The following graphic shows areas of application in which the institute works.
Application Area 1: Bundeswehr (Bw)
Within Bw, AI is increasingly one of the new core competencies. This applies to reconnaissance, cross-TSK data exchange, increasing the autonomy of platforms as well as areas such as training, logistics and medicine. The HSU-AI is already working in naval and reconnaissance (ELINT) projects. A developing focus is AI in the leadership process. Another focus is AI in and for the training of Bw members.
Application Area 2: Public Service, Offices and Administration
Not only in the area of Bw, but also for public services, offices and administration in general, the increasing integration of AI systems is accompanied by numerous questions and challenges that require the most holistic consideration and support possible. Due to its interdisciplinary orientation, the HSU-AI offers such support, which, in addition to technical questions, also includes, for example, the area of ethics, the political or socio-technical consequences of AI systems. Knowledge of such relationships will be indispensable in the future, especially for managers.
Application Area 3: Social Groups
In addition to concrete application or development scenarios, the HSU-AI also deals with the question of how knowledge about and design of AI systems can be thought of more strongly in terms of society as a whole and how participation processes can be supported. Examples here are the area of political disinformation or competence development approaches in the education sector.
Application Area 4: Cyber-Physical Systems (CPS) and Engineering Applications
This focus focuses on the increasing penetration of products, vehicles, machines and buildings with sensors and information processing as well as on the resulting possibilities for monitoring the systems as well as for generating and constantly updating digital models with high innovation potential. In particular, artificial intelligence (AI) is used to evaluate the large amounts of data collected with the sensor technology in a targeted manner and to optimize the systems in operation or to ensure their safe and correct operation.
Application Area 5: Medicine and Health
The question of how AI can influence healthcare in the areas of diagnosis, prognosis and prevention is becoming increasingly relevant. By analyzing large amounts of image and laboratory data, as well as applying complex algorithms, AI can help detect diseases earlier, create personalized treatment plans, and predict future health risks. AI plays a crucial role in diagnostics by helping doctors interpret medical data (e.g., X-rays, MRIs, and CT scans). Machine learning can be used to detect anomalies and patterns that are indicative of certain diseases, allowing them to be diagnosed early. In terms of prognosis, AI methods are used to predict the course of diseases and support treatment decisions. By analyzing patient data and medical trajectories, models can be created that can predict the likelihood of complications or relapses. Prevention is supported by AI-personalized health care by analyzing individual risk factors and recommending preventive measures. By processing patient data such as genetic information, medical histories, and lifestyle factors, AI can help healthcare providers develop preventive strategies tailored to the specific needs of each individual.
Application Area 6: Material Design
The development of new materials, such as those used in hydrogen storage or battery production, is unfortunately complex and time-consuming work. New materials are first analyzed in complex simulations, usually followed by expensive experiments. As a result, only a few new material configurations can be tried out. AI can help here by using neural networks to pre-evaluate new configurations based on data and neural networks, thus identifying promising candidates more quickly.
Letzte Änderung: 27. June 2024