Computer vision has been having a moment.
No more does an image recognition algorithm make dumb mistakes when looking at the world: these days, it can accurately tell you that an image contains a cat. But the way it pulls off the party trick may not be as familiar to humans as we thought.
Most computer vision systems identify features in images using neural networks, which are inspired by our own biology and are very similar in their architecture—only here, the biological sensing and neurons are swapped out for mathematical functions.
Now a study by researchers at Facebook and Virginia Tech says that despite those similarities, we should be careful in assuming that both work in the same way.