Category: Artificial Intelligence

Quantum State Encoding

Quantum state encoding is the process of representing classical or quantum information using the states of quantum systems, such as qubits. This involves mapping data onto the possible configurations of quantum bits, which can exist in a superposition of multiple states at once. The way information is encoded determines how it can be manipulated, stored,…

Graph-Based Inference

Graph-based inference is a method of drawing conclusions by analysing relationships between items represented as nodes and connections, or edges, on a graph. Each node might stand for an object, person, or concept, and the links between them show how they are related. By examining how nodes connect, algorithms can uncover hidden patterns, predict outcomes,…

Adaptive Learning Rates

Adaptive learning rates are techniques used in training machine learning models where the rate at which the model learns changes automatically during the training process. Instead of using a fixed learning rate, the algorithm adjusts the rate depending on how well the model is improving. This helps the model learn more efficiently, making faster progress…

Neural Pattern Recognition

Neural pattern recognition is a technique where artificial neural networks are trained to identify patterns in data, such as images, sounds or sequences. This process involves feeding large amounts of data to the network, which then learns to recognise specific features and make predictions or classifications based on what it has seen before. It is…

Decentralized AI Frameworks

Decentralised AI frameworks are systems that allow artificial intelligence models to be trained, managed, or run across multiple computers or devices, rather than relying on a single central server. This approach helps improve privacy, share computational load, and reduce the risk of a single point of failure. By spreading tasks across many participants, decentralised AI…

Privacy-Preserving Feature Models

Privacy-preserving feature models are systems or techniques designed to protect sensitive information while building or using feature models in software development or machine learning. They ensure that personal or confidential data is not exposed or misused during the process of analysing or sharing software features. Approaches often include methods like data anonymisation, encryption, or computation…

Homomorphic Inference Models

Homomorphic inference models allow computers to make predictions or decisions using encrypted data without needing to decrypt it. This means sensitive information can stay private during processing, reducing the risk of data breaches. The process uses special mathematical techniques so that results are accurate, even though the data remains unreadable during computation.

Federated Learning Scalability

Federated learning scalability refers to how well a federated learning system can handle increasing numbers of participants or devices without a loss in performance or efficiency. As more devices join, the system must manage communication, computation, and data privacy across all participants. Effective scalability ensures that the learning process remains fast, accurate, and secure, even…

Multi-Party Inference Systems

Multi-Party Inference Systems allow several independent parties to collaborate on using artificial intelligence or machine learning models without directly sharing their private data. Each party contributes their own input to the system, which then produces a result or prediction based on all inputs while keeping each party’s data confidential. This approach is commonly used when…

Encrypted Model Processing

Encrypted model processing is a method where artificial intelligence models operate directly on encrypted data, ensuring privacy and security. This means the data stays protected throughout the entire process, even while being analysed or used to make predictions. The goal is to allow useful computations without ever exposing the original, sensitive data to the model…