WG 1: Technological Enablers and Data Science
The greatest progress in Artificial Intelligence has been currently made in the field of machine learning. This new knowledge - in combination with extensive data sets and high computing capacity - has become a key economic factor in various areas of science and industry. Data science comprises the entire process of data management: curating, cleansing, analysing and saving data are part of this special procedure. New forms of learning have evolved over the previous years, such as creating and using new knowledge-based systems, analysing given databases (e.g. for recommendation systems), learning from large data streams and using the acquired knowledge in real time.
Topics and Organisation of the Working Group
The working group examines the technological principles and enablers of self-learning systems. It fulfils a cross-sectoral function for the entire platform and provides key impetus to all the other working groups.
Members of the Working Group
- Prof. Dr. Ulf Brefeld
- Leuphana University Lüneburg
- Dr. Carl-Helmut Coulon
- INVITE GmbH
- Dr. Wolfgang Ecker
- Infineon Technologies AG
- Prof. Dr. Kristian Kersting
- Technical University (TU) of Darmstadt
- Dr. Markus Kohler
- SAP SE
- Prof. Dr. Stefan Kramer
- Johannes Gutenberg University Mainz
- Prof. Dr.-Ing. Alexander Löser
- Beuth University of Applied Sciences Berlin
- Prof. Dr. Klaus-Robert Müller
- Technische Universität Berlin
- Prof. Dr. Erhard Rahm
- Leipzig University
- Prof. Dr. Wolfgang Rosenstiel
- University of Tübingen
- Prof. Dr. Kai-Uwe Sattler
- Ilmenau University of Technology
- Dr. Harald Schöning
- Software AG
- Prof. Dr. Volker Tresp
- Ludwig-Maximilians-Universität München
- Dr. Jilles Vreeken
- Helmholtz Center for Information Security (CISPA) / Max Planck Institute for Informatics
- Prof. Dr.-Ing. Gerhard Weikum
- Max Planck Institute for Informatics
- Prof. Dr. Stefan Wrobel
- Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS
We have received written consent from the listed persons for the publication of their data in accordance with the DSGVO. This list of members is an excerpt and will be completed continuously.
Key Questions for the Working Group
- What are the most important research areas for Artificial Intelligence, Machine Learning and Data Science? Which potential do they hold for disruptive applications?
- What are the strengths and weaknesses within German AI-research?
- How can we improve education for researchers and skilled employees for Machine Learning and Data Science?
- Which research expertise is required for application?
- How can we accelerate the transfer from successful research to application? Which factors could be obstructive?
Results and Contributions of the Working Group
- Machine Learning and Deep Learning
- Year of publication: 2019
- We are living in the golden age of Artificial Intelligence1 (AI). Continuing progress in algorithm development, particularly in Machine Learning2 (ML) and Deep Learning (DL), combined with the availability of huge data sets and advances in rapid, parallel computing have helped deliver breakthroughs in diverse fields of use. Applications that just a few years ago seemed to be plucked from the realms of science fiction are now already – or are soon to become – part of our daily lives. Knowledge of unimaginable breadth and astonishing depth is becoming accessible at the click of a mouse, voice-controlled assistance systems are helping us in many aspects of life, image recognition systems have achieved near-human performance levels, autonomous vehicles are increasingly becoming a reality, business models are changing rapidly and personalised medicine is supporting optimum and personalised treatment.
- Contact: Birgit Obermeier / Linda Treugut
Coordination of the Working Group at the Managing Office: Maximilian Hösl