The Computational Theory of Intelligence: Information Entropy

TL;DR

This paper proposes an information theoretic framework for understanding computational intelligence as an entropy minimization process, demonstrating its application through a data-driven clustering example.

Contribution

It introduces a novel probabilistic scheme linking intelligence to entropy minimization, with practical clustering applications.

Findings

Intelligence can be modeled as an entropy minimizing process.

A simple data-driven clustering method is developed.

The approach has potential applications in various AI tasks.

Abstract

This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computational intelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications.