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
How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial intelligence
Luke Zaphir, Jason M. Lodge, Jacinta Lisec, Dom McGrath, Hassan Khosravi
CollaFuse: Collaborative Diffusion Models
Simeon Allmendinger, Domenique Zipperling, Lukas Struppek, Niklas Kühl
Extracting Training Data from Unconditional Diffusion Models
Yunhao Chen, Xingjun Ma, Difan Zou, Yu-Gang Jiang
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle S Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu
An investigation into the scientific landscape of the conversational and generative artificial intelligence, and human-chatbot interaction in education and research
Ikpe Justice Akpan, Yawo M. Kobara, Josiah Owolabi, Asuama Akpam, Onyebuchi Felix Offodile
Large Language Models Playing Mixed Strategy Nash Equilibrium Games
Alonso Silva
Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence
Gianmarco Mengaldo