AlphaGo beats Lee Sedol again in match 2 of 5 • Hacker News Board

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#431

Post by [deleted] » Thu, Mar 10, 2016, 5:06 PM UTC

1024core wrote: post #292

ig1 wrote: post #254

Earlier quoted context omitted.

AlphaGo is essentially built on the work that IBM did on TD-Gammon (a reinforcement learning backgammon player) in the 90s. Pretty much the same thing happened with TD-Gammon with it playing unconventional moves, in the longer term humans ended up adopting some of TD-Gammon's tactics once they understood how they played out, it wouldn't be surprising to see the same happen with Go.

> AlphaGo is essentially built on the work that IBM did on TD-Gammon (a reinforcement learning backgammon player) in the 90s. Citation needed.

[deleted]

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#432

Post by kllrnohj » Thu, Mar 10, 2016, 5:07 PM UTC

yeonhoyoon wrote: post #285

taneq wrote: post #243

Earlier quoted context omitted.

Yeah, that explanation from the DeepMind team member today put a whole new spin on some of the 'odd' late game moves. It doesn't 'care' about about margins so it will shore up its odds of a win in preference to increasing the margin if it wins.

that's very interesting exlanation.. do you have the link to the interview of the DeepMind team?

It was during the game on the main stream:

https://youtu.be/l-GsfyVCBu0?t=2510

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#433

Post by mbrock » Thu, Mar 10, 2016, 5:07 PM UTC

angli wrote: post #403

JabavuAdams wrote: post #362

Earlier quoted context omitted.

I've seen this happen with "modern tennis" versus how I was taught to play.

This is interesting. Could you (or someone else whose had this experience) elaborate?

If you're in the mood for some long form literary tennis journalism about this subject, check out David Foster Wallace's Federer as Religious Experience from 2006.

http://www.nytimes.com/2006/08/20/sports/playmagazine/20fede...

Although it takes a few paragraphs until it gets into the details of "today's power-baseline game."

Re: AlphaGo beats Lee Sedol again in match 2 of 5

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Post by netheril96 » Thu, Mar 10, 2016, 5:08 PM UTC

VladKovac wrote: post #260

gsklee wrote: post #156

Earlier quoted context omitted.

On a more realistic side note... Professional Go players devote decades in training ever since their youth, giving up normal educations and lots of other more lucrative opportunities for their lives. It's very easy to imagine their frustrations now that their life-time devotion actually means nothing in front of the AI. It's an upright denial to the way of life they so chose and devoted. IMHO Google should donate the…

Isn't this a good thing? Why are high IQ people devoting their entire lives to a game? Maybe this will make them shift their priorities to solving problems that only really smart humans (like them) can solve.

Uh, talent for Go doesn't translate automatically into talent for math, physics, finance or other branches of science. Even if they are, being the top Go player is probably more attractive than being a meh quant or programmer.

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#435

Post by iopq » Thu, Mar 10, 2016, 5:08 PM UTC

pgodzin wrote: post #39

Is it best of 5 or are they definitely playing 5 matches?

Even at 0-3 there's still money for Lee Sedol for each match that he wins.

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#436

Post by lpage » Thu, Mar 10, 2016, 5:08 PM UTC

bduerst wrote: post #418

lpage wrote: post #406

Earlier quoted context omitted.

> he will later point out to as achieving very good results some 20 moves later This. It's a fairly common feature of any AI that uses some form of tree search/minimax, and the effect is very pronounced in chess. Even the best human players can only think 6-8 plies into the feature versus ~18 for a computer. What we can (could?) do is apply smarter evaluation functions to the board states resulting from candidate pla…

Wasn't the breakthrough with AlphaGo that it doesn't consider every board combination in the future? Because that there are too many combinations?

Yes, but pruning (not considering everything) is as old as game tree search. Previous Go AIs used MCTS as well. What's new in AlphaGo is a more sophisticated approach to scoring game boards - policy networks that help the AI prune even more aggressively, and a value network that's used to "guess" the winner in lieu of searching to endgame. Note that guessing the winner is just a special case of an evaluation function. For any game, if you could consistently search to the end, your evaluation function is always a -1/1 corresponding to lose/win. AlphaGo is still using MCTS - just a more sophisticated form.

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#437

Post by larakerns » Thu, Mar 10, 2016, 5:08 PM UTC

veeragoni wrote: post #407

fhe wrote: post #239

As someone who studied AI in college and am a reasonably good amateur player, I have been following the matches between Lee and AlphaGo. AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. I've been giving it…

so, is it not possible to get the log of its thinking and take a look at why it took certain step later?!

It might look something like attention detailed in Show, Attend, Tell: http://arxiv.org/abs/1502.03044

Which attempts to visualize machine areas of attention that look like: http://www.wildml.com/wp-content/uploads/2015/12/Screen-Shot...

Re: AlphaGo beats Lee Sedol again in match 2 of 5

#438

Post by greg_data » Thu, Mar 10, 2016, 5:13 PM UTC

poppingtonic wrote: post #301

fhe wrote: post #239

As someone who studied AI in college and am a reasonably good amateur player, I have been following the matches between Lee and AlphaGo. AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them. I've been giving it…

There's an interesting angle to this phrase "intelligent species opening up a new way of looking at the world", which is that we (humans) designed go as a game - a subset of the real world we interact with. Go is "reality" to alphago. The superset of all possible sense data it could have, in principle. Whatever "chunks" AlphaGo uses, if it does use them, all of its policies are built only from subsets of the sense da…

that's an absolutely fascinating way to think about it.

Re: AlphaGo beats Lee Sedol again in match 2 of 5

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Post by CyberDildonics » Thu, Mar 10, 2016, 5:14 PM UTC

astrofinch wrote: post #417

CyberDildonics wrote: post #390

Earlier quoted context omitted.

> the "intelligent species" can sometimes work really intelligently towards a goal that wasn't quite what you had in mind. That's called programming

Right. Skynet and Terminator are science fiction, but the slippery, unpredictable reality of how computers actually behave is right in front of your eyes as a programmer every day. Sometimes I wonder if science fiction writers do more harm than good: once they make a movie about some possible future, people feel free to dismiss it as "just science fiction", even if they have easily available empirical evidence that s…

Not unlike the Simpsons episode where the military school graduation speech tells them the wars of the future will be fought with robots and that their jobs will be to maintain those robots.

Re: AlphaGo beats Lee Sedol again in match 2 of 5

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Post by btilly » Thu, Mar 10, 2016, 5:15 PM UTC

EGreg wrote: post #419

btilly wrote: post #408

Earlier quoted context omitted.

Absolutely! And it doesn't matter in the end... Remember that AlphaGo has spent months developing its own style and theory of the game in a way that no human has ever seen. Its style is sure to have weaknesses, but humans will have a hard time figuring them out on first sight. Similarly chess computers do better in some positions than others (they love open tactics!) and one of the games that Kasparov won against Dee…

But when the weaknesses it has require looking 20 ply into the game, can anyone exploit those weaknesses? And furthermore, if the computer itself is able to see 20 ply into the game, then it can spot its own weaknesses and you need to look even further, making the question of whether it's really a weakness. Weaknesses are only relative to capabilities of the opponent to exploit them. If a tank has a weak spot that ro…

The weaknesses that it has will be of the form that it has wrong opinions about certain kinds of positions. In the case of chess, those weaknesses showed up in closed positions where the program owned the center and large amounts of space. In the case of AlphaGo, the weaknesses will be much more subtle, but will be discoverable and exploitable in time.

Additionally AlphaGo has the advantage that it started with a database of human play, so it has some ideas what kinds of positions humans miscalculate.

As for your tank vs horseback analogy, that's flawed at the moment. AlphaGo is probably reasonably close in strength to the human facing him. Improved human knowledge could tip the balance.

However in the future it will become an apt analogy. Computers are going to become so good that knowing the relative weaknesses in their style of play may reduce the handicap you need against them, but won't give you a chance of becoming even with them. That happened close to 20 years ago in chess, and is now only a question of time in Go.