Neuronal Networks solution to the Bayesian Problem

Neural Networks (which are heavily used in AI) appear to be the solution to the complexity of the Bayesian Perception Problem. They break the problem in levels.

You feed a collection of images of faces to a neuronal network and it predicts:

• low-level features: edges and corners
• mid-level features: noses or eyes
• high-level features: facial details

Since this hierarchical predictions are computationally efficient, we can think our mind also solves the complexity of a Bayesian prediction by breaking up the problem in hierarchical levels.