Once it gets to enough nines, its monte carlo trees will run out of sample resolution. If it can resolve to three nines, then a 99.93% win branch has a 70% chance of being reported as 99.9% and a 30% chance of being reported as 100%. When all the branches here get rolled up, they report some average around 99.93% but not necessarily exactly it. This propagates upwards in the tree, adding more meaningless digits. Adding the evaluation network in increases the number of decimals, but doesn't really change the effect. It's similar to how ray tracing renderers start to return weird speckle patterns when the room is dark enough. And the policy network chooses branches to investigate, not which one to choose. It adds sample resolution to places pros might play, but doesn't add to the estimated probability of winning. Edit: Actually, since places pros might play have higher sample resolution, they're less random. So worse moves get worse evaluation, and a higher chance of leading the pack. This might actually bias AlphaGo to play some pretty bad moves - but, again, this is all assuming it's going to win anyway.

ig1 11y ago

AlphaGo is essentially built on the work that IBM did on TD-Gammon (a reinforcement learning backgammon player) in the 90s. Pretty much the same thing happened with TD-Gammon with it playing unconventional moves, in the longer term humans ended up adopting some of TD-Gammon's tactics once they understood how they played out, it wouldn't be surprising to see the same happen with Go.

bdalgaard 11y ago
From my understanding, computers have also had this affect on chess. The play styles of younger champions has evolved to the point where unpredictability is actually part of the strategy. I'm not a chess expert by any means, but this quote by Viswanathan Anand (former World Chess Champion) describes it. “Top competitors who once relied on particular styles of play are now forced to mix up their strategies, for fear that powerful analysis engines will be used to reveal fatal weaknesses in favoured openings....Anything unusual that you can produce has quadruple, quintuple the value, precisely because your opponent is likely to do the predictable stuff, which is on a computer” [1] [1] http://www.businessinsider.com/anand-on-how-computers-have-changed-chess-2013-11 http://www.businessinsider.com/anand-on-how-computers-have-c...
- slm_HN 11y ago
  >powerful analysis engines will be used to reveal fatal weaknesses in favoured openings... Anand isn't really talking about strategy here, he's just talking about choice of opening. Players with narrow opening repertoires, like Fischer, have always been easier to prepare for than players who play a wide variety of openings. As far as actual changes to strategy, the most obvious one is that computers tend to value material more highly than humans. So a computer will take a risky pawn if it looks sound, while a human will see that taking the pawn is very complicated and prefer a simpler move.
- oli5679 11y ago
  Computers and the internet have changed chess in several ways: (1) Online game databases have made it easier for players to track developments in opening theory and prepare to play specific opponents (2) Chess engines add to this be used to search for antidotes to complicated opening systems (3) Young players have greater access to high-quality sparring partners - either engines or fellow humans on online servers. This has lead to the best players becoming younger, and players playing more varied and less 'sharp' openings.

astrofinch 11y ago

There's also the fact that some of the unexpected moves were apparently more about solidifying against a loss than increasing the magnitude of a win. Which has its own kind of eerie implication: since AIs (like all computer programs) do what you say, not what you mean, the "intelligent species" can sometimes work really intelligently towards a goal that wasn't quite what you had in mind. (Gets especially interesting for any AlphaHuman/AlphaCEO/AlphaPresident successors that are given goals more complicated & nuanced than "maximize Go win probability regardless of ending score". BTW, if you haven't already read the Wait But Why series on the future of AI, I recommend it: http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html http://waitbutwhy.com/2015/01/artificial-intelligence-revolu...)

colanderman 11y ago
About a year ago I wrote an AI to play the board game "Hive" (shares some similarities with chess). Because I scored all wins equally, it behaved almost exactly like this. It would simply try to minimize my advantage while always keeping open the possibility for it to win, almost like a cat toying with prey. It never actually would make the winning move – however obvious – until it had no other options! I fixed this behavior by scoring earlier wins higher than later wins. Now it will actually finish games (and win), but almost invariably its edge is very small, no matter how well or poorly I play. Because of the new win scoring, it willingly sacrifices its own advantage if it means securing a win even one turn earlier. (And since scoring is symmetrical, this has the added advantage of working to delay any win it sees for me, thus increasing the possibility of me making a mistake!) I suppose I could try modifying the scoring rules again, to weight them by positional advantage. A "show off" mode if you like :) And again, with the flip side of working to create the least humiliating losses for itself.
- reacweb 11y ago
  In go, the purpose is to have more territory than the opponent. There is no point in humiliating the opponent by having a big advantage. I think the aim of the strange moves was to increase the confidence of the program in its advance, not to increase the advance.

ap22213 11y ago

Sometimes optimal solutions don't make sense to the human mind because they're not intuitive. For instance, I developed a system that used machine learning and linear solver models to spit out a series of actions to take in response to some events. The actions were to be acted on by humans who were experts in the field. In fact, they were the ones from whom we inferred the relevant initial heuristics. Everyday, I would get a support call from one of the users. They'd be like, 'this output is completely wrong. You have a bug in your code.' I'd then have to spend several hours walking through each of the actions with them and recording the results. In every case, the machine would produce recommended actions that were optimal. However, they were rarely intuitive. In the end, it took months of this back and forth until the experts began to trust the machine outputs. This is the frightening thing about AI - not only can an AI outperform experts, but it often makes decisions that are incomprehensible.

caskance 11y ago
The only frightening part of your story is the insecurity of the human experts.
- ghaff 11y ago
  Or, maybe, there could have been bugs in the code. If I'm an expert in some domain and a computer is telling me to do something completely different ("Trust me--just drive over the river!") I'm certainly going to question the result.
- grayclhn 11y ago
  Not really. The alternative is like driving your car into a lake because the GPS told you to.
sigmar 11y ago
What you said about the expert calling something a bug reminded me of how the commentator in the first game would see a move by alphaGo and say that it was wrong. He did this multiple times for alphaGo but never once questioned the human's move. Yet even with all those "wrong" moves alphaGo won. Didn't watch the second game, so not sure if he kept doing that.

1024core 11y ago

The analogy I can come up with, based on your post, is of something like addition. We don't know how we add numbers in our heads; but we somehow do it. Some people can do it very, very quickly[1], but won't be able to explain how they did it. On the other hand: a computer doesn't look at digits and numbers; it just looks at bits and shifts them around as appropriate. [1] https://en.wikipedia.org/wiki/Shakuntala_Devi https://en.wikipedia.org/wiki/Shakuntala_Devi

teekert 11y ago

I like your statement: "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." I think this will the theme of our future interactions with AIs. We simply can't imagine in advance how they will see and interact with the world. There will be many surprises.

keitmo 11y ago
That quote reminds me of "The Two Faces of Tomorrow" by James P. Hogan. One of the subplots is that humans can communicate because of shared experience. We all must eat, sleep, breathe, seek shelter, etc. Communication with an alien or artificial intelligence may be difficult or even impossible without this shared framework.

poppingtonic 11y ago

There's an interesting angle to this phrase "intelligent species opening up a new way of looking at the world", which is that we (humans) designed go as a game - a subset of the real world we interact with. Go is "reality" to alphago. The superset of all possible sense data it could have, in principle. Whatever "chunks" AlphaGo uses, if it does use them, all of its policies are built only from subsets of the sense data that is the interactions (self-plays) and inferences from past games. There's nothing outside the game to bring into its decision process. With humans, however, our policies are noisy and are rife with what, for lack of a better term, I would call leaky abstractions.

greg_data 11y ago
that's an absolutely fascinating way to think about it.
mattmcknight 11y ago
I think it's more metaphor than leaky abstraction in this case, except to the extent that metaphor is mapping an abstraction of a domain we are trying to understand to an abstraction of one we are better able to understand.

forgotpwtomain 11y ago

> 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. I have been watching Myungwan Kim's commentary for the games - and it seems notable that a few moves he finds very peculiar immediately when they are made, he will later point out to as achieving very good results some 20 moves later. So it also seems quite possible that AlphaGo is actually reading this far ahead, to find those peculiar moves achieve better results than from the more standard approaches. Whether these constitute a 'new way' or not I think depends highly on whether these kind of moves can fit into some general heuristics useful for considering positions, or whether the ability to make them is limited to intelligence's with extremely high computational power for reading ahead.

statsaresimple 11y ago
It seems that you are trying to create a new word that describe this new way of looking at the world. If human are able to decode the information contained in those unexpected moves, perhaps by creating a new heuristic, that could be viewed as a way of understanding the features the machine use internally, that is reading the machine brain. If human are able to decode that information creating new heuristics we could say that we are in a new state in IA in which learning among different intelligent species should be studied.
bpicolo 11y ago
I would imagine it's absolutely thinking that far ahead. That said, it can't possibly search every possible solution, just needs to find an adequate one
lpage 11y ago
> he will later point out to as achieving very good results some 20 moves later This. It's a fairly common feature of any AI that uses some form of tree search/minimax, and the effect is very pronounced in chess. Even the best human players can only think 6-8 plies into the feature versus ~18 for a computer. What we can (could?) do is apply smarter evaluation functions to the board states resulting from candidate plays and stop considering moves that look problematic earlier in the search (game tree pruning). AI tends to use very simple evaluation functions that can be computed quickly. They do so given that 1) it allows for deeper search, and a weak heuristic evaluated far in the future often beats a strong one evaluated a few plies prior and 2) for some games (like Go) it's really hard to codify the "intuitions" that human players speak of. Because search based AI considers board states __very__ far in the future, the results are often completely counterintuitive in a game with an established theory of play. Those theories are born of humans, for humans. The introduction of MCTS some years back was the first leap towards a human level Go AI (incidentally, MCTS is more human-like than exhaustive tree search in that it prunes aggressively by making early judgement calls as to what merits further consideration). AlphaGo's use of deep policy and evaluation networks to score the board is very cool, and the next step in that journey. What's interesting to me is that, unlike chess AI, AlphaGo might actually advance the human theory of Go. It's possible that these "strange moves" will lead to some very interesting insights if DeepMind traces them through the eval and policy networks and manages to back out a more general theory of play.

Ensorceled 11y ago

> 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. Could AlphaGO be winning in a way similar to left handed fencers having an advantage over right handers by wrong footing them rather than simply being better? Would giving Lee more chance to see this style give him a chance to catch up?

sdenton4 11y ago
Seems unlikely. Training was partly from human games, and partly from self play; if there's some new, off book heuristics at play, there's no way to know that humans would respond poorly to them. Though I suppose it's possible it would notice that humans do poorly on simply off book moves generally.
- Ensorceled 11y ago
  Why does this seem unlikely? Humans do poorly with "off book" moves in general in sports and other games; it's why new styles of play or management work really well until others get used to them. Why would it be unlikely in Go?
fma 11y ago
I'm not a Go player but play other competitive sports. Humans have a herd mentality...as Op mentioned there's certain styles of playing...which has their own strengths and weaknesses. Sometimes people will not examine other styles that may have better strengths and just focus on the exist one. Then comes along someone who 'thinks outside the box' with a new style and revolutionize the playing field. Think Bruce Lee and the creation of Jeet Kune Do. Before him everyone concentrated on improving one style by following it classically, rather than just thinking of 'how do I defeat someone'. IMHO Lee is the best at the current style of Go. AlphaGO is the best at playing Go. Maybe humans can devise a better style and defeat AlphaGo, but I'm sure AlphaGo can adapt easily if another style exists.
- 11y ago

makmanalp 11y ago

I wonder if this is similar to how musket battles were fought in the american civil war era, with soldiers lining up across each other in a battlefield and taking turns shooting at each other. I hear they did this because the rifles were very inaccurate so it made sense to use a bunch of them at the same time as an area-effect weapon, in effect like a gigantic shotgun. Until someone got better weapons and suddenly the "rules" of the battlefield that dictated standing in lines across each other made no sense to follow anymore because the original principles that dictated those rules to be good were not valid anymore.

anantzoid 11y ago

> It's like another intelligent species opening up a new way of looking at the world. And this is just the beginning with AlphaGo. As we keep on training Deep Learning systems for other domains, we'll realise how differently they approach problems and solve them. It'll, in turn, help us in adapting these different perspectives and applying them to solve other problems as well.

statsaresimple 11y ago

A great breakthrough could be to decode the information contained in the feature space of the nn or the rnn. A topological language in which shapes and chains are explained by analogies with real world situations and actions. Being able to share our vision and communicate our intentions (the weight given to the distinct features and the links among the several layers of the nn - the overall plan) should transform the concept of AI into one of CAI communication between intelligent agents to create a synergistic approach).

astazangasta 11y ago

>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. It's not like this at all; let's not do this sort of thing. Humans are inveterate myth makers (viz. your description of how people conceive the Go board as army units), and our impositions on the world are easily confused for reality. In this case, there's no "intelligent species" at work other than humans. We made this, and it is not an intelligence, it is a series of mathematical optimization functions. We have been doing this for decades, and these systems, while sophisticated, are mathematical toys that we have applied. We built and trained this thing to do exactly this. As a student of AI you know that convolutional neural networks are black boxes and are hard to interpret. A different choice of machine would have yielded more insight about how it is operating (for example, decision trees are easier to interpret). The inscrutability of the system is not a product of its complexity; even a simple neural network is hard to understand. This, actually, is my primary objection to using CNNs as the basic unit of machine learning - they don't help US learn, they require us to put our faith in machines that are trained to operate in ways that are resistant to inspection. In the future I hope that this research will move more towards models that provide interpretable results, so they ARE actually a tool for improved understanding.

statsaresimple 11y ago
As a follow up to your idea, we should explore two paths: first create the most powerful AI, second create subsystems devised to be interpretable. The powerful method could be used to train the interpretable method, that is we need an interpreter to translate from machine AI to human AI, and interpretable systems provide a middle ground.
- astazangasta 11y ago
  I think training one function to approximate another function wouldn't help much; we'd inevitably lose the subtleties of the higher-order function and any insights that come with it. If we could train a decision tree to do what a CNN does and then interpret the outcome, why not use decision trees in the first place? I think the answer must be in figuring out how to decompose the black box of a CNN - it is, after all, just a set of simple algebraic operations at work, and we should be able to get something out of inspection. I have to imagine Hinton et al. have done work in this regard, but this is far afield for me, so if it exists I don't know it.

hitekker 11y ago

I want to thank you for this comment. It's this kind of subtle, low-key, informed speculation that generates good, hard sci-fi concepts, which are absolutely relevant to my WIP novel. "oh what if the machine suddenly came alive!?" has been done 1000 times. But such concepts like: a computer can detect and act patterns which we cannot, in ways that are almost, if not possibly intelligence, are magnitudes more believable, and therefore, compelling. Thanks! :-)

Jobbers 11y ago
Is it about a tyrannical super-AI that maintains power over the human race by strategically releasing butterflies into the wild at specific times and locations?
- Anderkent 11y ago
  Why would it bother if it can just convince people to do the thing it wants done by talking to them?
- deleted 11y ago
  [deleted]
- Falcon9 11y ago
  It is now.
- hitekker 11y ago
  Actually, it's a Soviet knock-off of a PDP-10. Constructed in 1970's India, the machine has a 12mhz clock rate, a 4M of RAM, and a directive to bring about "World Peace". Of course, those fools underestimated it. They should have known better...

Mizza 11y ago

Can you give an example of an "unusual" move? I'm a (very) novice Go player, and I think it'd be really interesting to see some specific commentary on how the machine is playing the game.

z0r 11y ago

I think AlphaGo is playing very natural go! The 5th move shoulder hit that is the subject of so much commentary would fit into the theory of go that players like Takemiya espouse. It has chosen to emphasize influence and speed and has not been afraid to give solid territory early in the games so far. It's very exciting play but not inhuman play, and if professionals are allowed to train with AlphaGo it will surely usher in the next decade's style of play. Don't forget that the game has changed every 10 years for the past 100 years, it should not be surprising that it is continuing to change now!

zeven7 11y ago
It didn't look like a Takemiya-style move to me. Takemiya tends to play for a huge moyo in the center. AlphaGo had no such moyo. It wasn't only a strange move; it was also a strange time to play it, and it definitely went against conventional wisdom.
- z0r 11y ago
  The result of the shoulder hit coordinated with black's bottom formation, and the extension on the 4th line that threatened to cut white's stones off was flexible and could have easily formed an impressive moyo on the bottom. It did not play out that way, but I think that black's strategy was as cosmic as anything Takemiya might have played. His games did not always end with a giant moyo, he was also very flexible. I hope to see written reactions from professional players, and maybe Takemiya will give AlphaGo's style his endorsement :) Some examples of 5th line early shoulder hits in recent professional play - these situations are not the same as the one seen in today's game, but something like a 5th line shoulder hit is always going to be highly contextual and creative. http://ps.waltheri.net/database/game/26929/ http://ps.waltheri.net/database/game/26929/ (move 23) http://ps.waltheri.net/database/game/69545/ http://ps.waltheri.net/database/game/69545/ (move 22) http://ps.waltheri.net/database/game/71408/ http://ps.waltheri.net/database/game/71408/ (move 22) http://ps.waltheri.net/database/game/4663/ http://ps.waltheri.net/database/game/4663/ (move 9)
  - zeven7 11y ago

jonahx 11y ago

> 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. The excellent point you're making applies in general to nearly every type of human thinking. The way we think about other people, our intuitions about probabilities, our predictions about politics, and so on -- all are based on our peculiarly effective, yet woefully approximate, analogy based reasoning. It shouldn't be surprising in the least when commonly accepted "expert" heuristics are proved wrong by AIs that actually search the space of possibilities with orders of magnitude more depth than we can. What's surprising -- and I think still a mystery -- is how human heuristics are able to perform so well to begin with. I'm not a Go player, but I saw this same phenomenon as poker bots have surpassed humans in ability. As with AlphaGo, they make plays that fly in the face of years of "expert" wisdom. Of course, as with any revolutionary thinking, some of the new strategies are "obvious" in hindsight, and experts now use them. Others seem to require the computational precision of a computer to be effective in practice, and so can't be co-opted. That is, we can't extract a new human-compatible "heuristic" from them -- the complexity is just irreducible.

netheril96 11y ago
> all are based on our peculiarly effective They are peculiarly effective only because of lack of comparison. Humans have been the most intelligent species on this planet for millennia, where no other species come even close. We don't know how ineffective those strategies are seen by a more advanced species. Well, until now.
- jonahx 11y ago
  This is a good point. I was coming from the point of view that we've had powerful computers for a while, and yet humans were still dominating them, at least until recently, in games like Go, poker, and many visual and language tasks. Of course, the counterpoint could be that it's only the case because humans, with their laughable reasoning abilities, are the ones programming those computers.

jdietrich 11y ago

The same thing happened in chess. Computers play in a very "computerish" way that was initially mocked, but became hugely influential on how humans play chess. Computer analysis opened up new approaches to the game. http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/ http://www.nybooks.com/articles/2010/02/11/the-chess-master-...

atom-morgan 11y ago

As a competitive speedcuber (Rubik's Cubes) this makes sense. If I watch a fellow cuber solve a cube, I understand their process even if it's a different method than the one I'd use. But a robot solving it? To my brain it looks like random turns until...oh shit it's finished.

semi-extrinsic 11y ago
Have you ever managed to learn the human Thistlethwaite algo? It basically lets you solve the cube like a robot would. I'm pretty rusty at cubing nw, but I always wanted to learn it.
- atom-morgan 11y ago
  I have not. It's just not something I'm very interested in.

veeragoni 11y ago

so, is it not possible to get the log of its thinking and take a look at why it took certain step later?!

larakerns 11y ago
It might look something like attention detailed in Show, Attend, Tell: http://arxiv.org/abs/1502.03044 http://arxiv.org/abs/1502.03044 Which attempts to visualize machine areas of attention that look like: http://www.wildml.com/wp-content/uploads/2015/12/Screen-Shot-2015-12-30-at-1.42.58-PM-1024x762.png http://www.wildml.com/wp-content/uploads/2015/12/Screen-Shot...

mtgx 11y ago

I believe that when Google talked last year about DeepMind playing those 70's Atari games, it also surprised the team with some of the tricks that it learned to be more effective in the game. So this is quite interesting stuff.

phantarch 11y ago

Your metaphor about army units has got me thinking: When are we going to see the next generation of AlphaGo, but applied to a real world army?

return0 11y ago

> It's like another intelligent species opening up a new way of looking at the world .. that we'll be probably unable to comprehend ourselves.

deleted 11y ago

[deleted]

nihilnegativum 11y ago

Abstraction is the domain we need to research before we understand intelligence in general, the ways our abstraction is determined by nature and more importantly the ways that will become possible when we surpass it.