The use of artificial intelligence has radically transformed the way the computer understands reality. It has given rise to such phenomena as facial recognition, analysis of images in medicine, driverless cars, and visual search. However, although very complicated, all these innovations have originated from something much simpler. Namely, in 1958, an American psychologist and computer scientist, Frank Rosenblatt, introduced his invention named the Perceptron at the Cornell Aeronautical Laboratory in Buffalo, New York. The development was funded by the United States Navy. It was an important step forward, as it showed that the computer is not only able to follow pre-programmed instructions but can also learn from experience and improve. The Perceptron itself was capable of doing only simple things. Still, it was the foundation for machine learning, which eventually gave rise to artificial neural networks and computer vision. The machine that learned from examplesBefore the emergence of the Perceptron, computers functioned mostly based on explicit programming. It required setting up firm rules for each task that limited opportunities for adaptation to new data from the machine side. This time, the scientist suggested something totally new. Inspired by neurons' work, he developed a model that could alter its own settings based on example-based learning. In its first public demonstration in 1958, the Perceptron used an IBM 704 computer and punch cards to identify the difference between cards marked on the left side and those marked on the right. While the task may seem rather easy now, it was one of the first demonstrations of machine self-adjustment to new data through training rather than only predefined programming. People's interest in the invention was caused by its implication that it might be possible someday for machines to recognise patterns and objects and make decisions independently. Rosenblatt defined the Perceptron as a system