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Lee Sedol Beats AlphaGo in Game 4

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Re: Lee Sedol Beats AlphaGo in Game 4

#311

Earlier quoted context omitted.

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 do…

I concur that better time management may have made a difference.

Beyond that, I think that AlphaGo may still be missing a type of component. From the descriptions of it, the policy network generates possible moves from board positions, and the value network evaluates the probability of desirable outcomes. How this is different than human play is that strategic assessment and planning are implicit in the middle layers rather than a 'conscious' element to searching and decision making. I'm not saying that this is a necessary component as AlphaGo has already done exceptionally well. I do believe this kind of 'middle-out' processing producing and evaluating strategic concepts could make it better handle unusual circumstances. Being trained on high amateur and pro games, it will best respond to the most conventional of those types of games, more unconventional the game becomes, the worse it would fare in terms of efficiency of move generation and choices of which to evaluate.

Re: Lee Sedol Beats AlphaGo in Game 4

#312

Earlier quoted context omitted.

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 do…

It would be interesting to see a neural net evolve to take into account player state, not just game state. I play chess, and no where near professional levels, so I don't know if this anecdote is valuable, but if I see my opponent looking at a particular area of the board, I tend to take a second look.

I suppose beating humans isn't AlphaGo's primary motive though - learning to play a perfect game of Go in general is probably more difficult than playing the perfect game against a particular person.

Re: Lee Sedol Beats AlphaGo in Game 4

#313
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?

Re: Lee Sedol Beats AlphaGo in Game 4

#314
post #307

Earlier quoted context omitted.

He made $30k for losing that game, so I don't feel too sorry for him.

The best (like absolute best) football|basketball|baseball players in the world make approximately what per game?

Best is around 10x that, but at the cost of their body and long term health. Nobody is playing professional football|basketball|baseball in there 50's, and even just 40 is pushing it. Where pro go players can 60+.

Re: Lee Sedol Beats AlphaGo in Game 4

#315
post #267
post #266

Earlier quoted context omitted.

OT: What's with using monotype for quotes? That's breaks line wrapping and makes it hard to read on mobile or small screens. I don't get why people do it.

Looks good in desktop browsers.

It's also customary when quoting large sections of text. I'm not sure if this carried over from email, or from academic texts (where if your quote is more than a line or two, it needs to be formatted differently).

Re: Lee Sedol Beats AlphaGo in Game 4

#316

Earlier quoted context omitted.

If it really was about money, I don't think he would have spent so much of his life dedicated to Go ...

yeah, not to belabor it, but if you can do something like be a champion in go or chess, chances are you have the mental skillset to do something exponentially more lucrative.

I dunno, Bobby Fischer was kinda deranged, for instance, and that often hurts outcomes in otherwise "lucrative" positions. Incidentally, he is credited with raising chess player compensation through his demands.

Generally I'd guess you're right though.

Re: Lee Sedol Beats AlphaGo in Game 4

#317
post #217

Earlier quoted context omitted.

AlphaGo doesn't feel pressure. 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 Polic…

Could this be overcome by throwing more hardware?

Yes, to a certain extent and certain complexity.

https://en.wikipedia.org/wiki/Go_and_mathematics#Game_tree_c...

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).

Re: Lee Sedol Beats AlphaGo in Game 4

#318

Earlier quoted context omitted.

If it really was about money, I don't think he would have spent so much of his life dedicated to Go ...

yeah, not to belabor it, but if you can do something like be a champion in go or chess, chances are you have the mental skillset to do something exponentially more lucrative.

With the number of tournaments he has won (many of which have a winner's purse of <$100 000) I suspect he is already a millionaire.

Re: Lee Sedol Beats AlphaGo in Game 4

#319
post #291
post #240

Earlier quoted context omitted.

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.

> Frankly many pre-modern, pre-civilizarion humans were assholes.

While it's somewhat news to me, I'm very glad to hear we've moved past that.

Re: Lee Sedol Beats AlphaGo in Game 4

#320
https://gogameguru.com/lee-sedol-defeats-alphago-masterful-c...

> 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.

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