What "AI" Actually Means

"AI" (artificial intelligence) is a broad umbrella term for software that performs tasks which normally require human intelligence — recognizing an image, answering a question, translating a sentence. It's not one specific technology; it's a whole category, the same way "vehicle" covers everything from a bicycle to a jet.

Machine learning (ML) is a subset of AI: instead of a programmer writing exact rules for every situation, an ML system learns patterns directly from examples. Show it thousands of labeled photos of cats and dogs, and it gradually learns to tell them apart on its own — nobody hand-wrote a rule for "pointy ears usually mean cat."

Deep learning is a subset of machine learning that uses neural networks — layered structures loosely inspired by neurons in a brain — with many layers stacked on top of each other. Almost every AI tool that's made headlines recently (chatbots, image generators, voice assistants) is built on deep learning specifically, not just "AI" in general.

So the relationship nests like Russian dolls: AI is the biggest category, machine learning is one way of building AI, and deep learning is one particular kind of machine learning. A large language model like the ones behind modern chatbots is a deep learning system — meaning it's AI, and it's ML, but not every AI system is a deep learning system.

Which of these correctly describes how the three terms relate?

A system that learns patterns from labeled examples instead of following hand-written rules is using ____.

Every AI system is a deep learning system.