Geometric algebra expands n-dimensional embeddings into a 2^n-dimensional structure that can represent both base concepts and their interactions in a single unified framework, potentially solving long-standing problems in how neural networks compose meanings.
This paper proposes using geometric algebra (Clifford algebras) instead of conventional linear algebra as the mathematical foundation for representing word and sentence meanings in AI.