Machine learning for rediscovering revolutionary ideas of the past
| العنوان: | Machine learning for rediscovering revolutionary ideas of the past |
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| المؤلفون: | Michael J. O'Brien, Joshua Borycz, Damian J. Ruck, R. Alexander Bentley, Simon Carrignon |
| المصدر: | Adaptive Behavior. 30:279-286 |
| بيانات النشر: | SAGE Publications, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0106 biological sciences, World Wide Web, 0303 health sciences, 03 medical and health sciences, Behavioral Neuroscience, Experimental and Cognitive Psychology, Sociology, Social learning, 010603 evolutionary biology, 01 natural sciences, Scientific revolution, 030304 developmental biology |
| الوصف: | 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. |
| تدمد: | 1741-2633 1059-7123 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::a445a86b3c03e1c1bd8a817b3b238aef https://doi.org/10.1177/1059712320983045 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........a445a86b3c03e1c1bd8a817b3b238aef |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17412633 10597123 |
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