Neuromorphic AI architectures are computer systems designed to mimic how the human brain works, using networks that resemble biological neurons and synapses. These architectures use specialised hardware and software to process information in a way that is more similar to natural brains than traditional computers. This approach can make AI systems more efficient and better…
Category: Artificial Intelligence
Quantum Machine Learning
Quantum Machine Learning combines quantum computing with machine learning techniques. It uses the special properties of quantum computers, such as superposition and entanglement, to process information in ways that are not possible with traditional computers. This approach aims to solve certain types of learning problems faster or more efficiently than classical methods. Researchers are exploring…
Data-Driven Decision Systems
Data-driven decision systems are tools or processes that help organisations make choices based on factual information and analysis, rather than intuition or guesswork. These systems collect, organise, and analyse data to uncover patterns or trends that can inform decisions. By relying on evidence from data, organisations can improve accuracy and reduce the risk of mistakes.
Customer Experience Automation
Customer Experience Automation refers to the use of technology to manage and improve how customers interact with a business across different channels, such as websites, emails, and customer support. It involves automating repetitive tasks, personalising communication, and streamlining processes to provide faster and more consistent service. The goal is to make each stage of the…
Blockchain-Based AI Governance
Blockchain-based AI governance is a method of using blockchain technology to oversee and manage artificial intelligence systems. It offers a transparent and secure way to record decisions, rules, and changes made to AI models. This approach helps ensure that AI systems are operated fairly, ethically, and are accountable to all stakeholders.
Decentralized Model Training
Decentralised model training is a way of teaching computer models by spreading the work across many different devices or locations, instead of relying on a single central computer. Each participant trains the model using their own data and then shares updates, rather than sharing all their data in one place. This approach helps protect privacy…
Federated Learning Protocols
Federated learning protocols are rules and methods that allow multiple devices or organisations to train a shared machine learning model without sharing their private data. Each participant trains the model locally on their own data and only shares the updates or changes to the model, not the raw data itself. These protocols help protect privacy…
Secure Multi-Party Learning
Secure Multi-Party Learning is a way for different organisations or individuals to train machine learning models together without sharing their raw data. This method uses cryptographic techniques to keep each party’s data private during the learning process. The result is a shared model that benefits from everyone’s data, but no participant can see another’s sensitive…
Encrypted Neural Networks
Encrypted neural networks are artificial intelligence models that process data without ever seeing the raw, unprotected information. They use encryption techniques to keep data secure during both training and prediction, so sensitive information like medical records or financial details stays private. This approach allows organisations to use AI on confidential data without risking exposure or…
Privacy-Preserving Inference
Privacy-preserving inference refers to methods that allow artificial intelligence models to make predictions or analyse data without accessing sensitive personal information in a way that could reveal it. These techniques ensure that the data used for inference remains confidential, even when processed by third-party services or remote servers. This is important for protecting user privacy…