AI workflows can improve image-based quality control of agricultural crops

The joint project between DTU and FOSS focuses on how explainable AI can be used for quality control of biological materials – for example, grain, which varies greatly in appearance. The result is not only new insights but also a workflow that can help others develop explainable AI models for biological materials.

PERSIST: Autonomous Drones to protect Denmark’s Infrastructure

The PERSIST project aims to demonstrate how cutting-edge AI can be turned into practical solutions that protect people, the environment, and critical infrastructure while reducing costs, enhancing safety, and supporting a greener, more resilient energy supply.

REINS: Adaptive AI for Industry – Without the Cloud

The REINS project unites B&O, Leica Geosystems, the Alexandra Institute, DTU, and SDU in a shared mission: to develop AI that runs efficiently on devices that operate in dynamic environments with very limited computing power. The result is technology that saves energy, responds instantly, and can operate anywhere.

MOTUS: Safe and Responsible Co-bots in Healthcare and Industry

Many researchers are working on bringing advanced AI capabilities to co-bots. However, AI is often unaware of its own mistakes. When the AI is simply ChatGPT writing an e-mail, at worst, its mistake will mean that e-mail makes no sense. But if the AI controls a co-bot, its malfunction can injure workers or destroy the robot’s surroundings. 

GREENSQL: Green digitalization starts in the database

Databases track information, move it back and forth, and ensure seamless integration across systems — but all of this consumes energy. A lot of energy. What if it could be done more efficiently? This question is central to this project. By optimizing code and databases with AI, GREENSQL has the potential to drastically reduce energy consumption.

1813AI: Responsible AI for the Emergency Hotline

1813AI focuses on citizens with injuries and is developing a citizen-facing, adaptive AI chat solution that, through a new self-service app, will provide guidance and retrieve information during wait time. This creates faster access to help, reduces staff stress, and contributes to a more equitable and efficient healthcare service.

FAIRFM: Towards equal access to ultrasound scans

The quality of a fetal scan depends on the operator’s experience and the mother’s individual physiology, meaning some women receive more accurate assessments than others. The FairFM project aims to eliminate this disparity by developing AI that detects and corrects biases, ensuring all pregnant women have equal access to early and accurate fetal diagnostics.