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Lee Sedol Beats AlphaGo in Game 4

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Re: Lee Sedol Beats AlphaGo in Game 4

#291

Post by simonh »

esun wrote: post #240
Bud wrote: post #211

Earlier quoted context omitted.

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?

Not really related, but dolphins are assholes! http://www.deepseanews.com/2013/02/10-reasons-why-dolphins-a...

Frankly many pre-modern, pre-civilizarion humans were assholes. Read many studies or accounts of life in relatively isolated tribal societies and hair curlingy awful accounts of violent and sometimes institutionalised abuse are uncomfortably common. Leave the safety catches off basic human urges for long and it can get pretty ugly.

Re: Lee Sedol Beats AlphaGo in Game 4

#292

Post by bluecalm »

dba7dba wrote: post #128
dannysu wrote: post #109

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 p…

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 'experien…

Another reason could be that Lee is a much bigger name in go than Ke Jie. Sure, Ke Jie is stronger now but Lee was a dominant/top player for a long long time.

Re: Lee Sedol Beats AlphaGo in Game 4

#293

Post by hibikir »

nopinsight wrote: post #289
mikeyouse wrote: post #11

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! H…

Not so coincidentally, "This was when things got weird. From 87 to 101 AlphaGo made a series of very bad moves." [1] The bad moves in the eyes of humans could be risky bets or the horizon effect. [1] [2] AlphaGo's use of deep neural nets (value networks) to evaluate board positions should significantly help counter the horizon effect, but since move 78 by Lee Sedol which turned the situation around was unexpected by…

78 was hard, but not impossible: If you watch the AGA commentary of the game, they had two pros at the time 78 happened, and they found 78 as the best answer a few minutes before Lee did, expecting AlphaGo to go with a stronger, yet still good not good enough 79, that left the game even, instead of basically lost. Then they were elated about how AlphaGo seemed to have failed to read the whole thing, and instead of doing preparatory moves for a nasty ko fight, the best option at the time, AlphaGo just took a route that provided almost no compensation.

If anything, it seems to me that AlphaGo's problem here might be the time management: Seeing a really scary situation, Lee Seido just sank many minutes into reading the problem, going pretty much all the way to byoyomi time. A human, after seeing something like that, would figure out that their assessment of the situation and their opponent's is very different, and spend a lot of budget trying to figure out what was wrong. AlphaGo just didn't see the problem, and didn't just budgets its time to analyze the position to death. It moved slower than before, but not really that much, and ended up making moves a kyu player could see as terrible.

Either way, I'd love to see Deepmind giving us all a good postmortem of the 70-100 range of moves.

Re: Lee Sedol Beats AlphaGo in Game 4

#294

Post by emcq »

dllu wrote: post #156
emcq wrote: post #27

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 appro…

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 t…

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.

Re: Lee Sedol Beats AlphaGo in Game 4

#295

Post by simonh »

comex wrote: post #104
jballanc wrote: post #56

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 a…

Or maybe it was just the computer version of grasping at straws. None of the future gamestates looked good, so it ended up picking whatever could at least theoretically lead to a comeback, even if that would require Lee Sedol to miss a completely obvious move.

This looks like a possibility to me. I'm not sure that Alphago has any idea how strong an opponent it is facing at any given moment. It's played games against venues and it's played games against professionals. But if a set of complex advanced moves are still likely to lead to a narrow defeat, it might well pick some stupid moves that in theory could allow it to win back a dominant position if the opponent plays badly.Since it doesn't have a model of the competence of its opponent, that might appear to be a viable strategy because against some opponents and in some past games it's played, that could work.

This is really interesting, because forming a model of our opponent and tailoring our strategies appropriately is fundamental to how humans approach competitions.

Re: Lee Sedol Beats AlphaGo in Game 4

#296

Post by Artoemius »

denim_chicken wrote: post #190
mikeyouse wrote: post #11

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! H…

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 accelerat…

> For helping to advance an amoral field of study that can potentially delegitimize everything that currently makes humans unique and extraordinary?

If you care about being unique and extraordinary more than about reason, knowledge, truth, the observable reality, and the search for what it really means to be sentient, then and only then you may call AI "amoral".

Also, you are being racist against artificial sentient beings, and being racist is hopefully not what makes humans extraordinary.

Re: Lee Sedol Beats AlphaGo in Game 4

#297

Post by hibikir »

ktRolster wrote: post #273
fhe wrote: post #166

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 move…

AlphaGo's mid game today was really strange. Many experts have praised Lee's move 78 as a "divine-inspired" move. Add to that the moves where AlphaGo basically threw away stones by adding to formations that would be removed from the table. Even I, a complete, lousy, amateur, could see that they were a mistake.

To be fair, those moves were made when AlphaGo was already behind. It's just not any good at dealing with being that far back. The AI just has no concept of what to do while behind: What a human would do is to go for positions that are very complicated, making the chances of sloppy play much higher. Instead, it makes moves that have to be answered in only one or two ways, but that are very easy to read by even an amateur human.

Training an ai to make good play in a bad situation would require it to train in ways that are very different than the AlphaGo vs AlphaGo training that it spent a lot of time doing. And why do that, instead of trying to make itself good while the game is even, or when it's winning?

It's a bit like how it's different to train in chess to play in pro games, vs training to hustle amateurs in the park: You are not making the best move, but a good move that will confuse the opponent the most. You are trying to exploit a bad opponent: Very different play.

Re: Lee Sedol Beats AlphaGo in Game 4

#298

Post by lettergram »

sltkr wrote: post #260
avar wrote: post #259

Earlier quoted context omitted.

They've said it doesn't require that, AlphaGo running on a single machine beats the cluster they're using 25% of the time.

So based on current data, Lee Sedol is exactly as good AlphaGo running on a single machine.

Even on a single machine it has the "memory" of virtually any Go game AlphaGo could be fed.

Re: Lee Sedol Beats AlphaGo in Game 4

#299

Post by jonbarker »

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).

Re: Lee Sedol Beats AlphaGo in Game 4

#300

Post by jonbarker »

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?
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