The work is devoted to the problem of platform solutions using for the digital transformation of production and logistics systems. The purpose of this article was a development of approaches to the services formation and filling of the digital platforms. The role and place of digital platforms in the transition of industrial enterprises to work on the models of "manufacturing as a service", "sharing using" of a circular economy, integrated execution by an international supply chain links of a geographically distributed business process are determined. The need for solving the problems of cooperation between production and logistics links based on digital platforms has been identified. In contrast to the existing researches, the article addressed the methodological issues of designing the digital platforms functionality to solve the logistic problems of business, cooperation and coordination of participants in digital platforms. In the work, general scientific methods were used to cognize economic phenomena and processes (analysis and synthesis, a systematic approach, the dialectical method, etc.). The main study results were methodological approaches to the formation of a digital platform as an ecosystem; designing its functional component. The author offered methodological approaches to services designing based on continuous engineering and agile design. The using area of the proposed solutions: the design of applied digital platforms and logistics service systems for certain production and logistics systems.

This article briefly reviews the Chinese experience of the development of the digital economics, with reference to which there have been formed recommendations for the Republic of Belarus. In the Chinese experience of digitalization of the economy, theoretical foundations are summarized, summarized in the stages of the digital economy and its comparison with industries, and practical results on creating drivers of economic growth and development are systematized. In conclusion on the international regulation of digital globalization and borrowing Chinese experience in Belarus.

Today, in the era of information technologies, the economy, as a sphere of public life, is expanding its borders by moving to the Internet space, where its main structural unit is the platform. The economy of platforms or platform economy is the transformation of economic markets from the real world to the virtual world. But such a transformation cannot take place without consequences, and to understand what they are, we need to understand the concept of "platform economy", its structure as an economic sector, as well as the effect it has on the economic rules themselves. Being something new, at first glance, the platform economy embodies the cooperation of experience accumulated over the centuries with modern technologies to meet the needs of today's society. For a more illustrative example, at this article will be considering the consumer market.

The article shows that in the analysis of the labor market statistical and administrative data are used as an empirical base, which is insufficient for understanding current trends on it and making decisions. The authors determined the scope and features of the presentation of information about the labor market on online job portals, revealed differences in the formation and structuring of information, substantiated the need to use the data of online job portals in the Republic of Belarus to obtain a comprehensive picture of the labor market, since they cover different segments of the Belarusian labor market. Online job portals in the Republic of Belarus were evaluated and ranked. The authors formulated the difficulties of forming a single portal of vacancies for labor market analysts: - different criteria for structuring information in the description of vacancies and resumes, the use of different classifiers; - lack of unification in the use of methodological and methodological approaches (understanding the labor market situation, division into types of economic activities, categories of vacancies and resumes); - weak structuring and arbitrariness in the description of the required skills; - closed information. The directions of formation of the digital ecosystem of the labor market in the country are determined, including the collection and processing of information about the labor market from different sources, their relationship and integration with the education system, business entities, management bodies, ensuring the unity of methodological approaches in the formation of information (terminology, taxonomy, classifiers, etc.) and its provision to all interested users (management bodies for the development of programs and forecasts, personnel services of organizations, educational institutions, the population - employed, unemployed, who want to change jobs, find additional work, students, applicants, etc.).

The aim of the work is to comparatively evaluate the sizes of FIFO and LIFO stacks required for image segmentation based on growing regions. The coordinates (y, x) of the pixels that need to be attached to the cultivated area are placed in stacks during the segmentation process. The size of the stack needed to store the coordinates depends on the structure of the image and cannot be determined before segmentation. To avoid stack overflow, its size is determined for maximum load conditions when the image contains a single maximum area. In this case, the stack size is equal to the image size. This approach does not take into account the process of stack unloading and leads to memory overrun. Expressions are obtained in the paper that allow one to increase the accuracy of determining the sizes of FIFO and LIFO stacks necessary for storing the coordinates of adjacent pixels in a segmentation algorithm based on growing regions under maximum load conditions. In this case, the initial position of the region growth point and the direction of the selection of adjacent pixels in the scanning window are taken into account. A comparative assessment of the stack sizes required for image segmentation showed that using the FIFO stack is preferable to the LIFO stack and leads to significant memory savings.

Currently, the process of digital transformation is actively going on in the economy, science, education, and society as a whole. This process has a number of restrictions and risks we consider. The mathematical theory of complexity reveals a large class of the restrictions. The exact solution of a number of simple-looking problems with a small amount of input data requires resources many times greater than the capabilities of all available computers.

On the "border" between natural and artificial intelligence lies the "cognitive barrier". This, as a rule, makes it impossible to use the results of a number of artificial intelligence systems to adjust our strategies. We and computers "think" differently. They have to be considered as "black boxes". It is very likely that the tester of artificial intelligence systems will become one of the mass professions in the close future.

We give examples to show that the "translation" from "continuous" to "discrete" language can lead to qualitatively different behavior of mathematical models. In a number of problems associated with a computational experiment this can be quite significant.

Great risks arise when passing to the "fast world", approaching the "Lem's barrier". It happens when artificial intelligence systems are assigned strategically important tasks that they must solve at a speed inaccessible to humans.

The analysis shows that managing the risks of digital transformation and its limitations requires the attention of the scientific and expert community, as well as active participants in this process.

Analysis of methods and tools for image colorization was performed. It was explained why artificial neural network model was chosen for graphics information processing. The task of automatic colorization of arbitrary images was formulated. Initial data, conditions and constraints necessary for colorization model are listed. As a result of text classification, set of neural network hypercolumns was retrieved for each image processed. Colorization model was created which allows to determine color of each pixel based on hypercolumns set. In fact, this model consists of two related parts: classifier and colorizer. Classifier is based on using convolutional neural network, and colorizer is based on hash table which stores mapping of hypercolumns and colors. Algorythm of using this model for image colorization is proposed. Comparison of colorization results for developed and existing models was performed. Software tool was created which allows to perform learning of different neural networks and colorization of graphical information. Experiments shown that developed model determines image color quite correctly. Proposed algorithm allows to use convolutional neural network for colorizing black-and-white images, for color correction of pictures, etc.