Philosophy of cognitive science in the age of deep learning.

IF 3.2 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Wiley Interdisciplinary Reviews-Cognitive Science Pub Date : 2024-05-21 DOI:10.1002/wcs.1684
Raphaël Millière
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Abstract

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the center stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful. This article is categorized under: Philosophy > Artificial Intelligence Computer Science and Robotics > Machine Learning.

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深度学习时代的认知科学哲学。
深度学习在人工智能研究的大多数领域都取得了重大进展。这一令人瞩目的进步已超越了单纯的工程学成就,对认知科学哲学具有重要意义。深度神经网络在克服老式联结主义模型的局限性方面取得了长足进步,而老式联结主义模型曾一度占据认知哲学争论的中心舞台。这一发展与认知科学哲学中长期存在的理论争论直接相关。此外,目前与深度神经网络比较评估相关的方法论挑战也将大大受益于与哲学和认知科学的跨学科合作。哲学家们探索与深度学习和认知相关的基础性问题的时机已经成熟;这篇视角论文探讨了哲学家们的贡献尤其富有成效的关键领域。本文归类于哲学 > 人工智能 计算机科学与机器人 > 机器学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.30
自引率
7.70%
发文量
50
期刊最新文献
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