ISSN :2582-9793

Why Learning and Machine Learning Are Different

Review Article (Published On: 20-Aug-2021 )
Why Learning and Machine Learning Are Different
DOI : 10.54364/AAIML.2021.1109

János Végh

Adv. Artif. Intell. Mach. Learn., 1 (2):136-154

János Végh : Kalimános BT, Komlóssy u 26, Debrecen, 4032, Hungary

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DOI: 10.54364/AAIML.2021.1109

Article History: Received on: 20-Jul-21, Accepted on: 05-Aug-21, Published on: 20-Aug-21

Corresponding Author: János Végh

Email: Vegh.Janos@gmail.com

Citation: János Végh and Ádám József Berki (2021). Why Learning and Machine Learning Are Different. Adv. Artif. Intell. Mach. Learn., 1 (2 ):136-154

          

Abstract

    

Machine learning intends to be a biology-mimicking learning method, implemented by means of technical computing. Their technology and methods, however, differ very much; mainly because technological computing is based on the time-unaware classic computing paradigm. Based on the time-aware computing paradigm, the paper discovers the mechanism of biological information storing and learning; furthermore, it explains, why biological and technological information handling and learning are entirely different. The consequences of the huge difference in transmission speed in those computing systems may remain hidden in "toy"-level technological systems but comes to the light in systems having large size and/or mimicking neuronal operations. The biology-mimicking technological operations are in resemblance to the biological operations only when using time-unaware computing paradigm. The difference leads also to the need of introducing "training" mode (with desperately low efficiency) in technological learning, while biological systems have the ability of life-long learning. It is at least misleading to use technological learning methods to complement biological learning studies. The examples show evidence for the effect of transmission time in published experiments.

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