What Is a Neural Network?

Neural networks are the architecture behind most modern AI, including the large language models that power ChatGPT and Claude. The name is a nod to the brain, loosely inspired by how neurons connect and communicate, but the comparison only goes so far.

A neural network is a system of layers. Information goes in at one end, passes through multiple layers of mathematical transformations, and an answer comes out the other end. Each layer extracts increasingly abstract features from the input.

In an image recognition network, the first layers might detect edges and colours. Middle layers detect shapes and textures. Later layers detect objects, a wheel, a face, a logo. By the final layer, the system can identify what is in the image without anyone writing rules about what wheels or faces look like.

The "learning" happens by adjusting the strength of connections between these layers based on how wrong the output was. Do this billions of times across millions of examples and the network gets very good at its task.

Why does this matter to you?

You do not need to understand the mathematics to use neural networks effectively. But understanding that they learn from examples, improve with more data, and can fail on inputs very different from their training data helps you set realistic expectations.

It also explains why AI tools sometimes make strange errors. The network has learned patterns from its training data. If it encounters something genuinely outside those patterns, it extrapolates in ways that can be confidently wrong.

The best business application of this knowledge: treat AI outputs as a starting point, not a final answer. The more a task resembles the data the system was trained on, the more you can trust it.