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Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more

Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. Just to play Devil's Advocate ever so slightly: there are people out there who would say that there hasn't been any "progress in AI" for quite some time, or at least very little so. And they would probably argue further that the apparent progress you are referring to is just progress in "mere pattern recognition" or something like that. I'm not sure myself. I do lean at least a little towards the idea that there is a qualitative difference between most of modern ML and many aspects of what we would call "intelligence". As such, my interests in all of this remain around the intersection of ML and GOFAI, and the possibility of hybrid systems that use elements of both. But I can't rule out the possibility that it will eventually be found to be the case that all of "intelligence" does indeed reduce to "mere pattern recognition" in some sense. And Geoffrey Hinton may be correct in saying that we don't need, and never will need, any kind of "hybridization" and that neural networks can do it all.

galaxyLogic 6y ago
Neural Networks can do it all after they emerge symbolic representations, a way to represent logic and mathematics. A neural network that just "feels" that Fermat's Last Theorem is true, is much less intelligent than one that can produce the proof of it and present that to us, so we can trust that what it's saying is true. If you can't do symbolic manipulation, you are not really intelligent, artificial or otherwise, I would say.
- The_rationalist 6y ago
  neural network are extremely inefficient at learning symbolic reasoning by design and I doubt a radically new kind of neural network will be discovered, we have pretty much made an exhaustive analysis.

galaxyLogic 6y ago

Neural Networks can do it all after they emerge symbolic representations, a way to represent logic and mathematics. A neural network that just "feels" that Fermat's Last Theorem is true, is much less intelligent than one that can produce the proof of it and present that to us, so we can trust that what it's saying is true. If you can't do symbolic manipulation, you are not really intelligent, artificial or otherwise, I would say.

The_rationalist 6y ago
neural network are extremely inefficient at learning symbolic reasoning by design and I doubt a radically new kind of neural network will be discovered, we have pretty much made an exhaustive analysis.