AI Overview Glossary Flashcards
Machine Learning
A branch of AI that uses data and algorithms to make predictions or decisions.
Artificial Intelligence
Software that imitates human behaviors and capabilities, such as reasoning and problem-solving.
Generative AI
AI that creates original content, such as text, images, and audio, based on input data.
Supervised Learning
A type of machine learning where models are trained using labeled data.
Unsupervised Learning
A type of machine learning where models identify patterns without labeled data.
Deep Learning
An advanced subset of machine learning using neural networks to mimic the human brain.
Clustering
An unsupervised learning technique that groups data based on similarities.
Regression
A supervised learning task that predicts continuous numerical values.
Classification
A supervised learning task that predicts categorical outcomes.
Natural Language Processing
AI that enables computers to understand, interpret, and respond to human language.
Computer Vision
AI capabilities that enable systems to process and interpret visual data.
Document Intelligence
AI for extracting, processing, and analyzing data from documents.
Knowledge Mining
Extracting insights from large volumes of unstructured data to create searchable knowledge.
Optical Character Recognition
A technique to detect and digitize text from images or scanned documents.
Bounding Box
A rectangular region used in computer vision to identify object locations in images.
Endpoint
The location or URL to access a resource, such as an Azure AI service.
Key
A private string used to authenticate access to AI resources.
REST API
An interface that allows applications to interact with services over HTTP.
AutoML
Automated Machine Learning that simplifies model training and optimization tasks.
Responsible AI
Principles ensuring ethical, fair, and safe development and deployment of AI systems.
Azure AI Vision
Microsoft’s service for analyzing and processing visual data using AI.
Azure AI Speech
Microsoft’s service for speech recognition, synthesis, and real-time translations.
Azure AI Language
A service for building NLP solutions like text analysis and conversational AI.
Fairness in AI
The principle that AI systems should treat all users and data without bias.
Interpretability
The ability to understand and explain how AI systems make decisions.
Reliability and Safety
Ensuring AI systems operate consistently and without causing harm.
Transparency
Being open about AI systems’ purpose, functionality, and limitations.
Inclusiveness
Designing AI systems that empower and engage diverse communities.
Privacy and Security
Protecting data used in AI systems to ensure confidentiality and safety.
MLOps
Practices for managing the deployment and lifecycle of machine learning models.
