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AI - Machine Learning, Computer Vision, Natural Language Processing

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.

Projects

Bridge project

Deep Learning and Automation of Image-Based Quality of Seeds and Grains

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 industrial partners, 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. 

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Bridge project

Verifiable and Safe AI for Autonomous Systems

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.

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Bridge project

Edge-based AI Systems for Predictive Maintenance

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.

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Bridge project

Embedded AI

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.

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SciTech project

Privacy and Machine Learning

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. This project aims to develop AI methods and tools that enable secure and privacy-preserving use of sensitive data for machine learning. The goal is to address the lack of trust in cloud service providers and the risk of data breaches, while still enabling the use of analytical tools. 

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Bridge project

AI and Blockchains for Complex Business Processes

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. 

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Bridge project

Business Transformation and Organisational AI-based Decision Making

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. 

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Bridge project

Multimodal Data Processing of Earth Observation Data

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.

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SciTech project

Machine Learning Algorithms Generalisation

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.

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Bridge project

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

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.

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Workstream manager

Mads Nielsen

Professor

University of Copenhagen
Department of Computer Science
E: madsn@di.ku.dk
T: +45 24 60 05 99​

Contributing researchers

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Aalborg University
Department of Computer Science

Technical University of Denmark
DTU Compute

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

Roskilde University
Department of People and Technology