Managing contextual artificial neural networks with a service-based mediator

TL;DR

This paper proposes a mediator framework to manage diverse artificial neural networks and AI systems across platforms, enabling integrated analysis and high-level reasoning for complex real-world data.

Contribution

It introduces a novel mediator architecture for managing heterogeneous AI systems, facilitating signal integration and high-level logic processing.

Findings

Proposed multiple architectures supporting the mediator framework

Demonstrated potential benefits in combining signals from different networks

Explored applications in academic and industrial contexts

Abstract

Today, a wide variety of probabilistic and expert AI systems used to analyze real world inputs such as unstructured text, sounds, images, and statistical data. However, all these systems exist on different platforms, with different implementations, and with very different, often very specific goals in mind. This paper introduces a concept for a mediator framework for such systems and seeks to show several architectures which would support it, potential benefits in combining the signals of disparate networks for formalized, high level logic and signal processing, and its possible academic and industrial uses.