What is the difference between AI and machine learning?

AI is the big, broad goal — building machines that can do things normally requiring human intelligence — and machine learning is just one strategy for reaching that goal, which makes it a subset of AI, not a separate or competing thing.

AI has been around since the 1950s, and plenty of AI systems never "learn" anything at all. Old-school expert systems worked by having programmers hand-code thousands of rules — "if patient has a fever and a rash, suspect measles." Classic chess and checkers programs won by brute-force searching through millions of possible moves, not by learning from experience. That's real AI, with zero machine learning involved.

Machine learning showed up as a different way to build AI: instead of a human typing out the rules, you feed the system huge piles of examples and let it find the patterns itself. Show it a million photos labeled "cat" or "dog," and it works out the difference on its own — nobody wrote a rule that says "cats have pointy ears."

The nesting keeps going

Deep learning is a subset of machine learning that uses neural networks stacked in many layers, and it's what let machine learning finally handle messy problems like images, speech, and language well. Large language models (LLMs) like ChatGPT, Claude, and Gemini are a specific application of deep learning, trained on massive amounts of text.

Picture nesting dolls: AI is the outermost doll, machine learning sits inside it, deep learning sits inside that, and LLMs are the smallest doll in the middle. Every LLM is machine learning, but not every machine learning model is an LLM, and not every AI system uses machine learning at all.