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
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak Mishra, Ninghao Liu, Song Gao, Tianming Liu, Gao Cong, Yingjie Hu, Chris Cundy, Ziyuan Li, Rui Zhu, Ni Lao
Communications-Aware Robotics: Challenges and Opportunities
Daniel Bonilla Licea, Giuseppe Silano, Mounir Ghogho, Martin Saska
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection
Ghazal Alinezhad Noghre, Armin Danesh Pazho, Vinit Katariya, Hamed Tabkhi
Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidates
Carlos Raoni Mendes, Emilio Vital Brazil, Vinicius Segura, Renato Cerqueira
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
Zhixuan Chu, Jianmin Huang, Ruopeng Li, Wei Chu, Sheng Li