János Végh
Adv. Artif. Intell. Mach. Learn., 1 (2):136-154
János Végh : Kalimános BT, Komlóssy u 26, Debrecen, 4032, Hungary
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
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.