Machine Teaching: A New Paradigm for Building Machine Learning Systems

• DOI: 10.48550/arxiv.1707.06742
• Publication Date: 2017-01-01
• Authors: Patrice Y. Simard; Saleema Amershi; David M. Chickering; Alicia Edelman Pe...; Soroush Ghorashi; Christopher Meek; Gonzalo Ramos; Jina Suh; Johan Verwey; Mo Wang; John Wernsing
• Keywords: Decoupling (probability)

The current processes for building machine learning systems require practitioners with deep knowledge of learning. This significantly limits the number that can be created and has led to a mismatch between demand ability organizations build them. We believe in order meet this growing we must increase individuals teach machines. postulate achieve goal by making process teaching machines easy, fast above all, universally accessible. While focuses on creating new algorithms improving accuracy "learners", discipline efficacy "teachers". Machine as is paradigm shift follows extends principles software engineering programming languages. put strong emphasis teacher teacher's interaction data, well crucial components such techniques design visualization. In paper, present our position regarding articulate fundamental principles. also describe how, decoupling about from teaching, accelerate innovation empower millions uses models.