对深层神经网络玩魔术解开人类欺骗
Playing magic tricks to deep neural networks untangles human deception
摘要：
魔术是在观众中制造不可能的幻觉的艺术。虽然对魔术的科学研究还处于起步阶段，但最近基于深度学习的跟踪算法的出现，现在可以以前所未有的分辨率和鲁棒性来量化魔术师在自然条件下的技能。在这项研究中，我们将舞台魔术解构为纯粹的运动动作，并训练了一个人工神经网络（DeepLabCut）来跟踪硬币，因为专业魔术师让它们在一系列技巧中出现和消失。而不是使用人工智能作为一个纯粹的跟踪工具，我们设想它作为一个“人工观众”。当硬币不可见时，算法被训练来推断它们的位置，就像人类观众一样（即在左拳中）。这就造成了人类被愚弄而人工智能（人类所看到的）没有被愚弄的情况，反之亦然。从机器的角度看魔术揭示了我们自己的认知偏见。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
