AI Glossary: Letter "O"

Object Detection is a computer vision task that combines image classification and localization, identifying what objects are in an image and outputting bounding boxes around their coordinates.

Observability in AI refers to the ability to measure, trace, and audit the internal states, reasoning paths, tool execution parameters, and model outputs of an AI system. It enables developers to debug complex reasoning steps and optimize agent behaviors.

Ollama is a lightweight, open-source tool that allows developers to run, manage, and bundle Large Language Models locally on consumer devices. It provides a simple command-line interface and a local API server for seamless model integration.

One-Hot Encoding is a data preprocessing technique that converts categorical variables (like "dog", "cat") into binary vector representations where only a single element is 1 (hot) and the rest are 0.

One-Shot Learning is a machine learning setup where a model is trained or prompted to perform a task or classify inputs after being shown only a single demonstration example.

OpenAI is an artificial intelligence research and deployment company behind ChatGPT, GPT-4, and Sora, dedicated to building safe and beneficial artificial general intelligence (AGI).

An Orchestration Layer is the control center of an agentic system that manages the execution loop, schedules task transitions, calls external tools, updates state databases, and routes inputs/outputs between the user, tools, and the LLM brain.

Out-of-Distribution (OOD) data refers to inputs that originate from a different probability distribution than the dataset used to train the machine learning model, often causing models to make confident mistakes.

An Outcome Reward Model (ORM) is a feedback mechanism that scores only the final response generated by a model, without evaluating the correctness of intermediate reasoning steps. It is simpler to train but less granular than step-by-step reward models.

Overfitting is a common training error where a model learns the details and noise in the training dataset to the extent that it negatively impacts its performance on new, unseen test data. The model performs exceptionally well on training data but fails to generalize.