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
Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments
Kosuke Imai, Kentaro Nakamura
PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI System
Gary D. Lopez Munoz, Amanda J. Minnich, Roman Lutz, Richard Lundeen, Raja Sekhar Rao Dheekonda, Nina Chikanov, Bolor-Erdene Jagdagdorj, Martin Pouliot, Shiven Chawla, Whitney Maxwell, Blake Bullwinkel, Katherine Pratt, Joris de Gruyter, Charlotte Siska, Pete Bryan, Tori Westerhoff, Chang Kawaguchi, Christian Seifert, Ram Shankar Siva Kumar, Yonatan Zunger
Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modeling
Ville Heilala, Roberto Araya, Raija Hämäläinen
The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles
Hanwen Zhang, Dusit Niyato, Wei Zhang, Changyuan Zhao, Hongyang Du, Abbas Jamalipour, Sumei Sun, Yiyang Pei
How will advanced AI systems impact democracy?
Christopher Summerfield, Lisa Argyle, Michiel Bakker, Teddy Collins, Esin Durmus, Tyna Eloundou, Iason Gabriel, Deep Ganguli, Kobi Hackenburg, Gillian Hadfield, Luke Hewitt, Saffron Huang, Helene Landemore, Nahema Marchal, Aviv Ovadya, Ariel Procaccia, Mathias Risse, Bruce Schneier, Elizabeth Seger, Divya Siddarth, Henrik Skaug Sætra, MH Tessler, Matthew Botvinick
Measuring Human Contribution in AI-Assisted Content Generation
Yueqi Xie, Tao Qi, Jingwei Yi, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu