ISSN 1008-2204
CN 11-3979/C
王东. 机器学习与科学发现的逻辑刍议[J]. 北京航空航天大学学报社会科学版, 2021, 34(2): 99-104. DOI: 10.13766/j.bhsk.1008-2204.2021.0003
引用本文: 王东. 机器学习与科学发现的逻辑刍议[J]. 北京航空航天大学学报社会科学版, 2021, 34(2): 99-104. DOI: 10.13766/j.bhsk.1008-2204.2021.0003
WANG Dong. A Preliminary Study of Machine Learning and Logic of Scientific Discovery[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2021, 34(2): 99-104. DOI: 10.13766/j.bhsk.1008-2204.2021.0003
Citation: WANG Dong. A Preliminary Study of Machine Learning and Logic of Scientific Discovery[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2021, 34(2): 99-104. DOI: 10.13766/j.bhsk.1008-2204.2021.0003

机器学习与科学发现的逻辑刍议

A Preliminary Study of Machine Learning and Logic of Scientific Discovery

  • 摘要: 是否存在科学发现的逻辑一直存在争议,人工智能(AI)发展早期通过基于规则的和大规模数据挖掘的方法探索自动科学发现存在局限,包括需要先验知识或者只能发现特定领域的经验规律。通过近期两个案例分析介绍基于机器学习的研究可以不需要先验知识就能发现科学概念甚至是简单的理论,但仍然存在训练数据的来源、观察和实验的选择、科学理论的构建以及因果建模等问题,需要结合科学哲学和哲学史做跨学科的研究。

     

    Abstract: Whether there is the logic of scientific discovery has always been controversial. In the early development of artificial intelligence (AI), there were limitations in the research of automatic scientific discovery through rule-based and data mining methods, including the need for prior knowledge or only the discovery of empirical laws in specific fields. Through two case studies, it was shown that the recent research based on machine learning can find the concept of science and even a simple theory without prior knowledge. But there are still some problems such as the source of training data, the choice of observation and experiment, the construction of scientific theory and the causal modeling which need interdisciplinary research combination with philosophy of science and history of philosophy.

     

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