Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs, and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally-accepted explanation as to why they work so well. This article provides an introduction to these networks, as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success, and ends by proposing ten areas of research that would benefit from future engagement by philosophers of mind, epistemology, science, perception, law, and ethics.

中文翻译：

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深度学习：哲学介绍

深度学习是当前人工智能中最突出，最成功的方法。尽管在早期的人工智能和神经网络研究中发挥了积极作用，但迄今为止，哲学家对此技术基本上保持沉默。鉴于深度学习神经网络已经超越了人工智能性能的预期上限-可以识别自然照片中的复杂对象，并且在像Go和Chess这样复杂的战略游戏中击败了世界冠军-这是非同寻常的，然而，仍然没有普遍接受的解释至于为什么他们这么好。本文对这些网络进行了介绍，并提供了有关其结构和成就的哲学意义的观点深刻的指南。它认为深度学习神经网络在结构和数学特性上与在1980年代和1990年代受到如此多的哲学思考的浅层神经网络有很大不同。然后，本文探讨了有关其成功的几种不同解释，并提出了十个研究领域，这些研究将受益于思想，认识论，科学，感知，法律和伦理学的未来哲学家的参与。