Generative AI Model
Generative AI models are computational systems designed to create new content, such as text, images, and audio, by learning patterns from existing data. Current research emphasizes improving efficiency and scalability of these models, particularly focusing on architectures like transformers and diffusion models, and addressing challenges like bias mitigation, data security, and responsible AI practices. The widespread adoption of generative AI across diverse fields, from medicine and law to art and entertainment, necessitates rigorous research into its capabilities, limitations, and societal impact.
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
Large Scale Generative AI Text Applied to Sports and Music
Aaron Baughman, Stephen Hammer, Rahul Agarwal, Gozde Akay, Eduardo Morales, Tony Johnson, Leonid Karlinsky, Rogerio Feris
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation
Yuanhuiyi Lyu, Xu Zheng, Lin Wang
IoT in the Era of Generative AI: Vision and Challenges
Xin Wang, Zhongwei Wan, Arvin Hekmati, Mingyu Zong, Samiul Alam, Mi Zhang, Bhaskar Krishnamachari
Can AI Be as Creative as Humans?
Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi