“Playing Magic Tricks to Deep Neural Networks Untangles Human Deception”, Regina Zaghi-Lara, Miguel Ángel Gea, Jordi Camí, Luis M. Martínez, Alex Gomez-Marin2019-08-20 (adversarial examples (human), neuroscience; similar)⁠:

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 (ie. 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.

[ML techniques, compared to humans, look a bit like autistic or idiot savants: skilled at fine detail but missing the big picture. Another example of CNNs being ‘smarter by being stupider’ is “Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes”, Eckstein et al 2017.]

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