This report enlists 13 functional conditions cashed out in computational
terms that have been argued to be constituent of conscious valenced experience.
These are extracted from existing empirical and theoretical literature on,
among others, animal sentience, medical disorders, anaesthetics, philosophy,
evolution, neuroscience, and artificial intelligence.

该报告概述了从现有经验和理论文献中提取出的 13 个与有意识感知经验的构成要素相关的功能条件，在计算术语中进行了解释，主要涉及动物感知、医学障碍、麻醉、哲学、进化、神经科学和人工智能领域。

意识化体验计算指标候选报告

Report on Candidate Computational Indicators for Conscious Valenced  Experience

Whether current or near-term AI systems could be conscious is a topic of
scientific interest and increasing public concern. This report argues for, and
exemplifies, a rigorous and empirically grounded approach to AI consciousness:
assessing existing AI systems in detail, in light of our best-supported
neuroscientific theories of consciousness. We survey several prominent
scientific theories of consciousness, including recurrent processing theory,
global workspace theory, higher-order theories, predictive processing, and
attention schema theory. From these theories we derive "indicator properties"
of consciousness, elucidated in computational terms that allow us to assess AI
systems for these properties. We use these indicator properties to assess
several recent AI systems, and we discuss how future systems might implement
them. Our analysis suggests that no current AI systems are conscious, but also
shows that there are no obvious barriers to building conscious AI systems.

当前和近期 AI 系统是否会具备意识是一个受到科学界关注和公众担忧的话题。本报告提出并举例了一种严格的和以实证为基础的 AI 意识研究方法，即在我们最有支持的神经科学意识理论的指导下，对现有 AI 系统进行详细评估。我们调查了几个著名的科学意识理论，包括循环处理理论、全局工作空间理论、高阶理论、预测处理和注意图式理论。根据这些理论，我们以计算术语阐明了意识的 “指示性属性”，从而可以评估 AI 系统是否具备这些属性。我们利用这些指示性属性评估了几个最近的 AI 系统，并讨论了未来系统如何实现它们。我们的分析表明目前没有任何 AI 系统具备意识，但也表明建立意识的 AI 系统并没有明显的障碍。