AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
O
Object Detection
A computer vision task that identifies and locates specific objects within an image or video, providing both the object class and its position (usually as a bounding box).
Observability
The ability to understand the internal state and behavior of an AI system through its external outputs, including logging, tracing, and monitoring of LLM calls and agent actions.
Online Learning
A training paradigm where the model updates continuously as new data arrives, one example at a time (or in small batches), rather than training on a fixed dataset.
ONNX
Open Neural Network Exchange — an open format for representing machine learning models that enables interoperability between different ML frameworks and deployment targets.
Ontology
A formal representation of knowledge within a domain that defines concepts, categories, properties, and the relationships between them. It provides a shared vocabulary and structure for organizing information.
Open Source AI
AI models and tools released with open licenses that allow anyone to use, modify, and distribute them. Open-source AI democratizes access and enables community-driven improvement.
OpenAI
The AI research company that created GPT, ChatGPT, DALL-E, and Whisper. Originally founded as a nonprofit in 2015, OpenAI became the most prominent AI company after launching ChatGPT.
Optical Character Recognition
Technology that converts images of text (typed, handwritten, or printed) into machine-readable digital text. Modern OCR uses deep learning for high accuracy even on difficult inputs.
Orchestration
The coordination and management of multiple AI components, tools, and services to accomplish complex workflows. Orchestration handles routing, sequencing, error handling, and resource allocation.
Overfitting
When a model learns the training data too well — including its noise and random fluctuations — and performs poorly on new, unseen data. The model essentially memorizes rather than generalizes.
Overfitting Prevention
The collection of techniques used to ensure a model generalizes well to unseen data rather than memorizing training examples. Includes regularization, dropout, early stopping, and data augmentation.