Well, consider a neural net for distinguishing dogs from cats. This neural network might develop features that look like “dog-like eyes” and “cat-like eyes,” which are pattern-matched across the image. Images with more activation on the first feature are claimed to be dogs and images with more activation on the second feature are claimed to be cats, along with input from many other features. This is fairly typical-sounding.
Now imagine how bonkers a neural net would have to be in order to reproduce the generative process behind the images! Leaving aside simulations of the early universe, our neural network should still have a solid understanding of the biology of dogs and cats, the different grooming and adornment practices, macroscopic physics and physiology that leads to poses, and the preferences of people taking and storing photographs.
Isn’t the idea more that the neural network just learns rough subgraphs of the underlying DAG that captures the causal structure up to quantum detail? Whole-part relationships are such subgraphs: a person being present causes a face to be present, which causes eyes to be present etc.
Well, consider a neural net for distinguishing dogs from cats. This neural network might develop features that look like “dog-like eyes” and “cat-like eyes,” which are pattern-matched across the image. Images with more activation on the first feature are claimed to be dogs and images with more activation on the second feature are claimed to be cats, along with input from many other features. This is fairly typical-sounding.
Now imagine how bonkers a neural net would have to be in order to reproduce the generative process behind the images! Leaving aside simulations of the early universe, our neural network should still have a solid understanding of the biology of dogs and cats, the different grooming and adornment practices, macroscopic physics and physiology that leads to poses, and the preferences of people taking and storing photographs.
Isn’t the idea more that the neural network just learns rough subgraphs of the underlying DAG that captures the causal structure up to quantum detail? Whole-part relationships are such subgraphs: a person being present causes a face to be present, which causes eyes to be present etc.