Visual & Physical AI glossary

Most model problems are data problems. Definitions for the datasets, embeddings, quality signals, and curation methods that decide what is worth labeling and training on in the first place.

Annotation is where model quality is won or lost. Definitions for the label types, formats, and quality measures that turn raw data into training data, from bounding boxes to ground truth.

A model is only as trustworthy as the way you measure it. Definitions for the metrics and methods that tell you whether a model works, and where a strong headline number can still hide failure.

The building blocks that learn from your data. Definitions for the networks, foundation models, and algorithms behind modern visual AI, and what each one is actually good for.

Physical AI is AI that perceives and acts in the real world, in robots, autonomous vehicles, and drones. Definitions for the multimodal and sensor concepts behind it, from LiDAR, point clouds, and sensor fusion to world models and sim-to-real.

Visual AI is AI that interprets images and video to detect, segment, track, and make sense of what appears in a scene. Definitions for the core tasks and techniques behind it, from object detection, segmentation, and tracking to depth estimation, optical flow, and 3D scene reconstruction.