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Complex Systems and the Evolution of Artificial Life and Intelligence

  • Klaus Mainzer

Abstract

The algorithmic mechanization of thinking with program-controlled computers has some severe obstacles which cannot be overcome by growing computational capacities. For example, pattern recognition, the coordination of movements, and other complex tasks of human learning cannot be mastered by Turing-like computer programs. Artificial neural networks realize the principles of complex dynamical systems. They are inspired by the successful technical applications of nonlinear dynamics to solid-state physics, spin-glass physics, chemical parallel computers, optical parallel computers, laser systems, and the human brain (Sect. 6.1).

Keywords

Boolean Function Cellular Automaton Cellular Automaton Truth Table Artificial Life 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Klaus Mainzer
    • 1
  1. 1.Lehrstuhl für Philosophie und Wissenschaftstheorie, Institut für Interdisziplinäre InformatikUniversität AugsburgAugsburgGermany

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