A multimodal neural network model by Sber, under the supervision of HSE University’s expert commission, has successfully passed the Unified State Exam in social studies. GigaChat completed all exam tasks and scored 67 points.
Top development teams around the world are trying to create a neural network similar to a curious but bored three-year-old kid. IQ.HSE shares why this approach is necessary and how such methods can bring us closer to creating strong artificial intelligence.
A team of researchers from HSE University, jointly with the Yandex School of Data Analysis and Yandex Cloud, have developed a neural network for anticipating El Niño climate anomalies. The new algorithm enables more precise predictions of changes in the average surface temperature of oceanic waters that can trigger natural disasters in specific regions of the world. At present, the model is capable of predicting El Niño events one and a half years in advance, and the researchers are working towards extending the forecast period to two years.
On September 4, the HSE University building on Pokrovsky Bulvar hosted ARTificial Fest, an event devoted to neural network art. The festival was organised by the HSE University Faculty of Creative Industries, the HSE Career centre, and the Chisty List (‘Blank Page’) student organisation. The event was open not only to students and staff of HSE University, but also to anyone interested in the blending of machine algorithms and art.
A scientist from HSE University has developed an image recognition algorithm that works 40% faster than analogues. It can speed up real-time processing of video-based image recognition systems. The results of the study have been published in the journal Information Sciences.
Researchers Teach Neural Networks to Recognize Similar Objects on Videos without Accuracy Degradation
Andrey Savchenko, Professor at HSE University, has developed a method that can help to enhance image identification on videos. In his project, a network was taught by a new algorithm and can now make decisions on image recognition and classification 10 times faster than before.