An Epistemological Analysis of the Social and Humanitarian Significance of Artificial Intelligence Innovations in Context of Artificial General Intelligence

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance of new AI achievements, especially regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues.

1. Anokhin K.V. (2021) In Search of a Fundamental Neuroscientific Theory. Journal of Higher Nervous Activity. Vol. 71, no. 1, pp. 39–71 (in Russian).

2. Dubrovsky D.I. (2015) “Consciousness and Brain” Problem: A Theoretical Solution. Moscow: Kanon+ (in Russian).

3. Dubrovsky D.I. (2019) “The Hard Problem of Consciousness.” Theoretical Solution of Its Main Questions. AIMS Neuroscience. Vol. 6, no. 2, pp. 85–103.

4. Dubrovsky D.I. (2020) Mental Phenomena and the Brain. Philosophical Analysis of the Problem in Connection with Actual Problems of Neurophysiology, Psychology and Cybernetics (2nd ed.). Moscow: LENAND (in Russian).

5. Dubrovsky D.I. (2021) The Task of Creating General Artificial Intelligence and the Problem of Consciousness. Russian Journal of Philosophical Sciences = Filosofskie nauki. Vol. 64, no. 1, pp. 13‒44 (in Russian).

6. Dubrovsky D.I. (2022) The Value of Neuroscience Research of Consciousness for the Development of Artificial General Intelligence (Methodological Issues). Voprosy filosofii. No. 2, pp. 83−93 (in Russian).

7. Efimov A.R. (2020) Post-Turing Methodology: Breaking the Wall toward Artificial General Intelligence. Intellekt. Innovatsii. Invesititsii. No. 2, pp. 74–80 (in Russian).

8. Fujiwara Y., Miyawaki Y., & Kamitani Y. Estimating Image Bases for Visual Image Reconstruction from Human Brain Activity. Advances in Neural Information Processing Systems. Vol. 22, pp. 576–584.

9. Lepskiy V.E. (2010) Reflexively Active Environments of Innovative Development. Moscow: Kogito-tsentr (in Russian).

10. Lepskiy V.E. (2017) The Seventh Socio-Humanitarian Technological Order − the Outlines of the Future of Mankind. In: Zapesotsky A.S., Markov A.P., Paseshnikova L.A. (Eds.) Global World: Systemic Shifts, Challenges and Outlines of the Future: 17th International Likhachev Scientific Readings, May 18–20, 2017 (pp. 357–360). Saint Petersburg: SPbGUP (in Russian).

11. Lepskiy V.E. (2018) Socio-Humanitarian Criteria for Evaluating the Innovations of Digital Reality. Sotsial’noye vremya. No. 4, pp. 16−26 (in Russian).

12. Lepskiy V.E. (2020) Philosophical and Methodological Foundations for Assessing the Socio-Psychological Consequences of the Introduction of New Technologies. Psychological Journal. Vol. 41, no. 4, pp. 105−108 (in Russian).

13. Makin J.G., Moses D.A., & Chang E.F. (2020) Machine Translation of Cortical Activity to Text with an Encoder-Decoder Framework. NatureNeuroscience Technical Report. Vol. 23, no. 4, pp. 575−582.

14. Miyawaki Y., Uchida H., Yamashita O., Sato M.A., Morito Y., Tanabe H.C., Sadato N., & Kamitani Y. (2008) Visual Image Reconstruction from Human Brain Activity Using a Combination of Multiscale Local Image Decoders. Neuron. Vol. 60, no. 5, pp. 915–929.

15. Shen G., Horikawa T., Majima K., & Kamitani Y. (2019) Deep Image Reconstruction from Human Brain Activity. PLoS Computational Biology. Vol. 15, no. 1, e1006633.

16. Singh S. (1999) The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography. New York: Doubleday (Russian translation: Moscow: AST, Astrel, 2007).