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.
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.
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.
Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.
Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.
Together with industry, the project aims to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.
Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.
The rapidly growing application of machine learning techniques in cyber-physical systems leads to better solutions and products in terms of adaptability, performance, efficiency, functionality and usability. However, cyber-physical systems are often safety critical, e.g., self-driving cars or medical devices, and the need for verification against potentially fatal accidents is of key importance.
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. This results in unnecessary service costs and occasional interruptions of production due to unexpected failures.
How can we leverage the potential benefits of having multiple robots working in parallel to reduce time to completion? If robots are given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance. Together with industrial partners, this project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems.
AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.
Today, manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. Together with FOSS, this project aims to develop and validate a method of automated imaging-based solutions that can replace subjective manual inspection and improve performance, robustness and consistency of the inspection.
Together with industry, this project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.
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
