Generative AI System
Generative AI systems, encompassing models like LLMs and diffusion models, aim to create diverse content (text, images, audio, video) from various inputs, often leveraging natural language. Current research emphasizes improving model safety and trustworthiness through techniques like red teaming and auditing, addressing biases and ethical concerns related to content generation and societal impact, and exploring more nuanced human-AI interaction paradigms beyond simple prompting. These advancements hold significant implications for diverse fields, from creative industries and healthcare to scientific research, demanding careful consideration of ethical and societal consequences alongside technical progress.
Papers
Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction
Andrea Cristina McGlinchey, Peter J Barclay
Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Models
Sebastian Gutierrez, Irene Hou, Jihye Lee, Kenneth Angelikas, Owen Man, Sophia Mettille, James Prather, Paul Denny, Stephen MacNeil