P35: Hybrid AI Integration Using Implicit Representations With Scruff

Authors: Avi Pfeffer, Michael Harradon, Sanja Cvijic, Joseph Campolongo

Abstract: In this paper, we present a general framework for building hybrid AI systems called Scruff. The key idea behind Scruff is \emph{implicit programming} which provides a principled framework for combining different kinds of model components in a joint model. It enables algorithms that typically have been defined only for neural or symbolic components to generalize and jointly execute on all components while maintaining their meaning. PARSNIP’s implicit programming is based on hierarchical predictive processing (HPP). Implicit programming enables non-generative components such as neural networks to be included in generative predictive processing models, as if they were generative. These components are interpreted as participating implicitly in a generative process, through their ability to support well-defined mathematical operations on the process. This powerful abstraction enables algorithms such as importance sampling, belief propagation, and gradient descent, to generalize across representations while maintaining their well-understood properties.