Artificial intelligence relies on digital neural networks inspired by the structure and function of the human brain.

The foundation of artificial neural networks was established in 1943 when Warren McCulloch and Walter Pitts developed a mathematical model for how neurons compute. The field made a critical leap forward in 1958 when Frank Rosenblatt created the Perceptron at Cornell Aeronautical Laboratory—the first network capable of learning through trial and error, a breakthrough that paved the way for modern deep learning.Today's AI systems use deep learning, which stacks many layers of artificial neurons to process information. Data flows through an input layer, passes through multiple hidden layers where complex patterns are extracted, and emerges from an output layer. Each connection between nodes carries a "weight" that determines its strength. During backpropagation, the network calculates how far its output missed the target and adjusts these weights to improve accuracy.A watershed moment came in 2012 when AlexNet, designed by Alex Krizhevsky, won the ImageNet Large Scale Visual Recognition Challenge—proving that deep neural networks could surpass hand-coded algorithms at image recognition. Modern networks now contain billions of parameters. OpenAI's GPT-3, for instance, has 175 billion parameters, enabling it to generate human-like text by predicting the next word based on patterns learned from vast datasets.