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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

#461

Post by Houshalter »

drawnwren wrote: post #191
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.

Sorry, you're off base a bit. The Atari system did use a Deep Neural Network / Reinforcement algorithm, but as the original poster was trying to point out, the rules of Go were very much hard coded into AlphaGo. From what this [1] says, multiple DNNs are learning how to traverse Monte Carlo trees of Go games. The reinforcement piece comes in choosing which of the Go players is playing the best games. While the higher…

It's best to say that alphago uses neural networks, which are extremely general. The same way planes and cars both use internal combustion engines. ICEs are extremely general. They produce mechanical energy from gas, and are totally uncaring whether you put them into a plane or a car. The body of the plane is necessary, but isn't really the interesting part.

Likewise NNs are uncaring what application you put them into. Give them a different input and a different goal, and they will learn to do that instead. Alphago gave it's NN's control over a monte carlo search tree, and that turned out to be enough to beat Go. They could plug the same AI into a car and it would learn to control that instead.

Note that even without the monte carlo search system, it was able to beat most amateurs, and predict the moves experts would make most of the time.

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

#462

Post by escoz »

YeGoblynQueenne wrote: post #416
21 wrote: post #12

The thing that was supposed to take at least 10 years happened. Only last month people were still saying that no way AlphaGo will beat the champion and that it will be crushed. Today everybody will have seen it coming and say that it was normal. Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out.

Go is literally infinitely more easy to solve than general intelligence. "Literally" in the sense that Go has a finite number of board states, while a general intelligence must be able to deal with an infinite amount of novel situations, presumably by generalising from previously experienced ones. Infinity is a real problem. When you try to learn from examples, you first need to see "enough" examples of whatever you'…

Yes, go have a finite number of board states, just 2.08168199382×10^170, just a bit over the 10^80 atoms in the universe.

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

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

#463

Post by Florin_Andrei »

21 wrote: post #16
dwaltrip wrote: post #8

This is my generation's Gary Kasparov vs. Deep Blue. In many ways, it is more significant. Several top commentators were saying how AlphaGo has improved noticeably since October. AlphaGo's victory tonight marks the moment that go is no longer a human dominated contest. It was a very exciting game, incredible level of play. I really enjoyed watching it live with the expert commentary. I recommend the AGA youtube chann…

I know absolutely nothing about Go, and I enjoyed the deepmind channel and found the commenting very good. I was actually thinking about playing a game with another total noob, just for fun, since the rules can be explained in 1 minute (unlike chess).

> I was actually thinking about playing a game with another total noob, just for fun, since the rules can be explained in 1 minute (unlike chess).

That's actually the recommended way to get started. Learn the rules, and then play a bunch of games with another beginner.

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

#464

Post by riffraff »

CamperBob2 wrote: post #437
riffraff wrote: post #79

Earlier quoted context omitted.

it is a big deal, but it is still very different from general AI, and we are very good at underestimating complexity. Remember that "solve computer vision" was considered a summer project.

Remember that "solve computer vision" was considered a summer project. And they're pretty much there. Have you seen some of the latest results in that field?

I think you misunderstood, I am referring to the fact that Marvin Minsky in 1966, asked Gerald Sussman to "spend the summer linking a camera to a computer and getting the computer to describe what it saw".

We certainly got nearly there, but it was nearly 50 years later, not 3 months. Similarly, something that might look somewhat simple to us right now, might also be a lot more difficult.

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

#465

Post by teraflop »

exDM69 wrote: post #172
gearhart wrote: post #132

Earlier quoted context omitted.

1202 CPUs and 176 GPUs apparently edit: according to the livestream

1202 CPUs and 176 GPUs is the figure mentioned in the Nature paper. But it's important to understand that this is the computer used to train the networks used by the algorithm. It took about 30+ days worth of wallclock to train it. That's about 110 megawatt-hours (MWh) worth of energy required! During the play, the computational requirements are vastly less (but I don't know the figures). It's still probably more tha…

That's not correct; those numbers refer to the system requirements while actually playing. To quote from the paper:

> Evaluating policy and value networks requires several orders of magnitude more computation than traditional search heuristics. AlphaGo uses an asynchronous multi-threaded search that executes simulations on CPUs, and computes policy and value networks in parallel on GPUs. The final version of AlphaGo used 40 search threads, 48 CPUs, and 8 GPUs. We also implemented a distributed version of AlphaGo that exploited multiple machines, 40 search threads, 1202 CPUs and 176 GPUs.

In fact, according to the paper, only 50 GPUs were used for training the network.

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

#466

Post by Houshalter »

argonaut wrote: post #240
nullc wrote: post #173

Earlier quoted context omitted.

Something being invented previously doesn't mean that it existed as a matter of engineering practicality; improved performance is some but not all of that. Just describing something in a paper isn't enough to make it have impact, many things described in papers simply don't work as described. A decade ago I was trying and failing to build multi-layer networks with back-propagation-- it doesn't work so well. More mode…

Backpropagation and convolutional neural nets were breakthroughs that were immediately put to use.

The point is that no one could train deep nets 10 years ago. Not just because of computing power, but because of bad initializations, and bad transfer functions, and bad regularization techniques, etc.

These things might seem like "small iterative refinements", but they add up to 100x improvement. Even when you don't consider hardware. And you should consider hardware too, it's also a factor in the advancement of AI.

Also reading through old research, there is a lot of silly ideas along with the good ones. It's only in retrospect that we know this specific set of techniques work, and the rest are garbage. At the time it was far from certain what the future of NNs would look like. To say it was predictable is hindsight bias.

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

#467

Post by amoonki »

d0m wrote: post #404
geebee wrote: post #387

Terrific accomplishment. Just a question to throw out there - does anyone feel like statements like this one "But the game [go] is far more complex than chess, and playing it requires a high level of feeling and intuition about an opponent’s next moves." … seem to show a lack of understanding of both go and chess? I understand there may be some cross-sports trash talking, but chess, played at a high level by humans ,…

I know I will offend chess players by saying this but.. I feel like chess is more of a IQ contest, while Go is more Art-ish in how you move and slowly cripple and surround your opponent. Realistically speaking, there aren't that many moves you can do in chess. Most of them are just blunder that would get you insta-killed by a good player. Contrast that to Go where there are so many good moves. This is why I think the…

Why wouldn't the same be true for Go? Because the search space of good moves is larger?

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

#468

Post by sago »

vessenes wrote: post #452
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 really enjoyed Myungwan's down-and-dirty commentary, and watching him get lost in some variations, and it was just incredibly exciting to see him get won over to AlphaGo during the game. From about move 50, I was just viscerally excited to see where things went, and the game did not disappoint in any way. I've read a few different reviews and watched Michael Redmond's live commentary as well, who obviously has a sl…

I'm planning to watch the AGA coverage later, after watching the DeepMind coverage live. I found the DeepMind pair a bit underwhelming. Redmond was excellent at playing through some variations, but they did get very distracted at times, and away from what was actually happening. His co-host was playing a little too strong on the 'I'm so nervous' line, I felt. So I didn't spot the significance of the bottom right pivotal moves. Thanks for the recommendation, I'm looking forward to the AGA coverage even more now.

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

#469

Post by sago »

cm2012 wrote: post #429

After Go, the next AI challenge they're looking at is Starcraft: https://twitter.com/deeplearning4j/status/706541229543071745

The obvious problem is that speed of tactical execution can make up for a lot of strategic thought. The famous example: you can rush a line of siege tanks with zerglings if you can micro them fast enough[0].

[0]:https://www.youtube.com/watch?v=IKVFZ28ybQs

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

#470

Post by CamperBob2 »

GolDDranks wrote: post #203
knaik94 wrote: post #53

Earlier quoted context omitted.

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…

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

This is what struck me as especially interesting, as a non-player watching the commentary. The commentators, a 9-dan pro and the editor of a Go publication, were having real problems figuring out what the score was, or who was ahead. When Lee resigned the game, it came as a total surprise to both of them.

Just keeping score in Go appears to be harder than a lot of other games.

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