Artificial intelligence (AI) is the field of building computer systems that perform tasks we normally associate with human intelligence: recognising speech, understanding text, spotting patterns, making predictions and taking decisions.
Narrow AI and General AI
Every AI system in use today is narrow: it is built for a specific kind of task. A model that transcribes speech cannot diagnose an X-ray, and a chess engine cannot write an email. General AI — a system that can learn and reason across any domain as flexibly as a person — remains a research goal and a subject of debate, not a product you can buy.
How Modern AI Works
Early AI systems were built from hand-written rules. Most modern AI instead uses machine learning: rather than being told the rules, the system is shown many examples and adjusts itself to find the patterns that connect inputs to outputs. Large language models, image recognisers and recommendation engines are all built this way.
What AI Is Good and Bad At
AI excels at tasks with lots of examples and clear patterns, and at doing them fast and at scale. It struggles when data is scarce, when the situation differs from what it was trained on, and when it needs common sense or guaranteed correctness. It can also reproduce biases in its training data.
Key Terms
- Model — the learned function that makes predictions.
- Training — adjusting the model using examples.
- Inference — using the trained model on new inputs.