Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
The simulation of human intelligence processes by machines, especially computer systems.
A field of AI that enables machines to interpret and understand the visual world.
The practice of examining large pre-existing databases in order to generate new information.
A subset of ML based on artificial neural networks with representation learning. It can automatically discover representations needed for detection or classification from raw data.
AI that is programmed to describe its purpose, rationale, and decision-making process in a way that is understandable to humans.
A class of ML systems where two neural networks contest with each other in a game (generally a zero-sum game, where one agent's gain is another's loss).
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
The process of determining the ideal parameters of a mathematical model. This phase involves feeding the model with data and allowing it to learn from it.
A field of AI that gives machines the ability to read, understand, and derive meaning from human languages.
Computing systems vaguely inspired by the biological neural networks that constitute animal brains. An artificial neural network consists of layers of nodes, or neurons, which process information.
The use of data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes based on historical data.
