Sandra Goettisheim, KIT

Artificial intelligence (AI) can play a key role in the energy transition. To ensure its reliable application, its methods must be transparent and comprehensible. This is the goal pursued by the Helmholtz Investigator Group “Data-Driven Analysis of Complex Systems” (DRACOS), led by Tenure-Track Professor Benjamin Schäfer, which was evaluated and rated by KIT’s Executive Board as “extraordinarily successful.” Schäfer’s goal is to make AI models used to analyze large volumes of data from energy systems more transparent. Until now, it has often been unclear which factors an AI uses to predict household energy consumption.

Klobučar et al.

Inside cells, ribonucleic acids (RNA) and proteins form distinct membrane-less droplets. These biomolecular condensates act as organizational hubs, supporting a wide range of cellular functions from gene regulation to stress responses. By combining experimental analyses with deep learning to determine which RNAs tend to cluster, researchers around Miha Modic identified a previously unknown RNA class active during early development. Their results also provide a conceptual and mechanistic framework to interpret pathogenic condensates in disease.

Markus Götz, KIT

Wildfires, floods and droughts: a new artificial intelligence (AI) from KIT promises to be a game-changer in providing more precise, faster, and energy-efficient predictions of such events. In the "WOW - a World model of Our World" project, researchers will develop an AI world model that combines multiple specialized AI sub-models through shared “latent spaces”. "Modern AI methods can not only cost-effectively imitate physics-based simulations, but can even learn correlations directly from observational data," says project coordinator Peer Nowack. The Carl Zeiss Foundation is funding the project with six million euros.