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AI – Artificial Intelligence

Technology simulating human intelligence in machines, such as decision-making and pattern recognition.

ML – Machine Learning

A subset of AI where machines learn from annotated data to make predictions or decisions.

NLP – Natural Language Processing

AI’s ability to understand, interpret, and generate human language.

CV – Computer Vision

The field of AI focused on enabling machines to interpret and process visual data (images and videos).

DL – Deep Learning

An advanced ML technique using neural networks with many layers to model complex patterns in data.

RLHF – Reinforcement Learning from Human Feedback

Training AI systems using human input to align outputs with user expectations.

RLAIF – Reinforcement Learning with AI Feedback

Similar to RLHF, but using AI-generated feedback to accelerate training.

RAG – Retrieval-Augmented Generation

Combining AI’s generative abilities with information retrieval for accurate, context-based outputs.

Bounding Box (BB)

A rectangular box used to define the position of an object in an image.

Semantic Segmentation

Assigning a label to every pixel in an image based on the object it represents.

Instance Segmentation

A more granular form of segmentation, distinguishing between multiple objects of the same class.

3D Cuboid Annotation

Marking objects in 3D space for depth estimation and spatial relationships.

NER – Named Entity Recognition

Identifying entities like names, locations, and dates in text.

OCR – Optical Character Recognition

Converting scanned images or handwritten text into machine-readable text.

Keypoint Annotation

Identifying specific points on objects, such as facial landmarks or joints in motion analysis.

LiDAR – Light Detection and Ranging

A sensor technology used in annotation for mapping environments in autonomous systems.

Sensor Fusion

Combining data from multiple sensors (e.g., LiDAR, radar, cameras) for robust annotations.

Frame-by-Frame Annotation

Annotating each frame in a video for tasks like object tracking or action recognition.

Speech-to-Text (STT)

Transcribing spoken words into text.

Diarization

Identifying and labeling multiple speakers in an audio file.

Class Imbalance

A common issue in datasets where some classes are underrepresented, affecting ML performance.

Active Learning

A method of selecting the most informative data samples for annotation to improve model training.

Workflow and Process Terms

WFM – Workforce Management

The process of optimizing staffing and workflows in data annotation projects.

QC – Quality Control

Processes to ensure accuracy and consistency in annotated datasets.

QA – Quality Assurance

Ongoing checks to maintain high standards in data labeling projects.

HIT – Human Intelligence Task

A small, discrete task designed for human annotators, such as labeling or categorizing data.

FTE – Full-Time Equivalent

A unit to measure workloads based on full-time staffing needs for annotation projects.

Pipeline Automation

The use of automated tools to streamline data annotation workflows.

Hybrid Model

A mix of traditional (office-based) and gig-based (remote) annotation setups.

Performance Metrics

mIoU – Mean Intersection over Union

A metric for evaluating the accuracy of object detection and segmentation models.

F1 Score

The harmonic mean of precision and recall, used to measure model performance.

Accuracy

The percentage of correctly labeled data samples.

Precision

The proportion of correctly predicted positive annotations out of all predicted positives.

Recall

The proportion of correctly predicted positives out of all actual positives.

TAT – Turnaround Time

The time it takes to complete a data annotation project.

Crowdsourcing

Obtaining annotations by outsourcing tasks to a large group of freelance or gig workers.

GiG Platform

A system to source and manage gig-based annotation talent globally.

LLM – Large Language Model

AI models trained on vast datasets to perform tasks like text generation and summarization.

Zero-Shot Learning (ZSL)

An AI’s ability to make predictions without direct training on a specific task or class.

Few-Shot Learning

Training AI models with very few annotated samples per class.

Synthetic Data

Artificially generated data used to train models when real data is scarce.

Human-in-the-Loop (HITL)

Integrating human judgment into AI training and decision-making workflows.

Federated Learning

A privacy-preserving method where AI models are trained across decentralized devices without sharing raw data.