10 ms·

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 tot

• 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
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  • 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
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  • 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.