Your comprehensive reference for AI terminology. Clear, practical definitions to help you navigate the world of artificial intelligence with confidence.
117+ terms defined
Showing 117 of 117 terms
Artificial Intelligence (AI)
Fundamentals
Computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making.
Examples:
Chatbots like ChatGPT
Image recognition systems
Recommendation engines
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Artificial General Intelligence (AGI)
Fundamentals
A theoretical form of AI that would have the ability to understand, learn, and apply intelligence across a wide range of tasks at a level equal to or beyond human capability.
Examples:
Hypothetical AI that could write poetry, solve math problems, and drive cars equally well
AI that could replace human workers in any job
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Artificial Narrow Intelligence (ANI)
Fundamentals
AI systems designed to perform specific tasks or solve particular problems, representing the current state of AI technology.
Examples:
Chess-playing AI
Voice assistants
Image recognition systems
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Automation
Fundamentals
The use of technology to perform tasks with minimal human intervention, often powered by AI to handle complex decision-making.
Examples:
Automated customer service
Manufacturing robots
Automated data entry
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Cognitive Computing
Fundamentals
AI systems that simulate human thought processes to solve complex problems, often involving natural language processing and pattern recognition.
Examples:
IBM Watson
Medical diagnosis systems
Financial analysis tools
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Data Science
Fundamentals
An interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Examples:
Customer behavior analysis
Predictive analytics
Business intelligence
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Expert System
Fundamentals
An AI program that mimics the decision-making ability of a human expert in a specific domain using rules and knowledge bases.
Examples:
Medical diagnosis systems
Tax preparation software
Equipment troubleshooting systems
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Heuristic
Fundamentals
A problem-solving approach that uses practical rules or shortcuts to find satisfactory solutions when optimal solutions are impractical.
Examples:
Chess move evaluation
Search algorithms
Route planning
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Intelligence Augmentation (IA)
Fundamentals
The use of AI to enhance human intelligence and capabilities rather than replace them, focusing on human-AI collaboration.
Examples:
AI writing assistants
Medical decision support
Research tools
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Knowledge Graph
Fundamentals
A structured representation of information that connects entities, concepts, and their relationships in a network format.
Examples:
Google Knowledge Graph
Medical knowledge bases
Product recommendation systems
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Algorithm
Fundamentals
A set of rules or instructions that a computer follows to solve problems or complete tasks. In AI, algorithms enable machines to learn from data.
Examples:
Decision tree algorithm
Linear regression
Neural network
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Training Data
Fundamentals
The dataset used to teach an AI model how to make predictions or decisions. The quality and quantity of training data directly affects model performance.
Examples:
Images labeled with objects
Text paired with sentiment
Historical sales data
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Model
Fundamentals
A mathematical representation of a process that has been trained on data to make predictions or decisions about new, unseen data.
Examples:
Language model like GPT
Image classifier
Recommendation system
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Inference
Fundamentals
The process of using a trained AI model to make predictions or generate outputs on new data.
Examples:
Generating text response
Classifying an image
Making a recommendation
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Machine Learning (ML)
Machine Learning
A subset of AI that enables computers to learn and improve from data without being explicitly programmed for every task.
Examples:
Email spam detection
Stock price prediction
Customer segmentation
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Anomaly Detection
Machine Learning
The identification of unusual patterns or outliers in data that do not conform to expected behavior.
Examples:
Fraud detection
Network security monitoring
Quality control
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Backpropagation
Machine Learning
An algorithm used to train neural networks by calculating gradients and updating weights to minimize prediction errors.
Examples:
Training image classifiers
Language model training
Pattern recognition
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Bagging
Machine Learning
An ensemble method that combines multiple models trained on different subsets of data to improve accuracy and reduce overfitting.
Examples:
Random Forest algorithm
Voting classifiers
Model averaging
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Boosting
Machine Learning
An ensemble technique that sequentially builds models, with each new model focusing on correcting the errors of previous models.
Examples:
XGBoost
AdaBoost
Gradient boosting machines
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Classification
Machine Learning
A supervised learning task where the algorithm learns to assign input data to predefined categories or classes.
Examples:
Email spam detection
Image recognition
Medical diagnosis
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Clustering
Machine Learning
An unsupervised learning technique that groups similar data points together without predefined labels.
Examples:
Customer segmentation
Gene sequencing
Market research
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Cross-Validation
Machine Learning
A technique for assessing how well a model will generalize to new data by testing it on multiple subsets of the training data.
Examples:
K-fold cross-validation
Leave-one-out validation
Stratified sampling
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Decision Tree
Machine Learning
A tree-like model that makes decisions by splitting data based on feature values, creating a hierarchy of if-then rules.
Examples:
Medical diagnosis trees
Credit approval systems
Customer segmentation
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Dimensionality Reduction
Machine Learning
Techniques for reducing the number of input variables or features while preserving important information.
Examples:
Principal Component Analysis
Data visualization
Noise reduction
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Ensemble Learning
Machine Learning
A method that combines multiple machine learning models to create a stronger predictor than any individual model.
Examples:
Random Forest
Voting classifiers
Stacking models
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Feature Engineering
Machine Learning
The process of selecting, modifying, or creating input variables (features) to improve machine learning model performance.
Examples:
Creating interaction terms
Normalizing data
Encoding categorical variables
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Feature Selection
Machine Learning
The process of identifying and selecting the most relevant input variables for building effective machine learning models.
Examples:
Recursive feature elimination
Correlation analysis
Importance scoring
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Gradient Descent
Machine Learning
An optimization algorithm that iteratively adjusts model parameters to minimize the difference between predicted and actual outcomes.
Examples:
Neural network training
Linear regression optimization
Loss minimization
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K-Means Clustering
Machine Learning
An unsupervised learning algorithm that partitions data into k clusters based on similarity of features.
Examples:
Customer segmentation
Image segmentation
Market research
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Linear Regression
Machine Learning
A statistical method that models the relationship between a dependent variable and independent variables using a linear equation.
Examples:
Price prediction
Sales forecasting
Risk assessment
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Logistic Regression
Machine Learning
A statistical method used for binary classification that predicts the probability of an instance belonging to a particular category.
Examples:
Medical diagnosis
Email spam detection
Marketing response prediction
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Random Forest
Machine Learning
An ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting.
Examples:
Feature importance ranking
Classification tasks
Regression problems
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Regression
Machine Learning
A supervised learning task where the algorithm predicts continuous numerical values rather than discrete categories.
Examples:
House price prediction
Stock price forecasting
Temperature estimation
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Support Vector Machine (SVM)
Machine Learning
A powerful classification algorithm that finds the optimal boundary (hyperplane) to separate different classes of data.
Examples:
Text classification
Image recognition
Bioinformatics
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Time Series Analysis
Machine Learning
Statistical techniques for analyzing data points collected over time to identify trends, patterns, and make predictions.
Examples:
Stock market prediction
Weather forecasting
Sales projection
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Supervised Learning
Machine Learning
A type of machine learning where models learn from labeled training data to make predictions on new, unlabeled data.
Examples:
Email classification
Medical diagnosis
Price prediction
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Unsupervised Learning
Machine Learning
Machine learning that finds hidden patterns in data without labeled examples or specific target outputs.
Examples:
Customer grouping
Market segmentation
Fraud detection
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Reinforcement Learning
Machine Learning
A learning method where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment.
Examples:
Game playing AI
Autonomous vehicles
Trading algorithms
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Overfitting
Machine Learning
When a model learns the training data too specifically and fails to generalize well to new, unseen data.
Examples:
Memorizing instead of learning
Perfect training accuracy but poor test performance
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Deep Learning
Deep Learning
A subset of machine learning using neural networks with multiple layers to learn complex patterns from large amounts of data.
Examples:
Image recognition
Language translation
Speech synthesis
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Activation Function
Deep Learning
Mathematical functions that determine the output of neural network nodes, introducing non-linearity to enable complex pattern learning.
Examples:
ReLU function
Sigmoid function
Tanh function
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Attention Mechanism
Deep Learning
A technique that allows neural networks to focus on specific parts of input data when making predictions, improving performance on sequential tasks.
Examples:
Machine translation
Text summarization
Image captioning
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BERT
Deep Learning
Bidirectional Encoder Representations from Transformers - a pre-trained language model that understands context from both directions in text.
Examples:
Question answering
Text classification
Named entity recognition
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Dropout
Deep Learning
A regularization technique that randomly sets some neural network connections to zero during training to prevent overfitting.
Examples:
Image classification
Text processing
Speech recognition
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Generative Pre-trained Transformer (GPT)
Deep Learning
A type of large language model that generates human-like text by predicting the next word in a sequence.
Examples:
ChatGPT
GPT-4
Text completion
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Long Short-Term Memory (LSTM)
Deep Learning
A type of recurrent neural network designed to remember information for long periods, solving the vanishing gradient problem.
Examples:
Language modeling
Speech recognition
Time series prediction
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Multilayer Perceptron (MLP)
Deep Learning
A feedforward neural network with multiple layers of nodes, each fully connected to the next layer.
Examples:
Pattern recognition
Function approximation
Classification tasks
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Pre-training
Deep Learning
The process of training a model on a large, general dataset before fine-tuning it for specific tasks.
Examples:
BERT pre-training
GPT pre-training
Vision transformer training
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Residual Network (ResNet)
Deep Learning
A deep neural network architecture that uses skip connections to allow gradients to flow directly through the network.
Examples:
Image classification
Object detection
Medical imaging
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Self-Attention
Deep Learning
A mechanism where each position in a sequence can attend to all positions in the same sequence to compute representations.
Examples:
Language modeling
Machine translation
Document understanding
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Neural Networks
Deep Learning
Computing systems inspired by biological neural networks, consisting of interconnected nodes (neurons) that process and transmit information.
Examples:
Feedforward networks
Convolutional networks
Recurrent networks
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Transformer
Deep Learning
A neural network architecture that uses attention mechanisms to process sequential data, forming the basis of modern language models.
Examples:
GPT models
BERT
T5
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Convolutional Neural Network (CNN)
Deep Learning
A type of neural network particularly effective for processing grid-like data such as images, using convolution operations.
Examples:
Image classification
Object detection
Medical imaging
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Recurrent Neural Network (RNN)
Deep Learning
Neural networks designed to process sequential data by maintaining memory of previous inputs through recurrent connections.
Examples:
Language modeling
Speech recognition
Time series prediction
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Natural Language Processing (NLP)
Natural Language
The field of AI focused on enabling computers to understand, interpret, and generate human language.
Examples:
Chatbots
Translation services
Text summarization
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Embeddings
Natural Language
Dense vector representations of words, sentences, or documents that capture semantic meaning and relationships.
Examples:
Word embeddings
Sentence embeddings
Document vectors
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Few-Shot Learning
Natural Language
The ability of AI models to learn and perform new tasks with only a few examples or training samples.
Examples:
GPT with examples in prompt
Quick task adaptation
Learning from demonstrations
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In-Context Learning
Natural Language
The ability of large language models to perform tasks by learning from examples provided in the input prompt without parameter updates.
Examples:
Translation with examples
Classification with demonstrations
Pattern completion
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Named Entity Recognition (NER)
Natural Language
The task of identifying and classifying named entities (people, places, organizations) in text.
Examples:
Extracting person names
Identifying locations
Finding organizations
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Part-of-Speech Tagging
Natural Language
The process of assigning grammatical categories (noun, verb, adjective) to each word in a text.
Examples:
Identifying nouns and verbs
Grammar checking
Syntax analysis
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Question Answering (QA)
Natural Language
AI systems designed to automatically answer questions posed in natural language by finding relevant information.
Examples:
Search engines
Virtual assistants
Customer support bots
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Retrieval-Augmented Generation (RAG)
Natural Language
A technique that combines information retrieval with text generation to produce more accurate and factual responses.
Examples:
Document-based QA
Fact-checking systems
Knowledge-grounded chatbots
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Text Classification
Natural Language
The task of automatically categorizing text documents into predefined classes or categories.
Examples:
Email filtering
News categorization
Review classification
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Text Generation
Natural Language
The process of automatically creating human-like text using AI models, often based on prompts or context.
Examples:
Story writing
Code generation
Email composition
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Text Summarization
Natural Language
The process of automatically creating shorter versions of text while preserving the most important information.
Examples:
News article summaries
Document abstracts
Meeting notes
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Word2Vec
Natural Language
A technique for learning vector representations of words that capture semantic relationships based on context.
Examples:
Word similarity
Analogy completion
Document clustering
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Large Language Model (LLM)
Natural Language
AI models trained on vast amounts of text data to understand and generate human-like language across various tasks.
Examples:
ChatGPT
Claude
Gemini
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Prompt Engineering
Natural Language
The practice of designing and optimizing text inputs (prompts) to get desired outputs from AI language models.
Examples:
Crafting specific instructions
Providing examples
Setting context
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Tokenization
Natural Language
The process of breaking down text into smaller units (tokens) that AI models can process, such as words or subwords.
Examples:
Word tokenization
Subword tokenization
Character tokenization
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Sentiment Analysis
Natural Language
The use of NLP to determine the emotional tone or opinion expressed in text, typically positive, negative, or neutral.
Examples:
Social media monitoring
Customer feedback analysis
Product reviews
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Computer Vision
Computer Vision
The field of AI that enables computers to interpret and understand visual information from the world, such as images and videos.
Examples:
Facial recognition
Medical imaging
Autonomous driving
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Image Recognition
Computer Vision
The ability of AI systems to identify and classify objects, people, places, or activities in digital images.
Examples:
Photo tagging
Medical diagnosis
Quality control
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Object Detection
Computer Vision
A computer vision technique that identifies and locates objects within images or videos, often drawing bounding boxes around them.
Examples:
Autonomous vehicles
Security systems
Retail analytics
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Generative Adversarial Network (GAN)
Computer Vision
A machine learning architecture where two neural networks compete: one generates fake data while the other tries to detect it.
Examples:
Deepfakes
Art generation
Data augmentation
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Bounding Box
Computer Vision
A rectangular border drawn around objects in images to indicate their location and boundaries for object detection tasks.
Examples:
Car detection in traffic
Face detection in photos
Product detection in retail
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Edge Detection
Computer Vision
A computer vision technique that identifies boundaries between different regions in an image by detecting discontinuities in brightness.
Examples:
Medical imaging
Industrial inspection
Autonomous navigation
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Feature Map
Computer Vision
The output of a convolutional layer in a neural network, representing detected features at different spatial locations.
Examples:
Edge detection maps
Object feature maps
Texture detection
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Image Segmentation
Computer Vision
The process of dividing an image into multiple segments or regions to identify and locate objects and boundaries.
Examples:
Medical image analysis
Autonomous driving
Photo editing
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Optical Character Recognition (OCR)
Computer Vision
Technology that converts images of text into machine-readable text format, enabling digital text extraction from images.
Examples:
Document scanning
License plate reading
Receipt processing
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YOLO (You Only Look Once)
Computer Vision
A real-time object detection algorithm that identifies and locates multiple objects in images with a single neural network pass.
Examples:
Surveillance systems
Autonomous vehicles
Sports analysis
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AI Ethics
Ethics & Safety
The study of moral principles and guidelines for developing and using AI systems responsibly and beneficially for society.
Examples:
Fair hiring algorithms
Unbiased medical diagnosis
Transparent decision-making
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Algorithmic Accountability
Ethics & Safety
The principle that AI systems and their creators should be responsible and answerable for the decisions and outcomes of their algorithms.
Examples:
Algorithm audits
Impact assessments
Regulatory compliance
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Data Privacy
Ethics & Safety
The protection of personal and sensitive information used in AI systems, ensuring proper handling and user consent.
Examples:
Personal data protection
Medical record privacy
Financial information security
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Differential Privacy
Ethics & Safety
A mathematical framework for protecting individual privacy while allowing useful analysis of datasets.
Examples:
Census data analysis
Medical research
Location services
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Fairness
Ethics & Safety
The principle that AI systems should treat all individuals and groups equitably without discrimination or bias.
Examples:
Equal opportunity in hiring
Fair loan decisions
Unbiased medical treatment
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Human-in-the-Loop
Ethics & Safety
A model where humans are involved in the AI decision-making process to provide oversight, validation, or intervention.
Examples:
Medical diagnosis review
Content moderation
Financial decision approval
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Robustness
Ethics & Safety
The ability of AI systems to maintain performance and reliability when faced with unexpected inputs or adversarial attacks.
Examples:
Spam detection resilience
Image classifier stability
Fraud detection accuracy
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Transparency
Ethics & Safety
The principle that AI systems should be open and understandable in their operations, decisions, and limitations.
Examples:
Algorithm explanation
Decision reasoning
Model documentation
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Business Intelligence (BI)
Business
Technologies and practices for collecting, analyzing, and presenting business data to support decision-making.
Examples:
Sales dashboards
Performance metrics
Market analysis
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Customer Relationship Management (CRM)
Business
Technology for managing interactions and relationships with customers, often enhanced with AI capabilities.
Examples:
Salesforce
Lead scoring
Customer segmentation
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Data-Driven Decision Making
Business
The practice of basing business decisions on data analysis and interpretation rather than intuition alone.
Examples:
A/B testing results
Customer behavior analysis
Performance optimization
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Key Performance Indicator (KPI)
Business
Measurable values that demonstrate how effectively AI systems or business processes are achieving objectives.
Examples:
Model accuracy rates
Customer satisfaction scores
Revenue growth
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Machine Learning Operations (MLOps)
Business
Practices for deploying, monitoring, and maintaining machine learning models in production environments.
Examples:
Model versioning
Performance monitoring
Automated retraining
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Predictive Analytics
Business
The use of data, statistical algorithms, and machine learning to predict future outcomes based on historical data.
Examples:
Sales forecasting
Risk assessment
Demand planning
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Process Automation
Business
The use of AI and technology to automate repetitive business processes and workflows.
Examples:
Invoice processing
Customer onboarding
Data entry
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Batch Processing
Technical
Processing data or running AI models on large datasets in batches rather than in real-time.
Examples:
Nightly data processing
Model training
Report generation
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Cloud Computing
Technical
The delivery of computing services including AI capabilities over the internet rather than local servers.
Examples:
AWS AI services
Google Cloud AI
Azure Cognitive Services
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Container
Technical
A lightweight, portable package that includes an AI application and all its dependencies for consistent deployment.
Examples:
Model deployment
Application packaging
Environment consistency
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Data Pipeline
Technical
A series of data processing steps that move and transform data from source systems to AI models or analytics platforms.
Examples:
Real-time streaming
Batch processing
Data transformation
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Inference Engine
Technical
The component of an AI system that applies learned knowledge to make predictions or decisions on new data.
Examples:
Real-time predictions
Batch inference
Edge inference
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Latency
Technical
The time delay between input and output in AI systems, crucial for real-time applications.
Examples:
Voice assistant response
Image recognition speed
Trading algorithms
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Model Serving
Technical
The process of deploying trained AI models to production environments where they can make predictions on new data.
Examples:
REST API endpoints
Model servers
Batch prediction services
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Scalability
Technical
The ability of AI systems to handle increasing amounts of data or users without performance degradation.
Examples:
Horizontal scaling
Auto-scaling
Load distribution
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Version Control
Technical
Systems for tracking and managing changes to AI models, code, and data over time.
Examples:
Git repositories
Model registries
Experiment tracking
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Bias
Ethics & Safety
Systematic prejudice in AI systems that can lead to unfair or discriminatory outcomes, often inherited from training data or design choices.
Examples:
Gender bias in hiring
Racial bias in criminal justice
Age bias in lending
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Explainable AI (XAI)
Ethics & Safety
AI systems designed to provide clear, understandable explanations for their decisions and reasoning processes.
Examples:
Medical diagnosis explanations
Loan approval reasoning
Legal decision factors
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AI Safety
Ethics & Safety
The research and practices focused on ensuring AI systems operate safely and align with human values and intentions.
Examples:
Robustness testing
Fail-safe mechanisms
Human oversight
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Hallucination
Ethics & Safety
When AI models generate false or nonsensical information that appears confident and plausible but is factually incorrect.
Examples:
False historical facts
Non-existent citations
Incorrect mathematical calculations
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AI as a Service (AIaaS)
Business
Cloud-based platforms that provide AI capabilities and tools as services, allowing businesses to use AI without building infrastructure.
Examples:
Google Cloud AI
AWS AI Services
Azure Cognitive Services
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Return on Investment (ROI)
Business
The measure of financial benefit gained from AI implementations relative to the cost of investment.
Examples:
Automation savings
Increased productivity
Revenue growth
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Digital Transformation
Business
The integration of AI and digital technologies into all areas of business to fundamentally change operations and value delivery.
Examples:
Automated customer service
Predictive maintenance
Personalized marketing
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AI Governance
Business
The framework of policies, procedures, and oversight mechanisms for responsible AI development and deployment in organizations.
Examples:
AI ethics committees
Usage policies
Audit procedures
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API (Application Programming Interface)
Technical
A set of protocols and tools that allows different software applications to communicate and share data or functionality.
Examples:
OpenAI API
Google Translate API
Computer Vision API
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GPU (Graphics Processing Unit)
Technical
Specialized processors originally designed for graphics that are highly effective for parallel computing tasks in AI training and inference.
Examples:
NVIDIA GPUs
Google TPUs
Cloud GPU instances
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Hyperparameters
Technical
Configuration settings that control the learning process of AI models, set before training begins and not learned from data.
Examples:
Learning rate
Batch size
Number of layers
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Fine-tuning
Technical
The process of taking a pre-trained AI model and adapting it for a specific task or domain with additional training.
Examples:
Customizing ChatGPT
Domain-specific models
Task adaptation
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Edge Computing
Technical
Running AI computations on local devices rather than in the cloud, enabling faster responses and reduced data transmission.
Examples:
Smartphone AI
IoT devices
Autonomous vehicles
Related Terms:
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