Features of artificial intelligence simulation models and risks when using them

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Abstract

This paper is aimed at emphasizing that ample opportunities in robotics have made their appearance at the current stage of development of computer technologies. Modern computer technologies make it possible to analyze an enormous number of input and output actions on the management systems. Ample opportunities in robotics make their appearance, making it possible to create and commission machines with Artificial Intelligence. The authors of the paper give consideration to the problem of creation of Artificial Intelligence which is capable of carrying out operation an infinite aggregate of operations. The management processes can be implemented using the Artificial Intelligence simulation modeling. The simulation modeling is able to provide feedback and to achieve the evolution of the capabilities of human intelligence through the use of the “method of successive approximations”. The methodological foundation of this paper consists in the economics and statistics analysis of the most common probability theory distributions: normal, exponential and uniform. The authors analyze difficulties on the way toward the selection of methods and algorithms able to solve the problem of creation of Artificial Intelligence. The result of work consists in the assessment of managerial decision-making risk functions in every distribution of random variables under consideration. In addition, the generalization of risk functions in the multidimensional stochastic process – a multiplicative model with functional shift – has been constructed. We note finally: using the capabilities of the simulation modeling and modern computer technologies will allow making the best choice in the creation and control of machines endowed with Artificial Intelligence.

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APA

Silaev, A. A., Bukhtoyarova, M. V., Parshikova, G. Y., & Atoyan, S. V. (2020). Features of artificial intelligence simulation models and risks when using them. In Advances in Intelligent Systems and Computing (Vol. 1100 AISC, pp. 394–403). Springer. https://doi.org/10.1007/978-3-030-39319-9_45

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