Artificial Intelligence as an Object of Statistical Study

The article addresses the questions regarding organizing a statistical study of one of the most dynamic sectors of the economy, based on the use of artificial intelligence AI. Given the novelty and fundamental nature of the topic, the paper focuses on the subject of artificial intelligence, understanding its essential aspects, economic nature, and its driving force for development in order to have clear guidelines for their reflection in current statistical accounting and reporting. The article demonstrates the contradictions of AI development, duality, and the variability of building strong and weak AI models.

The methodological basis for the statistical study of artificial intelligence is provided by the basic OECD Guidelines for big technologies such as biotechnology, nanotechnology, information, and communication technologies. To be specific, the paper elaborates on science and research, technology and technology base. The author outlines the main technical questions related to statistical observation: basic and list-based definitions of artificial intelligence, identifies characteristics of enterprises and organizations that are subject to statistical accounting, including start-ups. Specific statistical indicators are indicated; are shown drivers and growth segments of the AI market.

Specific attention is aimed at the topic of interdisciplinarity. Particularly the author touches upon brief historical background of the origin of the AI concept, nature of weak and strong artificial intelligence, and also shows major trends in worldview transformations. The paper examines areas of concern for the formation of the research potential of universal (strong) artificial intelligence.

The author describes the technological aspects of the progress in artificial intelligence, the relevance of its analysis from the perspective of a complex of big technologies, the basic contours of interaction between various fundamental and applied technologies in building the single technology platform for the creation and study of artificial intelligence.

With the use of specific statistical materials, the article presents forms of the global and Russian market of artificial intelligence technologies and demonstrates its key growth drivers.

1. Kazantsev A.K. et al. NBIC-Technologies: Innovations Civilization of the XXI Century. Moscow: INFRA-M; 2014. 383 p. (In Russ.)

2. Rybak O.P. Information Cognition and Statistics. Voprosy Statistiki. 2017;(7):3-16. (In Russ.)

3. Anderson J.R. The Architecture of Cognition. Cambridge, MA; Harvard University Press: 1983. 314 p.

4. Simon G. Structure of Complexity in the Developing World. In: Velichkovskii B.M., Solov’ev V.D. (eds.) Computers, Brain, Cognition: The Success of Cognitive Sciences. Moscow: Nauka Publ.; 2008. (In Russ.)

5. Markram H. Human Brain Project. ‘‘V mire nauki / Scientifi c American’’ magazine. 2012;(8). (In Russ.).

6. Kasavin I.T. The Philosophy of Knowledge and Notion of Interdisciplinarity. Epistemology & Philosophy of Science. 2004;(2.). (In Russ.)

7. Rybak O.P. Methodological Problems in Developing Statistics of Cognitive Technologies. Voprosy Statistiki. 2016;(7):10-25. (In Russ.)

8. A Push to Map All the Brain’s Neurons. Scientific American Mind. 2013;24(2):18-18.

9. Alivisatos A.P. et al. The Brain Activity Map Project and the Challenge of Functional Connectomics. Neuron. 2012;74(6):970-974.

10. Church G., Yuste R. New Century of the Brain. ‘‘V mire nauki / Scientific American’’ magazine. 2014;(5). (In Russ.).

11. Insel T.R., Landis S.C., Collins F.S. Research Priorities. The NIH BRAIN Initiative. Science. 2013;340(6133):687-688.

12. Gardner H. The Mind’s New Science: A History of Cognitive Revolution. N.Y.: Basic Books; 1985.

13. Strategy for Development of Rosstat for the Year 2024 (Draft). Voprosy Statistiki. 2019;26(4):3-24. (In Russ.)

14. Shastri D. Demystifying Artificial Intelligence. Deloitte University Press; 2016.