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News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Макроэкономические прогнозы и макроэкономические прогнозисты

Вопросы экономики. 2024. № 2. С. 23–48.
Smirnov S. V., Kondrashov N. V., Качур А. С.

The specificity of macroeconomic forecasts is determined not so much by the list of predicted indicators or by mathematical tools used, but by the unavoidable human factor, which often generates great difference between forecasts made by various professionals. In Russian-language literature, this psychological aspect of macroeconomic forecasting has not received any attention; our work is designed to fill this gap. As a source of statistical data we used the forecasts extracted from the quarterly Poll of Professional Forecasters (PPF), which began in the first quarter of 2000. An analysis of real GDP and CPI forecasts made it possible to identify optimists and pessimists among independent experts, and also to show that the official forecasts by the Russian Ministry of Economic Development and the Bank of Russia are often too optimistic (with the possible exception of forecasts for a current year). It was also confirmed that the accuracy of consensus-forecasts exceeds (in the long run) not only that of official forecasts,
but also forecasts made by the vast majority of independent experts. This asserts consensus-forecasts as a benchmark against which macroeconomic forecasts of other experts and organizations should be compared. An analysis of errors for different forecast horizons showed that in the medium term, the most significant ones are associated with underestimation of the resilience of the Russian economy to external shocks. This aspect of macroeconomic forecasting is closely related to forecasting cyclical dynamics (in particular, recessions, their beginning, depth and duration). Currently, this is just what requires additional attention by macroeconomic forecasters.

Research target: Economics and Management
Language: Russian
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Keywords: рецессияeconomic cyclesrecessionэкономический циклmacroeconomic forecastingмакроэкономическое прогнозированиеSurvey of Professional Forecastersопросы профессиональных прогнозистов
Publication based on the results of:
Development of macro-forecasting tools that are effective in conditions of a sharp change in trends, using expert surveys and big data (2023)
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