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.
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 method has the potential of providing the grain industry with a disruptive new tool for ensuring quality and optimising the value of agricultural commodities.
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.
Together with industrial partners, the aim of this project is to develop methods and tools that will enable industry to automatically synthesize correct-by-construction and near-optimal controllers for safety critical systems within a variety of domains.
In the Western world, approx. one in ten medical diagnoses is estimated to be incorrect, which results in the patients not getting the right treatment. The explanation may be lack of experience and training on the part of the medical staff.
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.
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.
The COVID-19 pandemic, and the attendant lockdown, demonstrated the potential benefits and possibilities of remote work practices, as well as the glaring deficiencies such practices bring. ‘Zoom fatigue’, resulting from high cognitive loads and intense amounts of eye contact, is just the tip of an uncomfortable iceberg.
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.
Low-volume production represents a large share of the Danish manufacturing industry. An unmet need in this industry is flexibility and adaptability of manufacturing processes. Existing solutions for automating industrial logistics tasks include combinations of automated storage, conveyor belts, and mobile robots with special loading and unloading docks.
However, these solutions require major investments and are not cost efficient for low-volume production, and today, low-volume production is often labor intensive.
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.
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.
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 projects aims to address multi-robot collaboration and design and evaluate technological solutions that enables users to engage and control autonomous multi-robot systems.
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.
Although it sounds simple, there are several challenges involved with creating a robot database. For example, robot data are complicated as they consist of images, trajectories, force vectors, information on different materials, CAD-files etc.
With input from industry and international experts, this completed project has gained a much better understanding of the challenges. Next step is to delevop software that allows for the reuse of robot data.
Both society and industry have a substantial interest in well-functioning outdoor and indoor mobility infrastructures that are efficient, predictable, environmentally friendly, and safe. For outdoor mobility, reduction of congestion is high on the political agenda as is the reduction of CO2 emissions, as the transportation sector is the second largest in terms of greenhouse gas emissions. For indoor mobility, corridors and elevators represent bottlenecks for mobility in large building complexes.
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.
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.
Therefore, it is imperative in many domains to transition from regular maintenance to predictive maintenance. The report ‘An AI Nation: Harnessing the Opportunity of Artificial Intelligence in Denmark’ estimates that enabling predictive maintenance via AI has a 14-19 billion potential for the private sector in Denmark.
Together with industrial partners, this project aims to uncover how AI and data-driven methods can solve the problem, so that companies have the least possible wasted time when production is at a standstill.
AI is currently limited by the need for massive data centres and centralized architectures, as well as the need to move data to algorithms. To overcome this key limitation, AI needs to evolve from today’s highly structured, controlled, and centralized architecture to a more flexible, adaptive, and distributed network of devices.
Together with industrial partners, this project aims to develop methods and tool to migrate AI algorithms from the cloud to a distributed network of AI-enabled edge-devices. This transformation is enabled by the merging of AI and IoT into “Artificial Intelligence of Things” (AIoT) and has created an emerging sector of embedded AI, where each part of the AI processing is done on the sensor devices at the edge, rather than sent to the cloud.
The expected outcome of the project is an AI framework that supports the autonomous discovery and processing of disparate data from a distributed collection of AI-enabled edge devices.
IoT devices are blending into the infrastructure of both society and our personal lives. However, many of these devices run in uncontrolled, potentially hostile environments, which makes them vulnerable to security attacks.
Moreover, with the increasing number of safety critical IoT devices, such as medical and industrial IoT devices, IoT security is a public safety issue.
Together with industrial partners, this project aims to develop a modelling formalism with automated tool support, for performing risk assessments and allowing for extensive “what‐if” scenario analysis.
A recurring problem of digitalised industries is to design and coordinate hybrid systems that include IoT, 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.
Together with industrial partners, this project aims to explore the applicability of forefront technologies and methods for the design of reactive hybrid IoT-edge-cloud architectures in industry.
These technologies are based on unambiguous formal languages, which can be processed by computers to check for desirable design properties and to deploy components for monitoring the correct functioning of systems. Adopting these techniques has shown to substantially increase the productivity of digital industries (for example, up to 4x increase in development speed)
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.
Today, business processes in private companies and public organizations are widely supported by Enterprise Resource Planning, Business Process Management, and Electronic Case Management systems to improve the efficiency of the processes.
The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors, and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on companies and organizations.
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 to tools for the use of process insights for business intelligence and transformation.
Today, business processes in private companies and public organisations are 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.
The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on the companies and organisations.
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.
