What is artificial intelligence and how does it work?

Artificial intelligence (AI) is the field of science and engineering that tries to give machines abilities we usually associate with human minds, such as reasoning, planning, learning and perception. Most modern AI is not programmed with step-by-step rules. Instead, it learns patterns from large numbers of examples and uses them to make predictions, recommendations, decisions or new content.

Below we cover what the term means, how machine learning works under the hood, how chatbots like ChatGPT generate text, where AI already shows up in daily life, and the limits worth keeping in mind.

What is artificial intelligence? A simple definition

The name dates back to the 1956 Dartmouth Summer Research Project, proposed in 1955 by John McCarthy and colleagues. For decades, AI mostly meant "symbolic AI": experts wrote explicit rules that a computer followed.

According to an academic overview, that rule-based view dominated until the end of the 1980s. After that, approaches such as neural networks, fuzzy systems and evolutionary computation gained ground. In short, the field moved from hand-written rules to learning from data.

Policymakers use a similar framing. The OECD's definition, revised in November 2023, describes an AI system as a machine-based system that infers from its input how to generate outputs like predictions, content, recommendations or decisions, which can influence physical or virtual environments. Systems vary in how autonomous and adaptive they are, and the EU AI Act uses closely matching wording.

Two distinctions help beginners. First, everything that exists today is narrow AI, built for specific tasks; artificial general intelligence (AGI) remains hypothetical, and even its definition is disputed. Second, the terms nest inside each other: AI contains machine learning, which contains deep learning, which in turn powers large language models and generative AI.

How does machine learning work?

Traditional software follows rules a programmer writes. Machine learning flips this: you give a model examples, called training data, and it works out the patterns itself.

There are three main styles. In supervised learning, examples come with labels, such as emails marked "spam" or "not spam". In unsupervised learning, there are no labels and the model finds groupings on its own. In reinforcement learning, the model learns by trial and error through rewards and penalties, as game-playing systems like AlphaGo did.

Most powerful systems today use neural networks: layers of simple mathematical units connected by numerical settings called parameters or weights. During training, the model makes a guess, compares it to the correct answer and nudges its weights to shrink the error. The technical names for this are backpropagation and gradient descent.

It also helps to separate two phases. Training is the costly stage where the model learns. Inference is when the finished model is put to work answering questions or making predictions.

How do large language models like ChatGPT work?

Large language models (LLMs) first split text into tokens, which are whole words or pieces of words. Languages with many suffixes, such as Turkish, can need more tokens for the same amount of text, though the exact ratio depends on the model.

In pre-training, the model practises one task across an enormous body of text: predicting the next token. That simple objective forces it to pick up grammar, facts and many other patterns along the way. The underlying design is the Transformer architecture, introduced by Google researchers in the 2017 paper "Attention Is All You Need".

Post-training comes next. The model is fine-tuned on examples of instructions and shaped with feedback, through methods such as reinforcement learning from human feedback (RLHF), so it behaves like a helpful assistant. ChatGPT, launched by OpenAI on 30 November 2022, brought this kind of assistant to a mass audience; Gemini, Claude, Copilot, Llama, DeepSeek, Mistral and Grok are other well-known names.

The technology keeps shifting. Recent trends include reasoning models that work through problems step by step before answering, multimodal models that handle images and audio, agents that use tools to complete tasks, and retrieval-augmented generation (RAG), where a model looks up documents or the web first. Model versions change often, so check makers' own pages for what is current.

What is AI used for in everyday life?

You probably use AI many times a day without noticing. It drives recommendations on streaming and shopping sites, filters spam, predicts traffic in navigation apps, unlocks phones with your face, powers voice assistants and translates text.

Banks rely on it to flag fraud, and photo apps use it for automatic editing. Generative AI has added writing help, summaries, coding assistance, image creation and study support to the list.

In medicine, AI tools help clinicians read scans. In science, its impact has been recognised at the highest level: part of the 2024 Nobel Prize in Chemistry went to Demis Hassabis and John Jumper for AlphaFold, which predicts protein structures, and the 2024 Nobel Prize in Physics went to John Hopfield and Geoffrey Hinton for foundational work on neural networks.

What are the limits and risks of AI?

The best-known problem is hallucination. Because language models produce plausible-sounding text rather than verified facts, they can state false things with confidence. How often this happens varies widely by model and test, so double-check anything important.

Models also have a knowledge cutoff: unless connected to search, they know nothing about events after their training ended. They can reproduce biases present in their training data, too.

Privacy deserves attention. Think before typing sensitive personal, health or business information into a chatbot, and check each provider's settings to see whether your conversations may be used for training.

Wider debates include copyright lawsuits, energy and water use, deepfakes and misinformation, and the effect on jobs, where forecasts differ sharply. Finally, today's systems should not be assumed to understand or be conscious in a human sense; that question remains philosophically contested.

Is artificial intelligence regulated?

Increasingly, yes. The EU AI Act entered into force on 1 August 2024 and its obligations are being phased in over several years. Some deadlines may shift under later EU proposals, so it is worth checking official EU sources for the current timetable.

Other countries are building their own frameworks. Türkiye, for example, has a National AI Strategy for 2021–2025 prepared by the Presidency's Digital Transformation Office and the Ministry of Industry and Technology. Because policy is moving quickly, follow announcements from the relevant authorities for the latest status.