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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 Y uk 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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Generative adversarial networks(GANs) took the Machine Learning field by storm last year with those impressive fake human-like faces. Bonus Point* They are basically generated from nothing. Irrefutably, GANs implements implicit learning methods where the model learns without the data directly passing through the network, unlike those explicit techniques where weights are learned directly from the data. Okay, suppose in the city of Rio de Janeiro, money forging felonies are increasing so a department is appointed to check in these cases. Detectives are expected to classify the legit ones and fake ones.