I found an interesting counter perspective on the Mindscape podcast[0]:
And Ted Chiang, in that article, suggests that ChatGPT and language models generally can be thought of as a kind of blurry jpeg of the web, where they get trained to compress the web and then at inference time, when you're generating a text with these models, it's a form of lossy decompression that involves interpolation in the same way. And I think it's a very interesting test case for intuitions because I think this metaphor, this analogy, parts of it are pumping the right intuitions.
There is definitely a deep connection between machine learning and compression that has long been observed and studied. [...] I think comparing it to lossy image decompression is pumping the wrong intuitions because, again, that suggests that all it's doing is this kind of shallow interpolation that amounts to a form of approximate memorization where you have memorized some parts of the data and then you are loosely interpolating what's in between.
[...] the intuition is that there would be a way, presumably, to characterize what large language models and image generation models are doing when they generate images and texts as involving a form of interpolation but this form of interpolation would be very very different from what we might think of when we think of nearest neighbour pixel interpolation in lossy image decompression. So different, in fact, that this analogy is very unhelpful to understand what generative models are doing because, again, instead of being analogous to brute force memorization, there's something much more generally novel and generative about the process of inference in these models.
I found an interesting counter perspective on the Mindscape podcast[0]:
[0] https://www.preposterousuniverse.com/podcast/2023/03/20/230-...