The Geometry of Knowledge and Computational Discovery

Modern neural networks transform vast datasets into continuous embedding spaces, translating semantic relationships into geometric structures. The key to unlocking their full potential lies in making these representations interpretable. This talk presents a simple theory underlining the interpretability of the embedding space and how this principle can allow us to analyze the data, design new metrics, and model the dynamics of the system, moving beyond black-box models to data-driven interpretable insights.