Is (Artificial) Intelligence Possible Without Mathematics?
The last decade saw spectacular progress in AI applications, with deep learning and neural networks outperforming many traditional approaches. However, the theoretical part of this revolution remains incomplete in many ways, with limited understanding of when and why machine-learning works, how reliable its decisions are, and what its limitations are. Mathematics has historically underpinned most of the progress in science and engineering. How could mathematical methods advance the state of AI and help uncover why it works in some cases and fails in others?
Elena Bunina, General Director, Director of Organizational Development and HR Management, Yandex Russia
Stanislav Smirnov, Fields Laureate; Professor, University of Geneva
Dmitriy Vetrov, Research Professor, National Research University Higher School of Economics; Head of Machine Learning, Artificial Intelligence (AI) Center in Russia, Samsung
Alexander Kraynov, Head of Computer Vision and Artificial Intelligence Technologies, Yandex Group of Companies
Terrence Sejnowski, Professor, Laboratory Head of the Computational Neurobiology Laboratory, Salk Institute for Biological Studies; Distinguished Professor, The Biological Sciences at University of California San Diego
Artem Yamanov, Senior Vice President, Business Development Director, Tinkoff Bank
Front row participants
Arutyun Avetisyan, Director, Institute for System Programming of the Russian Academy of Sciences
Stephen Brobst, Chief Technology Officer, Teradata
Andrey Fursenko, Aide to the President of the Russian Federation