Tag " artificial intelligence"

The HSE Artificial Intelligence Centre has developed software for modelling radio channels in 5G and 6G wireless networks, based on ray tracing and machine learning techniques. Their software solutions enable modelling radio wave propagation between transmitters and receivers and can convert ray tracing data into a frame sequence format, configure and train neural networks based on this data, and subsequently save the trained models. 

Developments in the field of artificial intelligence are gradually taking over the world. AI has the potential to bring incredible benefits to the global economy and our quality of life, but it also creates new challenges. Panos Pardalos, Professor at the University of Florida, Academic Supervisor of the Laboratory of Algorithms and Technologies for Networks Analysis (Nizhny Novgorod), covered these issues, along with other related topics, in his recent report.

A student of the HSE Faculty of Computer Science, Andrey Kuznetsov, has become the winner of the 2024 Data Fusion Contest. He took first place in solving geoanalytics tasks, and also won the special ‘Companion’ category. The competition took place as part of the 2024 Data Fusion conference on big data and AI technologies. Researchers from HSE University presented the results of their work and demonstrated applied developments at the conference.

The Baltic Federal University (Kaliningrad) recently hosted an International Congress entitled ‘The World Concept of Philosophy’ in honour of the 300th anniversary of the birth of the philosopher and thinker Immanuel Kant. The event brought together about 500 scientists and experts from 23 countries. HSE Rector Nikita Anisimov took part in the opening plenary session of the congress titled ‘Critique of Artificial Intelligence: Being and Cognition in the Context of Artificial Intelligence Development.’

Researchers from the HSE AI Research Centre have built a system for the automated control of manual operations, which finds application in industrial production. The system facilitates the process of monitoring objects and actions, as well as controlling the quality of their execution.

Researchers from HSE University in Nizhny Novgorod, MISIS and the Artificial Intelligence Research Institute (AIRI) have developed an algorithm that selects the best available neural network for facial recognition, taking into account the features of a mobile device. This new approach accelerates the selection of the most suitable neural network and allows the identification of people with an accuracy rate of up to 99%. The study was published in the IEEE Access journal. The source code is available on GitHub.

Today, text bots like ChatGPT are doing many tasks that were originally human work. In our place, they can rewrite ‘War and Peace’ in a Shakespearean style, write a thesis on Ancient Mesopotamia, or create a Valentine’s Day card. But is there any way to identify an AI-generated text and distinguish it from works done by a human being? Can we catch out a robot? The Deputy Head of the HSE School of Data Analysis and Artificial Intelligence, Professor of the HSE Faculty of Computer Science Vasilii Gromov explained the answer in his lecture ‘Catch out a Bot, or the Large-Scale Structure of Natural Intelligence’ for Znanie intellectual society.

Specialists at HSE Campus in Perm and clinicians at Perm City Clinical Hospital No. 4, have been collaborating to develop a neural network capable of determining the root cause of a stroke. This marks the world's first attempt to create such a system, the developers note.

Specialists at the HSE AI Research Centre have developed an AI-powered fact-checking assistant. This software solution will improve the quality of working with information, reduce the risks of errors and biases, and save both time and resources. A notable advantage of the program lies in its capability to process a wide variety of statement types.

Researchers at the HSE University AI Research Centre and Faculty of Computer Science have proposed a novel algorithm for detecting structural changes in time series. The method uses a neural network to compare various segments of a series, enabling rapid detection of changes in its behaviour. The results of their work have been presented at the 26th International Conference on Artificial Intelligence and Statistics— AISTATS (A*).