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AI Glossary: Unlock the Language of AI & Stay Ahead
No more confusion—get straightforward definitions of key AI concepts and buzzwords.
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AI Glossary of Terms
Here is a (complete as can be in this fast-changing industry) alphabetical glossary of commonly used artificial intelligence (AI) terms, explained clearly and simply without technical jargon.
A
Adversarial attack: Intentionally tricking an AI with misleading information, causing it to make mistakes.
Agent: An AI program or robot that can look around, understand its environment, and take action based on what it sees.
Algorithm: A clear set of instructions a computer follows to do a task or solve a problem.
Artificial general intelligence (AGI): A future type of AI that could understand most things and do tasks just as broadly and flexibly as a human being.
Artificial intelligence (AI): Teaching computers to do things normally needing human thinking, like recognizing images, answering questions, or making predictions.
Artificial neural network: A computer system loosely modeled after how the human brain works, capable of learning from examples.
Augmented intelligence: When AI works as a helpful tool to humans, rather than replacing them fully.
Automated machine learning (AutoML): Systems that automatically build or improve AI models without requiring deep technical expertise.
B
Backpropagation: How AI learns by correcting itself based on identifying its own mistakes.
Bias (in AI): When AI systems unfairly favor or discriminate against certain people or groups due to issues in their training data or design.
Big data: Very large and complicated sets of information that AI helps to analyze, because humans would struggle to process it manually.
Bot: Short for robot, usually means a software tool that performs automated tasks online.
C
Chatbot: A type of AI that talks with people through messages or voice, usually answering questions or managing simple tasks.
Classification: Putting information into groups based on their shared characteristics— like sorting emails as “spam” or “not spam.”
Clustering: Finding similarities between things and grouping them together, even without being told beforehand what the groups should be.
Computer vision: Teaching computers to "see" and understand images and videos.
Conversational AI: Systems that can have human-like conversations by understanding context and responding appropriately.
D
Data mining: Exploring large amounts of data to find hidden patterns or useful insights.
Data science: The process of studying information and data to discover meaningful insights and answers.
Decision tree: A clear, step-by-step decision-making tool that helps AI choose between different options.
Deep learning: A technique inspired by the human brain that teaching computers how to learn patterns in information, particularly good at tasks like recognizing objects or speech.
Digital twin: A detailed virtual model of a real-world object, used by AI to predict outcomes and behaviors.
E
Edge AI: AI that runs directly on devices like phones, cameras, or sensors instead of relying on remote servers.
Ethics in AI: Guidelines to make sure that AI technologies are developed and used safely, fairly, and responsibly.
Explainable AI (XAI): An approach that helps humans clearly understand why and how an AI makes decisions.
F
Face recognition: An AI technology that can identify a person based on their facial features using images or video footage.
Federated learning: AI learning technique where many devices (like smartphones) learn separately and then share their learning, protecting personal data privacy.
Feature: An important characteristic or trait that an AI uses to spot patterns in data, like recognizing a car by its shape or wheels.
G
Generative AI: An AI that can create new content, including text, images, music, or videos, similar to those a human would produce.
Genetic algorithm: A way of solving problems by simulating evolutionary processes— testing lots of ideas, then combining and improving the best ones gradually.
GPT (Generative Pre-trained Transformer): A popular type of AI capable of understanding and producing very human-like writing and conversation.
H
Hallucination (in AI): When an AI confidently gives incorrect or made-up information, even though it sounds believable.
Human-in-the-loop: Situation where human judgment is included in an AI system to supervise, guide or correct it.
I
Image recognition: AI’s ability to understand and identify content within a picture, such as recognizing objects, people, or scenes.
Inference: When an AI applies its learned knowledge to make decisions or predictions about new information or situations.
Intelligent automation: Using AI technology to automate tasks that typically need human thought or judgment.
K
Knowledge graph: A structured model that shows relationships between various ideas, entities, or concepts—helping AI understand context and connections.
L
Large language model (LLM): AI programs trained on vast amounts of text, capable of understanding, producing, and responding to language in very human-like ways.
Learning rate: How quickly or slowly an AI learns from new examples or feedback.
M
Machine learning (ML): Teaching an AI system to identify patterns on its own by studying examples and then making decisions or predictions from that knowledge.
Metadata: Information about other information; like date, time, or location data associated with photographs or documents.
Model: The method or program that an AI uses to understand patterns and relationships, and then make decisions.
N
Natural language processing (NLP): Teaching AI to understand, interpret, and respond to human language naturally and effectively.
O
Object detection: Recognizing and locating specific objects within images or video, like tagging each person in a group photograph.
Optical character recognition (OCR): A technology that lets AI read text from images and convert it into editable text.
Overfitting: An error where an AI system learns training examples too literally and fails to recognize similar but slightly different examples later.
P
Pattern recognition: The ability of AI to identify repeating structures, events, or trends within data.
Prediction: When AI uses its understanding of past information to guess what might happen next or answer questions.
Prompt engineering: The practice of carefully designing instructions or queries to help AI models produce better, clearer responses.
R
Recommendation system: AI systems that suggest books, movies, music, or products based on your past activity or preferences.
Reinforcement learning: Teaching AI through rewarding good choices and discouraging bad ones; similar to how animals learn by receiving positive and negative feedback.
Robotics: Combining AI with machinery to create robots that can complete tasks and interact with the physical world.
S
Sentiment analysis: Using AI to understand emotions, attitudes, and opinions expressed in text or speech—such as detecting whether an online review is positive or negative.
Supervised learning: Teaching an AI by showing it lots of examples with clearly-labeled correct answers so it learns patterns.
Synthetic data: Artificially generated information created to help train AI models, especially when real data is scarce or sensitive.
T
Transfer learning: Taking knowledge an AI gained from solving one task, and using that understanding to help it do better on a related, new task.
Transformer: A popular AI structure used especially for understanding language or sequences, making AI very good at processing text.
Turing test: A benchmark in AI to decide whether an AI can convincingly mimic human behavior or conversation enough that a human couldn’t reliably tell it’s not human.
U
Underfitting: When an AI does not learn enough detail from training examples, making its decisions too simplistic or inaccurate.
Unsupervised learning: Teaching an AI by letting it sort out patterns and relationships in data by itself, without any examples labeled with the correct answers.
User Experience (UX): How comfortable, clear, and enjoyable it is for a human user to interact with an AI technology.
V
Virtual assistant: AI software designed to help users complete everyday tasks like scheduling, answering questions, or setting reminders; examples include Siri, Alexa, or Google Assistant.
Voice recognition: AI’s ability to interpret and understand spoken words, converting human speech into text that a computer can understand.
Z
Zero-shot learning: AI’s ability to accurately identify or predict something it has never seen before, based purely on descriptions or context, without direct training examples.
A
Adversarial attack: Intentionally tricking an AI with misleading information, causing it to make mistakes.
Agent: An AI program or robot that can look around, understand its environment, and take action based on what it sees.
Algorithm: A clear set of instructions a computer follows to do a task or solve a problem.
Artificial general intelligence (AGI): A future type of AI that could understand most things and do tasks just as broadly and flexibly as a human being.
Artificial intelligence (AI): Teaching computers to do things normally needing human thinking, like recognizing images, answering questions, or making predictions.
Artificial neural network: A computer system loosely modeled after how the human brain works, capable of learning from examples.
Augmented intelligence: When AI works as a helpful tool to humans, rather than replacing them fully.
Automated machine learning (AutoML): Systems that automatically build or improve AI models without requiring deep technical expertise.
B
Backpropagation: How AI learns by correcting itself based on identifying its own mistakes.
Bias (in AI): When AI systems unfairly favor or discriminate against certain people or groups due to issues in their training data or design.
Big data: Very large and complicated sets of information that AI helps to analyze, because humans would struggle to process it manually.
Bot: Short for robot, usually means a software tool that performs automated tasks online.
C
Chatbot: A type of AI that talks with people through messages or voice, usually answering questions or managing simple tasks.
Classification: Putting information into groups based on their shared characteristics— like sorting emails as “spam” or “not spam.”
Clustering: Finding similarities between things and grouping them together, even without being told beforehand what the groups should be.
Computer vision: Teaching computers to "see" and understand images and videos.
Conversational AI: Systems that can have human-like conversations by understanding context and responding appropriately.
D
Data mining: Exploring large amounts of data to find hidden patterns or useful insights.
Data science: The process of studying information and data to discover meaningful insights and answers.
Decision tree: A clear, step-by-step decision-making tool that helps AI choose between different options.
Deep learning: A technique inspired by the human brain that teaching computers how to learn patterns in information, particularly good at tasks like recognizing objects or speech.
Digital twin: A detailed virtual model of a real-world object, used by AI to predict outcomes and behaviors.
E
Edge AI: AI that runs directly on devices like phones, cameras, or sensors instead of relying on remote servers.
Ethics in AI: Guidelines to make sure that AI technologies are developed and used safely, fairly, and responsibly.
Explainable AI (XAI): An approach that helps humans clearly understand why and how an AI makes decisions.
F
Face recognition: An AI technology that can identify a person based on their facial features using images or video footage.
Federated learning: AI learning technique where many devices (like smartphones) learn separately and then share their learning, protecting personal data privacy.
Feature: An important characteristic or trait that an AI uses to spot patterns in data, like recognizing a car by its shape or wheels.
G
Generative AI: An AI that can create new content, including text, images, music, or videos, similar to those a human would produce.
Genetic algorithm: A way of solving problems by simulating evolutionary processes— testing lots of ideas, then combining and improving the best ones gradually.
GPT (Generative Pre-trained Transformer): A popular type of AI capable of understanding and producing very human-like writing and conversation.
H
Hallucination (in AI): When an AI confidently gives incorrect or made-up information, even though it sounds believable.
Human-in-the-loop: Situation where human judgment is included in an AI system to supervise, guide or correct it.
I
Image recognition: AI’s ability to understand and identify content within a picture, such as recognizing objects, people, or scenes.
Inference: When an AI applies its learned knowledge to make decisions or predictions about new information or situations.
Intelligent automation: Using AI technology to automate tasks that typically need human thought or judgment.
K
Knowledge graph: A structured model that shows relationships between various ideas, entities, or concepts—helping AI understand context and connections.
L
Large language model (LLM): AI programs trained on vast amounts of text, capable of understanding, producing, and responding to language in very human-like ways.
Learning rate: How quickly or slowly an AI learns from new examples or feedback.
M
Machine learning (ML): Teaching an AI system to identify patterns on its own by studying examples and then making decisions or predictions from that knowledge.
Metadata: Information about other information; like date, time, or location data associated with photographs or documents.
Model: The method or program that an AI uses to understand patterns and relationships, and then make decisions.
N
Natural language processing (NLP): Teaching AI to understand, interpret, and respond to human language naturally and effectively.
O
Object detection: Recognizing and locating specific objects within images or video, like tagging each person in a group photograph.
Optical character recognition (OCR): A technology that lets AI read text from images and convert it into editable text.
Overfitting: An error where an AI system learns training examples too literally and fails to recognize similar but slightly different examples later.
P
Pattern recognition: The ability of AI to identify repeating structures, events, or trends within data.
Prediction: When AI uses its understanding of past information to guess what might happen next or answer questions.
Prompt engineering: The practice of carefully designing instructions or queries to help AI models produce better, clearer responses.
R
Recommendation system: AI systems that suggest books, movies, music, or products based on your past activity or preferences.
Reinforcement learning: Teaching AI through rewarding good choices and discouraging bad ones; similar to how animals learn by receiving positive and negative feedback.
Robotics: Combining AI with machinery to create robots that can complete tasks and interact with the physical world.
S
Sentiment analysis: Using AI to understand emotions, attitudes, and opinions expressed in text or speech—such as detecting whether an online review is positive or negative.
Supervised learning: Teaching an AI by showing it lots of examples with clearly-labeled correct answers so it learns patterns.
Synthetic data: Artificially generated information created to help train AI models, especially when real data is scarce or sensitive.
T
Transfer learning: Taking knowledge an AI gained from solving one task, and using that understanding to help it do better on a related, new task.
Transformer: A popular AI structure used especially for understanding language or sequences, making AI very good at processing text.
Turing test: A benchmark in AI to decide whether an AI can convincingly mimic human behavior or conversation enough that a human couldn’t reliably tell it’s not human.
U
Underfitting: When an AI does not learn enough detail from training examples, making its decisions too simplistic or inaccurate.
Unsupervised learning: Teaching an AI by letting it sort out patterns and relationships in data by itself, without any examples labeled with the correct answers.
User Experience (UX): How comfortable, clear, and enjoyable it is for a human user to interact with an AI technology.
V
Virtual assistant: AI software designed to help users complete everyday tasks like scheduling, answering questions, or setting reminders; examples include Siri, Alexa, or Google Assistant.
Voice recognition: AI’s ability to interpret and understand spoken words, converting human speech into text that a computer can understand.
Z
Zero-shot learning: AI’s ability to accurately identify or predict something it has never seen before, based purely on descriptions or context, without direct training examples.
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