Machine Teaching Group

Established: April 28, 2015

The demand for Machine Learning (ML) models far exceeds the supply of “machine teachers” that can build those models. Categories of common-sense understanding tasks that we would like to automate with computers include interpreting commands, feedbacks, requests, alarms and personalized interests. For each such category, there are hundreds or thousands of modeling tasks. For example, we might be interested in building a model to understand voice commands for controlling a television. The solution to the increasing demand is to make teaching machines easy, fast, and universally accessible.

A large fraction of the ML community is focused on creating new algorithms to improve the accuracy of the “learners” (ML algorithms) on given data sets. The “Machine Teaching” discipline, in contrast to machine learning, is focused on the efficacy of the teachers, given the learners. The metrics of machine teaching measure performance relative to human costs, such as productivity, interpretability, scaling (with complexity or contributors), and robustness.

Machine Teaching is a paradigm shift from machine learning, akin to how other fields like programming language have shifted from optimizing performance to optimizing productivity. Concepts such as functional programming, programming interfaces, and source control, for instance, aim at improving productivity.

Many problems that affect productivity are not addressed by traditional ML. For instance, teachers often do not understand their problems at the onset. Concepts definition, schemas, and labels are changing as new islands of rare positives are discovered or when teachers simply change their mind. Evolving decomposition leading to new features and training of sub-models is part of the teaching process. Interpretability, sharing, interchangeability of ML algorithms, and interchangeability of teachers, are explicit goals of the machine teaching languages, “design patterns”, and tools.

The discipline of Machine Teaching lives at the intersection of the HCI, ML, Visualization, Systems and Engineering fields.

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Alumni

  • Aparna Lakshmiratan
  • Denis Charles
  • David Grangier
  • Leon Bottou