Know Your AI Terms
AI Glossary for
Small Business Owners
Welcome to our AI Glossary — a simple, easy-to-follow resource to help small business owners understand the most common artificial intelligence terms. Whether you’re exploring automation, chatbots, or machine learning, this guide is here to help.
A-M
Algorithm
A step-by-step set of rules or calculations a computer follows to solve a problem or perform a task.
API (Application Programming Interface)
A set of tools that allows different software systems–including AI–to communicate with each other.
Artificial Intelligence (AI)
The ability of machines to perform tasks that typically require human intelligence.
Automation
The use of technology to perform tasks without human intervention.
Bias in AI
When an AI system produces unfair or unbalanced results due to flawed or limited training data.
Chatbot
A software application that simulates conversation with users, typically online.
Classification
The process of sorting data into categories based on learned patterns.
Clustering
Grouping similar data points together without prior labeling.
Computer Vision
A field of AI that enables computers to interpret and understand images or videos.
Conversational AI
AI systems designed to simulate human-like conversations, often used in chatbots.
Data Labeling
Tagging data with categories to help AI learn and make predictions.
Data Mining
Analyzing large datasets to identify patterns and useful information.
Deep Learning
A type of machine learning that uses neural networks with many layers to analyze data.
Generative AI
AI that can create new content–like text, images, or code–based on learned patterns.
Machine Learning
A subset of AI that enables computers to learn from data to improve over time.
N-Z
Natural Language Processing (NLP)
A branch of AI focused on understanding and interacting with human language.
Neural Network
A model designed to mimic the way the human brain processes information.
Predictive Analytics
Using historical data to forecast future outcomes or trends.
Prompt
The input (usually a question or instruction) given to an AI system to generate a response.
RPA (Robotic Process Automation)
The use of bots to automate repetitive and rule-based digital tasks across applications.
Scalability
The ability of an AI system to handle increasing amounts of work or data without performance loss.
Structured Data
Data organized into rows and columns, making it easy for AI systems to read and process.
Supervised Learning
A type of machine learning where the AI is trained using labeled data to learn from examples.
Synthetic Data
Artificially created data used to train AI models when real data is limited or sensitive.
Training Data
The data used to teach AI systems how to perform a task or recognize patterns.
Transfer Learning
When an AI model trained on one task is reused for another related task, saving time and resources.
Tuning
Adjusting the parameters of an AI model to improve performance or accuracy.
Unstructured Data
Data that doesn’t follow a set format–like emails, videos, or social media posts.
Use Case
A specific way AI is applied to solve a problem or automate a business task.
Voice Assistant
An AI-powered tool that responds to voice commands (like Siri or Alexa) to help users with tasks.
Workflow Automation
Using AI to connect and streamline multiple business tasks or processes automatically.
Zero-shot Learning
An AI’s ability to complete tasks it hasn’t been specifically trained for, using reasoning.