AI systems excel in pattern recognition, so much so that they can stalk individual zebrafish and fruit flies even when the animals are in groups of up to a hundred.
To demonstrate this, a group of researchers from the Champalimaud Foundation, a private biomedical research lab in Portugal, trained two convolutional neural networks to identify and track individual animals within a group. The aim is not so much to match or exceed humans’ ability to spot and follow stuff, but rather to automate the process of studying the behavior of animals in their communities.
“The ultimate goal of our team is understanding group behavior,” said Gonzalo de Polavieja. “We want to understand how animals in a group decide together and learn together.”
The resulting machine-learning software, known as idtracker.ai, is described as “a species-agnostic system.” It’s “able to track all individuals in both small and large collectives (up to 100 individuals) with high identification accuracy—often greater than 99.9 per cent,” according to a paper published in Nature Methods on Monday.
The idtracker.ai software is split into a crossing-detector network and an identification network. First, it was fed video footage of the animals interacting in their enclosures. For example in the zebrafish experiment, the system pre-processes the fish as coloured blobs and learns to identify the animals as individuals or which ones are touching one another or crossing past each other in groups. The identification network is then used to identify the individual animals during each crossing event.
Surprisingly, it reached an accuracy rate of up to 99.96 per cent for groups of 60 zebrafish and increased to 99.99 per cent for 100 zebrafish. Recognizing fruit flies is harder. Idtracker.ai was accurate to 99.99 per cent for 38 fruit flies, but decreased slightly to 99.95 per cent for 72 fruit flies.
Robin Edgar
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