AI and Data

The simulation of human intelligence processes by computer systems, enabling machines to learn, reason, solve problems, and make decisions. It covers speech, vision, and automated decision making.

A subset of artificial intelligence where systems learn from data and improve their performance over time without being explicitly programmed.

A type of artificial intelligence capable of generating new content, such as text, images, code, or audio, based on patterns learned from training data.

A type of artificial intelligence model trained on massive amounts of text data to understand, generate, and manipulate human language. Typically transformer-based, it contains billions of parameters.

A mathematical representation of a real-world process, trained on data to recognize patterns and make predictions or decisions. Once trained, it makes predictions or outputs without human involvement.

A basic unit of text, such as a word or a part of a word, that a language model uses to process and generate language. It is the structural unit for model input and output.

The maximum amount of text, measured in tokens, that an AI model can read and process at a single time. It defines the combined input and output token limit per processing step.

The text, question, or set of instructions provided to an AI model to guide its output and generate a response. Its phrasing determines the output style.

The process of feeding data to an AI model to help it learn patterns, adjust its internal mathematical weights, and make accurate predictions.

The process of using a trained AI model to make predictions, generate content, or solve tasks based on new, unseen input data. Its speed drives user experience.

A mathematical representation of data, such as words or images, as a list of numbers that captures its meaning and relationships. They power semantic search.

A specialized database designed to store, index, and rapidly search high-dimensional mathematical vectors, such as embeddings. It enables similarity search.

A technique that improves AI accuracy by searching external databases for relevant facts before generating a response. It grounds outputs in verified data.

A phenomenon where an AI model generates incorrect or fictional information but presents it with high confidence. It arises from data gaps or lack of context.

An autonomous AI system designed to perceive its environment, make decisions, use digital tools, and execute multi-step tasks to achieve a specific goal.

The process of taking an existing trained AI model and training it further on a smaller, specialized dataset to adapt it for a specific task.