Interpretable Machine Learning through Teaching

We've designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept -- for instance, the best images to describe the concept of dogs -- and experimentally we found our approach to be effective at teaching both AIs and humans. Some of the most transformative applications of powerful AI will come from computers and humans collaborating, but getting them to speak a common language is hard. Think about trying to guess the shape of a rectangle when you're only shown a collection of random points inside that rectangle: it's much faster to figure out the correct dimensions of the rectangle when you're given points at the corners of the rectangle instead. Our machine teaching approach works as a cooperative game played between two agents, with one functioning as a student and the other as a teacher.