The field of AI that lets computers look at images and work out what's in them.
Computer vision is about teaching computers to see. To a computer, a photo is just millions of dots, each stored as numbers for its colour. The goal is to find edges, shapes and objects in those dots and work out what they mean: a cat, a crosswalk, a face.
In 1989 a neural network learned to read handwritten ZIP codes. In 2012 a deep neural network won the ImageNet contest by a wide margin, and the whole field switched to deep learning. Today it powers face unlock, photo search and lane detection in cars. Studies have also found that some face systems were less accurate depending on skin colour and gender.
Think of it like this
Think of a page from Where's Waldo. At first it's a mess of lines, but you spot small clues, like a striped shirt or a bobble hat, and put them together until you find him. Computer vision works the same way: tiny edges first, then bigger shapes, then the whole object.
For example
Search "dog" in your photo app and it finds every picture with a dog in it: that's computer vision.
Records about this
Source-checked moments from AI history.
Established in a paper the combination of GPUs, large-scale data and deep CNNs. The conventional starting point of the current deep learning era
Released a large-scale image dataset based on the WordNet hierarchy, creating the recognition that data is the bottleneck
An early case of combining weight sharing with backpropagation to apply a neural network to a real-world task