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
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks
Zhaoyang Ma, Xupeng He, Hyung Kwak, Jun Gao, Shuyu Sun, Bicheng Yan
Cognitive Architecture Toward Common Ground Sharing Among Humans and Generative AIs: Trial on Model-Model Interactions in Tangram Naming Task
Junya Morita, Tatsuya Yui, Takeru Amaya, Ryuichiro Higashinaka, Yugo Takeuchi
Brief for the Canada House of Commons Study on the Implications of Artificial Intelligence Technologies for the Canadian Labor Force: Generative Artificial Intelligence Shatters Models of AI and Labor
Morgan R. Frank
Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems
Derek Lilienthal, Paul Mello, Magdalini Eirinaki, Stas Tiomkin
Beyond Words: A Mathematical Framework for Interpreting Large Language Models
Javier González, Aditya V. Nori
Joint Composite Latent Space Bayesian Optimization
Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat, Eytan Bakshy
Supermind Ideator: Exploring generative AI to support creative problem-solving
Steven R. Rick, Gianni Giacomelli, Haoran Wen, Robert J. Laubacher, Nancy Taubenslag, Jennifer L. Heyman, Max Sina Knicker, Younes Jeddi, Hendrik Maier, Stephen Dwyer, Pranav Ragupathy, Thomas W. Malone
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023
Srijoni Majumdar, Soumen Paul, Debjyoti Paul, Ayan Bandyopadhyay, Samiran Chattopadhyay, Partha Pratim Das, Paul D Clough, Prasenjit Majumder
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Jinbo Wen, Jiangtian Nie, Jiawen Kang, Dusit Niyato, Hongyang Du, Yang Zhang, Mohsen Guizani
Large-scale Foundation Models and Generative AI for BigData Neuroscience
Ran Wang, Zhe Sage Chen
From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI
Alex Kim, Maximilian Muhn, Valeri Nikolaev
Exploring the Potential of Generative AI for the World Wide Web
Nouar AlDahoul, Joseph Hong, Matteo Varvello, Yasir Zaki
Techniques for supercharging academic writing with generative AI
Zhicheng Lin