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Why AlphaGo Is Such a Big Deal

192 points by retupmoc01 11y ago

andreyk 11y ago
A good summary on the differences between DeepBlue and AlphaGo, and one that recognizes the latter is part of a broader trend of successful applications of deep neural networks. I thought this was pretty well written and accurate.
- michael_nielsen 11y ago
  Thanks. It's for a general audience, so I gloss over many details, but tried to do so in a way that remains broadly accurate. The Monte Carlo tree search, in particular, involves many finnicky details that I couldn't get into in this kind of essay.
  - Kiro 11y ago
    Thanks for writing this. I've been looking for an article to send to my friends who don't understand what's so special about AlphaGo. I think you nailed it.
pmichaud 11y ago
I don't think this is really why AlphaGo is a big deal. He's not wrong about the ways that it works vs. DeepBlue, but there's a much simpler way to understand why AlphaGo is a big deal. (I am not an AI researcher, but I work with AI researchers, like by putting on training programs and recruiting for them, and working out of the same office, and this is a summary of [my understanding of] their discussions about this topic.) AGI is a Hard Problem (TM), and no one really knows how hard it is. General intelligence is made of algorithms that are somewhere on a spectrum between dizzyingly complex math that we have decades+ more work to develop, and simple components that someone in their basement could stumble upon at any moment if only we had a couple key insights. (Sorry for the gross oversimplification.) Before AlphaGo there was a whole area of probability space that seemed plausible--a whole area of complexity that had yet to be worked out in detail. AlphaGo proved that more or less current AI tech was sufficient for that problem (current meaning one incremental step forward, as opposed to some revolutionary insight). That's why they "moved the field forward by 10 years"--by proving that the work that AI researchers basically expected to have to do over the next 10 years is not necessary. Said another way, we're closer to the "simple components" end of the spectrum than we collectively thought. AlphaGo proved that, and that's why it's a big deal.
- partycoder 11y ago
  Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means. I could play my entire life and never achieve the level of play Lee Sedol has. AlphaGo surpassed that level in a matter of months. As a matter of fact, many professionals that play the game for a living as their main occupation, since they're kids, will never achieve the level that Lee Sedol has. Also from here, they can add more infrastructure and feed the bot with more training data and let it train for a longer time, and it will be distancing it more and more from human level play. Then, this task specifically is not just any random task... it's one of the most complex games, one the oldest games, and is also professionally played and studied. Then, AGI is not a requirement for disrupting our world. You can have ANI for one specific task that is performed by millions of humans and you have disrupted the world enough to make it a problem.
  - shostack 11y ago
    I think that velocity is the fascinating part. I remember being intrigued while reading "Influx" by Daniel Suarez (great tin foil hat thriller that is hitting scarily close to home these days) about the training of the various AIs that were used by the government. I wonder what happens once we grow an AI whose purpose is to excel at training other AI's. Will we see an exponential leap forward in the speed with which they are trained?
    - catshirt 11y ago
      start here https://en.m.wikipedia.org/wiki/Technological_singularity https://en.m.wikipedia.org/wiki/Technological_singularity
    - pdkl95 11y ago
      > exponential leap forward A recent(-ish) Computerphile video has a good overview of the AI-self-improvement problem. https://www.youtube.com/watch?v=5qfIgCiYlfY https://www.youtube.com/watch?v=5qfIgCiYlfY I recommend watching the previous video first, which introduces the problem of strong-AI rapidly finding solutions (especially with poorly-specified questions) that may not be in humanity's best interest. https://www.youtube.com/watch?v=tcdVC4e6EV4 https://www.youtube.com/watch?v=tcdVC4e6EV4 Before anybody jumps to overly-sensational conclusions, note that the last video in that series, Rob Miles explains how exponential self-improvement is an extreme point in the space of possible AI development. We don't know how to predict discoveries[1], so we need more research into AI, so we can hopefully make something that isn't exponentially growing beyond our understanding. https://www.youtube.com/watch?v=IB1OvoCNnWY https://www.youtube.com/watch?v=IB1OvoCNnWY [1] see James Burke's non-teleological view of change
SonicSoul 11y ago
I really liked Jeff Atwoods explanation [0], and the DeepMinds CEO talk referenced in his post [1] [0] http://blog.codinghorror.com/thanks-for-ruining-another-game-forever-computers/ http://blog.codinghorror.com/thanks-for-ruining-another-game... [1] https://www.youtube.com/watch?v=rbsqaJwpu6A https://www.youtube.com/watch?v=rbsqaJwpu6A
- thomasahle 11y ago
  I'm not sure Atwoods whole point about computation power: If AlphaGo was intimidating today, having soundly beaten the best human Go player in the world, it'll be no contest after a few more years of GPUs doubling and redoubling their speeds again. In the AlphaGo paper they said their cluster version (with the 170 GPUs Atwoods mention) were the limit of what hardware could do for their algorithms. They tried using much larger clusters, but it didn't really improve performance. This actually fits his point that for chess, most of the performance gain over the last ten years has come from better algorithms. In particular for chess, better tuning of variables, through projects like fishtest (http://stockfishchess.org/ http://stockfishchess.org/) It seems like many of these problems can only be solved using algorithms with require a certain amount of computing power. However once we've reached that particular amount of power, further progress really lies in the algorithms.
jonbarker 11y ago
Would be interested to see a rematch between Lee and AlphaGo. The analogy between tricking image recognition with specific artifacts and finding weak points or flaws in other AI applications in the article is great; given enough games I'm confident Lee will find the flaws and be able to reliably exploit them.
- xbmcuser 11y ago
  Alphago is not static either it also improves. Lee would improve his game by a lot by playing Alphago regularly. But Alphago in the same time period would probably improve a few fold. At the end of the day Alphago has probably changed the game of Go for ever as it has already taught Lee other ways to think about Go.
- fizx 11y ago
  Does the classic "wear funky makeup" to fool face recognition even work with recent deep learning advances? I'd doubt it's nearly as effective. If you're referring to the recent "deep learning classifies this random noise as a dog" news, keep in mind that humans are far more suited to more abstract tricks like wearing makeup than learning the perfect static to fool an image recognition algorithm. Even with computer assistance and access to AlphaGo in order to train the perfect pathological boards, the high branching factor of go makes it impossible to for a human to remember what the relevant pathological board would be halfway through the game. (Also, keep in mind that the adversarial reinforcement learning is essentially training against as many pathological boards as possible. Image recognition isn't trained this way.) So I don't think you're right about this. I feel like the era of "makeup" tricks is finished (though maybe there's still one or two wins left from this approach). If Lee beats AlphaGo, I think it's because their strengths are truly similar.
vessenes 11y ago
One quick nitpick on your well written and helpful article: it's easily conceivable that Lee Sedol has reviewed on the order of 100k games. I think your general point stands, AI learns slowly in terms of number of iterations (consider how many times a five year old needs to be told the pronunciation of an unusual word or what emotion a certain tone conveys vs training a neural net).
- kqr 11y ago
  > it's easily conceivable that Lee Sedol has reviewed on the order of 100k games. ...but highly unlikely. In the best case, that works out to something like reviewing 10 new games every single day. If a game takes 1 hour to properly review, and a person needs 8 hours of sleep, that leaves just 6 hours in a day to eat, manage hygiene, exercise, be with family, travel to competitions, play yourself and do all kinds of other studies you need to.
  - vessenes 11y ago
    Okay, fair enough -- but it's definitely not 10k games. Asian pros work very, very hard. It's like any sport; time spent matters, and the best players put in a lot of time.
    - kqr 11y ago
      It's probably not 10k, but it's on the order of 10k, i.e. somewhere between 10,000 and 99,999.
sgt101 11y ago
The proof of the pudding will be in the applications that come from this specific line of research. I predict that there will be folks pointing to anything with an activation network, or a search algorithm, and shouting "look it works like alpha-go". What is the class of problems beyond go and closed world games which this work attacks?
- whitegrape 11y ago
  More like anything with a guided search algorithm will be said to be like AlphaGo. In general I think the field may be warming up to the idea that "whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought."[1] MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. [1] http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/ http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/
  - gcp 11y ago
    MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. The tricky part is that using the "best (rather than better) than random guidance" in the Monte Carlo simulations makes the performance worse. We don't understand very deeply why that is.
- gcp 11y ago
  The proof of the pudding will be in the applications that come from this specific line of research. What is "this specific line of research" though? Monte Carlo simulations with tree search? This is a bit tricky because simulation the playout requires a "fixed" world or something that can be simplified to it. Deep Convolutional Neural Networks? Those are very broadly applicable, most famously in image recognition, in fact one reason why Google and Facebook were on this is because they are probably using them a ton...
lossolo 11y ago
It's a big deal because it makes better decisions than human in decision space that cannot be brute forced to get all solutions. Life is also that kind of decision space, which you can't brute force to make best decision there is. AlhpaGo has better analytical "skills" in Go than human has, without knowing all the results of all the moves, just like human.
- astazangasta 11y ago
  No, life is not like Go. You don't choose one of 64 squares to play your next stone on in life. The state space is infinite. This is why AI will continue beating humans at games but be totally inept at, say, tech support.
  - JoeAltmaier 11y ago
    Indeed not. Most folks can consider only a few moves, and hardly anybody plots more than 1 move ahead. Truly, the human state-space is finite (and quite tiny). Most folks sit in their hometown and vegetate. Instead of starting a business/catching a bus to the city/joining the circus/hiking into the woods to become a hermit/running for office/and on and on.
    - astazangasta 11y ago
      This is just misanthropy.
      - JoeAltmaier 11y ago
        The statistics favor misanthropy?
        mrec 11y ago
        I'm guessing astazangasta is objecting less to the statistics of how many people stay in their hometown, and more to the pejorative framing of that as "sitting" and "vegetating". Which strikes me as a fair point. I'm sure there are lots of positives to staying near family and friends, and if you have stronger community ties you may well be more active in that community.
grumpopotamus 11y ago
>To get over this hurdle, the developers’ core idea was for AlphaGo to play the policy network against itself, to get an estimate of how likely a given board position was to be a winning one. That probability of a win provided a rough valuation of the position. This part is confusing in my opinion. It makes it sounds like AlphaGo is not using Monte Carlo Tree Search, which is mentioned earlier. However, this is exactly what AlphaGo is doing, except with the policy network in place of previous methods for initializing move priors, and the value network in place of traditional methods for terminating and scoring simulations.
- gcp 11y ago
  If I read their papers right, they don't actually use the SL policy network, because it played worse when combined with MCTS than the RL one, even if it was stronger without tree search. (But you need the tree search to make a strong player)
llSourcell 11y ago
If you guys like this, check out my '4 Reason why AlphaGo is a Huge Deal' video https://www.youtube.com/watch?v=2QhVarCzscs https://www.youtube.com/watch?v=2QhVarCzscs
vph 11y ago
Somehow, I feel that the big deal here is the cleverness to use words like "neurons", "deep learning" and "human intuition" (the "intuition" here appears no more than simply taking the route giving max probability of success).
- zero_iq 11y ago
  I think you're greatly underestimating how difficult it is to come up with an algorithm to play Go. Even evaluating a single play state is a difficult problem, let alone looking forwards to 'simply taking the route giving max probability of success'. That's not simple at all. Go is fiendishly difficult to evaluate. The traditional techniques that work in chess don't work well in Go (if at all). All the pieces have the same 'value'. More or less pieces isn't necessarily better or worse, it depends on the board layout. It's not at all easy to say who's winning or losing at most points in the game, or if there even is a winner or a loser. There is no mathematically-obvious way to compare two play states and say one is better than the other. The branching factor is huge, making the usual forward-looking techniques extraordinarily expensive. The number of possible legal boards states is something like 90 orders of magnitude greater than the number of atoms in the observable universe, making backward-looking techniques like minimax impossible, even if you could easily assign an evaluation function to each state.
  - gcp 11y ago
    It's not at all easy to say who's winning or losing at most points in the game, or if there even is a winner or a loser. You play out the game to the end. making backward-looking techniques like minimax impossible, even if you could easily assign an evaluation function to each state. That's exactly how AlphaGo works though. Read this paper, it was the landmark result that showed how to do this: http://www.remi-coulom.fr/CG2006/ http://www.remi-coulom.fr/CG2006/ AlphaGo uses the same technique, just optimized a bit more.
    - zero_iq 11y ago
      You play out the game to the end. Nope. You could play out to one or several ends, but there is a huge number of possible end games at any given point. Simply saying 'play out the game to the end' is a gross simplification. The average branching factor is something like 200, so just to fully evaluate just 5 moves ahead would require traversing 320 billion game states. Go games are 100-200 moves per game, so 5 moves ahead doesn't even get you close to the end of a game. At the first move there are 208168199381979984699478633344862770286522453884530548425639456820927419612738015378525648451698519643907259916015628128546089888314427129715319317557736620397247064840935 legal board states you'd have traverse to reach all end games. Yes, that's an exact figure. (http://tromp.github.io/go/legal.html http://tromp.github.io/go/legal.html) How big is that figure? If you counted up the number of fundamental particles in the entire universe, and for each fundamental particle there was another complete universe of fundamental particles and you counted all of those too, and then do all of that once for every person in the USA add up their answers, you'd be at about 3% of the way to that value. You simply cannot do a full evaluation to the end of the game: it's computationally infeasible. That's exactly how AlphaGo works though (minimax) No, it's not. Minimax requires working back from all end-game states, and there are too many end-game states to be evaluated. I suggest you read the paper yourself. It's about doing random partial evaluations and forward-looking searches of the game space, and choosing clever subsets to evaluate, precisely because you can't evaluate them all. The 'backing up' referred to in the paper is backing evaluation values up the tree after you've looked ahead a while, not starting at all possible end games and backing up from there, as in classic minimax. Some of the basic concepts are similar to classic tic-tac-toe or chess algorithms, but combining them in new ways. AlphaGo uses the same technique, just optimized a bit more. And a fighter jet uses the same technique as a paper aeroplane, only optimized a bit more...
unabst 11y ago
Using words such as "intuition" just makes the line between Strong and Weak AI blurrier, but that line is absolute. Think of it this way. Strong AI would lose to Weak AI at Go. So Strong AI is not in AphaGo's evolutionary path. Strong AI is the kind that authors and expresses things. Weak AIs are computational marvels. The brilliance of Strong AI would be in its capacity to build targeted Weak AI systems. And that is how Strong AI will beat any Weak AI system. If Strong AI is what we have, then just look at our relationship with AlphaGo. We created it, and it beat us at Go. Also, science is unpredictable. So saying we're 10 years ahead of schedule is not a scientific statement at all. Someone said it, and it was wrong, but they weren't speaking for the thousands of scientists working on new ideas as opposed to refining old ones.
- pixl97 11y ago
  >And that is how Strong AI will beat any Weak AI system. When humans build a calculator, do we say that the calculator beat humans at math, or humans beat calculators because we create them? Not really, they are just a tool. The problem with humans and calculators is the interface is rather clunky. You can't directly interface with the digital inputs of the calculator, but instead do so millions of times slower than a computer can. With strong AI this is not apt to be the case. Yes, it will create weak 'computational' AIs to do some tasks very well, but the speed that it will be able to interface and classify results at will be much faster than we are able to obtain. Where you and your calculator are very different things, ASI and it's calculators will not be.
  - unabst 11y ago
    > humans beat calculators because we create them The new calculator will beat the old calculator. Humans don't beat calculators themselves. But only humans have the capacity to improve on calculators. And this is truly all we've been doing with science and technology ever since day one. And yes, they're all just tools. AphaGo is just a tool. It told the dude moving the pieces where to move the pieces to beat the world champion who was moving pieces without any help. AlphaGo2 will be a better tool. > much faster than we are able to obtain Would that be that important though? Compare IBM Watson with the Jeopardy champion, except, this time he gets to use Google. Watson has a direct line, Ken Jennings does not. So what? Calculators are the same thing. We don't benefit from a direct link, at least at that level. It gives us the answer, and we can read it. We only want the answer anyway. Having to download all the details is redundant. The mind reading interface is what is clunky, when we're already wireless. Seeing is enough. > ASI and it's calculators will not be. Right, so you're switching to hardware now. Artificial human enhancement is the go-to answer many will give you here, but it's a bit too sci-fi for me. But regardless, the strong-weak divide still stands. We would create tools to solve the problem. And this is where strong AI and calculators will differ, regardless of what other similarities they may have, physical or otherwise.
ericjang 11y ago
Coincidentally, Quora.com just had a knowledge prize contest to answer "How Significant is AlphaGo?". https://www.quora.com/How-significant-is-the-AlphaGo-victory-for-AI-and-technology-in-general-How-generalizable-is-the-success-in-this-one-use-case https://www.quora.com/How-significant-is-the-AlphaGo-victory... I thought that many of the answers were quite insightful. Disclaimer: I also wrote an answer, but the submission deadline for the knowledge prize is over.
Houshalter 11y ago
It's a big deal because AIs can now beat humans at all perfect information games. And NNs and maybe even MCTS are applicable to non perfect information games. Soon they will be beaten too. Then what? The list of things humans are better at is narrowing.
graycat 11y ago
As we see, the OP has > We have learned to use computer systems to reproduce at least some forms of human intuition. Hmm .... Let's examine that claim: To set aside the game of Go, we start with an analogy: A human can walk 10 miles to town, and a car can help one drive the same 10 miles to town. But what a car does to get to town is very different from what a walking human does. Both approaches get to town, and the car is much better in various ways, but this does not mean that a car walked like a human. To be clear, specifically the car did not "reproduce" the way a human gets to town; e.g., the car did not have two legs. Both the human and the car got to town? Yes. The car "reproduced" how a human got to town on two legs? No. Similarly, a motor boat does not "reproduce" some forms of human swimming. This pattern of using science, engineering, etc. to get results comparable with those of unaided humans is very old with many great examples from especially the last 100 years. Further, on "intuition", there are many tasks that humans do with human intuition where science, engineering, and computing do comparably well, but that does not mean that the science, etc. has "reproduced" human intuition. Similarly, on the game of Go, AlphaGo got some game successes that humans get via human intuition, but that does not mean that AlphaGo "reproduced" human intuition, not in general and not even in the specific case of the game of Go. Early in IBM's mainframe computing, in the common publicity, there was a related claim that IBM's computers were "gigantic electronic human brains". Those computers were no more "human brains" than Henry Ford's Model T was gigantic mechanical human legs. A car is not human legs, and an early IBM computer was not a human brain. IMHO, there is an important point here: From the publicity about IBM's early computers to the claim in the OP I quoted above, there are claims that computing and computer science are reproducing some of how humans do things. But so far this claim is false, seriously false, not even close to the truth: Computers are no closer to reproducing human intelligence or "intuition" than Ford's Model T was to reproducing human legs. Instead, apparently AlphaGo is a some high dimensional data fitting to yield a classification system, that is, given a board position in Go, AlphaGo reports 0 for bad for the AlphaGo player and 1 good for the AlphaGo player. So, they have a classifier. But classifiers go way back, to Leo Breiman's random forests, classification and regression trees (CART), logistic regression, a huge range of statistical tests, e.g., in some cases of advanced radar target detection, and much more. Many such solutions have been very valuable, and maybe AlphaGo or its techniques will also be valuable, but none of this in any sense reproduces human intuition or really anything about humans -- not yet, not even close. Net, while it's a free country and people can disagree, which is one role of HN, IMHO, the quote I gave above from the OP is scientifically irresponsible. It just isn't really true. At best it is just hype.
mrcactu5 11y ago
I am concerned... OK the AlphaGo AI plays better than any human, but does it understand why? When I learn Go, there are all sorts of techniques such as joseki, life-death, tesuji. AlphaGo implicitly learned these techniques, since it learned from professional games, which are experts in those topics. This is my problem with neural networks in general, since they do not discuss how go boards divide into smaller problems or heuristics for putting them back together. to be corny, Go is really a teaching tool for how people think logically and abstractly. It's not about teaching computers.
- drjesusphd 11y ago
  > This is my problem with neural networks in general, since they do not discuss how go boards divide into smaller problems or heuristics for putting them back together. This strategy is called "chunking", and is an adaptation humans have developed to solve complex problems. There's no reason to artificially constrain AI to use the same crutches we do.
YeGoblynQueenne 11y ago
Reading this article I find myself thinking again how unhappy I am that a neural network won Go. I am unhappy because neural networks are not smart. They're the epitome of artificial stupidity: they learn, but they don't understand anything that they learn. They can't take what they learned and apply it to another domain, not without training again. And every time you train one in a new thing, it forgets all about the old thing. They got intelligence alright, but it's the dumbest kind of intelligence possible, mechanical, unaware of itself and devoid of all purpose other than what we give it. And what about ourselves? Building AlphaGo is a feat of human intelligence, but how does it improve that human intelligence itself? How do we become smarter? We can build machines to solve problems that we don't understand, but they solve them in ways that we still don't understand. Except- we're not even trying anymore. We've given up. Knowledge is hard. Logic is hard. Just throw a bunch of GPUs at the problem and make it go away. Far from this being a promising time for artificial intelligence, I fear we're about to see a great dumbing down as funding is cut to everything but neural networks research. A kind of AI summer, as it were.
- jamespitts 11y ago
  Imagine that we augmented alphago by placing a system nearby that can introspect alphago and generate abstractions of what occurred, and then provide a human-understandable narrative about alphago's simulations during a game. What if the same narrative system could, either on its own or through human interaction, bring alphago back to previous states or new simulations? Might this combined system be closer to understanding, logically, what it has learned? Sometimes I wonder if inside of ourselves we come up with logical reasoning after intuition has pre-illuminated the path forward (based on experience). Or perhaps many different forms of thinking run in parallel, influencing each other. Each of us may have a very, very different combination of instinct, intuition, logic, etc.
- azag0 11y ago
  > neural networks are not smart That's like saying computers are not smart. It's meaningless. Computers are as smart as the algorithms they run and neural networks are as smart as the neural connections within them. Neural networks are models of computation as much as current computer architectures are. As far as we know, neural networks indeed are at the core of human intelligence. Human brain is an extremely complicated neural network with plenty of side mechanisms, many of which we possibly do not know about yet. Nonetheless it's unlikely that the underlying building blocks are not neural networks. What is probably several orders of magnitude more complex than uncovering the static architecture of the human brain, is understanding the way it is trained. Note that this is the case with artificial neural networks as well. I doubt that we will ever understand human intelligence "from above". It doesn't seem implausible to me that there is some general complexity theory law which prohibits intelligence "understanding" itself.
colllectorof 11y ago
Like most articles about AlphaGo this one does not mention two (uncomfortable for many) facts. 1. AlphaGo doesn't just use neural networks. If it did, it would almost certainly loose 0-5. It also uses Monte-Carlo tree search, which is an algorithms that made a huge leap in the strength of Go bots. The rub is that MCTS is a flavor of "brute-force" algorithm. What does that tell you about the nature of Go? 2. None of the individual components in AlphaGo are theoretically novel. In fact, all of them are very well-known: conv. nets, MCTS, data-mining, reinforcement learning. What does that tell you about the nature of this particular game-playing AI compared to its many brethren? (By the way, I wonder how many of the journalists praising Google for Alpha Go even heard of Rémi Coulom. Shouldn't he be at least be mentioned somewhere in all these articles?)
- unexistance 11y ago
  https://www.tastehit.com/blog/google-deepmind-alphago-how-it-works/ https://www.tastehit.com/blog/google-deepmind-alphago-how-it... somewhat answers your question, tho with questionable analogy to the human mind
- Houshalter 11y ago
  I don't see anything wrong with them using existing tech. Most technology is iterative. It's like saying going to the moon isn't impressive because rockets are 20 year old tech. NNs have shown a lot of promise in the last few years. They are just starting to make their way to new applications like Go playing. So in that sense AlphaGo is a sign of things to come.
  - argonaut 11y ago
    Not a great analogy, because during the Space Race, hundreds of technologies were invented. Google would be hard-pressed to patent any of the specific component technologies in AlphaGo, if they wished to.
  - colllectorof 11y ago
    > NNs have shown a lot of promise in the last few years. Which is interesting in its own right, since ANNs have been the subject of research for more than half a century. If anything, all their ups and downs should make people more cautious about bold predictions.
return0 11y ago
I like that he doesn't use the typical AI jargon. I think its time to stop using science-fiction-writer terms to talk about our layered nonlinear unit systems. There is so much talk about "General AI", "Strong" and "Weak" AI, and also about "Intelligence", but none of these are well-defined enough to be useful. These are just words, not things that exist in the world out there. What we have here is a system that generalizes in a way that is human-like. It's a mystery why this looks so human-like to us, given that neural networks are only barely resembling of actual neural networks, and that is a problem. Reading the neuroscience of plasticity, it's still not clear whether learning takes place via synapse modifications. What these amazingly capable neural networks do, however, is to bolster the idea of connectionism in neuroscience, which is an interesting, given that the relationship between the two started the other way around. P.S. Michael Nielsen must be one of the very few people who can explain any concept in almost absolute clear and simple terms.
nsxwolf 11y ago
The problem I have being wowed by AlphaGo is that games like go and chess are so inscrutable to me that I cannot tell the difference between a game played by two strong players and a game played by a players making random legal moves. I have never won a game of chess in my entire life. Not against any human player or any Commodore 64 chess program on the easiest setting. I don't expect I ever will. I know how the pieces move and that's about it. Watson playing Jeopardy was different. That I could "get", and that impressed me.

192 points by retupmoc01 11y ago

A good summary on the differences between DeepBlue and AlphaGo, and one that recognizes the latter is part of a broader trend of successful applications of deep neural networks. I thought this was pretty well written and accurate.

michael_nielsen 11y ago
Thanks. It's for a general audience, so I gloss over many details, but tried to do so in a way that remains broadly accurate. The Monte Carlo tree search, in particular, involves many finnicky details that I couldn't get into in this kind of essay.
- Kiro 11y ago
  Thanks for writing this. I've been looking for an article to send to my friends who don't understand what's so special about AlphaGo. I think you nailed it.

pmichaud 11y ago

I don't think this is really why AlphaGo is a big deal. He's not wrong about the ways that it works vs. DeepBlue, but there's a much simpler way to understand why AlphaGo is a big deal. (I am not an AI researcher, but I work with AI researchers, like by putting on training programs and recruiting for them, and working out of the same office, and this is a summary of [my understanding of] their discussions about this topic.) AGI is a Hard Problem (TM), and no one really knows how hard it is. General intelligence is made of algorithms that are somewhere on a spectrum between dizzyingly complex math that we have decades+ more work to develop, and simple components that someone in their basement could stumble upon at any moment if only we had a couple key insights. (Sorry for the gross oversimplification.) Before AlphaGo there was a whole area of probability space that seemed plausible--a whole area of complexity that had yet to be worked out in detail. AlphaGo proved that more or less current AI tech was sufficient for that problem (current meaning one incremental step forward, as opposed to some revolutionary insight). That's why they "moved the field forward by 10 years"--by proving that the work that AI researchers basically expected to have to do over the next 10 years is not necessary. Said another way, we're closer to the "simple components" end of the spectrum than we collectively thought. AlphaGo proved that, and that's why it's a big deal.

partycoder 11y ago
Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means. I could play my entire life and never achieve the level of play Lee Sedol has. AlphaGo surpassed that level in a matter of months. As a matter of fact, many professionals that play the game for a living as their main occupation, since they're kids, will never achieve the level that Lee Sedol has. Also from here, they can add more infrastructure and feed the bot with more training data and let it train for a longer time, and it will be distancing it more and more from human level play. Then, this task specifically is not just any random task... it's one of the most complex games, one the oldest games, and is also professionally played and studied. Then, AGI is not a requirement for disrupting our world. You can have ANI for one specific task that is performed by millions of humans and you have disrupted the world enough to make it a problem.
- shostack 11y ago
  I think that velocity is the fascinating part. I remember being intrigued while reading "Influx" by Daniel Suarez (great tin foil hat thriller that is hitting scarily close to home these days) about the training of the various AIs that were used by the government. I wonder what happens once we grow an AI whose purpose is to excel at training other AI's. Will we see an exponential leap forward in the speed with which they are trained?
  - catshirt 11y ago
    start here https://en.m.wikipedia.org/wiki/Technological_singularity https://en.m.wikipedia.org/wiki/Technological_singularity
  - pdkl95 11y ago
    > exponential leap forward A recent(-ish) Computerphile video has a good overview of the AI-self-improvement problem. https://www.youtube.com/watch?v=5qfIgCiYlfY https://www.youtube.com/watch?v=5qfIgCiYlfY I recommend watching the previous video first, which introduces the problem of strong-AI rapidly finding solutions (especially with poorly-specified questions) that may not be in humanity's best interest. https://www.youtube.com/watch?v=tcdVC4e6EV4 https://www.youtube.com/watch?v=tcdVC4e6EV4 Before anybody jumps to overly-sensational conclusions, note that the last video in that series, Rob Miles explains how exponential self-improvement is an extreme point in the space of possible AI development. We don't know how to predict discoveries[1], so we need more research into AI, so we can hopefully make something that isn't exponentially growing beyond our understanding. https://www.youtube.com/watch?v=IB1OvoCNnWY https://www.youtube.com/watch?v=IB1OvoCNnWY [1] see James Burke's non-teleological view of change

SonicSoul 11y ago

I really liked Jeff Atwoods explanation [0], and the DeepMinds CEO talk referenced in his post [1] [0] http://blog.codinghorror.com/thanks-for-ruining-another-game-forever-computers/ http://blog.codinghorror.com/thanks-for-ruining-another-game... [1] https://www.youtube.com/watch?v=rbsqaJwpu6A https://www.youtube.com/watch?v=rbsqaJwpu6A

thomasahle 11y ago
I'm not sure Atwoods whole point about computation power: If AlphaGo was intimidating today, having soundly beaten the best human Go player in the world, it'll be no contest after a few more years of GPUs doubling and redoubling their speeds again. In the AlphaGo paper they said their cluster version (with the 170 GPUs Atwoods mention) were the limit of what hardware could do for their algorithms. They tried using much larger clusters, but it didn't really improve performance. This actually fits his point that for chess, most of the performance gain over the last ten years has come from better algorithms. In particular for chess, better tuning of variables, through projects like fishtest (http://stockfishchess.org/ http://stockfishchess.org/) It seems like many of these problems can only be solved using algorithms with require a certain amount of computing power. However once we've reached that particular amount of power, further progress really lies in the algorithms.

jonbarker 11y ago

Would be interested to see a rematch between Lee and AlphaGo. The analogy between tricking image recognition with specific artifacts and finding weak points or flaws in other AI applications in the article is great; given enough games I'm confident Lee will find the flaws and be able to reliably exploit them.

xbmcuser 11y ago
Alphago is not static either it also improves. Lee would improve his game by a lot by playing Alphago regularly. But Alphago in the same time period would probably improve a few fold. At the end of the day Alphago has probably changed the game of Go for ever as it has already taught Lee other ways to think about Go.
fizx 11y ago
Does the classic "wear funky makeup" to fool face recognition even work with recent deep learning advances? I'd doubt it's nearly as effective. If you're referring to the recent "deep learning classifies this random noise as a dog" news, keep in mind that humans are far more suited to more abstract tricks like wearing makeup than learning the perfect static to fool an image recognition algorithm. Even with computer assistance and access to AlphaGo in order to train the perfect pathological boards, the high branching factor of go makes it impossible to for a human to remember what the relevant pathological board would be halfway through the game. (Also, keep in mind that the adversarial reinforcement learning is essentially training against as many pathological boards as possible. Image recognition isn't trained this way.) So I don't think you're right about this. I feel like the era of "makeup" tricks is finished (though maybe there's still one or two wins left from this approach). If Lee beats AlphaGo, I think it's because their strengths are truly similar.

vessenes 11y ago

One quick nitpick on your well written and helpful article: it's easily conceivable that Lee Sedol has reviewed on the order of 100k games. I think your general point stands, AI learns slowly in terms of number of iterations (consider how many times a five year old needs to be told the pronunciation of an unusual word or what emotion a certain tone conveys vs training a neural net).

kqr 11y ago
> it's easily conceivable that Lee Sedol has reviewed on the order of 100k games. ...but highly unlikely. In the best case, that works out to something like reviewing 10 new games every single day. If a game takes 1 hour to properly review, and a person needs 8 hours of sleep, that leaves just 6 hours in a day to eat, manage hygiene, exercise, be with family, travel to competitions, play yourself and do all kinds of other studies you need to.
- vessenes 11y ago
  Okay, fair enough -- but it's definitely not 10k games. Asian pros work very, very hard. It's like any sport; time spent matters, and the best players put in a lot of time.
  - kqr 11y ago
    It's probably not 10k, but it's on the order of 10k, i.e. somewhere between 10,000 and 99,999.

sgt101 11y ago

The proof of the pudding will be in the applications that come from this specific line of research. I predict that there will be folks pointing to anything with an activation network, or a search algorithm, and shouting "look it works like alpha-go". What is the class of problems beyond go and closed world games which this work attacks?

whitegrape 11y ago
More like anything with a guided search algorithm will be said to be like AlphaGo. In general I think the field may be warming up to the idea that "whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought."[1] MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. [1] http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/ http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/
- gcp 11y ago
  MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. The tricky part is that using the "best (rather than better) than random guidance" in the Monte Carlo simulations makes the performance worse. We don't understand very deeply why that is.
gcp 11y ago
The proof of the pudding will be in the applications that come from this specific line of research. What is "this specific line of research" though? Monte Carlo simulations with tree search? This is a bit tricky because simulation the playout requires a "fixed" world or something that can be simplified to it. Deep Convolutional Neural Networks? Those are very broadly applicable, most famously in image recognition, in fact one reason why Google and Facebook were on this is because they are probably using them a ton...

lossolo 11y ago

It's a big deal because it makes better decisions than human in decision space that cannot be brute forced to get all solutions. Life is also that kind of decision space, which you can't brute force to make best decision there is. AlhpaGo has better analytical "skills" in Go than human has, without knowing all the results of all the moves, just like human.

astazangasta 11y ago
No, life is not like Go. You don't choose one of 64 squares to play your next stone on in life. The state space is infinite. This is why AI will continue beating humans at games but be totally inept at, say, tech support.
- JoeAltmaier 11y ago
  Indeed not. Most folks can consider only a few moves, and hardly anybody plots more than 1 move ahead. Truly, the human state-space is finite (and quite tiny). Most folks sit in their hometown and vegetate. Instead of starting a business/catching a bus to the city/joining the circus/hiking into the woods to become a hermit/running for office/and on and on.
  - astazangasta 11y ago
    This is just misanthropy.
    - JoeAltmaier 11y ago
      The statistics favor misanthropy?
      - mrec 11y ago
        I'm guessing astazangasta is objecting less to the statistics of how many people stay in their hometown, and more to the pejorative framing of that as "sitting" and "vegetating". Which strikes me as a fair point. I'm sure there are lots of positives to staying near family and friends, and if you have stronger community ties you may well be more active in that community.

grumpopotamus 11y ago

>To get over this hurdle, the developers’ core idea was for AlphaGo to play the policy network against itself, to get an estimate of how likely a given board position was to be a winning one. That probability of a win provided a rough valuation of the position. This part is confusing in my opinion. It makes it sounds like AlphaGo is not using Monte Carlo Tree Search, which is mentioned earlier. However, this is exactly what AlphaGo is doing, except with the policy network in place of previous methods for initializing move priors, and the value network in place of traditional methods for terminating and scoring simulations.

gcp 11y ago
If I read their papers right, they don't actually use the SL policy network, because it played worse when combined with MCTS than the RL one, even if it was stronger without tree search. (But you need the tree search to make a strong player)

llSourcell 11y ago

If you guys like this, check out my '4 Reason why AlphaGo is a Huge Deal' video https://www.youtube.com/watch?v=2QhVarCzscs https://www.youtube.com/watch?v=2QhVarCzscs

vph 11y ago

Somehow, I feel that the big deal here is the cleverness to use words like "neurons", "deep learning" and "human intuition" (the "intuition" here appears no more than simply taking the route giving max probability of success).

zero_iq 11y ago
I think you're greatly underestimating how difficult it is to come up with an algorithm to play Go. Even evaluating a single play state is a difficult problem, let alone looking forwards to 'simply taking the route giving max probability of success'. That's not simple at all. Go is fiendishly difficult to evaluate. The traditional techniques that work in chess don't work well in Go (if at all). All the pieces have the same 'value'. More or less pieces isn't necessarily better or worse, it depends on the board layout. It's not at all easy to say who's winning or losing at most points in the game, or if there even is a winner or a loser. There is no mathematically-obvious way to compare two play states and say one is better than the other. The branching factor is huge, making the usual forward-looking techniques extraordinarily expensive. The number of possible legal boards states is something like 90 orders of magnitude greater than the number of atoms in the observable universe, making backward-looking techniques like minimax impossible, even if you could easily assign an evaluation function to each state.
- gcp 11y ago
  It's not at all easy to say who's winning or losing at most points in the game, or if there even is a winner or a loser. You play out the game to the end. making backward-looking techniques like minimax impossible, even if you could easily assign an evaluation function to each state. That's exactly how AlphaGo works though. Read this paper, it was the landmark result that showed how to do this: http://www.remi-coulom.fr/CG2006/ http://www.remi-coulom.fr/CG2006/ AlphaGo uses the same technique, just optimized a bit more.
  - zero_iq 11y ago
    You play out the game to the end. Nope. You could play out to one or several ends, but there is a huge number of possible end games at any given point. Simply saying 'play out the game to the end' is a gross simplification. The average branching factor is something like 200, so just to fully evaluate just 5 moves ahead would require traversing 320 billion game states. Go games are 100-200 moves per game, so 5 moves ahead doesn't even get you close to the end of a game. At the first move there are 208168199381979984699478633344862770286522453884530548425639456820927419612738015378525648451698519643907259916015628128546089888314427129715319317557736620397247064840935 legal board states you'd have traverse to reach all end games. Yes, that's an exact figure. (http://tromp.github.io/go/legal.html http://tromp.github.io/go/legal.html) How big is that figure? If you counted up the number of fundamental particles in the entire universe, and for each fundamental particle there was another complete universe of fundamental particles and you counted all of those too, and then do all of that once for every person in the USA add up their answers, you'd be at about 3% of the way to that value. You simply cannot do a full evaluation to the end of the game: it's computationally infeasible. That's exactly how AlphaGo works though (minimax) No, it's not. Minimax requires working back from all end-game states, and there are too many end-game states to be evaluated. I suggest you read the paper yourself. It's about doing random partial evaluations and forward-looking searches of the game space, and choosing clever subsets to evaluate, precisely because you can't evaluate them all. The 'backing up' referred to in the paper is backing evaluation values up the tree after you've looked ahead a while, not starting at all possible end games and backing up from there, as in classic minimax. Some of the basic concepts are similar to classic tic-tac-toe or chess algorithms, but combining them in new ways. AlphaGo uses the same technique, just optimized a bit more. And a fighter jet uses the same technique as a paper aeroplane, only optimized a bit more...

unabst 11y ago

Using words such as "intuition" just makes the line between Strong and Weak AI blurrier, but that line is absolute. Think of it this way. Strong AI would lose to Weak AI at Go. So Strong AI is not in AphaGo's evolutionary path. Strong AI is the kind that authors and expresses things. Weak AIs are computational marvels. The brilliance of Strong AI would be in its capacity to build targeted Weak AI systems. And that is how Strong AI will beat any Weak AI system. If Strong AI is what we have, then just look at our relationship with AlphaGo. We created it, and it beat us at Go. Also, science is unpredictable. So saying we're 10 years ahead of schedule is not a scientific statement at all. Someone said it, and it was wrong, but they weren't speaking for the thousands of scientists working on new ideas as opposed to refining old ones.

pixl97 11y ago
>And that is how Strong AI will beat any Weak AI system. When humans build a calculator, do we say that the calculator beat humans at math, or humans beat calculators because we create them? Not really, they are just a tool. The problem with humans and calculators is the interface is rather clunky. You can't directly interface with the digital inputs of the calculator, but instead do so millions of times slower than a computer can. With strong AI this is not apt to be the case. Yes, it will create weak 'computational' AIs to do some tasks very well, but the speed that it will be able to interface and classify results at will be much faster than we are able to obtain. Where you and your calculator are very different things, ASI and it's calculators will not be.
- unabst 11y ago
  > humans beat calculators because we create them The new calculator will beat the old calculator. Humans don't beat calculators themselves. But only humans have the capacity to improve on calculators. And this is truly all we've been doing with science and technology ever since day one. And yes, they're all just tools. AphaGo is just a tool. It told the dude moving the pieces where to move the pieces to beat the world champion who was moving pieces without any help. AlphaGo2 will be a better tool. > much faster than we are able to obtain Would that be that important though? Compare IBM Watson with the Jeopardy champion, except, this time he gets to use Google. Watson has a direct line, Ken Jennings does not. So what? Calculators are the same thing. We don't benefit from a direct link, at least at that level. It gives us the answer, and we can read it. We only want the answer anyway. Having to download all the details is redundant. The mind reading interface is what is clunky, when we're already wireless. Seeing is enough. > ASI and it's calculators will not be. Right, so you're switching to hardware now. Artificial human enhancement is the go-to answer many will give you here, but it's a bit too sci-fi for me. But regardless, the strong-weak divide still stands. We would create tools to solve the problem. And this is where strong AI and calculators will differ, regardless of what other similarities they may have, physical or otherwise.

ericjang 11y ago

Coincidentally, Quora.com just had a knowledge prize contest to answer "How Significant is AlphaGo?". https://www.quora.com/How-significant-is-the-AlphaGo-victory-for-AI-and-technology-in-general-How-generalizable-is-the-success-in-this-one-use-case https://www.quora.com/How-significant-is-the-AlphaGo-victory... I thought that many of the answers were quite insightful. Disclaimer: I also wrote an answer, but the submission deadline for the knowledge prize is over.

Houshalter 11y ago

It's a big deal because AIs can now beat humans at all perfect information games. And NNs and maybe even MCTS are applicable to non perfect information games. Soon they will be beaten too. Then what? The list of things humans are better at is narrowing.

graycat 11y ago

As we see, the OP has > We have learned to use computer systems to reproduce at least some forms of human intuition. Hmm .... Let's examine that claim: To set aside the game of Go, we start with an analogy: A human can walk 10 miles to town, and a car can help one drive the same 10 miles to town. But what a car does to get to town is very different from what a walking human does. Both approaches get to town, and the car is much better in various ways, but this does not mean that a car walked like a human. To be clear, specifically the car did not "reproduce" the way a human gets to town; e.g., the car did not have two legs. Both the human and the car got to town? Yes. The car "reproduced" how a human got to town on two legs? No. Similarly, a motor boat does not "reproduce" some forms of human swimming. This pattern of using science, engineering, etc. to get results comparable with those of unaided humans is very old with many great examples from especially the last 100 years. Further, on "intuition", there are many tasks that humans do with human intuition where science, engineering, and computing do comparably well, but that does not mean that the science, etc. has "reproduced" human intuition. Similarly, on the game of Go, AlphaGo got some game successes that humans get via human intuition, but that does not mean that AlphaGo "reproduced" human intuition, not in general and not even in the specific case of the game of Go. Early in IBM's mainframe computing, in the common publicity, there was a related claim that IBM's computers were "gigantic electronic human brains". Those computers were no more "human brains" than Henry Ford's Model T was gigantic mechanical human legs. A car is not human legs, and an early IBM computer was not a human brain. IMHO, there is an important point here: From the publicity about IBM's early computers to the claim in the OP I quoted above, there are claims that computing and computer science are reproducing some of how humans do things. But so far this claim is false, seriously false, not even close to the truth: Computers are no closer to reproducing human intelligence or "intuition" than Ford's Model T was to reproducing human legs. Instead, apparently AlphaGo is a some high dimensional data fitting to yield a classification system, that is, given a board position in Go, AlphaGo reports 0 for bad for the AlphaGo player and 1 good for the AlphaGo player. So, they have a classifier. But classifiers go way back, to Leo Breiman's random forests, classification and regression trees (CART), logistic regression, a huge range of statistical tests, e.g., in some cases of advanced radar target detection, and much more. Many such solutions have been very valuable, and maybe AlphaGo or its techniques will also be valuable, but none of this in any sense reproduces human intuition or really anything about humans -- not yet, not even close. Net, while it's a free country and people can disagree, which is one role of HN, IMHO, the quote I gave above from the OP is scientifically irresponsible. It just isn't really true. At best it is just hype.

mrcactu5 11y ago

I am concerned... OK the AlphaGo AI plays better than any human, but does it understand why? When I learn Go, there are all sorts of techniques such as joseki, life-death, tesuji. AlphaGo implicitly learned these techniques, since it learned from professional games, which are experts in those topics. This is my problem with neural networks in general, since they do not discuss how go boards divide into smaller problems or heuristics for putting them back together. to be corny, Go is really a teaching tool for how people think logically and abstractly. It's not about teaching computers.

drjesusphd 11y ago
> This is my problem with neural networks in general, since they do not discuss how go boards divide into smaller problems or heuristics for putting them back together. This strategy is called "chunking", and is an adaptation humans have developed to solve complex problems. There's no reason to artificially constrain AI to use the same crutches we do.

YeGoblynQueenne 11y ago

Reading this article I find myself thinking again how unhappy I am that a neural network won Go. I am unhappy because neural networks are not smart. They're the epitome of artificial stupidity: they learn, but they don't understand anything that they learn. They can't take what they learned and apply it to another domain, not without training again. And every time you train one in a new thing, it forgets all about the old thing. They got intelligence alright, but it's the dumbest kind of intelligence possible, mechanical, unaware of itself and devoid of all purpose other than what we give it. And what about ourselves? Building AlphaGo is a feat of human intelligence, but how does it improve that human intelligence itself? How do we become smarter? We can build machines to solve problems that we don't understand, but they solve them in ways that we still don't understand. Except- we're not even trying anymore. We've given up. Knowledge is hard. Logic is hard. Just throw a bunch of GPUs at the problem and make it go away. Far from this being a promising time for artificial intelligence, I fear we're about to see a great dumbing down as funding is cut to everything but neural networks research. A kind of AI summer, as it were.

jamespitts 11y ago
Imagine that we augmented alphago by placing a system nearby that can introspect alphago and generate abstractions of what occurred, and then provide a human-understandable narrative about alphago's simulations during a game. What if the same narrative system could, either on its own or through human interaction, bring alphago back to previous states or new simulations? Might this combined system be closer to understanding, logically, what it has learned? Sometimes I wonder if inside of ourselves we come up with logical reasoning after intuition has pre-illuminated the path forward (based on experience). Or perhaps many different forms of thinking run in parallel, influencing each other. Each of us may have a very, very different combination of instinct, intuition, logic, etc.
azag0 11y ago
> neural networks are not smart That's like saying computers are not smart. It's meaningless. Computers are as smart as the algorithms they run and neural networks are as smart as the neural connections within them. Neural networks are models of computation as much as current computer architectures are. As far as we know, neural networks indeed are at the core of human intelligence. Human brain is an extremely complicated neural network with plenty of side mechanisms, many of which we possibly do not know about yet. Nonetheless it's unlikely that the underlying building blocks are not neural networks. What is probably several orders of magnitude more complex than uncovering the static architecture of the human brain, is understanding the way it is trained. Note that this is the case with artificial neural networks as well. I doubt that we will ever understand human intelligence "from above". It doesn't seem implausible to me that there is some general complexity theory law which prohibits intelligence "understanding" itself.

colllectorof 11y ago

Like most articles about AlphaGo this one does not mention two (uncomfortable for many) facts. 1. AlphaGo doesn't just use neural networks. If it did, it would almost certainly loose 0-5. It also uses Monte-Carlo tree search, which is an algorithms that made a huge leap in the strength of Go bots. The rub is that MCTS is a flavor of "brute-force" algorithm. What does that tell you about the nature of Go? 2. None of the individual components in AlphaGo are theoretically novel. In fact, all of them are very well-known: conv. nets, MCTS, data-mining, reinforcement learning. What does that tell you about the nature of this particular game-playing AI compared to its many brethren? (By the way, I wonder how many of the journalists praising Google for Alpha Go even heard of Rémi Coulom. Shouldn't he be at least be mentioned somewhere in all these articles?)

unexistance 11y ago
https://www.tastehit.com/blog/google-deepmind-alphago-how-it-works/ https://www.tastehit.com/blog/google-deepmind-alphago-how-it... somewhat answers your question, tho with questionable analogy to the human mind
Houshalter 11y ago
I don't see anything wrong with them using existing tech. Most technology is iterative. It's like saying going to the moon isn't impressive because rockets are 20 year old tech. NNs have shown a lot of promise in the last few years. They are just starting to make their way to new applications like Go playing. So in that sense AlphaGo is a sign of things to come.
- argonaut 11y ago
  Not a great analogy, because during the Space Race, hundreds of technologies were invented. Google would be hard-pressed to patent any of the specific component technologies in AlphaGo, if they wished to.
- colllectorof 11y ago
  > NNs have shown a lot of promise in the last few years. Which is interesting in its own right, since ANNs have been the subject of research for more than half a century. If anything, all their ups and downs should make people more cautious about bold predictions.

return0 11y ago

I like that he doesn't use the typical AI jargon. I think its time to stop using science-fiction-writer terms to talk about our layered nonlinear unit systems. There is so much talk about "General AI", "Strong" and "Weak" AI, and also about "Intelligence", but none of these are well-defined enough to be useful. These are just words, not things that exist in the world out there. What we have here is a system that generalizes in a way that is human-like. It's a mystery why this looks so human-like to us, given that neural networks are only barely resembling of actual neural networks, and that is a problem. Reading the neuroscience of plasticity, it's still not clear whether learning takes place via synapse modifications. What these amazingly capable neural networks do, however, is to bolster the idea of connectionism in neuroscience, which is an interesting, given that the relationship between the two started the other way around. P.S. Michael Nielsen must be one of the very few people who can explain any concept in almost absolute clear and simple terms.

nsxwolf 11y ago

The problem I have being wowed by AlphaGo is that games like go and chess are so inscrutable to me that I cannot tell the difference between a game played by two strong players and a game played by a players making random legal moves. I have never won a game of chess in my entire life. Not against any human player or any Commodore 64 chess program on the easiest setting. I don't expect I ever will. I know how the pieces move and that's about it. Watson playing Jeopardy was different. That I could "get", and that impressed me.