Artificial intelligence, or AI, has become a term that appears everywhere: in the news, in policy documents, in the workplace, and in legislation. Yet many people are unsure what it actually means, and related terms such as machine learning, deep learning, and neural networks are frequently used interchangeably. This article provides an accessible conceptual framework for anyone who wants to understand AI better, without any technical background.

What is artificial intelligence?

Artificial intelligence is the umbrella term for computer systems that perform tasks normally requiring human intelligence. Examples include understanding language, recognising faces, making recommendations, or taking decisions based on data.

The EU AI Act (Regulation (EU) 2024/1689) provides a formal definition in Article 3(1): an AI system is "a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments."

In simpler terms: an AI system receives input, learns or reasons on the basis of that input, and produces an output that influences something.

What is machine learning?

Machine learning (ML) is a subfield of AI. In traditional software, a programmer writes explicit rules: "if X, then Y." In machine learning, the system learns patterns from data itself, without every step needing to be manually programmed.

A spam filter that learns which emails are unwanted, or a recommendation engine that anticipates what you are likely to buy, are examples of machine learning in practice. The system is trained on examples and adjusts its behaviour based on what it learns. That process of training on data is central to the European Commission's guidance: machine learning methods learn "from data how to achieve certain objectives."

What are neural networks?

Neural networks are a specific technique within machine learning, inspired by the workings of the human brain. They consist of layers of artificial "neurons" connected to one another. Each neuron processes information and passes the result to the next layer.

The input layer receives raw data, such as the pixels of an image or the words of a sentence. The output layer produces the final result, such as "this is a dog" or "this message is negative." The layers in between are called hidden layers; this is where the actual processing and pattern recognition take place.

Neural networks have demonstrated a striking capacity to outperform traditional software on tasks such as image recognition, translation, and speech conversion.

What is deep learning?

Deep learning is a further specialisation within neural networks. Its defining characteristic is depth: a deep learning model contains many hidden layers, sometimes dozens or hundreds. This depth allows the system to recognise increasingly abstract patterns.

A deep learning model that recognises faces first learns simple shapes such as edges, then more complex structures such as eyes and noses, and finally entire faces. This multi-stage learning process makes deep learning well suited to complex tasks such as generating text, images, or speech.

Chatbots, virtual assistants, and medical image analysis systems are largely based on deep learning.

How the concepts relate

The four terms stand in a hierarchical relationship to one another:

  • AI is the broad category: all systems that perform intelligent tasks.
  • Machine learning is a subfield of AI: systems that learn from data.
  • Neural networks are a technique within machine learning: systems modelled on the brain.
  • Deep learning is an application of neural networks: models with many layers.

Each concept is therefore a further specification of the one before it. Not all AI makes use of machine learning, and not all machine learning uses neural networks.

Why this conceptual framework matters

The EU AI Act, which entered into force on 1 August 2024, imposes obligations on organisations that provide or use AI systems. Article 4 requires providers and deployers to ensure a sufficient level of AI literacy among their staff and any other persons operating or using AI systems on their behalf.

AI literacy begins with a shared conceptual framework. Those who understand the basic concepts are better placed to assess which systems are deployed within their organisation, what risks are associated with them, and which obligations apply. Without this foundation, it is difficult to engage with AI in a responsible manner.

What this means in practice

The distinction between AI, machine learning, deep learning, and neural networks is not merely academic. It determines, for example, how a system was trained, how explainable it is, and what risks it may carry. A rule-based system behaves differently from a deep learning model that learns its own patterns. That difference is relevant for supervision, liability, and compliance with the AI Act.

For professionals who work with AI or make decisions about its deployment, an understanding of these concepts is a necessary starting point.