Natural Language Processing: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Natural Language Processing” means the ability of a machine to process, analyze, and mimic human language, either spoken or written.
Predictive Analysis: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Predictive Analysis” means the organization of analyses of structured and unstructured data for inference and correlation that provides a useful predictive capability to new circumstances or data.
Profiling: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Profiling” means any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyze or predict aspects concerning that natural person’s performance at work, economic situation,…
Reinforcement Learning: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Reinforcement Learning” means a type of machine learning in which the algorithm learns by acting toward an abstract goal, such as “earn a high video game score” or “manage a factory efficiently.” During training, each effort is evaluated based on its contribution…
Structured Data: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Structured Data” means data that has a predefined data model or is organized in a predefined way.
Unstructured Data: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Unstructured Data” means data that does not have a predefined data model or is not organized in a predefined way.
Synthetic Data: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Synthetic Data” means data generated from data/processes and a model that is trained to reproduce the characteristics and structure of the original data aiming for a similar distribution. The degree to which synthetic data is an accurate proxy for the original data…
Transfer Learning: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Transfer Learning” means a technique in machine learning in which an algorithm learns to perform one task, such as recognizing cars, and builds on that knowledge when learning a different but related task, such as recognizing cats.
Supervised Learning: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Supervised Learning” means machine learning that makes use of labeled data during training.
Unsupervised Learning: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Unsupervised Learning” means machine learning that makes use of unlabeled data during training.
(AI) Accuracy: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “(AI) Accuracy” means: Closeness of computations or estimates to the exact or true values that the statistics were intended to measure. The goal of an AI model is to learn patterns that generalize well for unseen data. It is important to check…
Test: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Test” means: Technical operation to determine one or more characteristics of or to evaluate the performance of a given product, material, equipment, organism, physical phenomenon, process, or service according to a specified procedure.
Evaluation: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Evaluation” means: Systematic determination of the extent to which an entity meets its specified criteria.
Verification: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Verification” means: Provides evidence that the system or system element performs its intended functions and meets all performance requirements listed in the system performance specification.
Validation: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Validation” means: Confirmation by examination and provision of objective evidence that the particular requirements for a specific intended use are fulfilled.
Test and Evaluation, Verification and Validation (TEVV): According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Test and Evaluation, Verification and Validation (TEVV)” means: A framework for assessing, incorporating methods and metrics to determine that a technology or system satisfactorily meets its design specifications and requirements, and that it is sufficient for its…
Adaptive Learning: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Adaptive Learning” means: An adaptive AI is a system that changes its behavior while in use. Adaptation may entail a change in the weights of the model or a change in the internal structure of the model itself. The new behavior of…
Algorithm: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Algorithm” means: An algorithm consists of a set of step-by-step instructions to solve a problem (e.g., not including data). The algorithm can be abstract and implemented in different programming languages and software libraries.
Classification: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Classification” means: A classification system is a set of “boxes” into which things are sorted. Classifications are consistent, have unique classificatory principles, and are mutually exclusive. In AI design, when the output is one of a finite set of values (such as sunny,…
Federated Learning: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Federated Learning” means: Federated learning is a machine learning model which addresses the problem of data governance and privacy by training algorithms collaboratively without transferring the data to another location. Each federated device shares its local model parameters instead of sharing the…
