Large AI Model
Large AI models, encompassing multimodal and language models, aim to create systems capable of understanding and generating diverse forms of data, including text, images, and video. Current research emphasizes improving model reasoning abilities, particularly in handling ambiguous instructions and complex tasks, as well as developing efficient training methods to reduce computational costs and environmental impact. These advancements are driving significant progress in various fields, including education, healthcare, and weather forecasting, by enabling more powerful and accessible AI applications. However, challenges remain in ensuring model safety, robustness, and equitable performance across different communities.
Papers
Mechanistic understanding and validation of large AI models with SemanticLens
Maximilian Dreyer, Jim Berend, Tobias Labarta, Johanna Vielhaben, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
Kristian G. Barman, Sascha Caron, Emily Sullivan, Henk W. de Regt, Roberto Ruiz de Austri, Mieke Boon, Michael Färber, Stefan Fröse, Faegheh Hasibi, Andreas Ipp, Rukshak Kapoor, Gregor Kasieczka, Daniel Kostić, Michael Krämer, Tobias Golling, Luis G. Lopez, Jesus Marco, Sydney Otten, Pawel Pawlowski, Pietro Vischia, Erik Weber, Christoph Weniger