Senior AI research scientist Benjamin Hoffman is working on self-supervised methods for interpreting data collected from animal-borne tags, known as bio-loggers. Using bio-loggers, scientists are able to record an animal’s motions, as well as audio and video footage from the animal’s perspective. However, these data are often difficult to interpret, and there is often too much data to analyze by hand. A solution is to use self-supervised learning to discover repeated behavioral patterns in these data. This will allow behavioral ecologists to rapidly analyze recorded data, and measure how an individual’s behavior is affected by external factors, such as human disturbance or communication signals from other individuals. Ben is working closely with our partners, Dr. Christian Rutz and Dr. Ari Friedlaender, to source datasets from their labs and among researchers in the ethology community.

In December 2021, we published our first scientific paper in the peer-reviewed journal Scientific Reports which already has multiple citations. This publication focused on automatic source separation so that researchers are more easily able to distinguish between animal vocalizations when more than one animal is speaking at the same time. The research was the outcome of a close collaboration with marine biologist Dr. Laela Sayigh, who provided a dataset of bottlenose dolphin signature whistles for our project.