Level : Beginner
AI vs machine learning vs deep learning: what's the difference?
By The Info Agent IA editorial team ·
Artificial intelligence is the broad field, machine learning is one way to achieve it by learning from examples, and deep learning is a machine learning technique built on neural networks with many layers. The three fit inside each other like nesting dolls: all deep learning is machine learning, and all machine learning is AI, but not the other way around.
The short answer: three nested circles
Picture three circles. The largest one is AI. Inside it sits a smaller circle: machine learning. At the center is the smallest circle: deep learning.
That picture is enough to follow most articles on the subject. When people talk about ChatGPT, voice transcription or image generators, they are talking about the center circle. When they talk about an old chess program or software that follows hand-written rules, they are in the big circle, without stepping into the other two.
If you are just getting started, our beginner’s guide to artificial intelligence sets out the big picture. Here, we focus on what separates the three terms.
Artificial intelligence: the goal
Artificial intelligence covers all the techniques that let a machine do tasks we associate with human intelligence: understanding a sentence, recognizing a face, playing a game, planning, writing.
The term first appears in a research proposal dated August 31, 1955, signed by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon. It announced the Dartmouth workshop of summer 1956, widely seen as the birth of the field.
Here is the key point: “AI” says nothing about the method. For decades, much of AI relied on rules written by people. The “expert systems” of the 1980s, for example, chained together thousands of instructions along the lines of “if this symptom, then this hypothesis.” That is AI, but not machine learning: the machine learns nothing, it just applies.
Machine learning: learning from examples
Machine learning flips that logic. Instead of writing every rule, you show the program a large number of examples that are already labeled, and it works out the useful clues on its own.
The classic example is the spam filter in your inbox. Writing every rule for a junk email by hand is impossible. So you feed the system emails marked “spam” or “not spam.” These examples are the training data. From them, the program builds a model, which can then sort emails it has never seen.
The idea goes back a long way. In 1959, IBM researcher Arthur Samuel published a paper on a checkers program that improved by playing, and used the phrase “machine learning.” Today, machine learning flags credit card fraud, forecasts electricity demand and recommends your next show.
Deep learning: neural networks in layers
Deep learning is a family of machine learning techniques. What sets it apart is that it uses artificial neural networks stacked in many layers. “Deep” refers to that stacking, not to deep thinking.
In a photo, the first layers pick up edges, the next ones shapes, then whole objects. Nobody writes these steps: the network discovers them from examples. That is the big difference from traditional machine learning, where people often decide in advance which features to look at (the amount of a payment, the time, the country).
The foundations are old: the first mathematical model of an artificial neuron dates from 1943. The breakthrough came in 2012, when Geoffrey Hinton’s team clearly won the ImageNet image recognition competition. Two ingredients had been missing: massive amounts of digital data and suitable computer chips, the GPUs originally built for video games.
Side-by-side comparison
| Artificial intelligence | Machine learning | Deep learning | |
|---|---|---|---|
| In one sentence | The field: getting machines to do “intelligent” tasks | A method: learning from examples | A machine learning technique based on deep neural networks |
| Historical marker | Dartmouth proposal, 1955 | Arthur Samuel’s paper, 1959 | Artificial neuron in 1943, breakthrough in 2012 |
| Who sets the rules? | People or data, depending on the approach | The data, often from clues chosen by people | The data, including which clues matter |
| Data needed | Varies, sometimes none | Moderate to large | Very large |
| Computing power | Varies | Moderate | Very high, often on GPUs |
| Explainability | Good for hand-written rules | Often reasonable | Hard |
| Everyday example | GPS working out a route | Spam filter, fraud alert | AI assistant, voice transcription, photos grouped by face |
This table describes general trends, not laws. Some traditional machine learning models are hard to explain, and some neural networks are small.
Where do ChatGPT, generative AI and LLMs fit?
These terms all sit inside the smallest circle. A large language model, or LLM, is a deep neural network trained on enormous amounts of text to predict how a text continues. Nearly all of them use the Transformer architecture, introduced by Google researchers in 2017.
ChatGPT is an assistant built on this kind of model. So it is AI, machine learning and deep learning, and more precisely generative AI, because it produces new content. To dig deeper, read what an LLM is and how ChatGPT works, both explained without jargon.
Check yourself Tax software that applies the official tax brackets, line by line: is that machine learning?
No. It follows rules written by people and learns nothing from examples. Depending on the definition you use, you could call it conventional software or rule-based AI, but it is neither machine learning nor deep learning.
Three common mix-ups
“Anything smart is deep learning.” No. Your GPS route planner, for example, relies on path-finding algorithms that have been around for decades, not on learning.
“Machine learning learns on its own, with no humans involved.” People choose the data and the goal, and check the results. The learning is automatic; the design is not.
“An AI that learns keeps learning while I use it.” Usually not. A model is trained, then frozen before release. Your conversations may be used for a future version, unless you have opted out in the settings.
Which concept matters for you?
- You use ChatGPT, Gemini or Claude every day: focus on deep learning and LLMs. They explain why answers sound fluent but are not always accurate. Also read what generative AI covers.
- You work with spreadsheets of numbers (sales, inventory, finance): traditional machine learning is often the right starting point, as it needs less data and is easier to explain.
- You follow public debates about AI: keep the broad definition in mind. The EU AI Act, for instance, covers “AI systems” in a wide sense, not just deep learning.
To place these milestones in time or check what you have learned, these free tools can help, including our interactive AI timeline.
Frequently asked questions
Are machine learning and artificial intelligence the same thing?
No. Artificial intelligence is the broad field of getting machines to do tasks we think of as intelligent. Machine learning is one way to get there, by learning from examples. It is the dominant approach today, but not the only one.
Is ChatGPT machine learning or deep learning?
Both, because deep learning is a part of machine learning. ChatGPT runs on a large language model, a deep neural network built on the Transformer architecture and trained on huge amounts of text. So it is deep learning, which also makes it machine learning and AI.
Do I need to know how to code to understand these terms?
No. Just remember how they fit together: AI is the goal, machine learning is a way of learning, and deep learning is a machine learning technique based on neural networks. Coding only becomes useful if you want to build your own models.
Has deep learning replaced every other method?
Not entirely. It dominates for language, images and speech. For spreadsheets of numbers, such as sales or payment histories, more traditional machine learning methods are still widely used because they need less data and computing power.