Wildlife imaging shows that AI models aren’t as smart as we think - University of Exeter

Using AI to identify wildlife reveals a potential “transferability crisis”, researchers say.

Marketing for AI imaging systems often suggests that models can easily tackle novel scenarios across ecosystems and settings, much in the same way as human observers.

But in a new article, two University of Exeter researchers argue that this is based on a “flawed assumption”.

They use examples from species identification and diagnostic imaging to illustrate this.

While AI models work reliably within the environments in which they were trained—the researchers say that this rarely carries over to new locations, making generalisability difficult to predict.

“The take-home message is that despite being considered as the ‘gold standard’, performance benchmarks (tests used to assess AI) do not reliably indicate the true ability of AI models,” said Dr Thomas O’Shea-Wheller, from the Environment and Sustainability Institute at Exeter’s Penryn Campus in Cornwall.

“We see lots of claims purporting to compare the ability of the latest models to humans across very broad scenarios. However, these are derived from performance testing on datasets that do not always carry over to real-world tasks. A model trained to identify cats using stock images will perform well when tested against other stock images of cats, but this is not going to translate into effective cat detection in the wild. The danger is that such benchmark metrics - often composed of arbitrary image categories - are being used to overstate model performance and generalisability.”