Glossary – 100 “AI” Terms – The Data Community

Below is a glossary that includes 100 common “AI (Artificial Intelligence)” terms and phrases in alphabetical order. Enjoy!

Term	Definition & Example
Accuracy	Percentage of correct predictions. Example: 92% accuracy.
Agent	AI entity performing tasks autonomously. Example: Task-planning agent.
AI Alignment	Ensuring AI goals match human values. Example: Safe AI systems.
AI Bias	Systematic unfairness in AI outcomes. Example: Biased hiring models.
Algorithm	A set of rules used to train models. Example: Decision tree algorithm.
Artificial General Intelligence (AGI)	Hypothetical AI with human-level intelligence. Example: Broad reasoning across tasks.
Artificial Intelligence (AI)	Systems that perform tasks requiring human-like intelligence. Example: Chatbots answering questions.
Artificial Neural Network (ANN)	A network of interconnected artificial neurons. Example: Credit scoring models.
Attention Mechanism	Focuses model on relevant input parts. Example: Language translation.
AUC	Area under ROC curve. Example: Model comparison.
AutoML	Automated model selection and tuning. Example: Auto-generated models.
Autonomous System	AI operating with minimal human input. Example: Self-driving cars.
Backpropagation	Method to update neural network weights. Example: Deep learning training.
Batch	Subset of data processed at once. Example: Batch size of 32.
Batch Inference	Predictions made in bulk. Example: Nightly scoring jobs.
Bias (Model Bias)	Error from oversimplified assumptions. Example: Linear model on non-linear data.
Bias–Variance Tradeoff	Balance between bias and variance. Example: Choosing model complexity.
Black Box Model	Model with opaque internal logic. Example: Deep neural networks.
Classification	Predicting categorical outcomes. Example: Email spam classification.
Clustering	Grouping similar data points. Example: Customer segmentation.
Computer Vision	AI for interpreting images and video. Example: Facial recognition.
Concept Drift	Changes in underlying relationships. Example: Fraud patterns evolving.
Confusion Matrix	Table evaluating classification results. Example: True positives vs false positives.
Data Augmentation	Expanding data via transformations. Example: Image rotation.
Data Drift	Changes in input data distribution. Example: New user demographics.
Data Leakage	Using future information in training. Example: Including test labels.
Decision Tree	Tree-based decision model. Example: Loan approval logic.
Deep Learning	ML using multi-layer neural networks. Example: Image recognition.
Dimensionality Reduction	Reducing number of features. Example: PCA for visualization.
Edge AI	AI running on local devices. Example: Smart cameras.
Embedding	Numerical representation of data. Example: Word embeddings.
Ensemble Model	Combining multiple models. Example: Random forest.
Epoch	One full pass through training data. Example: 50 training epochs.
Ethics in AI	Moral considerations in AI use. Example: Avoiding bias.
Explainable AI (XAI)	Making AI decisions understandable. Example: Feature importance charts.
F1 Score	Balance of precision and recall. Example: Imbalanced datasets.
Fairness	Equitable AI outcomes across groups. Example: Equal approval rates.
Feature	An input variable for a model. Example: Customer age.
Feature Engineering	Creating or transforming features to improve models. Example: Calculating customer tenure.
Federated Learning	Training models across decentralized data. Example: Mobile keyboard predictions.
Few-Shot Learning	Learning from few examples. Example: Custom classification with few samples.
Fine-Tuning	Further training a pre-trained model. Example: Custom chatbot training.
Generalization	Model’s ability to perform on new data. Example: Accurate predictions on unseen data.
Generative AI	AI that creates new content. Example: Text or image generation.
Gradient Boosting	Sequentially improving weak models. Example: XGBoost.
Gradient Descent	Optimization technique adjusting weights iteratively. Example: Training neural networks.
Hallucination	Model generates incorrect information. Example: False factual claims.
Hyperparameter	Configuration set before training. Example: Learning rate.
Inference	Using a trained model to predict. Example: Real-time recommendations.
K-Means	Clustering algorithm. Example: Market segmentation.
Knowledge Graph	Graph-based representation of knowledge. Example: Search engines.
Label	The correct output for supervised learning. Example: “Fraud” or “Not Fraud”.
Large Language Model (LLM)	AI trained on massive text corpora. Example: ChatGPT.
Loss Function	Measures model error during training. Example: Mean squared error.
Machine Learning (ML)	AI that learns patterns from data without explicit programming. Example: Spam email detection.
MLOps	Practices for managing ML lifecycle. Example: CI/CD for models.
Model	A trained mathematical representation of patterns. Example: Logistic regression model.
Model Deployment	Making a model available for use. Example: API-based predictions.
Model Drift	Model performance degradation over time. Example: Changing customer behavior.
Model Interpretability	Ability to understand model behavior. Example: Decision tree visualization.
Model Versioning	Tracking model changes. Example: v1 vs v2 models.
Monitoring	Tracking model performance in production. Example: Accuracy alerts.
Multimodal AI	AI handling multiple data types. Example: Text + image models.
Naive Bayes	Probabilistic classification algorithm. Example: Spam filtering.
Natural Language Processing (NLP)	AI for understanding human language. Example: Sentiment analysis.
Neural Network	Model inspired by the human brain’s structure. Example: Handwritten digit recognition.
Optimization	Process of minimizing loss. Example: Gradient descent.
Overfitting	Model learns noise instead of patterns. Example: Perfect training accuracy, poor test accuracy.
Pipeline	Automated ML workflow. Example: Training-to-deployment flow.
Precision	Correct positive predictions rate. Example: Fraud detection precision.
Pretrained Model	Model trained on general data. Example: GPT models.
Principal Component Analysis (PCA)	Technique for dimensionality reduction. Example: Compressing high-dimensional data.
Privacy	Protecting personal data. Example: Anonymizing training data.
Prompt	Input instruction for generative models. Example: “Summarize this text.”
Prompt Engineering	Crafting effective prompts. Example: Improving LLM responses.
Random Forest	Ensemble of decision trees. Example: Classification tasks.
Real-Time Inference	Immediate predictions on live data. Example: Fraud detection.
Recall	Ability to find all positives. Example: Cancer detection.
Regression	Predicting numeric values. Example: Sales forecasting.
Reinforcement Learning	Learning through rewards and penalties. Example: Game-playing AI.
Reproducibility	Ability to recreate results. Example: Fixed random seeds.
Robotics	AI applied to physical machines. Example: Warehouse robots.
ROC Curve	Performance visualization for classifiers. Example: Threshold analysis.
Semi-Supervised Learning	Mix of labeled and unlabeled data. Example: Image classification with limited labels.
Speech Recognition	Converting speech to text. Example: Voice assistants.
Supervised Learning	Learning using labeled data. Example: Predicting house prices from known values.
Support Vector Machine (SVM)	Algorithm separating data with margins. Example: Text classification.
Synthetic Data	Artificially generated data. Example: Privacy-safe training.
Test Data	Data used to evaluate model performance. Example: Held-out validation dataset.
Threshold	Cutoff for classification decisions. Example: Probability > 0.7.
Token	Smallest unit of text processed by models. Example: Words or subwords.
Training Data	Data used to teach a model. Example: Historical sales records.
Transfer Learning	Reusing knowledge from another task. Example: Image model reused for medical scans.
Transformer	Neural architecture for sequence data. Example: Language translation models.
Underfitting	Model too simple to capture patterns. Example: High error on all datasets.
Unsupervised Learning	Learning from unlabeled data. Example: Customer clustering.
Validation Data	Data used to tune model parameters. Example: Hyperparameter selection.
Variance	Error from sensitivity to data fluctuations. Example: Highly complex model.
XGBoost	Optimized gradient boosting algorithm. Example: Kaggle competitions.
Zero-Shot Learning	Performing tasks without examples. Example: Classifying unseen labels.