Neural Networks
Neural networks are computational systems loosely inspired by biological brains, designed to learn patterns from data. They form the backbone of modern Artificial Intelligence, enabling machines to recognize images, understand language, play games, and solve problems that would be difficult to program explicitly.
How They Work
A neural network consists of interconnected nodes (artificial "neurons") organized in layers. Each connection has an adjustable weight; during training, these weights are refined so the network produces correct outputs for given inputs. Through repeated exposure to examples, a network learns to map inputs to outputs—a process called Machine learning.
The simplest networks have input, hidden, and output layers. Deeper architectures, called "deep neural networks," stack many layers and have enabled breakthrough applications in recent years. When trained on vast datasets, they discover subtle patterns humans might never explicitly define.
Why They Matter
Neural networks power The Internet's most visible intelligence: voice assistants, recommendation systems, translation, and creative tools. Organizations like Anthropic, founded by Dario Amodei and others, research how to build neural networks that are safer and more aligned with human values.
Their flexibility is remarkable—the same basic architecture scales from specialized tasks to general-purpose reasoning. Yet they remain somewhat mysterious: even their creators don't always understand exactly why a trained network makes particular decisions.