Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&ML applications.

The Republic of Kazakhstan has significant deposits of fossil fuels and is one of the largest energy producers among the countries of Central Asia. At the same time, The Republic of Kazakhstan is one of the richest countries of the world in terms of renewable resources, evaluated to over 1000 billion kWh/year. The application of therenewable energy sources (RES), both on a large scale and at the level of a single household, ensures the transformation of the energy system to a ''green state''. However, these initiatives should be substantiated by relevant supportive information to promote transformation of the country's economy to a qualitative ecological state.The paper covers developed multi-criteria decision-making system (MCDM) and software tools for processing of spatial heterogeneous data which could be applied for evaluation of the RES potential.The developed system serves to evaluate the potential of usable RES as it allows the assessment of a territory of the country in terms of installing photovoltaic and wind generators.A feature of the proposed MCDM is the use of an analytical hierarchical process (AHP) in combination with the Bayesian approach, which allows obtaining two complementary assessments of the territory areas. The method allows a rough estimate in an event of lack of data.The verification performed based on the available data on the installed solar and wind power stations shows that the system gives a relatively small root-mean-square error within 15%.INDEX TERMS Decision making support methods, geo information systems, intelligent information technologies, heterogeneous data, machine learning, renewable energy, spatially distributed resources, spatial decision making (SDM), multiple-criteria decision analysis, multiple-criteria decision making (MCDM).

The subject matter of the study is the processes of planning supply logistics taking into account swings in demand and prices for products. The goal is to develop models and applied information technology for managing enterprise supplies taking into consideration the unforeseen demand swings. The following tasks were solved: a process model of supply logistics was developed, a model for forecasting demand for products was developed, a model for calculating the optimal volume of orders for various demand options was developed, the structure and modules of the applied information technology for supply logistics management was developed. The following methods were used: structural process models, methods for regression analysis and time series forecasting, inventory management models, STATISTICA software package, object-oriented programming methods. The following results were obtained: the generalized pattern of supply logistics was developed; the supplement of the first block of this pattern with the processes of marketing research of demand for products and planning supply volumes according to the forecasted demand and the probability of a shortage or surplus of products due to unforeseen swings in demand was substantiated; the application of the methods of regression analysis and forecasting of time series to assess the market factors of supply logistics was considered; the model for determining the optimal stock size was studied taking into account storage costs and probable shortages; the architecture of the applied information technology for planning supply logistics was developed; the proposed IT enables analyzing and predicting changes in the main market factors and, in accordance with the results obtained, solving inventory management tasks efficiently. In this case, the deficit and backordered demand can be taken into account. The operation of IT modules was illustrated by a test case. Conclusions. The use of IT is efficient in making decisions on logistics planning of business processes, as well as in analyzing the efficiency of logistics for a certain period of time. Further, the specified technology is going to be supplemented with the capabilities of solving inventory logistics problems.

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.