This paper is a mosaic of vivid fragments describing the industrial aspects of artificial intelligence (AI). These are sketches of the overall picture, which will likely never be completed, as each day brings information about new achievements, ideas, and threats. Discussions cover issues of civilian AI in short-term workstations, the development of algorithms for intelligent games, the threats and dangers posed by AI, AI ethics, and standards and international norms for artificial intelligence. Each fragment is a review of the latest (mid-January 2026) Russian and international sources, including quotes, translations, screenshots, and links to original documents.


This text remains an immense "fragment" on the benefits of AI applications, which was presented with the greatest speed. Perhaps this will be the beginning of a separate, never-ending study.

The heat flux distribution in the North Atlantic calculated using a stochastic difference equation scheme, namely, a first-order autoregressive scheme with random coefficients, is studied. The ERA5 database, containing geophysical data for 40 years, from 1979 to 2018, is used. The coefficients for the autoregressive series were previously determined based on these data, and it is shown that the conditions on the coefficients ensure the existence and uniqueness of a solution to this difference equation. The method for calculating distributions is based on successive integration using an autoregressive scheme. Computational experiments are conducted and analyzed. Moreover, it is shown that the theoretically calculated distributions are in good agreement with their empirical counterparts. Further, after the division of the original time series into a distinguished mean (trend) and a residual, the latter is analyzed as a stationary random process. Selected correlation functions were calculated and it is shown that they are well approximated by known analytical expressions. Those approximations allow explicitly filtering and prediction of the process under study. Numerical calculations were performed on the Lomonosov-2 supercomputer at Moscow State University.

This article examines the main problems of teaching functional programming to students already familiar with the imperative paradigm. The learner model and the main problems that arise when teaching functional programming in this case (mutable variables, loops, sequential calculations) are described. A detailed example of the transition from an imperative to a functional paradigm is given. The return of a functional value is examined in detail using examples of numerical differentiation and interpolation. An implementation of lazy evaluation based on anonymous functions is discussed. It is shown that the multi-paradigm Lisp language is a convenient introduction to the functional paradigm.