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
Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
Siya Zhu, Raymundo Arróyave, Doğuhan Sarıtürk
Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation
Colin Diggs, Michael Doyle, Amit Madan, Siggy Scott, Emily Escamilla, Jacob Zimmer, Naveed Nekoo, Paul Ursino, Michael Bartholf, Zachary Robin, Anand Patel, Chris Glasz, William Macke, Paul Kirk, Jasper Phillips, Arun Sridharan, Doug Wendt, Scott Rosen, Nitin Naik, Justin F. Brunelle, Samruddhi Thaker
Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government
Amol Khanna, Adam McCormick, Andre Nguyen, Chris Aguirre, Edward Raff
On-Device LLMs for SMEs: Challenges and Opportunities
Jeremy Stephen Gabriel Yee Zhi Wen, Pai Chet Ng, Zhengkui Wang, Ian McLoughlin, Aik Beng Ng, Simon See
Opportunities and Challenges of Generative-AI in Finance
Akshar Prabhu Desai, Ganesh Satish Mallya, Mohammad Luqman, Tejasvi Ravi, Nithya Kota, Pranjul Yadav