AI Glossary: Essential Terms for Beginners

AI Glossary of Terms

This glossary provides clear, beginner-friendly definitions of essential AI terms. Each definition includes both a technical explanation and a simple analogy to help newcomers understand complex concepts. These terms are fundamental to understanding AI and are referenced throughout our business-focused AI curriculum.

For Business Leaders: See also our AI Business Glossary for executive-focused terminology.

Term	Definition	Simple Explanation
Artificial Intelligence (AI)	The field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.	Teaching computers to do things that usually need human thinking, like recognizing faces or understanding speech.
Machine Learning (ML)	A subset of AI where computers learn from data to make predictions or decisions without being explicitly programmed for every scenario.	Computers learn from examples instead of following strict rules - like learning to recognize cats by seeing thousands of cat photos.
Neural Network	A computing system inspired by the human brain, made up of layers of interconnected nodes (‘neurons’) that process information.	A computer system that works a bit like a simplified brain, with connections that get stronger as it learns.
Deep Learning	A type of machine learning using large neural networks with many layers to analyze complex data patterns.	Using big, layered networks to help computers learn from lots of data - like how we recognize complex patterns.
Generative AI	A type of machine learning that focuses on creating new content or data that resembles existing data.	Teaching computers to create new things, like art or music, by learning from examples.
Algorithm	A set of rules or instructions a computer follows to solve a problem or complete a task.	Step-by-step instructions for a computer to follow, like a recipe for solving problems.
Data	Information used to train AI systems, such as text, images, numbers, or any digital content.	The ‘food’ that feeds AI systems - examples and information they learn from.
Training	The process of teaching an AI system by showing it examples and letting it learn patterns.	Like teaching a child to recognize animals by showing them pictures and telling them what each one is.
Model	The result of training an AI system; the ‘brain’ that can make predictions or decisions based on new data.	The ‘smart program’ that results after an AI system has finished learning from examples.
Prediction	An AI system’s best guess about something unknown, based on patterns it learned from training data.	The AI’s educated guess about what will happen or what something is, based on what it learned.
Supervised Learning	A machine learning method where the model learns from labeled data (examples with correct answers provided).	Teaching a computer by showing it examples with the right answers - like flashcards with questions and answers.
Unsupervised Learning	A machine learning method where the model finds patterns in data without being given the correct answers.	Letting a computer find patterns on its own, like finding groups of similar customers without being told what to look for.
Classification	A task where AI sorts data into predefined categories or groups.	Sorting things into groups, like separating spam emails from regular emails.
Natural Language Processing (NLP)	The field of AI focused on helping computers understand, interpret, and generate human language.	Teaching computers to understand and use human language, like reading text or having conversations.
Computer Vision	The field of AI that enables computers to interpret and understand images and videos.	Teaching computers to ‘see’ and understand pictures or videos, like recognizing objects or people.
Chatbot	An AI program designed to simulate conversation with human users through text or voice.	A computer program that can chat with people, like a virtual assistant that answers questions.
Bias	Systematic errors or unfairness in AI decisions, often caused by unrepresentative or prejudiced training data.	When a computer makes unfair decisions because it learned from biased examples - like hiring software that discriminates.
Big Data	Extremely large datasets that require special tools and techniques to store, process, and analyze.	Huge amounts of information that are too big for regular computer programs to handle easily.
Cloud Computing	Delivering computing services (including AI) over the internet instead of using local computers.	Using computers and software over the internet instead of installing everything on your own device.
Automation	Using technology (including AI) to perform tasks without human intervention.	Having machines do work automatically without people having to control every step.
Pattern Recognition	The ability of AI systems to identify regularities and similarities in data.	How computers learn to spot similarities and trends in information, like recognizing handwriting styles.
Virtual Assistant	An AI-powered software that can perform tasks or services based on voice commands or text input.	A computer helper that understands what you say and can do things for you, like Siri or Alexa.
Recommendation System	AI that suggests products, content, or actions based on user preferences and behavior patterns.	Computer programs that suggest things you might like, such as movies on Netflix or products on Amazon.
Facial Recognition	Technology that can identify or verify people by analyzing their facial features.	Computer programs that can recognize who someone is by looking at their face, like photo tagging on social media.
Voice Recognition	Technology that can identify and respond to spoken words and commands.	Computer programs that understand what people are saying, like voice-to-text or smart speakers.
Robotics	The field that combines AI with physical machines to create robots that can perform tasks in the real world.	Smart machines that can move around and do physical tasks, from factory robots to robot vacuum cleaners.
Internet of Things (IoT)	Network of physical devices embedded with sensors and AI to collect and exchange data.	Everyday objects (like refrigerators or thermostats) that are connected to the internet and can think for themselves.

Further Learning Resources

AI Business Overview: Executive introduction to AI’s transformative potential and business impact
AI Fundamentals for Business: Core AI concepts explained in business terminology
AI Business Glossary: Executive-focused terminology and definitions for business leaders
Generative AI Tools Guide: Comprehensive guide to GPT (aka genAI) tools and their business applications

How to Use This Glossary

For Quick Reference: Use this glossary while reading AI articles, attending presentations, or participating in AI discussions to understand unfamiliar terms.

For Learning: Start with basic terms like “Artificial Intelligence” and “Machine Learning,” then progress to more specific concepts as your understanding grows.

For Business Context: Combine this technical glossary with our AI Business Glossary to understand both the technical concepts and their business implications.