Neural Network Algorithms in Current Processes of Transformation of Traditional Worldview and Ideological Systems.
Abstract
This study is devoted to identifying potential risks, challenges, and threats of intensive development and active implementation of artificial intelligence technologies and in particular self-learning neural network algorithms in the current practice of socio-political communications associated with active information impact on mass consciousness. This study proves that contemporary digital technological transformations and the active spread of digital technologies most directly influence the transformation of traditional worldview systems and ideologies. Based on the results of the study, it is concluded that with the help of digital tools, there is a significant information and communication impact on mass consciousness in terms of influencing traditional worldview and ideological systems. In this regard, the author puts forward the thesis about the need to form and maintain the digital sovereignty of contemporary states as a key conclusion. It is viewed as a critical condition for preserving and protecting national worldview and ideological spaces in the conditions of current global technological transformations
Keywords
digitalization,
technological transformations,
artificial intelligence,
neural networks,
world view,
political ideology,
digital sovereignty
Supporting agencies: The research was conducted with the support of the Interdisciplinary Scientific and Educational School of Moscow University «Preservation of the World Cultural and Historical Heritage»
About the Author
S. V. Volodenkov
Lomonosov Moscow State University
Russian Federation
Russian Federation
Sergey V. VOLODENKOV, Dr. Sc. (Political Sciences), Associate Professor, Professor of the Public Policy Department
Lomonosovsky Рrospect, 27, 4, Moscow, 19991
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Review
For citations:
Volodenkov S.V. Neural Network Algorithms in Current Processes of Transformation of Traditional Worldview and Ideological Systems. Outlines of global transformations: politics, economics, law. 2024;17(2):6-30. (In Russ.)
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