The two-day program showcases recent research on the interface between mathematics and AI. There will be plenty of time for informal discussions, as well as strategic sessions. The second AIM workshop is organized by the Dutch Mathematics Clusters and its goal is to highlight the role of mathematics as a key enabling technology within the emerging field of machine learning in science and bring together researchers across mathematics and connected to computer science.

Topics

This workshop AIMs to connect the mathematics cluster. The topics are the following:

• Geometric Deep Learning (session chairs: Remco Duits (TU/e, NDNS+), Bram Mesland (LEI, GQT))
  
• Learned Optimization session chairs: Barbara Franci (UM, STAR), Julia Olkhovskaya (VU, DIAMANT)
• Robust AI (session chairs: Tim van Erven (UvA, STAR), Sophie Langer (UT, STAR))
  
• Scientific Machine Learning (chairs: Benjamin Sanderse (CWI), Tristan van Leeuwen (CWI))

Program and Details

The program and more details are available here. 

Registration

You can register directly via the AIM2023 website at UT.

Background

Artificial Intelligence (AI) will have a growing impact on all sciences and business sectors, our private lives, and society as a whole. It is pre-eminently a multi-disciplinary technology that connects scientists from a wide variety of research areas, from behavioral science and ethics to mathematics and computer science. Without downplaying the importance of its interdisciplinary nature, it is apparent that mathematics can and should play an active role.
As Robbert Dijkgraaf observed in NRC in May 2019: ”Artificial intelligence is in its adolescent phase, characterized by trial and error, self-aggrandizement, credulity and lack of systematic understanding”. Mathematics can contribute to this much-needed systematic understanding of AI and at the same time lay the groundwork for further improvements.