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.

翻译：当前或近期的人工智能系统是否可能具备意识，是一个科学界关注且日益引发公众担忧的话题。本报告论证并示范了一种严谨且基于实证的人工智能意识研究方法：基于我们最受支持的神经科学意识理论，对现有AI系统进行详细评估。我们梳理了几种重要的科学意识理论，包括递归加工理论、全局工作空间理论、高阶理论、预测加工理论及注意图式理论。从这些理论中，我们提炼出意识的"指标属性"，并以计算术语加以阐释，从而能够评估AI系统是否具备这些属性。我们利用这些指标属性评估了多个近期AI系统，并讨论了未来系统如何实现这些属性。分析表明，当前尚无AI系统具备意识，但也揭示了构建有意识AI系统并不存在明显的障碍。