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Complexity, Artificial Life, and Artificial Intelligence.

Carlos Gershenson1

  • 1State University of New York at Binghamton, School of Systems Science and Industrial Engineering, Universidad Nacional Autónoma de México, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Centro de Ciencias de la Complejidad. cgg@binghamton.edu.

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Complexity, Artificial Life (ALife), and artificial intelligence (AI) share deep connections. This personal account explores their shared history, methods, and limitations, using concepts like self-organization and emergence to foster future collaboration.

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Area of Science:

  • Interdisciplinary study bridging complexity science, Artificial Life (ALife), and artificial intelligence (AI).

Background:

  • Shared origins in cybernetics from the 1940s.
  • Development contingent on advancements in modern information technology.
  • Exploration of historical, conceptual, methodological, and philosophical commonalities.

Purpose of the Study:

  • To provide a personal perspective on the expectations and limitations of complexity, ALife, and AI.
  • To analyze the influence of formal systems on these fields.
  • To facilitate alignment and progress by addressing inherent limitations.

Main Methods:

  • Comparative analysis of complexity, ALife, and AI.
  • Focus on key concepts: interactions, self-organization, emergence, and balance.
  • Personal reflection and biased account of field development.

Main Results:

  • Identification of shared traits and developmental trajectories across the three fields.
  • Highlighting limitations stemming from formal systems.
  • Emphasis on overarching questions rather than definitive answers.

Conclusions:

  • The fields of complexity, ALife, and AI possess significant overlap and shared challenges.
  • Understanding and addressing the limits of formal systems is crucial for future advancements.
  • Further dialogue and collaboration are encouraged to overcome or accept these limitations.