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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

#321

Earlier quoted context omitted.

There is a lot of progress being made in AI right now. Hard problems that were expected to take decades, are being beaten regularly. Who is to say how much AI could advance in the next 20-30 years? Do you really believe there's less than a 50% chance strong AI won't be invented in your lifetime?

There is a lot of progress being made in AI right now. Hard problems that were expected to take decades, are being beaten regularly. Who is to say how much AI could advance in the next 20-30 years? "We've beaten some hard problems more quickly than expected therefore we'll likely beat other hard problems more quickly than expected" is logical induction. It's equivalent to "I just flipped a coin and got heads. I'll pr…

>I don't really see the value in speculating.

There is all kinds of value in speculating. We do it all the time in things like war games. 'If neighbor $x attacked us, what would happen?, would they win?, what can we do to prevent this?'.

Of course this may in your mind hold very little value now, but I promise if and when it occurs you will change your mind quickly on that topic.

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

#322
post #172

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…

If your 110MWh to train is accurate, and the 25W used by the human brain reported in this thread is as well.

This is equivalent to one person expending 500 years solely to learn Go.

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

#323

Isn't this jumping the shark a bit? It's a 5-game match. The first was really, really close.

It's worth mentioning that Lee Sedol mentioned in an interview that even if he loses a single game against AlphaGo, he will have lost the match. He was expecting to win all 5 games.

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

#324
post #35
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.

It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it . Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.

Then you can say that, in 10 years, if we indeed have reached that point. Otherwise it'd just an empty prediction, and his perfectly valid point stands.

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

#327
post #63
post #35

Earlier quoted context omitted.

It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it . Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.

> What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it . I don't think that's quite true as a description of what we knew about computer Go previously, though it depends on what precisely you mean. Recent systems (meaning the past 10 years…

"Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated)."

This is exactly the opposite of my sense based on following the computer go mailing list (which featured almost all the top program designers prior to Google/Facebook entering the race). They said that scaling was quite bad past a certain point. The programs had serious blindspots when dealing with capturing races and kos[1] that you couldn't overcome with more power.

Also, DNNs were novel for Go--Google wasn't the first one to use them, but no one was talking about them until sometime in 2014-2015.

[0] Not the kind of weaknesses that can be mechanically exploited by a weak player, but the kind of weaknesses that prevented them from reaching professional level.

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

#328

Isn't this jumping the shark a bit? It's a 5-game match. The first was really, really close.

It's worth mentioning that Lee Sedol mentioned in an interview that even if he loses a single game against AlphaGo, he will have lost the match. He was expecting to win all 5 games.

I hope that doesn't shake his determination and ability to concentrate. He could still win.

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

#329
Extremely interesting news and kind of sad as a human being :)

I don't really know that much about AI, but hopefully some experts can tell me - how different are the networks that play go vs chess for example? Or recognise images vs play go?

What I mean is - if you train a network to play go and recognise images at the same time, will the current techniques of reinforcement learning/deep learning work or are the techniques not sufficient at the moment?

If that works, then it really does seem like a big step towards AGI.

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

#330
post #306

I was really hoping to see a more technical discussion than what I found here in the comments. It's too bad that such a cool accomplishment gets reduced to arguments about the implications for an AI apocalypse and "moving the goalposts". This isn't strong AI, and it was at least believed to be possible (albeit incredibly difficult), but it is still a remarkable achievement. To my mind, this is a really significant ac…

> without having all of the training data in advance What? They certainly trained the algorithm on a huge database of professional go games. It's even in the abstract. [1] [1]: http://www.nature.com/nature/journal/v529/n7587/full/nature1...

> What?

Exactly

They used the game database to learn the value network, then reinforcement learning of the policy network was performed on self-play games. I.e., the machine learned to play from existing data, then played against itself to learn the search heuristics (the policy network) without the need for expert data.

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