Version 1
: Received: 15 March 2021 / Approved: 16 March 2021 / Online: 16 March 2021 (11:33:04 CET)
How to cite:
Végh, J.; Berki, Á. On the Role of Information Transfer’s Speed in Technological and Biological Computations. Preprints2021, 2021030414. https://doi.org/10.20944/preprints202103.0414.v1
Végh, J.; Berki, Á. On the Role of Information Transfer’s Speed in Technological and Biological Computations. Preprints 2021, 2021030414. https://doi.org/10.20944/preprints202103.0414.v1
Végh, J.; Berki, Á. On the Role of Information Transfer’s Speed in Technological and Biological Computations. Preprints2021, 2021030414. https://doi.org/10.20944/preprints202103.0414.v1
APA Style
Végh, J., & Berki, Á. (2021). On the Role of Information Transfer’s Speed in Technological and Biological Computations. Preprints. https://doi.org/10.20944/preprints202103.0414.v1
Chicago/Turabian Style
Végh, J. and Ádám-József Berki. 2021 "On the Role of Information Transfer’s Speed in Technological and Biological Computations" Preprints. https://doi.org/10.20944/preprints202103.0414.v1
Abstract
Information is commonly considered as a mathematical quantity that forms the basis of computing. In mathematics, information can propagate instantly, so its transfer speed is not the subject of information science. In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so the information’s propagation speed cannot exceed the speed of the carrier. Because of this limitation, for any implementation, one must consider the transfer time between computing units. We need a different mathematical method to take this limitation into account: classic mathematics can only describe infinitely fast and infinitely small computing system implementations. The difference between the mathematical handling methods leads to different descriptions of the behavior of the systems. The correct handling also explains why biological implementations can have lifelong learning and technological ones cannot. The conclusion about learning evidences matches others’ experimental evidence, both in technological and biological computing.
Keywords
computing paradigm; technological computing; biological computing; information transfer speed; information storage; lifelong learning; redundancy; temporal behavior; machine learning; artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.