AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
A neural network is the underlying structure most modern AI models use to learn patterns, loosely inspired by how the brain connects signals. You rarely need to think about neural networks directly, but they explain why AI models are so capable and also why they can be hard to predict or explain.
A neural network is a series of filters stacked in layers. Each layer learns to detect slightly higher-level patterns than the last: raw pixels to edges to shapes to faces. No one programs the filters; the network discovers them from data.
A retailer's quality control system uses a neural network to spot defective products on a conveyor belt at 300 items per minute, catching defects a human inspector would miss at that speed.
For tasks involving images, audio, or natural language, where the signal is too complex for explicit rules and you have enough labeled data to train on.
When you need to explain why the system made a specific decision, when data is scarce, or when a simpler model would do the job at a fraction of the compute cost.
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