Lee Sedol Beats AlphaGo in Game 4 - <AUTHOR> https://gogameguru.com/alphago-4/ <comment><USER2> Relevant tweets from Demis;

        Lee Sedol is playing brilliantly! #AlphaGo thought it
        was doing well, but got confused on move 87. We
        are in trouble now...

        Mistake was on move 79, but #AlphaGo only came to
        that realisation on around move 87

        When I say 'thought' and 'realisation' I just mean the
        output of #AlphaGo value net. It was around 70% at
        move 79 and then dived on move 87

        Lee Sedol wins game 4!!! Congratulations! He was
        too good for us today and pressured #AlphaGo into
        a mistake that it couldn't recover from

From: [https://twitter.com/demishassabis](https://twitter.com/demishassabis)

<comment><USER3> It feels really weird to see someone being showered with congratulations for beating a computer program. What exactly is he being congratulated for? For probably triggering and then capitalizing on a bug in AlphaGo's AI? For showing that human resolve, perseverance and a "fighting spirit" can trump a flawed AI, at least until the AI gets fixed? For giving DeepMind extremely valuable test data that will only accelerate the refinement of AlphaGo's AI? For helping to advance an amoral field of study that can potentially delegitimize everything that currently makes humans unique and extraordinary?

<comment><USER4> Perhaps you'd like a shot at explaining why AI is an "amoral field of study", and why it could "delegitimize everything that currently makes humans unique and extraordinary"?

Dolphins are about as intelligent as us, too. Are dolphins amoral? Do they delegitimize Beethoven, Tesla, Gödel, Einstein, and da Vinci?

<comment><USER5> > Dolphins are about as intelligent as us, too

How are you making this comparison?

<comment><USER6> I think Bud wanted to make a different point. Ie that AI doesn't delegitimize us---whatever that even means. Dolphins were just an example. He could have used Neanderthals, or Aliens.

</comment></comment></comment></comment></comment><comment><USER7> If it's true that AlphaGo started making a series of bad moves after its mistake on move 79, this might tie into a classic problem with agents trained using reinforcement learning, which is that after making an initial mistake (whether by accident or due to noise, etc.), the agent gets taken into a state it's not familiar with, so it makes another mistake, digging an even deeper hole for itself - the mistakes then continue to compound. This is one of the _biggest_ challenges with RL agents in the real, physical world, where you have noise and imperfect information to confront.

Of course, a plausible alternate explanation is that AlphaGo felt like it needed to make risky moves to catch up.

<comment><USER8> There is a possibility that the AlphaGo team, knowing that it has already won a decisive victory, wanted to spare the champion a crushing 5-0 defeat and so commanded the AI to lose on purpose.

<comment><USER9> The AlphaGo team has stated that they no longer have the ability to make changes to the AI used for the match. The rules of the exhibition match prohibit that.

They maybe could have anyway, but it would be cheating: just the same as if they'd Mechanical Turk'ed it by e.g. having Ke Jie actually choose the moves to play.

</comment></comment></comment><comment><USER10> In the post-game press conference I think Lee Sedol said something like "Before the matches I was thinking the result would be 5-0 or 4-1 in my favor, but then I lost 3 straight... I would not exchange this win for anything in the world."

Demis Hassabis said of Lee Sedol: "Incredible fighting spirit after 3 defeats"

I can definitely relate to what Lee Sedol might be feeling. Very happy for both sides. The fact that people designed the algorithms to beat top pros and the human strength displayed by Lee Sedol.

Congrats to all!

<comment><USER11> I've read some articles in Korean press that suggested AlphaGo team picked Lee as their match and not Ke Jie (currently #1 ranked, 19 years young) because there's a lot more public records of Lee's plays over his much longer pro career (nearly 20 years now). Thus more material for AlphaGo to train with and against.

So it's not totally arrogant of Ke Jie to suggest he could beat AlphaGo. AlphaGo has not much 'experience' in dealing with Ke Jie.

And Lee winning of game 4 shows a human is still indeed more capable than any AI. He basically reprogrammed his game on his own. Sorta.

<comment><USER12> That was specifically discussed in the after match conference today. Demis said that it wasn't trained specifically against Lee but was trained against strong amateurs on the Internet initially before playing against itself. He said that even if they'd wanted to, alpha go requires a much larger number of games than are available by Lee Seoul to train so it wouldn't have been possible anyway.

<comment><USER13> It was discussed pre-match for game 2 or 3 as well, and Lee's games were described as "a drop in the ocean" I believe.

Came up because of an assertion from the interviewer that chess AI had been trained against it's opponent specifically.

</comment></comment></comment></comment><comment><USER14> My friends and I (many of us are enthusiastic Go lovers/players) have been following all of the games closely. AlphaGo's mid game today was really strange. Many experts have praised Lee's move 78 as a "divine-inspired" move. While it was a complex setup, in terms of number of searches I can't see it be any more complex than the games before. Indeed because it was a very much a local fight, the number of possible moves were rather limited. As Lee said in the post-game conference, it was the only move that made any sense at all, as any other move would quickly prove to be fatal after half a dozen or so exchanges.

Of course, what's obvious to a human might not be so at all to a computer. And this is the interesting point that I hope the DeepMind researchers would shed some light on for all of us after they dig out what was going on inside AlphaGo at the time. And we'd also love to learn why did AlphaGo seem to go off the rails after this initial stumble and made a string of indecipherable moves thereafter.

Congrats to Lee and the DeepMind team! It was an exciting and I hope informative match to both sides.

As a final note: I started following the match thinking I am watching a competition of intelligence (loosely defined) between man and machine. What I ended up witnessing was incredible human drama, of Lee bearing incredible pressure, being hit hard repeatedly while the world is watching, sinking to the lowest of the lows, and soaring back up winning one game for the human race.. Just incredible up and down in a course of a week. Many of my friends were crying as the computer resigned.

<comment><USER15> Given the 78 was a "divine inspired" move, and Demis tweeted that AlphaGo's mistake was move 79, my guess is that Black 78 was not in AlphaGo's search tree at all. It had pruned that move as being too "unlike what a human pro would do", and had not considered play beyond that point. Then it had to suddenly backtrack, drop its entire search tree, and start rebuilding from scratch. It took only a couple minutes to do so, but that's still not enough time to build up the depth and breadth of a tree that it had been constructing up to that point in the game. So it ended up playing a suboptimal move simply because Lee Sedol played a move that was simultaneously brilliant/strong and novel/unexpected, and it had to respond somehow.

<comment><USER16> The problem may well have occurred before then, too. If move 78 was incorrectly pruned from the tree for being unlikely, then AlphaGo had many turns in which it should have been considering counter-moves to move 78 (and thus prevent the situation that allowed move 78 to even happen in the first place), but it didn't.

</comment></comment></comment><comment><USER17> So AlphaGo is just a bot after all...

Toward the end AlphaGo was making moves that even I (as a double-digit kyu player) could recognize as _really_ bad. However, one of the commentators made the observation that each time it did, the moves forced a highly-predictable move by Lee Sedol in response. From the point of view of a Go player, they were non-sensical because they only removed points from the board and didn't advance AlphaGo's position at all. From the point of view of a programmer, on the other hand, considering that predicting how your opponent will move has got to be one of the most challenging aspects of a Go algorithm, making a move that easily narrows and deepens the search tree makes complete sense.

<comment><USER18> Do you think Lee could use this as a way to crack AlphaGo?

<comment><USER17> From my limited knowledge of the game, a few of the moves that Lee made before AlphaGo "lost its mind" were a tad on the aggressive side. The conventional wisdom in Go is to prefer more conservative moves (increasingly so as the game progresses). Usually, if your opponent is being overly aggressive then you want to play more conservatively and wait for them to make a mistake, but in AlphaGo's case, it attempted to match Lee's aggressiveness move-for-move, and Lee was able to capitalize.

If I had to guess (and this is pure speculation), AlphaGo has no concept of waiting for its opponent to make a mistake. Instead, it assumes its opponent will continue to make the best possible follow-ups, and so AlphaGo feels overly compelled to "keep up". In this case, that did it in.

If this is what happened, then _yes_ , I would expect Lee to be able to capitalize.

<comment><USER19> _> If I had to guess (and this is pure speculation), AlphaGo has no concept of waiting for its opponent to make a mistake. Instead, it assumes its opponent will continue to make the best possible follow-ups_

One of the DeepMind guys just confirmed that this is how AlphaGo operates in the press conference.

<comment> david-given "When you're up against someone smarter than you are, do something insane and let them think themselves to death."

\-- Pyanfar Chanur (C.J. Cherryh)

</comment></comment></comment></comment></comment><comment><USER20> The crucial play here seems to have been Lee Seedol's "tesuji" at White 78. From what I understand this phrase in Go means something like "clever play" but is something like sneaking up on your opponent with something that they did not see coming. Deepmind CEO confirmed that the machine actually missed the implications of this move as the calculated win percentage did not shift until later. [https://twitter.com/demishassabis/status/708928006400581632](https://twitter.com/demishassabis/status/708928006400581632)

Another interesting thing I noticed while catching endgame is that AlphaGo actually used up almost all of its time. In professional Go, once each player uses their original (2 hour?) time block, they have 1 minute left for each move. Lee Sedol had gone into "overtime" in some of the earlier games, and here as well, but previously AlphaGo still had time left from its original 2 hours. In this game, it came down quite close to using overtime before resigning, which is does when the calculated win percentage falls below a certain percentage.

<comment><USER21> AlphaGo was in over time in game 2.

<comment><USER22> To add to this, it seemed to have no problem makkng moves wothin the 60 second window (at that lte tage of the game).

<comment><USER16> I suspect that in almost all cases it has already converged on a move well before sixty seconds, and so if forced by time pressure to choose a move before it has achieved the confidence it's looking for, it will probably still do the right thing most of the time (especially in the endgame, when the branching factor is cut down enormously). It just uses additional time because it has it. I would love to see a graph of AlphaGo's thinking showing certainty of time, and how long it was taking to come up with the move it ultimately ended up playing, and how that certainty evolved over its thinking period.

</comment></comment></comment></comment><comment><USER23> Another way to look at this is just how efficient the human brain is for the same amount of computation.

On one hand, we have racks of servers (1920 CPUs and 280 GPUs) [1] using megawatts (gigawatts?) of power, and on the other hand we have a person eating food and using about 100W of power (when physically at rest), of which about 20W is used by the brain.

[1] [http://www.economist.com/news/science-and- technology/2169454...](http://www.economist.com/news/science-and- technology/21694540-win-or-lose-best-five-battle-contest-another-milestone)

<comment><USER4> Definitely not anything close to gigawatts: ALL of Google (not DeepMind, but the entire company) uses only about 260 megawatts.

Probably on the order of one megawatt or so.

[http://inhabitat.com/infographic-how-much-energy-does- google...](http://inhabitat.com/infographic-how-much-energy-does-google-use/)

<comment><USER24> Still, wow! And a human is using 100W or so: (~400 max) [https://sustainability.blogs.brynmawr.edu/2012/07/31/underst...](https://sustainability.blogs.brynmawr.edu/2012/07/31/understanding- energy-part-1/)

<comment><USER25> And don't forget, the human grows organically from a complex system of DNA that also codes for way more than playing Go! And is able to perform a lot of tasks very efficiently, including cooperating together on open ended activities.

<comment><USER26> And building an AI that can play better than itself :)

</comment></comment></comment></comment></comment><comment><USER27> There were a few jokes made during the round about how AlphaGo resigns. Turns out it's just a popup window! [http://i.imgur.com/WKWMHLv.png](http://i.imgur.com/WKWMHLv.png)

<comment><USER28> For anyone wondering, that's Ubuntu Linux (version most likely 14.04; Unity interface).

off-topic: DeepMind should switch to a tiling window manager like i3 for increased keyboard-only productivity :)

<comment><USER29> The developers probably use whatever window manager they want. I'm personally a fan of i3 also.

But there is nothing wrong with keeping the frontend machine used in this Go match in default Ubuntu desktop environment since its only purpose is to play Go with a graphical user interface anyway.

<comment><USER28> I know. I was half-joking.

Also given the progress of DeepMind so far, it's very likely that whatever desktop setups they have, is working very well for them.

</comment></comment></comment></comment><comment><USER30> AlphaGo's weakness was stated in the press conference inadvertently: it considers only the opponent moves in the future which it deems to be the most profitable for the opponent. This leaves it with glaring blind spots when it has not prepared for lines which are surprising to it. Lee Sedol has now learned to exploit this fact in a mere 4 games, whereas the NN requires millions of games to train on in order to alter its playing style. So Lee only needs to find surprising and strong moves (no small feat but also the strong suit of Lee's playing style generally).

<comment><USER31> You are saying, its weakness is making the mistake of not looking at all the profitable moves.

The contra argument for the sake of the argument goes, he just was lucky to find a local maximum (?) outside of the search space (?) by chance, rather than learning in a few days the universal function (?) that the NN thinks solves go, or at least one fixed point (?) [i.e. the surprisingly wrong expectation].

</comment></comment><comment><USER32> We were discussing the probability that Sedol would win this game. Everyone, including me, bet 90% that no human would _ever_ win again, let alone this specific game: [http://predictionbook.com/predictions/177592](http://predictionbook.com/predictions/177592)

I tried to estimate it mathematically. Using a uniform distribution across possible win rates, then updating the probability of different win rates with bayes rule. You can do that with Laplace's law of succession. I got a 20% that Sedol would win this game.

However a uniform prior doesn't seem right. Eliezer Yudkowsky often says that AI is likely to be much better than humans, or much worse than humans. The probability of it falling into the exact same skill level is pretty implausible. And that argument seems right, but I wasn't sure how to model that formally. But it seemed right, and so 90% "felt" right. Clearly I was overconfident.

So for the next game, with we use Laplace's law again, we get 33% chance that Sedol will win. That's not factoring in other information, like Sedol now being familiar with AlphaGo's strategies and improving his own strategies against it. So there is some chance he is now evenly matched with AlphaGo!

I look forward to many future AI-human games. Hopefully humans will be able to learn from them, and perhaps even learn their weaknesses and how to exploit them.

Depending how deterministic they are, you could perhaps even play the same sequence of moves and win again. That would really embarrass the Google team. I hear they froze AlphaGo's weights to prevent it from developing new bugs after testing.

<comment><USER33> The argument that AI is either much better or much worse does not apply here. It's not an accident that they chose this point in time to play against Lee Sedol instead of 10 years ago or 10 years in the future. They chose this point in time because they thought that they have a reasonable chance of winning.

Also, he won with white but he will play with black next time, so playing the same sequence of moves can't happen. Additionally, even if the AI didn't incorporate any randomness in the opening (I think it does) it may choose different moves if it gets a different amount of time to think, so Lee Sedol would have to play his moves at exactly the same time as the last game. A couple of seconds deviation only has to lead to a different move in one of the 80 or so moves before the mistake was made to invalidate this strategy.

<comment><USER16> > It's not an accident that they chose this point in time to play against Lee > Sedol instead of 10 years ago or 10 years in the future. They chose this > point in time because they thought that they have a reasonable chance of > winning.

Most importantly, they chose this point in time because they know that there are several other AI research teams that are also on the right track (including at Facebook). Like circumnavigating the globe or landing on the Moon, 90% of the benefit of it is lost if someone else does it before you. So you don't wait for 100% certainty of winning -- you are maximizing for the chance of winning _first_ , not winning for certain.

Given that, it seems reasonable that they would go after Lee Sedol when they were sure they were better than him, but not too much better than him. So a non-5-0 outcome is, in hindsight, not horribly surprising.

</comment></comment></comment><comment><USER34> According to the commentary of both streams I was watching, after losing an important exchange in the middle (apparently move 79 [https://twitter.com/demishassabis/status/708928006400581632](https://twitter.com/demishassabis/status/708928006400581632)) it seems AlphaGo sort of bugged out and started making wrong moves on an already dead group on the right side of the board. After that it kept repeating similar mistakes until it resigned a lot of moves after. But the game was already won for Lee Sedol after that middle exchange. It was really interesting seeing everyone's reactions to AlphaGo's bad moves though.

<comment><USER20> I would avoid thinking of this like a traditional computer program that just "bugged out" due to a glitch or a problem in the software. More accurately, it failed to account for the implications of a move on the board and therefore focused its attention in the wrong place. This happens often in games, I can imagine that a chess master playing an amateur might move his knight or bishop into a position that does immediately seem threatening to an amateur, so the amateur responds by moving a pawn somewhere else on the board, while the correct move would have been to attempt to counter the threat. As should be well known by now, in Go you cannot examine the implications of all possible moves, so some things will be missed. In this case, it seems something important was missed at the time, and the implications were not realized by the program until 10 or 15 turns later.

<comment><USER34> I mean that it bugged out because the moves it made after missing the exchange in the middle were moves that were obviously wrong, not in a "maybe it's up to something" kind of way, but in an objectively 100% bad kind of way. Even if you can't analyze all possibilities AlphaGo made a number of moves that made absolutely no sense at all even to way lower level players.

<comment><USER35> > a number of moves that made absolutely no sense at all even to way lower > level players.

Another possibility is that it was looking way deeper than anybody, and there was a 1% chance or turning the whole game around with those seemingly bad moves. But Lee Sedol blocked that deep move in a way nobody was able to see.

<comment><USER36> No. That is not what happened. They were genuinely bad moves.

<comment><USER37> You don't have the authority to make that call unless you intimately understand how it AlphaGo works. Please stop.

<comment><USER38> He's right. The 9d commentators just laughed them off. They were the kinds of moves a 25k might play hoping that his opponent would make a silly mistake. That's exactly what AlphaGo was trying to do. The moves it played did have a "slight" chance of working, if Lee Sedol had responded incorrectly, but that would have never happened. They were the kinds of moves that are insulting when an opponent plays them against me and I'm just a weak amateur player. The moves clearly had no depth to them, and everyone that understands the game agrees on that.

</comment></comment></comment></comment></comment></comment></comment><comment><USER39> I found this comment on that thread quite insightful: [https://gogameguru.com/alphago-4/#comment-13410](https://gogameguru.com/alphago-4/#comment-13410)

Edit: here's another great one on MCTS: [https://gogameguru.com/alphago-4/#comment-13479](https://gogameguru.com/alphago-4/#comment-13479)

</comment><comment><USER40> That was really cool! It seemed after the brilliant play in the middle the most probable moves for winning required Lee Sedol to make impossibly bad mistakes for a professional, which would be a prior that AlphaGo doesn't incorporate. I've heard the training data was mostly amateur games so perhaps the value/policy networks were overfit? Or maybe greedily picking the highest probability, common with tree search approaches, is just suboptimal?

<comment><USER29> Failure to generalize is not always caused by overfitting. Even if there is no overfitting, deep neural networks seem to learn a surprisingly discontinuous function. Consequently, in rare cases, they can misclassify things with great confidence. [0]

From the cited paper,

> [Experimental results] suggest that adversarial examples are somewhat > universal and not just the results of overfitting to a particular model or > to the specific selection of the training set.

Anyway, Monte Carlo Tree Search is bad at losing positions. In general, you want to delay the impending catastrophe as long as possible instead of making stupid moves that make your position worse and worse. However, MCTS uses random rollouts to the end of the game, which sometimes make it difficult to ascertain if the inevitable doom is near or far.

Also, MCTS converges very, very slowly and is likely to miss a unique, single winning continuation.

I think it is probably a combination of both AlphaGo's value network failing to realize the good position of Lee Sedol after his brilliant play, and the MCTS failing to spot the unique winning sequence for Lee, that caused it to make the mistake. But we should probably wait for official analysis from the Deepmind team to see what exactly went wrong.

[0] "Intriguing properties of neural networks" [http://arxiv.org/pdf/1312.6199.pdf](http://arxiv.org/pdf/1312.6199.pdf)

<comment><USER40> I'm hoping someone creates adversarial formations for AlphaGo if Google ever releases their model :)

One thing I've been pondering is if many adversarial samples exist. The board is rather low dimensional (19 x 19) and discrete. While certainly a massive state space, one of the suggestions for why adversarial images work is that the real number line is incredibly dense.

For example our possible Go board space is 2^(log2(3) * 19^2) for Go but 2^(24 * 28^2) for greyscale [0, 1] normalized single precision float imagery for MNIST. Thats an exponentially bigger space (I think something like 1e910 times bigger!), and gets only larger if you train with double precision, have larger images, add multiple color channels, add more nonlinear layers, etc.

</comment></comment></comment><comment><USER41> I wouldn't be surprised if, in a month, Lee Sedol was able to beat AlphaGo in another match. This is what happened in chess. The best computers were able to beat the best humans, until the best humans learned how to play anti-computer chess. This bought them a year or so more, until computers finally dominated for good.

</comment><comment><USER42> Right now I don't know if I'm more impressed by AlphaGo's artificial intelligence or its artificial stupidity.

Lee Sedol won because he played extremely well. But when AlphaGo was already losing it made some very bad moves. One of them was so bad that it's the kind of mistake you would only expect from someone who's starting to learn how to play Go.

<comment><USER43> AlphaGo kept making bad moves in such a way the rest of the game becomes more and more predictable - each of the moves Lee Sedol makes could be described as the only obvious one.

On the surface, as an analogy, it sounds like investors in financial markets, _capitulating_ , selling at a loss for a more risky outcome. In hindsight almost always bad moves, but at the time of making them it feels right because it's removing risk. Investors are losing, and then when capitulating they make even worse moves, like selling at market bottoms.

[http://www.investopedia.com/terms/c/capitulation.asp?layout=...](http://www.investopedia.com/terms/c/capitulation.asp?layout=infini&v=4B&adtest=4B)

<comment><USER43> Correction: _for a more risky outcome_ should be _for a less risky outcome_

</comment></comment></comment><comment><USER44> [https://twitter.com/StatsmanBruno/status/708941065877737472/...](https://twitter.com/StatsmanBruno/status/708941065877737472/photo/1)

<comment><USER45> That got me for a second hehe (it's a fake)

</comment></comment><comment> herrvogel- Am I right by asumming, that if they would play another game (AlphaGo black and Lee Sedol white), that Lee Sedol could pressure AlphaGo into makeing the same mistake again?

<comment><USER40> This is an interesting question - if Lee Sedol simply replays the game exactly, does he repeat a win?

I think the answer would be most likely not - the monte carlo tree search is randomized so AlphaGo's responses to Sedol may not be exactly the same, requiring Sedol to not be able to repeat the exact same play.

<comment><USER46> Assuming it's a parallel monte carlo computation i doubt it would make exactly the same moves.

</comment></comment></comment><comment><USER47> After AlphaGo won the first three games, I wondered not if the computer had reached and surpassed human mastery, but instead how many orders of magnitude better it was. Given today's result, it may be only one order, or even less. Perhaps the best human players are relatively close to the maximum skill level for go, and that the pros of the future will not be categorically better than Lee Sedol is today.

<comment><USER48> Yudkowski argues that on the scale of intelligence, Einstein and a village idiot are basically right next to each other [1]. So once artificial intelligence gets close to matching the village idiot, it is not far from completely thrashing Einstein.

Now if that same picture held for Go, then a situation like this would seem to be impossible. Either the computer should be much worse than a human player, or much better. It would be an incredible coincidence that, at the end of six months of training, the computer happened to be of comparable skill to humans.

For the game of Go, at least, Yudkowski is wrong. What other aspects of intelligence are this way? Yudkowski's picture seems appealing, but perhaps it is wrong for many areas of intelligence.

[1] [http://lesswrong.com/lw/ql/my_childhood_role_model/](http://lesswrong.com/lw/ql/my_childhood_role_model/)

<comment><USER49> AlphaGo is not an AI in the sense meant by Yudkowsky, I believe. He speaks more of a recursively self-improving AI, an AI which is capable of upgrading itself to be faster and more intelligent.

In the linked article, Yudkowsky even says "On the right side of the scale, you would find Deep Thought—Douglas Adams's original version, thank you, not the chessplayer." The implication is clear that these programs playing chess/Go are nothing like what he is talking about - general AI.

Or so I assume, from my less-than-complete understanding of Yudkowsky's writings.

</comment></comment></comment><comment><USER50> I was not expecting Lee Sedol to come back and win a game after his first three losses. AlphaGo seemed to be struggling at the end of the match.

<comment><USER51> I was hoping Lee Sedol would be able to at least win one, humans can 'learn fast'. We cant learn 24/7 in parallel like a computer, but we do seem to have quite the talent at learning 'situational' things very quickly, quite likely because our very survival depended on it in the deep distant past. :-)

I don't believe AlphaGo had the time to do any additional training between matches. So effectively Lee has the ability to 'learn his opponent' while AlphaGo cannot until the entire match set is over because of how long it would take do do additional training.

<comment><USER52> It's been said elsewhere that AlphaGo studying such a corpus of matches that even every match Lee ever recorded wouldn't be enough to bias it. I presume this also means that adding the last 3 games to that corpus would still not be enough to affect AlphaGo meaningfully.

</comment></comment></comment><comment><USER53> Bizarre. I felt a palpable sense of relief when I read this. Silly meat-brain that I am.

<comment><USER54> Living flesh is not meat. Meat is what we call dead flesh.

A living brain is made of neural nano-processors.

<comment><USER55> This is a matter of perspective. If you were a tiger, you would consider anything that walks or crawls to be "meat".

<comment><USER56> What special insight do you have into the perspective of a tiger? I think this is anthropomorphization.

</comment></comment></comment></comment><comment><USER57> GoGameGuru just published a commentary of the game with some extra insight [https://gogameguru.com/lee-sedol-defeats-alphago- masterful-c...](https://gogameguru.com/lee-sedol-defeats-alphago-masterful- comeback-game-4/)

The author thinks that Lee Sedol was able "to force an all or nothing battle where AlphaGo's accurate negotiating skills were largely irrelevant."

[...]

"Once White 78 was on the board, Black's territory at the top collapsed in value."

[...]

"This was when things got weird. From 87 to 101 AlphaGo made a series of very bad moves."

"We've talked about AlphaGo's 'bad' moves in the discussion of previous games, but this was not the same."

"In previous games, AlphaGo played 'bad' (slack) moves when it was already ahead. Human observers criticized these moves because there seemed to be no reason to play slackly, but AlphaGo had already calculated that these moves would lead to a safe win."

Which, I add, is something that human players also do: simplify the game and get home quickly with a win. We usually don't give up as much as AlphaGo (pride?), still it's not different.

"The bad moves AlphaGo played in game four were not at all like that. They were simply bad, and they ruined AlphaGo's chances of recovering."

"They're the kind of moves played by someone who forgets that their opponent also gets to respond with a move. Moves that trample over possibilities and damage one's own position — achieving less than nothing."

And those moves unfortunately resemble what beginners play when they stubbornly cling to the hope of winning, because they don't realize the game is lost or because they didn't play enough games yet not to expect the opponent to make impossible mistakes. At pro level those mistakes are more than impossible.

Somebody asked an interesting question during the press conference about the effect of those kind of mistakes in the real world. You can hear it at [https://youtu.be/yCALyQRN3hw?t=5h56m15s](https://youtu.be/yCALyQRN3hw?t=5h56m15s) It's a couple of minutes because of the translation overhead.

</comment><comment><USER58> Lee Sedol definitely did not look like he was in top form there. I would say (as an amateur) his play in Game 2 was far better. It was the funky clamp position that perhaps forced AlphaGo to start falling apart this game. [0]

I wonder if Lee Sedol can find a way to replicate that in Game 5.

[0]: [https://twitter.com/demishassabis/status/708928006400581632](https://twitter.com/demishassabis/status/708928006400581632)

</comment><comment><USER59> Here's the post-game conference livestream:

[https://www.youtube.com/watch?v=yCALyQRN3hw](https://www.youtube.com/watch?v=yCALyQRN3hw)

At the end, Lee asked to play white in the last match, and the Deepmind guys agreed. He feels that AlphaGo is stronger as white, so he views it as more worthwhile to play as black and beat AlphaGo.

Conference over, see you all tomorrow.

<comment><USER60> Lee asked to be black, because there's 7.5 points advantage for the white who follows the black, and Lee won as a white this time.

<comment><USER22> Given that Lee was black in games one (due to luck of the draw) and three I expected he would be in game five as well. Perhaps the system permits loser to choose?

<comment><USER61> normally the color of the 5th game is chosen by the result of the 4th game, but Lee asked to change that rule and make him black, so he can have a shot at beating AlphaGo as black.

<comment><USER16> Are you sure? I thought I heard the commentators saying that it was randomized again for game 5, but maybe I was mistaken?

</comment></comment></comment></comment></comment><comment><USER62> This is a great day for humans. Glad to see all those years of human research finally pay off.

</comment><comment><USER63> On a tangential note, apparently AlphaGo has been added to [http://www.goratings.org/](http://www.goratings.org/), though its current rating of 3533 looks off. Shouldn't it be much higher?

<comment><USER64> Probably not. Nine games of data is not really that strong of evidence. The algorithm probably gives each player a prior such has assigning one fake loss in order to limit the maximum ranking of players with few games and no losses. This prior is discussed in the paper on the ranking algorithm used here: [http://www.remi-coulom.fr/WHR/WHR.pdf](http://www.remi-coulom.fr/WHR/WHR.pdf)

</comment></comment><comment><USER45> So where can we see this "move 78" that everyone is talking about, without having to go through the entire match counting?

<comment><USER65> [https://online-go.com/review/116862](https://online-go.com/review/116862)

<comment><USER66> "look at that move. that's an exciting move"

[https://www.youtube.com/watch?v=yCALyQRN3hw&feature=youtu.be](https://www.youtube.com/watch?v=yCALyQRN3hw&feature=youtu.be)

</comment></comment></comment><comment><USER67> The post match conference analysis with Lee Sedol and the CEO of deepmind about the different aspects of the game is beautiful to watch. There seems to be a sense of sincerity rather than the greed to win from each of the side.

</comment><comment><USER68> Now that we have two points for interpolation, expectations are down to near best human competency in go using distributed computation. Also from move 79 to 87 the machine wasn't able to detect the weak position, that shows its weakness. Now Lee can try and aggressive strategy creating multiple hot points of attacks to defeat his enemy. The human player is showing the power of intelligence.

</comment><comment><USER69> <joke> AlphaGo let Lee Sedol win to lull us all into a false sense of security. The robot apocalypse is well underway. </joke>

</comment><comment><USER70> okay human race, let's sit back and enjoy our last moments of glory!

</comment><comment><USER25> Wow! Incredible! Now we know that they have a chance against each other. I would say that this was a very major point... otherwise we wouldn't know whether AlphaGo's powers have progressed to the point where no one can ever beat it. Now I take what Ke Je said much more seriously: [http://www.telegraph.co.uk/news/worldnews/asia/china/1219091...](http://www.telegraph.co.uk/news/worldnews/asia/china/12190917/Google- AlphaGo-cant-beat-me-says-China-Go-grandmaster.html)

</comment><comment><USER71> This game is a great example for the people that said that AlphaGo didnt play mistakes when it had a better position because it lowered the margin, because it only looks at winning probability.

AlphaGo made a mistake and realized it was behind, and crumbled because all moves are "mistakes"(they all lead to loss) so any of them is as good as any other.

Im very suprrised and glad to see Humans still have something against AlphaGo, but ultimately, these kind of errors might dissapear if AlphaGo trains 6 more months. It made a tactical mistake, not a theory one.

<comment><USER72> That doesn't make sense to me. Even if the objective function is win probability, it's used to order all potential moves. Thus given a menu of bad options, it should choose the least-bad one, not start choosing at random.

I think there's something more subtle going on.

<comment><USER6> Alphago only has an approximation of win probability, and to be more precise of `win probability playing against itself'. That works well in an even match against humans---but when far to the losing side, Alphago's win probability estimate is not very good.

</comment></comment></comment><comment><USER73> Has anyone heard the crazy theory that alphago bugged out because of daylight savings (the cutover happened mid-game)? Anyone know the exact time at which alphago made its first wonky move?

</comment><comment><USER74> Also:

Lee Sedol doesn't have RAM that can be crammed with faithfully recalled gigabytes of information, and that allow exhaustive, yet precise searching of vast information spaces. The amount of short-term information Sedol can remember perfectly is very small by comparison, and doing so requires a lot of concentration and effort.

Secondly, the faculty with which Lee Sedol plays Go wasn't designed for the exclusive task of playing Go. Without having to load a different program, Sedol's brain can do many other things well.

</comment><comment><USER75> AlphaGo obviously made mistakes in game 4 under the pressure from LSD's brilliant play. I'd like to know if the "dumb moves" are caused by the lack of pro data or some more fundamental flaws with the algorithm/methodology. AlphaGo was trained on millions of amateur games, but if Google/Deepmind builds a website where people (including prop players) can play with AlphaGo, it would be interesting to see who improves faster.

<comment><USER76> AlphaGo doesn't feel pressure.

[http://i.imgur.com/ny3RhD4.png](http://i.imgur.com/ny3RhD4.png)

My guess is that Sedol won because he introduced sufficient complexity through cutting points and numerous black groups (see the image). Since AlphaGo uses Value and Policy networks to determine the hot spots to analyse using Monte Carlo tree searches, by making a game rife with lots of simultaneous fights, Sedol dodged the one-two punch of Value and Policy networks combined with MCTS.

In other words, if Sedol can make over a dozen points of interest on the board, AlphaGo cannot deeply assess them all. In the image, there are at least 13 interesting moves and cuts plus up to 15 groups (depending if lone stones are considered groups by AlphaGo). I suspect that this position was far more complex than at any point during any of the three previous games.

It might also explain the meltdown of playing out an unfavourable ladder (the P10 group, as P8 is another possible move).

<comment><USER77> Could this be overcome by throwing more hardware?

<comment><USER76> Yes, to a certain extent and certain complexity.

[https://en.wikipedia.org/wiki/Go_and_mathematics#Game_tree_c...](https://en.wikipedia.org/wiki/Go_and_mathematics#Game_tree_complexity)

Eventually, math wins. There will come a point where humans cannot make the game sufficiently complex to beat a domain-specific machine intelligence (such as AlphaGo).

</comment></comment></comment></comment><comment><USER78> Apparently AlphaGo made two rather stupid moves on the sidelines, judging from the commentary. Which incidentally is the kind of edgecase one would expect machine learning against itself is bad at learning, since there is a possibility that AlphaGo just tries to avoid such situations. It will be interesting to see if top players are able to exploit such weaknesses once AlphaGo is better understood by high level Go players.

<comment><USER65> From my perspective as a weak player, those stupid moves are the sorts of moves I would make if I already knew I was losing (but not by a huge margin, otherwise resign) but I was in byo-yomi overtime. It settles already dead stones and gives me a bit more time to keep searching for a better move that could maybe turn the game around, and if the opponent fails to make the obvious response then I can turn the game around from their blunder. The only odd thing is AlphaGo makes these moves without having entered byo-yomi time.

<comment><USER78> Thanks, this just raises further questions. I have a lot to learn before I can call myself a weak player, but it does sound like AlphaGo regressed to the level of an amateur, which needs a explanaition by itself.

</comment></comment></comment><comment><USER79> LSD maybe the only human to ever win against AlphaGo.

<comment><USER80> Ke Jie won 8 out of 10 when went against Lee though. Lee is probably not the strongest in the world right now.

<comment><USER81> I think esturk meant that AlphaGo is barely beatable now, so by the time anyone else gets a chance, it will have improved in the meantime and even a stronger human player won't be able to beat it.

<comment><USER39> By that measure, Fan Hui beat AlphaGo before Lee Sedol did, since he was playing an earlier and bit weaker version of more or less the same complete, distributed, system.

</comment></comment></comment></comment><comment><USER82> It's worth mentioning that while 79 is where Black goes bad, not everyone is sure that 78 actually works for White ([http://lifein19x19.com/forum/viewtopic.php?f=15&t=12826](http://lifein19x19.com/forum/viewtopic.php?f=15&t=12826)). I'm sure we'll eventually get a more complete analysis.

</comment><comment><USER51> I was hoping to see how AlphaGo would play in overtime. Now I'm curious, does it know how to play in overtime? Can the system handle evaluating how much time it can give itself to 'think' about each move, or does it fall into the halting problem territory and it was programmed to evaluate its probability of winning given the 'fixed' time it had left.

<comment><USER83> It's called scorboarding. You start coming up with solutions and ranking them and putting the best one up on the scoreboard. When you run out of time, you just go with what you got. Pretty much what humans have.

I'm sure there are many levels of watchdogs in this program.

</comment></comment><comment><USER31> I just noticed a pun in the name: All Phago, devourer of worlds. Especially funny as beating a stone could be imaged as swallowing.

</comment><comment><USER84> [https://gogameguru.com/lee-sedol-defeats-alphago- masterful-c...](https://gogameguru.com/lee-sedol-defeats-alphago-masterful- comeback-game-4/)

> This was when things got weird. From 87 to 101 AlphaGo made a series of very > bad moves.

It seems to me, that these bad moves were a direct result of AlphaGo's min- maxing tree search.

According to @demishassabis' tweet, it had had the "realisation" that it had misestimated the board situation at move 87. After that, it did a series of bad moves, but it seems to me that those moves were done precisely because it couldn't come up with any other better strategy – the min-max algorithm used traversing the play tree expects that your opponent responds the best he possibly can, so the moves were optimal in that sense.

But if you are an underdog, it doesn't suffice to play the "best" moves, because the best moves might be conservative. With that playing style, the only way you can do a comeback is to wait for your opponent to "make a mistake", that is, to stray from a series of the best moves you are able to find, and then capitalize that.

I don't think AlphaGo has the concept of betting on the opportunity of the opponent making mistakes. It always just tries to find the "best play in game" with its neural networks and tree search – in terms of maximising the probability of winning. If it doesn't find any moves that would raise the probability, it picks one that will lower it as little as possible. That's why it picks uninteresting sente moves without any strategy. It just postpones the inevitable.

If you're expecting the opponent to play the best move you can think of, expecting mistakes is simply not part of the scheme. In this situation, it would be actually profitable to exchange some "best-of-class" moves to moves that aren't that excellent, but that are confusing, hard to read and make the game longer and more convoluted. Note that this totally DOESN'T work if the opponent is better at reading than you, on average. It will make the situation worse. But I think that AlphaGo is better in reading than Lee Sedol, so it would work here. The point is to "stir" the game up, so you can unlock yourself from your suboptimal position, and enable your better-on-average reading skills to work for you.

It seems to me that the way skilful humans are playing has another evaluation function in addition to the "value" of a move – how confusing, "disturbing" or "stirring up" a move is, considering the opponent's skill. Basically, that's a thing you'd need to skilfully assess your chances to perform an OVERPLAY. And overplay may be the only way to recover if you are in a losing situation.

<comment><USER84> It would be interesting to see how AlphaGo would play with a handicap – because the skill of overplaying is required from the start in that situation. It might actually suck at handicap games, at the moment.

</comment></comment><comment><USER85> So I am a completely ignorant of the game go. I mean I've heard about it my whole life but never bothered to understand it ever.

But after watching the summary video of AlphaGos win... I'm fascinated.

I'm sure there are thousands of resources that can teach me the rules, but HN; can you point me to a resource you recommend to get up to speed?

</comment><comment><USER30> Would it not be beneficial to the deepmind team to open at least the non- distributed version to the public to allow for training on more players? I was surprised to learn that the training set was strong amateur internet play, why not train on the database of the history of pro games?

<comment><USER6> That's just the initial training set, to get things started.

Alphago learns mostly from playing against itself. (And in the future they are planning to remove the crutch of starting with human generated data entirely.)

</comment></comment><comment><USER18> Does this mean Lee found AlphaGo's weakness, and AlphaGo wasn't player at a out-of-reach level?

</comment><comment><USER86> As a human, I'm pulling for the human. As a computer programmer, I'm pulling for the human. As a romantic, I'm pulling for the human. As a fan of science fiction, I'm pulling for the human. To me it will matter even he can pull off a 3-2 loss over a 4-1 loss.

</comment><comment><USER87> I wonder what the chances are of a cosmic ray or some stray radiation causing AlphaGo to have problems. It's quite a rare event, but when you have 1920 CPUs and 280 GPUs, it might up the probability enough to be something you have to worry about.

<comment><USER88> I would expect them to be running on Google's distributed infrastructure which has error corrections through end to end checksums

<comment><USER89> Actually they were running on a single PC!

[https://twitter.com/demishassabis/status/708489093676568576](https://twitter.com/demishassabis/status/708489093676568576)

ed: oops I misread!

<comment><USER90> "Using distributed for match but single machine AG very strong also"

Doesn't this imply they weren't using a single PC?

<comment><USER91> They're using the distributed version for these matches, this question was just asked in the post match press conference.

</comment></comment></comment></comment></comment><comment><USER92> I am super excited and all about the Progress AI has made in AlphaGo, but a part of me feels kind of relieved that humans won at least one match. :). Sure, won't last for long.

</comment><comment><USER93> I wonder if Lee Sedol were to start as white again, and follow the exact same starting sequences, would AlphaGo's algorithms follow the exact same moves as it did before?

</comment><comment><USER94> It seems Lee Sedol fares better at late to end game than AlphaGo. Makes one wonder if Lee might have won the earlier games had Lee pushed on until the late game stages.

<comment><USER39> I don't think so. Everyone seems to agree that MCTS only gets stronger in the endgame, and that AlphaGo started making weird moves towards the end because it thought it was already losing, which by definition means there's no strategy leading to a winning position.

...However if it's true that it couldn't find a way out, shouldn't its probability of winning have hit ~0% much earlier? There was still a long time between when it started acting strangely and when it resigned.

<comment><USER61> maybe the probability was calculated by playing against other alphagos?

</comment></comment></comment><comment><USER95> Way to go humans. (I felt that AlphaGo was unbeatable and a milestone in computing overthrowing organic brains... I gave in the buzz a bit prematurely).

</comment><comment><USER96> Was it AlphaGo losing the game, or Lee Sedol winning it?

<comment><USER97> Given the number of errors by the AlphaGo in the last 10 minutes, probably the former.

<comment><USER98> I think Lee Sedol won the game earlier by destroying AlphaGo's territory in the center. The commentator (Michael Redmond) was quite impressed with what he did there.

</comment></comment></comment><comment><USER99> Montecarlo bots behave weirdly when losing.

</comment><comment><USER100> Some pretty questionable moves from AlphaGo in that game, but I'm glad LSD managed to close it out.

</comment><comment><USER101> Wow that is awesome news. Very happy to read this this morning. It's a good day to be human.

</comment><comment><USER102> Is there a place I can go to quickly flip through all the board states from the game?

<comment><USER16> See the bottom of [https://gogameguru.com/lee-sedol-defeats-alphago- masterful-c...](https://gogameguru.com/lee-sedol-defeats-alphago-masterful- comeback-game-4/)

</comment></comment><comment><USER103> Move 78 gives us hope in the war against the machines.

78 could come to symbolize humanity.

What a special moment.

<comment><USER104> Machines retreat and regroup, and fix the problems. Next time there is no chance for a move 78.

</comment></comment><comment> another-hack Humans strike back! :P

</comment><comment><USER105> I hope it won't turn out they let him win.

<comment><USER18> Hopefully they're not that disrespectful

</comment></comment><comment><USER106> But, did he pull a Kirk vs Kobayashi Maru? :) (yes, I went there)

</comment><comment><USER107> Now THIS is news!

</comment><comment><USER108> The game seemed to be going in AlphaGo's favour when it was half way through. Black (AG) had secured a large area on the top that seemed nearly impossible to invade.

It was amazing to see how Lee Sedol found the right moves to make the invasion work.

This makes me think that if the time for match was three hours instead of two, maybe a professional player will have enough time to read the board deeply enough to find the right moves.

<comment><USER109> The thing is, Lee Sedol didn't "find the right moves"; the wedge at L11 shouldn't have worked. If black's 79th move had been at L10 instead of K10, Sedol would likely have resigned on the spot.

<comment><USER108> As far as I can tell from watching Redmond's commentary at the time, there were other options for white in the area.

<comment><USER109> I was on the other stream; Myungwan Kim and Hajin Lee went way deeper than Redmond typically goes (since they have access to an SGF editor instead of a clumsy demo board, and they aren't performing for the camera as much). They seemed pretty confident in their conclusion that L10 killed white.

<comment><USER110> Just from Redmond's commentary, killing white in the center is just one of the outcomes that Alphago was impelled to achieve. [for just an arbitrary example] If Alphago killed white in the center but lost the bottom and the top right, it could have still lost. (and Sedol had to acheive multiple objectives also, of course).

Sure, maybe Alphago missed a winning move. But the situation was fluid both strategically and tactically, which might have been why that machine chose it's losing move and moreover, why I don't think it was just a matter of the machine failing to find a kill - especially since I think computers have been able to beat humans on pure tesuji for a while now.

</comment></comment></comment></comment></comment><comment><USER111> negativist paranoid skeptic could say that it would be a good move for the team to intentionally make αgo lose single battle in the moment it has already won the war..

</comment><comment><USER71> Maybe Alpha Go understood it won the 5 series, so its reading that it can lose the last 2 games and still win and hence plays suboptimal :P

</comment><comment><USER112> I believe that after winning 3 out of 5, AlphaGo team started experimenting with variables now that they can relax. Which will in turn be even more helpful for future AlphaGo version than the previous 3 wins.

<comment><USER113> They've declared AlphaGo's code and network frozen for the purposes of this competition, in part to avoid the PR issues that Deep Blue got for doing exactly that.

</comment></comment><comment><USER114> Don't want to sound all Conspiracy Theory but somehow this feels planned.. It plays into DeepMind's hand to not have the machine completely trouncing the human. It's less scary and keeps people engaged further into the future.

Also seems in-line with the way Demis was "rooting" for the human this time – they already won so now they focus on PR.

<comment><USER115> Don't post unfounded conspiracy theories if you don't want to sound like a conspiracy theorist.

<comment><USER114> A lot of comments here are unfounded. It was a thought I had that imo warrants some discussion (which you may disagree)

<comment><USER54> I agree, this point does warrant discussion.

How could DeepMind _prove_ that AlphaGo played its best in match 4 ?</comment></comment></comment></comment>


Paper airplane to fly from space to Earth - <AUTHOR> http://www.msnbc.msn.com/id/23827045/

<comment><USER2> and by 'flying' we mean 'falling'...</comment>