人工智能是愚蠢的，因果推理不会解决它
Artificial Intelligence is stupid and causal reasoning won't fix it
摘要：
人工神经网络已经在各种游戏中达到了大师级甚至超人级的性能：从涉及完美信息的游戏（如围棋）到涉及非完美信息的游戏（如星际争霸）。人工智能实验室的这种技术发展已经在商业世界中带来了相应的应用-人工智能品牌标签正在迅速变得无处不在。如此广泛的商业部署的一个必然结果是，当人工智能出错时-自动驾驶汽车坠毁;聊天机器人表现出种族主义行为;自动信用评分过程歧视性别等-通常会产生重大的财务，法律和品牌后果，事件成为重大新闻。正如Judea Pearl所看到的，这些错误的根本原因是，“深度学习所有令人印象深刻的成就都只是曲线拟合”。朱迪亚·珀尔认为，关键是用联想推理（causal reasoning）--从观察到的现象中推断原因的能力--取代推理。Gary Marcus和Ernest Davis最近在《纽约时报》的一篇文章中也表达了这一观点：“我们需要停止构建那些仅仅在数据集中检测统计模式方面变得越来越好的计算机系统--通常使用一种被称为深度学习的方法--并开始构建那些从组装的那一刻起就天生掌握三个基本概念的计算机系统：时间、空间和空间。”在这篇论文中，我将突出吉尔伯特·赖尔（Gilbert Ryle）在1949年所称的范畴错误，为人工智能的错误提供另一种解释：与其说人工智能机器无法理解因果关系，不如说人工智能机器--作为计算--根本无法理解任何事情。
英文摘要：
Artificial Neural Networks have reached Grandmaster and even super-human performance across a variety of games: from those involving perfect-information (such as Go) to those involving imperfect-information (such as Starcraft). Such technological developments from AI-labs have ushered concomitant applications across the world of business - where an AI brand tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits racist behaviour; automated credit scoring processes discriminate on gender etc. - there are often significant financial, legal and brand consequences and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that, 'all the impressive achievements of deep learning amount to just curve fitting'. The key, Judea Pearl suggests, is to replace reasoning by association with causal-reasoning - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: 'we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets - often using an approach known as Deep Learning - and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality'. In this paper, foregrounding what in 1949 Gilbert Ryle termed a category mistake, I will offer an alternative explanation for AI errors: it is not so much that AI machinery cannot grasp causality, but that AI machinery - qua computation - cannot understand anything at all.
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。
