Organizational Recognition and Intelligent Computation: A Generative Continuum Perspective on Artificial Intelligence

This paper develops a philosophical framework for artificial intelligence derived from Generative Continuum Ontology (GCO). While contemporary AI paradigms differ in their mathematical formulations and learning mechanisms, they generally share a common assumption that reasoning proceeds within a computational framework established prior to inference. This paper argues that such an assumption overlooks a logically prior condition of intelligent computation: the recognition of the organizational regime within which computation becomes appropriate. Drawing upon the ontological commitments of GCO, the paper proposes that computational laws, like scientific laws, derive their intelligibility from organizational regimes rather than possessing universal applicability independent of organization. Building upon this principle, the paper introduces three related concepts: organizational recognition, regime-dependent computation, and organizational intelligence. Organizational recognition is proposed as the foundational capacity through which intelligent systems identify the organizational conditions governing computational appropriateness. Regime-dependent computation follows by treating computational strategies as organizationally conditioned rather than universally applicable, while organizational intelligence is defined as the capacity to recognize organizational regimes, select appropriate computational strategies, evaluate their continued adequacy, and revise them as organizational conditions evolve. The paper further argues that explainability, adaptation, and computational safety should be understood as consequences of organizational recognition rather than independent computational objectives. Rather than proposing a new algorithm or machine learning architecture, the paper offers a philosophical foundation for future computational systems in which organizational conditions precede and guide intelligent reasoning. In doing so, it establishes a conceptual bridge between ontology, scientific understanding, explainable artificial intelligence, and adaptive computation.