Computer Vision

Overview

Computer vision teaches machines to interpret visual information the way other AI interprets text or speech. It spans a range of tasks, recognizing what an image contains, locating and outlining objects within it, segmenting a scene into its parts, and tracking movement across video frames. Modern computer vision is built almost entirely on deep neural networks, which learn the relevant visual features directly from large image datasets.

How it works

A vision model is trained on many labeled images, learning a hierarchy of features: early layers detect edges and textures, deeper layers assemble these into shapes, objects, and ultimately whole scenes. Convolutional networks long dominated the field, and vision transformers have since joined them, applying the same attention mechanism that reshaped language modeling to patches of an image.

Why it matters

Computer vision underpins a vast range of practical applications, from medical imaging and industrial inspection to autonomous systems and content moderation. Both training these models on large image and video datasets and serving them at scale are compute-intensive, particularly for high-resolution inputs and real-time video, which makes GPU performance the deciding factor in what is achievable.