Introduction to the E-Sense Artificial Intelligence System

Abstract

This paper describes the E-Sense Artificial Intelligence system. It comprises a memory model with two levels of information and then a more neural layer above that. The lower memory level stores source data in a Markov (n-gram) structure that is unweighted. Then, a middle ontology level is created from a further three aggregating phases that may be deductive. Each phase re-structures from an ensemble to a tree, where the information transposition is from horizontal set-based sequences into more vertical, typed-based clusters. The base memory is essentially neutral, but bias can be added to any of the levels through associative networks. The success of the ontology typing is open to question, but the results suggested related associations more than direct ones. The third level is more functional, where each function can represent a subset of the base data and learn how to transpose across it. The functional structures are shown to be quite orthogonal, or separate, and are made from nodes with a progressive type of capability, including unordered to ordered. Comparisons with the columnar structure of the neural cortex can be made and the idea of ordinal learning, or just learning relative positions, is introduced. While this is still a work in progress, it offers a different architecture to the current frontier models and is probably one of the most biologically inspired designs.

Keywords

• brain model
• memory model
• neural model
• cortex
• statistical clustering