Project Exzellenzcluster 2064 "Maschinelles Lernen: Neue Perspektiven für die Wissenschaft"

Basic data

Title:
Exzellenzcluster 2064 "Maschinelles Lernen: Neue Perspektiven für die Wissenschaft"
Duration:
01/01/2019 to 31/12/2025
Abstract / short description:
The rise of “intelligent” technology is transforming engineering, industry and the economy at an increasing pace and on an unprecedented scale. At the core of this revolution are breakthroughs in the field of machine learning which allow machines to perform tasks that, until recently, could only be performed by humans. Less prominently discussed, developments in machine learning have the potential to transform science at an equally fundamental level. While machine learning methods have been used in the past to tackle isolated prediction problems, recent breakthroughs open up an exciting new opportunity: Automated inference methods will become increasingly useful in the process of scientific discovery itself, supporting scientists in identifying which hypotheses to test, which experiments to perform, and how to extract principles describing a broad range of phenomena.

The aim of this cluster is to enable machine learning to take a central role in all aspects of scientific discovery and to understand how such a transformation will impact the scientific approach as a whole. To this end, a substantial research effort is required in the field of machine learning itself.
In the cluster, we are going to target the following four research areas:
A Beyond prediction, towards understanding: We will design algorithms that reveal complex structure and causal relationships from data in order to integrate machine learning into the scientific discovery process.
B Managing uncertainty: We will develop tools to estimate and handle the uncertainty in data-driven scientific models and algorithms, and exploit this information for experimental design.
C Interface between algorithms and scientists: We will develop techniques to allow scientists to understand and control all stages of the machine learning process in the scientific discovery pipeline.
D Philosophy and ethics of machine learning in science: The fact that machine learning algorithms will play a central role in the process of scientific discovery challenges our traditional understanding of the scientific process and raises fundamental questions about concepts of scientific discovery and the role of the scientists. We will study these questions from the perspective of philosophy and ethics of science.

Involved staff

Managers

Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Research Center for Ophthalmology
Center for Ophthalmology, Hospitals and clinical institutes, Faculty of Medicine

Contact persons

International Center for Ethics in the Sciences and Humanities (IZEW)
Centers
Faculty of Science
University of Tübingen
Institute for Theoretical Physics (ITP)
Department of Physics, Faculty of Science
Institute of Linguistics (SfS)
Department of Modern Languages, Faculty of Humanities
Faculty of Science
University of Tübingen
Institute for Theoretical Physics (ITP)
Department of Physics, Faculty of Science
Faculty of Science
University of Tübingen
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Department of Informatics
Faculty of Science
Faculty of Science
University of Tübingen
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Center for Bioinformatics (ZBIT)
Centers
Quantitative Biology Center (QBIC)
Centers
Faculty of Science
University of Tübingen
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Faculty of Science
University of Tübingen
Department of Informatics
Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Faculty of Science
University of Tübingen
Department of Geoscience
Faculty of Science
Geography Research Area
Department of Geoscience, Faculty of Science
Faculty of Science
University of Tübingen
Department of Psychology
Faculty of Science
Institute of Psychology
Department of Psychology, Faculty of Science
Knowledge Media Research Center (IWM)
Other institutions
Faculty of Science
University of Tübingen
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Faculty of Science
University of Tübingen
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science

Local organizational units

Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics
Faculty of Science

Funders

Bonn, Nordrhein-Westfalen, Germany
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