Generative AI Model
Generative AI models are computational systems designed to create new content, such as text, images, and audio, by learning patterns from existing data. Current research emphasizes improving efficiency and scalability of these models, particularly focusing on architectures like transformers and diffusion models, and addressing challenges like bias mitigation, data security, and responsible AI practices. The widespread adoption of generative AI across diverse fields, from medicine and law to art and entertainment, necessitates rigorous research into its capabilities, limitations, and societal impact.
Papers
Exploring compressibility of transformer based text-to-music (TTM) models
Vasileios Moschopoulos, Thanasis Kotsiopoulos, Pablo Peso Parada, Konstantinos Nikiforidis, Alexandros Stergiadis, Gerasimos Papakostas, Md Asif Jalal, Jisi Zhang, Anastasios Drosou, Karthikeyan Saravanan
Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models
Daniel Lopez-Martinez
RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection
Liting Huang, Zhihao Zhang, Yiran Zhang, Xiyue Zhou, Shoujin Wang
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
Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement
Haowen Xu, Femi Omitaomu, Soheil Sabri, Sisi Zlatanova, Xiao Li, Yongze Song
Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts
S. Mostafa Mousavi, Marc Stogaitis, Tajinder Gadh, Richard M Allen, Alexei Barski, Robert Bosch, Patrick Robertson, Nivetha Thiruverahan, Youngmin Cho, Aman Raj