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Title: Knowledge Transfer Between Artificial Intelligence Systems
Authors: Tyukin, Ivan Y.
Gorban, Alexander N.
Sofeykov, Konstantin I.
Romanenko, Ilya
First Published: 13-Aug-2018
Publisher: Frontiers Media
Citation: Frontiers in Neurorobotics, 2018, 12:49.
Abstract: We consider the fundamental question: how a legacy “student” Artificial Intelligent (AI) system could learn from a legacy “teacher” AI system or a human expert without re-training and, most importantly, without requiring significant computational resources. Here “learning” is broadly understood as an ability of one system to mimic responses of the other to an incoming stimulation and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the “student” Artificial Intelligent system have the structure of an n-dimensional topological vector space and n is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals. In particular, for n sufficiently large, with probability close to one, the “student” system can successfully and non-iteratively learn k ≪ n new examples from the “teacher” (or correct the same number of mistakes) at the cost of two additional inner products. The concept is illustrated with an example of knowledge transfer from one pre-trained convolutional neural network to another.
DOI Link: 10.3389/fnbot.2018.00049
ISSN: 1662-5218
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Mathematics

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