AI model interpretability is the ability to understand how and why an artificial intelligence model makes its decisions. It involves making the workings of complex models, like deep neural networks, more transparent and easier for humans to follow. This helps users trust and verify the results produced by AI systems.
Transfer learning optimisation refers to the process of improving how a machine learning model adapts knowledge gained from one task or dataset to perform better on a new, related task. This involves fine-tuning the model’s parameters and selecting which parts of the pre-trained model to update for the new task. The goal is to reduce…
Efficient model inference refers to the process of running machine learning models in a way that minimises resource use, such as time, memory, or computing power, while still producing accurate results. This is important for making predictions quickly, especially on devices with limited resources like smartphones or embedded systems. Techniques for efficient inference can include…
Neural network sparsity refers to making a neural network use fewer connections or weights by setting some of them to zero. This reduces the amount of computation and memory needed for the network to function. Sparsity can help neural networks run faster and be more efficient, especially on devices with limited resources.
Model quantisation is a technique that reduces the size and computational requirements of machine learning models by using fewer bits to represent numbers. This can make models run faster and use less memory, especially on devices with limited resources. However, it may also lead to a small drop in accuracy, so there is a balance…
Intelligent Process Discovery is the use of artificial intelligence and data analysis to automatically identify and map out how business processes happen within an organisation. It gathers data from system logs, user actions, and other digital traces to understand the real steps people take to complete tasks. This helps businesses see where work can be…
Hyperautomation pipelines are systems that combine different technologies to automate complex business processes from start to finish. They use tools like artificial intelligence, machine learning, robotic process automation, and workflow management to handle repetitive tasks, data analysis, and decision-making. These pipelines allow organisations to speed up operations, reduce manual work, and improve accuracy by connecting…
Encrypted model inference is a method that allows machine learning models to make predictions on data without ever seeing the raw, unencrypted information. This is achieved by using special cryptographic techniques so that the data remains secure and private throughout the process. The model processes encrypted data and produces encrypted results, which can then be…
Federated Differential Privacy is a method that combines federated learning and differential privacy to protect individual data during collaborative machine learning. In federated learning, many users train a shared model without sending their raw data to a central server. Differential privacy adds mathematical noise to the updates or results, making it very hard to identify…
Knowledge graph completion is the process of filling in missing information or relationships within a knowledge graph. A knowledge graph is a structured network of facts, where entities like people, places, or things are connected by relationships. Because real-world data is often incomplete, algorithms are used to predict and add missing links or facts, making…