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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Между культурой и экономикой: факторы, мотивирующие регионы поддерживать креативные индустрии

Экономика региона. 2024. Т. 20. № 4. С. 1041–1057.
Boos V., Shubina V., Kutsenko E.

Due to the lack of a unified approach to supporting creative industries and to the differentiation of initial socioeconomic conditions in Russian regions, different government participation models in the development of creative industries are emerging. Understanding the balance of factors influencing the creative industries is key to more effective goal-setting and support for the creative sector in Russian regions. Using the polynomial logistic regression method, this study defines objective factors that influence the quality of policies in the field of creative industries in the regions of Russia and, using k-means clustering, determines groups of regions with similar profiles of implemented creative policies. As a result, three groups of policies were identified: “organized”, “independent” and “unmanifested”. It was revealed that eight “organized” regions were ahead of other groups in terms of the level of organizational support (by 5 and 7 times), by federal support measures attracted (by 1.5 and 1.8 times), regulatory legal and strategic support (by 4 % and 8 %) and were approximately on the same level as twenty “independent” regions in terms of the scale of regional support measures. “Organized” regions were 1.4 times ahead of “independent” regions in terms of the number of students, and 1.9 times ahead of “unmanifested” regions. In terms of the number of patent applications “organized” regions were 2.2 and 2.5 times ahead, respectively, and in terms of attendance at cultural institutions 22% and 18%. Modelling showed that government authorities’ support for creative industries at the regional level is facilitated by a combination of innovative and cultural development factors.

Research target: Economics and Management Political Science, International Relations, and Public Administration
Language: Russian
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Keywords: региональная политикаинновационный потенциалregional policycreative industriesкреативные индустрииStrategic planningcultural industrieshuman potentialcreative clustersкультурный потенциалкультурный секторкреативные кластерыкреативный сектор
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