Time series analysis with dynamic law exploration

In this paper we examine, how the dynamic laws governing the time evolution of a time series can be identified. We give a finite difference equation as well as a differential equation representation for that. We also study, how the required symmetries, like time reversal can be imposed on the laws. We study the compression performance of linear laws on sound data.

Understanding understanding: a renormalization group inspired model of (artificial) intelligence

This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we treat the transformation made by an intelligent actor not as a loss of information, but instead a reorganization of the information in the framework of a new coordinate system. We introduce, following the ideas of physical renormalization group, the notions of relevant and irrelevant parameters, and discuss, how the different AI tasks can be interpreted along these concepts, and how the process of learning can be described. We show, how scientific understanding fits into this framework, and demonstrate, what is the difference between a scientific task and pattern recognition. We also introduce a measure of relevance, which is useful for performing lossy compression.