AI governance is the set of rules, processes, and structures that guide how artificial intelligence systems are developed, used, and managed. It covers everything from who is responsible for AI decisions to how to keep AI safe, fair, and transparent. The goal is to make sure AI benefits society and does not cause harm, while…
Data labelling is the process of adding meaningful tags or labels to raw data so that machines can understand and learn from it. This often involves identifying objects in images, transcribing spoken words, or marking text with categories. Labels help computers recognise patterns and make decisions based on the data provided.
Model monitoring is the process of regularly checking how a machine learning or statistical model is performing after it has been put into use. It involves tracking key metrics, such as accuracy or error rates, to ensure the model continues to make reliable predictions. If problems are found, such as a drop in performance or…
An AI adoption strategy is a plan that guides how an organisation introduces and uses artificial intelligence in its operations. It outlines the steps, resources, and goals for using AI to improve efficiency, solve problems, or create new opportunities. This strategy often includes assessing needs, preparing teams, choosing the right tools, and ensuring that changes…
Zero Resource Learning is a method in artificial intelligence where systems learn from raw data without needing labelled examples or pre-existing resources like dictionaries. Instead of relying on human-annotated data, these systems discover patterns and structure by themselves. This approach is especially useful for languages or domains where labelled data is scarce or unavailable.
Model pruning is a technique used in machine learning where unnecessary or less important parts of a neural network are removed. This helps reduce the size and complexity of the model without significantly affecting its accuracy. By cutting out these parts, models can run faster and require less memory, making them easier to use on…
Knowledge calibration is the process of matching your confidence in what you know to how accurate your knowledge actually is. It helps you recognise when you are sure about something and when you might be guessing or uncertain. Good calibration means you are neither overconfident nor underconfident about what you know.
Contrastive pretraining is a method in machine learning where a model learns to tell how similar or different two pieces of data are. It does this by being shown pairs of data and trying to pull similar pairs closer together in its understanding, while pushing dissimilar pairs further apart. This helps the model build useful…
Evaluation benchmarks are standard tests or sets of criteria used to measure how well a system, tool, or model performs. They provide a way to compare different approaches fairly by using the same tasks or datasets. In technology and research, benchmarks help ensure that results are reliable and consistent across different methods or products.
Quantisation-Aware Training is a method used to prepare machine learning models for running efficiently on devices with limited computing power, such as smartphones or embedded systems. It teaches the model to handle the reduced precision of numbers, which happens when large models are made smaller by using fewer bits to represent data. This approach helps…