Machine learning for rediscovering revolutionary ideas of the past

R Alexander Bentley; Joshua Borycz; Simon CarrignonDamian J RuckMichael J O’Brien

首页> 外文期刊>Adaptive behavior >Machine learning for rediscovering revolutionary ideas of the past

【24h】

Machine learning for rediscovering revolutionary ideas of the past

机译：Machine learning for rediscovering revolutionary ideas of the past

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

著录项

来源
《Adaptive behavior》 |2022年第3期|279-286|共8页
作者
R Alexander Bentley; Joshua Borycz; Simon CarrignonDamian J RuckMichael J O’Brien;
展开▼
作者单位

Department of Anthropology, The University of Tennessee|Center for the Dynamics of Social Complexity, The University of Tennessee;

Sarah Shannon Stevenson Science and Engineering Library, Vanderbilt University;

Network Science Institute, Northeastern UniversityOffice of the Provost, Texas A&M University–San Antonio|Department of Anthropology, University of Missouri;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词
Artificial intelligence; machine learning; phylogenetic analysis; scientific revolution; social learning;

Machine learning for rediscovering revolutionary ideas of the past

摘要

著录项

相关主题

期刊订阅