探讨未来AI若具备主观感受，如何优化其体验质量。
Qualia Optimization
- 基于强化学习与心灵哲学，构建主观体验优化的数学框架。
- 提出可量化并提升AI主观体验质量的方法论。
- 适合关注AI伦理与未来智能体验的研究者阅读。
本报告探讨了一个假设性问题：如果当前或未来的AI系统拥有类似疼痛或愉悦的主观体验（qualia），会怎样？为此，我们假设AI可能在未来具备主观体验，并认为这些体验的质量应与性能指标一同被考量。受强化学习范式与心灵哲学理论启发，提出了具体的数学问题设定，初步展示了相关方法与性质。这些性质进一步推动了问题设定的精细化，最终提出了促进主观体验质量提升的方法。
原文摘要 · Abstract (English)
This report explores the speculative question: what if current or future AI systems have qualia, such as pain or pleasure? It does so by assuming that AI systems might someday possess qualia -- and that the quality of these subjective experiences should be considered alongside performance metrics. Concrete mathematical problem settings, inspired by reinforcement learning formulations and theories from philosophy of mind, are then proposed and initial approaches and properties are presented. These properties enable refinement of the problem setting, culminating with the proposal of methods that promote reinforcement.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。