Category theory is a useful tool because it provides a systematic way to produce mathematically sound definitions and structures that work together reliably.

“And so it tends to produce meaningful definitions that domain experts like in a mathematical domain will have independently come up with very often. And so it uh it’s uh it’s for lazy people who don’t want to or uncreative people. You know, you don’t have to be clever.”

Category theory is the unifying framework needed to transform deep learning from an trial-and-error alchemy into a principled engineering science.

“Categorical deep learning is an attempt to find that periodic table for neural networks. Deep learning, despite its remarkable success, is a field permeated by ad hoc design choices.”