Global AI Trends Discussed at International Foresight Workshop at HSE University
At an international foresight workshop on artificial intelligence held at HSE University, Russian and foreign scholars discussed the trends and challenges arising from the rapid development of AI.
The discussion centred on three key foresight areas: architectures, machine learning algorithms, and optimisation and mathematics; fundamental and generative models; governance, decision-making, and agent-based (multi-agent) models.
The event was organised by the HSE AI and Digital Science Institute, led by Director Alexey Naumov. Leading researchers from India, the UAE, and Germany took part in the workshop, together with heads of laboratories from the institute and other HSE divisions.
Foresight is a field of future studies that helps identify priorities for social, economic, and technological development, noted Alexander Sokolov, Director of the Educational Research and Educational Foresight Centre and Deputy Director of the HSE Institute for Statistical Studies and Economics of Knowledge (ISSEK), opening the discussion. ‘Foresight allows us to define the main challenges that are important in the global world. We see what changes are coming, which trends they will shape, how markets will evolve, and what technologies will be required for success,’ he said.
This year, ISSEK identified key trends in the development and dissemination of AI technologies, and this work is currently being updated. As part of this process, experts are taking part in interviews, foresight seminars, and data validation. The aim is to determine promising avenues for further AI research. A detailed report on the subject will be published at the end of 2025, Alexander Sokolov noted, inviting experts to contribute to its preparation.
Several speakers addressed machine learning, algorithms, and optimisation during the session. Among them was Maxim Rakhuba, Associate Professor at the HSE Faculty of Computer Science. ‘The high cost of training large neural network models makes the speed of this process a truly important parameter. Several promising methods were published last year, and I believe we can expect a rethinking and significant progress in this area in the near future,’ he said.
Aparajita Ojha, professor at the Indian Institute of Information Technology, Design, and Manufacturing, talked about the future of medical image analysis using generative technologies. Multimodal generative AI, she explained, accelerates the analysis of medical images. However, the technology is still at an early stage. ‘Neural networks are not making use of different methods, and some approaches are simply being ignored,’ she noted.
Denis Derkach, Head of the HSE Laboratory of Methods for Big Data Analysis, stressed that hybridisation in AI would soon be essential. This involves a comprehensive approach where different AI methods and technologies are combined with classical models to create systems that are more powerful and flexible than solutions based on a single methodology.
Incorporating scientific knowledge into AI models is one of the key ways of building reliable models in science, where data is always limited. At the same time, scientific knowledge can be expressed not only in mathematical formulae but also, for example, in information about which system parameters influence a response, said Michael Medvedev, Associate Professor at the HSE Joint Department of Organic Chemistry with the RAS Zelinsky Institute of Organic Chemistry.
The international foresight workshop at HSE University was organised in accordance with the decree of Russian President Vladimir Putin to hold a strategic session (an international foresight) in Russia on fundamental and exploratory research aimed at the further development of artificial intelligence.
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