Artificial Intelligence (AI) sits at the core of today’s most significant breakthroughs, influencing how we communicate, create, and solve complex problems. As these technologies continue to evolve, understanding the language behind them has become just as important as using the tools themselves.
We have created this glossary to provide you with a solid foundation in the most widely used terms across AI and machine learning. Whether you’re just getting started or already building with large language models, knowing the vocabulary helps you make sense of the systems, their capabilities, and their limitations.
A clear grasp of core AI concepts empowers you to ask better questions, build smarter solutions, and take part in more thoughtful conversations — not just about what AI can do, but also how it should be used.
AI Glossary of Terms:
As this field shapes the future, we believe it’s essential that developers, engineers, and curious minds alike stay informed, engaged, and ready to lead.
A
Ablation — A technique to evaluate the importance of a feature or component in an AI/ML system by temporarily removing it and retraining; if performance degrades, the removed part was likely necessary.
Accuracy — A standard metric for classification tasks, measuring the ratio of correct predictions (true positives + true negatives) to total predictions.
Active Learning — A learning paradigm where the model selectively chooses which data points to get labels for, aiming to achieve good performance with fewer labeled examples.
Adaptive Learning Rate — In optimization, a technique where the learning rate changes during training (often reducing over time) to help convergence.
Agent / AI Agent — An autonomous or semi-autonomous AI entity that can perform tasks or decisions on behalf of users or systems.
Anomaly Detection — The process of identifying data points that deviate significantly from the majority, often used for detecting fraud or errors.
Anthropomorphism — The attribution of human traits, emotions, or intentions to non-human entities (like AI), sometimes leading to overestimation of AI’s human-like abilities.
Architecture (AI Architecture) — The overall design and structure of an AI system, including data pipelines, model layers, interfaces, and supporting infrastructure.
Artificial General Intelligence (AGI) — A hypothetical AI system that can understand, learn, and perform any intellectual task that a human can, across diverse domains.
Artificial Intelligence (AI) — The broader field of computer science focused on creating machines or systems that mimic aspects of human intelligence: reasoning, learning, perception, language understanding, and decision-making.
Artificial Neural Network (ANN) — A computational model inspired by biological neural networks; composed of interconnected “neurons” (nodes) that process information and learn patterns.
Adversarial Attack — A technique where carefully crafted perturbations are added to input data (e.g., images, text) to trick a model into making incorrect predictions — used to test robustness/security of AI.
Adversarial Training — A defense mechanism where models are trained on adversarial examples (potential attacks) to make them more robust against malicious perturbations.
Adaptive Computation Time (ACT) — A mechanism in some neural architectures where the model dynamically adjusts how many computational steps to spend per input — helpful to save compute or allow variable-length reasoning.
Attention Mechanism — A technique enabling models to focus on relevant parts of input when producing outputs; it assigns varying “attention weights” to input elements for better context handling.
Autoencoder — A neural network trained to reconstruct its input — learns compressed “latent” representations; useful for dimensionality reduction, denoising, anomaly detection, or as a building block for generative models.
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B
Backpropagation — An algorithm used in neural networks to compute gradients (errors) and update weights by propagating the error backward through the layers.
Batch Processing — Processing multiple data samples together (in a batch) during training or inference, often for computational efficiency.
Bias (in data/model) — Systematic error or skew in data or model predictions, often arising due to nonrepresentative training data, can lead to unfair or inaccurate outputs.
Boosting — A method in ensemble learning where multiple weak learners are combined sequentially to build a strong learner, improving performance over individual models.
C
Capacity (Model Capacity) — The ability of a model to fit a wide variety of functions; high-capacity models can represent complex patterns but risk overfitting.
Chunking — Dividing large documents or data into smaller, manageable parts (chunks) for processing — useful in text processing, retrieval, or summarization.
Classification — A supervised learning task where the model assigns input data to predefined categories or labels.
Clustering — An unsupervised learning technique that groups similar data points together based on their features, without predefined labels.
Confusion Matrix — A table used to evaluate a classification model by showing counts of true positives, true negatives, false positives, and false negatives.
Context Window — In language models, the maximum number of tokens (words or subwords) that the model can consider at once when generating output — defines “memory” span per prompt.
Convolutional Neural Network (CNN) — A type of neural network especially suited for processing structured grid data like images; uses convolution layers to detect local patterns.
Cross-Validation — A technique to assess how well a model generalizes to unseen data by splitting data into multiple train/test partitions and averaging performance.
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D
Data Ingestion — The process of collecting, importing, and storing raw data from various sources before processing or analysis.
Data Preprocessing — Steps taken to clean, normalize, and transform raw data before feeding it into a model; critical to the model’s performance and reliability.
Deep Learning (DL) — A subset of ML using neural networks with many (deep) layers — powerful for complex tasks like image recognition, language understanding, and speech.
Decision Tree — A model that uses a tree-like structure to make decisions: each node represents a test on a feature, branches correspond to outcomes, and leaves yield predictions.
Dimensionality Reduction — Techniques used to reduce the number of input variables/features (e.g., via PCA, t-SNE) while preserving relevant information — helps with visualization and efficiency.
Distillation (Model Distillation) — The process of transferring knowledge from a large, complex model (“teacher”) to a smaller, simpler model (“student”), retaining performance but reducing size/compute.
Drift (Data Drift / Concept Drift) — When statistical properties of input data change over time, causing a model to degrade; requires monitoring and possible retraining.
Data Augmentation — Technique to artificially expand training datasets by transforming or perturbing existing data (e.g., rotations for images, noise insertion), helping models generalize better.
Deep Reinforcement Learning (DRL) — Combines deep learning and reinforcement learning: uses deep neural networks as function approximators for policies or value functions, enabling RL in high-dimensional spaces (e.g., games, robotics).
E
Epoch — One complete pass through the entire training dataset during model training. Multiple epochs are often needed for effective learning.
Ethical AI — Practices, frameworks, and principles aimed at ensuring AI systems are fair, transparent, accountable, and avoid harmful biases or misuse.
Evaluation Metric — Quantitative measure used to assess model performance (e.g., accuracy, precision, recall, F1-score) — guides model selection and tuning.
Exploratory Data Analysis (EDA) — The approach of summarizing and visualizing datasets to understand patterns, spot anomalies, and gain insights before modeling.
Embedding (Vector Embedding / Representation) — Mapping of high-dimensional data (text, images, graph nodes, etc.) into a continuous vector space such that similar inputs map to close vectors — fundamental for similarity search, clustering, retrieval.
Ensemble Methods — Combine predictions of multiple models (e.g., via voting, averaging, stacking) to reduce variance or bias and improve predictive performance over single models.
Error-Driven Learning — A Learning paradigm that uses prediction error (difference between output and target) as feedback to update model parameters.
F
Feature — An input variable used by the model; feature engineering involves creating, selecting, or transforming features to improve model performance.
Feature Selection — The process of selecting a subset of relevant features from the original feature set — helps reduce overfitting and improve model efficiency.
Fine-Tuning — Further training a pre-trained model on a new (often smaller) dataset to adapt it to a specific task or domain.
Foundation Model — A large, versatile model trained on vast amounts of data that can be adapted for many kinds of downstream tasks (e.g., via fine-tuning).
F1 Score — A metric combining precision and recall to give a balanced measure of a classification model’s performance — helpful when classes are imbalanced. (Key evaluation metric.)
Feedback Loop — In iterative AI systems, using model outputs (or user interactions) as new data for retraining or refining the model.
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G
GAN (Generative Adversarial Network) — A class of deep learning models comprising two networks (generator and discriminator) competing to generate realistic synthetic data (images, text, etc.).
Generative AI — A Branch of AI focused on generating new content — text, images, audio, video — often via models trained on vast datasets to mimic human-like output.
Gradient Descent — An optimization algorithm that iteratively adjusts model parameters in the direction that minimally reduces error (loss), guiding the model to learn.
Grounding (in AI / NLP) — Anchoring AI outputs in real-world knowledge, context, or data so that generated output is relevant, accurate, and meaningful.
GPU (Graphics Processing Unit) — Specialized hardware that accelerates parallel computation; widely used to train deep learning models efficiently.
Generative Models — Models (such as autoencoders, GANs, VAEs, and others) that can generate new data similar to training data; used for image synthesis, data augmentation, creative content generation, etc.
Grokking (in ML) — A surprising phenomenon where, after long training on small datasets, a model abruptly shifts from overfitting (poor generalization) to generalized performance—even though earlier validation loss plateaued.
Graph Neural Network (GNN) — A neural architecture designed to work on graph-structured data (nodes and edges), capable of learning representations for nodes, edges, or entire graphs — useful for social networks, molecules, and knowledge graphs.
H
Hyperparameter — A parameter that defines model behavior (e.g., learning rate, number of layers) set before training, not learned during training.
Hyperparameter Tuning — The process of searching for the best combination of hyperparameters (via grid search, random search, etc.) to optimize model performance. (Important ML practice.)
Hallucination (in Generative AI) — When an AI model (especially a generative one) produces outputs that are incorrect, nonsensical, or not grounded in real data/facts.
Holding Out (Holdout Set) — Reserving a portion of data (not used in training) for final evaluation to test how well a model generalizes to unseen data.
Human-in-the-Loop (HITL) — Design paradigm where humans are involved in some part of the ML/AI workflow — e.g., labeling data, verifying outputs, providing feedback — often used to improve reliability, fairness, and safety of AI systems.
I
Inference — The stage where a trained model makes predictions or generates output on new, unseen data.
Intent — The purpose or goal behind a user’s input (e.g., a request to “book flight”), used in conversational AI and chatbots to understand what the user wants.
Intelligence Augmentation — The use of AI to amplify human capabilities, helping humans make better decisions or perform tasks more efficiently.
Iterative Training — Repeated cycles of training and evaluation — used to refine models over multiple passes, improving performance or stability.
J
Jaccard Similarity — A measure of similarity between two sets, defined as the size of their intersection divided by the size of their union. In AI/ML, it’s often used for comparing sets of features, tags, or token sets.
Jensen–Shannon Divergence — A symmetric, smoothed version of Kullback–Leibler divergence that measures how different two probability distributions are. Common in generative modeling (e.g., evaluating GANs) and when comparing model output distributions.
Joint Probability Distribution — A probability distribution over multiple random variables at once (e.g., p(X, Y)). Many generative models and graphical models implicitly or explicitly model a joint distribution over inputs and labels or over latent and observed variables.
Joint Embedding — A shared vector space where different modalities (e.g., text and images) or other domains are mapped so that semantically related items lie close together. Used in multimodal models.
Joint Training — Training multiple components or tasks of a model together rather than separately, allowing shared parameters or shared representations to improve performance.
Jacobian — The matrix of all partial derivatives of a model’s outputs with respect to its inputs or parameters. It appears in backpropagation, sensitivity analysis, and some interpretability and robustness methods.
K
K-Nearest Neighbors (KNN) — A simple ML algorithm that classifies a data point based on the majority label of its k nearest neighbors in feature space.
K-Shot Learning — A learning paradigm where the model learns from only k examples per class (often small, such as 1–5), which is useful when labeled data is scarce.
Kernel (in ML) — A function used in algorithms (like SVM) to implicitly map input data into a higher-dimensional space for better separation — functional for non-linear patterns.
Knowledge Graph — A structured representation of entities and their relationships, enabling AI systems to reason over and query complex interlinked data.
K-Fold Cross-Validation — A variant of cross-validation where the data is split into K subsets (folds), training is repeated K times, each time using a different fold as validation, and helps assess generalization reliably.
L
Label (in Supervised Learning) — The target value or class given for each input in training data, used by supervised algorithms to learn a mapping from input to output.
Large Language Model (LLM) — A high-capacity language model (often based on transformer architecture) trained on massive text data to perform tasks like text generation, summarization, translation, etc.
Learning Rate — A hyperparameter that controls the size of the update step during training when using gradient descent; too high may cause divergence, while too low slows down learning.
Loss Function — A function that quantifies the difference between the model’s predictions and actual labels; the optimization algorithm tries to minimize this loss.
Low-Code / No-Code AI — Tools or platforms that allow building AI applications with minimal to no coding by using visual interfaces — enabling broader adoption beyond expert engineers.
M
Machine Learning (ML) — A subset of AI where algorithms learn patterns from data (rather than being explicitly programmed) and improve over time as they get more data.
Model — The representation learned by an algorithm during training, which can be used to make predictions or generate outputs on new data.
Model Chaining — The technique of linking multiple models in a sequence (pipeline), where the output of one serves as input to another — standard in complex AI systems.
Multimodal Model — Models capable of processing and generating more than one data type (e.g., text + image, audio + text), enabling richer AI applications.
Multitask Learning — Training a model to perform multiple related tasks simultaneously — can improve generalization by leveraging shared patterns across tasks.
Monte Carlo Dropout — A technique for approximating model uncertainty: dropout is applied at inference time, running multiple forward passes to obtain a distribution of predictions — used in Bayesian / uncertainty-aware neural nets.
N
Natural Language Processing (NLP) — A subfield of AI focused on enabling machines to understand, interpret, and generate human language (text or speech).
Neural Network — A computational model composed of layers of interconnected “neurons” (units) that can learn complex patterns — the basis for deep learning.
Normalization (Feature Normalization / Data Normalization) — Scaling or transforming data features to a standard scale, improving training stability and performance.
N-gram — A contiguous sequence of N items (words, characters) from text, used in language modeling, text analysis, and NLP tasks.
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O
Overfitting — When a model learns the training data too well (including noise) and performs poorly on unseen data — a common pitfall in ML.
Optimization Algorithm — Algorithms (like gradient descent) used to adjust model parameters to minimize loss — critical for effective learning.
Ontology (in AI / Knowledge Representation) — A structured representation of concepts and relationships in a domain — used to enable AI systems to reason and understand semantics.
Outlier Detection — Identifying data points that deviate significantly from the rest — helps in cleaning data and detecting anomalies, such as fraud.
P
Parameter (Model Parameter) — Numeric values internal to a model (e.g., weights in a neural network) that are learned during training.
Precision (in Classification) — A performance metric: among predicted positives, how many are actually positive — useful when false positives are costly.
Prompt — In generative AI, a prompt is the input or query given to a model.
Pre-trained Model — A model trained on a large generic dataset beforehand, which can then be fine-tuned for specific tasks — saves time and resources.
Privacy & Data Masking (PII Masking) — Techniques to anonymize or mask personally identifiable information when using data for training, ensuring compliance and ethical standards.
Parameter-Efficient Fine-Tuning (PEFT) — Techniques for adapting large pre-trained models to new tasks using only a small subset of parameters, making fine-tuning cheaper and more efficient.
Prompt Engineering — In generative AI / LLMs: designing inputs (prompts) carefully to elicit desired behavior/output — subtle changes can influence output style, content, and reliability significantly.
Reinforcement Learning from Human Feedback (RLHF) — A method where human feedback (e.g., preferences, rankings) is used to fine-tune model behavior — widely used in training modern conversational and generative AI to align with human values/desires.
Q
Query (in AI / Databases) — A request for information from a model, database, or AI system; in NLP or search, it’s the user input for which a model returns an answer.
Quantization (Model Quantization) — The process of reducing the precision (e.g., from 32-bit to 8-bit) of model parameters to make models smaller and faster — useful for deployment on devices.
Out-of-Distribution (OOD) Detection — The task of identifying inputs that differ significantly from the training data distribution — essential to avoid unpredictable or erroneous model behavior in real-world deployment.
Overparameterization — When a model has far more parameters than strictly necessary for a task, often in deep networks, it can enable flexibility and learning capacity but may risk overfitting without proper regularization.
R
Recall (in Classification) — A metric: among actual positives, how many did the model correctly identify — important when missing a positive is costly.
Reinforcement Learning (RL) — A learning paradigm where an agent learns to make decisions by receiving feedback (rewards or penalties) from its environment — valuable for sequential decision tasks.
Regularization — Techniques (like L1, L2 penalties, dropout) used during training to prevent overfitting by discouraging overly complex models.
Representation Learning — Methods by which a model automatically discovers useful features or representations from raw data, reducing the need for manual feature engineering.
Retrieval-Augmented Generation (RAG) — A technique where a generative model augments its generation by retrieving relevant documents/data from external sources, thereby improving accuracy and grounding.
S
Semi-Supervised Learning — A learning approach combining a small amount of labeled data with a large amount of unlabeled data — valid when labels are expensive or scarce.
Supervised Learning — A type of ML where models are trained on labeled data (input + correct output) to learn a mapping; everyday tasks are classification and regression.
Support Vector Machine (SVM) — A classic ML algorithm used for classification and regression by finding a hyperplane that best separates classes in feature space.
System Prompt / Meta Prompt — Hidden instructions given to a generative AI model (before user messages) to set its behavior, tone, or constraints.
Self-Supervised Learning — A training paradigm where models learn useful representations from unlabeled data by generating pseudo-labels (e.g., predicting masked parts) — reduces dependency on labeled data and scales to big data.
Stochastic Gradient Descent (SGD) — An optimization algorithm that updates model parameters based on a random subset (mini-batch) of data per iteration — more efficient than full-batch gradient descent, widely used in training neural nets.
Synthetic Data Generation — Creating artificial but realistic data (images, text, tabular) using generative models — useful for augmenting datasets, balancing classes, or training when real data is scarce or sensitive.
T
Transformer (Model / Architecture) — A neural network architecture using attention mechanisms to process sequential data (like text), highly effective for NLP tasks and the foundation of many LLMs.
Token (in NLP / LLMs) — A unit of text (e.g., word, sub-word, character) that language models process; models operate on sequences of tokens rather than raw text.
Training (Model Training) — The process of feeding data to an algorithm so that it learns patterns, adjusts parameters, and becomes able to make accurate predictions or generate outputs.
Tokenization (in NLP) — The process of breaking raw text into tokens (words, subwords, characters) to prepare input for language models — foundational in NLP preprocessing.
Transfer Learning — The technique of leveraging a model pre-trained on one task/data, and adapting it to a different but related task — helps reduce data, compute, and time requirements for new tasks.
U
Unsupervised Learning — A type of ML where the model learns from unlabeled data, discovering hidden patterns or structure (e.g., via clustering, dimensionality reduction).
Underfitting — When a model is too simple to capture underlying patterns in data, resulting in poor performance even on training data — the opposite of overfitting.
Utility Function (in RL / AI Agents) — A function defining preferences or rewards in reinforcement-learning or agentic systems — guides agent decisions toward desired outcomes.
Uncertainty Quantification (UQ) — Methods to measure how confident a model is about its predictions (e.g., using probabilistic outputs, Bayesian approaches, or ensembles), critical for safety-critical AI deployment.
V
Validation Set — A subset of data held out from training, used to tune model hyperparameters and assess performance during development.
Variance (Model Variance) — The variability of model predictions across different training data subsets. High variance indicates that the model might overfit (be too sensitive to training data noise).
Vision AI / Computer Vision — Subfield of AI focused on enabling machines to interpret and understand visual data (images, video), used in facial recognition, object detection, etc.
Variational Autoencoder (VAE) — A type of generative model that learns latent representations of data and can generate new samples by sampling from learned latent space — widely used for image/speech generation, anomaly detection, and data compression.
W
Weights (in Neural Networks) — Numeric parameters in a neural network that determine the strength of connections between neurons; learned during training.
Weight Regularization (L1, L2, Dropout, etc.) — Techniques to penalize large weights or randomly drop connections during training — help prevent overfitting, encourage simpler models, and improve generalization.
Weak Supervision — A training setup where labels come from noisy, distant, or heuristic sources (rules, weak models, user clicks) instead of fully hand-labeled data, often combined to approximate high-quality labels at scale.
Weight Initialization — The strategy used to set a neural network’s weights before training (e.g., Xavier, He initialization); good initialization helps gradients flow and speeds up convergence.
Word Embedding — A dense vector representation of words where similar words lie close in vector space (e.g., Word2Vec, GloVe); a specific case of embeddings widely used in NLP.
Warm Start — Starting training or optimization from an already-trained model or previous solution instead of random initialization, often reducing training time or helping models adapt to new data.
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X
XAI (Explainable AI) — A branch of AI focused on building models or tools that produce interpretable, transparent, and understandable decisions/outputs (rather than “black-box”).
XGBoost (Extreme Gradient Boosting) — A highly optimized gradient-boosted decision tree library widely used for tabular ML tasks, known for strong performance on classification and regression benchmarks.
Xavier Initialization (Glorot Initialization) — A weight initialization method that sets layer weights so that activations and gradients stay in a reasonable range across layers, improving stability in deep networks.
XOR Problem — A classic example showing that a single-layer perceptron cannot model linearly inseparable functions (like XOR), historically motivating the use of multi-layer neural networks.
Y
YOLO (You Only Look Once) — A family of real-time object detection models that predict bounding boxes and class probabilities in a single forward pass, widely used in practical computer vision applications.
Y-hat (ŷ) — Notation for a model’s predicted output (e.g., ŷ for regression, predicted class probabilities in classification), in contrast to the actual target value y.
Z
Zero-Shot Learning — A paradigm where a model can perform a task for classes or categories it has never seen during training, by leveraging generalized knowledge or embeddings.
Zero-Shot Prompting — In generative models / LLMs: asking a model to perform a task it has not been explicitly trained for, purely via prompt — e.g., “Translate this to Japanese,” even if the model is not fine-tuned for translation.
Z-Score Normalization (Standardization) — A feature-scaling technique that transforms values to have zero mean and unit variance, helping many models train more reliably.
Zipf’s Law (in NLP) — An empirical law stating that word frequency is inversely proportional to its rank in the frequency table; explains why a few words dominate text corpora while many words are rare.
