AI Glossary

A type of artificial intelligence that can understand, learn, and apply knowledge across a wide range of tasks at a human level or beyond.

A set of rules or instructions given to an AI system to help it learn and make decisions.

A set of rules that allows different software applications to communicate with each other, commonly used to access AI services.

A technique used in neural networks that allows the model to focus on specific parts of the input data when processing information.

An algorithm used to train neural networks by calculating gradients and adjusting weights to minimize error.

Systematic prejudice in AI systems that can lead to unfair or discriminatory outcomes, often reflecting biases in training data.

An AI application that can simulate human conversation through text or voice interactions.

A field of AI that enables computers to interpret and understand visual information from the world.

A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.

A numerical representation of words, phrases, or other data that captures semantic meaning in a high-dimensional space.

The process of taking a pre-trained model and training it further on specific data to improve performance for particular tasks.

AI systems that can create new content such as text, images, music, or video based on learned patterns.

When an AI system generates false or misleading information that appears plausible but is not based on factual data.

A type of AI model trained on vast amounts of text data to understand and generate human language.

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.

A mathematical representation of patterns learned from data that can make predictions or generate outputs.

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

A computing system inspired by biological brains, consisting of interconnected nodes that process information.

When a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on new data.

The learnable weights and biases in a neural network that are adjusted during training to improve performance.

The input text or instructions given to an AI model to guide its response or behavior.

The practice of designing and optimizing prompts to get better results from AI models.

A type of machine learning where an agent learns to make decisions by taking actions and receiving rewards or penalties.

A machine learning approach where the model learns from labeled training data to make predictions.

The basic unit of text that an AI model processes, which can be a word, part of a word, or punctuation.

The dataset used to teach an AI model patterns and relationships for making predictions or generating outputs.

A technique where a model trained on one task is adapted for a related task, improving efficiency and performance.

A neural network architecture that uses attention mechanisms to process sequential data, commonly used in language models.

A machine learning approach where the model finds patterns in data without labeled examples.

A mathematical representation of data points in multi-dimensional space, often used to represent words or concepts.