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

Post by Houshalter » Thu, Mar 10, 2016, 7:39 PM UTC

YeGoblynQueenne wrote: post #532

Houshalter wrote: post #456

Earlier quoted context omitted.

I think you are confusing utility functions with intelligence. All AIs need utility functions. An AI without a utility function would just do nothing. It would have no reason to beat Atari games, because it wouldn't get any reward for doing so. Even humans have utility functions. For example, we get rewards for having sex, or eating food, or just making social relationships with other humans. Or we have negative rein…

>> I think you are confusing utility functions with intelligence. No, what I'm really saying is that you can't have an autonomous agent that needs to be told what to do all the time. In machine learning, we train algorithms by giving them examples of what we want them to learn, so basically we tell them what to learn. And if we want them to learn something new, we have to train them again, on new data. Well, that's n…

Post by Houshalter » Thu, Mar 10, 2016, 7:48 PM UTC

argonaut wrote: post #505

Houshalter wrote: post #443

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I don't know what you are trying to say. I'm making an observation that whenever AI beats a milestone, there are a bunch of pessimists that come out and say "but obviously X was beatable by stupid algorithms. I will beleive AI is making progress when it beats Y!" Those arguments absolutely are wrong. For one thing it's classic hindsight bias. When you make a wrong prediction, you should update your model, not come up…

I said exactly what I said. Calling out "moving the goalposts" does not refute the assertion that this does not get us nontrivially closer to AGI. Whenever AI beats a milestone, there are a bunch of over-optimists that come out and make predictions about AGI. They have been wrong over and over again over the course of half a century. It's classic hindsight bias.

Post by chillacy » Thu, Mar 10, 2016, 9:29 PM UTC

geebee wrote: post #400

jscahefer wrote: post #394

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think of it this way: The search space for chess is much smaller, so we can lean very heavily on brute forcing in our A.I. implementations. The search space for Go is much larger, so while brute force searches are critical in tight fighting, and in endgame play, something more has to happen to play go well in the middle game. Chess fell to a much earlier generation of A.I. While Go held out until A.I. as a field had…

Agreed. The part I object to is the unqualified statement that go requires a high degree of intuition, whereas chess doesn't. As humans play the game, I think it's safe to say that this is generally inaccurate. Both games, for humans, rely very heavily a high degree of intuition. I would tend to agree that there is something interesting and new at work here, though, in that computers didn't get better than humans at…

Post by conceit » Thu, Mar 10, 2016, 9:41 PM UTC

eternalban wrote: post #558

conceit wrote: post #539

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remember that chess is a war game and that war is most often fought over resources and territory, so they had their "chess" alright.

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…

Post by YeGoblynQueenne » Fri, Mar 11, 2016, 1:29 AM UTC

Houshalter wrote: post #561

YeGoblynQueenne wrote: post #532

Earlier quoted context omitted.

>> I think you are confusing utility functions with intelligence. No, what I'm really saying is that you can't have an autonomous agent that needs to be told what to do all the time. In machine learning, we train algorithms by giving them examples of what we want them to learn, so basically we tell them what to learn. And if we want them to learn something new, we have to train them again, on new data. Well, that's n…

Machine learning doesn't "need to be told what to do all the time". No one told alphaGo what strategies were the best. It figured that out on it's own, by playing against itself. There is also unsupervised and semi-supervised learning, which can take advantage of unlabelled data. Even supervised learning can work really well on weakly labelled data. E.g. taking pictures from the internet and using the words that occu…