{"title":"IEEE Computer Society Jobs Board","authors":"","doi":"10.1109/mis.2024.3359896","DOIUrl":"https://doi.org/10.1109/mis.2024.3359896","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"55 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neurosymbolic Value-Inspired Artificial Intelligence (Why, What, and How)","authors":"Amit Sheth, Kaushik Roy","doi":"10.1109/mis.2023.3344353","DOIUrl":"https://doi.org/10.1109/mis.2023.3344353","url":null,"abstract":"The rapid progression of artificial intelligence (AI) systems, facilitated by the advent of large language models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as a part of human society. Toward this end, neurosymbolic AI systems are attractive because of their potential to enable and interpretable interfaces for facilitating value-based decision making by leveraging explicit representations of shared values. In this article, we introduce substantial extensions to Kahneman’s System 1 and System 2 framework and propose a neurosymbolic computational framework called value-inspired AI (VAI). It outlines the crucial components essential for the robust and practical implementation of VAI systems, representing and integrating various dimensions of human values. Finally, we further offer insights into the current progress made in this direction and outline potential future directions for the field.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"79 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Embracing LLMs for Point-of-Interest Recommendations","authors":"Tianxing Wang, Can Wang","doi":"10.1109/mis.2023.3343489","DOIUrl":"https://doi.org/10.1109/mis.2023.3343489","url":null,"abstract":"A point-of-interest (POI) recommendation becomes the core function of location-based services. Unlike a traditional item recommendation, a POI recommendation has distinct features, such as geographical influences, complex mobility patterns, and a balance between local and global user preferences. Past POI recommendation system research has focused mainly on integrating deep learning models like convolutional neural networks, recurrent neural networks, and attention-based architectures, demonstrating their effectiveness in addressing the dynamic nature of spatial–temporal data in POI recommendation areas. In recent years, with the rise of large language models (LLMs), POI recommendation has produced a number of promising directions. This article first discusses the characteristics and state-of-the-art solutions of POI recommendation, then it introduces potential research directions by integrating the latest LLMs.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"21 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EEG Emotion Recognition Based on Manifold Geomorphological Features in Riemannian Space","authors":"Yanbing Wang, Hong He","doi":"10.1109/mis.2024.3363895","DOIUrl":"https://doi.org/10.1109/mis.2024.3363895","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"31 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reflecting on Algorithmic Bias with Design Fiction: the MiniCoDe Workshops","authors":"T. Turchi, A. Malizia, S. Borsci","doi":"10.1109/mis.2024.3352977","DOIUrl":"https://doi.org/10.1109/mis.2024.3352977","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}