271–280 of 428 posts
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Sorry, but what's the difference?
Yeah, isn't Lee Sedol playing with the same objective? It sounds like people are implying he's trying to thrash the AI. Or do they mean he can't help but have such an objective, subconsciously?
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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.
The same statement holds for humans too. I don't think you can teach a person how to play Go and expect him/her to learn anything else than how to play Go.
It would be nice if AlphaGo emitted the estimated probability of it winning every time a move is made. I wonder what this curve looks like. I would imagine mistakes by the human opponent would give nice little jumps in the curve. If the commentary is correct, we would expect very high probability 40-60 minutes into the game. Perhaps something crushing, like 99,9%
http://www.nature.com/news/the-go-files-ai-computer-wins-fir...
For me, the key moment came when I saw Hassabis passing his iPhone to other Google executives in our VIP room, some three hours into the game. From their smiles, you knew straight away that they were pretty sure they were winning – although the experts providing the live public commentary on the match that was broadcast to our room weren’t clear on the matter, and remained confused up to the end of the game just before Lee resigned. (I'm told that other high-level commentators did see the writing on the wall, however).
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…
What do you mean by a one-stone handicap? Just no komi?
> In the case of AlphaGo, an extremely optimized strategy seems to have thrown away the 'typical' production of a visible point lead that characterizes human play. Maximizing win-probability in Go, at this level of play against a human 9p, is not strongly correlated with what a human can see as visible extra territory - so that gets thrown out even though it was previously associated with 'trying to win' in human play.
Pros have been aware for centuries that "visible extra territory" is a poor indicator of win probability. They use the term "thickness" (in English) to denote the positive potential resulting from a strong, safe, influential position, independent of current territory. Quite often a pro game will be a battle of territory vs thickness.
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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.
The same statement holds for humans too. I don't think you can teach a person how to play Go and expect him/her to learn anything else than how to play Go.
There's a lot that suggests that humans and machine learning algorithms learn in very different ways. For instance, by the time a human can master a game like Go they can also perform image processing, speech recognition, handwriten digit recognition, word-sense disambiguation and other similar cognitive tasks. Machine learning algorithms can only do one of those things at a time. A system trained to do image processing might do it well, but it won't be able to go from recognising images to recognising the senses of words in a text without new training, and not without the new training clobbering the previous training.
To make it perfectly clear: I'm talking about separate instances of possibly the same algorithm, trained on a different task every time. I'm not saying that CNNs can't do speech recognition because they're good at image processing. I'm saying that an instance of a CNN that's learned to tag images must be trained on different data in a different time if you also want it to do word-sense disambiguation.
And that that is a limitation, that stands in the way of machine learning algorithms achieving general intelligence.
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> change other human's minds Computer says no > contribute to the state of the art of human knowledge Genetic algorithms have designed circuitry that we failed to even understand at first but that did work. > determine the difference between a human and a machine. For now.
I'm afraid I don't understand the point you're making with "computer says no". > Genetic algorithms have designed circuitry that we failed to even understand at first but that did work. This is an excellent example of what I think of as not machine intelligence. If humans can't understand it then it's something entirely different that we need a different word for - an "artefact", perhaps. Meaningfully contributing to…
Savant literally means 'one who knows', and they're not required to explain to you how they know, it's up to you to verify that they do. Just like a chess grand master doesn't have to prove to you he or she is intelligent, it's enough that they beat you. They are under no obligation to prove their intelligence to you by teaching you the same (assuming you could follow in the first place).
> Meaningfully contributing to the state of the art of human knowledge requires being built upon.
No, it requires us to understand. But we will not always be able to (in the case of those circuits we eventually figured it out, but not at first). And in Chess we did too, computer chess made some (the best) chess players better at chess. But there is no reason to assume this will always be the case and that's a limit of our intelligence.
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…
I agree with you as this makes perfect sense. I'm new to the game Go but have been riveted by these games and the commentary. Here's where I think you're right. It seems from the commentary during the matches that the pro player thought the game to be close...on every match. Then the tides turned...or so we thought...and the game always swayed in AlphaGo's favor. I like how they were saying that the computer changed…
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Sorry, but what's the difference?
There really isn't, from the point of view of AlphaGo. But for someone playing Go (or any game), it's a hint that perhaps one should focus on things that can go wrong more than furthering your own aggressive plan. It's certainly a feature of the best Magic: the Gathering pros, for example - their play is marked by the cards they play around, even when seemingly far ahead.
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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.
Too many chiefs in a village. Can you imagine trying to explain why this move is correct because "20 moves in the future" it proves to be right. It would probably take an hour per move. Also I mentioned in a previous post...the human style of playing go needs to adapt to AlphaGo. That's why the commentators say "oh that was odd" since a human would not make that move as its unorthodox, but turns out to be right. If t…
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Hopefully Google does not do what IBM did and stop playing after they win. IBM lost in 1996, 2-4, and then won in 1997, 3.5-2.5. If they had played a third match with Kasparov, especially a longer match, it is not at all clear that they would have won. Kasparov asked for a third match of 10 games, to be played over 20 days, but IBM would not give it to him.
What is the point? What a few years and you can get an equivalent AI on your home computer. Play as many games as you want. Of course, you can change rules as much as you want and generate new marketing events. Personally, I would like a match where the AI is only allowed the energy a human uses. I guess, AlphaGo would lose with only 2000 kcal (2.3 kWh). Not sure about chess.