Term tag: Computer Vision

Autonomous Vehicle Safety

Autonomous vehicle safety technologies, standards, and testing protocols designed to ensure self-driving cars can operate safely without human intervention.

Computer Use (GUI Agents)

How AI operates software by reading screenshots and issuing clicks and keystrokes: the perception-action loop, why grounding is hard, and what OSWorld measures.

Computer Vision (CV)

How computers turn a grid of pixel values into labels, boxes and masks — the task ladder, the 2012 hinge, and where vision is reliable versus brittle.

Convolution

Convolution slides a small kernel over an image, multiplying and summing each patch into one output number. The arithmetic of stride, padding and kernels.

Convolutional Neural Network (CNN)

The architecture that assumes image features are local and position-independent — how the stack builds a hierarchy, and where CNNs still beat transformers.

Generative Adversarial Network (GAN)

Two networks trained against each other: one invents data, the other judges it. The generator never sees a real example — only the judge's verdict.

Image Generation

How AI turns noise into a picture: the diffusion loop, the noise schedule on numbers, why it runs in a 48x-compressed latent space, and what guidance costs.

Multimodal AI

How one model reads an image and text at once: the picture is cut into fixed patches, each patch becomes a token, and those tokens cost context.

One-shot Learning

Learning a new class from exactly one example — and the split that matters: one-shot prompting updates no weights; classical one-shot training does.

Pattern Recognition (PR)

Pattern recognition is the automatic discovery and classification of regularities in data — the older name for what became statistical machine learning.

Pooling

A downsampling operation in CNNs that shrinks a feature map by summarizing each small region into one value, cutting resolution and compute.

Residual Connections (Skip Connections)

Residual connections add a block's input back to its output — F(x) + x — the trick that lets gradients flow through deep nets and made transformers trainable.

Robotics

Robotics builds machines that sense, plan, and act physically — where AI perception and control meet Moravec's paradox and the sim-to-real gap.

Video Generation

Video generation is an AI technology that creates moving images and scenes from text descriptions, images, or other video clips.

Vision Transformer (ViT)

A Vision Transformer (ViT) cuts an image into fixed patches, embeds each as a token, and runs a transformer encoder — trading CNN inductive bias for scale.

Zero-shot Learning

Performing a task with zero examples, on prior knowledge alone — and the split that matters: zero-shot prompting versus classical zero-shot learning.