AI Glossary

An AI agent is a system that doesn't just answer a question but pursues a goal on your behalf — breaking it into steps, using tools like search, software, or other programs, and adjusting as it goes until the task is done.

An algorithm is a precise set of step-by-step instructions for solving a problem or completing a task — the fixed procedure a computer follows to turn an input into a result.

Artificial general intelligence (AGI) is a hypothetical kind of AI that could understand, learn, and handle virtually any intellectual task a human can, rather than being limited to the one narrow job it was built for.

Artificial Intelligence (AI) is the field of computer science focused on building systems that can perform tasks that would normally require human intelligence — things like understanding language, recognizing images, making decisions, and solving problems.

A chatbot is a software program that holds a conversation with people in everyday language, through text or voice, answering questions and helping with tasks as if you were messaging a person.

ChatGPT is an AI assistant developed by OpenAI that uses a large language model to hold conversations, answer questions, write content, analyze information, and help with a wide range of tasks through a simple chat interface.

Computer vision is a field of artificial intelligence that enables computers to extract meaningful information from digital images and video, and to act on what they find.

A dataset is an organized collection of data — such as a table of records, a folder of images, or a body of text — gathered together so it can be analyzed or used to train and test an AI model.

Deep learning is a branch of machine learning that uses neural networks with many layers to learn complex patterns from large amounts of data, powering most of today's advanced AI systems.

A diffusion model is a type of AI that creates images and other content by starting from random visual noise and gradually cleaning it up, step by step, into a finished result — having learned how to do that by studying how real images dissolve into noise.

Fine-tuning is the process of taking an AI model that has already been trained on broad, general data and training it a bit further on a smaller, focused set of examples, so it specializes in a particular task, domain, or style.

A foundation model is a large, general-purpose AI model trained on a huge, broad sweep of data, built to serve as a reusable base that can be adapted to many different tasks rather than being made for just one.

Generative AI is a category of artificial intelligence that can create new content — including text, images, audio, video, and code — by learning patterns from existing data.

A hallucination in AI is when a system, especially a chatbot, produces information that sounds confident and plausible but is actually false, made up, or not supported by any real source.

A large language model is a type of AI system trained on massive amounts of text that can understand, summarize, translate, and generate human language with remarkable fluency.

Machine learning is a branch of artificial intelligence in which systems learn from data to make predictions and decisions, improving their performance over time without being explicitly programmed for every step.

Multimodal AI is artificial intelligence that can work with more than one kind of information at once — such as text, images, audio, and video — understanding and connecting them within a single system rather than handling just one.

Natural language processing (NLP) is a field of AI that enables computers to read, understand, and generate human language in all its complexity and variation.

A neural network is a computing system loosely modeled on the human brain, made up of interconnected nodes that process data and learn patterns from examples.

Prompt engineering is the practice of crafting and refining the instructions you give to an AI system to get more accurate, useful, and consistent results.

Reinforcement learning (RL) is a type of machine learning in which a system learns by trial and error, taking actions and adjusting its behavior based on rewards or penalties it receives, rather than being shown the correct answers in advance.

Retrieval-augmented generation (RAG) is a technique that lets an AI look up relevant information from an outside source — like a company's documents or a database — and use what it finds to answer your question, instead of relying only on what it memorized during training.

A token is the small chunk of text — often a whole word, a piece of a word, or a punctuation mark — that an AI language model reads and generates, since these models work in tokens rather than in letters or whole sentences.

Training data is the collection of examples an AI model learns from during training — the photos, text, sounds, or other information it studies to discover the patterns it will later use to make predictions.

A Transformer is a type of neural network architecture that processes language by learning which parts of a text are most relevant to each other, and is the foundation on which most modern AI language systems are built.