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EXC 2064:  Machine Learning: New Perspectives for Science

Subject Area Computer Science
Term since 2019
Website Homepage
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 390727645
 
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.Our team of principal investigators consists of researchers in machine learning and its applications in various disciplines, including medicine, neuroscience, bioinformatics, vision, cognitive science, physics, geoscience, linguistics and social science, as well as experts in philosophy and ethics. Our cluster will build on the internationally renowned strength of Tübingen as a hub for machine learning as well as on the established excellence in the contributing scientific fields.Machine learning is changing the world, and we want to - and should - take an active role in this process in the area where we are most qualified: in science.
DFG Programme Clusters of Excellence (ExStra)
 
 

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