AI Glossary

A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments. Artificial intelligence systems use machine and human-based inputs to (a) perceive real and virtual environments, (b) abstract such perceptions into models through analysis in an automated manner; and (c) use model inference to formulate options for information or action.

A subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from insights and then make informed decisions.

A subset of machine learning that uses artificial neural networks to process and analyze information. Deep learning algorithms are inspired by the neural networks of the human brain and are used for analysis of data with a logical structure.

Artificial intelligence systems that utilize statistical analysis and machine learning algorithms to make predictions about potential future outcomes, causation, risk exposure, and more.

a category of deep learning models trained on immense amounts of data, making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks

e.g. ChatGPT, Claude, Gemini

The growing gap between individuals, communities, and companies who have access to, use of, and skills related to cutting-edge Artificial Intelligence technologies.

An instance where an AI model generates misleading, inaccurate, or entirely fabricated content, often without a clear basis in its training data. Many favor alternative terms (bullshit, machine errors, AI mirage), so as not to assign human qualities to AI technology.

The ability to understand how Artificial Intelligence (AI) works, recognize its strengths and limitations, critically evaluate AI tools and outputs, and thoughtfully consider the ethical, social, and practical implications of its use. 

Related to the AI Divide, AI privilege refers to the benefits of one’s access to, use of, and skills related to cutting-edge Artificial Intelligence technologies. 

(Dis)information – False or inaccurate information that is deliberately created and spread with the intent to deceive or cause harm. (Mis)information has the same effects but is not intentional.

The process of using data to teach a machine learning model to identify patterns and make predictions or decisions.

Excessive dependence on AI tools or outputs, potentially leading to a decline in human skills like critical thinking and independent decision making. 

The right of individuals to control the collection, use, and dissemination of their personal information. In AI, it relates to how the user data and information used for training models are handled.