Our website provides a comprehensive guide to key artificial intelligence terms. It covers essential concepts like AI Copilots, Generative AI, and large language models (LLMs), explaining their roles in modern enterprise environments. This glossary is designed to help professionals understand the evolving AI landscape and its practical applications across industries. With terms ranging from machine learning to natural language processing, it’s an accessible resource for both beginners and experts.
AI Terms Glossary not only defines key AI terms but also illustrates how these technologies apply in real-world business scenarios. For example, AI copilots help automate customer support, while generative AI transforms content creation. Large language models (LLMs) streamline workflows, such as analyzing vast datasets to generate insights. Use cases span industries—from automating IT support in enterprise environments to enhancing employee productivity through AI-driven tools. Understanding these terms helps businesses integrate AI effectively, reducing operational costs and improving efficiency.
a
- Absolute Value Function
- Action Recognition
- Activation Function
- Active Learning
- Adversarial Learning
- Agent-Based Modeling
- Agentic AI
- AI copilot
- AI Plugin
- AI Search
- Algorithmic Transparency
- Anomaly Detection
- Artificial General Intelligence
- Associative Memory
- Asynchronous Learning
- Attention Mechanism
- Automated Machine Learning (AutoML)
- Automated Speech Recognition (ASR)
- Autonomous Systems
- Autoregressive Model
b
- Bag of Words
- Batch Normalization
- Batch Processing
- Bayesian Decision Theory
- Bayesian Filtering
- Bayesian Inference
- Bayesian Network
- Bayesian Neural Network
- Bayesian Optimization
- Bayesian Regression
- Behavioral Analytics
- Behavioral Cloning
- Benchmark Dataset
- Benchmarking
- Bias Mitigation
- Bias-Variance Tradeoff
- Bidirectional LSTM (BiLSTM)
- Bidirectional Search
- Bimodal Distribution
- Binary Classification
- Binary Search Tree
- Black Box Model
- Blended Learning Models
- Boolean Logic
- Boosting Algorithm
- Bootstrap Aggregation (Bagging)
- Bot Framework
- Botnet Detection
- Bounding Box
- Brute Force Search
- Business Process Automation (BPA)
- Business Rules Engine
c
- Canonical Correlation Analysis (CCA)
- Capsule Network
- Causal Forecasting
- Causal Inference
- Centroid
- Cluster Analysis
- Clustering
- Cognitive Analytics
- Cognitive Automation
- Cognitive Computing
- Cognitive Search
- Cold Start Problem
- Collaborative AI
- Collaborative Filtering
- Combinatorial Optimization
- Concept Drift
- Conditional Random Field (CRF)
- Confidence Interval
- Confidence Score
- Confusion Matrix
- Constraint Satisfaction Problem (CSP)
- Contextual AI
- Contextual Bandits
- Contextual Embeddings
- Control Systems
- Correlation Analysis
- Cost Function
- Covariance Matrix
- Curse of Dimensionality
- Customer Churn Prediction
- Customer Sentiment Analysis
d
- Data Augmentation
- Data Bias
- Data Drift
- Data Imputation
- Data Monetization
- Data Partitioning
- Data Pipeline
- Data Provenance
- Data Sampling
- Data Transformation
- Data Wrangling
- DataRobot
- Decision Boundary
- Deep Q-Network (DQN)
- Deep Reinforcement Learning
- Dense Layer
- Deterministic Model
- Dimensionality Reduction
- Discriminative Model
- Distributed AI
- Document Classification
- Domain Adaptation
- Domain Knowledge
- Dynamic Pricing
- Dynamic Scheduling
- Dynamic Time Warping (DTW)
e
- E-commerce AI
- E-commerce Personalization
- Edge AI
- Edge Computing
- Edge Device
- Edge Intelligence
- ElasticNet
- Embedded AI
- Emotion Recognition
- Enriched Data
- Ensemble Learning
- Ensembling
- Entity Resolution
- Episodic Memory
- Error Analysis
- Error Rate
- Evolutionary Algorithm
- Exploratory Data Analysis
- Exponential Growth Model
- Exponential Smoothing
f
- F1 Score
- Faceted Search
- Factor Analysis
- Factorization Machines
- False Discovery Rate (FDR)
- Fast Gradient Sign Method
- Fault Detection
- Feature Engineering
- Feature Extraction
- Feature Importance
- Feature Map
- Feature Selection
- Feedback Control
- Few-shot Learning
- Finite State Machine
- Fitness Landscape
- Fog Computing
- Forecasting Accuracy
- Forward Chaining
- Forward Propagation
- Fraud Detection
- Functional Programming
- Fuzzy Clustering
- Fuzzy Logic
- Fuzzy Matching
g
- Gated Recurrent Unit (GRU)
- Gaussian Blur
- Gaussian Naive Bayes
- Gaussian Noise
- Gaussian Process Regression
- Generalization
- Generalized Linear Models (GLM)
- Genetic Algorithm
- Gesture Recognition
- Gibbs Sampling
- Gini Index
- Global Optimization
- Gradient Boosting
- Gradient Clipping
- Gradient Descent
- Graph Clustering
- Graph Embeddings
- Graph Neural Networks
- Graph Theory
- Graphical Models
- Greedy Algorithm
- Grid Search
- Guided Learning
- Gumbel Softmax
h
- Hardware Acceleration
- Health Analytics
- Hessian Matrix
- Heterogeneous Computing
- Heterogeneous Data
- Heteroscedasticity
- Heuristic Function
- Heuristic Search
- Hidden Layer
- Hierarchical Clustering
- Hinge Loss
- Histogram of Oriented Gradients (HOG)
- Human-AI Collaboration
- Human-Centered AI
- Human-Machine Interface (HMI)
- Hybrid AI
- Hyperbolic Tangent
- Hypergraph
- Hyperparameter Tuning
- Hyperplane
- Hyperspectral Imaging
- Hypothesis Testing
i
- Image Annotation
- Image Classification
- Image Segmentation
- Image Synthesis
- Imbalanced Data
- Imputation
- Incremental Learning
- Inductive Learning
- Industrial AI
- Inference Engine
- Information Extraction
- Information Retrieval
- Instance Normalization
- Intelligent Agents
- Intelligent Automation
- Intelligent Document Processing (IDP)
- Intelligent Edge
- Intelligent Search
- Intelligent Systems
- Intelligent Tutoring Systems
- Intent-Based Networking
- Interactive AI
- Interpretability
- Inverse Reinforcement Learning (IRL)
- Iterative Learning
j
k
l
- L1 Regularization (Lasso)
- L2 Regularization
- Label Encoding
- Label Propagation
- Label Smoothing
- Latent Dirichlet Allocation
- Latent Semantic Analysis (LSA)
- Latent Space
- Latent Variable
- Latent Variable Models
- Layer Normalization
- Learning Curve
- Learning from Data
- Learning Rate
- Learning to Rank
- Least Squares Method
- Lexical Analysis
- Likelihood Function
- Linear Discriminant Analysis (LDA)
- Linear Programming
- Link Prediction
- Logical Inference
- Longitudinal Data
- Loss Function
m
- Machine Translation
- Manifold Learning
- Margin of Error
- Markov Chain
- Markov Decision Process
- Masked Autoencoder
- Masked Language Model
- Matrix Factorization
- Maximum Likelihood Estimation
- Mean Absolute Error
- Mean Shift Clustering
- Mean Squared Error
- Memory Networks
- Minimax Algorithm
- Mixture of Gaussians
- Model Compression
- Model Drift
- Model Evaluation
- Model Optimization
- Model Selection
- Model Training
- Model-Based Reinforcement Learning
- Monte Carlo Tree Search
- Multi-Armed Bandit Problem
- Multi-Class Classification
- Multilayer Perceptron
- Multimodal Learning
- Multinomial Logistic Regression
- Multivariate Analysis
- Mutual Information
n
- Named Entity Recognition
- Nash Equilibrium
- Natural Language Generation
- Nearest Neighbor Search
- Negative Sampling
- Nesterov Momentum
- Network Analysis
- Neural Architecture Search
- Neural Search
- Neuro-Symbolic AI
- Noise in Data
- Noise Reduction
- Non-Negative Matrix Factorization
- Nonlinear Programming
- Nonlinear Regression
- Normalization
- Normalization Layer
o
p
- Parallel Coordinates Plot
- Parallel Processing
- Parameter Tuning
- Partial Dependence Plot (PDP)
- Pattern Recognition
- Perceptron Learning Algorithm
- Perturbation
- Pose Estimation
- Precision Agriculture
- Precision-Recall Curve
- Prediction Interval
- Predictive Maintenance
- Predictive Text
- Preprocessing
- Probability Distribution
- Product Recommendation Engine
- Public Cloud
q
r
- Random Search
- Random Walk
- Real-Time Fraud Detection
- Real-Time Monitoring
- Recursive Feature Elimination (RFE)
- Regression Trees
- Regularization
- Representation Learning
- Resampling
- Residual Block
- Residual Network (ResNet)
- Resource Allocation
- Resource Scheduling
- Ridge Regression
- Risk Mitigation
- Risk Modeling
- Robustness
- Root Mean Square Error (RMSE)
s
- Scalability
- Scenario Planning
- Self-Learning
- Semi-Supervised Learning
- Sensor Fusion
- Sentiment Classification
- Shapley Value
- Siamese Networks
- Similarity Search
- Simulation Modeling
- Smart Analytics
- Smart Manufacturing
- Smart Supply Chain
- Softmax Function
- Sparse Data
- Sparse Matrix
- Sparsity
- Stochastic Gradient Descent (SGD)
- Stochastic Modeling
- Stochastic Processes
- Super Resolution
- Support Vector Machine (SVM)
- Support Vectors
- Survival Analysis
- Swarm Intelligence
- System Identification
t
- Tabular Data
- Target Encoding
- Target Variable
- Task Automation
- Temporal Data
- Tensors
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Test Set
- Text Analytics
- Text Classification
- Text Mining
- Thompson Sampling
- Time Complexity
- Time Series Analysis
- Time Series Forecasting
- Topic Modeling
- Traffic Prediction
- Training Data
- Transfer Function
- Transferable Skills
- True Negative (TN)
- Turing Completeness
u
- Uncertainty Propagation
- Uncertainty Quantification
- Underfitting
- Unified Data Analytics
- Uniform Distribution
- Univariate Analysis
- Universal Approximation Theorem
- Universal Robots
- Unsupervised Learning
- Uplift Modeling
- Upper Confidence Bound
- Upsampling
- User Segmentation
- User-Centric
- User-Centric Design (USD)
- Utility Function
v
w
- Wavelet Transform
- WaveNet
- Weak Supervision
- Weakly Supervised Learning
- Wearable Sensors
- Web Personalization
- Web Scraping
- Weight Decay
- Weighted Average
- Whitelisting
- Wireless Sensor Networks
- Word Embeddings
- Word Error Rate (WER)
- Word Segmentation
- Word Sense Disambiguation
- Workflow Orchestration
- Workforce Analytics
- Workforce Optimization
- Workplace AI
x