Philosophical Forecasting of Artificial Intelligence Dynamics: Data Reconfiguration and Environment Design

Dynamic scaling of artificial intelligence (AI) systems in the space of modern culture actualizes the need for philosophical prognostic reflection on realistic strategies for the development of “machine intelligence”. The article intends to define relevant markers for predicting the development of AI, classify the types of data that “computer intelligence” operates with, and present a description of the dynamics of changes in the types of tasks and conditions for solving them for AI. Methodologically, the study is substantiated by the theoretical de-velopments of L. Floridi and is based on conceptual modeling, as well as structural analysis of the epistemo-logical and ontological properties of different types of data. Based on the results of the study, it was found that the dynamics of AI is characterized by a paradigm shift from focusing on historical data as a resource for ma-chine learning to algorithmically generated synthetic data produced on the basis of constitutive rules. This can create original patterns of problem solving, but creates problems of their verification and transparency. It is also argued that the strategy for increasing the efficiency of AI should be based on the creation of specialized sys-tems integrated into the designed environments that transform complex tasks into controlled processes by re-ducing cognitive-motor requirements. A general prognostic conclusion is formulated: the evolution of AI will be associated with the hybridization of types of operated data, minimization of access to empirical information and optimization of polymodal interaction with essentially relevant environments.