Playing magic tricks to deep neural networks untangles human deception
This work provides insights into human deception and perception biases by comparing AI and human responses to magic tricks, though it is incremental in applying existing tracking methods to a novel context.
The study used deep learning to track a magician's coin tricks and trained an AI as an 'artificial spectator' to infer coin locations, revealing situations where humans and AI were fooled differently, highlighting human cognitive biases.
Magic is the art of producing in the spectator an illusion of impossibility. Although the scientific study of magic is in its infancy, the advent of recent tracking algorithms based on deep learning allow now to quantify the skills of the magician in naturalistic conditions at unprecedented resolution and robustness. In this study, we deconstructed stage magic into purely motor maneuvers and trained an artificial neural network (DeepLabCut) to follow coins as a professional magician made them appear and disappear in a series of tricks. Rather than using AI as a mere tracking tool, we conceived it as an "artificial spectator". When the coins were not visible, the algorithm was trained to infer their location as a human spectator would (i.e. in the left fist). This created situations where the human was fooled while AI (as seen by a human) was not, and vice versa. Magic from the perspective of the machine reveals our own cognitive biases.