AI Glossary | MMA Global

AI Glossary

AI (Artificial Intelligence) A branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that responds in a manner similar to human intelligence.
Algorithm A set of rules to be followed in calculations or problem-solving operations, often by a computer.
Anomaly Detection The identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.
AutoML (Automated Machine Learning) The process of automating the process of applying machine learning to real-world problems.
Bias An error introduced in your model due to oversimplification of the machine learning algorithm. It can lead to underfitting.
Big Data Extremely large data sets that may be analyzed computically to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
Chatbots Programs that can have a conversation with a human, often used in customer service.
Classification A process in machine learning where a model is trained to predict the category of a given input.
Computer Vision The ability for a computer to understand and interpret visual information from the world.
Data Mining The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Data Set A collection of related sets of information composed of separate elements but can be manipulated as a unit by a computer.
Deep Learning (DL) A subset of machine learning that makes the computation of multi-layer neural networks feasible. It is responsible for advancements in image and speech recognition.
Ensemble Methods Machine learning paradigm where multiple models are trained to solve the same problem and combined to get better results.
Evaluation Metrics Metrics used to measure the quality of the machine learning model such as accuracy, precision, recall, F1 score, ROC, etc.
Explainable AI (XAI) Techniques in AI which can be trusted and easily understood by humans.
Feature Selection/Extraction The process of selecting a subset of relevant features for use in model construction.
Generative Adversarial Networks (GANs) A class of AI algorithms used in unsupervised machine learning, implementing two neural networks contesting with each other in a game.
Image Recognition The process of identifying and detecting an object or a feature in a digital image or video.
Machine Learning (ML) A subset of AI that involves the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something.
Natural Language Processing (NLP) A branch of AI that helps computers understand, interpret, and respond to human language in a valuable way.
Neural Networks (NN) Computational models inspired by the human brain. They are used to model complex patterns and prediction problems.
Overfitting A modeling error that occurs when a function is too closely aligned to a limited set of data points.
Personalization The use of technology and customer information to tailor digital experiences to the individual.
Predictive Analytics The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
Predictive Modeling A statistical technique using machine learning and data mining to predict and forecast likely future outcomes.
Programmatic Advertising Automated bidding on advertising inventory in real time, for the opportunity to show an ad to a specific customer, in a specific context.
Recommendation Engine A system that predicts the preferences or ratings a user would give to a product.
Regression A process in machine learning where a model is trained to predict a continuous or semi-continuous outcome.
Reinforcement Learning A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal.
Semantic Analysis The process of relating syntactic structures and objects in the world to computational representations of meaning.
Sentiment Analysis The use of natural language processing to identify, extract, quantify, and study affective states and subjective information.
Speech Recognition The ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format.
Supervised Learning A type of machine learning where the AI is trained using labeled data.
Test Data The dataset used to provide an unbiased evaluation of a final model fit on the training dataset.
Text Analysis The process of converting unstructured text data into meaningful data for analysis.
Training Data The dataset from which the machine learning algorithm learns the model.
Transfer Learning A machine learning method where a model developed for a task is reused as the starting point for a model on a second task.
Underfitting A modeling error that occurs when a function is too simplistic to accurately represent the underlying data.
Unsupervised Learning A type of machine learning where the AI is trained using unlabeled data.
Variance An error introduced in your model due to the complex machine learning algorithm. It can lead to overfitting.