Purpose: This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).Design/methodology/approach: The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).Findings: The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.Research limitations/implications: Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.Practical implications: Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.Originality/value: The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

The modern stage of technology development is characterized by the emergence of new paradigms for the construction of anthropogenic systems, such as cyber-physical systems, socio-cybernetic systems, etc. The task of data acquisition about the state of a multi-level system and managing the structure and behavior of a system consisting of many thousands of elements of different physical nature is a complex task. This article describes one of possible approaches to solving the problem of data acquisition and management of the structure of a large-scale heterogeneous system. The proposed approach is based on the idea of using dynamic digital twins, which are dynamic models of the observed system. This approach was used for the development of systems in various subject domains, in particular, in production management systems built on the Industry 4.0 principle, in the development of a technical support system for cable television networks and in the development of support systems for the construction of educational trajectories.

Recurrent neural network (RNN) models continue the theory of the autoregression integrated moving average (ARIMA) model class. In this paper, we consider the architecture of the RNN with embedded memory—«Process of Nonlinear Autoregressive Exogenous Model» (NARX). Though it is known that NN is a universal approximator, certain difficulties and restrictions in different NN applications are still topical and call for new approaches and methods. In particular, it is difficult for an NN to model noisy and significantly nonstationary time series. The paper suggests optimizing the modeling process for a complicated-structure time series by NARX networks involving wavelet filtering. The developed procedure of wavelet filtering includes the application of the construction of wavelet packets and stochastic thresholds. A method to estimate the thresholds to obtain a solution with a defined confidence level is also developed. We introduce the algorithm of wavelet filtering. It is shown that the proposed wavelet filtering makes it possible to obtain a more accurate NARX model and improves the efficiency of the forecasting process for a natural time series of a complicated structure. Compared to ARIMA, the suggested method allows us to obtain a more adequate model of a nonstationary time series of complex nonlinear structure. The advantage of the method, compared to RNN, is the higher quality of data approximation for smaller computation efforts at the stages of network training and functioning that provides the solution to the problem of long-term dependencies. Moreover, we develop a scheme of approach realization for the task of data modeling based on NARX and anomaly detection. The necessity of anomaly detection arises in different application areas. Anomaly detection is of particular relevance in the problems of geophysical monitoring and requires method accuracy and efficiency. The effectiveness of the suggested method is illustrated in the example of processing of ionospheric parameter time series. We also present the results for the problem of ionospheric anomaly detection. The approach can be applied in space weather forecasting to predict ionospheric parameters and to detect ionospheric anomalies.

The problem of transferring data from a database of one type to a database of another type is relevant today due to the increase in data volumes and the complexity of tasks solved in various fields of human activity. The existing databases are less and less satisfying the needs of users. New types of databases appear that are more suitable for solving large-scale problems. This article proposes an algorithm for solving the problem of optimizing the document structure of a database based on metadata about the structure of a distributed relational database from which data are transferred. The approach also takes into account information about the structure of the priority database queries. The priority of database queries is user-defined. The system of automatic translation of the database, taking into account these metadata, allows the user to create a distributed document database that is optimal in two parameters: in terms of the volume of stored data and in terms of the execution time of priority database queries.

This paper proposes algorithms for optimization of the Remote Core Locking (RCL) synchronization method in multithreaded programs. We propose an algorithm for the initialization of RCL-locks and an algorithm for thread affinity optimization. The algorithms consider the structures of hierarchical computer systems and non-uniform memory access (NUMA) in order to minimize the execution time of multithreaded programs with RCL. The experimental results on multi-core computer systems show the reduction of execution time for programs with RCL.