AI for security monitoring means using artificial intelligence to help detect, analyse and respond to security threats. It can automatically scan data from cameras, sensors or network traffic to spot suspicious activity. This helps organisations respond faster to issues and reduces the chances of missing important warning signs.
Decentralised AI training is a method where multiple computers or devices work together to train an artificial intelligence model, instead of relying on a single central server. Each participant shares the workload by processing data locally and then combining the results. This approach can help protect privacy, reduce costs, and make use of distributed computing…
Blockchain-AI synergies refer to the ways in which blockchain technology and artificial intelligence can work together to solve problems or create new tools. Blockchain provides a secure, transparent way to store and share data, while AI can analyse and learn from that data to make decisions or predictions. By combining these technologies, organisations can create…
AI-powered threat detection uses artificial intelligence to identify security threats, such as malware or unauthorised access, in digital systems. It analyses large amounts of data from networks, devices or applications to spot unusual patterns that might signal an attack. This approach helps organisations respond faster and more accurately to new and evolving threats compared to…
Predictive asset management is a method of using data and technology to anticipate when equipment or assets will need maintenance or replacement. By analysing information from sensors, usage patterns, and historical records, organisations can predict problems before they occur. This helps reduce unexpected breakdowns, saves money on emergency repairs, and extends the life of valuable…
AI-driven logistics optimisation uses artificial intelligence to improve how goods and materials are moved, stored, and delivered. It analyses large amounts of data to find the most efficient routes, schedules, and resource allocations. This helps companies save time, reduce costs, and respond quickly to changes or unexpected events.
Deepfake mitigation techniques are methods and tools designed to detect, prevent, or reduce the impact of fake digital media, such as manipulated videos or audio recordings. These techniques use a mix of computer algorithms, digital watermarking, and human oversight to spot and flag artificial content. Their main goal is to protect people and organisations from…
Synthetic data pipelines are organised processes that generate artificial data which mimics real-world data. These pipelines use algorithms or models to create data that shares similar patterns and characteristics with actual datasets. They are often used when real data is limited, sensitive, or expensive to collect, allowing for safe and efficient testing, training, or research.
AI accelerator design involves creating specialised hardware that speeds up artificial intelligence tasks like machine learning and deep learning. These devices are built to process large amounts of data and complex calculations more efficiently than general-purpose computers. By focusing on the specific needs of AI algorithms, these accelerators help run AI applications faster and use…
TinyML deployment strategies refer to the methods and best practices used to run machine learning models on very small, resource-constrained devices such as microcontrollers and sensors. These strategies focus on making models small enough to fit limited memory and efficient enough to run on minimal processing power. They also involve optimising power consumption and ensuring…