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>but that AI machinery - qua computation - cannot understand anything at all Ugh, here we go. I swear this was all gone over with a very similar post just a we

>but that AI machinery - qua computation - cannot understand anything at all Ugh, here we go. I swear this was all gone over with a very similar post just a week ago where it was pointed out that if an author says physical things can't 'understand' or whatever else, they are implying some non-physical soul-spark in humans.

nurettin 6y ago
Then it was pointed out wrongly and this isn't some spiritual talk. If you are able to retrofit new knowledge into the observations and categories you've previously formed by correcting what you already know and by changing perspective so that you can have proper inputs without causing contradictions, you understand it. Do you think curve fitting does this?
- SpicyLemonZest 6y ago
  I'm not sure what you mean by "curve fitting" here. I would definitely say that a deep neural net has observations and categories, and I think it's pretty reasonable to characterize the training process as retrofitting new knowledge into it by correcting its knowledge and changing its perspective.
  - nurettin 6y ago
    This is the reply I was expecting, and I think you are right, but a CNN doesn't take into account any contradictions. It doesn't test the input for correctness, it just tries to categorize and fucked if the results make no sense.
    - SpicyLemonZest 6y ago
      I'd agree, but I'm not sure how much this is an inherent problem. To what degree does it just reduce to needing a good conceptual framework for a "makes no sense" category?
      - nurettin 6y ago
        I would be interested in the kind of model which will tell you something is wrong when you show it a black square and call it a black square, then show it a white square and call it a black square. Not the kind of model which would average the results and adapt to nonsense.
- carrolldunham 6y ago
  you didn't read the part in the abstract where it says " - qua computation - ". This author is trotting out the same old reflex that a computer (any computer, with any algorithm) can never "understand" something because it's "not conscious" because it's "not alive". It absolutely is philosophically equivalent to positing a spirit, because when you start drilling down into what's different between a brain and a (general) computer.. that's all you'll have left.
  - nurettin 6y ago
    The article is about state of the art computation, which does not model understanding as per my definition. Not spiritualism.
Animats 6y ago
Right. Same old bad arguments. Undecidability, Chinese Room, Penrose, etc. There's a problem, but that stuff doesn't help. Machine learning as currently done clearly has limits. The big problem is lack of an underlying model of the real world. Systems which do have real-world knowledge tend to store it as something like predicate form. Cyc is the classic example of that. A useful question: what should knowledge about the real world look like? More specifically, is there some low-level form in which info about the real world could be represented that makes it useful as training data for machine learning? Skin contact and muscle tension, perhaps?

nurettin 6y ago

Then it was pointed out wrongly and this isn't some spiritual talk. If you are able to retrofit new knowledge into the observations and categories you've previously formed by correcting what you already know and by changing perspective so that you can have proper inputs without causing contradictions, you understand it. Do you think curve fitting does this?

SpicyLemonZest 6y ago
I'm not sure what you mean by "curve fitting" here. I would definitely say that a deep neural net has observations and categories, and I think it's pretty reasonable to characterize the training process as retrofitting new knowledge into it by correcting its knowledge and changing its perspective.
- nurettin 6y ago
  This is the reply I was expecting, and I think you are right, but a CNN doesn't take into account any contradictions. It doesn't test the input for correctness, it just tries to categorize and fucked if the results make no sense.
  - SpicyLemonZest 6y ago
    I'd agree, but I'm not sure how much this is an inherent problem. To what degree does it just reduce to needing a good conceptual framework for a "makes no sense" category?
    - nurettin 6y ago
      I would be interested in the kind of model which will tell you something is wrong when you show it a black square and call it a black square, then show it a white square and call it a black square. Not the kind of model which would average the results and adapt to nonsense.
carrolldunham 6y ago
you didn't read the part in the abstract where it says " - qua computation - ". This author is trotting out the same old reflex that a computer (any computer, with any algorithm) can never "understand" something because it's "not conscious" because it's "not alive". It absolutely is philosophically equivalent to positing a spirit, because when you start drilling down into what's different between a brain and a (general) computer.. that's all you'll have left.
- nurettin 6y ago
  The article is about state of the art computation, which does not model understanding as per my definition. Not spiritualism.

Animats 6y ago

Right. Same old bad arguments. Undecidability, Chinese Room, Penrose, etc. There's a problem, but that stuff doesn't help. Machine learning as currently done clearly has limits. The big problem is lack of an underlying model of the real world. Systems which do have real-world knowledge tend to store it as something like predicate form. Cyc is the classic example of that. A useful question: what should knowledge about the real world look like? More specifically, is there some low-level form in which info about the real world could be represented that makes it useful as training data for machine learning? Skin contact and muscle tension, perhaps?