Is it just, like, whatever transformations of a big buffer of camera pixels let me find conditional independence patterns probably correspond to regularities in the real world? Is it “that easy”??
Roughly speaking, yes.
Features are then typically the summary statistics associated with some abstraction. So, we look for features which induce conditional independence patterns in the big buffer of camera pixels. Then, we look for higher-level features which induce conditional independence between those features. Etc.
It’s funny that you should mention this, because I’ve considered working on a machine learning system for image recognition using this principle. However, I don’t think this is necessarily all of it. I bet we come pre-baked with a lot of rules for what sorts of features to “look for”. To give an analogy to machine learning, there’s an algorithm called pi-GAN, which comes pre-baked with the assumption that pictures originate from 3D scenes, and which then manages to learn 3D scenes from the 2D images it is trained with. (Admittedly only when the images are particularly nice.)
Roughly speaking, yes.
Features are then typically the summary statistics associated with some abstraction. So, we look for features which induce conditional independence patterns in the big buffer of camera pixels. Then, we look for higher-level features which induce conditional independence between those features. Etc.
This gave me a blog story idea!
“Feature Selection”
It’s funny that you should mention this, because I’ve considered working on a machine learning system for image recognition using this principle. However, I don’t think this is necessarily all of it. I bet we come pre-baked with a lot of rules for what sorts of features to “look for”. To give an analogy to machine learning, there’s an algorithm called pi-GAN, which comes pre-baked with the assumption that pictures originate from 3D scenes, and which then manages to learn 3D scenes from the 2D images it is trained with. (Admittedly only when the images are particularly nice.)