Artificial intelligence is not one thing — it is an umbrella term for many different technologies that allow computers to perform tasks that previously required human intelligence. Under that umbrella live machine learning, natural language processing, computer vision, robotics, and much more.
Machine learning: the key paradigm
Most modern AI uses machine learning: instead of programming explicit rules, you feed the system vast amounts of data and let it find patterns. A spam filter trained on millions of emails learns to identify characteristics of spam without being told the specific rules. The more data, the better the patterns, the better the model.
Deep learning and neural networks
The most powerful machine learning models use artificial neural networks — mathematical structures loosely inspired by the brain. These networks, with millions or billions of parameters, are trained on enormous datasets and become able to recognise images, generate text, translate languages, and play strategy games at superhuman level.
Large language models (LLMs)
ChatGPT, Claude, Gemini and similar tools are large language models: neural networks trained on vast amounts of text that learn to predict the next word in a sequence. They produce remarkably coherent and useful text but do not "understand" in the human sense — they are sophisticated pattern-matching engines. Their limitations include hallucination (generating false information confidently), inability to reason reliably, and cutoff dates in their training data.
