Artificial intelligence is currently leading to one breakthrough after the other, both in public life with, for instance, autonomous driving and speech recognition, and in the sciences in areas such as medical imaging or molecular dynamics. However, one current major drawback worldwide, in particular, in light of regulations such as the EU AI Act and the G7 Hiroshima AI Process, is the lack of reliability of such methodologies.
In this lecture, we will provide an introduction into this vibrant research area, focussing specifically on deep neural networks. We will then survey recent advances, in particular, concerning performance guarantees and explainability approaches. Finally, we will discuss fundamental limitations of the training phase of deep neural networks on classical hardware, and reveal a surprising connection to novel computing approaches such as neuromorphic computing and quantum computing.
Personal website of Gitta Kutyniok