Artificial intelligence as a factor in the transformation of contemporary cognitive practices in the digital age

Abstract

In the present article, a philosophical analysis is carried out of the transformation of epistemic practices in the contemporary digital age, driven by the development of artificial intelligence (AI). The study explains the need for philosophical inquiry “in advance,” since the pace of technological improvement of AI systems is so rapid that philosophical reflection on these processes often occurs with a certain delay. Moreover, the question of the role of AI in cognitive activity cannot be considered separately from the philosophical problem of its potential epistemic agency. The article analyzes the main contemporary approaches to the possibility of reducing thinking to computational functions, which brings artificial intelligent systems closer to “natural intelligence”.
The theoretical basis of the study includes a wide range of conceptual developments, from the ideas of A. Turing to the phenomenological realism of T. Nagel, the biological naturalism of John Searle, and the Dennett’s functionalism. In addition, through the perspective of epistemic structural realism, the study distinguishes the problem of “AI self-consciousness” from its role in the production of new knowledge through the detection of stable correlational patterns that can be identified by AI systems independently, without the participation of a human researcher.
The article also examines models of “human–AI” interaction, both purely instrumental ones and those in which AI systems are delegated a leading role in the research process. Furthermore, the study highlights risks, associated with the “epistemic opacity” of complex neural networks (the black-box problem), as well as the possibility of generating “chimeric entities” that may potentially distort research results. At the same time, the article emphasizes that the identification of correlations in large data sets is not sufficient for the formation of a full scientific theory. This requires a complex path from the intuitive formulation of a hypothesis to its support by empirical data, as well as recognition by the scientific community. The study concludes that despite their high computational power, AI systems cannot replace the human researcher in matters of goal-setting, creative inquiry, and the acceptance of epistemic responsibility for the results of knowledge production.