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:
