Short, practical, and easy to understand — key AI concepts explained for small and midsize businesses. No jargon, just clarity.
34 terms
A step-by-step set of rules or calculations a computer follows to solve a problem or perform a task.
A set of tools that allows different software systems — including AI — to communicate with each other efficiently and reliably.
The ability of machines to perform tasks that typically require human intelligence.
The use of technology to perform tasks without human intervention.
When an AI system produces unfair or unbalanced results due to flawed or limited training data.
A software application that simulates conversation with users, typically online.
The process of sorting data into categories based on learned patterns.
Grouping similar data points together without prior labeling.
A field of AI that enables computers to interpret and understand images or videos.
AI systems designed to simulate human-like conversations, often used in chatbots and virtual assistants.
Tagging data with categories to help AI learn and make predictions.
Analyzing large datasets to identify patterns and useful information.
A type of machine learning that uses neural networks with many layers to analyze data.
AI that can create new content — like text, images, or code — based on learned patterns.
A subset of AI that enables computers to learn from data and improve over time.
A branch of AI focused on understanding and interacting with human language.
A model designed to mimic the way the human brain processes information.
Improving AI performance by adjusting processes, data, or parameters to achieve better results.
The coordination of multiple AI agents or systems to work together toward a shared goal.
Using historical data to forecast future outcomes or trends.
The input (usually a question or instruction) given to an AI system to generate a response.
The use of bots to automate repetitive and rule-based digital tasks across applications.
The ability of an AI system to handle increasing amounts of work or data without performance loss.
Data organized into rows and columns, making it easy for AI systems to read and process.
A type of machine learning where the AI is trained using labeled data to learn from examples.
Artificially created data used to train AI models when real data is limited or sensitive.
The data used to teach AI systems how to perform a task or recognize patterns.
When an AI model trained on one task is reused for another related task, saving time and resources.
Adjusting the parameters of an AI model to improve performance or accuracy.
Data that doesn't follow a set format — like emails, videos, or social media posts.
A specific scenario where AI is applied to solve a problem or automate tasks.
An AI-powered tool that responds to voice commands (like Siri or Alexa) to help users with tasks.
Using AI to connect and streamline multiple business tasks or processes automatically.
An AI's ability to complete tasks it hasn't been specifically trained for, using reasoning.
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