14 ms·

The thing that was supposed to take at least 10 years happened. Only last month people were still saying that no way AlphaGo will beat the champion and that it

• simonh 11y ago
  Highly optimised single-function algorithms like this are impressive stuff and can lead to useful tools, but that's it. This gets us no closer to strong AI than a tic tac toe program. Until we have systems that can tackle a wide range of fundamentally different problems and independently adapt strategies for dealing with one class of problems to deal with other classes of problems, systems like Alphago will remain one trick wonders with little relevance to 'true' AI. Edit: I do understand that the techniques used to implement Alphago can be used to implement other single-function solvers. That doesn't make it a general purpose strong AI.
  • lololomg 11y ago
    'True AI will always be defined as anything a computer can not yet do'
    • ubernostrum 11y ago
      Sort-of repeating a comment I made last time AlphaGo came up: As far as I know there is nothing particularly novel about AlphaGo, in the sense that if we stuck an AI researcher from ten years ago in a time machine to today, the researcher would not be astonished by the brilliant new techniques and ideas behind AlphaGo; rather, the time-traveling researcher would probably categorize AlphaGo as the result of ten years' incremental refinement of already-known techniques, and of ten years' worth of hardware development coupled with a company able to devote the resources to building it. So if what we had ten years ago wasn't generally considered "true AI", what about AlphaGo causes it to deserve that title, given that it really seems to be just "the same as we already had, refined a bit and running on better hardware"?
      • lololomg 11y ago
        > As far as I know there is nothing particularly novel about AlphaGo, By that standard there's nothing particularly novel about anything. Everything we have today is just a slight improvement of what we already had yesterday. World experts in go and ML as recently as last year thought it would be many more years before this day happened. Who are you to trivialize this historic moment?
        • argonaut 11y ago
          Some experts in Go less than 10 years ago believed it would be accomplished within 10 years. Also, you didn't actually refute his argument. Can you point to an algorithm that is not an incremental improvement over algorithms that existed 10 years ago? MCTS and reinforcement learning with function approximators definitely existed 20 years ago.
          • kqr 11y ago
            No, that's what they're saying. Take any invention and you can break it down into just a slight improvement of the sub-inventions it consists of. A light bulb is just a metal wire encased in a non-flammable gas and you run electricity through it. It was long known that things get hot when you run electricity through them, and that hot things burst into fire, and that you can prevent fire by removing oxygen, and that glass is transparent. It's not a big deal to combine these components. A lot of people still celebrate it as a great invention, and in my opinion it is! Think about how inconvenient gas lighting is and how much better electrical light is. Same thing with AlphaGo. Sure, if you break it down to its subcomponents it's just clever application of previously known techniques, like any other invention. But it's the result that makes it cool, not how they arrived at it! All algorithms are incremental improvements of existing techniques. This isn't a card you can use to diminish all progress as "just a minor improvement what's the fuss".
            • argonaut 11y ago
              No, not all inventions are incremental improvements of existing techniques. Backpropagation and convolutional nets, for example. Now, you might counter with the fact that it's just the chain rule (and convolution existed before that), but the point is it that algorithm had never been used in machine learning before. People have used neural nets as function approximators for reinforcement learning with MCTS for game playing well before AlphaGo (!!). Your lightbulb example actually supports my point. The lightbulb was the product of more than a half-century of work by hundreds of engineers/scientists. I have no problem with pointing to 70 years of work as a breakthrough invention.
  • Eliezer 11y ago
    In case you're not aware, AlphaGo's key component is based on the same type of Deepmind system that learned to play dozens of Atari games, to superhuman levels, by watching the pixels, without any programmatic adaptation to the particular Atari game. At least the version of AlphaGo that played in October was far less specialized for Go than Deep Blue was for chess. Demis Hassabis says that next up after this is getting Deepmind to play Go without any programmatic specialization for Go. Your reply would be appropriate if we were talking about Deep Blue, chess, and 1997.
    • Fede_V 11y ago
      That's incorrect. The features that AlphaGo uses are not pixel level features, but board states - and the architecture between AlphaGo and the Atari network is completely different. It's still an incredibly achievement - but it's important to be accurate.
      • DavidSJ 11y ago
        For AlphaGo, a "pixel" is a point on the board. It uses essentially the same convolutional neural networks (CNNs) that are in state-of-the-art machine vision systems. But yes, the overall architecture is rather different from the Atari system, due to the integration of that CNN with Monte Carlo Tree Search.
        • drawnwren 11y ago
          Sorry, you're off base a bit. The Atari system did use a Deep Neural Network / Reinforcement algorithm, but as the original poster was trying to point out, the rules of Go were very much hard coded into AlphaGo. From what this [1] says, multiple DNNs are learning how to traverse Monte Carlo trees of Go games. The reinforcement piece comes in choosing which of the Go players is playing the best games. While the higher portions do share some similarities with the Atari system, at a basic level this is a machine that was designed and trained to play Go. AlphaGO is 'essentially the same' as the Atari system in the same way that all Neural Networks are 'essentially the same.' Is this an extremely impressive accomplishment? Yes. However, doesn't seem to qualify as anything close to generalizable. [1] http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html http://googleresearch.blogspot.com/2016/01/alphago-mastering...
  • danielbarla 11y ago
    Regarding your edit, I'd wager that it won't stay true for long. Eventually the single-function problem of orchestrating and directing a bunch of sub-solvers in a similar manner to the human brain will become feasible. At that point true general purpose AI will exist, for all intents and purposes.
    • PascalsMugger 11y ago
      And when that happens I guarantee someone will be on HN saying it is "trivial" and "not a real advancement" and "we already basically knew how to do this". Because that's what people say every time something interesting happens in AI, without fail.
  • Houshalter 11y ago
    Welcome to the AI effect! Every time AI makes an accomplishment, it is disregarded. The goalposts are perpetually moved. "AI is whatever computers can't do yet." People said for years that Go would never be beaten in our lifetime. They said this because Go has a massive search space. It can't be beaten by brute force search. It requires intelligence, the ability to learn and recognize patterns. And it requires doing that at the level of a human. A brute force algorithm can beat humans by doing a stupid thing far faster than a human can. But a pattern recognition based system has to beat us by playing the same way we do. If humans can learn to recognize a specific board pattern, it also has to be able to learn that pattern. If humans can learn a certain strategy, it also has to be able to learn that strategy. All on it's own, through pattern recognition. And this leads to a far more general algorithm. The same basic algorithm that can play Go, can also do machine vision, it can compose music, it can translate languages, or it could drive cars. Unlike the brute force method that only works one one specific task, the general method is, well, general. We are building artificial brains that are already learning to do complex tasks faster and better than humans. If that's not progress towards AGI, I don't know what is.
    • argonaut 11y ago
      The "moving goalposts" argument is one that really needs to die. It's a classic empty statement. Just because other people made <argument> in the past does not mean it's wrong. It proves nothing. People also predicted AGI many times over-optimistically; probably just as often as people have moved goalposts.
      • Houshalter 11y ago
        I don't know what you are trying to say. I'm making an observation that whenever AI beats a milestone, there are a bunch of pessimists that come out and say "but obviously X was beatable by stupid algorithms. I will beleive AI is making progress when it beats Y!" Those arguments absolutely are wrong. For one thing it's classic hindsight bias. When you make a wrong prediction, you should update your model, not come up with justifications why your model doesn't need to change. But second, it's another bias, where nothing looks like AI, or AI progress. People assume that intelligence should be complicated, that simple algorithms can't have intelligent behavior. That human intelligence has some kind of mystical attribute that can't be replicated in a computer.
  • Confusion 11y ago
    You are not a general purpose strong AI either. Your mind could easily consist of a whole bunch of single-function solvers, combined into networked components. See this interview between Kurzweil and Minsky: https://www.youtube.com/watch?v=RZ3ahBm3dCk#action=share https://www.youtube.com/watch?v=RZ3ahBm3dCk#action=share
  • taneq 11y ago
    I don't think anyone's claiming that AlphaGo is an AGI in and of itself, just that it's a significant step towards one. There's still a lot to go before we can toss a standardized piece of hardware+code into an arbitrary situation and have it 'just figure it out'.
  • JabavuAdams 11y ago
    > This gets us no closer to strong AI than a tic tac toe program. We don't know that, actually. Maybe GAI isn't one shining simple algo, but cobbling together a bunch of algorithms like this one.
  • empath75 11y ago
    1) this isn't a single function algorithm 2) the human mind is FULL of ugly, highly optimized hacks that accomplish one thing well enough for us to survive. Don't assume that human intelligence is this magical general intelligence, rather than a collection of single function algorithms.
    • argonaut 11y ago
      It is. Value and policy networks are nonlinear approximators for value and policy functions. You're making the mistake of assuming anything about how the human brain learns.
• panic 11y ago
  DeepMind is an algorithm which clearly improves a lot on traditional tree search. But how will a better tree search algorithm lead to AI taking over? Why does winning at Go mean AI is closer to taking over the world than when it won at Chess?
  • 21 11y ago
    The issue is that we don't take the threat seriously, since we believe we have plenty of time to solve it. Some don't even believe it's a threat at all (will unplug it, right?) This 10 years to beat go prediction shows that our time estimates are wildly ignorant.
  • kevinwang 11y ago
    ah, but it's not just tree search. It uses neural nets, which makes it different than the chess algorithms.
  • Eliezer 11y ago
    It's not an improved tree search. Deepmind was almost pro level purely using the deep learning network before doing any Monte Carlo search. One way of looking at the significance of this is that it might tell us that relatively simple machine learning algorithms can capture key aspects of the versatile human cortical capacity to learn things like Go using sheer pattern recognition. (It's amazing that human visual cortex can do that.) If the human brain were more mystical in its power, then human-level ability to recognize Go patterns wouldn't have been penetrable at all to a comparatively simple neural algorithm like deep learning. From another standpoint, this could show that we're reaching the point where, when an AI algorithm starts to reach interesting levels of performance, a much-encouraged Google dumps 100,000 tons of GPU time and 5 months of a few dozen researchers' time to improve the algorithm right past the human performance level. In N years from now when it's a more interesting AI system doing more interesting cognition, we could see a more interesting result materialize when a big organization, encouraged by some key milestone, invests 5 months of time and massively more computing power to further improve an AI system.
    • panic 11y ago
• Teodolfo 11y ago
  Many of the people saying not to worry ALSO predicted AlphaGo. So let's not get ahead of ourselves.
  • Eliezer 11y ago
    [Citation needed.] I for one did not say to not worry, and I bet $1500 vs. $2250 on Sedol winning the match before getting cold feet and arbing my outstanding bets down to $400 vs. $700.
    • argonaut 11y ago
      Why does Teodolfo even need a citation for that? Are you really suggesting that there is no group of people who believe AI is not a threat to worry about, and believed that AlphaGo would win?
      • Eliezer 11y ago
        I'm sure there's someone on the planet. Which is a very different state of affairs from the one that would obtain if a well-known AI researcher had, say, made a $1500 public bet on AlphaGo winning, while also loudly declaring that worrying about AGI was like worrying about overpopulation on Mars, in the company of other researchers declaring similar combined expectations. What actually happened was the reverse of that; AI moved faster than I publicly bet a large sum of money on it moving, and I was already worried before then. I'm not aware of the reverse-reverse having happened. If you're claiming something as a successful advance prediction to bolster belief in a general model, it's fair play to ask for a record of that advance prediction.
        • argonaut 11y ago
          Now you're moving the standard-of-citation-evidence goalposts ;)
      • davidiach 11y ago
        @Eliezer, but you might still win the bet, this was just the first match.
• onion2k 11y ago
  Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out. As hard as writing AI for a problem space like "how to win at Go" is, it's several orders of magnitude easier than creating a general AI with the self-awareness required to see us as a threat.
  • CamperBob2 11y ago
    The interesting thing is that this algorithm taught itself to master the game. If you're writing a chess engine, you just "write the AI for the problem space" in a few dozen lines of code, as you put it, and let brute force do the rest. In the case of DeepMind, faced with a game where a traversal approach is numerically impossible, its programmers gave it the ability to improve by playing against itself. That's the difference between a neural net -- which until about an hour ago was a phrase that reliably set off my snake-oil detector -- and a simple tree search. DeepMind just beat a Go master... and nobody can say exactly how it did it. That's a big deal.
    • riffraff 11y ago
      it is a big deal, but it is still very different from general AI, and we are very good at underestimating complexity. Remember that "solve computer vision" was considered a summer project.
      • faizshah 11y ago
        I think it's the same as what happened when the perceptron was the big thing in the 50s/60s. Many made far overreaching conclusions about how the problem of AI had been solved.
      • CamperBob2 11y ago
        Remember that "solve computer vision" was considered a summer project. And they're pretty much there. Have you seen some of the latest results in that field?
• tim333 11y ago
  Some people may say AI being a worry is 50 years but not usually smart ones who know anything much about it.
• exDM69 11y ago
  It's only been the first round and I'm not throwing in the towel yet. Unlike AlphaGo, Lee Sedol has an opportunity to learn from their opponents since AlphaGo takes about 30 days of wall clock time to train the networks. There will be 5 games during the next week. Despite my optimism, the writing is on the wall. AlphaGo and algorithms like it will only improve as you throw more CPU time at them. I actually want Lee Sedol to win, not because it would uphold some kind of human supremacy but because I want to see the AI guys put some more effort (and CPU time) into it. It would be a real shame if they'd win on their first attempt.
  • deleted 11y ago
    [deleted]
• YeGoblynQueenne 11y ago
  Go is literally infinitely more easy to solve than general intelligence. "Literally" in the sense that Go has a finite number of board states, while a general intelligence must be able to deal with an infinite amount of novel situations, presumably by generalising from previously experienced ones. Infinity is a real problem. When you try to learn from examples, you first need to see "enough" examples of whatever you're trying to learn. If there are infinitely many such examples, no matter how clever you are in tackling your search space there will always be infinitely many examples of infinitely many situations you've never come across, and that you won't be able to learn. The typical example of this is language. You could give a learner all phrases of a given language every produced and it would still be missing an infinite amount of necessary examples. Somehow (and it's freaky when you stop to think about it) humans get around this and we can produce and understand parts of infinity, without sweating it. Machine learning is simply incapable of generalising like that and anyone who thinks AGI is just around the corner has just failed to consider what "general" really, really means. Though to be fair, now that I had my little rant I have to admit that you don't need to go "general intelligence" before you can be really, really dangerous. Even if AI doesn't "take over" it can do a lot of damage, frex if we start using autonomous weapon systems or hand over critical infrastructure maintenance to limited and inflexible mechanical intelligence.
  • escoz 11y ago
    Yes, go have a finite number of board states, just 2.08168199382×10^170, just a bit over the 10^80 atoms in the universe. https://en.wikipedia.org/wiki/Go_and_mathematics https://en.wikipedia.org/wiki/Go_and_mathematics
    • argonaut 11y ago
      Not all of those board states are "novel." The true "dimensionality" of "novel patterns" in Go is much smaller.
    • YeGoblynQueenne 11y ago
      Yes, but the point is that you 're not going to brute-force this. If it's finite you can hope to approximate it. If it's not finite, you can't even approximate it. Look- take Monte Carlo methods. You can sample a very big number of events and hope to get some useful information from that. If you sample infinity, though, what do you get? Infinity.
• z0r 11y ago
  I was one of the people who rooted for the champion last month, and my position hasn't changed. Last night's game was very exciting, but if the pressure doesn't get to Sedol he has a chance. He made some mistakes in the game and AlphaGo played incredibly well. General opinion I've seen so far is that the lead switched hands in a few places, although I'm waiting for professional commentaries to illuminate what possibilities were there for both players - the played game is always just the tip of the iceberg