AI Glossary — Every Term Explained Simply

A set of rules or instructions that a computer follows to solve a problem or make a decision. AI algorithms learn from data to improve over time.

The simulation of human intelligence in machines, enabling them to perform tasks like reasoning, learning, and problem-solving.

A system that can perform tasks independently without human intervention, often using AI for decision-making.

A computing model inspired by the human brain, consisting of layers of connected nodes that process and learn from data.

A training algorithm for neural networks that adjusts weights by calculating the gradient of errors from output back to input layers.

Systematic errors in AI models caused by skewed training data, leading to unfair or inaccurate predictions.

Extremely large datasets that require special tools to process, often used to train AI models.

An automated software program that performs repetitive tasks, often powered by AI for more intelligent behavior.

An AI program designed to simulate human conversation through text or voice, used in customer service and education.

A machine learning task where the model assigns input data into predefined categories or classes.

An unsupervised learning technique that groups similar data points together without predefined labels.

An AI field that enables machines to interpret and understand visual information from images and videos.

A type of deep neural network specialized for processing grid-like data such as images.

The process of discovering patterns and knowledge from large amounts of data using statistical and AI techniques.

An interdisciplinary field combining statistics, programming, and domain expertise to extract insights from data.

A flowchart-like model used in machine learning where decisions are made based on feature values at each node.

A subset of machine learning using neural networks with many layers to learn complex patterns from large datasets.

A structured collection of data used to train, validate, and test AI models.

Numerical representations of data (like words or images) in vector form, capturing semantic meaning for AI models.

One complete pass through the entire training dataset during model training.

The practice of designing and deploying AI systems that are fair, transparent, accountable, and beneficial to society.

An early form of AI that uses knowledge bases and rules to mimic the decision-making of a human expert.

The process of selecting, transforming, and creating input variables to improve machine learning model performance.

The process of further training a pre-trained model on a smaller, task-specific dataset.

A large AI model trained on broad data that can be adapted for many downstream tasks (e.g., GPT, BERT).

AI systems that can create new content — text, images, music, or code — based on patterns learned from training data.

Generative Pre-trained Transformer: a type of large language model by OpenAI that generates human-like text.

An optimization algorithm used in training neural networks to minimize error by adjusting model weights.

When an AI model generates confidently stated but factually incorrect or made-up information.

Configuration settings set before training (like learning rate or number of layers) that control how a model learns.

The ability of AI to identify and classify objects, people, or scenes within images.

The process of using a trained AI model to make predictions on new, unseen data.

A type of AI trained on massive text datasets that can understand and generate human language (e.g., ChatGPT, Claude).

A statistical model used for binary classification problems, predicting the probability of a category.

A subset of AI where systems learn from data and improve performance without being explicitly programmed.

The process of feeding data to an AI algorithm so it can learn patterns and optimize its parameters.

AI systems that can process and generate multiple types of data — text, images, audio, and video together.

A branch of AI focused on enabling computers to understand, interpret, and generate human language.

A computational model inspired by the brain, consisting of layers of connected nodes that learn from data.

When a model learns the training data too well, including noise, and performs poorly on new, unseen data.

The process of adjusting model parameters to minimize error and maximize performance.

The practice of crafting effective input prompts to guide AI models toward desired outputs.

The most popular programming language for AI and machine learning, known for its simplicity and rich library ecosystem.

A type of ML where an agent learns by taking actions and receiving rewards or penalties from an environment.

A type of supervised learning that predicts continuous numeric values based on input features.

Machine learning where models are trained on labeled data with known input-output pairs.

A learning approach using a small amount of labeled data combined with a large amount of unlabeled data.

A multi-dimensional array of numbers, the fundamental data structure used in deep learning frameworks like TensorFlow.

Reusing a pre-trained model for a new but related task, saving time and computational resources.

The dataset used to teach an AI model by exposing it to examples and their correct answers.

Machine learning where models find patterns in unlabeled data without predefined correct answers.

A portion of data set aside during training to evaluate and tune model performance before final testing.

Numerical parameters in a neural network that are adjusted during training to minimize prediction errors.

The ability of an AI model to handle tasks or recognize categories it has never seen during training.