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I'd say it's using computers for solving problems in a wide open model space. Any algorithm for solving practical problems embodies a model of the problem doma

I'd say it's using computers for solving problems in a wide open model space. Any algorithm for solving practical problems embodies a model of the problem domain. Traditionally we try to bake in as much of our own experience as possible in the model/algorithm. In some fields, we're now seeing for the first time that it's better to not do that, to make fewer assumptions, build in less of our own knowledge, and just use (new, powerful) general purpose learning algorithms. With enough data, they can make sense of it themselves - our attempts at explaining it through model-building just get in the way.

pron 11y ago
I think that you'll find that the learning algorithms that actually work well in practice have more built-in assumptions and heuristics than you may think. But, yeah, as a general rule that's a good categorization. A far cry from intelligence, though, so the name AI is still misleading. Not all adaptive behavior is intelligent, although it often gives the illusion of intelligence, so I guess we make this labeling mistake not only when computers are concerned. Anyway, statistical algorithms, adaptive algorithms or inductive algorithms would be much better, less misleading and less breathless -- if less inspiring -- names for those programs than "AI".
- vintermann 11y ago
  The main built-in assumptions in neural network are usually extremely high-level and abstract things, such as "this and this can be approximated with more or less smooth functions", or "things that are near are more likely to matter than things that are far away". Before, using machine learning to build models from the data was in some sense a last resort, for things you couldn't get a grip on modelling by hand from your own understanding. When you could, you preferred hand-made models because they did better. To take a concrete example, the top computer go program before Alphago, CrazyStone, still had hand-crafted patterns to prioritize which moves to explore during playouts. The author (and even more his competitors) tried to find better patterns with machine learning but they couldn't. The weights of the patterns were tuned with machine learning, but any attempt to e.g. evolve patterns with genetic algorithms just did worse. Now, it's going the other direction, we're seeing very general models only constrained by training data beat carefully hand-crafted models in area after area. And models are becoming more similar, more generalized as well - many people have noticed that state of the art recurrent and feedforward nets increasingly look more like each other. I disagree that it's a far cry from intelligence. I think people's ideas of intelligence are fuzzy and inconsistent, and it's far too often confused with things we associate with intelligence (from consciousness to independence and arrogance).
  - pron 11y ago
    > I think people's ideas of intelligence are fuzzy and inconsistent, and it's far too often confused with things we associate with intelligence (from consciousness to independence and arrogance). I can certainly agree with that -- it's not clear at all what intelligence is -- but induction and pattern recognition (really, statistical clustering) are either not sufficient for anything which we'd reasonably call intelligence, or can be found in creatures that we'd say have negligible intelligence (insects). Again, I'm not saying the achievements aren't impressive or that they don't resemble anything we can associate with biological beings, but even most biological beings aren't intelligent. In any event, no one claims (with any evidence) that the current "statistical induction" algorithms (inspired in part by some features of biological systems) are a simple, primitive form of "general intelligence" algorithms. They may well be, but we don't know that. So the "I" in AI is, if not a marketing gimmick, at most inspirational and aspirational. As far as we know, those algorithms are as far from human intelligence as solid-fuel rockets are from warp drives. Understanding it otherwise only leads to people conjuring up fantastical deadlines to achieving "true" intelligence. For all we know, we are as likely a century away from "true" AI as we are five years away.

pron 11y ago

I think that you'll find that the learning algorithms that actually work well in practice have more built-in assumptions and heuristics than you may think. But, yeah, as a general rule that's a good categorization. A far cry from intelligence, though, so the name AI is still misleading. Not all adaptive behavior is intelligent, although it often gives the illusion of intelligence, so I guess we make this labeling mistake not only when computers are concerned. Anyway, statistical algorithms, adaptive algorithms or inductive algorithms would be much better, less misleading and less breathless -- if less inspiring -- names for those programs than "AI".

vintermann 11y ago
The main built-in assumptions in neural network are usually extremely high-level and abstract things, such as "this and this can be approximated with more or less smooth functions", or "things that are near are more likely to matter than things that are far away". Before, using machine learning to build models from the data was in some sense a last resort, for things you couldn't get a grip on modelling by hand from your own understanding. When you could, you preferred hand-made models because they did better. To take a concrete example, the top computer go program before Alphago, CrazyStone, still had hand-crafted patterns to prioritize which moves to explore during playouts. The author (and even more his competitors) tried to find better patterns with machine learning but they couldn't. The weights of the patterns were tuned with machine learning, but any attempt to e.g. evolve patterns with genetic algorithms just did worse. Now, it's going the other direction, we're seeing very general models only constrained by training data beat carefully hand-crafted models in area after area. And models are becoming more similar, more generalized as well - many people have noticed that state of the art recurrent and feedforward nets increasingly look more like each other. I disagree that it's a far cry from intelligence. I think people's ideas of intelligence are fuzzy and inconsistent, and it's far too often confused with things we associate with intelligence (from consciousness to independence and arrogance).
- pron 11y ago
  > I think people's ideas of intelligence are fuzzy and inconsistent, and it's far too often confused with things we associate with intelligence (from consciousness to independence and arrogance). I can certainly agree with that -- it's not clear at all what intelligence is -- but induction and pattern recognition (really, statistical clustering) are either not sufficient for anything which we'd reasonably call intelligence, or can be found in creatures that we'd say have negligible intelligence (insects). Again, I'm not saying the achievements aren't impressive or that they don't resemble anything we can associate with biological beings, but even most biological beings aren't intelligent. In any event, no one claims (with any evidence) that the current "statistical induction" algorithms (inspired in part by some features of biological systems) are a simple, primitive form of "general intelligence" algorithms. They may well be, but we don't know that. So the "I" in AI is, if not a marketing gimmick, at most inspirational and aspirational. As far as we know, those algorithms are as far from human intelligence as solid-fuel rockets are from warp drives. Understanding it otherwise only leads to people conjuring up fantastical deadlines to achieving "true" intelligence. For all we know, we are as likely a century away from "true" AI as we are five years away.