Categories for AI
Category theory is a way of thinking and structuring one's knowledge grounded in the idea of compositionality. Originating in abstract mathematics, this is a formal language that has since spread to numerous fields, becoming a topic of interest for a growing number of researchers. It's helped build rigorous bridges between seemingly disparate scientific areas, showing great potential as a cohesive force in the scientific world. These fields include physics, chemistry, computer science, game theory, systems theory, database theory, and most importantly for us, machine learning, where it's seen a steady growth. Many machine learning concepts have started to be distilled in category theory. From general components of gradient-based learning, over specific architectures such as graph and recurrent neural networks, to equivariant learning, automatic differentiation, bayesian learning, topological data analysis, and more. Despite the steady growth and pervasive use in 21st century mathematic
Explore this link on the map →