Neural inference optimisation refers to improving the speed and efficiency of running trained neural network models, especially when making predictions or classifications. This process involves adjusting model structures, reducing computational needs, and making better use of hardware to ensure faster results. It is especially important for deploying AI on devices with limited resources, such as…
Category: Artificial Intelligence
Model Retraining Metrics
Model retraining metrics are measurements used to evaluate how well a machine learning model performs after it has been updated with new data. These metrics help decide if the retrained model is better, worse, or unchanged compared to the previous version. Common metrics include accuracy, precision, recall, and loss, depending on the specific task.
Graph Predictive Analytics
Graph predictive analytics is a method that uses networks of connected data, called graphs, to forecast future outcomes or trends. It examines how entities are linked and uses those relationships to make predictions, such as identifying potential risks or recommending products. This approach is often used when relationships between items, people, or events provide valuable…
AI for Optimization
AI for optimisation refers to the use of artificial intelligence techniques to find the best possible solutions to complex problems. This often involves improving processes, saving resources, or increasing efficiency in a system. By analysing data and learning from patterns, AI can help make decisions that lead to better outcomes than traditional methods.
Quantum Circuit Efficiency
Quantum circuit efficiency refers to how effectively a quantum circuit uses resources such as the number of quantum gates, the depth of the circuit, and the number of qubits involved. Efficient circuits achieve their intended purpose using as few steps, components, and time as possible. Improving efficiency is vital because quantum computers are currently limited…
Neural Representation Tuning
Neural representation tuning refers to the way that artificial neural networks adjust the way they represent and process information in response to data. During training, the network changes the strength of its connections so that certain patterns or features in the data become more strongly recognised by specific neurons. This process helps the network become…
Quantum Model Calibration
Quantum model calibration is the process of adjusting quantum models so their predictions match real-world data or expected outcomes. This is important because quantum systems can behave unpredictably and small errors can quickly grow. Calibration helps ensure that quantum algorithms and devices produce reliable and accurate results, making them useful for scientific and practical applications.
AI-Driven Insights
AI-driven insights are conclusions or patterns identified using artificial intelligence technologies, often from large sets of data. These insights help people and organisations make better decisions by highlighting trends or predicting outcomes that might not be obvious otherwise. The process usually involves algorithms analysing data to find meaningful information quickly and accurately.
Quantum Feature Analysis
Quantum feature analysis is a process that uses quantum computing techniques to examine and interpret the important characteristics, or features, in data. It aims to identify which parts of the data are most useful for making predictions or decisions. This method takes advantage of quantum systems to analyse information in ways that can be faster…
Neural Activation Tuning
Neural activation tuning refers to adjusting how individual neurons or groups of neurons respond to different inputs in a neural network. By tuning these activations, researchers and engineers can make the network more sensitive to certain patterns or features, improving its performance on specific tasks. This process helps ensure that the neural network reacts appropriately…