On Understanding and Machine Understanding

TL;DR

This paper proposes a self-similar network theory for understanding language, AI comprehension, and brain-environment interactions, emphasizing influence networks and societal effects to advance machine understanding.

Contribution

It introduces a novel self-similar network framework and extends natural language to an idealy sufficient language for AI understanding.

Findings

A new network model for language and brain understanding

Insights into influence networks in society

Discussion on inspirations for AI and brain research

Abstract

In the present paper, we try to propose a self-similar network theory for the basic understanding. By extending the natural languages to a kind of so called idealy sufficient language, we can proceed a few steps to the investigation of the language searching and the language understanding of AI. Image understanding, and the familiarity of the brain to the surrounding environment are also discussed. Group effects are discussed by addressing the essense of the power of influences, and constructing the influence network of a society. We also give a discussion of inspirations.