The conference brings together researchers, innovators, and entrepreneurs from the humanities, natural sciences, and technical disciplines to explore how artificial intelligence is developed, applied, and shared. The Kolleg highlights the importance of including diverse languages, especially minority languages, in AI, and examines how data choices influence meaning, translation, and social fairness.
It also looks at the scientific ideas behind AI, from mathematics and physics to computational modeling, showing how these fields shape the way intelligent systems work. In the natural sciences, the conference explores how machine learning is changing biology, chemistry, and ecology, opening new ways to understand life and complex systems.
The Kolleg also addresses ethical, educational, and societal challenges, from unequal access and representation to preparing the next generation of AI researchers. Together, these perspectives offer a space for thinking about AI that is inclusive, scientifically informed, and socially responsible.
In view of the recent interest of Alexander von Humboldt Foundation in promoting international talent and social entrepreneurship, a roundtable will also take place during the Kolleg. This session will bring together discussions on building innovation ecosystems, responsible AI products, and practical ways to turn research into technologies that benefit society. The Kolleg also features a plenary session on Alexander von Humboldt Foundation sponsorship programmes, as well as other opportunities for research funding.
Sessions:
**1. Minority Languages and AI: Building Datasets for a Multilingual Future**
Creating, curating, and validating datasets for low-resource languages.
Exploring translation mismatches, cultural meaning, and linguistic representation.
Ethical implications of training data imbalance and its social consequences.
**2. Foundations of Intelligence: Mathematics, Physics, and the Architecture of AI**
How fundamental scientific principles shape the theory and design of intelligent systems
Mathematical structures underlying AI algorithms, learning theory, and optimization
Physics-inspired models for complexity, emergence, and systems behavior in AI
Cross-disciplinary insights from mathematical modeling, statistical mechanics, and dynamical systems
**3. AI in the Natural Sciences: Data-Driven Insights Across Biology, Chemistry, and Ecology**
Leveraging machine learning for molecular modeling, biomolecular interactions, and ecological forecasting.
Connecting biological and chemical data to complex system modeling.
Highlighting interdisciplinary advances at the frontier of computational science.
How artificial intelligence reshapes our understanding of life and ecosystems.
Bridging biological intuition and computational prediction.
Toward a new synthesis of data, nature, and intelligence.
Applications of AI in life sciences — from protein modeling to ecological data.
**4. AI, Inequality, and Futures of Learning: Ethics, Culture, and Capacity Building**
How digital divides, cultural representation, and education shape an inclusive AI future
Inequalities in data access, infrastructure, and linguistic representation
Ethical challenges in generative AI for underrepresented communities
AI’s impact on cultural identity, creativity, and societal imagination
Education, capacity building, and preparing the next generation of AI innovators
Building sustainable ecosystems for responsible and inclusive AI
