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These labels describe related ideas, but they are not interchangeable.
Artificial intelligence is a broad field concerned with systems performing tasks such as perception, language processing or action selection. Machine learning develops behaviour from data. Deep learning uses neural networks with multiple layers. Generative AI produces outputs such as text, images or audio.
A useful distinction is the job being performed. A model predicting whether a message is spam is making a classification. A model drafting a reply is generating content. A single application may contain both.
For a language-model example, start with tokens and context windows; then use the prompt engineering template to turn the terminology into a practical task.
A fictional shop example
Imagine a shop that wants to handle customer enquiries:
- A fixed rule routes messages containing an order number to support.
- A trained classifier labels the enquiry as delivery, payment or returns.
- A language model drafts a response using approved policy text.
- Application code checks permissions before a message is sent.
Calling the whole system “AI” does not tell you which parts learned from data, which parts generate language or which parts are ordinary software.
Try this with a tool you use
Describe its input, output and the decision it supports. Then identify the parts you can actually verify. Avoid assuming that an attractive chat interface means the product is an autonomous agent or that every automated process uses machine learning.
Discussion: Which AI-related label causes the most confusion in conversations with clients or colleagues?
Further learning: Google Machine Learning glossary.
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