Keyword: «neural network technologies»

The article considers an example of creating a program capable of analyzing the price difference for cryptocurrency using neural network technologies. The program is designed to analyze price changes on the cryptocurrency exchange Binance.com. The authors propose to consider an example of the work of such a program. The paper defines the model by which forecasting is carried out, and describes its main properties. The relevance of the article is due to the need to create programs for automatic price analysis when trading on the stock exchange, which allows the trader to save a lot of time and effort.

This study examines the integration of neural network technologies into the educational process of higher education. Examples of the use of neural networks around the world are given, the problems and prospects of this area are considered.

The article is devoted to the analysis of the ethical and pedagogical challenges that arise when integrating neural network technologies into the educational process. The key problems are considered: a decrease in the level of critical thinking, the role of opaque algorithms in the formation of competencies, the place of digital inequality in access to neural network tools, difficulties in maintaining academic honesty. As their problems, recommendations are offered for identifying cheating, training in the use of neural network resources and the formation of a culture of responsible use of neural networks. The results are relevant for general teachers, subject teachers.

More and more people are now gaining access to programs that work with artificial intelligence. This is because such programs are becoming easier to use and cheaper. As a result, people have different opinions about how AI might change education. On the one hand, many hope that it will help teachers make lessons more interesting and automate some routine tasks. On the other hand, there are concerns that AI could replace teachers and put them out of work. The key question is: in which areas of learning do AI-powered computers help more effectively than a live teacher, and where is a human still needed? In this article, we compare who does a better job in education: the live teacher or computer programs. We want to understand the best way to use each of them in the learning process and figure out who has clear advantages in which areas. The article compares data from recent Russian publications and materials from conferences on children's and youth scientific and technical creativity. It also uses a method that divides the learning process into two parts: preparing materials and assisting students, versus the support and motivation provided by the teacher. The main results show that neural network technologies are highly effective in supporting tasks: speeding up the creation of varied exercises and adapting learning materials to the student's level. At the same time, there remains an area where the live teacher is irreplaceable: providing trustworthy feedback, tuning intrinsic motivation, taking responsibility for the meaningful content of the material, and developing independent critical thinking. Several ethical risks are also identified: the illusion of understanding despite a formally correct answer, the imitation of dialogue instead of real communication, and risks to academic integrity. The theoretical significance lies in clarifying the boundaries of how neural network technologies can be applied, as well as in overcoming both excessive faith in technology and outright rejection of it. The practical significance lies in using these findings to develop methodological recommendations for integrating AI into the learning process, updating teacher training programs, and shaping institutional policies on the use of digital tools in education.