Animal Language Processing: An AI Convergence In Animal Communication

Summary

Image credit: Elianne Dipp / Pexels Over the last decade, work on animal communication has reached a convergence as the growing availability of large-scale behavioral and bioacoustic data merges with relatively recent advances in machine learning. As a result, we’re starting to see the study of animal communication shift from narrowly defined, species-specific, and hypothesis-limited analyses toward data-driven discovery at scale. We refer to this convergence as Animal Language Processing (ALP), an approach that uses AI to study non-human communication at scale. The term was first used in a presentation given in Japanese by our senior AI researcher Masato Hagiwara in 2024, and it offers a useful framework for describing and organizing the shift we’re observing in the field. How Animal Language Processing Emerged For decades, ethologists, field biologists, and bioacousticians have built the foundations that make studying animal signals both possible and worthwhile. They’ve conducted long-term field studies that link vocal behavior to social context, ecology, and individual life histories. They’ve conducted playback experiments that test how animals respond to specific calls, sequences, or contexts. These experiments are vital for grounding any AI-based predictions in real behavior. We’ve also seen methodological innovations in recording, tagging, and annotation, from autonomous recorders to animal-borne sensors. This work has resulted in incredible breakthroughs, like discovering that elephants call out to each other using distinct names and that female beluga whales have a “come back!” call to coax their curious calves back from wandering too far. That’s all to say that this emergent field builds on decades of accumulated knowledge, methods, and data, and expands what is possible with modern AI tools. What’s Changed: Data, Models, and Scale Several shifts have made a fundamentally different approach possible: 1. Data volume and diversity First, advances in sensing technologies have led to an unprecedented accumulation of behavioral and acoustic data across many species and environments. Tools like passive acoustic monitoring, animal-borne tags, and long-term video recording now capture communication that was previously too hard to capture and annotate, including continuous, overlapping, and context-rich interactions. 2. Bigger questions are being asked acro…