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人工智能杂志AI(Artificial Intelligence)是目前公认的发表该领域最新研究成果的主要国际论坛。该期刊欢迎有关AI广泛方面的论文，这些论文构成了整个领域的进步，也欢迎介绍人工智能应用的论文，但重点应该放在新的和新颖的人工智能方法如何提高应用领域的性能，而不是介绍传统人工智能方法的另一个应用。关于应用的论文应该描述一个原则性的解决方案，强调其新颖性，并对正在开发的人工智能技术进行深入的评估。 官网地址：http://dblp.uni-trier.de/db/journals/ai/
Reinsuring AI: Energy, Agriculture, Finance & Medicine as Precedents for Scalable Governance of Frontier Artificial Intelligence
Arxiv
0+阅读 · 4月2日
How humans evaluate AI systems for person detection in automatic train operation: Not all misses are alike
How humans evaluate AI systems for person detection in automatic train operation: Not all misses are alike
Arxiv
1+阅读 · 4月3日
Limits of trust in medical AI
Arxiv
0+阅读 · 4月3日
Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval
Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval
Arxiv
0+阅读 · 4月3日
Antithetic Sampling for Top-k Shapley Identification
Arxiv
0+阅读 · 4月2日
The Author Is Sovereign: A Manifesto for Ethical Copyright in the Age of AI
Arxiv
0+阅读 · 4月3日
A Comprehensive Survey of Contamination Detection Methods in Large Language Models
Arxiv
0+阅读 · 4月2日
Networking Systems for Video Anomaly Detection: A Tutorial and Survey
Networking Systems for Video Anomaly Detection: A Tutorial and Survey
Arxiv
0+阅读 · 4月3日
Interpretable LLM-based Table Question Answering
Interpretable LLM-based Table Question Answering
Arxiv
0+阅读 · 4月2日
Can AI Solve the Peer Review Crisis? A Large Scale Cross Model Experiment of LLMs' Performance and Biases in Evaluating over 1000 Economics Papers
Can AI Solve the Peer Review Crisis? A Large Scale Cross Model Experiment of LLMs' Performance and Biases in Evaluating over 1000 Economics Papers
Arxiv
0+阅读 · 4月3日
Human-Centered Development of an Explainable AI Framework for Real-Time Surgical Risk Surveillance
Human-Centered Development of an Explainable AI Framework for Real-Time Surgical Risk Surveillance
Arxiv
0+阅读 · 4月3日
We Need Improved Data Curation and Attribution in AI for Scientific Discovery
Arxiv
0+阅读 · 4月3日
A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models
A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models
Arxiv
0+阅读 · 4月3日
Evaluating AI Recruitment Sourcing Tools by Human Preference
Evaluating AI Recruitment Sourcing Tools by Human Preference
Arxiv
0+阅读 · 4月3日
Investigating Simple Drawings of $K_n$ using SAT
Arxiv
0+阅读 · 4月3日
参考链接
父主题
人工智能
Elsevier
子主题
CV101
Jonathan Schmidt
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