The perceptron algorithm was designed to classify visual inputs, categorizing subjects into one of two types and separating groups with a line. Classification is an important part of machine learning and image processing. Machine learning algorithms find and classify patterns by many different means. The perceptron algorithm classifies patterns and groups by finding the linear separation between different objects and patterns that are received through numeric or visual input.
The perceptron algorithm was developed at Cornell Aeronautical Laboratory in 1957, funded by the United States Office of Naval Research. The algorithm was the first step planned for a machine implementation for image recognition. The machine, called Mark 1 Perceptron, was physically made up of an array of 400 photocells connected to perceptrons whose weights were recorded in potentiometers, as adjusted by electric motors. The machine was one of the first artificial neural networks ever created.
At the time, the perceptron was expected to be very significant for the development of artificial intelligence (AI). While high hopes surrounded the initial perceptron, technical limitations were soon demonstrated. Single-layer perceptrons can only separate classes if they are linearly separable. Later on, it was discovered that by using multiple layers, perceptrons can classify groups that are not linearly separable, allowing them to solve problems single layer algorithms can’t solve.