{"title":"从幸福的角度看什么是生成式人工智能的正确输出?- 从睡眠阶段估计的角度看人工智能","authors":"K. Takadama","doi":"10.1609/aaaiss.v3i1.31250","DOIUrl":null,"url":null,"abstract":"This paper explores an answer to the question of “what is a correct output by generative AI from the viewpoint of well-being?” and discusses an effectiveness of taking account of a biological rhythm for this issue. Concretely, this paper focuses on an estimation of the REM sleep stage as one of sleep stages, and compared its estimations based on random forest as one of the machine learning methods and the ultradian rhythm as one of the biological rhythms. From the human subject experiment, the following implications have been revealed: (1) the REM sleep stage is wrongly estimated in many areas by random forest; and (2) the integration of the REM sleep stage estimation based on the biological rhythm with that based on random forest improves the F-score of the estimated REM sleep stage.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"71 14","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What Is a Correct Output by Generative AI From the Viewpoint of Well-Being? – Perspective From Sleep Stage Estimation –\",\"authors\":\"K. Takadama\",\"doi\":\"10.1609/aaaiss.v3i1.31250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores an answer to the question of “what is a correct output by generative AI from the viewpoint of well-being?” and discusses an effectiveness of taking account of a biological rhythm for this issue. Concretely, this paper focuses on an estimation of the REM sleep stage as one of sleep stages, and compared its estimations based on random forest as one of the machine learning methods and the ultradian rhythm as one of the biological rhythms. From the human subject experiment, the following implications have been revealed: (1) the REM sleep stage is wrongly estimated in many areas by random forest; and (2) the integration of the REM sleep stage estimation based on the biological rhythm with that based on random forest improves the F-score of the estimated REM sleep stage.\",\"PeriodicalId\":516827,\"journal\":{\"name\":\"Proceedings of the AAAI Symposium Series\",\"volume\":\"71 14\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the AAAI Symposium Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1609/aaaiss.v3i1.31250\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the AAAI Symposium Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aaaiss.v3i1.31250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文探讨了 "从幸福的角度看,什么是生成式人工智能的正确输出?"这一问题的答案,并讨论了考虑生物节律对这一问题的有效性。具体而言,本文重点研究了作为睡眠阶段之一的快速眼动睡眠阶段的估算,并比较了基于随机森林(机器学习方法之一)和超昼夜节律(生物节律之一)的估算结果。通过人体实验,本文得出了以下结论:(1) 随机森林对快速动眼期睡眠阶段的估计在很多方面都是错误的;(2) 将基于生物节律的快速动眼期睡眠阶段估计与基于随机森林的快速动眼期睡眠阶段估计相结合,可以提高快速动眼期睡眠阶段估计的 F 分数。
What Is a Correct Output by Generative AI From the Viewpoint of Well-Being? – Perspective From Sleep Stage Estimation –
This paper explores an answer to the question of “what is a correct output by generative AI from the viewpoint of well-being?” and discusses an effectiveness of taking account of a biological rhythm for this issue. Concretely, this paper focuses on an estimation of the REM sleep stage as one of sleep stages, and compared its estimations based on random forest as one of the machine learning methods and the ultradian rhythm as one of the biological rhythms. From the human subject experiment, the following implications have been revealed: (1) the REM sleep stage is wrongly estimated in many areas by random forest; and (2) the integration of the REM sleep stage estimation based on the biological rhythm with that based on random forest improves the F-score of the estimated REM sleep stage.