## the machine pupil

2021, made using RunwayML, p5js, Bramble

This project uses an object-recognition machine learning model to explore the place of automation in teaching math.

The machine learning model was trained to recognise specific geometric patterns - triangles, circles, intersections, ‘cuts’ and ‘areas of complexity’. It then attempted to do so for randomised patterns, as well as trying to apply the same logic on a japanese visual multiplication system. It’s failures are paradoxical for a mathematical system and question ‘the right way’ of teaching logical reasoning.

This project uses an object-recognition machine learning model to explore the place of automation in teaching math.

The machine learning model was trained to recognise specific geometric patterns - triangles, circles, intersections, ‘cuts’ and ‘areas of complexity’. It then attempted to do so for randomised patterns, as well as trying to apply the same logic on a japanese visual multiplication system. It’s failures are paradoxical for a mathematical system and question ‘the right way’ of teaching logical reasoning.

semiotics key:

example set: