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
Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn
From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges
Sai Krishna Revanth Vuruma, Ashley Margetts, Jianhai Su, Faez Ahmed, Biplav Srivastava
Generative AI and Process Systems Engineering: The Next Frontier
Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar, Fengqi You
Generative AI in the Construction Industry: A State-of-the-art Analysis
Ridwan Taiwo, Idris Temitope Bello, Sulemana Fatoama Abdulai, Abdul-Mugis Yussif, Babatunde Abiodun Salami, Abdullahi Saka, Tarek Zayed
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence
Timothy R. McIntosh, Teo Susnjak, Nalin Arachchilage, Tong Liu, Paul Watters, Malka N. Halgamuge
An Inpainting-Infused Pipeline for Attire and Background Replacement
Felipe Rodrigues Perche-Mahlow, André Felipe-Zanella, William Alberto Cruz-Castañeda, Marcellus Amadeus
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System
Yan Zhao, Zhongyun Li, Yushan Pan, Jiaxing Wang, Yihong Wang
Governance of Generative Artificial Intelligence for Companies
Johannes Schneider, Rene Abraham, Christian Meske