Excellent Pub Quiz
Fahimi bar Skalitzer Str. 133, Berlin, GermanyDive into the wonderful world of research of the seven Berlin Clusters of Excellence: from literature to chemistry, from politics to AI, you and your team can find answers for
Dive into the wonderful world of research of the seven Berlin Clusters of Excellence: from literature to chemistry, from politics to AI, you and your team can find answers for
More details to follow. Image created with DALL-E by Maria Ott.
As artificial intelligence (AI) reshapes the landscape of scientific discovery, the need for openness, transparency, and reproducibility in research has never been more urgent. The 2025 Berlin Summer School on
This event is for registered attendees. Open data infrastructure refers to the systems, frameworks, and processes put in place to collect, store, manage, and share data generated or held by
Abstract: Since the release of ChatGPT and other generative AI applications, research institutions as well as various stakeholders such as research funders and publishers have been discussing how the use
Abstract: We explore how neuro-symbolic AI, i.e., combining neural networks with symbolic knowledge representation, can drive the next generation of open, transparent, and responsible scientific research. By combining the adaptability of
Artificial intelligence has advanced rapidly through larger and deeper neural networks, yet fundamental questions remain about how to optimize network dynamics for performance and adaptability. This study shows that deep
Abstract: Many social species use signals such as vocalizations to coordinate a range of group behaviors, from coming to consensus on where to move to banding together against threats. Despite
Dive into the wonderful world of research of the seven Berlin Clusters of Excellence: from literature to chemistry, from politics to AI, you and your team can find answers for
More details to follow.
More details to follow. Photo by Prince Patel on Unsplash.
Abstract The size and complexity of current Deep Artificial Neural Networks pose remarkable challenges to our attempts of explaining and understanding their workings. In this talk, I put forward a