Alan Turing’s 1950 paper, “Computing Machinery and Intelligence,” contains much more than its proposal of the “Turing Test.” Turing imagined the development of what we today call AI by a process akin to the education of a child. Thus, while Turing anticipated “machine learning,” his prescience brings to the foreground the yet unsolved problem of how humans might teach or shape AIs to behave in ways that align with moral standards. Part of the teaching process is likely to entail AIs’ absorbing lessons from human writings. Natural language processing tools are one of the ways computer systems extract knowledge from texts. An example is given of how one such technique, Latent Dirichlet Allocation, can draw out the most prominent themes from works of classical political theory.

中文翻译：

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人工智能可以学习政治理论吗？

艾伦·图灵（Alan Turing）在1950年发表的论文“计算机械与智能”中所包含的内容远远超过了“图灵测试”的提议。图灵通过类似于儿童教育的过程来想象我们今天所谓的人工智能的发展。因此，尽管图灵期望“机器学习”，但他的先知将尚未解决的问题提出来，即人类如何教导或塑造AI以符合道德标准的方式行事。教学过程的一部分可能需要AI吸收人类著作中的教训。自然语言处理工具是计算机系统从文本中提取知识的方法之一。举例说明了一种潜在的狄利克雷分配技术如何从古典政治理论著作中引出最突出的主题。