Ai Magazine最新文献_第7页

Human at the Center: A Framework for Human-Driven AI Development 以人为中心：人类驱动的人工智能发展框架

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-12-03 DOI: 10.1002/aaai.70043

Danniell Hu, Diana Acosta Navas, Susanne Gaube, Hussein Mozannar, Matthew E. Taylor, Krishnamurthy Dvijotham, Elizabeth Bondi-Kelly

{"title":"Human at the Center: A Framework for Human-Driven AI Development","authors":"Danniell Hu, Diana Acosta Navas, Susanne Gaube, Hussein Mozannar, Matthew E. Taylor, Krishnamurthy Dvijotham, Elizabeth Bondi-Kelly","doi":"10.1002/aaai.70043","DOIUrl":"https://doi.org/10.1002/aaai.70043","url":null,"abstract":"Artificial Intelligence (AI) systems increasingly shape many aspects of daily life, influencing our jobs, finances, healthcare, and online content. This expansion has led to the rise of human–AI systems, where humans communicate, collaborate, or otherwise interact with AI, such as using AI outputs to make decisions. While these systems have shown potential to enhance human capabilities and improve performance on benchmarks, evidence suggests that they often underperform compared to AI-only or human-only approaches in experiments and real-world applications. Here, we argue that human–AI systems should be developed with a greater emphasis on human-centered factors—such as usability, fairness, trust, and user autonomy—within the algorithmic design and evaluation process. We advocate for integrating human-centered principles into AI development through human-centered algorithmic design and contextual evaluation with real users. Drawing on interdisciplinary research and our tutorial at two major AI conferences, we highlight examples and strategies for AI researchers and practitioners to embed these principles effectively. This work offers a systematic synthesis that integrates technical, practical, and ethical insights into a unified framework. Additionally, we highlight critical ethical considerations, including fairness, labor, privacy, and human agency to ensure that systems meet performance goals while serving broader societal interests. Through this work, we aim to inspire the field to embrace a truly human-centered approach to algorithmic design and deployment.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foundation Models for Anomaly Detection: Vision and Challenges 异常检测的基础模型：愿景与挑战

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-12-02 DOI: 10.1002/aaai.70045

Jing Ren, Tao Tang, Hong Jia, Ziqi Xu, Haytham Fayek, Xiaodong Li, Suyu Ma, Xiwei Xu, Feng Xia

{"title":"Foundation Models for Anomaly Detection: Vision and Challenges","authors":"Jing Ren, Tao Tang, Hong Jia, Ziqi Xu, Haytham Fayek, Xiaodong Li, Suyu Ma, Xiwei Xu, Feng Xia","doi":"10.1002/aaai.70045","DOIUrl":"https://doi.org/10.1002/aaai.70045","url":null,"abstract":"As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection. They have demonstrated unprecedented capabilities in enhancing anomaly identification, generating detailed data descriptions, and providing visual explanations. This survey presents the first comprehensive review of recent advancements in FM-based anomaly detection. We propose a novel taxonomy for classifying FMs in anomaly detection tasks, based on the roles they play within the detection pipeline. Specifically, we categorize FMs as encoders, detectors, or interpreters, reflecting whether they are used for feature extraction, anomaly detection, or result interpretation, respectively. We provide a systematic analysis of state-of-the-art methods and discuss key challenges in leveraging FMs for improved anomaly detection. We also outline future research directions in this rapidly evolving field.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imitation learning is probably existentially safe 模仿学习可能是存在安全的

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-11-21 DOI: 10.1002/aaai.70040

Michael K. Cohen, Marcus Hutter

引用次数: 0

What do we want from AI? 我们想从人工智能中得到什么？

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-11-08 DOI: 10.1002/aaai.70042

Robert B. Fisher

引用次数: 0

RevOrder: A novel equation format for arithmetic operations in language models RevOrder：语言模型中用于算术运算的新型方程格式

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-11-06 DOI: 10.1002/aaai.70038

Si Shen, Peijun Shen, Danhao Zhu

{"title":"RevOrder: A novel equation format for arithmetic operations in language models","authors":"Si Shen, Peijun Shen, Danhao Zhu","doi":"10.1002/aaai.70038","DOIUrl":"https://doi.org/10.1002/aaai.70038","url":null,"abstract":"This paper proposes to understand arithmetic operations in Language Models (LM) by framing them as digit-based reasoning challenges. Our research focuses on arithmetic optimization challenges specific to LLMs, not on solving mathematical word problems. We introduce a metric called the Count of Sequential Intermediate Digits (CSID), which measures the complexity of arithmetic equations by counting the missing steps in digit reasoning. Our empirical findings suggest that increasing the model size does little to improve the handling of equations with high CSID values.We propose RevOrder, a method that incorporates techniques such as reversing the output order, step-by-step decomposition, and rollback mechanisms to maintain a low CSID, thereby enhancing the solvability of arithmetic equations in LMs. RevOrder also introduces a more compact reasoning process, which reduces the token requirements without affecting the CSID, significantly enhancing token efficiency.Comprehensive testing shows that RevOrder achieves perfect accuracy in operations such as addition, subtraction, and multiplication, and substantially improves performance in division tasks, especially with large numbers where traditional models falter.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence and the impact of the EU AI Act in business organizations 人工智能和欧盟人工智能法案对商业组织的影响

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-11-04 DOI: 10.1002/aaai.70039

Marc Selgas Cors, Renata Thiébaut

{"title":"Artificial intelligence and the impact of the EU AI Act in business organizations","authors":"Marc Selgas Cors, Renata Thiébaut","doi":"10.1002/aaai.70039","DOIUrl":"https://doi.org/10.1002/aaai.70039","url":null,"abstract":"Artificial intelligence (AI) is transforming industries worldwide, and the e-commerce sector is at the forefront of leveraging its capabilities to drive innovation and efficiency. The paper explores the integration of artificial intelligence in e-commerce, focusing on the ethical and regulatory implications introduced by the EU AI Act. This legislative framework aims to ensure the responsible deployment of AI by classifying AI systems into risk categories and imposing compliance requirements. It also underscores both the opportunities and challenges that AI presents to businesses, particularly in enhancing consumer experiences through automation and data-driven decision-making processes. The paper provides a comprehensive review of the AI landscape in Europe, analyzing the impact of the EU AI Act, particularly on small and medium-sized enterprises and startups. Through a mixed-methods approach, the study investigates how regulatory compliance may influence business innovation, market competitiveness, and consumer trust. The recommendations proposed aim to develop a trustworthy AI ecosystem that could stimulate long-term growth and enhance the global positioning of small European businesses.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From rights to runtime: Privacy engineering for agentic AI 从权利到运行：代理AI的隐私工程

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-10-26 DOI: 10.1002/aaai.70036

Keivan Navaie

引用次数: 0

A community-driven vision for a new knowledge resource for AI 社区驱动的人工智能新知识资源愿景

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-10-26 DOI: 10.1002/aaai.70035

Vinay K Chaudhri, Chaitan Baru, Brandon Bennett, Mehul Bhatt, Darion Cassel, Anthony G Cohn, Rina Dechter, Esra Erdem, Dave Ferrucci, Ken Forbus, Gregory Gelfond, Michael Genesereth, Andrew S. Gordon, Benjamin Grosof, Gopal Gupta, Jim Hendler, Sharat Israni, Tyler R. Josephson, Patrick Kyllonen, Yuliya Lierler, Vladimir Lifschitz, Clifton McFate, Hande Küçük McGinty, Leora Morgenstern, Alessandro Oltramari, Praveen Paritosh, Dan Roth, Blake Shepard, Cogan Shimizu, Denny Vrandečić, Mark Whiting, Michael Witbrock

{"title":"A community-driven vision for a new knowledge resource for AI","authors":"Vinay K Chaudhri, Chaitan Baru, Brandon Bennett, Mehul Bhatt, Darion Cassel, Anthony G Cohn, Rina Dechter, Esra Erdem, Dave Ferrucci, Ken Forbus, Gregory Gelfond, Michael Genesereth, Andrew S. Gordon, Benjamin Grosof, Gopal Gupta, Jim Hendler, Sharat Israni, Tyler R. Josephson, Patrick Kyllonen, Yuliya Lierler, Vladimir Lifschitz, Clifton McFate, Hande Küçük McGinty, Leora Morgenstern, Alessandro Oltramari, Praveen Paritosh, Dan Roth, Blake Shepard, Cogan Shimizu, Denny Vrandečić, Mark Whiting, Michael Witbrock","doi":"10.1002/aaai.70035","DOIUrl":"https://doi.org/10.1002/aaai.70035","url":null,"abstract":"The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose, widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered over 50 researchers to explore these questions. This paper synthesizes our findings and outlines a community-driven vision for a new knowledge infrastructure. In addition to leveraging contemporary advances in knowledge representation and reasoning, one promising idea is to build an open engineering framework to exploit knowledge modules effectively within the context of practical applications. Such a framework should include sets of conventions and social structures that are adopted by contributors.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145367001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CAGE challenge 4: A scalable multi-agent reinforcement learning gym for autonomous cyber defence CAGE挑战4：用于自主网络防御的可扩展多智能体强化学习健身房

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-10-21 DOI: 10.1002/aaai.70021

Mitchell Kiely, Metin Ahiskali, Etienne Borde, Benjamin Bowman, David Bowman, Dirk Van Bruggen, K. C. Cowan, Prithviraj Dasgupta, Erich Devendorf, Ben Edwards, Alex Fitts, Sunny Fugate, Ryan Gabrys, Wayne Gould, H. Howie Huang, Jules Jacobs, Ryan Kerr, Isaiah J. King, Li Li, Luis Martinez, Christopher Moir, Craig Murphy, Olivia Naish, Claire Owens, Miranda Purchase, Ahmad Ridley, Adrian Taylor, Sara Farmer, William John Valentine, Yiyi Zhang

{"title":"CAGE challenge 4: A scalable multi-agent reinforcement learning gym for autonomous cyber defence","authors":"Mitchell Kiely, Metin Ahiskali, Etienne Borde, Benjamin Bowman, David Bowman, Dirk Van Bruggen, K. C. Cowan, Prithviraj Dasgupta, Erich Devendorf, Ben Edwards, Alex Fitts, Sunny Fugate, Ryan Gabrys, Wayne Gould, H. Howie Huang, Jules Jacobs, Ryan Kerr, Isaiah J. King, Li Li, Luis Martinez, Christopher Moir, Craig Murphy, Olivia Naish, Claire Owens, Miranda Purchase, Ahmad Ridley, Adrian Taylor, Sara Farmer, William John Valentine, Yiyi Zhang","doi":"10.1002/aaai.70021","DOIUrl":"https://doi.org/10.1002/aaai.70021","url":null,"abstract":"As cyber threats become increasingly automated and sophisticated, novel solutions must be introduced to improve defense of enterprise networks. Deep reinforcement learning (DRL) has demonstrated potential in mitigating these advanced threats. Single DRL agents have proven utility toward execution of autonomous cyber defense. Despite the success of employing single DRL agents, this approach presents significant limitations, especially regarding scalability within large enterprise networks. An attractive alternative to the single-agent approach is the use of multi-agent reinforcement learning (MARL). However, developing MARL agents is costly with few options for examining MARL cyber defense techniques against adversarial agents. This paper presents a MARL network security environment, the fourth iteration of the cyber autonomy gym for experimentation (CAGE) challenges. This challenge was specifically designed to test the efficacy of MARL algorithms in an enterprise network. Our work aims to evaluate the potential of MARL as a robust and scalable solution for autonomous network defense.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-based AI planning and execution platforms for robotics 基于模型的机器人人工智能规划与执行平台

IF 3.2 4区计算机科学

Ai Magazine Pub Date : 2025-10-06 DOI: 10.1002/aaai.70034

Or Wertheim, Ronen I. Brafman

引用次数: 0