AI vs Machine Learning vs Neural Networks: Key Differences

Three nesting dolls: the big picture

The fastest way to put order in your head is to picture nesting dolls. The biggest on the outside, smaller ones inside, and each fits entirely within the previous one:

• Artificial intelligence (AI) — the biggest doll. Any system that does what normally requires human intelligence.
• Machine learning (ML) — the doll inside AI. Systems that learn from data instead of running on hand-written rules.
• Deep learning — the doll inside machine learning. Learning with "deep" (many-layered) neural networks.
• Neural networks — the "engine" inside deep learning, its workhorse.

The key point: these are not rivals, but levels of nesting. A neural network is always both machine learning and artificial intelligence. But the reverse is not true: there is AI without machine learning, and machine learning without neural networks. Next we go through each level in turn.

Artificial intelligence: the broadest

Artificial intelligence is the most general and the oldest term (it dates back to the 1950s). It covers any system that solves tasks normally requiring human intelligence: playing chess, understanding speech, driving a car, making a diagnosis.

An important nuance that surprises many: AI does not have to learn from data. A classic 1990s chess engine ground through millions of moves using strict, hand-written rules and beat champions — yet it "learned" nothing. A call-center voice menu that reacts to keywords along a rigid script is also AI, if a primitive one. This approach is called "symbolic" AI: a human writes the logic, the machine follows it.

Machine learning: learning from data

Writing every rule by hand is hard and often impossible (how do you put into words what tells a cat from a dog?). That is why machine learning was born — an approach where the system works out the rules itself, from examples.

Compare two worlds. In ordinary programming a human writes a rule: "if an email contains the words 'winnings' and 'prize,' it is spam." In machine learning the human writes no rules at all: they show the system thousands of emails labeled "spam" and "not spam," and it works out the signs of unwanted mail on its own — including ones a human would never have thought of. The rule emerges from the data, not from the programmer's head.

This is exactly why machine learning flourished where hand-written rules fail: spam filtering, movie recommendations, face recognition, demand forecasting. And — importantly — neural networks are not the only tool here. There are decision trees, random forests, support vector machines and dozens of other methods. For many practical tasks they work great, train faster and do not need mountains of data.

Deep learning and neural networks

Down we go into the smallest doll. Deep learning is a branch of machine learning that uses neural networks with many layers. The word "deep" is literally about depth: many layers stacked one after another.

Why does depth matter so much? Each layer learns to recognize something a bit more complex, building on the previous one. In image recognition the first layers catch edges and blobs, the next catch details like an eye or a wheel, the deepest catch whole objects. It was this many-layered structure that let neural networks solve what once seemed like science fiction: understanding natural speech, translating, generating text and images. How a single neuron works and what layers are is covered in detail in what a neural network is.

From this comes a simple criterion: a neural network with a couple of layers is just a neural network. A neural network with dozens or hundreds of layers is deep learning. And ChatGPT, Midjourney and voice assistants are all deep learning on very large neural networks.

How machines actually learn: three ways

Since machine learning is "learning from data," it is fair to ask: how exactly? There are three big families, and it helps to tell them apart:

• Supervised learning. The system is given examples with the correct answers attached: thousands of photos labeled "cat"/"dog." It learns from these "answers at the back of the textbook." The most common approach.
• Unsupervised learning. There are no correct answers — the system finds structure on its own and groups similar things together. For example, it splits a store's customers into segments nobody thought of in advance.
• Reinforcement learning. The system acts by trial and error and gets a "reward" for good moves. This is how you teach systems to play games and control robots. The same family includes RLHF — the final polish of ChatGPT using human ratings (covered in our breakdown, how ChatGPT works).

A walkthrough with real examples

Let's bring it all together with concrete cases. Notice how the same thing can belong to several levels of the doll at once:

Read the table like this: the "smarter" and more complex the task, the deeper we descend into the doll. A rule-based chess engine is AI, but without learning. A modern assistant is AI, machine learning and a neural network all at once. And that is exactly why "ChatGPT is a neural network" is correct but incomplete: it is also machine learning and artificial intelligence. Those words simply describe it at different depths.

Now the hierarchy is in your head: AI is the broadest umbrella, machine learning is its "learn from data" part, and deep learning with neural networks is the most powerful core inside. Want to reinforce it by looking under the hood of image generation? Read about generative AI. And for the language of models — our breakdown of how ChatGPT works.