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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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?

О построении нейросетевого агрегатора лингвистических оценок

С. 44–45.
Demidovskij A.

The construction of integrated sub-symbolic systems is an important scientific task. The implementation of symbolic rules, such as the aggregation of linguistic assessments in decision-making, played a vital role in creating such systems in the form of neural network architectures. The pre-clade discusses the idea of ​ ​ combining symbolic and sub-symbolic calculation levels for multi-criterion decision-making problems. In particular, the evaluation aggregation algorithm's input is represented as structures, and the aggregation operator is represented as operations on these structures. Existing neural primitives are considered, the combination of which will allow expressing the aggregation algorithm fully. The partial implementation of the proposed approach was carried out using the Keras software framework.

Language: Russian
Full text
Keywords: artificial neural networksискусственные нейронные сетиdistributed representationssymbolic structuresраспределённые представлениясимволические структуры

In book

25 Нижегородская сессия молодых ученых (технические, естественные, гуманитарные науки): материалы тезисов и докладов
Н. Новгород: НРЛ, 2020.
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