当前AI意识评估缺乏可靠依据，应转向生物启发的神经架构研究。
From indicators to biology: the calibration problem in artificial consciousness
- 从行为测试转向内部结构指标，但现有指标无独立验证
- 意识科学理论分散，缺乏人工现象性的客观标准
- 建议聚焦生物神经架构，缩小与真实意识系统的差距
近期关于人工意识的研究将评估重心从行为转向内部架构，基于意识理论推导出指标并更新信念。这比简单的图灵测试更进一步。然而，以指标为导向的研究仍存在认识论上的校准不足：意识科学理论碎片化，指标缺乏独立验证，且不存在人工主观体验的客观基准。在此背景下，对当前AI系统进行概率性意识归因尚不成熟。更稳妥的短期策略是将研究重点转向生物基础的工程系统——包括生物混合、类脑计算和连接组级系统——以缩小与唯一有实证基础的意识领域（生命系统）之间的差距。
原文摘要 · Abstract (English)
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.
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