神经网络也能快速学习与创新，挑战了人类思维必须是符号系统的观点。
Whither symbols in the era of advanced neural networks?
- 用神经网络的类人能力反驳符号系统必要性
- 指出神经网络训练数据源自符号系统，说明其仍具重要价值
- 提出重新思考人类思维符号基础的新研究方向
人类心智为何应被视为符号系统，主要基于其组合思想、产生新意和快速学习的能力。我们论证现代神经网络——以及基于它们的人工智能系统——也展现出类似能力。这削弱了人类认知过程和表征必须是符号性的论据。尽管如此，这些神经网络通常在由符号系统生成的数据上训练，表明符号系统在刻画人类心智需解决的抽象问题中仍起重要作用。由此，我们提出了关于人类思维符号基础研究的新议程。
原文摘要 · Abstract (English)
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, produce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by symbolic systems illustrates that such systems play an important role in characterizing the abstract problems that human minds have to solve. This argument leads us to offer a new agenda for research on the symbolic basis of human thought.
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