How Machines Learn

At its core, AI is about machines improving their performance through exposure to data rather than following hand-written rules. But there are fundamentally different ways this happens. A supervised model learns from labelled examples - you show it thousands of photos tagged "cat" or "dog" and it learns to tell the difference. An unsupervised model finds patterns on its own without being told what to look for. Reinforcement learning works through trial and error, like a game player learning which moves lead to winning. Self-supervised learning - the approach behind most large language models - has the system generate its own training signal from the structure of the data itself. The learning approach shapes everything that follows: what data you need, how much of it, what the model can and can't do, and where it's likely to fail. Two AI systems can look similar on the surface but behave very differently because they learned in fundamentally different ways.