AI Glossary
Must-Know Terms and Definitions

Artificial Intelligence (AI)

The simulation of human intelligence in machines programmed to think, learn, and perform tasks that typically require human cognition, such as decision-making, pattern recognition, and problem-solving.

Algorithm

A set of rules or instructions that a computer follows to solve problems or make decisions. In AI, algorithms are the foundation for learning and pattern recognition.

Artificial General Intelligence (AGI)

A hypothetical form of AI that can understand, learn, and apply knowledge across a wide range of tasks at a human or superhuman level, as opposed to being limited to specific tasks.

Artificial Narrow Intelligence (ANI)

AI systems designed to perform a specific task or solve a narrow problem, such as facial recognition or language translation. Most AI today is ANI.

Annotation

The process of labeling data (such as images or text) with relevant information to help machine learning models learn and make accurate predictions.

Bias

Systematic errors or assumptions in AI models that can lead to unfair or inaccurate outcomes, often due to skewed training data or flawed algorithms.

ChatGPT

A conversational AI model based on the transformer architecture, trained to generate human-like responses in natural language. It is an example of a large language model (LLM).

Computer Vision

A field of AI focused on enabling machines to interpret and understand visual information from the world, such as images and videos.

Data

Information collected and used by AI systems to learn, make predictions, or identify patterns. Data can be structured (organized) or unstructured (raw text, images, etc.).

Deep Learning

A subset of machine learning that uses neural networks with multiple layers to model complex patterns in data, often achieving high performance in tasks like image and speech recognition.

Generative AI (GenAI)

AI systems capable of creating new content—such as text, images, audio, or code—by learning patterns from large datasets. Examples include ChatGPT and image generators like DALL-E.

Guardrails

Mechanisms and frameworks designed to ensure AI systems operate within ethical, legal, and technical boundaries, preventing harm or misuse.

Hallucination

When an AI system generates false or misleading information and presents it as factual, often due to gaps in its training data or limitations in its model.

Hyperparameter

Settings or configurations that control the learning process of an AI model, such as learning rate or number of layers, which are set before training begins.

Label

The correct answer or category assigned to a piece of data, used in supervised learning to train models to make accurate predictions.

Large Language Model (LLM)

A type of AI model trained on vast amounts of text data to understand and generate human-like language, enabling applications like chatbots and text summarization.

Machine Learning (ML)

A: A subset of AI focused on developing algorithms that allow computers to learn from data and improve over time without explicit programming.

Metaprompting

Providing a higher-level prompt that sets the overall context, persona, or rules for how an AI should behave or respond to subsequent prompts in a conversation or session.

Model

The output of the machine learning process—a mathematical representation of learned patterns from data, used to make predictions or decisions.

Multimodal AI

AI systems that can process and integrate multiple types of data (such as text, images, and audio) to better understand context and improve performance across tasks.

Natural Language Processing (NLP)

A field of AI focused on enabling machines to understand, interpret, and generate human language in a meaningful way.

Neural Network

A computational model inspired by the human brain, consisting of interconnected nodes ("neurons") that process data in layers to recognize patterns and make predictions.

Parameter

A variable that a machine learning model learns from data during training, as opposed to hyperparameters, which are set prior to training.

Prompt

The input text, instruction, or question given to an AI model to guide its output or behavior.

Prompt Engineering

The skill and practice of designing and refining prompts to elicit a specific, desirable, or optimized response from an AI model.

Reinforcement Learning

A type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions in a given environment.

Supervised Learning

A machine learning approach where models find patterns or groupings in unlabeled data without explicit guidance on what to predict.

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