Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification
Michelle Mohr Carney, Barron Webster, Irene Alvarado, Kyle Meredith Phillips, Noura Howell, Jordan Griffith, Jonas Jongejan, Amit Pitaru, Alexander Chen
Abstract
Teachable Machine (teachablemachine.withgoogle.com) is a web-based GUI tool for creating custom machine learning classification models without specialized technical expertise. (Machine learning, or ML, lets systems learn to analyze data without being explicitly programmed.) 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 like AI ethics at institutions including the Stanford d.school, NYU's Interactive Telecommunications Program, the MIT Media Lab, as well as creative experiments. 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 supports people accessing ML concepts.