These terms are often used interchangeably, but they are not the same thing. Here is the clearest way to understand the distinction.

Artificial intelligence is the broad concept of building systems that can perform tasks requiring human-like intelligence. It has been around since the 1950s and encompasses many different technical approaches, from simple rule-based systems to complex neural networks.

Machine learning is a specific technique within AI. Rather than programming a computer with explicit rules, machine learning lets a system learn its own rules from data. It is one of the primary methods used to build AI today, but it is not the only method.

Deep learning is a subset of machine learning that uses neural networks with many layers. It is the engine behind most of the impressive AI capabilities seen in recent years, including large language models, image recognition, and speech synthesis.

A simple analogy: AI is the destination (intelligent behaviour), machine learning is a vehicle for getting there (learning from data), and deep learning is a particularly powerful type of that vehicle (layered neural networks).

Not all AI uses machine learning. A chess program that uses fixed rules and exhaustive search is AI but not machine learning. And not all machine learning produces what most people would call intelligent behaviour. A recommendation algorithm is machine learning but not particularly intelligent in the broader sense.

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