4 ms·

Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction I can read the words here, but

• vanderZwan 10y ago
  You implicitly (and I think without realising) presume objectivity + complete knowledge in the observer. Human perception is heavily biased towards features that had evolutionary advantages, and limited by whatever technical flaws our eyes/brains/etc have. That's a selection bias in our perception of information, in our processing of said information, and therefore in the abstractions that result from it.
  • gcp 10y ago
    I agree with what you say, but it doesn't support your earlier statements. I presume it's possible that the limitations of our visual system means we may miss powerful features and hence the ability to build some more powerful abstractions. (I didn't even argue this, just pointed out the process is the same even if features differ) But I don't see how this supports your original claim of bias, which was: "If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognize each other's abstractions." If humans are good at recognizing each others' abstraction, that's a validation that low-pass (for lack of a better term) filtering the features due to human's physical design still creates very good abstractions and classifiers. That is to say, if anything you're confirming that humans are designed in a way that makes the abstractions they can make maximally useful.
    • johnlbevan2 10y ago
      "you're confirming that humans are designed in a way that makes the abstractions they can make maximally useful." ... to other humans.
      • gcp 10y ago
        What's the meaning of this? Are you arguing that the classifications themselves are biased?
        • vanderZwan 10y ago
          That's exactly what I and others have been arguing. Now to be clear: it's not that these classifications are wrong, just that out of all possible classifications we could have found, we will most likely find the ones that fit the human perspective of the world. Think of the Turing test and its criticisms; it's kind of has the same issues. PS: I've upvoted every comment of yours; asking questions like this should be encouraged :)
          • gcp 10y ago
            Thanks for taking the time to explain your argument!
          • johnlbevan2 10y ago
            Confirmed; thanks vanderZwan.
        • rzzzt 10y ago
          Classifications are also dependent on the capabilities of the language they are expressed in: https://en.wikipedia.org/wiki/Linguistic_relativity https://en.wikipedia.org/wiki/Linguistic_relativity
    • vanderZwan 10y ago
      > still creates very good abstractions and classifiers. My point is that "good" and "bad" are not objective here, but depend on human use-cases. Now to be clear: I'm not disagreeing with you! These are good abstractions, for humans. It lets us communicate concepts easily, which is great! But it might not be the best abstraction in every circumstance. For example, I recall reading an article that said that AI is better at spotting breast cancer from photos (which is essentially interpreting abstract blobs as cancer or not). The main reason seems to be that it is not held back by the human biases in perception.
• johnlbevan2 10y ago
  Think of Bugs Bunny. He looks nothing like a real rabbit, yet humans recognise him as a rabbit (presumably) because we look at the characteristics that separate him from a normal human, then compare those characteristics with our list of things with those characteristics (long ears, big feat, eats carrots) and get a rabbit. If he'd been made to look like a rabbit-octopus hybrid instead of rabbit-human, we may have struggled more. Computers don't look at things from a human perspective; they're still good at abstraction, just different to human abstraction. i.e. there's a human bias in there. That's OK though; the objective is to make a computer that sees things the way people do; so it's a bias we want. However the issue isn't that the computer's not a sentient being and therefore can't abstract things it's never seen before; only that the algorithm hasn't been written to sufficiently take account of human bias.
  • K0SM0S 10y ago
    I think the word you're looking for is "familiarity", insofar as it describes a particularly efficient means of recognition. E.g. humans have become pretty good at identifying cats. I don't see a fundamental difference between biological and electronic neural nets; so please take the following with a physicalist grain of salt. Imho, precisely because NNs will be fed with nothing else than the reality (physical or virtual) we live in, it should gradually develop the same familiarity as humans have; i.e. nothing more and nothing less than elements of our lives/civs. Visually lots of cats, lots of cars, mountains and coasts; functionally all the tasks we accomplish daily, like driving or cooking or cleaning. I don't really think you can hard-code "human bias" as it's an emergent property of our biology: too complex (we don't really understand much of it, imho you're bound to miss the mark and induce subjective biases), and somewhat contradictory to how NNs are supposed to evolve (thinking long term here). Basically, I don't think it would be practical nor cost efficient to induce too much perturbations in deep learning, better work on refining the process itself. Think of plants: you can tweak the growing all you want, but the root deciding factors lie in genetics (their potential, and in understanding how to maximize it). I realize another wording is that we should apply sound evolutionary (Darwin etc.) principles in "growing" AI at large. Because AI and humans share the same environment, we should see converging "intelligence" (skills, familiarity, etc). It's a quite fascinating time from an ontological perspective.