Generative Artificial Intelligence
Generative Artificial Intelligence (GenAI) focuses on creating new data samples—text, images, code, etc.—from existing datasets using deep learning models. Current research emphasizes diverse applications, including drug discovery, education, and industrial processes, with a focus on model architectures like transformers, diffusion models, and generative adversarial networks (GANs). The field's significance lies in its potential to automate complex tasks, accelerate scientific discovery, and reshape various industries, while also raising important ethical considerations regarding bias, data privacy, and the responsible use of AI.
Papers
Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency
Xijun Wang, Dongshan Ye, Chenyuan Feng, Howard H. Yang, Xiang Chen, Tony Q. S. Quek
Generative Design of Periodic Orbits in the Restricted Three-Body Problem
Alvaro Francisco Gil, Walther Litteri, Victor Rodriguez-Fernandez, David Camacho, Massimiliano Vasile
Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
Jinbo Wen, Jiawen Kang, Dusit Niyato, Yang Zhang, Shiwen Mao
The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes
Jasper Roe, Mike Perkins, Yulia Tregubova
Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives
Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat
Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach
Sourojit Ghosh, Pranav Narayanan Venkit, Sanjana Gautam, Shomir Wilson, Aylin Caliskan