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The current processes for building machine learning systems requirepractitioners with deep knowledge of machine learning. This significantlylimits the number of machine learning systems that can be created and has ledto a mismatch between the demand for machine learning systems and the abilityfor organizations to build them. We believe that in order to meet this growingdemand for machine learning systems we must significantly increase the numberof individuals that can teach machines. We postulate that we can achieve thisgoal by making the process of teaching machines easy, fast and above all,universally accessible.  While machine learning focuses on creating new algorithms and improving theaccuracy of "learners", the machine teaching discipline focuses on the efficacyof the "teachers". Machine teaching as a discipline is a paradigm shift thatfollows and extends principles of software engineering and programminglanguages. We put a strong emphasis on the teacher and the teacher'sinteraction with data, as well as crucial components such as techniques anddesign principles of interaction and visualization.  In this paper, we present our position regarding the discipline of machineteaching and articulate fundamental machine teaching principles. We alsodescribe how, by decoupling knowledge about machine learning algorithms fromthe process of teaching, we can accelerate innovation and empower millions ofnew uses for machine learning models.

Machine Teaching: A New Paradigm for Building Machine Learning Systems