Working remotely and in hybrid work arrangements is the future. This research is focused on developing the “Futures of Hybrid work.” We will design and develop artefacts and processes to support organizations in exploring and preparing for successful collaboration in the future. In particular we will focus on the role and representation of embodiment, artefact interaction, and physical surroundings in a digital/analog setting.
The centre operates with SciTech projects, which are strategic research projects with the purpose of building up research and education capacity at the universities. Bridge projects, which are joint research / development / innovation projects between universities, companies, the public sector and GTS institutes with the aim of increasing the capacity within digitization and innovation in companies. Explore projects, which are small agile research projects with the purpose to quickly screen new ideas, and Educational projects, the aim of which is to support the master and PhD capacity building.
When developing novel IoT services or products today, it is essential to consider the potential security implications of the system and to take those into account before deployment. Due to the criticality and widespread deployment of many IoT systems, the need for security in these systems has even been recognised at the government and legislative level, e.g., in the US and the UK, resulting in proposed legislation to enforce at least a minimum of security consideration in deployed IoT products.
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.
In this project, we will combine knowledge and experience in human-robot interaction, AI for multi-robot control, and business innovation to develop novel methods for human-swarm interaction. The methods will be evaluated in concrete case studies directly relevant to the industrial partners and their target markets.
Most research in medical AI never makes it to the clinic. We aim to create more clinically useful AI and increase technology acceptance among clinicians by establishing Human-AI collaboration as a target that can be optimized similarly to predictive performance. In terms of explainable AI, this deﬁnes a shift from researching what we can explain to also researching how we explain it well.
Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning, Business Process Management and Electronic Case Management systems, put into use with the aim to improve efficiency of the business processes.
Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning, Business Process Management and Electronic Case Management systems, put into use with the aim to improve efficiency of the business processes. Recently, also blockchain technologies are being proposed as a means to provide guarantees for security, computational integrity and pseudonymous agency.
The mobility of people and things is an important societal process that facilitates and affects the lives of most people. Thus, society, including industry, has a substantial interest in well-functioning outdoor and indoor mobility infrastructures that are efficient, predictable, environmentally friendly, and safe.
Today, the 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. In order to improve performance, robustness and consistency of this inspection, there is a need for automated imaging-based solutions to replace subjective manual inspection.
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 resulting need for verification against potentially fatal accidents is self-evident and of key importance. Most recently, in the EU White Paper: “On Artificial Intelligence – A European approach to excellence and trust” (February 2020) the safety risks that come with usage of AI are stipulated:
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. For instance, consider a consortium of banks that want to improve fraud detection by pooling their customers’ payment data and merge these with data from, e.g., Statistics Denmark.
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.
The challenge to the research community is how to extend existing verification technologies to cope with software systems comprising AI components. This is an unchartered territory and one of the most pressing research challenges in AI. The industrial importance of this topic is closely related to the question of liability in case of malfunctioning products. Over a 4-month period the explore project will provide a state-of-the-art survey and identify research directions to be followed.
Artificial Intelligence brings the promise of technological means to solve problems that previously were assumed to require human intelligence, and ultimately provide human-centered solutions that are both more effective and of higher quality in a synergy between the human and the AI system than solutions that are provided by humans or by an AI system alone.
The mix of students in digital technology is low in diversity (e.g. female students). This is a problem on a societal level which also impacts the study environment.
Teaching quality and student feedback is negatively impacted by lack of teachers and many students. There is a need to consider how learning technologies can help improve teaching quality and student feedback both in physical and digital learning environments.