According to computer scientists at Stanford and Google, an artificially intelligent machine has learned to cheat its way through a designated task.
The machine was built in 2017 to turn aerial images into street maps and back again, the point of which is to boost the efficiency and accuracy of Google Maps.
It involves a technology called CycleGAN, which uses neural networks (a generator and a discriminator) to transform an image from one type to another. The purpose of the generator is to “trick” the discriminator, and through practice the machine will get better and better at producing the desired outcome, at least so the theory goes. However, this time something far more interesting happened – it seems that this particular AI learned how to take a few shortcuts.
Scientists noticed something was up when some of the early results turned out to be a little too good. Specifically, they noticed that skylights removed in the process of generating the street map somehow managed to reappear when it was transformed back into an aerial image. This shouldn’t happen. If the machine had been doing its job properly, it would use data from the street map to recreate the aerial map. The street map does not include skylights. Therefore, the second version of the aerial image should not include skylights.