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通过系统性来解释可解释性:人工智能的艰难系统性挑战

Matthieu Queloz1

  • 1Institute of Philosophy, University of Bern, Laenggassstrasse 49a, 3012 Bern, Switzerland.

Minds and machines
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概括
此摘要是机器生成的。

人工智能 (AI) 需要的不仅仅是可解释性;它需要系统性来实现一致和连贯的思维. 本文重新定义了系统性,解决了挑战,并提出了人工智能发展的动态框架.

关键词:
复合性 复合性是指复合性.连接主义 连接主义可以解释的可解释性.系统化的功能.可以解释性 解释性思想的语言是思想的语言.人工智能的哲学 人工智能的哲学生产力 生产力 生产力系统性 系统性 系统性在XAI,XAI就是XAI.

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科学领域:

  • 认知科学 认知科学
  • 人工智能的人工智能
  • 思想的哲学 思想的哲学

背景情况:

  • 可解释性是人工智能的关键期望,但不是高级人工智能的唯一标准.
  • "系统性挑战"历史上质疑连接主义AI实现系统思维的能力.
  • 一个更丰富的系统性概念,包括一致性和连贯性,已经被忽视了.

研究的目的:

  • 为人工智能提出一个更广泛的理想,超越了解释性,专注于系统性.
  • 提供一个概念框架,区分"思想系统性"的四种感官.
  • 重新评估连接主义和系统性之间的紧张关系.

主要方法:

  • 对"思想的系统性"进行概念分析.
  • 区分多种意义上的系统性.
  • 检查系统化的理由及其可转移到人工智能模型.

主要成果:

  • 提出了一个概念框架,区分四种系统性的感觉.
  • 解决了连接主义和系统性之间的感知冲突.
  • 确定了系统化的五个理由,并应用于AI,揭示了"艰难的系统性挑战".

结论:

  • 人工智能的系统性理想比以前理解的要苛刻得多.
  • 建议对系统化的动态理解,调节AI对系统性的需求.
  • 这一框架指导了人工智能模型应该如何以及何时变得更加系统.