AI, machine learning, and generative AI
Tell apart learning from data, following rules, and generating new content.
In plain language
Artificial intelligence is a broad field concerned with systems that perform tasks associated with intelligence. Machine learning is one approach: instead of explicitly writing every decision rule, we use data to fit a model. Deep learning uses neural networks with multiple layers.
Generative AI describes systems that produce content such as text, images or audio. These categories overlap. A neural network can classify a photograph without generating a new one, while a language model can generate a draft. Start with the task you need to solve, then consider whether an AI method is useful.
A handwritten rule can apply a fixed shipping discount. A learned model might estimate late-delivery risk from past shipments. A generative model could draft a customer update. These are different jobs, even inside one application.
Try it yourself
Think of one everyday task for a fixed rule and one for a learned model. Explain why the requirements differ.
Go deeper · for developers and architects
A model learns relationships in its training data, which can include shortcuts and bias. Evaluate on separate, representative examples. Compare against a simple rule-based baseline before adding complexity.
Check your understanding
2 related questions, with an explanation for each answer.
Explore the original sources
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