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AlphaGo beats the world champion Lee Sedol in first of five matches

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Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#201

Post by kqr »

argonaut wrote: post #122
lololomg wrote: post #115

Earlier quoted context omitted.

> As far as I know there is nothing particularly novel about AlphaGo, By that standard there's nothing particularly novel about anything. Everything we have today is just a slight improvement of what we already had yesterday. World experts in go and ML as recently as last year thought it would be many more years before this day happened. Who are you to trivialize this historic moment?

Some experts in Go less than 10 years ago believed it would be accomplished within 10 years. Also, you didn't actually refute his argument. Can you point to an algorithm that is not an incremental improvement over algorithms that existed 10 years ago? MCTS and reinforcement learning with function approximators definitely existed 20 years ago.

No, that's what they're saying. Take any invention and you can break it down into just a slight improvement of the sub-inventions it consists of.

A light bulb is just a metal wire encased in a non-flammable gas and you run electricity through it. It was long known that things get hot when you run electricity through them, and that hot things burst into fire, and that you can prevent fire by removing oxygen, and that glass is transparent. It's not a big deal to combine these components. A lot of people still celebrate it as a great invention, and in my opinion it is! Think about how inconvenient gas lighting is and how much better electrical light is.

Same thing with AlphaGo. Sure, if you break it down to its subcomponents it's just clever application of previously known techniques, like any other invention. But it's the result that makes it cool, not how they arrived at it!

All algorithms are incremental improvements of existing techniques. This isn't a card you can use to diminish all progress as "just a minor improvement what's the fuss".

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#202

Post by [deleted] »

knaik94 wrote: post #53
esturk wrote: post #24

Earlier quoted context omitted.

I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.

The real achievement is in the algorithm. To make an analogy, the accomplishment of putting a man on the moon required that we understand enough to make a rocket. We could have put hundreds of car engines together but that wouldn't ever have gotten us to the moon.

[deleted]

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#203

Post by GolDDranks »

knaik94 wrote: post #53
esturk wrote: post #24

Earlier quoted context omitted.

I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.

The real achievement is in the algorithm. To make an analogy, the accomplishment of putting a man on the moon required that we understand enough to make a rocket. We could have put hundreds of car engines together but that wouldn't ever have gotten us to the moon.

This.

AlphaGo utilizes the "Monte Carlo tree search" as its base algorithm[1]. The algorithm has been used for ten years in Go AIs, and when it was introduced, it made a huge impact. The Go bots got stronger overnight, basically.

What novel thing AlphaGo did, was a similar jump in algorithmic goodness. It introduced two neural networks for

1) predicting good moves at the present situation

2) evaluating the "value" of given board situation

Especially 2) has been hard to do in Go, without playing the game 'till the end.

This has a huge impact on the efficiency of the basic tree search algorithm. 1) narrows down the search width by eliminating obviously bad choises and 2) makes the depth at where the evaluation can be done, shallower.

So I think it's not just the processing power. It's a true algorithmic jump made possible by the recent advances in machine learning.

[1] http://senseis.xmp.net/?MonteCarlo

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#204

Post by Fede_V »

eru wrote: post #196
DavidSJ wrote: post #135

Earlier quoted context omitted.

For AlphaGo, a "pixel" is a point on the board. It uses essentially the same convolutional neural networks (CNNs) that are in state-of-the-art machine vision systems. But yes, the overall architecture is rather different from the Atari system, due to the integration of that CNN with Monte Carlo Tree Search.

Actually, putting a piece of software in front that infers board states from a video feed would be an easy problem.

That's actually true - going from the pixel level to the board state is trivial and not particularly interesting.

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#205

Post by mijoharas »

versteegen wrote: post #141
awwducks wrote: post #21

Earlier quoted context omitted.

Yep, terrific commentary by Myungwan Kim 9p on the AGA channel. For the folks who aren't as familiar with the game, how did you find the commentary (for any channel)? What would you be interested in hearing for events like these?

I watched most of the game on the Deepmind Youtube channel. Although I barely know the rules of Go, it was really nice that explained a lot of the strategies, although aside from the basic explanations most of the rest still flew over my head. I was still hooked, though. However it was infuriating that many times they switched randomly between video feeds, so I couldn't actually see what the commentators were talking…

I've been finding it pretty unwatcheable. Does anyone know of a version that doesn't have the technical issues? (I'm very happy with the commentary, but the video keeps cutting to this 0 minutes screen and audio is patchy).

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#206

Post by eru »

sago wrote: post #91
atemerev wrote: post #77

Earlier quoted context omitted.

The only remaining are language-related. Natural languages are the next focal point of AI research.

Skill related. I'd be interesting to see how quickly driving AIs take to beat the best human drivers, in a weight-equal vehicle. An algorithmic competitor in formula one, would be interesting.

Like http://www.telegraph.co.uk/news/science/science-news/1141026... ?

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#207

Post by jules »

ragebol wrote: post #188
codecamper wrote: post #185

A human was beaten with some thousands of CPUS & GPUS. On a calorie level, the human is still more efficient. On a time to learn these skills... going from zero (computer rolls off assembly line) to mastery, the computer wins. Actually maybe the computer wins even on the caloric level, if you consider all the energy that was required to get the human to that point (and all the humans that didn't get to that point, bu…

But the computer certainly does not win on the amount of training samples required. The human is at the same level as the computer now for Go, but the computer has had much more training samples as Lee Sedol could process in his lifetime. The next step is to reduce the training time/samples for the computer to get the same performance.

That's silly. Why would you want to put human limitations on the computer? We don't artificially put computer limitations on the human.

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#208

Post by eru »

spacehome wrote: post #146
visarga wrote: post #31

Earlier quoted context omitted.

But now every amateur will have access to unlimited play against Lee Sedol-level opponents.

Yea, let me just go home and grab my hundreds of GPUs and CPUs.

Renting them in the cloud for a single game should actually not be all that expensive. Around 100 USD per game perhaps? And the price is only going to fall.

An Amateur can learn plenty from slightly weaker version on less hardware already.

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#209

Post by eru »

nkurz wrote: post #119
mathgenius wrote: post #83

Earlier quoted context omitted.

Yeah according to Redmond, it seemed that AlphaGo made a few "mistakes" whereas Sedol made none. And yet AlphaGo came out substantially ahead. So I'm not sure what that means. Perhaps we need to see more in-depth analysis of the moves, but it seems that AlphaGo just out-calculated Sedol.

I wonder if their move selection algorithm takes into account the "surprise" factor: given two moves that are almost equal in strength when analyzed to a depth of N, chose the one that looks worst at N-1. That is, if all else is equal, assume that you can search deeper than your human opponent, and lay traps accordingly.

If they do that, they didn't tell anyone at Google.

Re: AlphaGo beats the world champion Lee Sedol in first of five matches

#210

Post by kqr »

panic wrote: post #62
Eliezer wrote: post #55

Earlier quoted context omitted.

I don't think it's accurate to call AlphaGo 'improved tree search' the way that Deep Blue was improved tree search. You could with equal justice call it an improved neural net.

I'd say both characterizations are accurate. Neither the tree search nor the neural net could have accomplished this on their own. But the essential interface to the algorithm is the tree search: it's picking the best move from a set of legal moves determined by formal game rules. The real world doesn't follow formal game rules. I find it difficult to see the progression from this victory to some kind of real-world A…

Sure, but when you say "tree search" people think of the traditional kind of expert system-based tree search. Traditional tree search methods prune trees by approximating subtrees according to some very specific rules decided on by human experts. This means in a tree search system, the computer cannot actually evaluate a position any better than the humans that designed it. The way it performs better is by running very many of these evaluations deep down in the tree.

When you're talking about some other kind of evaluation function, such as Monte Carlo rollouts, you usually prefix that to the tree search (in the case of Monte Carlo rollouts, "Monte Carlo tree search" or MCTS) to indicate that besides the basic fundamental task common to almost all AIs (finding the optimal branches in a decision tree) it functions completely differently from the expert systems.

So is the case with this program, which (in a first pass) approximates subtrees by a trained neural net, rather than Monte Carlo rollouts or an expert system. So using terminology that suggests classical expert system tree search is bound to cause confusion (as you noticed).

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