Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho
{"title":"人工智能未来的七大支柱","authors":"Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho","doi":"10.1109/mis.2023.3329745","DOIUrl":null,"url":null,"abstract":"In recent years, artificial intelligence (AI) research has showcased tremendous potential to positively impact humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision making, sense disambiguation, sarcasm detection, and narrative understanding as these require advanced kinds of reasoning, e.g., common-sense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely, multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"231 1","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Seven Pillars for the Future of Artificial Intelligence\",\"authors\":\"Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho\",\"doi\":\"10.1109/mis.2023.3329745\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, artificial intelligence (AI) research has showcased tremendous potential to positively impact humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision making, sense disambiguation, sarcasm detection, and narrative understanding as these require advanced kinds of reasoning, e.g., common-sense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely, multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness.\",\"PeriodicalId\":13160,\"journal\":{\"name\":\"IEEE Intelligent Systems\",\"volume\":\"231 1\",\"pages\":\"\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2023-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/mis.2023.3329745\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mis.2023.3329745","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Seven Pillars for the Future of Artificial Intelligence
In recent years, artificial intelligence (AI) research has showcased tremendous potential to positively impact humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision making, sense disambiguation, sarcasm detection, and narrative understanding as these require advanced kinds of reasoning, e.g., common-sense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely, multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness.
期刊介绍：
IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
