Emerging Opportunity
Emerging research highlights the transformative potential of artificial intelligence across diverse fields, focusing on optimizing AI model fairness, leveraging AI for resource management and efficiency gains (e.g., congestion pricing), and developing AI-powered tools for various applications including healthcare, education, and scientific discovery. Current research emphasizes improving model interpretability, robustness, and addressing ethical concerns related to bias and transparency, often utilizing large language models (LLMs) and deep learning architectures. This work holds significant implications for advancing scientific understanding, improving decision-making processes, and creating more efficient and equitable systems across numerous sectors.
Papers
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C. Comeau, Rezarta Islamaj, Aadit Kapoor, Xin Gao, Zhiyong Lu
Opportunities for Large Language Models and Discourse in Engineering Design
Jan Göpfert, Jann M. Weinand, Patrick Kuckertz, Detlef Stolten
The Age of Synthetic Realities: Challenges and Opportunities
João Phillipe Cardenuto, Jing Yang, Rafael Padilha, Renjie Wan, Daniel Moreira, Haoliang Li, Shiqi Wang, Fernanda Andaló, Sébastien Marcel, Anderson Rocha
Challenges and Opportunities for the Design of Smart Speakers
Tao Long, Lydia B. Chilton
Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce
Editing Large Language Models: Problems, Methods, and Opportunities
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing
Jawid Ahmad Baktash, Mursal Dawodi
SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
Zijie J. Wang, David Munechika, Seongmin Lee, Duen Horng Chau
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, Oriel Kiss, Sofia Vallecorsa, Michele Grossi, Zoë Holmes