{"title":"神经符号人工智能是缩放定律的对立面。","authors":"Alvaro Velasquez, Neel Bhatt, Ufuk Topcu, Zhangyang Wang, Katia Sycara, Simon Stepputtis, Sandeep Neema, Gautam Vallabha","doi":"10.1093/pnasnexus/pgaf117","DOIUrl":null,"url":null,"abstract":"<p><p>The recent progress in machine learning has shifted the trends in artificial intelligence (AI) toward an overreliance on increasing amounts of data, computing power, and model parameters. These trends have resulted in success, but have also created a monolithic perspective for AI, increased the barriers to entry outside of large tech companies, and raised concerns about computational sustainability. Neurosymbolic AI is a growing area that promotes methodological heterogeneity and aims to push the frontiers of AI through affordable data and computing power.</p>","PeriodicalId":74468,"journal":{"name":"PNAS nexus","volume":"4 5","pages":"pgaf117"},"PeriodicalIF":2.2000,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084822/pdf/","citationCount":"0","resultStr":"{\"title\":\"Neurosymbolic AI as an antithesis to scaling laws.\",\"authors\":\"Alvaro Velasquez, Neel Bhatt, Ufuk Topcu, Zhangyang Wang, Katia Sycara, Simon Stepputtis, Sandeep Neema, Gautam Vallabha\",\"doi\":\"10.1093/pnasnexus/pgaf117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The recent progress in machine learning has shifted the trends in artificial intelligence (AI) toward an overreliance on increasing amounts of data, computing power, and model parameters. These trends have resulted in success, but have also created a monolithic perspective for AI, increased the barriers to entry outside of large tech companies, and raised concerns about computational sustainability. Neurosymbolic AI is a growing area that promotes methodological heterogeneity and aims to push the frontiers of AI through affordable data and computing power.</p>\",\"PeriodicalId\":74468,\"journal\":{\"name\":\"PNAS nexus\",\"volume\":\"4 5\",\"pages\":\"pgaf117\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084822/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PNAS nexus\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/pnasnexus/pgaf117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PNAS nexus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/pnasnexus/pgaf117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Neurosymbolic AI as an antithesis to scaling laws.
The recent progress in machine learning has shifted the trends in artificial intelligence (AI) toward an overreliance on increasing amounts of data, computing power, and model parameters. These trends have resulted in success, but have also created a monolithic perspective for AI, increased the barriers to entry outside of large tech companies, and raised concerns about computational sustainability. Neurosymbolic AI is a growing area that promotes methodological heterogeneity and aims to push the frontiers of AI through affordable data and computing power.
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