  The recent rapid advance of AI has been driven largely by innovations in
neural network architectures. A concomitant concern is how to understand these
resulting systems. In this paper, we propose a tool to assist in both the
design of further innovative architectures and the simple yet precise
communication of their structure. We propose the language Neural Markov Prolog
(NMP), based on both Markov logic and Prolog, as a means to both bridge first
order logic and neural network design and to allow for the easy generation and
presentation of architectures for images, text, relational databases, or other
target data types or their mixtures.
