Artificial Intelligence and Machine Learning

About this topic

Artificial intelligence (AI) is the field of building computer systems that perform tasks normally needing human intelligence, such as recognising images, understanding speech, translating languages and playing games. Machine learning (ML) is a major branch in which, instead of programming exact rules, we give a system many examples and let it learn patterns from data.

In supervised learning, the model trains on labelled examples, such as photos tagged cat or dog, and adjusts internal numbers called weights to reduce its errors. Unsupervised learning finds groups in unlabelled data, and reinforcement learning learns by trial and reward. Neural networks, loosely inspired by the brain, have layers of connected units and power modern speech recognition and language models. A trained model is tested on new data it has not seen to check it generalises.

Applications include spam filters, recommendations, medical image analysis and self-driving features. A key caution is that AI learns from data, so biased or poor data produces biased results, and models can be confidently wrong. A common misconception is that AI understands or thinks like a person; it finds statistical patterns. Question outputs, check sources and consider fairness.