6 ms·

Humans are great at learning abstraction from concrete examples. That's also what deep learning does and the big reason for its success as well. I'd guess that

Humans are great at learning abstraction from concrete examples. That's also what deep learning does and the big reason for its success as well. I'd guess that some neural nets architecture can do the same with your cat example (perhaps with adaptation). Can any expert weigh in? An idea: We can also run several cat photos through image processing algorithms to filter out details. The output would be outlines similar to the drawings in the Google Quickdraw app. We put those through the app to generalize (perhaps the app needs some training with a few categories of objects, not necessarily animals). Voila! Software can now recognize drawings based on photo examples.

gcp 10y ago
Basically: you are right.
vanderZwan 10y ago
> Humans are great at learning abstraction Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognise each other's abstractions.
- gcp 10y ago
  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 I don't understand the meaning. We abstract to find a common set of features in things that are supposed to be the same but that are not present in things that are not supposed to be the same. Grouping these features then produces higher level abstractions, and so on. Where would the bias be? Even if the features differ, the process is the same. And even the features are often the same. If you reverse a DCNN to see what it uses to classify things as "cats", expect to see whiskers and fur.
  - 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
- hanoz 10y ago
  Cats are probably a particularly unfortunate example to use in comparing abstraction forming cabilities, as given our history it's highly likely that we come supplied with some dedicated cat recognition circuitry.
TheGrumpyBrit 10y ago
Humans have a bit of an advantage on two levels here. First, we know what a cat looks like. Not a video or a picture or a drawing, but an actual cat. That gives us a solid frame of reference. "That is definitely a cat. That drawing looks kind of like what I know a cat to look like, so it's a drawing of a cat." The closest a computer can get is "This drawing has quite a bit in common with these other drawings, and apparently these other drawings are cats. So this is probably a cat too." Second, when we look at a picture of a cat, we're looking at a human's interpretation of what a cat looks like. If we asked a computer to draw a cat, it might look nothing like a cat to us, but another computer could look at it and go "Oh sure, that's a cat." I seem to recall Google did a thing with this a while ago, where they effectively created a feedback loop in a neural net - feeding its own drawing back into itself. As I recall, the result looked like the computer had done way too much LSD.

gcp 10y ago

Basically: you are right.

vanderZwan 10y ago

> Humans are great at learning abstraction Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognise each other's abstractions.

gcp 10y ago
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 I don't understand the meaning. We abstract to find a common set of features in things that are supposed to be the same but that are not present in things that are not supposed to be the same. Grouping these features then produces higher level abstractions, and so on. Where would the bias be? Even if the features differ, the process is the same. And even the features are often the same. If you reverse a DCNN to see what it uses to classify things as "cats", expect to see whiskers and fur.
- 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
hanoz 10y ago
Cats are probably a particularly unfortunate example to use in comparing abstraction forming cabilities, as given our history it's highly likely that we come supplied with some dedicated cat recognition circuitry.

TheGrumpyBrit 10y ago

Humans have a bit of an advantage on two levels here. First, we know what a cat looks like. Not a video or a picture or a drawing, but an actual cat. That gives us a solid frame of reference. "That is definitely a cat. That drawing looks kind of like what I know a cat to look like, so it's a drawing of a cat." The closest a computer can get is "This drawing has quite a bit in common with these other drawings, and apparently these other drawings are cats. So this is probably a cat too." Second, when we look at a picture of a cat, we're looking at a human's interpretation of what a cat looks like. If we asked a computer to draw a cat, it might look nothing like a cat to us, but another computer could look at it and go "Oh sure, that's a cat." I seem to recall Google did a thing with this a while ago, where they effectively created a feedback loop in a neural net - feeding its own drawing back into itself. As I recall, the result looked like the computer had done way too much LSD.