Abstract

This paper examines the intersection of existence and self-organizing principles within the evolution of artificial intelligence (AI) for collective systems. Existence, in this context, refers to the fundamental nature of system sustainability and evolution, while self-organization highlights how complex structures and behaviors emerge from simple local interactions without central control. By exploring these concepts, the paper elucidates how self-organizing principles underpin the development of advanced AI systems, particularly in managing collective dynamics. The study underscores how neural networks and swarm intelligence models, inspired by natural processes, enable AI to achieve adaptability and efficiency in complex, dynamic environments. The ultimate goal is to demonstrate how these principles can enhance AI's ability to address global challenges and optimize collective systems.