Arik Kershenbaum writes:

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.

Consider LLMs such as ChatGPT that have training data available to them that includes the entirety of text available on the internet. Such information on animal communication hasn’t been accessible in the past. It’s not just that human data corpora are many orders of magnitude larger than the kind of data we have access to for animals in the wild: More than 500 GB of words were used to train GPT-3, compared to just more than 8,000 “codas” (or vocalizations) for Project Ceti’s recent analysis of sperm whale communication.

Additionally, when working with human language, we already know what is being said. We even know what constitutes a “word,” which is a huge advantage over interpreting animal communication, where scientists rarely know whether a particular wolf howl, for instance, means something different from another wolf howl, or even whether the wolves consider a howl as somehow analogous to a “word” in human language. [Continue reading…]