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AlphaGo beats Lee Sedol again in match 2 of 5

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

#461

Post by sgw928 »

Shaanie wrote: post #151
sgw928 wrote: post #89

Earlier quoted context omitted.

Random human beings simply voting fared pretty well in chess though: https://en.wikipedia.org/wiki/Kasparov_versus_the_World Of course, Go has way too many eligible possible moves for random people to vote on, but a large enough group of top pros might be able to do well just by voting.

I think the "Random" part is a bit disingenuous. Four to five expert chess players suggested moves for the world team. I feel that for any reasonable non-expert it comes down to "choose between these suggested moves" rather than "pick a move". It's also not really random human beings, since the selection of participants is self-selected and therefore much more likely to contain very good chess players. This is mostly…

Fair points. Thanks for pointing this out. I used "random" to really mean "not-top-pros." Moves suggested by experts being voted on by a large sum of people are kind of similar to how democracy and capitalism work.

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

#462

Post by return0 »

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…

> It's like another intelligent species opening up a new way of looking at the world

.. that we'll be probably unable to comprehend ourselves.

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

#463

Post by jasonjei »

Isn't it kind of interesting that Google is pushing the lead for these projects? It reminds me when IBM took on the gusto of developing Chess AI when they had strong technical superiority. It's almost as if Google is taking the mantle from IBM to develop these renaissance projects.

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

#464

Post by imaginenore »

JabavuAdams wrote: post #367
imaginenore wrote: post #198

Earlier quoted context omitted.

We already have a successful AI classical music writer https://en.m.wikipedia.org/wiki/Emily_Howell We have all kinds of visual art made by computers and AI - prom painting from photos to abstract art to 3D renders. We have computers writing poems and haiku. The only thing that's missing is the conceptual creation, which, let's be honest, most human artists struggle at as well. So writing and interesting story is not…

None of this stuff is that good yet. I see no fundamental limit, but let's not pretend that machine-generated music or poetry is as good as the best human stuff, yet.

Actually my very first example of the classical music IS that good. The guy created and sold 13 different albums:

https://en.wikipedia.org/wiki/David_Cope#Discography

And they were well accepted by the music community.

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

#465

Post by cLeEOGPw »

hughperkins wrote: post #276
cLeEOGPw wrote: post #217

Earlier quoted context omitted.

AlphaGo developers don't understand how it works in the same way you wouldn't understand how the program you've written to find prime numbers actually found a big prime number. The sequence of operations is known, but numbers are too big to be comprehended.

I think its more like, real parents dont understand why their children do the bizarre things that they do.

I don't think so.

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

#466

Post by mattmcknight »

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…

I think it's more metaphor than leaky abstraction in this case, except to the extent that metaphor is mapping an abstraction of a domain we are trying to understand to an abstraction of one we are better able to understand.

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

#467

Post by Jach »

astrofinch wrote: post #245

So given that this victory seems to be happening a decade or so before experts predicted, how likely are we to see similar acceleration in reaching other AI milestones? (Especially given that AlphaGo is using the same algorithm that won the Atari games, so it has the potential to be very general in its application)

You probably saw this, but linking anyway: https://news.ycombinator.com/item?id=10983539 The general point is that it's more evidence improvement can come in discontinuous leaps, it doesn't have to be some smooth (even if accelerating) incremental process. So timeline predictions should probably be wide, with closer-to-present lower bounds (especially when successful generalizable techniques become public). I don't think this initial view would have changed much this week unless Sedol just totally crushed the bot, perhaps suggesting there's still something more.

Personally I think the approach of combining deep learning with MCTS to beat Go was obvious to anyone sort of familiar with each thing, and with a good funded team could be done in a year or less, but a lot of 'experts' were ignorant of one or more of the areas. The uncertainty should have revolved around when some group would get around to writing the software and scaling up with powerful hardware. Implementation details, the theory work was already known.

My own lesson from this is that if all that is needed is tough engineering work (but not really new theory) that work could literally arrive in a week instead of the x+delay time it might take from scratch, because some group could already have been in the process for about x time that isn't public knowledge. AlphaGo kind of came out of nowhere; there were early signs with papers on using deep learning techniques, but I don't remember any public commitments to much. That just indicates companies are still quite capable of doing secret projects. If they had a secret new theory, too, it could be even more amazing. I know of one startup in particular that's been securely at the top of its niche because internally they have secret CS research unknown to academia.

The OpenAI initiative may be useful from the perspective that if they've shared what probably shouldn't be shared, at least we can suspect something is imminent and try to plan a last minute stand of getting it right first, vs someone like Google doing all the research in secret and then bam, unleashing UFAI.

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

#468

Post by avar »

bad_user wrote: post #256
avar wrote: post #176

Earlier quoted context omitted.

The Turing test is a really narrow "can you act like a human" test of intelligence, not a general intelligence test.

That's a point of concern for using the Turing Test to assess the intelligence of a computer, but that's only because the computer has to also be trained to expose human-like features that are considered to be unintelligent. But on one hand, in order to have a shot at passing the Turing Test, a computer has to first be able to understand and speak human language. And that's pretty damn hard actually. Yes we can deplo…

    > So while passing the Turing Test may not be enough, not passing
    > the Turing Test is a sign that the computer is unintelligent.
You could have an intelligence that's just not smart in the human sense. Consider running into an alien intelligence evolved from our equivalent of octopuses, you ask it a questions but it only communicates via color changes on its body.

Similarly you can conceive of an AI that's smart, self-aware and intelligent just hasn't been developed to talk to humans.

The Turing test is a fine test to figure out if your AI is conversational with humans, but the OP I was replying to was suggesting it as a general AI intelligence test, it's not meant for that, and will give you both false positives & negatives.

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

#469

Post by maaku »

Cookingboy wrote: post #116

Someone somewhere asked why a lot of people in the Go community is taking this in a somewhat hard way, here is my hypothesis: Go, unlike Chess, has deep mytho attached to it. Throughout the history of many Asian countries it's seen as the ultimate abstract strategy game that deeply relies on players' intuition, personality, worldview. The best players are not described as "smart", they are described as "wise". I thin…

> And now an AI without emotion, philosophy or personality

Why do you think AI has no emotion, philosophy, or personality? We too are mere machines. Magnificent machines, no doubt, but machines nonetheless.

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

#470

Post by jtolmar »

nbouscal wrote: post #423
jtolmar wrote: post #346

Earlier quoted context omitted.

Building on that, I suspect that if AlphaGo thinks it has a 100% chance of winning with any of several moves, it has no way of distinguishing between them and chooses effectively at random. The longer that goes on - and once it hits 100% chance of winning, it will be that way for the rest of the game - the more chances it has to pick bad moves. As long as the move isn't bad enough to ruin its 100% chance of winning,…

I doubt the value network ever outputs a literal 100% chance of winning, it would at most be a lot of nines. Even if it did output an actual 100% chance, AlphaGo would still end up picking moves favored by the policy network, so it would probably just revert to playing like it predicts a human pro would.

Once it gets to enough nines, its monte carlo trees will run out of sample resolution. If it can resolve to three nines, then a 99.93% win branch has a 70% chance of being reported as 99.9% and a 30% chance of being reported as 100%. When all the branches here get rolled up, they report some average around 99.93% but not necessarily exactly it. This propagates upwards in the tree, adding more meaningless digits. Adding the evaluation network in increases the number of decimals, but doesn't really change the effect.

It's similar to how ray tracing renderers start to return weird speckle patterns when the room is dark enough.

And the policy network chooses branches to investigate, not which one to choose. It adds sample resolution to places pros might play, but doesn't add to the estimated probability of winning.

Edit: Actually, since places pros might play have higher sample resolution, they're less random. So worse moves get worse evaluation, and a higher chance of leading the pack. This might actually bias AlphaGo to play some pretty bad moves - but, again, this is all assuming it's going to win anyway.

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