“The bottleneck that all teams have to navigate”

Interview “The bottleneck that all teams have to navigate” The Helmholtz Foundation Model Initiative (HFMI), launched two years ago, supports AI projects focused on processing enormous amounts of data. In this interview, AI expert Dagmar Kainmueller offers an interim assessment—including her own HFMI project, AqQua. Ms. Kainmueller, two years ago, funding began for the first projects under the Helmholtz Foundation Model Initiative (HFMI). What is your interim assessment? In my view, the HFMI is a huge success. In a relatively short time, we have built a highly motivated interdisciplinary community with an extremely steep learning curve. The expertise gained in this way is incredibly valuable and will remain so, even after the project period ends. And it has gained significant international visibility. In what way? One example is the Helmholtz-ELLIS Workshop, which we organized in Berlin in the spring of 2025. ELLIS stands for “European Laboratory for Learning and Intelligent Systems” and is, so to speak, the flagship among European AI research networks. The event was highly productive and brought together representatives from a wide range of scientific disciplines, leading AI researchers, and high-profile speakers from global players such as Meta and Microsoft Research. In addition, the European Commission has cited the HFMI as a case study and now uses it as a reference for its own activities in the field of “Artificial Intelligence (AI) in Science.” In June, we will jointly hold a workshop in Brussels with newly funded EU pilot projects. There is great interest in our experiences and insights. What is the status of the HFMI research projects? The four projects funded from the outset have largely compiled their datasets, trained initial models on them, and are currently working on testing and refining them. A particularly advanced example is the “Human Radiome Project” (THRP), which is developing