"Leveraging Cybernetics to Advance Artificial Intelligence: Integrating Systems Theory for Enhanced Adaptive Intelligence"

EasyChair Preprint 14359

Abstract

This paper explores the integration of cybernetics and systems theory to advance artificial intelligence (AI), focusing on enhancing adaptive intelligence. Cybernetics, with its principles of feedback, self-regulation, and dynamic system behavior, offers a robust framework for addressing the complexities of AI systems. By leveraging systems theory, which emphasizes the interdependence and interactions within complex systems, this research proposes a novel approach to AI development. The study outlines how concepts such as feedback loops, adaptive control, and systemic interconnections can be utilized to improve AI's ability to learn, adapt, and respond to dynamic environments. The paper presents a series of case studies demonstrating the application of these principles in real-world AI systems, highlighting improvements in performance and adaptability. The findings suggest that integrating cybernetic principles with AI research not only enhances the capability of AI systems but also offers a pathway to more resilient and intelligent adaptive behaviors. This interdisciplinary approach paves the way for future advancements in AI, underscoring the importance of a holistic perspective in the evolution of intelligent systems.

Keyphrases: Algorithmic Adaptation, Processing Systems, Technology and Regulation

@booklet{EasyChair:14359,
  author    = {John Owen},
  title     = {"Leveraging Cybernetics to Advance Artificial Intelligence: Integrating Systems Theory for Enhanced Adaptive Intelligence"},
  howpublished = {EasyChair Preprint 14359},
  year      = {EasyChair, 2024}}