University of Messina, Department of Cognitive Science, v. Concezione 8, 98121 Messina, Italy.
Cognition. 2020 Oct;203:104365. doi: 10.1016/j.cognition.2020.104365. Epub 2020 Jun 17.
In recent years, the family of algorithms collected under the term "deep learning" has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system.
近年来，术语“深度学习”所收集的算法家族彻底改变了人工智能，使机器在许多复杂认知任务中达到了类似人类的性能。尽管深度学习模型基于连接主义范式，但它们最近的进展基本上是出于工程目标而开发的。尽管它们注重应用，但深度学习模型最终似乎对认知目的很有成效。这可以被视为一种生物适应，其中生理结构变得适用于与其被选择的功能不同的功能。在本文中，我们将认为认知科学是时候认真对待深度学习了，并尝试阐明为什么要这样做的原因。首先，从连接主义项目追溯深度学习的演变路径，展示了显著的连续性以及差异。然后，将考虑深度学习模型如何可用于许多认知主题，尤其是在那些它已经达到与人类相当的性能的主题，从感知到语言。我们将认为深度学习提出了认知科学应该尝试回答的问题。其中一个问题是为什么没有实体、不活跃、不了解上下文且静态的深度卷积模型迄今为止最接近大脑视觉系统的激活模式。
