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