Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It.
Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It.
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DOI：
10.3389/fpsyg.2020.513474
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发表时间：
2020
影响因子：
3.8
通讯作者：
Bishop JM
中科院分区：
文献类型：
--
作者：
Bishop JM
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）到涉及不完美信息的游戏（如星际争霸）。人工智能（AI）实验室的这种技术发展已经在商业世界中带来了伴随的应用，“AI”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、法律的和品牌后果，事件会成为重大新闻。正如朱迪亚·珀尔所看到的，这种错误的根本原因是“......深度学习的所有令人印象深刻的成就都只是曲线拟合。”正如珀尔所建议的，关键是用“因果推理”取代“联想推理”--从观察到的现象中推断原因的能力。加里·马库斯（Gary Marcus）和欧内斯特·戴维斯（Ernest Davis）最近在《纽约》上发表的一篇文章中也表达了同样的观点：“我们需要停止构建计算机系统，这些系统仅仅是越来越善于检测数据集中的统计模式--通常使用一种被称为”深度学习“的方法--并开始构建计算机系统，这些系统从组装的那一刻起就天生掌握了三个基本概念：时间、空间和因果关系。”在这篇论文中，突出了吉尔伯特赖尔在1949年所说的“类别错误”，我将为人工智能的错误提供另一种解释;与其说人工智能机器不能“掌握”因果关系，不如说人工智能机器（作为计算）根本不能理解任何东西。
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 artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, 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, as 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.
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影响因子：
1.7
作者：
Church, A
通讯作者：
Church, A
影响因子：
5.4
作者：
Bishop, John Mark
通讯作者：
Bishop, John Mark
影响因子：
3.5
作者：
BLOCK, N
通讯作者：
BLOCK, N
影响因子：
2.4
作者：
Bishop, M
通讯作者：
Bishop, M
DOI：
10.1080/09528130050111455
发表时间：
2000-07-01
影响因子：
2.2
作者：
Bringsjord, S;Xiao, H
通讯作者：
Xiao, H