Pub Date : 2024-06-24DOI: 10.1109/mis.2024.3407895
{"title":"IEEE Annals of the History of Computing","authors":"","doi":"10.1109/mis.2024.3407895","DOIUrl":"https://doi.org/10.1109/mis.2024.3407895","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524002","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}
Pub Date : 2024-06-24DOI: 10.1109/mis.2024.3395936
Utkarshani Jaimini, Cory Henson, Amit Sheth
Causal neurosymbolic AI (NeSyAI) combines the benefits of causality with NeSyAI. More specifically, it 1) enriches NeSyAI systems with explicit representations of causality, 2) integrates causal knowledge with domain knowledge, and 3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.
{"title":"Causal Neurosymbolic AI: A Synergy Between Causality and Neurosymbolic Methods","authors":"Utkarshani Jaimini, Cory Henson, Amit Sheth","doi":"10.1109/mis.2024.3395936","DOIUrl":"https://doi.org/10.1109/mis.2024.3395936","url":null,"abstract":"Causal neurosymbolic AI (NeSyAI) combines the benefits of causality with NeSyAI. More specifically, it 1) enriches NeSyAI systems with explicit representations of causality, 2) integrates causal knowledge with domain knowledge, and 3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"60 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524006","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}
Pub Date : 2024-06-24DOI: 10.1109/mis.2024.3397180
{"title":"IEEE Computer Society Career Center","authors":"","doi":"10.1109/mis.2024.3397180","DOIUrl":"https://doi.org/10.1109/mis.2024.3397180","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510084","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}
With the advent of foundation models like ChatGPT, educators are excited about the transformative role that artificial intelligence (AI) might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various industries force us to think deeply about the changes they will make to education, a domain that is critically important for the future of humans. In this article, we discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities, as well as the development of agent architecture tailored for education, which integrates AI agents with pedagogical frameworks to create adaptive learning environments. Furthermore, we highlight the risks and opportunities of AI overreliance and creativity. Finally, we envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.
{"title":"Foundation Models for Education: Promises and Prospects","authors":"Tianlong Xu, Richard Tong, Jing Liang, Xing Fan, Haoyang Li, Qingsong Wen","doi":"10.1109/mis.2024.3398191","DOIUrl":"https://doi.org/10.1109/mis.2024.3398191","url":null,"abstract":"With the advent of foundation models like ChatGPT, educators are excited about the transformative role that artificial intelligence (AI) might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various industries force us to think deeply about the changes they will make to education, a domain that is critically important for the future of humans. In this article, we discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities, as well as the development of agent architecture tailored for education, which integrates AI agents with pedagogical frameworks to create adaptive learning environments. Furthermore, we highlight the risks and opportunities of AI overreliance and creativity. Finally, we envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"4 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524007","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}
Pub Date : 2024-06-24DOI: 10.1109/mis.2024.3398190
Przemysław Kazienko, Erik Cambria
Recommender systems have transformed our digital experiences in many regards. We enumerate six of their positive effects on the economy and humans, such as greater user satisfaction, time savings, broadening user horizons, and positive behavioral nudging. However, it is crucial to acknowledge the potential downsides inherent in their design. One significant concern is that these algorithms often prioritize the interests of the company deploying them, aiming to maximize profits and user engagement rather than solely focusing on enhancing user experience. Therefore, we also list and consider two use cases and six negative long-term impacts on humans, including addiction, reduced ability to think critically, less autonomy, and weakened human relationships caused by more and more human-like virtual assistants. Despite the undeniable utility of recommender systems, it is imperative to approach them critically, advocating for transparency, ethical considerations, and user empowerment to ensure that they serve as tools for enrichment rather than exploitation. To accomplish this, the idea and challenges of responsible recommender systems (RRSs) are presented. RRSs extend common recommender systems with components related to individual human values and goals as well as widely accepted well-being and lifestyle guidelines.
{"title":"Toward Responsible Recommender Systems","authors":"Przemysław Kazienko, Erik Cambria","doi":"10.1109/mis.2024.3398190","DOIUrl":"https://doi.org/10.1109/mis.2024.3398190","url":null,"abstract":"Recommender systems have transformed our digital experiences in many regards. We enumerate six of their positive effects on the economy and humans, such as greater user satisfaction, time savings, broadening user horizons, and positive behavioral nudging. However, it is crucial to acknowledge the potential downsides inherent in their design. One significant concern is that these algorithms often prioritize the interests of the company deploying them, aiming to maximize profits and user engagement rather than solely focusing on enhancing user experience. Therefore, we also list and consider two use cases and six negative long-term impacts on humans, including addiction, reduced ability to think critically, less autonomy, and weakened human relationships caused by more and more human-like virtual assistants. Despite the undeniable utility of recommender systems, it is imperative to approach them critically, advocating for transparency, ethical considerations, and user empowerment to ensure that they serve as tools for enrichment rather than exploitation. To accomplish this, the idea and challenges of responsible recommender systems (RRSs) are presented. RRSs extend common recommender systems with components related to individual human values and goals as well as widely accepted well-being and lifestyle guidelines.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"207 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524005","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}
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