Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective

TL;DR

This paper introduces the 'core and periphery' principles, a new framework based on systems theory and the Law of Requisite Variety, demonstrating their practical relevance to biological and artificial intelligence systems.

Contribution

It extends prior theoretical work by empirically validating the core-periphery framework and mathematically defining core- and periphery-dominant systems.

Findings

Empirical evidence supports the applicability of core-periphery principles in AI and biology.

Mathematical definitions distinguish core-dominant and periphery-dominant systems.

Bridges abstract systems theory with real-world intelligence systems.

Abstract

Engineering methodologies predominantly revolve around established principles of decomposition and recomposition. These principles involve partitioning inputs and outputs at the component level, ensuring that the properties of individual components are preserved upon composition. However, this view does not transfer well to intelligent systems, particularly when addressing the scaling of intelligence as a system property. Our prior research contends that the engineering of general intelligence necessitates a fresh set of overarching systems principles. As a result, we introduced the "core and periphery" principles, a novel conceptual framework rooted in abstract systems theory and the Law of Requisite Variety. In this paper, we assert that these abstract concepts hold practical significance. Through empirical evidence, we illustrate their applicability to both biological and artificial intelligence systems, bridging abstract theory with real-world implementations. Then, we expand on our previous theoretical framework by mathematically defining core-dominant vs periphery-dominant systems.