Neural Network

A neural network is a computing system inspired by biological brain architecture—composed of interconnected nodes (neurons) organized in layers that process information by learning patterns from data. Neural networks are the fundamental building block of modern artificial intelligence.

The architecture is deceptively simple: input data flows through layers of connected nodes, each applying a mathematical transformation and passing results forward. Learning occurs by adjusting connection weights to minimize the difference between predictions and actual outcomes. This basic principle, scaled to billions of parameters across hundreds of layers, produces the capabilities that power language models, image generators, speech recognition, and autonomous systems.

Different neural network architectures excel at different tasks. Convolutional neural networks (CNNs) dominate computer vision. Recurrent networks (RNNs and LSTMs) process sequential data. Transformers—the architecture behind modern LLMs—use attention mechanisms to process entire sequences in parallel, enabling the scale and capability of current frontier models. Generative adversarial networks (GANs) and diffusion models power image and video generation.

The field continues advancing along multiple frontiers. Mixture-of-experts architectures activate only relevant sub-networks per input, improving efficiency. State-space models offer alternatives to transformers for long-sequence processing. Neuromorphic computing attempts to implement neural networks in hardware that more closely mimics biological neurons. For agentic systems, neural networks provide the perception, reasoning, and generation capabilities that enable autonomous agents to understand context, plan actions, and produce outputs across text, code, images, and interaction.