Computer vision
Computer vision is the AI subfield that gives computers the ability to derive meaning from visual input — images, video, and the physical world seen through cameras. Where natural language processing teaches machines to read text, computer vision teaches them to see.
The dominant approach is supervised machine learning on large sets of labelled images. Given enough images labelled "bicycle", an image-recognition model works out for itself what a bicycle looks like and how it differs from a car or a boat. It learns the visual features that distinguish one category from another, rather than being told what to look for explicitly.
Computer vision powers a broad range of applications:
- Image classification: assigning a single label to an image.
- Object detection: locating and naming multiple objects within a scene.
- Facial recognition: finding the subtle relationships between facial features that make a particular face distinct from all others.
- Photo organization: phones that automatically group photos into folders like "at the beach" use vision models to cluster images by visual content.
- Medical imaging: identifying tumours and other anomalies in scans at a volume and consistency that a human consultant cannot match.
A persistent risk is that biases in the training data produce biased classifiers. A model trained only on red cars and white vans may learn to classify by colour rather than shape, misclassifying a blue car as something else entirely — a manifestation of AI bias that carries real consequences when the classifier is making medical or driving decisions.
Image generation models adapt these learned visual patterns into the chameleon-like power of producing new images. A model that has learned what a bicycle, a sunset, or a face looks like can be steered to synthesize novel pictures from those learned patterns — the basis of modern text-to-image systems.
Self-driving cars are the most prominent applied case. Cameras, radar, and range-sensing lasers feed a vision model that identifies moving objects, reads road signs, and makes driving decisions in real time. The same perception stack underpins robotics, surveillance, and accessibility tools that describe the world to blind users.