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I'm reminded of Eliezer Yudkowski's article "There is no fire Alarm for Artificial General Intelligence." Is this smoke? https://intelligence.org/2017/10/13/fi

• deleted 9y ago
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• panic 9y ago
  The core technique of AlphaGo is using tree search as a "policy improvement operator". Tree search doesn't work on most real-world tasks: the "game state" is too complex, there are too many choices, it's hard to predict the full effect of any choice you might make, and there often isn't even a "win" or "lose" state which would let you stop your self-play.
  • habitue 9y ago
    This version explicitly does not use tree search.
    • panic 9y ago
      MCTS means "Monte-Carlo Tree Search". It's the core of the algorithm. The big difference is that it doesn't use rollouts, or random play: it chooses where to expand the tree based only on the neural network.
      • cjbprime 9y ago
        No, 'habitue is correct. This new blog post says that the new software no longer does game readouts and just uses the neural net.
        • Tarq0n 9y ago
          That's not what Monte Carlo Tree search is. The new version is still one neural network + MCTS. There's no way to store enough information to judge the efficiency of every possible move in a neural network, therefore a second algorithm to simulate outcomes is necessary.
        • Twirrim 9y ago
          Read the white paper. MCTS is still involved, right the way through.
    • ankeshanand 9y ago
      The new version does use MCTS, you should read the paper again. :)
    • AlexCoventry 9y ago
      It does, during training.
      • panic 9y ago
        Tree search is also used during play. In the paper, they pit the pure neural net against other versions of the algorithm -- it ends up slightly worse than the version that played Fan Hui, at about 3000 ELO.
        • AlexCoventry 9y ago
          Oh, so it's just not using rollouts to estimate the board position? Thanks for the clarification.
          • mrec 9y ago
            It doesn't use rollouts at all: > AlphaGo Zero does not use “rollouts” - fast, random games used by other Go programs to predict which player will win from the current board position. Instead, it relies on its high quality neural networks to evaluate positions.
    • dastbe 9y ago
      If you read the paper, they do in fact still use monte-Carlo tree search. They just simplify their usage in conjunction with reducing the number of neural networks to 1
• jorgemf 9y ago
  Games are a joke compare with real life. The number of variables and rules is well defined in games, while in real life it is not. That is why AGI is not coming anytime soon.
• Arcsech 9y ago
  While this is promising, there's a long way to go between this and the other things you mentioned. Go is very well-defined, has an unequivocal objective scoring system that can be run very quickly, and can be simulated in such a way that the system can go through many, many iterations very quickly. There's no way to train an AI like this for, say, health: We cannot simulate the human body to the level of detail that's required, and we definitely aren't going to be able to do it at the speed required for a system like this for a very long time. Producing a definitive, objective score for a paper clip collection is very difficult if not impossible. AlphaGo/DeepMind represents a very strong approach to a certain set of well-defined problems, but most of the problems required for a general AI aren't well-defined.
  • red75prime 9y ago
    > most of the problems required for a general AI aren't well-defined. Do you care to give an example? Are they more or less well defined than find-the-cat-in-the-picture problem? > Producing a definitive, objective score for a paper clip collection is very difficult if not impossible. Erm, producing of objective comparison of relative values of Go board positions is still not possible.
    • chrchang523 9y ago
      The absolute value of any Go board position is well-defined, and MCTS provides good computationally tractable approximations that get better as the rest of the system improves but already start better than random.
      • z0r 9y ago
        the absolute value of a go board position is well defined? where?
        • wetpaws 9y ago
          As a human go player I can say that evaluating board position is close to impossible. You may have a seemingly good position and in two turns it seems that you have lost the game already.
• geofft 9y ago
  ... yeah, before reading that link my position was "Wow, that's super neat, but Go is a pretty well-defined game," and after reading it I remembered that my position maybe a year or two ago was "Chess is a well-defined game that's beatable by AI techniques but Go is acknowledged to be much harder and require actual intelligence to play and won't be solved for a long while" and now I'm worried. Thanks for posting that.
  • goatlover 9y ago
    Go is still a well defined game within a limited space that doesn't change, and rules that don't change. It's just harder than Chess, but that doesn't make it similar to tons of real world tasks humans are better at.
    • geofft 9y ago
      That's probably true, but that's very much not what people were saying about Go a couple years ago. There were a lot of people talking about how there isn't a straightforward evaluation function of the quality of a given state of the board, how things need to be planned in advance, how there's much more combinatorial explosion than in chess, etc., to the point where it's a qualitatively different game. For me, as someone who accepted and believed these claims about Go being qualitatively different, realizing that no, it's not qualitatively different (or that maybe it is, but not in a way that impedes state-of-the-art AI research) is increasing my skepticism in other claims that board games in general are qualitatively different from other tasks that AIs might get good at. (If you didn't buy into these claims, then I commend you on your reasoning skills, carry on.)
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      • YeGoblynQueenne 9y ago
        About those claims- this is from Russel and Norvig, 3d ed. (from 2003, so a way back): Go is a deterministic game, but the large branching factor makes it challeging. The key issues and early literature in computer Go are summarized by Boozy and Cazenave (2001) and Muller (2002). Up to 1997 there were no competent Go programs. Now the best programs play most of their moves at the master level; the only problem is that over the course of a game they usually make at least one serious blunder that allows a strong opponent to win. Whereas alpha—beta search reigns in most games, many recent Go programs have adopted Monte Carlo methods based on the UCT (upper confidence bounds on trees) scheme (Kocsis and Szepesvari, 2006). The strongest Go program as of 2009 is Golly and Silver's MoGo (Wang and Golly, 2007; Gelly and Silver, 2008). In August 2008, MoGo scored a surprising win against top professional Myungwan Kim, albeit with MoGo receiving a handicap of nine stones (about the equivalent of a queen handicap in chess). Kim estimated MOGO's strength at 2-3 dan, the low end of advanced amateur. For this match, MoGo was run on an 800-processor 15 terailop supercomputer (1000 limes Deep Blue). A few weeks later, MoGo, with only a five-stone handicap, won against a 6-dan professional. In the 9 x 9 form of Go, MoGo is at approximately the 1-dan professional level. Rapid advances are likely as experimentation continues with new forms of Monte Carlo search. The Computer Go Newsletter, published by the Computer Go Association, describes current developments. There's no word about how Go is qualitatively different to other games, but maybe the referenced sources say something along those lines. Personally, I took a Masters course in AI two years ago, before AlphaGo and I remember one professor saying that the last holdout where humans can still beat computers in board games was GO, but I don't quite remember him saying anything about qualititative difference. Still, I can recall hearing about the idea that Go needs intuition or something like that, except I've no idea where I've heard that. I guess it might come from the popular press. I guess this will sound a bit like the perenial excuse that "if it works, it's not AI" but my opinion about Go is that humans just weren't that good at it, after all. We may have thought that we have something special that makes us particularly good at Go, better than machines- but AlphaGo[Zero] has shown that, in the end, we just have no idea what it means to be really good at it (which, btw, is a damn good explanation of why it took us so long to make AI to beat us at it). That, to my mind, is a much bigger and much more useful achievement than making a good AI game player. We can learn something from an insight into what we are capable of.
• tanilama 9y ago
  Wait for Alpha StarCraft for some real panic. So far RL based method has limited success outside of simple games(Not to say Go is simple, but rather the presentation and control parts of the format).
  • Tepix 9y ago
    I'd like to see a StarCraft player AI that wins using a mere 1/10th of the effective actions per minute (EPM) of world class players. To me it seems beating another player while using fewer actions indicates superior skill, understanding and/or intellect.
    • vitaflo 9y ago
      Not sure I agree with this fully. Certainly many actions used in a typical SC game are redundant, but there are reasons for it. Lag for one. If there's a possibility of lag or dropped packets, spamming a command will help nullify this problem. The other is the entire reason for high APM, the stop/start problem. Pro players keep high APM so that when they actually need high EPM their muscle memory is already at full tilt. If you slow down your APM during lulls in the action it becomes harder to suddenly increase it when a fight happens. Certainly that's an entirely human condition that a machine wouldn't need to worry about. But I'm not sure it means lack of skill.
• ohhhwell 9y ago
  Thanks for that link, well worth the read. This is an interesting question to ask in these "how far away is AGI" discussions: I was once at a conference where there was a panel full of famous AI luminaries, and most of the luminaries were nodding and agreeing with each other that of course AGI was very far off, except for two famous AI luminaries who stayed quiet and let others take the microphone. I got up in Q&A and said, “Okay, you’ve all told us that progress won’t be all that fast. But let’s be more concrete and specific. I’d like to know what’s the least impressive accomplishment that you are very confident cannot be done in the next two years.” There was a silence. Eventually, two people on the panel ventured replies, spoken in a rather more tentative tone than they’d been using to pronounce that AGI was decades out. They named “A robot puts away the dishes from a dishwasher without breaking them”, and Winograd schemas. Specifically, “I feel quite confident that the Winograd schemas—where we recently had a result that was in the 50, 60% range—in the next two years, we will not get 80, 90% on that regardless of the techniques people use.”
  • asah 9y ago
    IBM Watson on Winograd schemas? It beat jeopardy... ?
• ilaksh 9y ago
  The smoke is when things like the same simulated robot that learned to run around like a mentally challenged person also learns to simulate throwing and can read very basic language. It will seem quite stupid and inept at first. So people will dismiss it. But when they have a system with general inputs and outputs that can acquire multiple different skills, that will be an AGI, and we can grow it's skills and knowledge passed human level.
• durdn 9y ago
  You expressed my exact thoughts and I was about to link to the same insightful article. I guess my comment could've been shortened as a silent upvote, but I commented anyway.
• arikrak 9y ago
  > But the hockey-stick takeoff The hockey stick is lying horizontally though instead of vertically. If it took 3 days to go from 0 to beating the top player in the world, I wouldn't have expected it to take 21 days to beat next version. I guess something happens at the top levels of Go that make training much harder. On another note, I didn't look at the details closely but it seems AlphaGo Zero needed much less compute training time than Alpha Go Master. Could getting rid of any human inputs really make it that much more efficient? That implies it will be able to have an impact in many different areas, which is a bit scary... (Updated - it took 3 days to beat the top player in the world.)
  • gizmo686 9y ago
    This type of curve is what I would expect out of machine learning. At first there is rapid improvement as it learns the easy lessons. The rate then slows down as further incremental improvements become less impact. What is, perhaps, surprising is that human play happens to be relatively close to the asymptote. Although this could be explained by Alphago being the first system to beat humans. If its peek performance were orders of magnitude higher than humans, a weaker program would have already beaten us.
  • kazagistar 9y ago
    The horizontal hockey stick makes sense to me in terms of learning. Each increased layer of of understanding a complex system could mean a potentially exponentially increasing difficulty.
• nothis 9y ago
  I'm sure it's naive to jump to sci-fi conclusions just yet, but I admit it's equal parts fascinating and terrifying. The general message of the posts is that human knowledge is cute but not required to find new insights. Define the measure of success and momma AI will find the answer. At this point, the path to AGI is about who first defines its goals right and that seems... doable? Even scarier: We think the holy grail of AI is simulating a human being. The AI of the future might chuckle at that notion.
• empath75 9y ago
  You don't even need to produce an AGI for this kind of intelligence to be frightening. At some point, a military is going to develop autonomous weapons that are vastly superior to human beings on the battle field, with no risk of losing human lives, and there is going to be a blitzkrieg sort of situation as the relative power of nations shifts dramatically. If we have two such countries we could have massive drone and cyberwars being fought faster than people even can comprehend what's happening. Right now most countries insist on maintaining human control over the machinery of death. But that will only last for as long as autonomous death machines don't dominate the battlefield. It's a fun challenge right now to build a machine that can win in Starcraft, but it's really a hop skip and a jump from there to winning actual wars.
  • nocoder 9y ago
    In that case you just nuke the shit out of everybody or create army if autonomous suicide bomber with nukes, biological and chemical weapons of all kinds. Once all humans are extinct the harmony on earth will be restored and everyone will leave happily ever after.
  • Veedrac 9y ago
    Nuclear ICBMs already push us past that boundary. The world can no longer afford to fight a war seriously.
  • clarky07 9y ago
    i'm not sure robot soldier is scarier than nukes. generally speaking, if they are just single task robots performing functions in dangerous situations, that seems like an improvement to risking human lives.
• YeGoblynQueenne 9y ago
  I spent an hour of my life that I'll never get back reading Yudkowski's overly-long article and I believe I can summarise it thusly: "We don't know how AGI will arise; we don't know when; we don't know why; we don't know anything at all about it and we won't know anything about it until it's too late to do anything anyway; We must act now!!" The question is- if we don't know anything about this unknowable threat, how can we protect ourselves against it? In fact, since we're starting from 0 information, anything we do has equal chances of backfiring and bringing forth AGI as it has of actually preventing it. Yudkowski is calling for random action, without direction and without reason. Besides, if Yudkowski is none the wiser about AGI than anyone else, then how is he so sure that AGI _will_ happen, as he insists it will? Yudkowski is fumbling around in the dark like everyone else in AI. Except he (and a few others) has decided that it's a good strategy, under the circumstances, to raise a hell of a racket. "It's dark!" he yells. "Beware of the darkness!". Yeah OK, friend. It's dark- we can all tell. Why don't you pipe down and let us find the damn light?
  • gliptic 9y ago
    So, in your view, starting MIRI, doing fundamental research into AI safety and advocating for it, is not trying to find the damn light? You exemplify exactly the attitude he's trying to combat. "Oh, nobody knows anything, let's not care about consequences and do whatever."
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    • YeGoblynQueenne 9y ago
      Sorry but I don't really see Yudkowski's contributions as "fundamental research into AI safety". More like navel-gazing without any practical implications. At best, listening to him is just a waste of time. At worse, AGI is a real imminent threat and having people like him generating useless noise like he does will make it harder for legitimate concerns to be heard, when the time comes.
      • 9y ago