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 use of unmanned aerial vehicles (UAVs) in various spheres of human activity is a promising direction for countries with very different types of economies. This statement refers to resource-rich economies as well. The peculiarities of such countries are associated with the dependence on resource prices since their economies present low diversification. Therefore, the employment of new technologies is one of the ways of increasing the sustainability of such economy development. In this context, the use of UAVs is a prospect direction, since they are relatively cheap, reliable, and their use does not require a high-tech background. The most common use of UAVs is associated with various types of monitoring tasks. In addition, UAVs can be used for organizing communication, search, cargo delivery, field processing, etc. Using additional elements of artificial intelligence (AI) together with UAVs helps to solve the problems in automatic or semi-automatic mode. Such UAV is named intelligent unmanned aerial vehicle technology (IUAVT), and its employment allows increasing the UAV-based technology efficiency. However, in order to adapt IUAVT in the sectors of economy, it is necessary to overcome a range of limitations. The research is devoted to the analysis of opportunities and obstacles to the adaptation of IUAVT in the economy. The possible economic effect is estimated for Kazakhstan as one of the resource-rich countries. The review consists of three main parts. The first part describes the IUAVT application areas and the tasks it can solve. The following areas of application are considered: precision agriculture, the hazardous geophysical processes monitoring, environmental pollution monitoring, exploration of minerals, wild animals monitoring, technical and engineering structures monitoring, and traffic monitoring. The economic potential is estimated by the areas of application of IUAVT in Kazakhstan. The second part contains the review of the technical, legal, and software-algorithmic limitations of IUAVT and modern approaches aimed at overcoming these limitations. The third part—discussion—comprises the consideration of the impact of these limitations and unsolved tasks of the IUAVT employment in the areas of activity under consideration, and assessment of the overall economic effect.

Drones, or UAVs, are developed very intensively. There are many effective applications of drones for problems of monitoring, searching, detection, communication, delivery, and transportation of cargo in various sectors of the economy. The reliability of drones in the resolution of these problems should play a principal role. Therefore, studies encompassing reliability analysis of drones and swarms (fleets) of drones are important. As shown in this paper, the analysis of drone reliability and its components is considered in studies often. Reliability analysis of drone swarms is investigated less often, despite the fact that many applications cannot be performed by a single drone and require the involvement of several drones. In this paper, a systematic review of the reliability analysis of drone swarms is proposed. Based on this review, a new method for the analysis and quantification of the topological aspects of drone swarms is considered. In particular, this method allows for the computing of swarm availability and importance measures. Importance measures in reliability analysis are used for system maintenance and to indicate the components (drones) whose fault has the most impact on the system failure. Structural and Birnbaum importance measures are introduced for drone swarms’ components. These indices are defined for the following topologies: a homogenous irredundant drone fleet, a homogenous hot stable redundant drone fleet, a heterogeneous irredundant drone fleet, and a heterogeneous hot stable redundant drone fleet.