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Reflective Thinking, Machine Learning, and User Authentication Via Artificial K-Lines

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Executive Summary

Artificial K-Lines (AKL) is a structure that can be used to store different types of knowledge, as long as this knowledge is represented by series of events connected by causality. Unlike, and, perhaps, complementary to, Artificial Neural Networks (ANN), AKL can combine inter-domain knowledge and its knowledge base can be augmented dynamically without rebuilding of the entire system. In this paper, the authors demonstrate the diversity of AKL by illustrating, through examples, it's working for three applications across three completely different areas of study.

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