[gcp]

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]

"you're confirming that humans are designed in a way that makes the abstractions they can make maximally useful."

... to other humans.

[gcp]

What's the meaning of this?

Are you arguing that the classifications themselves are biased?

[vanderZwan]

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]

Thanks for taking the time to explain your argument!

[johnlbevan2]

Confirmed; thanks vanderZwan.

[rzzzt]

Classifications are also dependent on the capabilities of the language they are expressed in: https://en.wikipedia.org/wiki/Linguistic_relativity

[vanderZwan]

> 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.