Knowledge transferability is the ability to apply what has been learned in one situation to a different context or problem. It means that skills, information, or methods are not limited to their original use but can help solve new challenges. This concept is important in education, technology, and the workplace, as it helps people and…
Knowledge injection is the process of adding specific information or facts into an artificial intelligence system, such as a chatbot or language model, to improve its accuracy or performance. This can be done by directly feeding the system extra data, rules, or context that it would not otherwise have known. Knowledge injection helps AI systems…
Synthetic feature generation is the process of creating new data features from existing ones to help improve the performance of machine learning models. These new features are not collected directly but are derived by combining, transforming, or otherwise manipulating the original data. This helps models find patterns that may not be obvious in the raw…
Cross-validation techniques are methods used to assess how well a machine learning model will perform on information it has not seen before. By splitting the available data into several parts, or folds, these techniques help ensure that the model is not just memorising the training data but is learning patterns that generalise to new data….
Robust optimisation is a method in decision-making and mathematical modelling that aims to find solutions that perform well even when there is uncertainty or variability in the input data. Instead of assuming that all information is precise, it prepares for worst-case scenarios by building in a margin of safety. This approach helps ensure that the…
Out-of-Distribution Detection is a technique used to identify when a machine learning model encounters data that is significantly different from the data it was trained on. This helps to prevent the model from making unreliable or incorrect predictions on unfamiliar inputs. Detecting these cases is important for maintaining the safety and reliability of AI systems…
Knowledge amalgamation is the process of combining information, insights, or expertise from different sources to create a more complete understanding of a subject. This approach helps address gaps or inconsistencies in individual pieces of knowledge by bringing them together into a unified whole. It is often used in fields where information is spread across multiple…
Data augmentation strategies are techniques used to increase the amount and variety of data available for training machine learning models. These methods involve creating new, slightly altered versions of existing data, such as flipping, rotating, cropping, or changing the colours in images. The goal is to help models learn better by exposing them to more…
AutoML, short for Automated Machine Learning, refers to tools and techniques that automate parts of the machine learning process. It helps users build, train, and tune machine learning models without requiring deep expertise in coding or data science. AutoML systems can handle tasks like selecting the best algorithms, optimising parameters, and evaluating model performance. This…
Ensemble learning is a technique in machine learning where multiple models, often called learners, are combined to solve a problem and improve performance. Instead of relying on a single model, the predictions from several models are merged to get a more accurate and reliable result. This approach helps to reduce errors and increase the robustness…