Abstract: The article investigates the possibility of formation of creative competencies of students within the framework of scientific personnel training. It is noted that the available training courses within the framework of general training of scientific personnel allow to prepare a specialist who is able to use existing knowledge, but such a specialist is not trained in creativity. For the formation of creative competences it is necessary to take a training course of the general theory of systems, the purpose of which is to create a universal methodology of reliable representation of models of objects, based on the similarity (isomorphism) of the forms and laws of the universe. A detailed argumentation of the connection between creativity and holistic representation of being, the empirical manifestation of which is isomorphism, is given. The mechanism of knowledge translation within the framework of the mechanism of multisystem integration of human being, the mechanism of knowledge borrowing by human being from sensually comprehended forms of reality is investigated. It is stated that in order to meet the set tasks of cognition, a version of the general theory of systems, extended by the metaphysical component, is required. Further, starting from the metaphysical description of the foundations of the phenomenon of isomorphism observed in the practice of cognition, it is possible to form complete collections of patterns – typical forms, templates of objects and their relations. At present, the work on such a version of the general theory of systems is being finalized. The article concludes with a discussion of the problematics of teaching general systems theory. It emphasizes two components: the issue of positioning the training course "General Theory of Systems" in relation to other courses, as well as taking into account the specifics of the subject area of students in different specialties (directions).

Abstract: The article is devoted to the study of the problem of increasing the reliability of modeling of cognitive systems, to which the authors refer not only human intelligence, but also artificial intelligence systems, as well as intelligent control systems for production, technological processes and complex equipment. It is shown that the use of cognitive systems for solving control problems causes very high rapidity requirements for them. These requirements combined with the necessity to simplify modeling methods as the modeling object becomes more complex determine the choice of an approach to modeling cognitive systems. Models should be based on the use of simple algorithms in the form of trend detection, correlation, as well as (for solving intellectual problems) on the use of algorithms based on the application of various patterns of forms and laws. In addition, the models should be decentralized. An adequate representation of decentralized systems formed from a large number of autonomous elements can be formed within the framework of agent-based models. For cognitive systems, two models are the most elaborated: actor and reactor models. Actor models of cognitive systems have two possible realizations: as an instrumental model or as a simulation. Both implementations have the right to exist, but the possibilities of realizing a reliable description when using the tool model are higher, because it provides incommensurably higher rapidity, and also assumes variability of the modeled reality. The actor model can be realized by means of a large number of existing programming languages. The solution to the problem of creating simulative actor models is available in most languages that work with actors. Realization of instrumental actor models requires rapidity, which is unattainable in imperative programming. In this case, the optimal solution is to use actor metaprogramming. Such programming is realizable in many existing languages.

Abstract: The article deals with the management of complex systems. The general definitions of the concepts "control" and "control system" are formulated. It is stated that the control system in its basis is an information system, for which the most important characteristics are performance and rapidity. Definitions are given and differences between these characteristics are revealed. The problem of realizability of control of complex systems is stated, which consists in the necessity of providing sufficient rapidity, at which the whole necessary complex of control operations is placed in the control cycle. The relationship between the control parameters: the complexity of the control object, the duration of the control cycle and the rapidity of the control system is investigated. As a result, a number of significant dependencies are revealed: the duration of the control cycle is approximately inversely proportional to the complexity of the control object; the rapidity of the control system is approximately proportional to the square of the object complexity. It is stated that within the framework of the general theory of systems there are two main options for increasing the stability of a complex system: the option of monocentrism with a central element, or by increasing the number of links in the object. The first option does not allow increasing rapidity. The second variant of stability can be implemented in practice in the form of a decentralized system. The latter option is universally realized in living systems and is promising for the control of technical systems.

Abstract: The article analyzes the possibilities and limitations of artificial intelligence. The article considers the subjectivity of artificial intelligence, determines its necessity for solving intellectual problems depending on the possibility of representing the real world as a deterministic system. Methodological limitations of artificial intelligence, which is based on the use of big data technologies, are stated. These limitations cause the impossibility of forming a holistic representation of the objects of cognition and the world as a whole. As a tool for deterministic description of the universe it is proposed to use empirical-metaphysical general theory of systems, which is an extension of existing general theories of systems due to ontological justification of the phenomenon of isomorphism and definition of a limited set of laws, rules, patterns and primitives of forms and relations of objects in the universe. The distinction of natural (human) and artificial intelligence is considered, including the realization of multisystem integration of intelligence in physical, biological, social and spiritual systems. A philosophically grounded approach to ensuring the evolutionary properties of artificial intelligence is formulated, based on the inclusion of non-equilibrium mechanisms through which stability is realized.