GENETIC INTELLIGENCE AND TENSOR-UNITARY TRANSFORMATIONS
Keywords: genetic intelligence, development of organisms, configuration spaces, vector analysis, memory, stochastics, determinism.
Abstract: The article is devoted to the author's results of the analysis of the genetically inherited ability of living bodies to intellectual activity (for example, the ability to echolocate in dolphins), which resulted in the emergence of algebraic formalisms called tensor-unitary transformations. Genetic intelligence is understood as that part of the intellectual potential of living organisms that allows, on the basis of genetic information in DNA and RNA molecules, to build, for example, from one fertilized cell an organism with trillions of cells so that the parental traits are reproduced in it in a multichannel noise-resistant manner, despite strong noise and constantly changing conditions of nutrition and external influences during life. In this case, we are talking about the systematic growth in the course of ontogenesis of the number of parameters and degrees of freedom of the body with a corresponding increase in the dimensionality of its configuration space of states. With such growth, the organism at successive stages of its development, acquiring new degrees of freedom and knowledge, somehow retains the memory of the skills and knowledge that it possessed at previous stages of life. The author develops the algebraic foundations for modeling this fundamental feature of the development of living bodies in the tensor-matrix language of systems of multidimensional vector configuration spaces. Tensor-unitary transformations are operators that preserve the lengths of vectors during their tensor transformation into vectors of a space of increased dimension (in contrast to conventional unitary transformations that transform vectors into a space of the same dimension). They are operators of expansion of stochastic-deterministic memory with preservation of all previous memory. Possible applications of tensor-unitary transformations for the development of AI, genetic algorithms, etc. are discussed.
Submission Number: 40
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