Historically, the dispute between empiricists and nativists in philosophy and cognitive science has concerned human and animal minds (Margolis and Laurence in Philos Stud: An Int J Philos Anal Tradit 165(2): 693-718, 2013, Ritchie in Synthese 199(Suppl 1): 159–176, 2021, Colombo in Synthese 195: 4817–4838, 2018). But recent progress has highlighted how empiricist and nativist concerns arise in the construction of artificial systems (Buckner in From deep learning to rational machines: What the history of philosophy can teach us about the future of artificial intelligence. Oxford University Press.). This paper uses nativism and empiricism to address questions about the nature of artificial intelligence and its trajectory. It begins by defining the nativism/empiricism debate in terms of the generality of a system. Nativist systems have initial states with domain-specific features; empiricist systems have initial states with only domain-general features. With the debate framed in this way, it then explores a variety of arguments for nativism and empiricism in AI. These arguments revolve around two different questions which must be distinguished: whether nativism the only possible approach to developing human-level AI (HLAI); and whether nativism is the most practical approach to developing HLAI. On the first question, it argues that nativism is quite clearly not the only possible approach to developing HLAI, as is sometimes suggested. It argues that existing arguments for the necessity of nativism are unconvincing, because they analogize from poverty of the stimulus arguments about humans, while AIs often have access to much more data than humans. Then it argues that the case of evolution gives us a compelling argument against nativism. On the second, practical question, the paper argues that there is a tradeoff between the advantages of encoding innate machinery directly, and the advantages of evolving or learning it. However, as the past decade has shown, empiricism is a much more viable path to greater capability levels, given the ‘bitter lesson’ (Sutton in Reinforcement Learning: An Introduction. MIT press.) that encoding the ‘correct’ knowledge in AI systems is perennially outperformed by more empiricist methods that leverage large-scale data and computation.

从历史上看，哲学和认知科学中经验主义者和先天主义者之间的争论一直涉及人类和动物的心灵（Margolis 和 Laurence in Philos Stud: An Int J Philos Anal Tradit 165(2): 693-718, 2013, Ritchie in Synthese 199(Suppl 1): 159–176, 2021, 科伦坡综合 195: 4817–4838, 2018)。但最近的进展凸显了在人工系统的构建中如何出现经验主义和本土主义的担忧（巴克纳，《从深度学习到理性机器：哲学史可以告诉我们人工智能的未来》。牛津大学出版社。）。本文使用先天主义和经验主义来解决有关人工智能的本质及其轨迹的问题。它首先根据系统的普遍性来定义本土主义/经验主义辩论。本土主义系统具有具有特定领域特征的初始状态；经验主义系统的初始状态仅具有领域通用特征。以这种方式展开辩论，然后探讨了人工智能领域的本土主义和经验主义的各种论点。这些争论围绕着两个必须区分的不同问题：本土主义是否是开发人类水平人工智能（HLAI）的唯一可能方法？以及本土主义是否是发展 HLAI 最实用的方法。关于第一个问题，它认为本土主义显然不是发展 HLAI 的唯一可能的方法，正如有时所建议的那样。它认为，现有的关于本土主义必要性的论点并不令人信服，因为它们是从关于人类的刺激论的贫困进行类比的，而人工智能往往比人类能够获得更多的数据。然后它认为进化论的例子为我们提供了反对本土主义的令人信服的论据。关于第二个实际问题，本文认为，直接编码先天机制的优势与进化或学习它的优势之间存在权衡。然而，正如过去十年所表明的那样，鉴于在人工智能系统中编码“正确”知识的“惨痛教训”（萨顿强化学习：简介。麻省理工学院出版社） ，经验主义是实现更高能力水平的更可行的途径长期被利用大规模数据和计算的经验主义方法所超越。