Machine Learning

Machine learning is an approach to artificial intelligence where a system is trained using data instead of being programmed with a rule for every possible case. The system examines large amounts of training data, finds patterns in it and builds a model that it uses to make predictions or decisions about new data.

Learning from examples

Because the system works out the patterns for itself, its performance improves as it is given more or better data, without anyone writing an explicit rule for every situation. This is not ordinary programming with a very long list of rules. In ordinary programming you write the rules that turn input into output; in machine learning you supply examples and the system adjusts itself until it can produce the output.

For example, imagine a camera on a bird feeder that sorts photos of visitors into robins and sparrows. Nobody writes a rule for every combination of colours and markings. Instead, the system is trained on thousands of photos that people have already labelled robin or sparrow. It finds patterns that tend to separate the two, such as a red breast, and builds a model. When a new photo arrives, the model uses those patterns to predict which bird it is.

Why the data matters

The model can only learn from what it is shown. If the training photos are blurry or labelled wrongly, its predictions will be poor, so the training data needs to be accurate and to cover the situations the system will really meet. A model trained only on photos taken in daylight will struggle with a visitor at dusk, because it has never been shown those patterns.