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
Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication
John R. Lawson, Joseph E. Trujillo-Falcón, David M. Schultz, Montgomery L. Flora, Kevin H. Goebbert, Seth N. Lyman, Corey K. Potvin, Adam J. Stepanek
AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images
Jonas Ricker, Dennis Assenmacher, Thorsten Holz, Asja Fischer, Erwin Quiring
U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI
Tanja Šarčević, Alicja Karlowicz, Rudolf Mayer, Ricardo Baeza-Yates, Andreas Rauber
An Economic Solution to Copyright Challenges of Generative AI
Jiachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe, Boaz Barak, Weijie J. Su
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding
Zezhong Fan, Xiaohan Li, Chenhao Fang, Topojoy Biswas, Kaushiki Nag, Jianpeng Xu, Kannan Achan
Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi, Aneesha Bakharia, Shazia Sadiq, Dragan Gasevic, Paul Denny, Hassan Khosravi