Heuristic anchoring bias in large language models (LLMs) refers to the tendency of these models to rely too heavily on the first piece of information they receive when generating responses. This bias can influence the accuracy and relevance of their outputs, especially if the initial prompt or context skews the model’s interpretation. As a result,…
Category: Artificial Intelligence
Synthetic Oversight Loop
A Synthetic Oversight Loop is a process where artificial intelligence or automated systems monitor, review, and adjust other automated processes or outputs. This creates a continuous feedback cycle aimed at improving accuracy, safety, or compliance. It is often used in situations where human oversight would be too slow or resource-intensive, allowing systems to self-correct and…
Cognitive Load Balancing
Cognitive load balancing is the process of managing and distributing mental effort to prevent overload and improve understanding. It involves organising information or tasks so that people can process them more easily and efficiently. Reducing cognitive load helps learners and workers focus on what matters most, making it easier to remember and use information.
Latent Prompt Injection
Latent prompt injection is a security issue affecting artificial intelligence systems that use language models. It occurs when hidden instructions or prompts are placed inside data, such as text or code, which the AI system later processes. These hidden prompts can make the AI system behave in unexpected or potentially harmful ways, without the user…
Intelligent Document Processing
Intelligent Document Processing (IDP) refers to the use of artificial intelligence and automation technologies to read, understand, and extract information from documents. It combines techniques such as optical character recognition, natural language processing, and machine learning to process both structured and unstructured data from documents like invoices, contracts, and forms. This helps organisations reduce manual…
Anomaly Detection
Anomaly detection is a technique used to identify data points or patterns that do not fit the expected behaviour within a dataset. It helps to spot unusual events or errors by comparing new information against what is considered normal. This process is important for finding mistakes, fraud, or changes that need attention in a range…
Federated Learning
Federated learning is a way for multiple devices or organisations to work together to train a machine learning model without sharing their raw data. Instead, each participant trains the model on their own local data and only shares updates, such as changes to the model’s parameters, with a central server. This approach helps protect privacy…
Model Drift
Model drift happens when a machine learning model’s performance worsens over time because the data it sees changes from what it was trained on. This can mean the model makes more mistakes or becomes unreliable. Detecting and fixing model drift is important to keep predictions accurate and useful.
AI Governance
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
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.