{"title":"Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice","authors":"Dan Li","doi":"10.1007/s11023-023-09639-9","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":51133,"journal":{"name":"Minds and Machines","volume":"33 1","pages":"429 - 450"},"PeriodicalIF":4.2000,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minds and Machines","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11023-023-09639-9","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
期刊介绍：
Minds and Machines, affiliated with the Society for Machines and Mentality, serves as a platform for fostering critical dialogue between the AI and philosophical communities. With a focus on problems of shared interest, the journal actively encourages discussions on the philosophical aspects of computer science.
Offering a global forum, Minds and Machines provides a space to debate and explore important and contentious issues within its editorial focus. The journal presents special editions dedicated to specific topics, invites critical responses to previously published works, and features review essays addressing current problem scenarios.
By facilitating a diverse range of perspectives, Minds and Machines encourages a reevaluation of the status quo and the development of new insights. Through this collaborative approach, the journal aims to bridge the gap between AI and philosophy, fostering a tradition of critique and ensuring these fields remain connected and relevant.
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