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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

#581

Post by treebeard901 »

exDM69 wrote: post #186
taneq wrote: 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.

> Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone. I find this overly optimistic because of the huge amount of power required to run the Go application. Remember, we're getting closer and closer to the theoretical lower limit in the size of silicon chips, which is around 4nm (that's about a dozen silicon atoms). That's a 3-4x improvem…

A Go app likely wouldn't rely on the native processing power of the smartphone. An AlphaGo app could be created today for a smartphone. The bottleneck isn't the phone it's the cost of the cloud computing resources behind it. Perhaps a combination of Moore's law and economy of scale would make it affordable sooner than we think. The Xbox One, for example, already subcontracts difficult problems out to Azure.

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

#582

Post by randomgyatwork »

AI is good for rules based systems, but most of the worlds problems that need to be solved don't have rules in the same way a board game does. Sure it's cool that a computer beat a human at a board game, but thats like celebrating a penguin being better at fishing than a person with bare hand

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

#583

Post by Houshalter »

argonaut wrote: post #577
Houshalter wrote: post #560

Earlier quoted context omitted.

I'm not sure that's correct. MCTS has well known weaknesses, and isn't even a predictive algorithm. MCTS on it's own couldn't get anywhere near beating the top Go champion, that requires deepminds neural networks.

http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... The best Go program before AlphaGo was CrazyStone, ranked at 5-dan ("high amateur" range).

There's a massive skill difference between ameatures and professionals. It couldn't even beat the top ameatures.

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

#584

Post by Houshalter »

YeGoblynQueenne wrote: post #575
Houshalter wrote: post #574

Earlier quoted context omitted.

>Of course they did. They trained it with examples of Go games and they also programmed it with a reward function that led it to select the winning games. Otherwise, it wouldn't have learned anything useful. Yes, but it doesn't need to be trained with examples of Go games. It helps a lot, but it isn't 100% necessary. It can learn to play entirely through self play. The atari games were entirely self play. As for havi…

>> The atari games were entirely self play. That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples. Because you're actually telling it what to do to get a reward. >> Without a reward function, any AI would cease to function. I can't understand this comment, which you made before. Not all AI has a reward function. Specific algorithms do. "All" AI? Do you mean…

>That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples.

No one tells the computer what to do. They just let it do it's thing, and give it a reward when it succeeds.

>Not all AI has a reward function. Specific algorithms do. "All" AI?

Fine, all general AI. Like game playing etc. Minimax isn't general, and it does require a precise "value function" to tell it how valuable each state is. Classification also isn't general, but it also requires precise loss function.

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

#585

Post by eternalban »

conceit wrote: post #567
eternalban wrote: post #558

Earlier quoted context omitted.

The story of Chess, according to Iranian mythological sources (recounted in Shahnameh) is that it was presented to the Iranian Court by the emissaries of the Indian Court, as a 'semantic puzzle' invented by Indian sages. (These games, it should be noted, were pedagogical in nature and used as symbolic means of training monarchs by the intellectual elites.) The response of the Iranian sages was the invention of Backga…

The origin is unclear. The fact that the thematic of a game of war is prevailing now, to me means that it might have as well been in the beginning. Actually it shows at least that those semantics are relevant to war, and to live, so what I was saying stands.

Of course it involves war, but note how they teach the young prince it is (a) better to let the Vazier (your Queen) do all the heavy duty lifting, and (b) it is perfectly honorable to hide behind fortifications in a castle.

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

#586

Post by mjn »

davorb wrote: post #508
mjn wrote: post #63

Earlier quoted context omitted.

> 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…

> Play strength scales roughly with the log of computing power To be fair, a lot of the progress in recent years has been due to taking a different approach to solving the problem, and not just due to pure computing power. Due to the way go works, you can't do what we do with chess and try all combinations, no matter how powerful of a computer you have. Using deep learning, we have recently helped computers develop w…

There've definitely been algorithmic improvements, but from what I've read so far, the change in search algorithms, from traditional minimax search to MCTS, has been the biggest improvement, more than deep learning.

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

#587

Post by argonaut »

Houshalter wrote: post #583
argonaut wrote: post #577

Earlier quoted context omitted.

http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... The best Go program before AlphaGo was CrazyStone, ranked at 5-dan ("high amateur" range).

There's a massive skill difference between ameatures and professionals. It couldn't even beat the top ameatures.

Which is why I said the intuition was amateur-pro level. I did not say it could beat every amateur-pro in the world.

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

#588

Post by emcq »

rictic wrote: post #473
gcr wrote: post #409

Earlier quoted context omitted.

This isn't about Moore's Law though. From the AlphaGo paper: > During the match against Fan Hui, AlphaGo evaluated thousands of times > fewer positions than Deep Blue did in its chess match against > Kasparov; compensating by selecting those positions more intelli- > gently, using the policy network, and evaluating them more precisely, > using the value network—an approach that is perhaps closer to how > humans play.…

My understanding is that it is much more expensive for AlphaGo to evaluate a position than it was for Deep Blue. I'm not certain, but I would be surprised if AlphaGo did not need significantly more computation than Deep Blue. edit: some actual estimates. Deep Blue had 11.38 GFLOPS[1]. According to the paper in Nature, distributed AlphaGo used 1202 CPUs and 176 GPUs. A single modern GPU can do between 100 and 2000 dou…

This 1000 times. Extrapolating Deep Blue's 11GFlop supercomputer to today with Moore's law would be a 70TFlop cluster. AlphaGo is using 1+PFlops of compute (280GPUs referenced for competition in [0]). That's an insane amount of compute.

While it's fun to hate on IBM, it's not really fair to say Deep Blue was throwing hardware at the problem but AlphaGo isn't. Based on the paper AlphaGo will perform much worse in terms of ELO ranking on a smaller cluster.

[0] http://www.economist.com/news/science-and-technology/2169454...

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

#589

Post by YeGoblynQueenne »

Houshalter wrote: post #584
YeGoblynQueenne wrote: post #575

Earlier quoted context omitted.

>> The atari games were entirely self play. That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples. Because you're actually telling it what to do to get a reward. >> Without a reward function, any AI would cease to function. I can't understand this comment, which you made before. Not all AI has a reward function. Specific algorithms do. "All" AI? Do you mean…

>That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples. No one tells the computer what to do. They just let it do it's thing, and give it a reward when it succeeds. >Not all AI has a reward function. Specific algorithms do. "All" AI? Fine, all general AI. Like game playing etc. Minimax isn't general, and it does require a precise "value function" to tell it…

>> No one tells the computer what to do.

Sure they do. Say you have a machine learning algorithm, that can learn a task from examples, and let's notate it like so:

y = f(x)

Where y is the trained system, f the learning function and x the training examples.

The "x", the training examples, is what tells the computer what to learn, therefore, what to do once it's trained. If you change the x, the learner can do a different y. Therefore, you're telling the computer what to do.

In fact, once you train a computer for a different y, it may or may not be really good at it, but it certainly can't do the old y anymore. Which is what I mean by "machine learning can't lead to AGI". Because machine learning algorithms are really bad at generalising from one domain to another, and the ability to do so is necessary for general intelligence.

Edit: note that the above has nothing to do with supervised vs unsupervised etc. The point is that you train the algorithm on examples, and that necessarily removes any possibility of autonomy.

>> Fine, all general AI. Like game playing etc.

I'm still not clear what you're saying; game-playing AI is not an instance of general AI. Do you mean "general game-playing AI"? That too doesn't always necessarily have a reward function. If I remember correctly for instance, Deep Blue did not use reinforcement learning and Watson certainly does not (I got access to the Watson papers, so I could double-check if you doubt this).

Btw, every game-playing AI requires a precise evaluation function. The difference with machine-learned game-playing AI is that this evaluation function is sometimes learned by the learner, rather than hard-coded by the programmer.

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

#590

Post by jakub_h »

maaku wrote: post #510
sethbannon wrote: post #7

I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing computer in history. Kasparov drew that match, but it was clear it was the last stand of homo sapiens in the man vs machine chess battle. Back then, people took solace in the game of Go. Many boldly and confidently predicted we wouldn't see a computer beat the Go wor…

> Many boldly and confidently predicted we wouldn't see a computer beat the Go world champion in our lifetimes. Sadly as I write this my uncle and personal hero who spent 17 years of his life working towards a Ph.D. on abstraction hierarchies for use in Go artificial intelligence, has been moved into hospice care. I'm just glad that in the few days that are left he has a chance to see this happen, even if it is not t…

> This project is the sixth attempt to implement the model:

Finally, the sixth attempt is written in the right language! Now it will succeed for sure.

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