Mechanistic Interpretability in Brains and Machines and Category Theory | by Farshad Noravesh | Medium
Mechanistic interpretability is an approach to understanding how machine learning models — especially deep neural networks — process and represent information at a fundamental level. It seeks to go beyond black-box explanations and identify specific circuits, patterns, and structures within a model that contribute to its behavior. Circuit Analysis Feature Decomposition Activation Patching & Ablations Sparse Coding & Superposition Automated Interpretability Methods Think of a deep neural network like a brain. Mechanistic interpretability is about figuring out exactly how that brain processes information, rather than just knowing that it gets the right answer. Join Medium for free to get updates from this writer. Remember me for faster sign in Concept discovery in compositional interpretation can benefit from category theory by leveraging its structural and compositional properties to formalize and analyze the relationships between concepts. Here’s how category theory could play a role: