From cacophony to hierarchy: a principled framework for assessing AI consciousness· 从混乱到层次：评估 AI 意识的原则框架
The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a cacophony of competing theories that often talk past each other. Separating the hard problem from the mapping problem allows the deepest metaphysical disagreements to be set aside: granting that experience supervenes on a system's organisation, the tractable question becomes at which grain of description that supervenience base sits. We extend Marr's three levels of analysis into a five-level hierarchy of functional descriptions (behavioural, computational, intrinsic causal-structural, organismic, and organism-environment) grounded in supervenience, coarse-graining, and multiple realisability. The major theories of consciousness are positioned wi
提出多层级评估框架，解决 AI 意识评估难题。
- 核心方法
- 扩展 Marr 的三个分析层次为五个层级的功能描述框架，涵盖行为、计算、内在因果结构、有机体及有机体-环境层面。
- 适合谁读
- 研究者
- 要解决的问题
- 现有的 AI 意识理论分歧大，缺乏统一评估标准。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种原则性的多层级框架，有助于系统化评估和讨论 AI 意识。
- 意义与局限
- 推动了 AI 意识研究的标准化和可比较性，但理论框架的实际应用仍需进一步验证。