A field of AI that enables computers to interpret and understand images and video.
Computer vision systems analyze pixels to recognize objects, faces, text, gestures, and scenes. Early approaches relied on hand-crafted features like edges and corners, but modern systems mostly use deep learning, particularly convolutional neural networks and, more recently, vision transformers.
Everyday applications include unlocking a phone with your face, scanning documents into editable text, automatic photo tagging, and checkout-free stores. In industry, computer vision inspects products for defects, guides robots, and helps autonomous vehicles detect pedestrians and lane markings. In healthcare, it assists clinicians in reviewing medical images. Challenges include bias in training data, privacy concerns around surveillance, and handling unusual lighting or weather conditions.
Gathering information about the Earth's surface, oceans, and atmosphere using satellites, aircraft, and drones, often as imagery and sensor data.
LidarLight Detection and Ranging: a sensing method that measures distances with laser pulses to build detailed 3D maps of surroundings.
Multimodal AIAI systems that can process and combine multiple types of input or output, such as text, images, audio, and video.
Optical Character RecognitionTechnology, known as OCR, that reads printed or handwritten text in images and scanned documents and turns it into editable text.
Where AI actually pays off in logistics and supply chain, from demand planning to routing and warehouses, and the data you need before each one works.
Online enrollment, waitlists that fill themselves, skill tracking parents can see and automated pool chemistry are changing how swim schools run.
A plain-English guide to computer vision: how machines classify and detect objects in images, and where the technology already works in retail, factories and healthcare.