Scientific abstraction and mathematical idealization exist not because they reveal a deeper Platonic layer of reality, but because finite human knowers need to simplify an inexhaustibly complex world in order to gain any traction on it.

6:26um make often but it's runs through science as a kind of justification for the pursuit of mathematical representations even when they sort of depart from known facts about the concrete physical systems in reality. the idea that the mathematical representation is getting you more to the truth the underlying truth of how things are as opposed to what I call the diff like the downto- earthth view of what abstraction is and mathematical

6:56representation is that it's something that we do because of our complic um cognitive limitations. So instead of thinking that the abstraction gets you like the higher level of reality, just saying that we do abstraction because we're finite knowowers. There's limits to how much complexity any individual person or group of people can actually encompass in their modeling strategies or representations. And actually it's only by pretending things that are more

7:25simple than they actually are that we get some traction. So that's like the downto- earthth um mundane explanation of why abstraction is so much used in science. >> Yeah, it's it's so pervasive in the deep learning world. I mean um I also interviewed the the folks who pioneered this geometric deep learning blueprint and that's the same idea basically that you know the world is described with geometry and all we need to do is imbue

7:49these geometrical um inductive prior into deep learning models and and then they can you know essentially by reducing the degrees of freedom to ones which are aligned with how the universe works then then we get where we where we want to go. I think the notion of like patterns and real pan patterns um to uh invoke Danet's term there is a helpful one. So one one thing that you could say

8:14is going on here is that yes there's lots of complexity there in the natural world. It's apparent in the data, but like if you just um dn noiseise the data a bit underlying there, there's a real pattern and we should we don't have to be like plonist and weird about it, but there's just regularity that is sometimes masked by noise. That doesn't seem like too metaphysically problematic.