Review
Version 1
Preserved in Portico This version is not peer-reviewed
Emergent Information Processing: Observations, Experiments, and Future Directions
Version 1
: Received: 11 August 2023 / Approved: 11 August 2023 / Online: 11 August 2023 (10:18:56 CEST)
A peer-reviewed article of this Preprint also exists.
Kroc, J. Emergent Information Processing: Observations, Experiments, and Future Directions. Software 2024, 3, 81-106. Kroc, J. Emergent Information Processing: Observations, Experiments, and Future Directions. Software 2024, 3, 81-106.
Abstract
Scientists are gradually becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines ranging from cosmology, physics, across chemistry, biochemistry, and ending in biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively-parallel, self-organized, emergent, error-resilient, computational environments?” A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. This all together is opening the crucial question whether there is existing a deeper, beyond the Turing machine theoretical description of massively-parallel computing. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python software GoL-N24. The perspective, future directions including applications in robotics and biology of this research are discussed in the light of known information.
Keywords
emergent information processing; emergent, emergent logic; complex systems; cellular automaton; error-ressilient; self-organization; biocomputing; Turing machine equivalence
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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