Integrating Machine Learning with Human Knowledge

Changyu Deng; Xunbi Ji; Colton Rainey; Jianyu Zhang; Wei Lu

doi:10.1016/j.isci.2020.101656

Integrating Machine Learning with Human Knowledge

iScience. 2020 Oct 9;23(11):101656. doi: 10.1016/j.isci.2020.101656. eCollection 2020 Nov 20.

Authors

Changyu Deng¹, Xunbi Ji¹, Colton Rainey¹, Jianyu Zhang¹, Wei Lu^{1

2}

Affiliations

¹ Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
² Department of Materials Science & Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Abstract

Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed.

Keywords: Artificial Intelligence; Computer Science; Human-Centered Computing.

Publication types

Review