Core Concepts

The use of machines, particularly computer systems, to simulate processes associated with human intelligence.

A set of mathematical instructions or rules that a computer follows to complete a specific task efficiently.

The use of AI to support human decision-making through collaboration between people and machines.

A machine or system that can perform tasks and make decisions without human intervention.

Assumptions or predispositions in AI models that can affect decisions and fairness.

AI systems designed to mimic how the human brain functions and support natural, human-like interaction.

A branch of artificial intelligence that studies and analyzes the algorithms behind machine learning.

A subset of machine learning involving neural networks with many layers to analyze data.

Specific, identifiable elements in text, such as names, places, dates, often extracted by AI.

AI systems designed to provide insights into their decision-making processes for transparency.

Artificial intelligence that exhibits cognitive functions across a wide range of tasks and domains.

AI systems capable of generating new, original content or data that mimics real-world examples.

A parameter set before learning begins that influences how the training process works.

The underlying purpose or goal a user wants to achieve with a query or statement.

Hidden or unobservable variables that machine learning models infer from observable data.

A broad term for the ability of machines to learn from data and perform tasks.

The science of getting computers to learn and act without being explicitly programmed.

AI systems that can process and interpret multiple types of data, such as text, images, and sound.

The use of AI to generate coherent, contextually relevant text from structured data.

The field of AI focused on how computers interact with humans through natural language.

The ability of AI to understand and interpret spoken or written human language.

A machine learning error in which a model learns the details and noise in its training data too closely.

The automated detection of patterns and regularities in data using machine learning algorithms.

A type of machine learning in which an agent learns decisions by acting in an environment to earn rewards.

AI that can understand, learn, and apply knowledge in ways indistinguishable from human intelligence.

A machine learning approach that trains models on labeled examples so they can predict outcomes from new inputs.

The smallest unit an NLP model processes, which may be a word, part of a word, or a character.

A test of whether a machine can exhibit intelligent behavior that is indistinguishable from a human's.

A type of machine learning in which models learn patterns from unlabeled data without explicit instructions.

In machine learning, how much a model's predictions vary around the average prediction, showing how sensitive it is to training data.

AI designed and trained for a specific task rather than the general cognitive abilities associated with human intelligence.

An educational psychology concept applied to AI, describing tasks an AI can perform with guidance but not independently.