Convolutional Neural Networks (CNNs) are a key technique for image recognition. As Wired Magazine put it a few years ago, “Google’s Artificial Brain Learns to Find Cat Videos.”
“Artificial Brain” may be going a bit far, but neural networks really are constructed using a similar architecture to the brain, but on a much smaller scale. The brain contains billions of neurons but typical CNNs contain thousands. Google has moved on from recognizing cat videos, but CNNs are still the basis of the image recognition and classification in Google Photos.
I first heard of CNNs at Yann LeCun’s keynote at the 2014 Embedded Vision Summit. He was describing work that was largely done at New York University but he since also had taken a position being in charge of AI research at Facebook. Yann talked about how CNNs had become the dominant methodology for image recognition. He had a demo where he pointed a camera attached to his laptop at various things—a shoe, a bagel, a pencil—and interactively the computer identified what it was seeing. Facebook obviously has a lot of interest in automatically categorizing the ½ billion photographs that are uploaded there every day: what they are, who is in them. CNNs are and will be the basis of this.
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