We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data.
See https://arxiv.org/abs/2204.00607 .
Julius von Kügelgen is a PhD student in the Cambridge-Tübingen program, supervised by Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems and by Adrian Weller at the University of Cambridge. His research interests lie at the intersection of causal inference and machine learning, including causal reasoning for explainability, recourse and fairness; causal discovery; and identifiable causal representation learning. He has interned at Amazon (2019 - 2021) and was awarded a Google PhD Fellowship in Machine Learning in 2022. Previously, he studied Mathematics (BSc+MSci) at Imperial College London and Artificial Intelligence (MSc) at UPC Barcelona in Spain and at TU Delft in the Netherlands.
See https://sites.google.com/view/julius-von-kuegelgen/home .
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