Abstract
There are promising prospects on the way to widespread use of AI, as well as problems that need to be overcome to adapt AI&ML technologies in industries. The paper systematizes the AI sections and calculates the dynamics of changes in the number of scientific articles in machine learning sections according to Google Scholar. The method of data acquisition and calculation of dynamic indicators of changes in publication activity is described: growth rate (D1) and acceleration of growth (D2) of scientific publications. Analysis of publication activity, in particular, showed a high interest in modern transformer models, the development of datasets for some industries, and a sharp increase in interest in methods of explainable machine learning. Relatively small research domains are receiving increasing attention, as evidenced by the negative correlation between the number of articles and D1 and D2 scores. The results show that, despite the limitations of the method, it is possible to (1) identify fast-growing areas of research regardless of the number of articles, and (2) predict publication activity in the short term with satisfactory accuracy for practice (the average prediction error for the year ahead is 6%, with a standard deviation of 7%). This paper presents results for more than 400 search queries related to classified research areas and the application of machine learning models to industries. The proposed method evaluates the dynamics of growth and the decline of scientific domains associated with certain key terms. It does not require access to large bibliometric archives and allows to relatively quickly obtain quantitative estimates of dynamic indicators.
Highlights
For successful economically justified development of traditional and new industries, increasing production volumes and labor productivity, we need new technologies related to extraction, processing, and production technologies, and to the collection, processing, and analysis of data accompanying these processes
The evolution of each scientific direction, including Artificial intelligence (AI), is accompanied by an increase or decrease of the interest of researchers, which is reflected in the change of bibliometric indicators
4, we present the results of the analysis of publication activity based on Section 5 is devoted to a discussion of the results
Summary
For successful economically justified development of traditional and new industries, increasing production volumes and labor productivity, we need new technologies related to extraction, processing, and production technologies, and to the collection, processing, and analysis of data accompanying these processes. Many countries are working out or have adopted their strategies for the use and development of AI [3]. There are promising prospects and some obstacles on the way to the widespread use of AI, the overcoming of which means a new round of technological development of AI and expansion of its application sphere. The evolution of each scientific direction, including AI, is accompanied by an increase or decrease of the interest of researchers, which is reflected in the change of bibliometric indicators. The latter includes the number of publications, the citation index, the number of co-authors, the Hirsch index, and others. The identification of “hot” areas in which these indicators are more important allows us to better understand the situation in science and, if possible, to concentrate the efforts on breakthrough areas
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