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
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
Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It
Philipp Hacker, Brent Mittelstadt, Frederik Zuiderveen Borgesius, Sandra Wachter
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks
I. R. Slootweg, M. Thach, K. R. Curro-Tafili, F. D. Verbraak, F. H. Bouwman, Y. A. L. Pijnenburg, J. F. Boer, J. H. P. de Kwisthout, L. Bagheriye, P. J. González