# Terms & Definitions

<table data-header-hidden><thead><tr><th width="191">Term</th><th>Definition</th></tr></thead><tbody><tr><td><strong>Term</strong></td><td><strong>Definition</strong></td></tr><tr><td>AI</td><td>Artificial Intelligence: the ability of machines to perform tasks that are typically associated with human intelligence, such as learning and problem-solving. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.</td></tr><tr><td>ML</td><td>Machine Learning: an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. As a scientific endeavour, machine learning grew out of the quest for AI.</td></tr><tr><td>Dataset</td><td>A collection of related, discrete items of related data that may be accessed individually or in combination or managed as a whole entity.</td></tr><tr><td>ML Algorithm</td><td>A mathematical method to find patterns in a set of data.</td></tr><tr><td>ML Model Training</td><td>The process of running an ML algorithm on a dataset (called training data) and optimising the algorithm to find certain patterns or outputs. The resulting function with rules and data structures is called the trained machine learning model.</td></tr><tr><td>ML Model</td><td>A computer program that is used to recognise patterns in data or make predictions. ML models are created from ML algorithms, which are trained using either labelled, unlabelled, or mixed data.</td></tr><tr><td><p>Data Labelling</p><p>or<br>Data Annotation</p></td><td>A part of the preprocessing stage when developing an ML model. It requires the identification of raw data (i.e., images, text files, videos), and then the addition of one or more labels to that data to specify its context for the models, allowing the machine learning model to make accurate predictions.</td></tr><tr><td>Agent</td><td>An actor in the data value chain that is responsible for processing data.</td></tr><tr><td>Human Agent</td><td>A natural person that solves data processing (labelling) tasks as a part of the crowdsourcing community.</td></tr><tr><td>AI Agent</td><td>A computer program or system that is designed to solve data processing (labelling) autonomously, based on an ML model that has been previously trained using data labelled by Human Agents. </td></tr><tr><td>FL</td><td>Federated Learning: a sub-field of Machine Learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train an ML model while ensuring that their data remains decentralised.</td></tr></tbody></table>


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