Echoes of Intelligence: What AI Could Learn From Bats

The rapid progress of deep learning has been driven in part by general principles of biological information processing, yet current AI systems still fall short of biological intelligence in critical ways such as integration of the computational and physical domains, i.e., embodied AI, and achieving autonomy in complex environments. This perspective argues that bat biosonar offers a particularly valuable model system for advancing embodied AI and autonomy in complex environments beyond the currently dominating vision‐centered and data‐intensive paradigms. Bats navigate dense and dynamic environments based on sparse trains of clutter echoes with random waveforms. These capabilities suggest alternative computational principles for perception and action: exploiting statistical rather than deterministic signal structure, extracting task‐relevant information from low‐bandwidth streams, and integrating morphology, actuation, and neural processing into closed‐loop embodied intelligence. This perspective reviews how bat biosonar differs from engineered sonar, identifies desirable capabilities that could inspire next‐generation autonomous systems, and outlines future research directions for deciphering and reproducing bat‐like intelligence through multimodal biological data, machine learning, neuromorphic processing, and biomimetic robotic platforms. By framing bat biosonar as a model for dynamic embodied intelligence, this perspective highlights a path toward AI systems that are more robust, parsimonious, and highly capable in real‐world environments.