提出核心与边缘框架，揭示智能系统演化规律
Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective
- 从系统理论出发，构建核心-边缘双模架构
- 实证验证该框架在生物与人工系统中均适用
- 数学定义核心主导与边缘主导系统类型
工程方法主要基于分解与重组原则，即在组件层面划分输入输出，并保持组件属性在组合后不变。然而，这一视角难以适用于智能系统，尤其在应对智能作为系统属性的规模化时。我们前期研究指出，通用智能的工程化需要一套新的系统性原则。为此，我们提出了‘核心与边缘’原则，这是一个基于抽象系统理论和适当多样性定律的新概念框架。本文进一步论证这些抽象概念具有实际意义：通过实证证据，展示其在生物与人工智能系统中的适用性，实现了抽象理论与现实应用的衔接。同时，我们扩展了先前的理论框架，从数学上明确定义了核心主导型与边缘主导型系统。
原文摘要 · Abstract (English)
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.
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