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...
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Topics: 
Artificial intelligence
Machine learning