Comment by usrbinbash
3 years ago
> that it’s anthropomorphizing machines.
No, it's not. It's merely pointing out the similarity between the process of training artists (by ingesting publicly available works) and ML models (which ingest publicly available works).
> First, you need to prove that generative AI works fundamentally the same way as humans at the task of learning.
Given that there is no comprehensive model for how humans actually learn things, that would be an unfeasible requirement.
What a reductive way to describe learning art. The similarities are merely surface level.
> Given that there is no comprehensive model for how humans actually learn things, that would be an unfeasible requirement.
That is precisely why we should not be making this comparison.
> The similarities are merely surface level.
Then please, feel free to explain the deep differences.
> That is precisely why we should not be making this comparison.
Wrong. It's precisely why the claim "there is a big difference" doesn't have a leg to stand on. If you claim "this is different", I ask "how?" and the answer simply repeats the claim, I can apply Hitchens Razor[1] and dismiss the claim.
[1]: https://en.wikipedia.org/wiki/Hitchens%27s_razor
A person sitting in an art school/museum for a few hours ingests way more than just the art in question. The entire context is brought in too, including the artists own physical/emotional state. Arguably, the art is a miniscule component of all sensory inputs. Generative AI ingests a perfectly cropped image of just the art from a single angle with little context beyond labelling metadata.
It's the difference between reading about a place and actually visiting it.
Edit: This doesn't even touch how the act of creating something - often in a completely different context - interacts with the memories of the original work, altering those memories yet again.
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I’m being told repeatedly that the similarities are surface level, but no one seems to be able to give an example of a deep difference
The mechinism backing human learning isn't well understood. Machine learning is considerably clearer. Imo, it's a mistake to assume they're close because ML seems to work.
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