Recent work on artificial consciousness shifts evaluation from behaviour to internal architecture, deriving indicators from theories of consciousness and updating credences accordingly. This is progress beyond naive Turing-style tests. But the indicator-based programme remains epistemically under-calibrated: consciousness science is theoretically fragmented, indicators lack independent validation, and no ground truth of artificial phenomenality exists. Under these conditions, probabilistic consciousness attribution to current AI systems is premature. A more defensible near-term strategy is to redirect effort toward biologically grounded engineering -- biohybrid, neuromorphic, and connectome-scale systems -- that reduces the gap with the only domain where consciousness is empirically anchored: living systems.

翻译：近期关于人工意识的研究将评估重点从行为转向内部架构，从意识理论中推导出指标并相应更新置信度。这是对朴素图灵测试范式的超越。然而，基于指标的研究方案在认识论层面仍存在校准不足的问题：意识科学在理论上支离破碎，指标缺乏独立验证，且不存在人工现象性的基础真实标准。在此条件下，对现有AI系统进行概率性意识归属的判定为时过早。一个更具可辩护性的近期策略是，将研究重心转向基于生物学的工程系统——包括生物混合系统、神经形态系统及连接组尺度系统——从而缩短与唯一具有经验基础意识领域（即生物系统）之间的差距。