Artificial intelligence comes with a fast-growing vocabulary. This beginner-friendly A–Z glossary explains important AI, machine learning, and data science terms in plain English.
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70 beginner-friendly terms · Last reviewed August 28, 2026
A
AI Agent
An AI system that can pursue a goal by deciding what steps to take, using tools, and acting on information with limited human direction. For example, an agent might research several sources and organize the findings into a report.
Algorithm
A set of step-by-step instructions a computer follows to solve a problem or make a decision — like a recipe that always gives the same result when followed correctly.
AI Alignment
The effort to make an AI system’s behavior, objectives, and decisions match intended human goals and values.
Application Programming Interface (API)
A defined way for software systems to communicate. An AI API lets an app send information to an AI model and receive a response without hosting the model itself.
Artificial General Intelligence (AGI)
A hypothetical form of AI that could learn, reason, and perform a wide range of intellectual tasks at a human level or beyond. No generally accepted AGI system exists today.
Artificial Intelligence (AI)
Technology that allows machines or software to perform tasks that usually require human intelligence, such as understanding language, recognizing images, or making predictions. For example, when your phone suggests your next word while typing.
Artificial Neural Network (ANN)
A type of AI model inspired by the human brain, made of connected “nodes” that work together to recognize patterns and learn from data.
B
Bias (AI Bias)
When an AI system gives unfair or inaccurate results because the data it learned from was unbalanced or flawed. For example, if it mostly saw one type of person in training data, it may perform worse on others.
Big Data
Extremely large sets of data that are too big for traditional tools to handle, often used to train and improve AI models. Think of millions of photos, messages, or clicks.
Bot
A software program that runs automated tasks, such as answering simple questions in a chat, indexing web pages, or sending alerts.
C
Chatbot
A computer program that simulates conversation with people, often used in customer service, websites, apps, or tools like ChatGPT.
Classification
A type of AI task where the model sorts data into categories, such as “spam vs. not spam” or “cat vs. dog.”
Computer Vision
A field of AI that allows computers to understand and interpret images or videos. For example, your phone recognizing your face to unlock.
Context Window
The amount of information an AI model can consider at one time, measured in tokens. A larger context window lets a model work with longer conversations or documents.
D
Data Mining
The process of analyzing large datasets to discover useful patterns, trends, or insights.
Dataset
A structured collection of data used to train or test AI models, such as a folder of labeled images or a spreadsheet of past customer purchases.
Deep Learning
A type of machine learning that uses many layers of neural networks to learn complex patterns. It powers things like image recognition, voice assistants, and advanced language models.
Diffusion Model
A generative AI model that learns to create data—often images—by starting with random noise and gradually refining it into a coherent result.
E
Edge Computing
Processing data close to where it is created (like on your phone or a local device) instead of sending everything to a distant server, which often makes AI tools faster and more private.
Embedding
A numerical representation of text, images, or other data that captures meaning and similarity. Embeddings help AI systems find related items even when they do not use the same words.
Ethics in AI
A set of principles that guide how AI should be built and used so it is fair, transparent, safe, and respectful of people’s rights and privacy.
Explainable AI (XAI)
AI designed to be more transparent, so humans can understand how it arrived at a particular decision or prediction.
F
Fine-Tuning
Taking an existing AI model and training it further on more specific data so it performs better on a particular task, industry, or style.
Foundation Model
A large, general-purpose AI model trained on massive amounts of data that can be adapted and customized for many different uses, such as writing, coding, or answering questions.
G
Generative AI
AI that can create new content, such as text, images, music, code, or video, instead of just analyzing existing data. Tools like image generators and writing assistants use generative AI.
Grounding
Connecting an AI model’s response to verifiable sources or supplied information so the answer is more relevant and less likely to rely on unsupported claims.
Guardrails
Rules, filters, and checks designed to keep an AI system within intended safety, privacy, and behavior boundaries.
H
Hallucination (AI Hallucination)
When an AI confidently gives an answer that is wrong, misleading, or completely made up, even though it sounds believable.
Hyperparameter
A setting chosen before training an AI model (such as learning rate or batch size) that affects how the model learns and how well it performs.
I
Image Recognition
A type of computer vision where AI identifies objects, people, or scenes in images — for example, detecting cats in photos or reading street signs for self-driving cars.
Inference
The moment an AI model uses what it has already learned to make a prediction, answer a question, or generate content.
J
Jailbreak (AI)
A prompt or technique intended to make an AI system ignore its safety rules or operating instructions. Responsible testing reports these weaknesses instead of using them to cause harm.
K
Knowledge Graph
A network of connected information that shows relationships between people, places, things, and concepts. Search engines and AI assistants use knowledge graphs to better understand context.
L
Labeling
Adding tags or descriptions to data so an AI can learn from it — for example, marking photos as “cat,” “dog,” or “car.”
Language Model
An AI system trained to understand and generate human language. It powers tools that can write text, answer questions, or carry on conversations.
Large Language Model (LLM)
A language model trained on very large amounts of text and other data to understand and generate language. LLMs power many chatbots, writing assistants, and coding tools.
M
Machine Learning (ML)
A type of AI where systems learn from data by finding patterns, rather than being explicitly programmed with fixed rules.
Model
The trained AI system that can take in new data and make predictions, classifications, or generate content based on what it has learned.
Model Drift
A decline in an AI model’s real-world performance as the data, behaviors, or conditions it encounters change over time.
Multimodal AI
AI that can understand and work with more than one type of input at the same time, such as text + images, or text + audio.
N
Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand, interpret, and generate human language, both written and spoken.
Neural Network
An AI model made of layers of interconnected nodes (“neurons”) that work together to recognize patterns in data, inspired by how the human brain works.
O
Open-Source AI Model
An AI model released with source code, weights, or other components under terms that allow some level of inspection, modification, and reuse. What is included varies by license.
Optimization
The process of improving an AI model’s performance by adjusting how it is trained or how it makes predictions so that it becomes more accurate or efficient.
Overfitting
When an AI model learns the training data too well (including noise or mistakes) and then performs poorly on new, unseen data.
P
Parameter
A value inside an AI model that gets adjusted during training, such as the weights in a neural network.
Pattern Recognition
The ability of AI to detect trends, structures, or repeated shapes in data — essential for tasks like image recognition or fraud detection.
Prompt
The instructions, question, examples, or other input given to an AI model to guide its response.
Prompt Engineering
The practice of designing and refining prompts so an AI system produces more useful, accurate, or consistently formatted results.
Q
Quantum Computing (AI Context)
A new kind of computing based on quantum physics that could one day make certain AI tasks much faster. It is still highly experimental and not widely used yet.
R
Reasoning Model
An AI model designed to spend additional computational effort working through complex, multi-step problems before producing an answer.
Regression
A type of prediction task where the AI tries to forecast a number, like the price of a house or next month’s sales.
Reinforcement Learning
A type of machine learning where an AI learns by trial and error, receiving rewards for good actions and penalties for bad ones, similar to training a pet.
Reinforcement Learning from Human Feedback (RLHF)
A training method that uses human preferences about model outputs to help an AI system produce responses people judge as more useful, safe, or appropriate.
Retrieval-Augmented Generation (RAG)
A technique that retrieves relevant information from documents, databases, or search systems and adds it to a model’s context before the model generates an answer.
S
Speech Recognition
AI that converts spoken language into text. It is used for transcription, captions, dictation, and voice-controlled interfaces.
Supervised Learning
A machine learning approach where the AI is trained on labeled data, meaning examples with known correct answers.
Synthetic Data
Artificially created data used to train AI models when real data is limited, expensive, or sensitive — such as simulated images or generated text.
T
Temperature
A setting that influences how predictable or varied a generative model’s output is. Lower values usually produce more consistent responses; higher values can produce more variety.
Test Data
A portion of a dataset kept separate from training and used to estimate how well a finished model performs on unseen examples.
Token
A small piece of text that an AI language model reads, often a word or part of a word.
Training
The process of teaching an AI model by feeding it data and letting it adjust its internal parameters until it learns useful patterns.
Training Data
The examples used to teach an AI model patterns, relationships, or desired behavior during training.
Transformer
A neural-network architecture that uses attention to understand relationships between parts of a sequence. Transformers are the foundation of most modern large language models.
U
Unsupervised Learning
A type of machine learning where the AI looks for patterns in unlabeled data, grouping or organizing it without being told the “right” answers.
V
Validation Data
A portion of a dataset used during model development to compare settings and tune the model without using the final test data.
Vector Database
A database designed for AI that stores information as vectors (lists of numbers) so the AI can quickly find similar items, such as similar texts or images.
W
Weights
Values inside a neural network that get adjusted during training, controlling how strongly each input affects the output.
X
Y
YOLO (You Only Look Once)
A family of computer-vision models that can detect and locate multiple objects in an image in a single processing pass, making it useful for real-time detection.
Z
Zero-Shot Learning
When a model handles a class or task without having seen labeled training examples for that specific class or task, using knowledge learned elsewhere to make the connection.
AI Glossary FAQs
What is an AI glossary?
An AI glossary is a simple reference guide that explains artificial intelligence terms in clear language.
It helps beginners quickly understand key concepts without needing a technical background.
Who is this AI glossary for?
This glossary is for anyone who wants to understand AI better — students, creators, business owners,
professionals, or anyone curious about how artificial intelligence works.
How should I use this glossary?
You can scroll through the A–Z list, use the letter shortcuts at the top, or type a word into the search bar
to instantly filter terms. Whenever you see an unfamiliar AI term in an article, come back here and look it up.
What is the difference between AI and machine learning?
Artificial intelligence is the broad idea of machines acting smart, while machine learning is a specific
way of building AI systems that learn from data instead of being hard-coded with rules.
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