What is Machine Learning?

Traditional software follows explicit rules a programmer writes: "if X, do Y." Machine learning inverts this: instead of writing rules, you show the system examples and it learns the rules itself. Feed a machine learning model thousands of spam emails and thousands of legitimate emails, and it learns to distinguish between them — without anyone writing a single rule about what makes email spam.

There are three main approaches: supervised learning trains on labeled examples (spam/not spam, fraud/not fraud, churned/retained) and learns to categorize new examples; unsupervised learning finds hidden patterns in unlabeled data (customer clustering, anomaly detection); and reinforcement learning learns through trial and error to maximize a reward (how game-playing AI and robotics systems are trained).

Machine learning models are only as good as the data they're trained on. Biased or incomplete training data produces biased models. More data generally improves performance, but data quality matters as much as quantity. The recent explosion in AI capability is largely attributable to the availability of massive training datasets and the computational power to train large models on them.