Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective
This work addresses the challenge of engineering general intelligence for researchers and practitioners, but it appears incremental as it builds on prior theoretical frameworks.
The paper tackles the problem of scaling intelligence as a system property by introducing the 'core and periphery' principles, a novel conceptual framework, and demonstrates their practical applicability to biological and artificial intelligence systems through empirical evidence.
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.