Because of deep learning, there has been a surge in interest in automatic differentiation, especially from the functional programming community. As a result there are many recent papers that look at AD from a Category Theory perspective. However, Category Theorists have already been looking at differentiation and calculus in general since the late 60’s in the context of Synthetic Differential Geometry, but it seems that this work is largely ignored by those interested in AD. In this talk, we will provide a gentle introduction to the ideas of SDG, by relating them to dual numbers, and show how it provides a simple and purely algebraic approach to (automatic) differentiation.
The discussion and AMA following this talk will be moderated by Mitch Wand.
Erik Meijer has been trying to bridge the ridge between theory and practice for most of his career. He is perhaps best known for his work on, amongst others, Haskell, C#, Visual Basic, and Dart programming languages, as well as for his contributions to LINQ and the Reactive Framework (Rx). Most recently he is on a quest to make uncertainty a first-class citizen in mainstream programming languages.