In 2025 we will see AI and machine learning leveraged to make real progress in understanding animal communication, answering a question that has puzzled humans as long as we have existed: “What are animals saying to each other?” The recent Coller-Dolittle Prize, offering cash prizes up to half-a-million dollars for scientists who “crack the code” is an indication of a bullish confidence that recent technological developments in machine learning and large language models (LLMs) are placing this goal within our grasp.
Many research groups have been working for years on algorithms to make sense of animal sounds. Project Ceti, for example, has been decoding the click trains of sperm whales and the songs of humpbacks. These modern machine learning tools require extremely large amounts of data, and up until now, such quantities of high-quality and well-annotated data have been lacking.
