AI is seen as one of the technologies with most impact on society in the coming years with a potential to boost the Danish GDP with 1.6% annually. Denmark has a unique collection of public and registry data. And as one of the most digitised countries in the world, more digital data are generated every day.
The objectives are to increase the capacity and theoretical knowledge on AI as well as the engineering skills, and to show impact on real world applications working together with start-ups, industry and public institutions. A number of demonstration projects will show the potential and impact.
Obvious synergies with all other disciplines lie in the potential use of machine learning where rule-based decisions have been used earlier. Big data analysis relies on an increasing degree on machine learning, and image and text data make part of big data corpora. Likewise new ML algorithms and proof of their performance can find input from for example the Efficient Algorithms and Data Structures WS4.
Human computer interfaces and information visualisation exploit machine learning and insight into AI systems relies on the development on advanced information visualisation. Likewise, cyber physical systems, autonomous systems use machine learning and IoT make sensor systems for AI. Verification and cybersecurity rely on AI to an increasing degree. Dually, it is also promising with an emerging focus on robustness verification of deep neural networks.
Embedded AI will revert the current AI processing flow from collecting data at the edge and processing it at the cloud, to a flow where AI algorithms are migrated from the cloud to a distributed network of AI enabled edge-devices, which will increase responsiveness and functionality, reduced data transfer, and increased resilience, security, and privacy.
There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service providers and there is a risk of data breaches. A lot of data are private and stored locally for good reasons, but combining the information in a global machine learning system could lead to services that benefit all.
Downtime of equipment is costly and a source of safety, security and legal issues. Today, organisations adopt a conservative schedule of preventive maintenance independent of the condition of equipment.
Professor
University of Copenhagen
Department of Computer Science
E: madsn@di.ku.dk
T: +45 24 60 05 99
Professor
University of Southern Denmark
The Maersk Mc-Kinney Moller Institute
Professor
Aalborg University
Department of Computer Science
Professor
Technical University of Denmark
DTU Compute
Professor
University of Southern Denmark
The Maersk Mc-Kinney Moller Institute
Associate Professor
Copenhagen Business School
Department of Digitalization
Professor
Roskilde University
Department of People and Technology
The aim of DIREC is to expand the capacity within research, innovation and education in digital technologies in Denmark. In addition, DIREC shall contribute to the competitiveness of Denmark through collaboration with Danish businesses and the public sector on developing new innovative products and services based on the newest digital technologies.
DIREC is partially funded by Innovation Fund Denmark.
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