Glossary

The capability of a machine or a computer program to mimic human intelligence processes, learn from experiences, adapt to new inputs, and perform tasks that generally require human intelligence.

The goal of ensuring that an AI system's objectives and behavior are aligned with human values and intentions.

A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

In machine learning, a model is the result of training an algorithm on data. It represents what was learned by a machine learning algorithm.

Inspired by the human brain, a neural network is a computing model designed to process information in ways similar to how a biological brain operates.

A subfield of machine learning involving algorithms inspired by the structure and function of the brain called artificial neural networks.

A branch of AI that involves the interaction between computers and human languages. This technology allows computers to understand, interpret, and generate human language in a valuable way.

In the context of language models, a token can be as small as one character or as large as one word. Tokens are the units a language model reads when processing text.

A type of machine learning model that is trained to predict the next word in a sentence.

A type of AI model that has been trained on a vast amount of internet text. These models can generate human-like text when given a prompt.

A model architecture used in machine learning, particularly in natural language processing. Transformers form the backbone of models like GPT.

A type of large language model that uses machine learning to produce human-like text.

The maximum number of recent tokens or words a model can consider when generating a response.

The input or instruction given to an AI model to generate a response. For a language model like GPT, a prompt could be a question, a statement, or a topic.