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.
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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…
Generative Adversarial Network (GAN): According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Generative Adversarial Network (GAN)” means: Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. Generative modeling is an unsupervised learning task in machine learning that involves automatically…
Human Values for AI: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Human Values for AI” means: Values are idealized qualities or conditions in the world that people find good. AI systems are not value-neutral. The incorporation of human values into AI systems requires that we identify whether, how, and what we…
Human-Centric AI: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Human-Centric AI” means: An approach to AI that prioritizes human ethical responsibility, dynamic qualities, understanding, and meaning. It encourages the empowerment of humans in design, use, and implementation of AI systems. Human-Centric AI systems are built on the recognition of a meaningful…
Language Model: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Language Model” means: A language model is an approximative description that captures patterns and regularities present in natural language and is used for making assumptions on previously unseen language fragments.
Large Language Model (LLM): According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Large Language Model (LLM)” means: A class of language models that use deep-learning algorithms and are trained on extremely large textual datasets that can be multiple terabytes in size. LLMs can be classed into two types: generative or discriminatory. Generative…
Model: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Model” means: A function that takes features as input and predicts labels as output. Typical phases of an AI model’s work flow are: Data collection and preparation, Model development, Model training, Model accuracy evaluation, Hyperparameters’ tuning, Model usage, Model maintenance, Model versioning.
Neural Network: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Neural Network” means: A computer system inspired by living brains, also known as artificial neural network, neural net, or deep neural net. It consists of two or more layers of neurons connected by weighted links with adjustable weights, which takes input data…
Scalability: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Scalability” means: The ability to increase or decrease the computational resources required to execute a varying volume of tasks, processes, or services.
