Ai Magazine最新文献_第5页

Transforming the value chain of local journalism with artificial intelligence 用人工智能改造地方新闻价值链

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-05-24 DOI: 10.1002/aaai.12174

Bartosz Wilczek, Mario Haim, Neil Thurman

引用次数: 0

Improve robustness of machine learning via efficient optimization and conformal prediction 通过高效优化和保形预测提高机器学习的稳健性

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-05-11 DOI: 10.1002/aaai.12173

Yan Yan

{"title":"Improve robustness of machine learning via efficient optimization and conformal prediction","authors":"Yan Yan","doi":"10.1002/aaai.12173","DOIUrl":"10.1002/aaai.12173","url":null,"abstract":"The advance of machine learning (ML) systems in real-world scenarios usually expects safe deployment in high-stake applications (e.g., medical diagnosis) for critical decision-making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance-regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end-to-end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample-efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP-based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk-aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"270-279"},"PeriodicalIF":0.9,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128836","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

Measuring the benefit of increased transparency and control in news recommendation 衡量提高新闻推荐透明度和控制力的益处

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-04-17 DOI: 10.1002/aaai.12171

Nava Tintarev, Bart P. Knijnenburg, Martijn C. Willemsen

{"title":"Measuring the benefit of increased transparency and control in news recommendation","authors":"Nava Tintarev, Bart P. Knijnenburg, Martijn C. Willemsen","doi":"10.1002/aaai.12171","DOIUrl":"10.1002/aaai.12171","url":null,"abstract":"Personalized news experiences powered by recommender systems permeate our lives and have the potential to influence not only our opinions, but also our decisions. At the same time, the content and viewpoints contained within news recommendations are driven by multiple factors, including both personalization and editorial selection. Explanations could help users gain a better understanding of the factors contributing to the news items selected for them to read. Indeed, recent works show that explanations are essential for users of news recommenders to understand their consumption preferences and set intentions in line with their goals, such as goals for knowledge development and increased diversity of content or viewpoints. We give examples of such works on explanation and interactive interface interventions which have been effective in influencing readers' consumption intentions and behaviors in news recommendations. However, the state-of-the-art in news recommender systems currently fall short in terms of evaluating such interventions in live systems, limiting our ability to measure their true impact on user behavior and opinions. To help understand the true benefit of these interfaces, we therefore call for improving the realism of studies for news.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"212-226"},"PeriodicalIF":0.9,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693533","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

The business of news in the AI economy 人工智能经济时代的新闻业务

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-04-09 DOI: 10.1002/aaai.12172

Helle Sjøvaag

引用次数: 0

The 2023 International Planning Competition 2023 年国际规划竞赛

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-04-05 DOI: 10.1002/aaai.12169

Ayal Taitler, Ron Alford, Joan Espasa, Gregor Behnke, Daniel Fišer, Michael Gimelfarb, Florian Pommerening, Scott Sanner, Enrico Scala, Dominik Schreiber, Javier Segovia-Aguas, Jendrik Seipp

引用次数: 0

Audiences, automation, and AI: From structured news to language models 受众、自动化和人工智能：从结构化新闻到语言模型

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-04-05 DOI: 10.1002/aaai.12168

David Caswell

{"title":"Audiences, automation, and AI: From structured news to language models","authors":"David Caswell","doi":"10.1002/aaai.12168","DOIUrl":"10.1002/aaai.12168","url":null,"abstract":"The appearance of large language models (LLMs) and other forms of generative AI portend a new era of disruption and innovation for the news industry, this time focused on the production and consumption of news rather than on its distribution. Large news organizations, however, may be surprisingly well-prepared for at least some of this disruption because of earlier innovation work on automating workflows for personalized content and formats using structured techniques. This article reviews this work and uses examples from the British Broadcasting Corporation (BBC) and other large news providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI. Using the BBC's next-generation authoring and publishing stack as an example, the article also discusses how earlier innovation work has influenced the design of flexible infrastructure that can accommodate uncertainty in audience behavior and editorial workflows – capabilities that are likely to be well suited to the fast-approaching AI-mediated news ecosystem.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"174-186"},"PeriodicalIF":0.9,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740078","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

AI and agents 人工智能和代理

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-04-04 DOI: 10.1002/aaai.12170

Babak Hodjat

引用次数: 0

Engineering AI for provable retention of objectives over time 人工智能工程可证明目标可长期保持

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-03-23 DOI: 10.1002/aaai.12167

Adeniyi Fasoro

引用次数: 0

Institute for Foundations of Machine Learning (IFML): Advancing AI systems that will transform our world 机器学习基础研究所（IFML）：推进人工智能系统，改变我们的世界

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-03-19 DOI: 10.1002/aaai.12163

Adam Klivans, Alexandros G. Dimakis, Kristen Grauman, Jonathan I. Tamir, Daniel J. Diaz, Karen Davidson

引用次数: 0

Creating intelligent cyberinfrastructure for democratizing AI 创建智能网络基础设施，实现人工智能民主化

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-03-10 DOI: 10.1002/aaai.12166

Dhabaleswar K. Panda, Vipin Chaudhary, Eric Fosler-Lussier, Raghu Machiraju, Amit Majumdar, Beth Plale, Rajiv Ramnath, Ponnuswamy Sadayappan, Neelima Savardekar, Karen Tomko

{"title":"Creating intelligent cyberinfrastructure for democratizing AI","authors":"Dhabaleswar K. Panda, Vipin Chaudhary, Eric Fosler-Lussier, Raghu Machiraju, Amit Majumdar, Beth Plale, Rajiv Ramnath, Ponnuswamy Sadayappan, Neelima Savardekar, Karen Tomko","doi":"10.1002/aaai.12166","DOIUrl":"https://doi.org/10.1002/aaai.12166","url":null,"abstract":"Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF-funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advance edge-to-center, AI-as-a-Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge-cloud-HPC computing continuum, plug-and-play next-generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high-impact domains: animal ecology, digital agriculture, and smart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment to democratizing AI. ICICLE seeks to serve as the national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"22-28"},"PeriodicalIF":0.9,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164289","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