Interpreting Restricted Boltzmann Machines from Optics Theory Perspectives

Currently, lack of interpretability (or explainability) is one of the major drawbacks for artificial intelligence (AI) models. When we intend to build a physical artificial intelligence (PAI) systems, the model interpretability (MI) becomes a crucial problem. To tackle MI problem, we give the explanation of restricted Boltzmann machines (RBM) from optics theory perspectives in this work. Furthermore, we present a discussion about how to implement optical learning neural network with our developed Optics Theory and Design Methods - OTDMs. With OTDMs, we can better understand the principle behind the good performance of deep neural networks. OTDMs not only give us an alternative explanation of RBM with optics theories, but also provide the guidance on designing a reliable PAI system also. Consequently, OTDMs pave the road to PAI systems, and make it to become possible for realizing all-optical learning neural network.