Ai Magazine最新文献

Leveraging AI to improve health information access in the World's largest maternal mobile health program

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-12-10 DOI: 10.1002/aaai.12206

Shresth Verma, Arshika Lalan, Paula Rodriguez Diaz, Panayiotis Danassis, Amrita Mahale, Kumar Madhu Sudan, Aparna Hegde, Milind Tambe, Aparna Taneja

{"title":"Leveraging AI to improve health information access in the World's largest maternal mobile health program","authors":"Shresth Verma, Arshika Lalan, Paula Rodriguez Diaz, Panayiotis Danassis, Amrita Mahale, Kumar Madhu Sudan, Aparna Hegde, Milind Tambe, Aparna Taneja","doi":"10.1002/aaai.12206","DOIUrl":"https://doi.org/10.1002/aaai.12206","url":null,"abstract":"Harnessing the wide-spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in underserved communities, with maternal and infant health being a key area of such mHealth programs. Unfortunately, dwindling listenership is a major challenge, requiring targeted interventions using limited resources. This paper focuses on Kilkari, the world's largest mHealth program for maternal and child care – with over 3 million active subscribers at a time – launched by India's Ministry of Health and Family Welfare (MoHFW) and run by the non-profit ARMMAN. We present a system called CHAHAK that aims to reduce automated dropouts as well as boost engagement with the program through the strategic allocation of interventions to beneficiaries. Past work in a similar domain has focused on a much smaller scale mHealth program and used markovian restless multiarmed bandits to optimize a single limited intervention resource. However, this paper demonstrates the challenges in adopting a markovian approach in Kilkari; therefore, CHAHAK instead relies on non-markovian time-series restless bandits and optimizes multiple interventions to improve listenership. We use real Kilkari data from the Odisha state in India to show CHAHAK's effectiveness in harnessing multiple interventions to boost listenership, benefiting marginalized communities. When deployed CHAHAK will assist the largest maternal mHealth program to date.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 4","pages":"526-536"},"PeriodicalIF":2.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860681","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

Introduction to the special issue on Innovative Applications of Artificial Intelligence (IAAI 2024)

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-11-27 DOI: 10.1002/aaai.12205

Alexander Wong, Yuhao Chen, Jan Seyler

引用次数: 0

Deceptively simple: An outsider's perspective on natural language processing 简单得令人难以置信：从局外人的角度看自然语言处理

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-21 DOI: 10.1002/aaai.12204

Ashiqur R. KhudaBukhsh

引用次数: 0

AI-assisted research collaboration with open data for fair and effective response to call for proposals

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-21 DOI: 10.1002/aaai.12203

Siva Likitha Valluru, Michael Widener, Biplav Srivastava, Sriraam Natarajan, Sugata Gangopadhyay

{"title":"AI-assisted research collaboration with open data for fair and effective response to call for proposals","authors":"Siva Likitha Valluru, Michael Widener, Biplav Srivastava, Sriraam Natarajan, Sugata Gangopadhyay","doi":"10.1002/aaai.12203","DOIUrl":"https://doi.org/10.1002/aaai.12203","url":null,"abstract":"Building teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel deployed system to recommend teams using a variety of Artificial Intelligence (AI) methods, such that (1) each team achieves the highest possible skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced among the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short- and long-term objectives. We evaluate our system in two diverse settings in US and India of researchers and proposal calls, at two different time instants about 1 year apart (total 4 settings), to establish generality of our approach, and deploy it at a major US university. We validate the effectiveness of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams and higher goodness, and (2) qualitatively, by conducting a large-scale user study at a college-wide level, and demonstrate that users overall found the tool very useful and relevant.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 4","pages":"457-471"},"PeriodicalIF":2.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861856","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

Learning representations for robust human–robot interaction

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-21 DOI: 10.1002/aaai.12197

Yen-Ling Kuo

引用次数: 0

A survey of out-of-distribution generalization for graph machine learning from a causal view

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-18 DOI: 10.1002/aaai.12202

Jing Ma

{"title":"A survey of out-of-distribution generalization for graph machine learning from a causal view","authors":"Jing Ma","doi":"10.1002/aaai.12202","DOIUrl":"https://doi.org/10.1002/aaai.12202","url":null,"abstract":"Graph machine learning (GML) has been successfully applied across a wide range of tasks. Nonetheless, GML faces significant challenges in generalizing over out-of-distribution (OOD) data, which raises concerns about its wider applicability. Recent advancements have underscored the crucial role of causality-driven approaches in overcoming these generalization challenges. Distinct from traditional GML methods that primarily rely on statistical dependencies, causality-focused strategies delve into the underlying causal mechanisms of data generation and model prediction, thus significantly improving the generalization of GML across different environments. This paper offers a thorough review of recent progress in causality-involved GML generalization. We elucidate the fundamental concepts of employing causality to enhance graph model generalization and categorize the various approaches, providing detailed descriptions of their methodologies and the connections among them. Furthermore, we explore the incorporation of causality in other related important areas of trustworthy GML, such as explanation, fairness, and robustness. Concluding with a discussion on potential future research directions, this review seeks to articulate the continuing development and future potential of causality in enhancing the trustworthiness of GML.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 4","pages":"537-548"},"PeriodicalIF":2.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851524","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

Fusing remote and social sensing data for flood impact mapping

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-18 DOI: 10.1002/aaai.12196

Zainab Akhtar, Umair Qazi, Aya El-Sakka, Rizwan Sadiq, Ferda Ofli, Muhammad Imran

{"title":"Fusing remote and social sensing data for flood impact mapping","authors":"Zainab Akhtar, Umair Qazi, Aya El-Sakka, Rizwan Sadiq, Ferda Ofli, Muhammad Imran","doi":"10.1002/aaai.12196","DOIUrl":"https://doi.org/10.1002/aaai.12196","url":null,"abstract":"The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end-to-end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state-of-the-art natural language processing and computer vision models to identify flood exposure, ground-level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real-world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground-truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard-hit districts and enhancing disaster response.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 4","pages":"486-501"},"PeriodicalIF":2.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861676","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 virtual driving instructor: Multi-agent system collaborating via knowledge graph for scalable driver education

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-18 DOI: 10.1002/aaai.12201

Johannes Rehm, Irina Reshodko, Stian Zimmermann Børresen, Odd Erik Gundersen

引用次数: 0

Framework to enable and test conversational assistant for APIs and RPAs

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-18 DOI: 10.1002/aaai.12198

Jayachandu Bandlamudi, Kushal Mukherjee, Prerna Agarwal, Ritwik Chaudhuri, Rakesh Pimplikar, Sampath Dechu, Alex Straley, Anbumunee Ponniah, Renuka Sindhgatta

{"title":"Framework to enable and test conversational assistant for APIs and RPAs","authors":"Jayachandu Bandlamudi, Kushal Mukherjee, Prerna Agarwal, Ritwik Chaudhuri, Rakesh Pimplikar, Sampath Dechu, Alex Straley, Anbumunee Ponniah, Renuka Sindhgatta","doi":"10.1002/aaai.12198","DOIUrl":"https://doi.org/10.1002/aaai.12198","url":null,"abstract":"In the realm of business automation, conversational assistants are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through application programming interface (APIs) and robotic process automation (RPAs). To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the “build” phase to assist humans in creating skills for digital assistants. As a result, the system does not need to rely on LLMs during conversations with business users, leading to efficient deployment. Along with enabling digital assistants, our system employs LLMs as proxies to simulate human interaction and automatically evaluate the digital assistant's performance. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 4","pages":"443-456"},"PeriodicalIF":2.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851522","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

Data-efficient graph learning: Problems, progress, and prospects

IF 2.5 4区计算机科学

Ai Magazine Pub Date : 2024-10-18 DOI: 10.1002/aaai.12200

Kaize Ding, Yixin Liu, Chuxu Zhang, Jianling Wang

{"title":"Data-efficient graph learning: Problems, progress, and prospects","authors":"Kaize Ding, Yixin Liu, Chuxu Zhang, Jianling Wang","doi":"10.1002/aaai.12200","DOIUrl":"https://doi.org/10.1002/aaai.12200","url":null,"abstract":"Graph-structured data, ranging from social networks to financial transaction networks, from citation networks to gene regulatory networks, have been widely used for modeling a myriad of real-world systems. As a prevailing model architecture to model graph-structured data, graph neural networks (GNNs) have drawn much attention in both academic and industrial communities in the past decades. Despite their success in different graph learning tasks, existing methods usually rely on learning from “big” data, requiring a large amount of labeled data for model training. However, it is common that real-world graphs are associated with “small” labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph machine learning (graph ML) with low-cost human supervision for low-resource settings where limited or even no labeled data is available. This paper investigates a new research field—data-efficient graph learning, which aims to push forward the performance boundary of graph ML models with different kinds of low-cost supervision signals. Specifically, we outline the fundamental research problems, review the current progress, and discuss the future prospects of data-efficient graph learning, aiming to illuminate the path for subsequent research in this field.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 4","pages":"549-560"},"PeriodicalIF":2.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861732","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