AlphaGo beats the world champion Lee Sedol in first of five matches - <AUTHOR> https://twitter.com/mustafasuleymn/status/707469083458068480 <comment><USER3> I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing computer in history. Kasparov drew that match, but it was clear it was the last stand of homo sapiens in the man vs machine chess battle. Back then, people took solace in the game of Go. Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes.

Tonight, that happened. Google's DeepMind AlphaGo defeated the world Go champion Lee Sedol. An amazing testament to humanity's ability to continuously innovate at a continuously surprising pace. It's important to remember, this isn't really man vs machine, as we humans programmed the algorithms and built the computers they run on. It's really all just circuitous man vs man.

Excited for the next "impossible" things we'll see in our lifetimes.

<comment><USER4> I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.

<comment><USER5> It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go _no matter how much computing power we threw at it_. Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.

<comment><USER6> > Give it ten years and, between algorithmic optimizations and advances in > processing, you'll have an unbeatable Go app on your phone.

I find this overly optimistic because of the huge amount of power required to run the Go application. Remember, we're getting closer and closer to the theoretical lower limit in the size of silicon chips, which is around 4nm (that's about a dozen silicon atoms). That's a 3-4x improvement over the current state of the art.

The computer to run AlphaGo requires thousands of watts of power. A smartphone can do about one watt. A 3-4x increase in perf per watt isn't going to cut it.

If there will be a smartphone capable of beating the best human Go players, my guess is that it won't be based on general purpose silicon chips running on lithium ion batteries.

On the other hand, a desktop computer with a ~1000 watt power supply (ie. a gaming pc) might be able to do this in a matter of years or a few decades.

<comment><USER7> As solid as your argument may be, everyone saw arguments like this over and over. Every single time they were solid. For a time, it was the high frequency noise that would not be manageable (80s), then heat dissipation (90s), then limits on pipeline optimization (00s) and now size constraints on transistors. They were all hard barriers, deemed impossible and all were overcome.

I already know that your answer will be: "but this time it is a fundamental physics limit". Whatever. I'm jaded by previous doomsday predictions. We'll go clockless, or 3D, or tri-state or quantum. It'll be something that is fringe, treated as idiotic by current standards and an obvious choice in hindsight.

<comment><USER8> This looks like a good example of the Normalcy bias logical fallacy: [https://en.wikipedia.org/wiki/Normalcy_bias](https://en.wikipedia.org/wiki/Normalcy_bias)

That previous constraints have been beaten in no way supports the argument that we will beat the laws of physics this time.

<comment><USER9> The previous problems were solved because people were willing to spend hundreds of billions of dollars to solve them. And they are still spending that kinds of money.

If the normalcy bias was in effect, they wouldn't be spending that money.

<comment><USER10> Actually Normalcy Bias may in fact feed that kind of money spending until such time as reality hits. Assuming that people will automatically act more logically when large amounts of money is in play flies in the face of recent history. Just look at the recent housing loan crisis. Normalcy Bias played a part there.

It's certainly possible that we'll break more barriers with clever engineering and new scientific breakthroughs. But that doesn't mean the Normalcy Bias isn't in play here.

<comment><USER9> Normalcy bias may have people spending lots of money on fabs assuming that the problems would be solved by the time the fabs are built.

However, I'm talking hundreds of billions spent on R&D to specifically to solve problems associated with chip manufacture. It took on the order of 25 years to solve each of the problems listed in the grandparent's post. Nobody would spend that kind of money or time on something that they think somebody else would solve.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER11> This is my generation's Gary Kasparov vs. Deep Blue. In many ways, it is more significant.

Several top commentators were saying how AlphaGo has improved noticeably since October. AlphaGo's victory tonight marks the moment that go is no longer a human dominated contest.

It was a very exciting game, incredible level of play. I really enjoyed watching it live with the expert commentary. I recommend the AGA youtube channel for those who know how to play. They had a 9p commenting at a higher level than the deepmind channel (which seemed geared towards those who aren't as familiar).

<comment><USER12> Yep, terrific commentary by Myungwan Kim 9p on the AGA channel.

For the folks who aren't as familiar with the game, how did you find the commentary (for any channel)? What would you be interested in hearing for events like these?

<comment><USER13> I really enjoyed Myungwan's down-and-dirty commentary, and watching him get lost in some variations, and it was just incredibly exciting to see him get won over to AlphaGo during the game. From about move 50, I was just viscerally excited to see where things went, and the game did not disappoint in any way.

I've read a few different reviews and watched Michael Redmond's live commentary as well, who obviously has a slower Japanese style of play than Myungwan, and his variations all exhibited a very thorough style and sensibility, but I think he missed the key moment, and Myungwan called it -- the bottom right just killed Lee Sedol, and it was totally unexpected.

And, Sedol was thinking about it too, because right after he resigned, he wanted to clear out that bottom right corner and rework some variations. I presume that's one frustration playing with a computer -- they'll have to instrument AlphaGo to do a little kibbitzing and talking after a game. That would be just awesome.

If you are very, very inspired by AlphaGo's side of this, it's really incredible to imagine, just for a moment, that building that white wall down to the right was in preparation for the white bottom right corner variation. The outcome of that corner play was to just massively destroy black territory, on a very painful scale, and it made perfect use of the white wall in place from _much_ earlier in the game.

If AlphaGo was in fact aiming at those variations while the wall was being built, I would think at a fundamental level, Go professionals are in the position that chess grandmasters were ten years ago -- acknowledging they will never see as deeply as a computerized opponent. It's both incredibly exciting, and a blow to an admirable and very unusual group of worldwide game masters.

I loved every minute!!

<comment><USER14> Building the wall down to attack the bottom right corner isn't something outrageous, not to those at the level of Sedol. AlphaGo definitely played amazing, as the game was very technical in term of fighting. But the "flow" (chase out weak group then invade a corner) is a fairly common situation. I don't think it's a matter of AlphaGo seeing strategy further than Sedol. It might have had much deeper calculation and reading than Sedol - as showed in deflecting the attachment in lower right - but that's a bit of a different story.

</comment></comment></comment></comment><comment><USER15> I was really hoping to see a more technical discussion than what I found here in the comments. It's too bad that such a cool accomplishment gets reduced to arguments about the implications for an AI apocalypse and "moving the goalposts". This isn't strong AI, and it was at least believed to be possible (albeit incredibly difficult), but it is still a remarkable achievement.

To my mind, this is a really significant achievement not because a computer was able to beat a person at Go, but because the DeepMind team was able to show that deep learning could be used successfully on a complex task that requires more than an effective feature detector, and that it could be done without having all of the training data in advance. Learning how to search the board as part of the training is brilliant.

The next step is extending the technique to domains that are not easily searchable (fortunately for DeepMind, Google might know a thing or two about that), and to extend it to problems where the domain of optimal solutions is less continuous.

<comment><USER16> > without having all of the training data in advance

What? They certainly trained the algorithm on a huge database of professional go games. It's even in the abstract. [1]

[1]: [http://www.nature.com/nature/journal/v529/n7587/full/nature1...](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html)

<comment><USER15> > What?

 _Exactly_

They used the game database to learn the value network, then reinforcement learning of the policy network was performed on self-play games. I.e., the machine learned to play from existing data, then played against itself to learn the search heuristics (the policy network) without the need for expert data.

<comment><USER16> Your claim still doesn't make sense. They either used expert data or they didn't. If the algorithm would lose when they remove the expert data, then they really do _need_ expert data.

The tree search wasn't even the novel part of the algorithm... the authors even cite others who had used the identical technique in previous Go algorithms.

<comment><USER15> It seems that my original comment is unclear. My apologies for the ambiguity. I did not mean that they do not need _any_ expert data, but that part of the training did not require a training data set.

They definitely need training data to learn the value function, but training the policy network is based on self-play. While MCTS is not new, I believe bootstrapping reinforcement learning with self-play to train a policy network that guides the MCTS is novel.

</comment></comment></comment></comment></comment><comment><USER17> I posted in the earlier thread because this one wasn't up yet[1].

Some quick observations

1\. AlphaGo underwent a substantial amount of improvement since October, apparently. The idea that it could go from mid-level professional to world class in a matter of months is kinda shocking. Once you find an approach that works, progress is fairly rapid.

2\. I don't play Go, and so it was perhaps unsurprising that I didn't really appreciate the intricacies of the match, but even being familiar with deep reinforcement learning didn't help either. You can write a program that will crush humans at chess with tree-search + position evaluation in a weekend, and maybe build some intuition for how your agent "thinks" from that, plus maybe playing a few games. Can you get that same level of insight into how AlphaGo makes its decisions? Even evaluating the forward prop of the value network for a single move is likely to require a substantial amount of time if you did it by hand.

3\. These sorts of results are amazing, but expect more of the same, more often, over the coming years. More people are getting into machine learning, better algorithms are being developed, and now that "deep learning research" constitutes a market segment for GPU manufacturers, the complexity of the networks we can implement and the datasets we can tackle will expand significantly.

4\. It's still early in the series, but I can imagine it's an amazing feeling for David Silver of DeepMind. I read Hamid Maei's thesis from 2009 a while back, and some of the results presented mentioned Silver's implementation of the algorithms for use in Go[2]. Seven years between trying some things and seeing how well they work and beating one of the best human Go players. Surreal stuff.

\---

1\. [https://news.ycombinator.com/reply?id=11251526&goto=item%3Fi...](https://news.ycombinator.com/reply?id=11251526&goto=item%3Fid%3D11250748)

2\. [https://webdocs.cs.ualberta.ca/~sutton/papers/maei- thesis-20...](https://webdocs.cs.ualberta.ca/~sutton/papers/maei- thesis-2011.pdf) (pages 49-51 or so)

3\. Since I'm linking papers, why not peruse the one in Nature that describes AlphaGo? [http://www.nature.com/nature/journal/v529/n7587/full/nature1...](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html)

<comment><USER18> Since the European match went 5-0, how do we know the bot wasn't just as good months ago?

<comment><USER19> [https://gogameguru.com/go-commentary-deepmind-alphago-vs- fan...](https://gogameguru.com/go-commentary-deepmind-alphago-vs-fan-hui- game-5/)

</comment></comment></comment><comment><USER20> Just for context, this is the first of a five-game match. Next one tomorrow at the same time! (6am CEST, 8pm PT).

<comment><USER21> Thank you. The title on HN here didn't imply it was a 5 game series at all, nor did the tweet it linked to.

It's a cool win but despite the way the titles are being presented, this isn't over yet.

</comment></comment><comment><USER22> What an incredible moment - I'm so happy to have experienced this live. As noted in the Nature paper, the most incredible thing about this is that the AI was not built specifically to play Go as Deep Blue was. Vast quantities of labelled Go data were provided, but the architecture was very general and could be applied to other tasks. I absolutely cannot wait to see advancements in practical, applied AI that come from this research.

<comment><USER23> Here's the Nature article: [http://www.nature.com/news/google-ai-algorithm- masters-ancie...](http://www.nature.com/news/google-ai-algorithm-masters- ancient-game-of-go-1.19234) (it has a link to the free paper, as well)

The position evaluation heuristic was developed using machine learning, but it was also combined with more 'traditional' algorithms (meaning the monte-carlo algorithm). So it was built specifically to play go (in the same way deep blue used tree searching specifically to play chess.....though tree searching is applicable in other domains).

</comment></comment><comment><USER24> I just wrote a blogg about this. I was up to 1am this morning watching the game live. I became interested in AI in the 1970s and the game of Go was considered to be a benchmark for AI systems. I wrote a commercial Go playing program for the Apple II that did not play a very good game by human standards but did play legally and understood some common patterns. At about the same time I was fortunate enough to get to play both the woman's world Go champion and the national champion of South Korea in exhibition games.

I am a Go enthusiast!

The game played last night was a real fight in three areas of the board and in Go local fights affect the global position. AlphaGo played really well and world champion (sort of) Lee Sedol resigned near the end of the game.

I used to work with Shane Legg, a cofounder off DeepMind. Congratulations to everyone involved.

</comment><comment><USER25> I watched the commentary that Michael Redmond gave (9-dan-professional) and he didn't point out one obvious mistake that Lee Sedol made the entire match. Just really high quality play by AlphaGo.

Really amazing moment to see Lee Sedol resign by putting one of his opponent's stones on the board.

<comment><USER26> Yeah according to Redmond, it seemed that AlphaGo made a few "mistakes" whereas Sedol made none. And yet AlphaGo came out substantially ahead. So I'm not sure what that means. Perhaps we need to see more in-depth analysis of the moves, but it seems that AlphaGo just out-calculated Sedol.

<comment><USER27> I wonder if their move selection algorithm takes into account the "surprise" factor: given two moves that are almost equal in strength when analyzed to a depth of N, chose the one that looks worst at N-1. That is, if all else is equal, assume that you can search deeper than your human opponent, and lay traps accordingly.

<comment><USER28> Besides trap-laying, there's also a second useful "surprise" factor: your opponent is likely to have spent time on your clock to read out follow-ups to your most likely move. By throwing in an unlikely (but still good!) move you're forcing them to expend time on their clock to re-think their follow- ups.

</comment></comment></comment></comment><comment><USER29> I was really expecting Lee Sedol to win here. I'm very excited, and congratulations to the DeepMind team, but I'm a bit sad about the result, as a go player and as a human.

<comment><USER30> If it's any consolation, there are still tons of things humans are far better at than machines.

<comment><USER31> The only remaining are language-related. Natural languages are the next focal point of AI research.

<comment><USER32> Skill related. I'd be interesting to see how quickly driving AIs take to beat the best human drivers, in a weight-equal vehicle. An algorithmic competitor in formula one, would be interesting.

<comment><USER33> Would be a stomp for the computer; they don't have to respect G-forces.

<comment><USER32> Racing cars aren't limited by the driver's G tolerance, I don't think. They generate 4G-ish, from what I hear on the F1 coverage. Well within driver capabilities. Their G is limited by tyres.

</comment></comment></comment></comment></comment></comment><comment><USER34> "AlphaGos Elo when it beat Fan Hui was 3140 using 1202 CPUs and 176 GPUs. Lee Sedol has an equivalent Elo to 3515 on the same scale (Elos on different scales aren't directly comparable). For each doubling of computer resources AlphaGo gains about 60 points of Elo."

<comment><USER5> So has AlphaGo raised its level so far just by continuing with the games against itself? Or did they just throw their entire server farm at it? (Or both, probably.)

<comment><USER35> Demis said it used roughly the same hardware resources as against Fan Hui?

<comment><USER36> When playing, yes. Training is a different kettle of fish.

</comment></comment></comment></comment><comment><USER37> Terrific accomplishment.

Just a question to throw out there - does anyone feel like statements like this one "But the game [go] is far more complex than chess, and playing it requires a high level of feeling and intuition about an opponent's next moves."

… seem to show a lack of understanding of both go and chess?

I understand there may be some cross-sports trash talking, but chess, played at a high level _by humans_ , relies on these things as well. The more structured nature of chess means that it is (or at least was) more amenable to analysis by brute force computer algorithm, but no human evaluates and scores hundreds of millions of positions while playing chess or go.

Eh, the mainstream media is going to say this regardless, and I suppose it's just unrealistic to expect them to draw a distinction between _complex for humans_ and _amenable to brute force computation_ but statements like this always seemed to show a remarkable lack of awareness of how people actually play these games (though I am not an especially skilled chess or go player).

<comment><USER38> No, I think the statement's approximately correct. Chess has an average branching factor of 35, Go has an average branching factor of 250, intuition is required to prune candidate moves in Go in a way that it is only extremely minimally required in Chess.

<comment><USER37> But is this true as humans play it? I'm not good enough at either to really know for sure, but my impression is that while the branching factor makes a big difference for computers, it is essentially impossible for a human to manage the branching in either game (massive numbers of branches vs exceptionally massive numbers of branches). As a result, humans play both games at a high level by relying on intuition.

For instance, I read a while back (approximating and paraphrasing to follow...) that top chess players can think up to 10 moves ahead along a very few branches. So let's say that in chess, there are 30 million possible positions to evaluate, and in go, there are 300 trillion. They're both such an order of magnitude different for humans that it makes really no difference in terms of how _we_ play the game, so intuition takes over. For computers, it's a different story.

<comment><USER38> I think humans usually have ten or less plausibly-good moves to consider per turn in Chess, and simply consider all of them, compared to tens to hundreds in Go.

</comment></comment></comment></comment><comment><USER39> The funny thing about AI at this scale is we don't really know why the computer does what it does. It's more of a inductive extrapolation that we can verify that a technique works for a small problem, so we'll throw a whole bunch of GPU power and data at it and it SHOULD work for a big problem. How it actually works is fuzzy though as there's just a couple of gigabytes of floats representing weights in neural networks. No human can look at that and say: "Oh! I see why it made that move". It's so much data that it becomes kind of nebulous what the AI is doing.

<comment><USER40> It certainly seems incomprehensible now, but that isn't necessarily true in neural networks - the amazing thing about some of the experiments using the results of intermediate layers in image recognition is that they seem to be building up higher and higher order "understanding" as you get to deeper and deeper layers which correlates directly with how a human might explain their strategy.

You can imagine a means of interpreting intermediate layers of alphago's weighting function similar to the second image in [1] (not the best example, I apologise) that would produce images or other abstract representations of the strategy that layer was encoding, similar to how a human might classify moves or patterns into categories.

[1] [http://cs231n.github.io/convolutional- networks/](http://cs231n.github.io/convolutional-networks/)

<comment><USER39> There's a name for this phenomenon: "Subsystem inscrutablity". See this presentation on Alpha Go linked to from here: [http://nextbigfuture.com/2016/03/what-is-different-about- alp...](http://nextbigfuture.com/2016/03/what-is-different-about-alphago- versus.html?m=1)

</comment></comment></comment><comment><USER41> After Go, the next AI challenge they're looking at is Starcraft: [https://twitter.com/deeplearning4j/status/706541229543071745](https://twitter.com/deeplearning4j/status/706541229543071745)

<comment><USER32> The obvious problem is that speed of tactical execution can make up for a lot of strategic thought. The famous example: you can rush a line of siege tanks with zerglings if you can micro them fast enough[0].

[0]:[https://www.youtube.com/watch?v=IKVFZ28ybQs](https://www.youtube.com/watch?v=IKVFZ28ybQs)

<comment><USER42> I hope that in the interest of fair play they'll limit their AI to 300 APM or so. Make it win not on mechanical execution, but on decision making.

<comment><USER41> Even with that though, They say Starcraft is still 5-10 years out for AI to beat pros: [http://www.newyorker.com/tech/elements/deepmind- artificial-i...](http://www.newyorker.com/tech/elements/deepmind-artificial- intelligence-video-games) (ctrl+f for Starcraft at the bottom of this article) \-----

</comment></comment></comment></comment><comment><USER43> The YouTube video: [https://www.youtube.com/watch?v=vFr3K2DORc8](https://www.youtube.com/watch?v=vFr3K2DORc8)

<comment><USER44> Is this video laggy and constantly showing "The match will start in -0- seconds" for anyone else?

<comment><USER45> Yes, apparently it gets better towards the end, but I have had to give up watching the start because it is too annoying.

</comment></comment></comment><comment><USER46> We still have Arimaa. It's designed specifically to make it difficult for computers to play.

[http://arimaa.com/arimaa/](http://arimaa.com/arimaa/)

<comment><USER47> A computer won the Arimaa challenge last year.

[https://en.wikipedia.org/wiki/Arimaa](https://en.wikipedia.org/wiki/Arimaa)

<comment><USER48> I guess the only thing left is to design a game where you can change the rules of the game as a turn.

<comment><USER49> Might not work for long. There are already contests for best generic solutions[0] and it seems like quite popular topic in machine learning.

[0] [https://en.wikipedia.org/wiki/General_game_playing](https://en.wikipedia.org/wiki/General_game_playing)

</comment></comment></comment></comment><comment><USER50> A human was beaten with some thousands of CPUS & GPUS. On a calorie level, the human is still more efficient.

On a time to learn these skills... going from zero (computer rolls off assembly line) to mastery, the computer wins.

Actually maybe the computer wins even on the caloric level, if you consider all the energy that was required to get the human to that point (and all the humans that didn't get to that point, but tried).

<comment><USER51> But the computer certainly does not win on the amount of training samples required. The human is at the same level as the computer now for Go, but the computer has had much more training samples as Lee Sedol could process in his lifetime.

The next step is to reduce the training time/samples for the computer to get the same performance.

<comment><USER52> That's silly. Why would you want to put human limitations on the computer? We don't artificially put computer limitations on the human.

<comment><USER51> I don't want to put a limit on the computer, not at all. But I do think humans have an edge on computer because at least for now, humans can learn the same skills from less samples (at least in this example: Go)

Of course, if there are many samples, the computer can go through those faster, but if there are no samples already and the computer has to learn example by example as humans do as well, humans may still have an advantage.

Of course, this advantage will diminish as well as AI advances.

<comment><USER52> How do you count examples? The computer can generate its own examples by playing against itself. So in theory it needs 0 examples. This is not a useful metric at all.

<comment><USER51> I would count every played game as an example.

What I mean is that I am more impressed by anyone of anything that can do a task (go, golf, chess, learning a foreign language, doing the dishes even) well with just a single example, or e.g. an hour of training.

Being able to train in solitude is an advantage indeed. You need two humans to do this, but you also need two AlphaGo-instances as well.

<comment><USER52> Are you going to count all the games that the human played in their head too? What about the learning done in the human brain when sleeping? Do you count that too?

</comment></comment></comment></comment></comment></comment></comment><comment><USER53> Beating humans in Go is, in itself, not all that exciting. _Go bots have been beating strong humans for quite some time now_ (just not the very top humans).

There are other implications that make this AlphaGo progress super exciting though. Go captures strategic elements that go well beyond the microcosm of one nerdy board game.

That's the real reason Go has been around for >2,000 years, and why this AI progress is relevant, despite its limited "game domain".

I wrote about it here, from my perspective of an avid Go player & machine learning professional [1].

[1] [http://rare-technologies.com/go_games_life/](http://rare- technologies.com/go_games_life/)

<comment><USER54> I disagree with this due to the rate of AlphaGo's progress. Consider CrazyStone which was the previous state of the art in Go computers. That program reached 5dan after many years of development and has not shown any signs of being able to reach Lee Sedol level (9dan).

In October of this year AlphaGo beat a 5dan player, bringing it into the range of CrazyStone. Only ~6 months later it beats a 9dan player which means it is now ~400 Elo higher. This means the new version would be predicted to beat the old version ~99% of the time.

Such incredible consistent progress of a problem considered somewhat intractable is notable and exciting. Imagine where this machine will be in 6 more months.

<comment><USER54> Edit: Fan Hui was only 2dan so this is even more insane.

<comment><USER53> Yes, AlphaGo's progress is amazing. I don't think there's any disagreement there :)

But I don't think you know much about Go, if you can say Fan Hui is "just" 2 dan professional. What do you reckon the strength difference is between 2p and 9p?

Nitpick: while AlphaGo today is certainly stronger than AlphaGo last October, it doesn't follow in any way from the fact that both programs beat their respective opponents. _A > B_, _C > D_, _D > B_, therefore _C > A_? By "400 ELO", no less?

<comment><USER54> [https://en.wikipedia.org/wiki/Go_ranks_and_ratings#Elo- like_...](https://en.wikipedia.org/wiki/Go_ranks_and_ratings#Elo- like_rating_systems_as_used_in_Go)

You can use that table to calculate the win probability for a 9dan player versus a 2dan player.

<comment><USER53> I rest my case.

For your info: professional ranks do not reflect strength. They are honorary and based (typically) on achievement and seniority.

</comment></comment></comment></comment></comment></comment><comment><USER21> Can someone explain why this is more impressive than a computer beating top chess players over a decade ago? I'm not very familiar with Go, and while there were far more squares on a Go board, it seems less sophisticated than chess to me.

Maybe Go has way more moves possible and emergent strategies or something I'm not taking into account.

<comment><USER55> Here's a way to measure the sophistication of a game of skill. Consider two players A and Z. A is a ten-year-old who has just been told the rules; Z is God. Now, in between them, put a series of other players B, ..., Y, where B beats A 2/3 of the time, C beats B 2/3 of the time, ..., Z beats Y 2/3 of the time. (We assume God doesn't use his magical divine powers to cheat by, e.g., making Y play bad moves.)

Unfortunately, God is not readily available for comparison, so we'll use the best human players instead.

How many links are there in that chain? The more there are, the more there is to learn about the game, and hence the deeper and more sophisticated the game is. (So you might think, anyway.)

If you rate players using the Elo system, beating someone 2/3 of the time corresponds to being about 150 points stronger. A complete beginner at chess might have an Elo rating of 500, compared with the world champion somewhere around 2900, giving 16 links in the chain.

In go, beating someone 2/3 of the time corresponds to being about one kyu/dan rank stronger. A complete beginner might be 30 kyu; the best players are stronger than 9 amateur dan, so that's at least 40 links in the chain. (Lower- numbered kyu ranks are stronger; after 1 kyu comes 1 dan, and then higher- numbered dan ranks are stronger.)

So by this measure -- which you may or may not find convincing -- go is a more sophisticated game than chess.

Here is the best argument I know against this definition. Define the game of "tenchess" as follows. To play a game of tenchess, you play ten games of chess and the winner is whoever wins more games (a draw if the same number). Then it's easy to see that tenchess has a longer chain, as defined above, than chess; if I win 2/3 of my chess games then I win 79% of my tenchess games, so I can win 2/3 of my tenchess games with a smaller advantage. (I am ignoring the existence of draws for this calculation, just for simplicity.) But surely tenchess isn't a deeper game than chess; it's just _longer_. Perhaps go's longer chain is just the result of its being a longer game.

<comment><USER56> > In go, beating someone 2/3 of the time corresponds to being about one > kyu/dan rank stronger

This isn't true. One kyu/dan rank stronger means being 1 stone stronger (so winning 50% of the time when playing White with reverse komi). In practice this may correspond to winning 2/3 of the time with normal komi for high dan players, but that doesn't hold for low kyu players. A 29k has maybe a 51% chance of winning against a 30k because both will make huge mistakes. So although the 29k can score on average 13-15 more points than the 30k in a given game, this advantage is swamped by the large standard deviation of scores in beginner games, turning the win/loss outcome into essentially a coin flip.

<comment><USER55> Fair comment about very weak players. My impression is that the element of chance goes way down well before you get to high dan level, though. How sure are you that I'm wrong?

<comment><USER56> Quite sure. 17k players still make many huge mistakes, and at the other extreme, God (say 13d) would win 100% of the time against a 12d player. Given that the win ratio for a one rank difference starts at 50% for extreme beginners and ends at 100% for God, maybe you should be the one explaining why you'd expect a significant plateau at 2/3 instead of a smooth increase.

<comment><USER55> I agree. In particular, I wasn't arguing for anything special to happen at p=2/3\. I was hopeful, though, that p=2/3 might be a tolerable approximation over a reasonable range of skill levels.

Having played a bit with some toy models, I've changed my mind a bit; my guess is that p=2/3 is a reasonable approximation for few-dan and few-kyu amateurs, but that outside, say, the 5k-5d range it's far enough off to make a substantial difference.

So, what does this do to those (anyway fairly bogus) "depth" figures? My crappy toy model suggests that for a 2/3 win probability you need a 3-rank difference around 24k, a 2-rank difference around 12k, a 1-rank difference around 2d, a 0.5-rank difference around 8d. And I estimate God at 15 amateur dan (if Cho Chikun is 9p and needs 4 stones from God then God is 21p; if, handwavily, 9d=3p and one p-step is 1/3 the size of one d-step, then God is 21p = (3+18)p = (9+6)d = 15d). So we need maybe 20 steps from God to 5d, then maybe 10 from there to 5k, then maybe 5 from there to 15k, then maybe 5 from there to 30k. That's 40 steps -- not so very different from what we get just by pretending one rank = one "2/3 win probability" step, as it happens.

</comment></comment></comment></comment></comment></comment><comment><USER57> Isn't this jumping the shark a bit? It's a 5-game match. The first was really, really close.

<comment><USER58> It's worth mentioning that Lee Sedol mentioned in an interview that even if he loses a single game against AlphaGo, he will have lost the match. He was expecting to win all 5 games.

<comment><USER57> I hope that doesn't shake his determination and ability to concentrate. He could still win.

</comment></comment></comment><comment><USER59> I'm truly amazed also, I'm not surprised or shocked. Once I knew that the previous master was beaten, I knew it's just a matter of time to see the #1 player topped.

What would be shocking is to find out that a famous writer, musician or scientist is in fact, just an alias for an advanced AI system :) It needs a little trick, because people should be tricked into believing that there's a real person behind the name.

Oh wait, I just remembered that there's a (mediocre) movie made on the subject: S1m0ne ( [http://www.imdb.com/title/tt0258153/](http://www.imdb.com/title/tt0258153/) )

Are you saying it won't happen? Think of the guys saying the same of go :)

<comment><USER60> > What would be shocking is to find out that a famous writer, musician or > scientist is in fact, just an alias for an advanced AI system :)

so, Milli VanAIlli?

([https://en.wikipedia.org/wiki/Milli_Vanilli](https://en.wikipedia.org/wiki/Milli_Vanilli))

<comment><USER59> Nice catch, that would be the perfect working title for the project :)

</comment></comment></comment><comment><USER61> What this actually means is that "the approach" AlphaGo team developed to "computationally" play Go, which is an computationally intractable problem, will be very useful in other computationally intractable problems. The media is going to get crazy without understanding what actually happened. If you are going very hysteric over this and thinking that robots are going to take over then please try this:- Before the start of the game add/remove/update any rules of the game and tell both the players - the human and computer - at the start of the game about new rules and lets see who wins.

</comment><comment><USER62> This not only shows the insane advances in computer AI, but an incredible advancement between the Fan Hui games and this one. Im still going through the kifu to get a sense of how could it have improved so much in only 6 months.

<comment><USER23> I feel like it started being more aggressive, playing more fighting moves....whereas in the last match it was playing mostly a defensive game (I'm not an expert by any stretch of the imagination, though).

<comment><USER63> It's exactly the opposite – this last game was much more aggressive on both sides than the Fan Hui match.

</comment></comment></comment><comment><USER64> I want to scratch my itch and play some go. I suck, and playing against other players online I get destroyed so quickly I feel like I'm ruining their fun. Where can I find a fun bot with variable difficulty?

</comment><comment><USER65> Extremely interesting news and kind of sad as a human being :)

I don't really know that much about AI, but hopefully some experts can tell me - how different are the networks that play go vs chess for example? Or recognise images vs play go?

What I mean is - if you train a network to play go and recognise images at the same time, will the current techniques of reinforcement learning/deep learning work or are the techniques not sufficient at the moment?

If that works, then it really does seem like a big step towards AGI.

<comment><USER66> This is basically a combination. A "traditional" chess program would use a tree search, but trees get quickly ot of hand since they grow exponentially. The trick is to prune them, and they trained a network to do that. It selects just the moves that look good to it. (It has some level of randomness to it, too) After reaching deep enough in the search tree, they use another network to evaluate who's winning. Usually this is hard to do in Go, and that's why the second network is quite novel and helpful.

So, they use a combination of techniques. And they're doing well at it.

<comment><USER65> right, yes, but my question was meant to be a bit more general - this and various other results have shown that it is possible to train a deep net to do a specific task very successfully - my question was if it's possible to train it to do two or more tasks as successfully or will the network then have to be exponentially larger. I suppose there is no known way to "combine" trained networks together.

<comment><USER67> The standard is to have each neural net be trained just for one task, as you say; there may be research into multi-skill neural nets but I have yet to see any. AlphaGo in particular is extremely specialized to Go, even in terms of how the algorithm is implemented.

</comment></comment></comment></comment><comment><USER68> I had a feeling that AlphaGo would beat Lee Sedol yesterday after watching Fan Hui's interview [1].

According to Hui's recall, the defeat all came down to these things: the state of the mind, confidence and human error. The gaming psychology is a big part of the game, without the feelings of fear of being defeated and almost never making mistakes like humans do, machine intelligence beating human at the highest level of competitive sports/games is inevitable. However, to truly master to game of Go, which in ancient Chinese society, it's more of an philosophy or art form than a competitive sport, there is still a long way to go.

There were a ton of details Hui cannot speak of due to the non-disclosure agreement he signed with DeepMind, but those were the gist of the interview.

In the end, AlphaGo match is 'a win for humanity', as Eric Schmidt put it. [2]

[1] [http://synchuman.baijia.baidu.com/article/344562](http://synchuman.baijia.baidu.com/article/344562) (In Chinese)

Google Translate: [https://translate.google.com/translate?hl=en&sl=zh- CN&tl=en&...](https://translate.google.com/translate?hl=en&sl=zh- CN&tl=en&u=http%3A%2F%2Fsynchuman.baijia.baidu.com%2Farticle%2F344562)

[2] [http://www.zdnet.com/article/alphago-match-a-win-for- humanit...](http://www.zdnet.com/article/alphago-match-a-win-for-humanity- eric-schmidt/)

</comment><comment><USER69> That sequence on the right side was excellent, I am so impressed with the level of play.

</comment><comment><USER70> reference: SGF file on OGS: [https://online- go.com/demo/114161](https://online-go.com/demo/114161)

To my untrained eye, AlphaGo was already way ahead by move 29 in the match tonight with black having a weak group in the upper side, while black wasted a lot of moves on the right side as white kept pushing (Q13, Q12), which white erased later because those pushes were 4th line for black and the area was too big too control. Black never had a chance to recover this bad fight. After those reductions and invasion on right side white came back to the 3-3 at C17 which feels like solidified the win.

Some people are asking what was the losing move for Lee Sedol? I wanted to joke and say "the first one.." but maybe R8 was too conservative being away from the urgent upper side where white started all the damage.

</comment><comment><USER71> A historic moment here, folks.

Incredible, and in my opinion a little terrifying.

<comment><USER72> What's more terrifying is that AlphaGo can learn even further, from these and other matches. Just hard to imagine what lies ahead.

<comment><USER5> The Singolarity is here!

</comment></comment></comment><comment><USER73> No surprise at all, human brain is an organ with limited neurons, and computer doubles its performance very 18 months. In fact not just the chess, I would say that AI will beat human all around at unlimited ratio in the future, when they learned how to improve themselves especially.

</comment><comment><USER74> I was just thinking, does AlphaGo's game strategy also emulate some sort of psychological strategies used by real human, such as bullying, confusing or making fun of its opponent when it sees fit.

</comment><comment><USER75> What do you guys think of the future progress on the game Go? Will our only chance against AI be to team up with an AI to beat the lone AI? Like in this article about centaur chess players: [http://www.wired.co.uk/magazine/archive/2014/12/features/bra...](http://www.wired.co.uk/magazine/archive/2014/12/features/brain- power/page/2) (2014) It all sounds very Gundam Wing to me.

</comment><comment><USER76> _Deep Blue:_

Massive search +

Hand-coded search heuristics +

Hand-coded board position evaluation heuristics [1]

 _AlphaGo:_

Search via simulations (Monte Carlo Tree Search) +

Learned search heuristics (policy networks) +

Learned patterns (value networks) [2]

Human strongholds seem to be our ability to learn search heuristics and complex patterns. We can perform some simulations but not nearly as extensively as what machines are capable of.

The reason Kasparov could hold himself against Deep Blue 200,000,000-per- second search performance during their first match was probably due to his much superior search heuristics to drastically focus on better paths and better evaluation of complex positions. The patterns in chess, however, may not be complex enough that better evaluation function gives very much benefits. More importantly, its branching factor after using heuristics is low enough such that massive search will yield substantial advantage.

In Go, patterns are much more complex than chess with many simultaneous battlegrounds that can potentially be connected. Go's Branching factor is also multiple-times higher than Chess', rendering massive search without good guidance powerless. These in turn raise the value of learned patterns. Google stated that its learned policy networks is so strong "that raw neural networks (immediately, without any tree search at all) can defeat state-of-the-art Go programs that build enormous search trees". This is equivalent to Kasparov using learned patterns to hold himself against massive search in Deep Blue (in their first match) and a key reason Go professionals can still beat other Go programs.

AlphaGo demonstrates that combining algorithms that mimic human abilities with powerful machines can surpass expert humans in very complex tasks.

The big questions we should strive to answer before it is too late are:

1) What trump cards humans still hold against computer algorithms and massively parallel machines?

2) What to do when a few more breakthroughs have enabled machines to surpass us in all relevant tasks?

Note: It is not entirely clear from the IBM article that the search heuristics is hand-coded, but it seems likely from the prevalent AI technique at the time.

[1] [https://www.research.ibm.com/deepblue/meet/html/d.3.2.html](https://www.research.ibm.com/deepblue/meet/html/d.3.2.html) [2] [http://googleresearch.blogspot.com/2016/01/alphago- mastering...](http://googleresearch.blogspot.com/2016/01/alphago-mastering- ancient-game-of-go.html)

<comment><USER77> Strong AI is not necessarily a bad thing. Instead of worrying about questions 1 & 2, we could be thinking less about constraint and competition with AI and more about cooperation and goal-orientation: e.g. the work of Yudkowsky ([https://intelligence.org/files/CFAI.pdf](https://intelligence.org/files/CFAI.pdf)) or some of the thoughts provided by Nick Bostrom [http://nickbostrom.com/](http://nickbostrom.com/). Goal-orientation is preferable to capability constraint because the potential benefits are far larger.

Tl;dr: I, for one, welcome our robot overlords (so long as they don't behave like our robot overlords).

<comment><USER76> I agree that superintelligence could bring enormous benefits to humanity but the risks are very high as well. They are in fact existential risks, as detailed in the book Superintelligence by Bostrom.

That is why we need to invest much more research efforts on Friendly AI and trustworthy intelligent systems. People should consider contribute to MIRI ([https://intelligence.org/](https://intelligence.org/)) where Yudkowsky, who helped pioneer this line of research, works as a senior fellow.

</comment></comment></comment><comment><USER78> What an amazing game to watch. Congratulations to the AlphaGo team, and good luck to both players in the next four games!

</comment><comment><USER79> AI is good for rules based systems, but most of the worlds problems that need to be solved don't have rules in the same way a board game does. Sure it's cool that a computer beat a human at a board game, but thats like celebrating a penguin being better at fishing than a person with bare hand

</comment><comment><USER80> How much did this match cost the AlphaGo team? (From a computing resources perspective)

</comment><comment><USER81> It's almost kind of bad timing in the U.S., what with one of the most insane primary seasons in our history -- this will probably not make the news at all let alone the front page like Kasparov's and Magnus's games did.

</comment><comment><USER82> Learning from experience goes both to the program and to the champion. Does this mean if the champion keeps playing with the machine several times, he has a chance of winning?

</comment><comment><USER83> I don't think a computer could ever beat me at Calvinball

</comment><comment><USER84> It'll be interesting to see what new things we learn about Go itself from DeepMind. The game is very deep, and apparently we haven't found the bottom yet!

<comment><USER85> Instead of the prize money, if I were Lee Sedol I'd request unlimited play time against the latest AlphaGo.

</comment></comment><comment><USER86> I think it will be very interesting if Lee Sedol can win one. Humans have different blueprints and environments. Who is to say a human can't become better?

</comment><comment><USER87> When there's a computer that can beat the world champion at both go and chess with no modifications, then I'll be scared.

<comment><USER88> You just take your top-notch Go/Chess engines and detect the game in the initial step.

</comment></comment><comment><USER89> I have read a fair amount about how it was written without much detail. Anyone know what it was written in?

</comment><comment><USER90> AlphaGo can be beaten. It uses reinforcement learning so it will perform the set of moves that in the past led to its win. So predictable. Sedol just needs to take control and make it play in a predictable fashion. Also, perhaps play obscure moves that AlphaGo wouldn't have trained on. Perhaps next year's Go winner will have a PhD in computer science.

</comment><comment><USER91> Here's one: how long until a computer can beat a human assisted by a computer?

<comment><USER85> Will humans be able to keep up with the depth of analysis these AIs will have, or will it become a problem for the AI to dumb down its thinking in order for us to grasp it?

More generally, scientists using AI for research will probably have to do research on the research, to understand what the AI discoveries mean. Maybe they mean something we can't grasp at all, in which case they go completely over our heads, like ants trying to learn about the finer points of financial markets. We will probably have to learn new concepts and even new languages designed by the AI to convey the meaning.

<comment><USER92> I wouldn't say "dumb down" but it definitely needs to explain why it took some lines of reasoning. With deep learning, you need to rebuild the whole system with different test-cases to change a minor behavior.. but imagine if we could just say "Why did you do that? XYZ. And adjust it: "Oh, gotcha. You can't because of ABC", and then the AI has that problem solved. I guess that would be the next step in AI. I think it's called symbolic reasoning.

Here's a very good article: [http://dustycloud.org/blog/sussman-on- ai/](http://dustycloud.org/blog/sussman-on-ai/) (A conversation with Sussman on AI and asynchronous programming)

</comment></comment></comment><comment><USER93> Does Lee Sedol have access to AlphaGo training games and/or matches?

</comment><comment><USER94> i would like to see the same match, but switched placed. alphago plays itself, this time as black, to kind of see the choices it would make, and if they would align with lee's.

</comment><comment><USER95> Poll: [https://news.ycombinator.com/item?id=11250806](https://news.ycombinator.com/item?id=11250806)

</comment><comment><USER96> Does this mean in the next few decades, computers will make better sex partners and companions than any human?

<comment><USER97> Worrying about the effect of strong AI on sexual relationships is like worrying about the effect on US-Chinese trade patterns if the Moon crashes into the Earth.

<comment><USER98> Who says? I mean, computers can be used for multiple purposes, and although some applications of AI seem more "noble" or "intellectual" than others, the pursuit of knowledge and sexual relationships are both sense pleasures that we indulge in to make ourselves feel good. On a very large scale, one is hardly more noble than the other.

</comment></comment></comment><comment><USER99> after so much press about this, it would be funny if overall the human wins

</comment><comment><USER100> 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 will be crushed. Today everybody will have seen it coming and say that it was normal.

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.

<comment><USER101> 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.

<comment><USER102> 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.

<comment><USER103> As far as I know the goal post of Turing test has never moved.

<comment><USER18> Chatbots can already beat the turing test.

<comment><USER101> Chatbots ate trivially easy to beat. Just try teaching it a simple game and ask it to play it with you. Basically any questions that require it to form a mental model of something and mutate or interrogate the model state.

Many of the chatbot Turing test competitions have heavily rigged rules restricting the kinds of questions you're allowed to ask in order to give the bots a chance.

</comment></comment></comment></comment></comment></comment><comment><USER104> Some people were downplaying the victory of AlphaGo over the European champion because he was only a 2p player. I wonder what they have to say now.

<comment><USER19> The last victory was significant, but this victory was far more significant. The professional dan scale isn't exactly linear and the ranks can't simply be compared numerically even when they are granted by the same organization - <USER2> Korea, China and Japan all have at least one organization of professional go players that each maintain their own rankings. Sedol is a current top player who has won many, _many_ titles and Fan Hui is 10 years out of regular professional play and doesn't have a title to his name. What people were saying before is still true today. All of the reporting has suffered from the usual problems of describing something specialized to the general public, and all the typical inaccuracies of such journalism (compounded by Google's PR department being the source of some of it).

Congratulations to the team at Deepmind, and I'm wishing good luck for Sedol in the remaining matches - if he wins we would certainly get to see a second series rematch some months down the line, and that would be very exciting for go fans everywhere.

</comment></comment><comment><USER105> man I am fired up to watch tonights game...like I am fired up for UFC

there should be like a North American Go Nationals or something like that televised on twitch

Anyone putting money down on Sedol? He said it will be either 5-0 or 4-1 in his favor.

</comment><comment><USER106> Lee is not the best player NOW.

</comment><comment><USER107> Giant spoiler! Does Hacker News have any policy against these things?

</comment><comment><USER108> Lee Sedol should have played that top left 3,3 move earlier (at least before white covered it) WTF. Humanity is not longer at the top of the intelligence pyramid...

</comment><comment><USER109> He has lost 1 game of a 5 game match, on a handicap. Hardly a defeat.

<comment><USER110> What handicap?

<comment><USER109> I misread it, he had only the usual first move advantage handicap.</comment></comment></comment>


Interesting thread about "why did trackballs disappear" - <AUTHOR> https://retrocomputing.stackexchange.com/questions/8787/why-did-trackballs-disappear <comment><AUTHOR> I found this interesting as I use a trackball and pretty much always have. Right now I have an Logitech MX Ergo on my desk at home and work. For things like OmniGraffle I also have an Apple Track Pad for zooming in and out, etc. I just find the trackball better for 99% of my daily use. I also use a Filco Majestouch for the keyboard with Red Cherry switches. CMD-Shift for spotlight and everything seems to flow. I do miss the 3 button mouse, that needed a reflective mouse pad and my Type-5 Sun keyboard sometimes however.

</comment><comment><USER2> I figure I can shepherd my collection of Logitech Trackman wired wheeled trackballs until I find something else with the correct feel. I love just using my thumb as normal mice do cause pain.

<comment><AUTHOR> I have 2 left ... six dead over the years.

<comment><USER2> It just kills me the wireless version isn't as good.</comment></comment></comment>


Analysis of post-workout chocolate milks available in the UK - <AUTHOR> http://blog.jgc.org/2013/03/analysis-of-post-workout-chocolate.html

<comment><USER2> The variance in _recommendations on_ the best carb to protein ratio to take postworkout seems to be much bigger than the variance in the milkshakes you reviewed (between 1:1 and 5:1, with your shakes only varying between 2.7:1 and 4.4:1). Choosing one milkshake over another because the ratio is very slightly closer to a ratio you've arbitrarily picked as the ideal seems like over- optimisation. (Especially as some of the shakes are more than twice as big as others, which is going to make a lot more of a difference than minor ratio differences).</comment>


Help The competition launched before us and has a better looking site - <AUTHOR>

Hi Hacker news, I recently joined a start up and after a month of grinding out our site, we discovered a direct competitor that has already launched, has users and has a better looking site.<p>What do you do in a situation like that? <comment><USER2> Figure out what they're missing and make it a hallmark feature of your product. Alternatively, just out-do them at their own game.

</comment><comment><USER3> Fight harder

<comment><AUTHOR> Very well.

<comment><USER3> Just read some of your previous comments, so let me be more precise. Nothing is fair in the marketplace. It is like animals at the watering hole. Show your teeth, stomp around, and defend your space.

Also important to remember: even if you're only the third largest provider of your service, in most industries that'll still put you in the 95th-99th percentile for wealth and power for the overall population.

Damn the torpedoes. Full speed ahead.</comment></comment></comment>


I want a domain for my IP NOW! - <AUTHOR> http://blog.oxplot.com/2012/03/rompee-i-want-domain-for-my-ip-now.html

<comment><USER2> Title of linked page: _I want a domain for my IP NOW!_

Title here on HN: _I want an IP for my domain NOW_

Pls fix.</comment>


Programmed Visions: how do we analyze, organize, and build with the undead? - <AUTHOR> http://mastersofmedia.hum.uva.nl/2011/09/26/book-review-programmed-visions-by-wendy-hui-kyong-chun/

<comment><AUTHOR> From a longer review of the same book:

[http://computationalculture.net/review/review-programmed- vis...](http://computationalculture.net/review/review-programmed-visions- software-and-memory)

"Software is at once knowable (e.g., through programming languages) and not at all (e.g., computer performance exceeds human perception). Code is simultaneously accessible (e.g., "view source") and nebulous (e.g., subject to an array of both machine and human interpretations). Media represent both permanence (e.g., they can always be upgraded) and decay (e.g., they always have to be upgraded).

... our embodied interactions with screens are habituated (e.g., through interface design), and "our computers execute in unforeseen ways" (9). The visible and invisible coalesce at our fingertips, and whatever is on display is always elsewhere—read here, written there.

... When the ENIAC was being developed during the 1940s, male analysts would speak commands at female operators, who would then translate those commands into input for the "Giant Brain." In this real-time interaction between man, woman, and machine, women can be read more or less as software. They directly executed commands before there was a command line or a graphical user interface.

... technologies that store everything discretely and act as universal archives. The fetish for the former eclipses the ways in which the latter is really about "the undead of information"—its persistent de- and regeneration, its repeated resuscitation, and its simultaneous presence and absence (172, 177).

... how exactly do we develop interfaces that are more productively spectral? How and for whom do we create software that is inclined to surprise? What are some existing examples, and how would learning from them affect coding, designing, and architecting practices? In short, how do we analyze, organize, and build with the undead?"</comment>


Buffer App Now Making Over $100K In Monthly Revenue, With 600K Users - <AUTHOR> http://techcrunch.com/2013/04/25/buffer-scheduling-service-now-making-over-100k-in-monthly-revenue-with-600k-users-sending-5m-updates-per-month/

<comment><USER2> Congrats to Joel and team. I remember reading your post [1] on HN from a couple of weeks ago about how you guys barely had 100 signups the first month. You've come a long way since then and totally deserve the success you're seeing. [1] <https://news.ycombinator.com/item?id=5387168></comment>


Design annoyances - <AUTHOR> http://codefastdieyoung.com/2011/03/design-annoyances/

<comment><USER2> List mysteriously leaves out "gratuitous headshot of author"

<comment><USER3> Yes, he's been on a pretty consistent self-promotion tour with the blog and the twitter.

<comment><USER4> _That's what you do_ with a blog and 'the twitter'.

</comment></comment></comment><comment><USER5> Number three is interesting, especially if we consider what Canonical have done recently with Unity and moved scrollbars _outside_ the window[1], freeing-up the screen real-estate that they permanently occupy.

[1] [http://design.canonical.com/2011/03/introducing-overlay- scro...](http://design.canonical.com/2011/03/introducing-overlay-scrollbars- in-unity/)

<comment><USER6> It's interesting how reminiscent that design is of Google Wave's much- criticized scrollbars: the position indicator following the thumb, the page- up/page-down buttons, and the minimalist appearance when not in use. I wonder if Wave's design was just ahead of its time.

<comment><USER7> I think that the problem with Wave's scrollbars wasn't so much that they were bad, they were non-standard. Scrollbars are OS territory, individual webapps should probably not try to reinvent them. (I would argue that they should only ever try to reinvent them when they provide something substantially better and when the standard OS implementation is lacking.)

<comment><USER8> My problem with wave's scrollbars was that whatever code they had to do scrolling was dramatically slower and glitchier than native scrollbars.

</comment></comment></comment></comment><comment><USER9> Most of these have been pretty well known or obvious, which is why those that do them have little excuse. But I disagree strongly that #1 is a problem. Some things, like the header in his example, usually should be full width. While others, especially columns of text should be of a fixed maximum width to improve readability. Who cares about blank bars to the sides of the screen? Or at least, who cares as much as they do about struggling to read too wide columns of text?

<comment><USER7> I think the problem is that things don't line up when you mix full width and fixed width. It's not supposed to be an endorsement of either full or fixed width.

<comment><USER10> Agreed. I think the fundamental underlying error is not having a reason why a particular elements is fixed or full width.

If you can answer the question "why did you go with a full width header and a fixed width body?", you're probably OK (unless that answer is something like "well, the theme I downloaded from themeforest was fixed width, but the client insisted I use their old header in the new design, so I just mashed it into the template...")

Like most "design errors", I think what the author of e article is ranting about is designs that aren't fully though out - if you've asked yourself the questions "fixed or full width, or both?", "what sized fonts should the forms have?", "how much of the site it going to be long enough to scroll, and do the scroll bars behaviour need to be considered in the design?", then you won't have made these errors, or at least will have good reasons why they are not "errors" but behaviours you chose.

</comment></comment></comment><comment> Gorm-Casper I'm curious what others think about #3 (The vertical scrollbar). Traditionally it's considered messing with the defaults of the browser, and wasting precious screen space; but I hear from more and more (graphical) designers that they'd like me to stop the page from jumping by doing exactly this.

<comment><USER11> I think it's mostly just the usability perspective, like was mentioned in the article. If you don't force the vertical scrollbar and this causes elements that should be in a static position to move, you've got a frustrating usability issue. (Like on sites that have forward/back buttons that jump around so you can't repeatedly click the same button. How frustrating.)

<comment><USER4> That's not the usability perspective. Usability would be "don't mess with the semantics of the controls". E.g. if you're going to make something that looks like a button, it should do everything that buttons do.

Having the sidebar always on so as to avoid the bounce is not a usability thing. Usability would suggest that you leave the defaults the heck alone. In this case though, it is a pretty minor infraction.

Rather, it is an _aesthetics_ thing. They've decided the default behaviour is 'ugly', and they've decided to override it to make it look better. In this case they've decided that prettiness trumps usability.

</comment></comment></comment><comment><USER12> "body {min-height:101%;}" will work much better than "overflow-y: scroll".

Unlike the latter it's CSS2 (vs CSS3) and also works without bugs.

<comment><USER13> In practice this isn't true. As it turns out, the browsers that don't support overflow-y (certain versions of IE) happen to display a vertical scrollbar by default anyway.

Adding 'min-height: 100%' to the body will actually add a few pixels of empty space which the user can scroll to (confusing and ugly); whereas "html { overflow-y: scroll; }" will insert a placeholder for the scrollbar, the scrollbar itself will be grayed out. This is much better behavior in my opinion.

Also "html { overflow-y: scroll; }" is self-describing (i.e. the real goal is to add a vertical scrollbar to the html, not make the minimum height of the body 101%).

</comment></comment><comment><USER14> I'm certainly more likely to feel like I'm signing up for something magical when I'm faced with big pretty happy-looking form elements! :)

</comment><comment><USER15> The reason for #1 is because fluid layouts can force text lines to stretch on wide screen monitors. As such, the line becomes harder to read.

<comment><USER16> You can always combine a fluid layout with a max-width for your text. Besides its not the width of the monitor that matters, its the width of the windows and I like my windows narrow, thank you very much. I used to be able to do this, but then they went and broke Readability :-(.

<comment><USER15> Interestingly, in the actual example he uses that isn't a fixed with element. Look at the Heroku.com site, its the div with id "accordion". That space to the left of it is created because of the padding of the surrounding div (which is fluid width), and the margin of the accordion div.

Its sister element with id "rightelement" is actually fixed width, but I don't see the issue there since the majority of its contents are image elements and not text.

\---

That said, I agree with you set a max-width for text only enclosures, and a min-width for enclosures that have image elements.</comment></comment></comment>


Ask HN: What Happened to Logitech? - <AUTHOR> I've been hoping to order a webcam from them since the beginning of the pandemic via (amazon, newegg, logitech.com) but they've been out of stock, and the only places it is available are 3rd party sellers who are price gouging.<p>What happened to their supply chain? <comment><USER2> The same thing that happened to thousands if not tens of thousands of other businesses and I think you answered your own question. The pandemic is what happened to their supply chain. I have been trying to buy a desk from IKEA for months that has also been out of stock. This isn't some rare phenomenon right now.</comment>


BLOBs with Knex and PostgreSQL - <AUTHOR> https://spin.atomicobject.com/2019/06/17/knex-postgresql-blobs/#.XQeccts5kdk.hackernews <comment><USER2> > Initially, we implemented this solution using a bytea column, but we found > that the retrieval performance was unacceptable. It required the whole file > to be read into memory before being sent back to the user (as opposed to > streaming the content in smaller chunks).

How large are the objects on average?</comment>


Launching and landing rockets: A comparison between Blue Origin and SpaceX - <AUTHOR> http://i.imgur.com/ATkpdAX.png <comment><USER2> This graphic aptly shows the differences in complexity between the two flight plans, but it doesn't show the biggest difference between the two rockets. When the Falcon 9, enters space at the height of 100 km, it is traveling at 5000 km/h with 125 metric tons of payload[0]. Blue Origin's New Shepard, the best I can tell was traveling at ~0 km/h at that height, as it reached apogee at the height of 100.5 km and began falling back to Earth.

According to Musk[0], a first stage of a rocket is judged by the energy it can impart to its payload at the standardized height of 100 km. Merely getting to 100 km is the easy part. The Falcon 9 is able to deliver 120 giga-joules to its payload at the height of 100 km, while performing a return to launch site landing. While it appears the New Shepard had ~0 joules left at 100 km.

[0] - [http://www.spacex.com/news/2015/12/21/background-tonights- la...](http://www.spacex.com/news/2015/12/21/background-tonights-launch)

[1] - [https://www.blueorigin.com/news/news/blue-origin-makes- histo...](https://www.blueorigin.com/news/news/blue-origin-makes-historic- rocket-landing)

<comment><USER3> Potential energy is energy, so it's not quite correct that New Shepard had 0 joules at 100km. At 100km and 0 velocity it'll have about 1 gigajoule of energy per metric ton of mass.

<comment><USER4> Potential energy is not energy that you can give to a payload unless you want to have it fall 100km back to earth.

<comment><USER3> Sure, but it's energy that a rocket needs to impart nevertheless.

<comment><USER5> The point was that the measurement is what it can impart on a payload (presumably one going UP.) So your point doesn't seem relevant.

<comment><USER3> I think the point was to illustrate the difference in energy requirements for the rocket to impart to the payload.

That said, re-reading Elon's article, he did explicitly say _kinetic_ energy in the 120GJ figure, in which case 0 is the right number for New Shepard at 100km.

</comment></comment></comment></comment></comment></comment><comment><USER6> A more apt comparison is this: A single SpaceX F9 rocket can lift _three_ Blue Origin New Shepards _into orbit_ at 200km altitude (vs merely touching space at 100km) and then _come back to land_.

</comment><comment><USER7> Here's a size difference comparison as well: [http://i.imgur.com/g2VIKVC.jpg](http://i.imgur.com/g2VIKVC.jpg)

</comment><comment><USER8> So it turns out that guy messed up the SpaceX plot (the distance downrange was doubled), and posted a corrected one to reddit: [http://i.imgur.com/Z81NgAk.png](http://i.imgur.com/Z81NgAk.png)

</comment><comment><USER9> Jeff Bezos: "... Welcome to the club!" [https://twitter.com/jeffbezos/status/679116636310360067](https://twitter.com/jeffbezos/status/679116636310360067)

<comment><AUTHOR> The best part about that tweet is how Jeff double spaces after a period.

<comment><USER10> Why is that the best part?

<comment><USER11> It's sort of a nonstandard punctuation thing, especially on Twitter, where characters are limited and space-saving measures like single spaces after periods are standard.

</comment></comment></comment></comment><comment><USER12> I'm curious, why does the falcon reverse course after having so much lateral velocity? Why not launch somewhere in Arizona and land in Texas or Florida?

<comment><USER13> Probably to avoid a path which goes over populated land. Launches almost always go east to benefit from the Earth's rotation, so you want to launch from the eastern coast if at all possible.

</comment></comment><comment><USER14> It's also a great illustration why SpaceX landing on land requires substantially more reserve fuel than a barge landing. (look at the length of the burn at apogee)

</comment><comment> bobalouie1-_ For those trying to figure out the importance of who did what first please read . [http://www.theverge.com/2015/11/24/9793220/blue-origin-vs- sp...](http://www.theverge.com/2015/11/24/9793220/blue-origin-vs-spacex- rocket-landing-jeff-bezos-elon-musk)

</comment><comment> bobalouie1-_ Please read this Trisell and others who are so concerned about who did what first. [http://www.theverge.com/2015/11/24/9793220/blue-origin-vs- sp...](http://www.theverge.com/2015/11/24/9793220/blue-origin-vs-spacex- rocket-landing-jeff-bezos-elon-musk)

</comment><comment><USER15> A somewhat tangential question I've had since seeing the landing -- why are these rockets trying to land by themselves, which seems really difficult, compared to say getting the rocket _roughly_ in the right place and then having a robotic arm attached to the ground reach out and grab them?

<comment><USER16> Two big issues with that. First, the arm would have to grab an attachment point of some sort, so you'd be trying to position the rocket and the hand close together and still enough to lock on. That's a much smaller and tougher target than landing. Second, there's no way you could build a robotic arm and grasping mechanism that's strong enough to hold the rocket once the engine shuts down, while also being nimble enough to reach out and grab it. To even have a chance, you'd need three arms and three attachment points, evenly spaced around the rocket, and coordinated to all grab on at the same time.

This sort of thing works well in space when everything is just kind of floating next to each other, and you only have to deal with very small thrusts to make small position adjustments, and momentum when you try to move things. (Weightless != massless) But down on the surface everything that's not solidly on the ground is constantly accelerating towards the center of the planet, and it takes a lot of force to counter-act that.

</comment></comment><comment><USER17> What about hard numbers? Anyone has delta-v figures for first stage ascent, velocity both vertical and lateral components at meco, and delta-v for boostback, entry, and landing burns?

</comment><comment><USER18> This is pretty cool. Is the first stage trajectory to scale here?

</comment><comment><USER19> Dear Jeff Bezos: Suck it.

Sincerely, Science

</comment><comment><USER20> The problem that SpaceX has is that Blue Origin did it first. Even if the technical challenges were larger for SpaceX then Blue Origin.

In the mind of the general public that cares, it's all the same thing. The technicalities of a suborbital flight of a small capsule verses an orbital flight carrying a payload is meaningless. They just saw the Blue Origin rocket do it, and then the SpaceX rocket do it, and they looked about the same.

<comment><USER21> Nope.

SpaceX did it in 2011 w/ Grasshopper - [https://en.wikipedia.org/wiki/Grasshopper_(rocket)](https://en.wikipedia.org/wiki/Grasshopper_\(rocket\))

<comment><USER22> Uh, Grasshopper went < .5 miles up, Blue Origin went _into space_.

<comment><USER23> Too bad Blue Origin can't actually put anything into space that stays for a while (such as 11 satellites). The only thing Blue Origin exists for is to give millionaires a thrill ride that lasts a few minutes. BTW, this isn't fanboyism -- I really fail to see how the suborbital rockets will advance humanity's future in space -- whereas getting down the cost to orbit (and remember, leo is halfway to anywhere) is what is going to open up a new future for mankind.

<comment><USER16> If the millionaire gets out and jumps at apogee, they might stay in space for a while. But that'll use up Blue Origin's market pretty quickly.</comment></comment></comment></comment></comment>


Show HN: AnyStatus open source plugins library - <AUTHOR> An open source plugins library for AnyStatus extension for Visual Studio and Desktop Application.
The plugins library contains all monitors including Jenkins, TFS, VSTS, AppVeyor, TeamCity, Network, Services, Metrics, Dynamic Source Code, Database and more.
For more information about AnyStatus visit http://www.anystat.us
I look forward to receive your feedback, contributions and pull-requests.
Happy Coding! <comment><USER2> Nice work !</comment>


Fellow Economists Express Skepticism About Thomas Piketty - <AUTHOR> http://www.nytimes.com/2014/10/15/upshot/fellow-economists-express-skepticism-about-thomas-piketty.html

<comment><USER2> 'No, Mainstream Economists Did Not Just Reject Thomas Piketty's Big Theory'

[http://www.slate.com/blogs/moneybox/2014/10/15/piketty_igm_f...](http://www.slate.com/blogs/moneybox/2014/10/15/piketty_igm_forum_economists_did_not_just_reject_capital_in_the_21st_century.html)</comment>


Cybersecurity Metrics Simplified - <AUTHOR> https://sekurie.com/ <comment><AUTHOR> Take control of your information security metrics. Present a clear infosec dashboard to your executives and the board of directors.

The perfect solution for security leaders looking to simplify their data and focus on what really matters. Sekurie provides a better way to look at information security performance metrics and manage cybersecurity overall.</comment>


Alarming Consequences of the California Drought You May Not Have Expected - <AUTHOR> http://www.huffingtonpost.com/2015/02/26/california-drought-unexpected-consequences_n_6765240.html?utm_hp_ref=san-francisco

<comment><USER2> Enough with the click-bait titles.</comment>


The Histogram as the Image - <AUTHOR> http://www.ironicsans.com/2007/09/idea_the_histogram_as_the_imag.html <comment><USER2> Even though this is five years old, I hadn't seen it before. It's a very cute trick. The followup by Josh Millard is even better: [http://www.joshmillard.com/2007/10/04/retro-histo-making- an-...](http://www.joshmillard.com/2007/10/04/retro-histo-making-an-image-fit- your-histogram/)

<comment><USER3> It's a good thing he created (and you posted) that followup, because otherwise I would've spent the rest of my morning implementing the same idea.

</comment></comment><comment><USER4> Relevant: <http://xkcd.com/688/></comment>


Modeling a zombie outbreak with CUDA - <AUTHOR> http://dotslashed.com/gpu_project2.html

<comment><USER2> As one of the guys responsible for CUDA, this made my day :-)

<comment><AUTHOR> I came across this trying to learn more about CUDA and OpenCL. Specifically I was trying to find information on bending GPUs to be useful for actor model... zombies were an awesome bonus!

<comment><USER2> It's not really suited to CUDA/OpenCL; for the purposes of this discussion, we can treat the two as the same because the execution model of actual kernels is almost identical. The problems are

\- the size of the grid is fixed at kernel launch time, so you can't arbitrarily spawn more work

\- the completion of one block cannot depend on the execution of another block, which means inter-block communication within a kernel is forbidden

So you could do some sort of block-local actor operation then do an exchange of the halo cells between blocks in another kernel (the only global synchronization point allowed in CUDA/OpenCL), but that seems really painful, especially if actors are sparse within your grid. A work queue approach would probably work better; you'd certainly get better utilization and load balancing than trying to spatially partition a large grid.

<comment><AUTHOR> While extremely limited, I thought shared memory enabled interblock communications...? It's important to note I have no applications for this, just researching.

<comment><USER2> No, shared memory is only for intra-block communication. __syncthreads() only ensures that every thread in a block is at a particular point rather than every block in a grid.

Take Fermi, for example--you can potentially have 128 blocks running concurrently (8 blocks per multiprocessor, 16 multiprocessors on GF110), but you can launch a grid of 65535x65535 blocks in a single kernel. As a result, if you try to do arbitrary global synchronization, you'd have a state explosion (PDF: <http://www.gdiamos.net/papers/stateExplosion.pdf> ). The best way to solve a problem with significant interaction between data elements is to use a persistent work queue (as described in PDF: [http://www.tml.tkk.fi/~timo/publications/aila2009hpg_paper.p...](http://www.tml.tkk.fi/~timo/publications/aila2009hpg_paper.pdf) ).

</comment></comment></comment></comment></comment><comment><USER3> "View Simulation" (<http://dotslashed.com/sim2.swf>) link is 404.

<comment><USER4> This may be a different simulation, but try:

<http://dotslashed.com/sim1.swf>

</comment></comment><comment><USER5> Very cool, but the article itself is written very poorly.

</comment><comment><USER6> You can't go wrong with zombies: the public loves them.

 _It is necessary to initialize all variables. This will cause a run time error._

This probably should read: 'accessing uninitialized variables causes a runtime error'.

 _It is necessary to check your memory indexes for all data structures, if they are wrong you will have run time errors._

What would be a 'memory index'? An index into an array? Yes indeed, an out-of- bounds array index will cause an error sooner or later. If your language doesn't check array bounds, like C, the bugs will be subtle and insidious.

</comment><comment><USER7> I was in this class. We all had to do this assignment and it was really fun. Dr. Andy Johnson is a great professor.

</comment><comment><USER8> I can't think of a better way of utilizing spare GPU power. It's important to predict our zombie doom to give us time to prepare.</comment>


British Gut Project - <AUTHOR> http://www.britishgut.org/ <comment><USER2> Hearing a lot about gut bacteria these days influencing everything from weight loss to allergies. Can anyone with knowledge verify any of these claims or is it, yet, another passing fancy?

<comment><AUTHOR> The study linked here is one step in trying to find out how many of those claims are credible. What I found interesting about this link, which I heard about from a medical genetics researcher, is that the British Gut Study is connected to the Twins Early Development Study (TEDS), which has been going on for twenty years, so it will include some data that are from a genetically sensitive study design. That makes it easier (but not completely easy) to tease out environmental influences on gut microbes from individual genetic influences on gut microbes. The study also includes some data on the twins' health and some other variables of interest. The TEDS research program and all of its study subjects are based in Britain, but the study design is of such interest around the world that the United States federal government helps fund it.

</comment></comment><comment><USER3> I heard about this on the Today programme this morning, the presenter was taking great delight in repeating the name "The British Gut Project".

<comment><USER4> I bet :)

Try it in a Sean Connery voice... "The British Gut Project"

</comment></comment><comment><USER5> By sample do they mean I get to send someone my poo for £75 and they will tell me what microbes they found in it?

<comment><USER6> You could probably just go to a nearby clinical lab and order a stool culture.

Without a diagnosis though (for insurance) I fear what the actual cost would be.

</comment></comment><comment><USER7> Now I just need to wait for someone knowledgable to tell me more information about this project, after which I will probably sign up and send off my poo.

<comment><USER8> And pay £75 to pay for their research without, as far as I can find, much information on their methodologies, details on what will happen to your data and how it will be looked after.

<comment><USER9> It would be more helpful it i knew what kind of protections my data will get.

Where did you get the £75 fee? I only see a mention of £2.90 postage.

<comment><USER10> I suspect the £75 'donation' came from their 'fundrazr' page: [https://fundrazr.com/campaigns/4sSf3/](https://fundrazr.com/campaigns/4sSf3/)

You seem to have to join via this fundraising site. It's mildly concerning that their participant login site seems to be coming up with an SSL error too ( [https://www.microbio.me/BritishGut](https://www.microbio.me/BritishGut) ). This site also takes you back round to the fundraising page when you select the menu option to join.

</comment></comment></comment></comment><comment><USER11> The font on the website hurts my eyes, making me squint.

<comment><USER12> To be fair, given what these researchers look at all day, we can't expect too much from them aesthetically.

</comment></comment><comment><USER13> Surveillance from the inside.</comment>


Kindle 2.0 or 2.No? - <AUTHOR> http://www.thestandard.com/news/2009/02/04/kindle-2-0-or-2-no

<comment><USER2> What a bummer. I was hoping for something new about Kindle and this article does little more than try to funnel readers into the registration-required Standard Prediction Market. Oh well. Move along; nothing to see here.

<comment><USER3> Prediction markets are established ways of applying collective intelligence to predict outcomes. Kindle is a huge business for Amazon, and potentially for authors, developers, and others as well. Talking about the chances for the release of a 2.0 version next week is a worthy discussion, in my opinion.</comment></comment>