Tensor Networks: A Mathematical Bridge Between Neural and Symbolic AI
Neural networks excel at learning patterns from data. Symbolic AI excels at logical reasoning and interpretability. For decades, researchers have tried to combine them — with limited success. A new paper proposes an elegant mathematical framework that unifies both approaches: tensor networks. The key insight? Both neural and symbolic computations can be expressed as tensor decompositions, and inference in both reduces to tensor contractions. The Problem: Two Worlds That Don’t Talk Modern AI is split into two camps: ...