{"title":"基于透视主义和人工智能责任的智能","authors":"Christian Hugo Hoffmann","doi":"10.1016/j.jrt.2022.100047","DOIUrl":null,"url":null,"abstract":"<div><p>What does the I in the composite “AI” stand for? The answer to this guiding question appears to be straightforward, namely intelligence; however, at secunda facie, one might wonder what intelligence really is. Even though definitions of intelligence have been provided in psychology, neuroscience, and animal cognition research, among others, these crude approaches turn out to be overly systematic, rigid and reductive (<span>Hoffmann, 2022b</span>). At the same time, novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is to showcase the instrumental value of this framework for AI responsibilization.</p></div>","PeriodicalId":73937,"journal":{"name":"Journal of responsible technology","volume":"12 ","pages":"Article 100047"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666659622000245/pdfft?md5=085f80040985d3e9c57290c7d930a69a&pid=1-s2.0-S2666659622000245-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Intelligence in light of perspectivalism and AI responsibilization\",\"authors\":\"Christian Hugo Hoffmann\",\"doi\":\"10.1016/j.jrt.2022.100047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>What does the I in the composite “AI” stand for? The answer to this guiding question appears to be straightforward, namely intelligence; however, at secunda facie, one might wonder what intelligence really is. Even though definitions of intelligence have been provided in psychology, neuroscience, and animal cognition research, among others, these crude approaches turn out to be overly systematic, rigid and reductive (<span>Hoffmann, 2022b</span>). At the same time, novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is to showcase the instrumental value of this framework for AI responsibilization.</p></div>\",\"PeriodicalId\":73937,\"journal\":{\"name\":\"Journal of responsible technology\",\"volume\":\"12 \",\"pages\":\"Article 100047\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666659622000245/pdfft?md5=085f80040985d3e9c57290c7d930a69a&pid=1-s2.0-S2666659622000245-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of responsible technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666659622000245\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of responsible technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666659622000245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligence in light of perspectivalism and AI responsibilization
What does the I in the composite “AI” stand for? The answer to this guiding question appears to be straightforward, namely intelligence; however, at secunda facie, one might wonder what intelligence really is. Even though definitions of intelligence have been provided in psychology, neuroscience, and animal cognition research, among others, these crude approaches turn out to be overly systematic, rigid and reductive (Hoffmann, 2022b). At the same time, novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is to showcase the instrumental value of this framework for AI responsibilization.
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