15 ms·

I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing compute

I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing computer in history. Kasparov drew that match, but it was clear it was the last stand of homo sapiens in the man vs machine chess battle. Back then, people took solace in the game of Go. Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Tonight, that happened. Google's DeepMind AlphaGo defeated the world Go champion Lee Sedol. An amazing testament to humanity's ability to continuously innovate at a continuously surprising pace. It's important to remember, this isn't really man vs machine, as we humans programmed the algorithms and built the computers they run on. It's really all just circuitous man vs man. Excited for the next "impossible" things we'll see in our lifetimes.

esturk 11y ago
I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.
- taneq 11y ago
  It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.
  - mjn 11y ago
    > What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. I don't think that's quite true as a description of what we knew about computer Go previously, though it depends on what precisely you mean. Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated). So we've had systems that can in principle play to any arbitrary strength, if you can throw enough computing power at them. Though you might legitimately argue: by "in principle" do you mean some truly absurd amount, like more computing power than could conceivably fit in the universe? The answer to that is also no; scaling trends have been such that people expected computer Go to beat humans anywhere from, well, around now [1], to 5 to 10 years from now [2]. The two achievements of the team here, at least as I see them, are: 1) they managed to actually throw orders of magnitude more computing power at it than other recent systems have used, in part by making use of GPUs, which the other strong computer-Go systems don't use (the AlphaGo cluster as reported in the Nature paper uses 1202 CPUs and 176 GPUs), and 2) improved the scaling curve by algorithmic improvements over vanilla MCTS (the main subject of their Nature paper). Those are important achievements, but I think not philosophical ones, in the sense of figuring out how to solve something that we previously didn't know how to solve even given arbitrary computing power. While I don't agree with everything in it, I also found this recent blog post / paper on the subject interesting: http://www.milesbrundage.com/blog-posts/alphago-and-ai-progress http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... [1] A 2007 survey article suggested that mastering Go within 10 years was probably feasible; not certain, but something that the author wouldn't bet against. I think that was at least a somewhat widely held view as of 2007. http://spectrum.ieee.org/computing/software/cracking-go http://spectrum.ieee.org/computing/software/cracking-go [2] A 2012 interview though that mastering Go would need a mixture of inevitable scaling improvements plus probably one significant new algorithmic idea, also a reasonably widely held view as of 2012. https://gogameguru.com/computer-go-demystified-interview-martin-mueller/ https://gogameguru.com/computer-go-demystified-interview-mar...
    - GolDDranks 11y ago
      > Play strength scales roughly with the log of computing power That means that the problem is exponentially hard. EXPTIME, actually. You couldn't possibly scale it much.
    - mafribe 11y ago
      Play strength scales roughly with the log of computing power The rumor I have heard is that the new Deep Mind learning algorithm really improves on this and scales linearly with computing power.
      - GolDDranks 11y ago
        The game itself, however, scales exponentially, and there's nothing to do about that, so if you enlargen the board, no computer... And no human may be able to play it well. The achivement was a leap towards the human level of play (and quite possibly over it). There might be additional leaps, which will take AIs WAY beyond humans, but none of those will scale linearily in the end. (And yeah, I guess you didn't want to say that either)
        sdenton4 11y ago
        Branch and bound my friend, branch and bound. If you can build an awesome bounding function, even exponentially large spaces can be manageable.
    - hyperpape 11y ago
      "Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated)." This is exactly the opposite of my sense based on following the computer go mailing list (which featured almost all the top program designers prior to Google/Facebook entering the race). They said that scaling was quite bad past a certain point. The programs had serious blindspots when dealing with capturing races and kos[1] that you couldn't overcome with more power. Also, DNNs were novel for Go--Google wasn't the first one to use them, but no one was talking about them until sometime in 2014-2015. [0] Not the kind of weaknesses that can be mechanically exploited by a weak player, but the kind of weaknesses that prevented them from reaching professional level.
  - zamalek 11y ago
    Indeed. The first time a computer defeated a human in Chess it was this[1] size (1997). In 2009 it became possible to fit a grandmaster into this[2]. > Pocket Fritz 4 won the Copa Mercosur tournament in Buenos Aires, Argentina with 9 wins and 1 draw on August 4–14, 2009. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second. Pocket Fritz 4 achieves a higher performance level than Deep Blue.[3] The first steps are always the most inefficient. Make it work, make it right, make it fast. [1]: https://en.wikipedia.org/wiki/Deep_Blue_%28chess_computer%29#/media/File:Deep_Blue.jpg https://en.wikipedia.org/wiki/Deep_Blue_%28chess_computer%29... [2]: http://cdn.slashgear.com/wp-content/uploads/2008/10/htc_touch_hd.jpg http://cdn.slashgear.com/wp-content/uploads/2008/10/htc_touc... [3]: https://en.wikipedia.org/wiki/Human%E2%80%93computer_chess_matches https://en.wikipedia.org/wiki/Human%E2%80%93computer_chess_m...
    - chakkop 11y ago
      GM Michael Stean lost to Cyber 176 (a mainframe 'supercomputer') in 1977 (at blitz). AFAIK this was the first time a computer defeated a GM; they began defeating IMs and experts some ten years before that. Kasparov himself lost to Fritz 2 at blitz as early as 1992.
      - Ketsuban 11y ago
        "Under tournament conditions" is the condition everyone forgets. Go AIs were competing with ranked players given handicaps of varying degrees of absurdity.
  - exDM69 11y ago
    > Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone. I find this overly optimistic because of the huge amount of power required to run the Go application. Remember, we're getting closer and closer to the theoretical lower limit in the size of silicon chips, which is around 4nm (that's about a dozen silicon atoms). That's a 3-4x improvement over the current state of the art. The computer to run AlphaGo requires thousands of watts of power. A smartphone can do about one watt. A 3-4x increase in perf per watt isn't going to cut it. If there will be a smartphone capable of beating the best human Go players, my guess is that it won't be based on general purpose silicon chips running on lithium ion batteries. On the other hand, a desktop computer with a ~1000 watt power supply (ie. a gaming pc) might be able to do this in a matter of years or a few decades.
    - Cyph0n 11y ago
      Your making the same assumption people made about computing in the 50s, then 70s, then 90s, etc.
      - exDM69 11y ago
        Please do elaborate. I try to base my assumptions (which I accept may turn out to be completely wrong) on physics and experience in working in semiconductors. I just don't see a 1000x+ decrease in the power required happening in a decade or two without some revolutionary technology I can't even imagine. Is this what you meant? I'm sure most people couldn't imagine modern silicon chips in the 1950s vacuum tube era. But now we're getting close to the theoretical, well-understood minimums in silicon chips, so another revolutionary step is required if another giant leap like that is to be achieved.
        peteretep 11y ago
        > physics and experience in working in semiconductors > without some revolutionary technology I can't even > imagine I suspect (in the nicest possible way) that in a lineup of your imagination (on current assumptions) vs the combined ingenuiety of the human race driven by the hidden hand, the latter wins.
  - alienbaby 11y ago
    No, they haven't shown that the same kind of intelligence required to play go can be implemented in computer software. The methods AlphaGo uses are not the same as the intelligence a human uses at all. What they have done is prove an implementation of computer go in software is capable of beating a human player, not that they have implemented the same kind of intelligence as the human player.
  - andrepd 11y ago
    Then you can say that, in 10 years, if we indeed have reached that point. Otherwise it'd just an empty prediction, and his perfectly valid point stands.
  - Someone 11y ago
    "What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware" Not necessarily the same kind, and, if I had to make the call, I would say they aren't of the same kind.
- 21 11y ago
  A top smartphone chess program can beat pretty much all but the best few players in the world. Do you think it's fair to pit a 150 gram device against a 70 kg human?
  - chopin 11y ago
    Although a fairer comparison would be against 1 kg of brain. Comparing against a human would need to include all the infrastructure for the device such as energy production or the manufacturing equipment required. But nevertheless, fitting so much computing power in such a small device is a great achievement.
    - ivanca 11y ago
      Not really, the brain to operate also needs all the other systems. Just as a CPU needs all the other parts to function. And including the manufacturing equipment is just as false, in the sense that you would have to include his mother (as biological manufacturing)
      - lmm 11y ago
        The human can run off resources that are available "in the wild", self-repair, and self-replicate at better than 1:1 (that is, a group of n humans produce >n offspring), whereas the smartphone needs a huge amount of infrastructure to repair it and produce new ones. I don't think any mass comparison is really meaningful, mind, but it's not that simple.
        cbd1984 11y ago
        Advanced chess players require a society which produces enough surplus to afford enough leisure to allow someone to not only produce a brain not damaged by starvation, but to allow them to use that brain to learn chess at a high level. It took a very long time for humans to get to that level, even though chess is a fairly old game. My point is, humans "in the wild" likely didn't have any equivalent to chess, because they didn't have sufficient leisure time. Chess is a product of an environment that's just as "artificial" as the one which produced cell phones.
- knaik94 11y ago
  The real achievement is in the algorithm. To make an analogy, the accomplishment of putting a man on the moon required that we understand enough to make a rocket. We could have put hundreds of car engines together but that wouldn't ever have gotten us to the moon.
  - deleted 11y ago
    [deleted]
  - GolDDranks 11y ago
    This. AlphaGo utilizes the "Monte Carlo tree search" as its base algorithm[1]. The algorithm has been used for ten years in Go AIs, and when it was introduced, it made a huge impact. The Go bots got stronger overnight, basically. What novel thing AlphaGo did, was a similar jump in algorithmic goodness. It introduced two neural networks for 1) predicting good moves at the present situation 2) evaluating the "value" of given board situation Especially 2) has been hard to do in Go, without playing the game 'till the end. This has a huge impact on the efficiency of the basic tree search algorithm. 1) narrows down the search width by eliminating obviously bad choises and 2) makes the depth at where the evaluation can be done, shallower. So I think it's not just the processing power. It's a true algorithmic jump made possible by the recent advances in machine learning. [1] http://senseis.xmp.net/?MonteCarlo http://senseis.xmp.net/?MonteCarlo
    - fma 11y ago
      Neural networks have been around for a long time. They basically took two existing concepts of AI and threw some money.
      - GolDDranks 11y ago
        That's true, but the ways to train them and ways to apply them to real world problems have really improved. It's obvious by just reading Hacker News.
    - CamperBob2 11y ago
      Especially 2) has been hard to do in Go, without playing the game 'till the end. This is what struck me as especially interesting, as a non-player watching the commentary. The commentators, a 9-dan pro and the editor of a Go publication, were having real problems figuring out what the score was, or who was ahead. When Lee resigned the game, it came as a total surprise to both of them. Just keeping score in Go appears to be harder than a lot of other games.
- nefitty 11y ago
  Considering the complexity of the human brain, it seems only fair to balance out a competitor's handicap in some way. Your idea seems to anticipate the logical progression of these tests: "Nature made this mind inside this small object, the brain, why don't we do that?" Regardless, the trend of course is toward miniaturization. I see news like this recent story: "Glass Disc Can Store 360 TB" http://petapixel.com/2016/02/16/glass-disc-can-store-360-tb-photos-13-8-billion-years/ http://petapixel.com/2016/02/16/glass-disc-can-store-360-tb-... to back up imagined futures like the film Her: https://youtu.be/WzV6mXIOVl4 https://youtu.be/WzV6mXIOVl4 (and that film doesn't even address whether the OSes are connected through a wireless network). This stuff is happening fast, and we might have found ourselves, historically, in a place of unintelligible amounts of change. And possibly undreamt of amounts of self-progression.
- agildehaus 11y ago
  Has Google talked about the amount of computing resources they're throwing at this match? I'd be very interested to know.
  - lololomg 11y ago
    They did not say exactly but something like a couple hundred GPUs
  - Eliezer 11y ago
    I'd guess more on the order of 10,000 GPUs.
    - lololomg 11y ago
      They only used 175 GPUs in the match 5 months ago.
      - Eliezer 11y ago
        I was actually thinking primarily of distributed training time for the networks and playing time for the system, rather than the number of GPUs running this particular match. Also, I thought the number of GPUs in October was more on the order of 1,000? Happy to be told I'm mistaken though.
      - Fede_V 11y ago
        They used ~1000 CPUs and ~200 GPUs 5 months ago.
  - deleted 11y ago
    [deleted]
  - gearhart 11y ago
    1202 CPUs and 176 GPUs apparently edit: according to the livestream
    - exDM69 11y ago
- lololomg 11y ago
  Today a tiny $300 desktop computer can beat any human at chess. It only took a few years after the Deep Blue vs Kasparov game.
  - Johnny_Brahms 11y ago
    Not only a desktop computer. A two year old smartphone would do just fine.
- SmellyGeekBoy 11y ago
  Seems like a pretty arbitrary limitation. 70 years ago Colossus filled an entire room, now it can be emulated on a Raspberry Pi. The really groundbreaking part is the algorithm.
- Confusion 11y ago
  Chess engines and processing power have since then advanced to a point where my phone can now reliably beat Carlsen. There is no reason to suppose Go is different in that respect. In 10 years, DeepMind will fit into a phone.
  - dingo_bat 11y ago
    >my phone can now reliably beat Carlsen I've seen this written by many people but is there any solid evidence/study that proves this? Edit: seems like Pocket Fritz and Komodo are easily able to beat grandmasters.
  - bluecalm 11y ago
    It's way more about algorithmic improvements than hardware improvements though. Deep Blue evaluated 200 million positions per second. I don't think top programs of today could get to 2 million positions per second on a smartphone (I get about 10 million pos/second on my i7 3770 quad). It's all about improvements in search algorithms as well as position evaluation.
    - Confusion 11y ago
      True. Without hardware improvements the processors that can evaluate 2 million positions per second while 'fitting' into a phone (qua processing power and power usage) would not exist though.
- talles 11y ago
  Are you suggesting to measure computing power by kilograms? That's even stupider than measuring software complexity by LOC.
  - JabavuAdams 11y ago
    It's not obviously stupid, as a bounding argument as we approach physical limits.
    - Vraxx 11y ago
      That's based on the assumption that computing must exist as silicon transistors. When would we have reached the bounds of computation based on physical limits if we had stuck with vacuum tubes. The point is computation is an abstract concept and not tied to the physical medium that we use.
      - pixl97 11y ago
        >The point is computation is an abstract concept and not tied to the physical medium that we use. That's not exactly true https://en.wikipedia.org/wiki/Limits_to_computation https://en.wikipedia.org/wiki/Limits_to_computation
        talles 11y ago
        Of course there are physical constraints on computing, but measuring by weight is rather stupid. Measuring the energy consumption seems to be a way better metric (even though "computation per energy" is clearly a human win). Not to mention that we suddenly forgot that computers have their own units of measurement, such as clock speed (hertz) and memory size (bytes).
        pixl97 11y ago
        > (even though "computation per energy" is clearly a human win) Is it? The problem here is it is really hard to compare the TCO. For example prime human computation requires years and years of learning and teaching, in which the human cannot be turned off (this kills the human). A computer can save its state and go in a low or even a zero power mode. >such as clock speed (hertz) and memory size (bytes). Which are completely meaningless, especially in distributed hybrid systems. Clock speed is like saying you can run at 10 miles per hour, but it doesn't define how much you can carry. GPUs run a far slower clock speed than CPUs, but they are massively parallel and are much faster than CPUs on distributed workloads. Having lots of memory is important, but not all memory is equal and hierarchy is even more important. Computer memory is (hopefully) bit perfect and a massive amount of power is spent keeping it that way. That is nice when it comes to remembering exactly how much money you have in the bank. Human memory is wonderful and terrible at the same time. There is no 'truth' in human memory, only repetition. A computer can take a picture and then make a hash of the image, both of which can be documented and verified. A human can recall a memory, but the act of recalling that memory changes it, and the parts we don't remember so well are influenced by our current state. It is this 'inaccuracy' that helps us use so little power for the amount of thinking we do.
- nkrisc 11y ago
  It's the software that's impressive. Why does how many physical computers it takes to run the software matter? It's physical footprint will almost certainly shrink as computers get more powerful.
- ramblerman 11y ago
  What a strange sentiment. You would only delay the inevitable outcome. Sure, it wouldn't win now, but processing power will become stronger and machines get smaller. What was the point?
- eitally 11y ago
  Besides disagreeing with you, this actually isn't true at all. In competition, AlphaGo doesn't rely on particularly expansive hardware. For training, yes, but not for playing.
nsns 11y ago
...it was the last stand of homo sapiens in the man vs machine... But who made that machine? I'd say a more precise evaluation would be that the ability to program a machine to assist in playing chess outdid the ability to play chess without such assistance.
- eru 11y ago
  Your point is entirely valid, but was already made by the very comment you are replying too..
  - nsns 11y ago
    Indeed... Apologies to parent.
hosh 11y ago
The 2003 match was a brute force approach. AlphaGo's architecture resembles much closer to how humans think and learn. I initially learned Go to be able to have some chance of an AI. I then had some transformative experiences that coincided with my early kyu learning of basic Go lessons. On of the big lessons in Go is to learn how to let go of something. Taking solace in anything on the Go board is one of the blocks you work through when you develop as a Go player. I had already known about two years ago that just the Monte Carlo approach was already scalable. If Moore's Law continues, it was a matter of time before the Monte Carlo approach would start challenging the professional ranks -- it had already gotten to the point where you just needed to throw more hardware at it. AlphaGo's architecture adds a different layer to it. The Deep Learning isn't quite as flexible as the human mind, but it can do something that humans can't: learn non-stop, 24/7 on one subject. We're seeing a different tipping point here, possibly the same kind of tipping point when we witnessed the web browser back in the early 90s, and the introduction of the smartphone in the mid '00s. This is way bigger (to use a Go terminology) than what happened with chess.
- hosh 11y ago
  And just to emphasize the big point here: The AlphaGo that beat the 2p European champion five months ago was not as strong as the AlphaGo that beat Lee Sedol (9p). I don't think this was just the AlphaGo team throwing more hardware. I think they had been constantly running the self-training during the intervening months so that AlphaGo was improving itself. If that is so, then the big thing here isn't that AlphaGo is the first AI to win an official match with the currently world's strongest Go player. It's that within less than half a year, AlphaGo was able to learn and grow to go from challenging a 2p to challenging the world's strongest player. Think about that.
  - d0m 11y ago
    I think it's fair to say that in the future, people will look back and wonder how it was possible to live without having a good AI.. similar to how we look past at caveman and wonder how they could live without electricity. AI is really just a tool that we leverage, the same was as we leveraged the wheel or electricity.
areyousure 11y ago
> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Can anyone provide some written references to this effect? Last time I searched (extensively), I couldn't really find anyone saying this.
- Florin_Andrei 11y ago
  FWIW, before AlphaGo defeated Fan Hui 2-dan last year, everyone was saying that would not be possible before 2025 or so. That was the consensus.
  - soup10 11y ago
    People that try to predict the future in ai are breathing hot air more often then not.
    - darkmighty 11y ago
      Serious predictions actually inferred from the progress of existing (MCTS and others before) bots, which was something like 1 stone every two years (I don't recall the details, but it's easy to find out there). Top professionals were estimated to be something like 10 stones stronger than the best bot at 2008, so 2025 wouldn't sound too conservative. "At the US Congress 2008, he [Myungwan Kim] also played a historic demonstration game against MoGo running on an 800 processor supercomputer. With a 9 stone handicap, MoGo won by 1.5 points. At the 2009 congress, he played another demonstration game against Many Faces of Go running on 32 processors. Giving 7 stones handicap, Kim won convincingly by resignation." (Kim Myung Wan (born 1978) is a 9d Korean professional who has taken up residence in the Los Angeles area as of 2008) More information here, with a nice graph: http://senseis.xmp.net/?ComputerGo http://senseis.xmp.net/?ComputerGo http://i.imgur.com/RvQsf6v.png http://i.imgur.com/RvQsf6v.png You can see progress seemed to be slow at 2012.
      - ghaff 11y ago
        Go seemed to progress in a lot more fits and starts than did chess (which, admittedly, probably had a lot more effort put into it). Prior to 2005 or so, Go programs were relatively weak and there were people working on them who were saying that they didn't really see a path forward. Then people hit on using Monte Carlo which was the big step forward you show in your graphs. But then, that progress seemed to stall to the degree that various people were quoted in a Wired article a couple years ago about how they weren't sure what was going to happen. Yet, here we are today.
d0mine 11y ago
Francis Collins? said something like: people underestimate the change in the short term and overestimate it in the long term (there is my flying DeLorean and the hotel on the Moon).
- adenadel 11y ago
  I think you got it backwards. Bill Gates said "We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don't let yourself be lulled into inaction."
  - d0mine 11y ago
    I meant what I said (and I didn't mean the Gates' quote). There are no flying DeLoreans (Back to the Future). There are no hotels on the Moon (Mad Man). People overestimate long-term (20+ years) change. It just shows once more that for any maxim there is a maxim with the opposite meaning.
maaku 11y ago
> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Sadly as I write this my uncle and personal hero who spent 17 years of his life working towards a Ph.D. on abstraction hierarchies for use in Go artificial intelligence, has been moved into hospice care. I'm just glad that in the few days that are left he has a chance to see this happen, even if it is not the good old-fashioned approach he took. [1] He recently started rewriting the continuation of this research in golang, available on Github: https://github.com/Ken1JF/ah https://github.com/Ken1JF/ah
- new299 11y ago
  Thanks for the link. I started reading his thesis. While AlphaGo is obviously exciting, it seems like your uncles approach could help us better understand how humans play go which seems hugely valuable. I look forward to exploring his thesis further.
- jakub_h 11y ago
  > This project is the sixth attempt to implement the model: Finally, the sixth attempt is written in the right language! Now it will succeed for sure.
olau 11y ago
Man building tool vs. man. :)
bjourne 11y ago
> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. To add to that, in Godel Escher Bach, Hofstadter in 1979 predicted that no chess engine would ever beat a human grandmaster player. It just goes to show how hard it is to predict what is, and also will remain, impossible for machines!

esturk 11y ago

I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.

taneq 11y ago
It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.
- mjn 11y ago
  > What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. I don't think that's quite true as a description of what we knew about computer Go previously, though it depends on what precisely you mean. Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated). So we've had systems that can in principle play to any arbitrary strength, if you can throw enough computing power at them. Though you might legitimately argue: by "in principle" do you mean some truly absurd amount, like more computing power than could conceivably fit in the universe? The answer to that is also no; scaling trends have been such that people expected computer Go to beat humans anywhere from, well, around now [1], to 5 to 10 years from now [2]. The two achievements of the team here, at least as I see them, are: 1) they managed to actually throw orders of magnitude more computing power at it than other recent systems have used, in part by making use of GPUs, which the other strong computer-Go systems don't use (the AlphaGo cluster as reported in the Nature paper uses 1202 CPUs and 176 GPUs), and 2) improved the scaling curve by algorithmic improvements over vanilla MCTS (the main subject of their Nature paper). Those are important achievements, but I think not philosophical ones, in the sense of figuring out how to solve something that we previously didn't know how to solve even given arbitrary computing power. While I don't agree with everything in it, I also found this recent blog post / paper on the subject interesting: http://www.milesbrundage.com/blog-posts/alphago-and-ai-progress http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... [1] A 2007 survey article suggested that mastering Go within 10 years was probably feasible; not certain, but something that the author wouldn't bet against. I think that was at least a somewhat widely held view as of 2007. http://spectrum.ieee.org/computing/software/cracking-go http://spectrum.ieee.org/computing/software/cracking-go [2] A 2012 interview though that mastering Go would need a mixture of inevitable scaling improvements plus probably one significant new algorithmic idea, also a reasonably widely held view as of 2012. https://gogameguru.com/computer-go-demystified-interview-martin-mueller/ https://gogameguru.com/computer-go-demystified-interview-mar...
  - GolDDranks 11y ago
    > Play strength scales roughly with the log of computing power That means that the problem is exponentially hard. EXPTIME, actually. You couldn't possibly scale it much.
  - mafribe 11y ago
    Play strength scales roughly with the log of computing power The rumor I have heard is that the new Deep Mind learning algorithm really improves on this and scales linearly with computing power.
    - GolDDranks 11y ago
      The game itself, however, scales exponentially, and there's nothing to do about that, so if you enlargen the board, no computer... And no human may be able to play it well. The achivement was a leap towards the human level of play (and quite possibly over it). There might be additional leaps, which will take AIs WAY beyond humans, but none of those will scale linearily in the end. (And yeah, I guess you didn't want to say that either)
      - sdenton4 11y ago
        Branch and bound my friend, branch and bound. If you can build an awesome bounding function, even exponentially large spaces can be manageable.
  - hyperpape 11y ago
    "Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated)." This is exactly the opposite of my sense based on following the computer go mailing list (which featured almost all the top program designers prior to Google/Facebook entering the race). They said that scaling was quite bad past a certain point. The programs had serious blindspots when dealing with capturing races and kos[1] that you couldn't overcome with more power. Also, DNNs were novel for Go--Google wasn't the first one to use them, but no one was talking about them until sometime in 2014-2015. [0] Not the kind of weaknesses that can be mechanically exploited by a weak player, but the kind of weaknesses that prevented them from reaching professional level.
- zamalek 11y ago
  Indeed. The first time a computer defeated a human in Chess it was this[1] size (1997). In 2009 it became possible to fit a grandmaster into this[2]. > Pocket Fritz 4 won the Copa Mercosur tournament in Buenos Aires, Argentina with 9 wins and 1 draw on August 4–14, 2009. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second. Pocket Fritz 4 achieves a higher performance level than Deep Blue.[3] The first steps are always the most inefficient. Make it work, make it right, make it fast. [1]: https://en.wikipedia.org/wiki/Deep_Blue_%28chess_computer%29#/media/File:Deep_Blue.jpg https://en.wikipedia.org/wiki/Deep_Blue_%28chess_computer%29... [2]: http://cdn.slashgear.com/wp-content/uploads/2008/10/htc_touch_hd.jpg http://cdn.slashgear.com/wp-content/uploads/2008/10/htc_touc... [3]: https://en.wikipedia.org/wiki/Human%E2%80%93computer_chess_matches https://en.wikipedia.org/wiki/Human%E2%80%93computer_chess_m...
  - chakkop 11y ago
    GM Michael Stean lost to Cyber 176 (a mainframe 'supercomputer') in 1977 (at blitz). AFAIK this was the first time a computer defeated a GM; they began defeating IMs and experts some ten years before that. Kasparov himself lost to Fritz 2 at blitz as early as 1992.
    - Ketsuban 11y ago
      "Under tournament conditions" is the condition everyone forgets. Go AIs were competing with ranked players given handicaps of varying degrees of absurdity.
- exDM69 11y ago
  > Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone. I find this overly optimistic because of the huge amount of power required to run the Go application. Remember, we're getting closer and closer to the theoretical lower limit in the size of silicon chips, which is around 4nm (that's about a dozen silicon atoms). That's a 3-4x improvement over the current state of the art. The computer to run AlphaGo requires thousands of watts of power. A smartphone can do about one watt. A 3-4x increase in perf per watt isn't going to cut it. If there will be a smartphone capable of beating the best human Go players, my guess is that it won't be based on general purpose silicon chips running on lithium ion batteries. On the other hand, a desktop computer with a ~1000 watt power supply (ie. a gaming pc) might be able to do this in a matter of years or a few decades.
  - Cyph0n 11y ago
    Your making the same assumption people made about computing in the 50s, then 70s, then 90s, etc.
    - exDM69 11y ago
      Please do elaborate. I try to base my assumptions (which I accept may turn out to be completely wrong) on physics and experience in working in semiconductors. I just don't see a 1000x+ decrease in the power required happening in a decade or two without some revolutionary technology I can't even imagine. Is this what you meant? I'm sure most people couldn't imagine modern silicon chips in the 1950s vacuum tube era. But now we're getting close to the theoretical, well-understood minimums in silicon chips, so another revolutionary step is required if another giant leap like that is to be achieved.
      - peteretep 11y ago
        > physics and experience in working in semiconductors > without some revolutionary technology I can't even > imagine I suspect (in the nicest possible way) that in a lineup of your imagination (on current assumptions) vs the combined ingenuiety of the human race driven by the hidden hand, the latter wins.
- alienbaby 11y ago
  No, they haven't shown that the same kind of intelligence required to play go can be implemented in computer software. The methods AlphaGo uses are not the same as the intelligence a human uses at all. What they have done is prove an implementation of computer go in software is capable of beating a human player, not that they have implemented the same kind of intelligence as the human player.
- andrepd 11y ago
  Then you can say that, in 10 years, if we indeed have reached that point. Otherwise it'd just an empty prediction, and his perfectly valid point stands.
- Someone 11y ago
  "What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware" Not necessarily the same kind, and, if I had to make the call, I would say they aren't of the same kind.
21 11y ago
A top smartphone chess program can beat pretty much all but the best few players in the world. Do you think it's fair to pit a 150 gram device against a 70 kg human?
- chopin 11y ago
  Although a fairer comparison would be against 1 kg of brain. Comparing against a human would need to include all the infrastructure for the device such as energy production or the manufacturing equipment required. But nevertheless, fitting so much computing power in such a small device is a great achievement.
  - ivanca 11y ago
    Not really, the brain to operate also needs all the other systems. Just as a CPU needs all the other parts to function. And including the manufacturing equipment is just as false, in the sense that you would have to include his mother (as biological manufacturing)
    - lmm 11y ago
      The human can run off resources that are available "in the wild", self-repair, and self-replicate at better than 1:1 (that is, a group of n humans produce >n offspring), whereas the smartphone needs a huge amount of infrastructure to repair it and produce new ones. I don't think any mass comparison is really meaningful, mind, but it's not that simple.
      - cbd1984 11y ago
        Advanced chess players require a society which produces enough surplus to afford enough leisure to allow someone to not only produce a brain not damaged by starvation, but to allow them to use that brain to learn chess at a high level. It took a very long time for humans to get to that level, even though chess is a fairly old game. My point is, humans "in the wild" likely didn't have any equivalent to chess, because they didn't have sufficient leisure time. Chess is a product of an environment that's just as "artificial" as the one which produced cell phones.
knaik94 11y ago
The real achievement is in the algorithm. To make an analogy, the accomplishment of putting a man on the moon required that we understand enough to make a rocket. We could have put hundreds of car engines together but that wouldn't ever have gotten us to the moon.
- deleted 11y ago
  [deleted]
- GolDDranks 11y ago
  This. AlphaGo utilizes the "Monte Carlo tree search" as its base algorithm[1]. The algorithm has been used for ten years in Go AIs, and when it was introduced, it made a huge impact. The Go bots got stronger overnight, basically. What novel thing AlphaGo did, was a similar jump in algorithmic goodness. It introduced two neural networks for 1) predicting good moves at the present situation 2) evaluating the "value" of given board situation Especially 2) has been hard to do in Go, without playing the game 'till the end. This has a huge impact on the efficiency of the basic tree search algorithm. 1) narrows down the search width by eliminating obviously bad choises and 2) makes the depth at where the evaluation can be done, shallower. So I think it's not just the processing power. It's a true algorithmic jump made possible by the recent advances in machine learning. [1] http://senseis.xmp.net/?MonteCarlo http://senseis.xmp.net/?MonteCarlo
  - fma 11y ago
    Neural networks have been around for a long time. They basically took two existing concepts of AI and threw some money.
    - GolDDranks 11y ago
      That's true, but the ways to train them and ways to apply them to real world problems have really improved. It's obvious by just reading Hacker News.
  - CamperBob2 11y ago
    Especially 2) has been hard to do in Go, without playing the game 'till the end. This is what struck me as especially interesting, as a non-player watching the commentary. The commentators, a 9-dan pro and the editor of a Go publication, were having real problems figuring out what the score was, or who was ahead. When Lee resigned the game, it came as a total surprise to both of them. Just keeping score in Go appears to be harder than a lot of other games.
nefitty 11y ago
Considering the complexity of the human brain, it seems only fair to balance out a competitor's handicap in some way. Your idea seems to anticipate the logical progression of these tests: "Nature made this mind inside this small object, the brain, why don't we do that?" Regardless, the trend of course is toward miniaturization. I see news like this recent story: "Glass Disc Can Store 360 TB" http://petapixel.com/2016/02/16/glass-disc-can-store-360-tb-photos-13-8-billion-years/ http://petapixel.com/2016/02/16/glass-disc-can-store-360-tb-... to back up imagined futures like the film Her: https://youtu.be/WzV6mXIOVl4 https://youtu.be/WzV6mXIOVl4 (and that film doesn't even address whether the OSes are connected through a wireless network). This stuff is happening fast, and we might have found ourselves, historically, in a place of unintelligible amounts of change. And possibly undreamt of amounts of self-progression.
agildehaus 11y ago
Has Google talked about the amount of computing resources they're throwing at this match? I'd be very interested to know.
- lololomg 11y ago
  They did not say exactly but something like a couple hundred GPUs
- Eliezer 11y ago
  I'd guess more on the order of 10,000 GPUs.
  - lololomg 11y ago
    They only used 175 GPUs in the match 5 months ago.
    - Eliezer 11y ago
      I was actually thinking primarily of distributed training time for the networks and playing time for the system, rather than the number of GPUs running this particular match. Also, I thought the number of GPUs in October was more on the order of 1,000? Happy to be told I'm mistaken though.
    - Fede_V 11y ago
      They used ~1000 CPUs and ~200 GPUs 5 months ago.
- deleted 11y ago
  [deleted]
- gearhart 11y ago
  1202 CPUs and 176 GPUs apparently edit: according to the livestream
  - exDM69 11y ago
lololomg 11y ago
Today a tiny $300 desktop computer can beat any human at chess. It only took a few years after the Deep Blue vs Kasparov game.
- Johnny_Brahms 11y ago
  Not only a desktop computer. A two year old smartphone would do just fine.
SmellyGeekBoy 11y ago
Seems like a pretty arbitrary limitation. 70 years ago Colossus filled an entire room, now it can be emulated on a Raspberry Pi. The really groundbreaking part is the algorithm.
Confusion 11y ago
Chess engines and processing power have since then advanced to a point where my phone can now reliably beat Carlsen. There is no reason to suppose Go is different in that respect. In 10 years, DeepMind will fit into a phone.
- dingo_bat 11y ago
  >my phone can now reliably beat Carlsen I've seen this written by many people but is there any solid evidence/study that proves this? Edit: seems like Pocket Fritz and Komodo are easily able to beat grandmasters.
- bluecalm 11y ago
  It's way more about algorithmic improvements than hardware improvements though. Deep Blue evaluated 200 million positions per second. I don't think top programs of today could get to 2 million positions per second on a smartphone (I get about 10 million pos/second on my i7 3770 quad). It's all about improvements in search algorithms as well as position evaluation.
  - Confusion 11y ago
    True. Without hardware improvements the processors that can evaluate 2 million positions per second while 'fitting' into a phone (qua processing power and power usage) would not exist though.
talles 11y ago
Are you suggesting to measure computing power by kilograms? That's even stupider than measuring software complexity by LOC.
- JabavuAdams 11y ago
  It's not obviously stupid, as a bounding argument as we approach physical limits.
  - Vraxx 11y ago
    That's based on the assumption that computing must exist as silicon transistors. When would we have reached the bounds of computation based on physical limits if we had stuck with vacuum tubes. The point is computation is an abstract concept and not tied to the physical medium that we use.
    - pixl97 11y ago
      >The point is computation is an abstract concept and not tied to the physical medium that we use. That's not exactly true https://en.wikipedia.org/wiki/Limits_to_computation https://en.wikipedia.org/wiki/Limits_to_computation
      - talles 11y ago
        Of course there are physical constraints on computing, but measuring by weight is rather stupid. Measuring the energy consumption seems to be a way better metric (even though "computation per energy" is clearly a human win). Not to mention that we suddenly forgot that computers have their own units of measurement, such as clock speed (hertz) and memory size (bytes).
        pixl97 11y ago
        > (even though "computation per energy" is clearly a human win) Is it? The problem here is it is really hard to compare the TCO. For example prime human computation requires years and years of learning and teaching, in which the human cannot be turned off (this kills the human). A computer can save its state and go in a low or even a zero power mode. >such as clock speed (hertz) and memory size (bytes). Which are completely meaningless, especially in distributed hybrid systems. Clock speed is like saying you can run at 10 miles per hour, but it doesn't define how much you can carry. GPUs run a far slower clock speed than CPUs, but they are massively parallel and are much faster than CPUs on distributed workloads. Having lots of memory is important, but not all memory is equal and hierarchy is even more important. Computer memory is (hopefully) bit perfect and a massive amount of power is spent keeping it that way. That is nice when it comes to remembering exactly how much money you have in the bank. Human memory is wonderful and terrible at the same time. There is no 'truth' in human memory, only repetition. A computer can take a picture and then make a hash of the image, both of which can be documented and verified. A human can recall a memory, but the act of recalling that memory changes it, and the parts we don't remember so well are influenced by our current state. It is this 'inaccuracy' that helps us use so little power for the amount of thinking we do.
nkrisc 11y ago
It's the software that's impressive. Why does how many physical computers it takes to run the software matter? It's physical footprint will almost certainly shrink as computers get more powerful.
ramblerman 11y ago
What a strange sentiment. You would only delay the inevitable outcome. Sure, it wouldn't win now, but processing power will become stronger and machines get smaller. What was the point?
eitally 11y ago
Besides disagreeing with you, this actually isn't true at all. In competition, AlphaGo doesn't rely on particularly expansive hardware. For training, yes, but not for playing.

nsns 11y ago

...it was the last stand of homo sapiens in the man vs machine... But who made that machine? I'd say a more precise evaluation would be that the ability to program a machine to assist in playing chess outdid the ability to play chess without such assistance.

eru 11y ago
Your point is entirely valid, but was already made by the very comment you are replying too..
- nsns 11y ago
  Indeed... Apologies to parent.

hosh 11y ago

The 2003 match was a brute force approach. AlphaGo's architecture resembles much closer to how humans think and learn. I initially learned Go to be able to have some chance of an AI. I then had some transformative experiences that coincided with my early kyu learning of basic Go lessons. On of the big lessons in Go is to learn how to let go of something. Taking solace in anything on the Go board is one of the blocks you work through when you develop as a Go player. I had already known about two years ago that just the Monte Carlo approach was already scalable. If Moore's Law continues, it was a matter of time before the Monte Carlo approach would start challenging the professional ranks -- it had already gotten to the point where you just needed to throw more hardware at it. AlphaGo's architecture adds a different layer to it. The Deep Learning isn't quite as flexible as the human mind, but it can do something that humans can't: learn non-stop, 24/7 on one subject. We're seeing a different tipping point here, possibly the same kind of tipping point when we witnessed the web browser back in the early 90s, and the introduction of the smartphone in the mid '00s. This is way bigger (to use a Go terminology) than what happened with chess.

hosh 11y ago
And just to emphasize the big point here: The AlphaGo that beat the 2p European champion five months ago was not as strong as the AlphaGo that beat Lee Sedol (9p). I don't think this was just the AlphaGo team throwing more hardware. I think they had been constantly running the self-training during the intervening months so that AlphaGo was improving itself. If that is so, then the big thing here isn't that AlphaGo is the first AI to win an official match with the currently world's strongest Go player. It's that within less than half a year, AlphaGo was able to learn and grow to go from challenging a 2p to challenging the world's strongest player. Think about that.
- d0m 11y ago
  I think it's fair to say that in the future, people will look back and wonder how it was possible to live without having a good AI.. similar to how we look past at caveman and wonder how they could live without electricity. AI is really just a tool that we leverage, the same was as we leveraged the wheel or electricity.

areyousure 11y ago

> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Can anyone provide some written references to this effect? Last time I searched (extensively), I couldn't really find anyone saying this.

Florin_Andrei 11y ago
FWIW, before AlphaGo defeated Fan Hui 2-dan last year, everyone was saying that would not be possible before 2025 or so. That was the consensus.
- soup10 11y ago
  People that try to predict the future in ai are breathing hot air more often then not.
  - darkmighty 11y ago
    Serious predictions actually inferred from the progress of existing (MCTS and others before) bots, which was something like 1 stone every two years (I don't recall the details, but it's easy to find out there). Top professionals were estimated to be something like 10 stones stronger than the best bot at 2008, so 2025 wouldn't sound too conservative. "At the US Congress 2008, he [Myungwan Kim] also played a historic demonstration game against MoGo running on an 800 processor supercomputer. With a 9 stone handicap, MoGo won by 1.5 points. At the 2009 congress, he played another demonstration game against Many Faces of Go running on 32 processors. Giving 7 stones handicap, Kim won convincingly by resignation." (Kim Myung Wan (born 1978) is a 9d Korean professional who has taken up residence in the Los Angeles area as of 2008) More information here, with a nice graph: http://senseis.xmp.net/?ComputerGo http://senseis.xmp.net/?ComputerGo http://i.imgur.com/RvQsf6v.png http://i.imgur.com/RvQsf6v.png You can see progress seemed to be slow at 2012.
    - ghaff 11y ago
      Go seemed to progress in a lot more fits and starts than did chess (which, admittedly, probably had a lot more effort put into it). Prior to 2005 or so, Go programs were relatively weak and there were people working on them who were saying that they didn't really see a path forward. Then people hit on using Monte Carlo which was the big step forward you show in your graphs. But then, that progress seemed to stall to the degree that various people were quoted in a Wired article a couple years ago about how they weren't sure what was going to happen. Yet, here we are today.

d0mine 11y ago

Francis Collins? said something like: people underestimate the change in the short term and overestimate it in the long term (there is my flying DeLorean and the hotel on the Moon).

adenadel 11y ago
I think you got it backwards. Bill Gates said "We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don't let yourself be lulled into inaction."
- d0mine 11y ago
  I meant what I said (and I didn't mean the Gates' quote). There are no flying DeLoreans (Back to the Future). There are no hotels on the Moon (Mad Man). People overestimate long-term (20+ years) change. It just shows once more that for any maxim there is a maxim with the opposite meaning.

maaku 11y ago

> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Sadly as I write this my uncle and personal hero who spent 17 years of his life working towards a Ph.D. on abstraction hierarchies for use in Go artificial intelligence, has been moved into hospice care. I'm just glad that in the few days that are left he has a chance to see this happen, even if it is not the good old-fashioned approach he took. [1] He recently started rewriting the continuation of this research in golang, available on Github: https://github.com/Ken1JF/ah https://github.com/Ken1JF/ah

new299 11y ago
Thanks for the link. I started reading his thesis. While AlphaGo is obviously exciting, it seems like your uncles approach could help us better understand how humans play go which seems hugely valuable. I look forward to exploring his thesis further.
jakub_h 11y ago
> This project is the sixth attempt to implement the model: Finally, the sixth attempt is written in the right language! Now it will succeed for sure.

olau 11y ago

Man building tool vs. man. :)

bjourne 11y ago

> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. To add to that, in Godel Escher Bach, Hofstadter in 1979 predicted that no chess engine would ever beat a human grandmaster player. It just goes to show how hard it is to predict what is, and also will remain, impossible for machines!