Super Dual Process Machine Learning

This paper introduces the dual process machine learning paradigm, which builds upon the unified machine learning and physics field framework. By integrating machine learning architectures and physics models into a single field-theoretical entity and constructing hidden layers and learning weights based on physical systems, complex machine learning is interpreted as a set of physical interactions. The super dual process machine learning leverages duality relations inherent in physical systems, enabling a simplified "dual" process to replicate the statistical behavior of the original complex "primary" process. We demonstrate that the super dual process opens a new pathway for AI engineering, wherein algebraic structures from underlying physical principles guide model design and computation. We present both the theoretical foundations and practical implementations of super dual machine learning, achieving improved scalability and efficiency compared to traditional methods.