Google Research

Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification

  • Alex Chen
  • Amit Pitaru
  • Barron Webster
  • Irene Alvarado
  • Jordan Griffith
  • Kyle Phillips
  • Michelle Carney
  • Noura Howell
Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification (2020)

Abstract

Teachable Machine is a web-based GUI tool for creating custom machine learning (ML) classification models without specialized technical expertise. We created it to help students, teachers, designers, and others learn about ML by creating and using their own classification models. Its broad uptake suggests it has empowered people to learn, teach, and explore ML concepts: People have created curriculum, tutorials, and other resources using Teachable Machine on topics such as AI ethics at institutions including the Stanford d.school, NYU ITP, the MIT Media Lab, as well as creative experiments. Over 182,000 users in 201 countries have created over 125,000 classification models. Here we outline the project and its key contributions of (1) a flexible, approachable interface for ML classification models without ML or coding expertise, (2) a set of technical and design decisions that can inform future interactive machine learning tools, and (3) an example of how structured learning content surrounding the tool support people accessing ML concepts.

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