Knowledge Transfer Between Artificial Intelligence Systems

Understand

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 complete re-training and, most importantly, without requiring significant computational resources.

• Here "learning" is understood as an ability of one system to mimic responses of the other 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.