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
BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI
Shwetha Rajaram, Nels Numan, Balasaravanan Thoravi Kumaravel, Nicolai Marquardt, Andrew D. Wilson
Diffusion Model for Data-Driven Black-Box Optimization
Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang
SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores
Vidminas Vizgirda, Rui Zhao, Naman Goel
Autonomous Monitoring of Pharmaceutical R&D Laboratories with 6 Axis Arm Equipped Quadruped Robot and Generative AI: A Preliminary Study
Shunichi Hato, Nozomi Ogawa
Foundation Models and Information Retrieval in Digital Pathology
H. R. Tizhoosh
ARtVista: Gateway To Empower Anyone Into Artist
Trong-Vu Hoang, Quang-Binh Nguyen, Duy-Nam Ly, Khanh-Duy Le, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria
Sara Sterlie, Nina Weng, Aasa Feragen
RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education
Jieun Han, Haneul Yoo, Junho Myung, Minsun Kim, Tak Yeon Lee, So-Yeon Ahn, Alice Oh