261–270 of 428 posts
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Actually alpha go can be trained for vision or any other task. It's not specialized to win go.
Of course, but it has to be trained anew, with new data. You can't train it on Go data and expect it to perform at all well on vision tasks.
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There must be some limit because the number of the possible moves is finite. How many handicap stones the current professionals are from that limit... who knows. Apparently AlphaGo could set a better lower limit but it should play with many more pros, and start giving handicap if needed. I wonder if Google will make it available to pros outside these official matches. It's not that Deep Blue played so much after winn…
This hardware setup is crazy. Given alone 280 GPUs is insane.
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The question is not if you can see that or not. The question is if you can see that earlier than the computer can see that it is winning.
If that is what is meant then you can't see that you're losing against any player that's any stronger than yourself by definition, because if you could see it earlier you wouldn't have made that move.
It's common for the opening to pass by without feeling behind--Go doesn't have set openings to the same extent as chess, but if you play joseki, you might have an opening where the weaker player feels ok. Even that is not guaranteed, but once you start fighting, you will quickly feel the strength difference.
Of course you can play a move that looks good when you play it, but against a much stronger player, it doesn't take long for it to look bad.
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My pocket calculator is faster than a human and has better memory. I don't know that this means the same thing as "superhuman in arithmetic". I can concede that it means superhuman in speed and memory, but, arithmetic? I don't think so. What it really does is move bits around registers. We are the ones interpreting those as numbers and the results of arithmetic operations. AlphaGo is rather different in that it actua…
Your calculator does have a representation of arithmetic too. It's those bits is moves around in registers, which are very much isomorphic to the relevant arithmetic. Why would an intelligence built by humans not be able to be superhuman? The generally accepted definition seems to be "having better than human performance" in which case it seems we've done it many times (like with calculators).
I don't think there's a generally accepted definition and I don't agree that performance on its own is a good measure. Humans are certainly not as good at mechanical tasks as machines are -duh. But how can you call "superhuman" something that doesn't even know what it's doing, even as it's doing it faster and more accurately than us?
Take arithmetic again. We know that cat's can't do arithmetic, because they don't understand numbers, so it's safe to say humans have super-feline arithmetic ability. But then, how is a pocket calculator super-human, if it doesn't know what numbers are for, any more than a cat does? There's something missing from the definition and therefore the measurement of the task.
I don't claim to have this missing something, mind you.
>> Why would an intelligence built by humans not be able to be superhuman?
Ah. Apologies, I got carried away a bit there. I meant to discuss how I doubt we can create superhuman intelligence using machine learing specifically. My thinking goes like this: we train machine learning algorithms using examples; to train an algorithm to exhibit superhuman intelligence we'd need examples of superhuman intelligence; we can't produce such examples because our intelligence is merely human; therefore we can't train a superhuman intelligence.
I also doubt that we can create a superhuman intelligence in any other way, at least intentionally, or that we would be able to recognise one if we created it by chance, but I'm not prepared to argue this. Again, sorry about that.
It's important to remember that this is an accomplishment of humanity, not a defeat. By constructing this AI, we are simply creating another tool for advancing our state of being. (or something like that)
What is our purpose if computers can do everything better than us? It feels like computers have taken one aspect of humanness: logic. Computers could do arithmetic, do algebra, play chess, and now they can play go. It hurts because logic is usually thought to be one of the highest of human characteristics. Yes computers might never be able to replicate emotion, but even dogs have that. There's still some aspects we h…
My (long) commentary here: https://www.facebook.com/yudkowsky/posts/10154018209759228 Sample: At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol's probabili…
> The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points [snip] I wonder how many handicap stones do Lee Sedol, or Ke Jie for that matter, need to have a shot at winning even one game.
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>> What would you consider to be "true AI"? At what point are you willing to say, "Okay, that's it, computers are just plain smarter than we are?" Look, there's no doubt that computers can outperform humans in specific tasks. There's no doubt that AlphaGo is intelligent when it comes to Go, but on the other hand it would be completely incapable of tackling a different congitive task- say, language, or vision, or disc…
Actually alpha go can be trained for vision or any other task. It's not specialized to win go.
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Actually alpha go can be trained for vision or any other task. It's not specialized to win go.
Of course, but it has to be trained anew, with new data. You can't train it on Go data and expect it to perform at all well on vision tasks.
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Challenging Ke Jie is way too small a goal for DeepMind at this point. I wonder if even the idea from the AGA stream today, to get all the best pros in the world together and challenge AlphaGo as a team, is enough. Perhaps releasing the core AlphaGo as open source (to the extent it's not dependent on internal Google machinery), or at least publishing its trained model, may be the next step. Let people "challenge them…
I wonder if even the idea from the AGA stream today, to get all the best pros in the world together and challenge AlphaGo as a team, is enough. Has this been tried? That is, have Players 2-9 (or some subset) ever competed as a group against a dominant Player 1? Unless it's been tested, I wouldn't take it for granted that a group would beat an individual.
My (long) commentary here: https://www.facebook.com/yudkowsky/posts/10154018209759228 Sample: At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol's probabili…