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
Are Generative AI systems Capable of Supporting Information Needs of Patients?
Shreya Rajagopal, Subhashis Hazarika, Sookyung Kim, Yan-ming Chiou, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan
SCAPE: Searching Conceptual Architecture Prompts using Evolution
Soo Ling Lim, Peter J Bentley, Fuyuki Ishikawa