DIREC has launced a number of projects revolving around the future of work. The projects involve different aspects of AI, robots and hybrid work and will help develop the underlying technology that enables the modern workplace and provide us with new knowledge about the space of opportunity that opens up based on new advanced digital technology.
New AI models, such as chat robots and image, text and video generation, have shown us the opportunities offered by AI. Robots are slowly improving and, especially when combined with new AI solutions, they are able to handle an increasingly number of tasks.
Advanced digital technology can create great opportunities for Danish companies, unlock the competitive advantage, and improve collaboration, productivity, efficiency and innovation.
But how will the next generation of algorithms affect the way we work? How will the interaction between robots, humans and AI systems affect the workplace? How can Danish companies exploit the technology and what are the pitfalls?
AI technology can provide benefits for companies, but a crucial factor is that the system can be trusted to behave in a certain way. One way of creating trust is to be able to explain why the algorithms have chosen or recommended a certain action. This is called Explainable AI. Another way is to create safeguards that allow the AI only to act within certain constraints.
Explore the DIREC projects addressing this challenge.
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.
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.
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.
There are a multitude of reasons to embrace remote and hybrid work. Climate concerns are increasing, borders are difficult to cross, work/life balance may be easier to attain, new opportunities such as live translation, automatic transcription and summary of meetings, to name a few.
However, the increase of hybrid work does also have challenges and the problem of embodied presence remains a stubborn limitation. How can we develop the next generation of tools to support hybrid work?
Explore the DIREC project adressing this challenge.
Remote and hybrid work will certainly be part of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and speculate a different kind of workplace future? Together with companies, this project seeks a vision of the future that integrates hybrid work experiences with grace and decency.
With the slow adoption of robot technologies, a burning question is how can we easily program robots to support us in achieving our business goals without us being an expert engineer in robotics? How can we develop user interfaces that allow everybody to program robots? Or how do we control multiple robots working in e.g. a warehouse or factory?
Explore the DIREC projects addressing these challenges.
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.
A robot database with information on previous robot solutions can save manufacturing companies time and money and allow for smaller-scale companies to automate their production as well. This is the conclusion of the ReRoPro project. Although it sounds simple, there are several challenges involved with creating a robot database. With input from industry and international experts, the researchers have now gained a much better understanding of the challenges.
Today, robots and drones take on an increasingly broad set of tasks. However, such robots are limited in their capacity to cooperate with one another and with humans. 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.
The amount of mobility-related data has increased massively which enables an increasingly wide range of analyses. When combined with digital representations of road networks and building interiors, this data holds the potential for enabling a more fine-grained understanding of mobility and for enabling more efficient, predictable, and environmentally friendly mobility.
Physical products are increasingly getting a digital layer that can provide new functionalities, collect data and allow for easily monitoring and maintenance of physical products.
These devices do often have limited computing resources so how can we use advanced AI-algorithms on these devices? What is the best way to create software for these devices? And how do we ensure that they are secure?
Explore the DIREC projects addresssing these challenges.
A recurring problem of digitalised industries is to design and coordinate hybrid systems that include IoT (Internet of Things), edge, and cloud solutions. Currently adopted methods and tools are not effective to this end, because they rely too much on informal specifications that are manually written and interpreted by humans.
This project aims to identify safety and security requirements for IoT systems and develop algorithms for quantitative risk assessment and decision-making. The aim is furthermore to create tools for designing and certifying IoT security training programs that will enable Danish companies to obtain security certification for their IoT devices, thus giving them a lead in a market that is likely to demand such certification in the near future.
AI currently relies on large data centers and centralized systems, necessitating data movement to algorithms. To address this limitation, AI is evolving towards a decentralized network of devices, bringing algorithms directly to the data. This shift, enabled by algorithmic agility and autonomous data discovery, will reduce the need for high-bandwidth connectivity and enhance data security and privacy, facilitating real-time edge learning.
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.
Businesses are creating a huge amount of data about their business processes. How can businesses better make use of these data and how can we develop innovative AI-solutions that use these data?
Explore the DIREC projects addressing these challenges.
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 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.
