Convolutional neural networks (CNN), or ConvNets, have become the cornerstone of deep learning and show where artificial intelligence (AI) stands today.

Since the 1950s, the early days of artificial intelligence, computer scientists have been trying to build computers that can make sense of visual data. In the ensuing decades, the field, which has become known as computer vision, saw incremental advances. In 2012, computer vision took a quantum leap when a group of researchers from the University of Toronto developed an AI model that surpassed the best image recognition algorithms by a large margin.

The AI system, which became known as AlexNet (named after its main creator, Alex Krizhevsky), won the 2012 ImageNet computer vision contest with an amazing 85 percent accuracy. The runner-up scored a modest 74 percent on the test.

At the heart of the AlexNet was a convolutional neural network (CNN), a specialized type of artificial neural network that roughly mimics the human vision system. In recent years, CNNs have become pivotal to many computer vision applications. Here’s what you need to know about the history and workings of CNNs.

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