Artificial Intelligence
Artificial intelligence (AI) research focuses on creating systems capable of performing tasks that typically require human intelligence, with current efforts concentrating on improving model alignment with human values, enhancing transparency and accountability in AI systems, and mitigating risks associated with bias and malicious use. Prominent approaches involve large language models (LLMs), deep learning architectures like nnU-Net, and reinforcement learning techniques, often applied within specific domains such as healthcare, cybersecurity, and scientific research. The widespread adoption of AI across diverse fields necessitates rigorous investigation into its ethical implications, safety, and societal impact, driving ongoing research to develop more robust, reliable, and responsible AI systems.
Papers
Safety challenges of AI in medicine
Xiaoye Wang, Nicole Xi Zhang, Hongyu He, Trang Nguyen, Kun-Hsing Yu, Hao Deng, Cynthia Brandt, Danielle S. Bitterman, Ling Pan, Ching-Yu Cheng, James Zou, Dianbo Liu
How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions
Umm-e- Habiba, Markus Haug, Justus Bogner, Stefan Wagner
You Have Thirteen Hours in Which to Solve the Labyrinth: Enhancing AI Game Masters with Function Calling
Jaewoo Song, Andrew Zhu, Chris Callison-Burch
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI
Michele Laurelli
Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI
Cristian Trout
Insuring Uninsurable Risks from AI: The State as Insurer of Last Resort
Cristian Trout
AI for Mathematics Mathematical Formalized Problem Solving and Theorem Proving in Different Fields in Lean4
Xichen Tang
The Future of Software Testing: AI-Powered Test Case Generation and Validation
Mohammad Baqar, Rajat Khanda
Explainable AI: Definition and attributes of a good explanation for health AI
Evangelia Kyrimi, Scott McLachlan, Jared M Wohlgemut, Zane B Perkins, David A. Lagnado, William Marsh, the ExAIDSS Expert Group
Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution
Marga Don, Stijn Pinson, Blanca Guillen Cebrian, Yuki M. Asano
Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Cord Paralysis
Yucong Zhang, Xin Zou, Jinshan Yang, Wenjun Chen, Faya Liang, Ming Li
From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
Jifan Yu, Zheyuan Zhang, Daniel Zhang-li, Shangqing Tu, Zhanxin Hao, Rui Miao Li, Haoxuan Li, Yuanchun Wang, Hanming Li, Linlu Gong, Jie Cao, Jiayin Lin, Jinchang Zhou, Fei Qin, Haohua Wang, Jianxiao Jiang, Lijun Deng, Yisi Zhan, Chaojun Xiao, Xusheng Dai, Xuan Yan, Nianyi Lin, Nan Zhang, Ruixin Ni, Yang Dang, Lei Hou, Yu Zhang, Xu Han, Manli Li, Juanzi Li, Zhiyuan Liu, Huiqin Liu, Maosong Sun
Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects
Mattias Tiger, Daniel Jakobsson, Anders Ynnerman, Fredrik Heintz, Daniel Jönsson
Hyperbolic Brain Representations
Alexander Joseph, Nathan Francis, Meijke Balay
Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models
Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski
Nteasee: A mixed methods study of expert and general population perspectives on deploying AI for health in African countries
Mercy Nyamewaa Asiedu, Iskandar Haykel, Awa Dieng, Kerrie Kauer, Tousif Ahmed, Florence Ofori, Charisma Chan, Stephen Pfohl, Negar Rostamzadeh, Katherine Heller
The Role of Artificial Intelligence and Machine Learning in Software Testing
Ahmed Ramadan, Husam Yasin, Burhan Pektas