{"title":"智力的四种属性,一千个问题。","authors":"Matthieu Bardal, Eric Chalmers","doi":"10.1007/s00422-023-00979-4","DOIUrl":null,"url":null,"abstract":"<p><p>Jeff Hawkins is one of those rare individuals who speaks the languages of both AI and neuroscience. In his recent book, \"A Thousand Brains: A New Theory of Intelligence\", Hawkins proposes that current learning algorithms lack four attributes which will be necessary for true machine intelligence. Here we demonstrate that a minimal learning system which satisfies all four points can be constructed using only simple, classical machine learning techniques. We illustrate that such a system falls short of biological intelligence in some important ways. We suggest that Hawkins' list is a useful model, but the \"recipe\" for true intelligence-if there is one-may not be so easily defined.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Four attributes of intelligence, a thousand questions.\",\"authors\":\"Matthieu Bardal, Eric Chalmers\",\"doi\":\"10.1007/s00422-023-00979-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Jeff Hawkins is one of those rare individuals who speaks the languages of both AI and neuroscience. In his recent book, \\\"A Thousand Brains: A New Theory of Intelligence\\\", Hawkins proposes that current learning algorithms lack four attributes which will be necessary for true machine intelligence. Here we demonstrate that a minimal learning system which satisfies all four points can be constructed using only simple, classical machine learning techniques. We illustrate that such a system falls short of biological intelligence in some important ways. We suggest that Hawkins' list is a useful model, but the \\\"recipe\\\" for true intelligence-if there is one-may not be so easily defined.</p>\",\"PeriodicalId\":55374,\"journal\":{\"name\":\"Biological Cybernetics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biological Cybernetics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00422-023-00979-4\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/7 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological Cybernetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00422-023-00979-4","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/7 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
Four attributes of intelligence, a thousand questions.
Jeff Hawkins is one of those rare individuals who speaks the languages of both AI and neuroscience. In his recent book, "A Thousand Brains: A New Theory of Intelligence", Hawkins proposes that current learning algorithms lack four attributes which will be necessary for true machine intelligence. Here we demonstrate that a minimal learning system which satisfies all four points can be constructed using only simple, classical machine learning techniques. We illustrate that such a system falls short of biological intelligence in some important ways. We suggest that Hawkins' list is a useful model, but the "recipe" for true intelligence-if there is one-may not be so easily defined.
期刊介绍:
Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.