WilliamDhalgren
Joined 10 years ago
Joined 10 years ago
And, given the example of GeorgeRRMartin, perhaps something WordStar-like would fit the bill? If so, are there good free editors in the style of WordStar?
I have this friend, and I've seen the mess of unintended font changes, sizes, styles, bulleted lists, indentation changes. and all kinds of horrible stuff in his manuscripts when I had to repair some of that damage because it was getting unusable - think he'd appreciate a more focused alternative..
Still, I think AlphaGo does demonstrate amazing positional judgement in unseen board states, and that this is visible in the details of how it plays out particular situations. No two games are exactly alike - difficulty of go for computers is precisely in its extreme combinatorial explosion - and in particular tactical situations every detail of the situation matters. Yet you can see AlphaGo judging the correct sequences of moves, "knowing" how to make a particular group alive for eg, even when a particular other move seems more natural. And probably the most amazing thing about how it plays is how early it becomes completely sure that its got an advantage on the board, and how precisely it judges how much it needs to keep the advantage to the end. Every detail of the board is again relevant here, and basically no human would be so confident so soon. A go bot that couldn't adapt its tactics to unseen situation would be easy to beat; just ensnare it in a large complicated fight, and you're going to kill a big group and guarantee a win. Ofc people tried this in some masterP games, and turns out AlphaGo is tactically just as strong.
So, its basically like with other generalizations you can get from machine learning; a net trained on say ImageNet will generalize to different poses, occlusions, contexts and variations of objects similar to what it was exposed in training etc and still do a superhuman job of classifying such pictures, but will naturally be quite hopeless with completely unseen items. So too AlphaGo seems to know the game of go, generalizing from seen examples to correct judgements in other states, but would be quite hopeless if tested on even a slight variation of the game rules.
I don't think that's a very good description of how AlphaGo was trained at all; you're essentially saying it merely overfits the training set, yet it clearly generalizes rather well to unseen board situations and still evaluates them sucessfully. No machine learning system would be found usefull if all it could do is merely memorize the training data.
Re the use of deep reinforcement learning, well for one the role of reinforcement learning in the first version of AlphaGo, the one described in the Nature paper was rather limited, and a small part of its training; it just made a ~3d KGS policy network into a ~5d KGS bot, and used to generate a training sample for the value net. If we had enough recorded human games to train the value net directly, that'd be an unnecessary step anyhow. And you could create such a training set w/o reinforcement learning since there are pure monte carlo bots stronger than 5d KGS - but that'd be far more computationally expensive.
But its still not really true that there aren't obvious applications of deep reinforcement learning - indeed robotics is one promising application, and that seems rather relevant. this paper initially demonstrated an impressive improvement in manipulative tasks, and you can prob follow its numerous citations for newer stuff: http://arxiv.org/abs/1504.00702
I do agree that this exact architecture in AlphaGo prob doesn't have applications beyond just teaching us how to play go better; it seems too specialized. I believe they mean it in just the vaguest possible sense; that the kind of deep algorithms demonstrating incredible performance in AlphaGo have diverse applications; but this should not come as a surprise to anyone even loosely following what people have done with deep learning in the past couple of years anyhow.
There was a computer bot championship recently (UEC cup), but AlphaGo declined to participate. FineArts won, DeepZen was second. Think there's a few other chinese bots that could be stronger than Zen but didn't participate. So the real competition didn't bother to show up really.
FineArt is among the bots that have a positive score against top professionals, yes. But it also can lose to them too. MasterP showed that a computer can completely outclass humans!
After the series of games, it was revealed that MasterP is in fact AlphaGo. As far as we can tell from that series, AlphaGo is some serious ELO above other strong bots. So now the question remains - is it that dominant at longer time controls too, as those games were all quick. So that's this match.
could you mention a few more? And distinguish if its possible which of those were generally practiced as highly competitive full contact sports (within their rulesets of forbidden strikes etc), and which mostly as a martial art?
But actually looking for it now, the particular study I had in mind seems to be from a science or fiction segment from this episode:
http://media.libsyn.com/media/skepticsguide/skepticast2014-1...
Interesting to see how Steve's assesment of modafinil evolved between the two episodes
Somewhat relatedly, I think I heard on some old Skeptic's guide to the universe episode about an experiment with some nootropic, maybe it was a racetam though I think it was mondafinil, and this kind of reaction time to response accuracy tradeoff was observed, on young healthy adults. Those with less correct answers slowed down, presumably concentrated better and gave better answers, but those already good at whatever the task was simply were slower and yet no better.
I'm not confident at all they'll be able to completely ban geoblocking in one go. Hopeful they will at least poke some holes in it this round. Most parts of the single market needed a couple of revisions of a directive (quite a few years removed) before liberalizing a particular market fully..