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
RatGPT: Turning online LLMs into Proxies for Malware Attacks
Mika Beckerich, Laura Plein, Sergio Coronado
Education in the age of Generative AI: Context and Recent Developments
Rafael Ferreira Mello, Elyda Freitas, Filipe Dwan Pereira, Luciano Cabral, Patricia Tedesco, Geber Ramalho
Approaches to Generative Artificial Intelligence, A Social Justice Perspective
Myke Healy
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
Giorgio Franceschelli, Mirco Musolesi
Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc
Doug Lenat, Gary Marcus
Exploring how a Generative AI interprets music
Gabriela Barenboim, Luigi Del Debbio, Johannes Hirn, Veronica Sanz
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Richard Hartley, Ajmal Mian