Entropy of Artificial Intelligence

We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way...

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Bibliographic Details
Published in:Universe (Basel) 2022-01, Vol.8 (1), p.53
Main Authors: Biró, Tamás Sándor, Jakovác, Antal
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
deep learning
Entropy
Learning
Neural networks
Physics
Power
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Summary:We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the entropy, learning creates order, and we expect that it decreases a properly defined entropy.
ISSN:2218-1997
2218-1997
DOI:10.3390/universe8010053