that's an optimistic way to frame the situation; there's heavy opposition from the content industry to limits to geoblocking, and unsurprisingly the industry seems to have support within the commission (Oettinger at least - perhaps the fact he now left the Digital Economy and Society position helps).
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..
curious: what kind of txt editor(s) would make sense for non-code writing, say a novel, say w markdown or something similar? Especially if the person writing is non-technical so stuff like emacs, vim, atom don't seem like a great fit? I'm thinking something non-obtrusive and with limited options, so that UI is not getting in the way of writing, but with very litte learning curve?
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..
sounds like General Game Playing task. Well, MCTS alone is often used in that domain, and it wouldn't surprise me if Ke Jie was a weaker checkers player than just a brute MCTS, nevermind any attempts to use neural nets (if that would even make sense for checkers?)
Hm, well you are no doubt right that it doesn't generalize well to a change of rules. Reminds me of that game DeepZen played. It was trained with a komi of 7.5 and it played too soft and lost when in the actual match komi was 6.5 (or maybe it was the other way around?). A human does not have much trouble adapting to such small rules variations, but at least the version of DeepZen that played that match was hard-coded for that exact komi value, because that's what what used in all of its training examples, and wasn't given as a parameter. It shouldn't be a hard limit of the approach - indeed I think AyaMC was said to have been trained with some flexibility in its komi.
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.
> AlphaGo essentially baked good movies into value and policy network by playing millions of times.
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.
but humans obtain their watts very very inefficiently, so there's prob at least an order of magnitude to give for the same kind of system-level efficiency. Consider a field to feed a human vs a PV installation of the same physical size. And ofc there's all the other ways to obtain electricity...
It absolutely should be dominant in a long game too. Even if it loses some of its strength at such time settings, it shouldn't lose THAT much, it was just too superhuman. The play should be interesting though; Ke Jie both had access to other strong bots in China for a long time, and could study the records of the MasterP games; maybe he tries something interesting and gets interesting responses so we all learn a bit about the nature of go (haven't watched the recording of this game yet, just woke up).
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.
Nono, you missed the REAL computer supremacy event then; it was the 50ish (!!!) games MasterP bot played in january against the field of top go professionals on some asian go servers. The bot went 50-0, crushing all opponents often in interesting ways.
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.
> There are quite a small number of old martial art that's pretty decent still.
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?
could be, I really don't know. I can imagine perfectly benign scenarios too; if our long-term memory grows with time, maybe there's just more possible connections/associations to filter out with time too, so that decision just becomes harder, with only the deeper old age being simply tissue decline, the exhaustion of cognitive reserves etc.
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.
yeah, but apparently barely declines untill say 60+. Speed suffers however.
"The adult years were remarkable in that complexity remained at a high level for a protracted period, in spite of a slow decrease of speed during the same period. This suggest that during the adult period, people tend to invest more and more computational time to achieve a stable level of output complexity. Later in life (>70), however, speed stabilizes, while complexity drops in a dramatic way."
and
"These speed-accuracy trade-offs were evident in the adult years, including the turn toward old age. During childhood, however, no similar pattern is discernible. This suggests that aging cannot simply be considered a “regression”, and that CT (completion time) and complexity provide different complementary information. This is again supported by the fact that in the 25–60 year range, where the effect of age is reduced, CT and complexity are uncorrelated (r = −.012, p = .53). These findings add to a rapidly growing literature that views RIG tasks as good measures of complex cognitive abilities [21, for a review]."
well, if only categorisation of extremism online were better. Recently I was searching youtube with the word "Lokiarchaeota", an exciting very recent find in the origin of the eukaryotes, hoping for a scientific lecture, and was mostly getting creationist preachers as results.
Who the hell searches for priests by naming obscure microbes ?? And sadly this is hardly unique; i've been bombarded by UFOs, reptillian aliens and similar outlandish nonsense in search of factual science regularly. And its clearly not doing a reasonable job at predicting my interests at all, for those are not items I'd view.
Why google's imbecilic algorithms promote and push such extremist trash on the general populace is beyond me, degenerating many neutral search results page to worse than reading the yellow press, but this does sadly seem to be what's currently happening, and would reasonably lead many away from wonders that Internet could offer instead.
I don't exactly see what ITS brings to the table here, being a multi-stage rocket as anything else that puts stuff in GTO.
But anyhow yeah - the physics should favor them by practically an order of magnitude, but if they can ever survive to reap any percentage of that potential - they could just as likely just go bust at any further point in r&d, and they don't seem to have either much funding nor are they advancing particularly rapidly...
as far as I understood the starshot discussions, they weren't optimistic about managing to pack any interferometry equipment in such a small package, so there'd be little hope of proper chemical analysis... There was some discussion of what could be figured out from color alone, w/o spectra -- which sounds rather desperate.
A big telescope on earth/in orbit could possibly do more actual work on figuring out stuff about a nearby star than such seconds-long flybys of such wimpy payloads, which is all you get even after you manage the non-trivial challenges of building the phased array, surviving the ISM, establishing communications somehow etc.
at destination? There's no propulsion beam there; all acceleration would have been done at launch, in the vicinity of Earth. You can't point lasers over light-years of distance.
Why? Its supposed to be significantly more efficient, given that its breathing air for launch which enables much higher specific impulse than anything a chemical rocket even could do, and it'd have the operational advantages of being a pure single stage to orbit skyplane, which SpaceX also can't do.
Hard to imagine what else one could even want a launch system to do (except perhaps to be a nuclear turborocket ala http://www.youtube.com/watch?v=C46Dt-X0V8c , if that were politically feasable). And you can hardly say that of anything SpaceX is doing, given that those are simply incremental improvements of existing rocketry tech.
Presuming they ever manage to make it work (which is prob the big question), it should bring significantly more to cheap launch than SpaceX ever could on their current trajectory.
I dont want to go look it up to verify and properly link a citation etc, but I think Schumpeter argued that monopolies are actually good for innovation, at least of the (what's the term?) big bang, fundamental kind, because in a highly competitive marked you can't afford to set aside a significant % of budget for fundamental r&d that's only going to pay off in the very long term and not get outcompeted in the meantime.
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..