AI Glossary

Artificial Intelligence

Artificial Intelligence (AI) is the field of computer science focused on creating systems that can perform tasks that normally require human intelligence.

Machine Learning

Machine Learning (ML) is a branch of AI that enables systems to learn patterns from data and improve performance without being explicitly programmed for every task.

Deep Learning

Deep Learning is a specialized area of machine learning that uses large neural networks to process complex data and power modern AI systems.

Generative AI

Generative AI refers to AI systems capable of creating new content such as text, images, code, audio, and video.

Large Language Model

A Large Language Model (LLM) is an AI system trained on massive amounts of text data to understand and generate human language.

Neural Network

A neural network is a computational model inspired by the human brain that helps AI systems recognize patterns and process information.

Natural Language Processing

Natural Language Processing (NLP) is the field of AI focused on helping computers understand, interpret, and generate human language.

Prompt Engineering

Prompt Engineering is the practice of designing effective instructions and inputs to improve AI-generated responses and outputs.

AI Model

An AI model is a trained computational system that learns patterns from data to make predictions or generate outputs.

Training Data

Training data is the information used to teach AI models how to recognize patterns and perform tasks.

AI Hallucination

An AI hallucination occurs when an AI system generates incorrect or fabricated information while presenting it confidently.

Transformer Model

A transformer model is a deep learning architecture that enables modern AI systems to process and understand language efficiently.

Computer Vision

Computer Vision is the field of AI focused on enabling computers to analyze, interpret, and understand visual information.

AI Agent

An AI agent is a system capable of autonomously performing tasks, making decisions, and interacting with tools or environments.

Responsible AI

Responsible AI refers to the ethical, secure, transparent, and accountable development and use of artificial intelligence systems.

Robotics

Robotics is a field of technology that combines software, sensors, and machines to perform tasks automatically or with limited human intervention.

Expert Systems

An expert system is a computer program designed to make decisions or provide recommendations using predefined rules and knowledge from human experts.

Reinforcement Learning

Reinforcement learning is a type of machine learning where an AI system learns through trial and error by receiving rewards or penalties for its actions.

Supervised Learning

Supervised learning is a machine learning method where models learn from labeled examples containing both inputs and correct answers.

Unsupervised Learning

Unsupervised learning is a machine learning method where AI systems identify patterns in data without being given correct answers.

Semi-Supervised Learning

Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data to improve model performance.

Self-Supervised Learning

Self-supervised learning is a machine learning technique where AI creates its own learning tasks from unlabeled data.

Transfer Learning

Transfer learning is an AI technique where knowledge learned from one task is reused to help solve another related task.

Fine-Tuning

Fine-tuning is the process of further training an existing AI model on specialized data to improve performance for a specific task.

Foundation Model

A foundation model is a large AI model trained on broad datasets that can be adapted for many different applications.

Multimodal AI

Multimodal AI refers to AI systems that can process and understand multiple forms of information such as text, images, audio, and video.

Predictive Analytics

Predictive analytics uses historical data, statistics, and AI models to forecast future outcomes.

Data Science

Data science is the practice of collecting, analyzing, and interpreting data to generate insights and support decision-making.

Data Mining

Data mining is the process of discovering patterns, trends, and useful information within large datasets.

Pattern Recognition

Pattern recognition is the ability of an AI system to identify relationships, trends, or recurring structures within data. It is one of the foundational capabilities that allows modern AI systems to make predictions and generate useful outputs.

Knowledge Representation

Knowledge representation is the process of organizing information in a structured way so that a computer system can use, interpret, and reason about it.

Autonomous System

An autonomous system is a system that can perform tasks, make decisions, or take actions with limited or no direct human intervention.

Symbolic AI

Symbolic AI is an approach to artificial intelligence that relies on explicit rules, logic, and structured knowledge rather than learning patterns from large datasets.

Narrow AI

Narrow AI refers to artificial intelligence systems designed to perform specific tasks rather than possessing broad human-like intelligence.

General AI

General AI is a hypothetical form of artificial intelligence capable of performing a wide variety of intellectual tasks at a level similar to human intelligence.

Artificial Superintelligence

Artificial Superintelligence refers to a hypothetical future AI system that would surpass human intelligence across virtually all cognitive tasks.

Dataset

A dataset is a collection of information used to train, test, or evaluate an AI system. It provides the examples that help a model learn patterns and make predictions.

Features

Features are the individual pieces of information within a dataset that help an AI model learn patterns and make predictions.

Labels

Labels are the correct answers or outcomes associated with training data. They help supervised machine learning models learn what they are trying to predict.

Classification

Classification is a machine learning task where an AI system places information into predefined categories based on patterns learned from data.

Regression

Regression is a machine learning task that predicts numerical values, such as prices, sales, or future outcomes, based on patterns in data.

Clustering

Clustering is a machine learning technique that groups similar data together by identifying natural patterns and relationships within a dataset.

Decision Tree

A decision tree is a machine learning model that makes predictions by following a series of questions and branching decisions that lead to an outcome.

Random Forest

A Random Forest is a machine learning model that combines many decision trees to make more accurate and reliable predictions. It reduces the risk of relying on a single tree's mistakes.

Gradient Boosting

Gradient Boosting is a machine learning technique where new models are built to correct the errors made by previous models. Over time, the combined system becomes more accurate.

Support Vector Machine (SVM)

A Support Vector Machine is a machine learning model that separates data into categories by finding the clearest possible boundary between groups.

K-Nearest Neighbors (KNN)

K-Nearest Neighbors is a machine learning algorithm that makes predictions based on the most similar examples in a dataset.

Linear Regression

Linear Regression is a machine learning model used to predict numerical values by identifying relationships between variables.

Logistic Regression

Logistic Regression is a machine learning model used for classification tasks, helping determine the probability that something belongs to a particular category.

Overfitting

Overfitting occurs when a machine learning model learns the training data too closely, causing it to perform poorly on new, unseen data.