Generative AI
Generative AI focuses on creating new content, ranging from text and images to code and even simulations of complex systems like fluid flows, primarily using large language models (LLMs) and generative adversarial networks (GANs). Current research emphasizes improving model accuracy, addressing biases and ethical concerns, and exploring effective human-AI collaboration in diverse applications like education, healthcare, and software development. This rapidly evolving field holds significant potential to accelerate scientific discovery and transform various industries by automating tasks, generating insights from large datasets, and personalizing services.
Papers
How to Strategize Human Content Creation in the Era of GenAI?
Seyed A. Esmaeili, Kshipra Bhawalkar, Zhe Feng, Di Wang, Haifeng Xu
LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment
Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick Schramowski
Generative AI Models: Opportunities and Risks for Industry and Authorities
Tobias Alt, Andrea Ibisch, Clemens Meiser, Anna Wilhelm, Raphael Zimmer, Christian Berghoff, Christoph Droste, Jens Karschau, Friederike Laus, Rainer Plaga, Carola Plesch, Britta Sennewald, Thomas Thaeren, Kristina Unverricht, Steffen Waurick
Morescient GAI for Software Engineering
Marcus Kessel, Colin Atkinson
Evaluating and Mitigating IP Infringement in Visual Generative AI
Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan, Lingjuan Lyu