People use these four terms as if they mean the same thing. They don’t — they are circles inside circles.
In this article
| Term | What it means | Everyday example |
|---|---|---|
| Artificial intelligence | Any technique that makes a computer do something we would call “smart” | A chess program, a route planner, a spam filter |
| Machine learning | AI that learns rules from examples instead of being hand-coded | Predicting house prices from past sales |
| Deep learning | Machine learning with many-layered neural networks | Face unlock, speech-to-text, photo search |
| LLM | A very large deep-learning model trained to predict text | ChatGPT, Claude, Gemini, Llama |
Rules vs learning
An old-style spam filter was a list of rules a person wrote: if the subject contains “lottery”, mark as spam. A machine-learning filter is shown thousands of emails already labelled spam or not spam, and learns which patterns matter — including ones no person thought of.
Why “deep”?
A neural network is layers of simple calculations. “Deep” just means many layers. Each layer builds on the previous one: in an image model, early layers detect edges, middle layers shapes, later layers faces or cars.
Where LLMs fit
An LLM is a deep-learning model with billions of parameters, trained on a huge amount of text to predict the next word-piece. Chatting, summarising and code writing all come from that one skill — see how a language model works.