Prompt overfitting happens when an AI model is trained or tuned too specifically to certain prompts, causing it to perform well only with those exact instructions but poorly with new or varied ones. This limits the model’s flexibility and reduces its usefulness in real-world situations where prompts can differ. It is similar to a student…
Token drift refers to the gradual change in the meaning, value, or usage of a digital token over time. This can happen as a result of changes in the underlying technology, platform updates, or shifts in the way users interact with the token. Token drift can cause confusion, unexpected behaviour, or compatibility issues if not…
Neural collapse is a phenomenon observed in deep neural networks during the final stages of training, particularly for classification tasks. It describes how the outputs or features for each class become highly clustered and the final layer weights align with these clusters. This leads to a simplified geometric structure where class features and decision boundaries…
Graph predictive analytics is a method that uses the relationships and connections between items, often represented as a network or graph, to make predictions about future events or behaviours. Instead of looking at individual data points on their own, this approach considers how they are linked together, such as people in a social network or…
Quantum data analysis is the process of using quantum computing techniques to examine and interpret large or complex datasets. Unlike traditional data analysis, which uses classical computers, quantum data analysis leverages the special properties of quantum bits to perform calculations that might be too time-consuming or difficult for standard computers. This approach can help solve…
AI-powered forecasting is the use of artificial intelligence to predict future events or trends based on data. These systems analyse large amounts of information, identify patterns, and make predictions more quickly and accurately than traditional methods. Businesses and organisations use AI forecasting to make better decisions by anticipating what might happen next.
Neural activation optimization is a process in artificial intelligence where the activity levels of neurons in a neural network are adjusted for better performance. This involves fine-tuning how much each neuron responds to inputs so that the entire network can learn more effectively and make accurate predictions. The goal is to find the best settings…
Model performance frameworks are structured approaches used to assess how well a machine learning or statistical model is working. They help users measure, compare, and understand the accuracy, reliability, and usefulness of models against specific goals. These frameworks often include a set of metrics, testing methods, and evaluation procedures to ensure models perform as expected…
Graph knowledge modelling is a way of organising information using nodes and connections, much like a map of relationships. Each node represents an entity, such as a person, place, or concept, and the lines between them show how they are related. This approach helps to visualise and analyse complex sets of information by making relationships…
AI for Compliance refers to the use of artificial intelligence technologies to help organisations follow laws, regulations and internal policies. This can include monitoring transactions, analysing documents or spotting unusual activity that could signal a rule has been broken. By automating these tasks, AI can help reduce errors, save time and make it easier for…