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超越计算等价性:机器意识的行为推理原理
Stefano Palminteri1,2, Charley M Wu3,4,5
1Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL, Research University, 29 rue d'Ulm, 75005, Paris, France.
Neuroscience of consciousness
|February 18, 2026
概括
这篇意见稿批评了用于在大型语言模型 (LLM) 中评估机器意识的计算等价原则. 它提出了一种行为推理原理,为人工意识提供一个更有效和可操作的标准.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 思想的哲学 思想的哲学
背景情况:
- 大型语言模型 (LLM) 展示了先进的能力,引发了关于人工通用智能和机器意识的辩论.
- 目前关于机器意识的共识依赖于计算功能主义和计算等价原则.
- 在LLMs中出现的教师是意想不到的,加剧了关于将意识归因于人工实体的讨论.
研究的目的:
- 批判性地评估目前用于评估机器意识的计算等价原则.
- 倡导一种替代性的"行为推理原理",用于将意识归因于人工实体.
- 为机器意识建立一个认识论上有效和可操作的标准.
主要方法:
- 对计算等价原理的批判性分析.
- 发展和论证"行为推理原理".
- 在AI中对意识归因的哲学考察.
主要成果:
- 目前的计算等价原则被认为不足以评估机器意识.
- 提出的"行为推断原理"提供了一个更实用的方法.
- 归因意识应该基于其在解释和预测行为的实用性.
结论:
- "行为推断原理"为评估机器意识提供了一个强大的框架.
- 这个原则增强了我们对人工意识的评估进行操作和验证的能力.
- 考虑到人工智能和法学士的快速发展,重新考虑意识归因至关重要.
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