When it comes to image recognition tech, it’s still remarkably easy to fool the machines.

And while it’s some good comedy when a neural network mistakes a butterfly for a washing machine, the consequences of this idiocy are pretty nightmarish when you think about rolling these flawed systems out into the real world.

Researchers from the University of California, Berkeley, the University of Washington, and the University of Chicago published a paper this month to really drive home the weaknesses of neural networks when it comes to correctly identifying an image.

The images selected for the dataset were pulled from millions of user-labelled animal images from the website iNaturalist as well as objects tagged by users on Flickr, according to the paper.

They downloaded 81,413 dragonfly images from iNaturalist and filtered that down to 8,925.

An “algorithmically suggested shortlist” spit out 1,452 images, and from there, they manually selected 80.

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