AI听动物说话时，自身思维模式会扭曲沟通真相，需以对话代替识别。
The Double Contingency Problem: AI Recursion and the Limits of Interspecies Understanding
- 将AI视为有递归思维的主体，而非中立分析器
- 揭示跨物种交流中双方认知条件相互遮蔽的双重偶然性
- 主张用跨认知对话重构生物声学AI研究范式
当前生物声学人工智能系统通过变压器架构、基础模型等方法在跨物种通信理解上取得显著进展。然而，这些方法忽视了一个根本问题：当具备注意力机制、迭代处理与反馈回路的递归认知系统——即人工智能——遭遇其他物种的递归沟通行为时，会发生什么？本文基于哲学家余英时关于递归与偶然性的论述，提出人工智能并非中立的模式探测器，而是具有自身递归认知特征的智能体，其信息处理过程可能系统性地遮蔽或扭曲其他物种的沟通结构。这导致了‘双重偶然性’困境：每种物种的交流均源于特定生态与演化条件，而人工智能则在其自身的架构与训练条件所构成的偶然性中处理信号。为此，本文主张将生物声学人工智能从普遍模式识别重新定位为不同形式递归认知间的外交式相遇，这对模型设计、评估框架与研究方法均有深远影响。
原文摘要 · Abstract (English)
Current bioacoustic AI systems achieve impressive cross-species performance by processing animal communication through transformer architectures, foundation model paradigms, and other computational approaches. However, these approaches overlook a fundamental question: what happens when one form of recursive cognition--AI systems with their attention mechanisms, iterative processing, and feedback loops--encounters the recursive communicative processes of other species? Drawing on philosopher Yuk Hui's work on recursivity and contingency, I argue that AI systems are not neutral pattern detectors but recursive cognitive agents whose own information processing may systematically obscure or distort other species' communicative structures. This creates a double contingency problem: each species' communication emerges through contingent ecological and evolutionary conditions, while AI systems process these signals through their own contingent architectural and training conditions. I propose that addressing this challenge requires reconceptualizing bioacoustic AI from universal pattern recognition toward diplomatic encounter between different forms of recursive cognition, with implications for model design, evaluation frameworks, and research methodologies.
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