AlphaGo Zero: Learning from scratch • Hacker News Board

Re: AlphaGo Zero: Learning from scratch

#311

Post by wetpaws » Fri, Oct 20, 2017, 9:49 PM UTC

Jach wrote: post #171

conistonwater wrote: post #153

Earlier quoted context omitted.

That doesn't sound right because in Chinese rules, which is what AG uses, komi only changes in steps of two. Are you sure about 6.5? Could it be 5.5?

Well in Japanese rules the komi is 6.5 so that's the alternative that tends to come up. Some quick searching I found a transcript from one of the games where DeepMind said 7.5 slightly favors white but they didn't say anything about 6.5 or 5.5, while a random comment from r/baduk claims that pro game analysis shows 6.5 slightly favors black and 7.5 slightly favors white.

I guess they should then randomize komi so any player would have 50/50 chance of getting a small edge.

Re: AlphaGo Zero: Learning from scratch

#312

Post by geofft » Fri, Oct 20, 2017, 10:13 PM UTC

YeGoblynQueenne wrote: post #237

geofft wrote: post #204

Earlier quoted context omitted.

That's probably true, but that's very much not what people were saying about Go a couple years ago. There were a lot of people talking about how there isn't a straightforward evaluation function of the quality of a given state of the board, how things need to be planned in advance, how there's much more combinatorial explosion than in chess, etc., to the point where it's a qualitatively different game. For me, as som…

About those claims- this is from Russel and Norvig, 3d ed. (from 2003, so a way back): Go is a deterministic game, but the large branching factor makes it challeging. The key issues and early literature in computer Go are summarized by Boozy and Cazenave (2001) and Muller (2002). Up to 1997 there were no competent Go programs. Now the best programs play most of their moves at the master level; the only problem is tha…

s/2003/2009/, I think, but the point stands. (Also I think I have the second edition at home and now I want to check what it says about Go.)

> my opinion about Go is that humans just weren't that good at it, after all. We may have thought that we have something special that makes us particularly good at Go, better than machines- but AlphaGo[Zero] has shown that, in the end, we just have no idea what it means to be really good at it (which, btw, is a damn good explanation of why it took us so long to make AI to beat us at it).

I really like that interpretation!

Re: AlphaGo Zero: Learning from scratch

#313

Post by posterboy » Fri, Oct 20, 2017, 11:36 PM UTC

Spare_account wrote: post #302

kndyry wrote: post #104

Earlier quoted context omitted.

I don't think it's an overstatement to say that, since playing Lee Sedol in 2016, AlphaGo has completely revolutionized professional and amateur go. It's certainly not unprecedented — the last major revolution happened in the early 20th century (often called the 'Shin Fuseki' era [0]) — but AlphaGo has demonstrably surpassed any previous high-water mark. > I wonder if this system produced more new styles of play. Abs…

With respect to your very interesting comment (I genuinely appreciate your input), you appear to have mis-understood the comment you were replying to. You've commented on the differences in the style of play that AlphaGo introduced, but the post you were replying to (by aeleos) was going a step further and hypothesising about the potential for a newer, completely 'non-human' style that AlphaGo Zero may have created.…

"more new styles of play" seems to indicate that non-human play.

Re: AlphaGo Zero: Learning from scratch

#314

Post by vitaflo » Fri, Oct 20, 2017, 11:59 PM UTC

Tepix wrote: post #226

tanilama wrote: post #39

Earlier quoted context omitted.

Wait for Alpha StarCraft for some real panic. So far RL based method has limited success outside of simple games(Not to say Go is simple, but rather the presentation and control parts of the format).

I'd like to see a StarCraft player AI that wins using a mere 1/10th of the effective actions per minute (EPM) of world class players. To me it seems beating another player while using fewer actions indicates superior skill, understanding and/or intellect.

Not sure I agree with this fully. Certainly many actions used in a typical SC game are redundant, but there are reasons for it. Lag for one. If there's a possibility of lag or dropped packets, spamming a command will help nullify this problem.

The other is the entire reason for high APM, the stop/start problem. Pro players keep high APM so that when they actually need high EPM their muscle memory is already at full tilt. If you slow down your APM during lulls in the action it becomes harder to suddenly increase it when a fight happens.

Certainly that's an entirely human condition that a machine wouldn't need to worry about. But I'm not sure it means lack of skill.

Re: AlphaGo Zero: Learning from scratch

#315

Post by letlambda » Sat, Oct 21, 2017, 12:41 AM UTC

eutectic wrote: post #254

roenxi wrote: post #202

Earlier quoted context omitted.

At this point, there is no evidence that the limiting factor in these cases is AI/software. The limiting factor with the neighbors cat is the robotics of having a robust body and arm attachment. We know that the scope of current AI can: 1) Identify a request to feed a cat 2) Identify the cat, cat food and cat's bowl from camera data 3) Navigate an open space like a house Being able to clean an arbitrary building is a…

I remember seeing a demonstration by a deep learning guy of a commercially available robot cleaning a house under remote control. You are seriously underestimating the difficulty of developing software to solve these problems in an integrated way.

The hardware is certainly good enough to assist a person with a disability living in a ranch house with typical household tasks. As demonstrated by human in the loop operation.

https://www.youtube.com/watch?v=eQckUlXPRVk

Re: AlphaGo Zero: Learning from scratch

#316

Post by naveen99 » Sat, Oct 21, 2017, 12:46 AM UTC

Would be nice if there was an open source attempt at an alpha go clone on a 9x9 board, so it could be run on commodity hardware and maybe trained in more reasonable time. Also would be interesting to see if human would still win on a 190x190 or some arbitrary size board against alphagozero trained appropriately.

Re: AlphaGo Zero: Learning from scratch

#317

Post by thomasahle » Sat, Oct 21, 2017, 4:13 AM UTC

tc wrote: post #198

When things will start getting interesting is when we figure out how to get move simulation and search into the network itself, rather than programming that on the outside. As far as I know, no-one has even the faintest idea of how to do that. We have an existence proof that this should be possible. The networks are great at perception and snap-prediction. Anything a human can do in 200ms is fair game. And with cleve…

I think what you are suggestion is similar to Deep Mind's Sokoban bot: https://deepmind.com/blog/agents-imagine-and-plan/

Re: AlphaGo Zero: Learning from scratch

#318

Post by Spare_account » Sat, Oct 21, 2017, 3:30 PM UTC

posterboy wrote: post #313

Spare_account wrote: post #302

Earlier quoted context omitted.

With respect to your very interesting comment (I genuinely appreciate your input), you appear to have mis-understood the comment you were replying to. You've commented on the differences in the style of play that AlphaGo introduced, but the post you were replying to (by aeleos) was going a step further and hypothesising about the potential for a newer, completely 'non-human' style that AlphaGo Zero may have created.…

"more new styles of play" seems to indicate that non-human play.

Yes, aeleos was interested in that, and so am I, and it seems to be what this entire thread _should be about_. kndyry steered back towards AlphaGo. I'm not sure this merits any further disection.

Re: AlphaGo Zero: Learning from scratch

#319

Post by pedrohorta » Sun, Oct 22, 2017, 10:49 AM UTC

I think that we cannot full understand the implications of that in our present/near future. The use of tecnhology will make the difference. There is a small book in amazon called something like Alpha Go Zero - 10 prophecies for the world. Interesting point of view of the use of it.

Re: AlphaGo Zero: Learning from scratch

#320

Post by nilkn » Sun, Oct 22, 2017, 3:32 PM UTC

Florin_Andrei wrote: post #25

galkk wrote: post #21

Why don't use the same approach for chess? It's very interesting to see if it is able to handle much more advanced and tuned engines that exist for chess, game with considerable much more complicated rules?

I think chess is less compelling because, in a sense, it is a "solved problem" - superhuman AI chess players already exist. And chess, while it does have more complex base rules, has a much lower combinatorial complexity than Go.

That makes it even more interesting. I think it would be very notable and significant if a neural network with MCTS and self-play reinforcement learning could surpass Stockfish, which has superhuman strength but was developed with an utterly different approach involving lots of human guidance and grandmaster input.

Giraffe attempted this (with more standard tree search than MCTS and with only a value function rather than a combined policy/value network), but only reached IM level -- certainly impressive, but nowhere close to Stockfish.