A token window refers to the amount of text, measured in tokens, that an AI model can process at one time. Tokens are pieces of words or characters that the model uses to understand and generate language. The size of the token window limits how much information the model can consider for a single response…
Memory tracing is the process of monitoring and recording how a computer program uses memory over time. It helps developers track which parts of their code allocate, use, and free memory. This information is valuable for finding memory leaks, optimising performance, and ensuring efficient resource management.
Weighted sampling is a method for selecting items from a group where some items are given a higher chance of being chosen than others. Each item is assigned a weight, which indicates its importance or likelihood of selection. This approach is often used when some options are more relevant or common than others, so the…
Output buffering is a technique used by computer programs to temporarily store data in memory before sending it to its final destination, such as a screen or a file. This allows the program to collect and organise output efficiently, reducing the number of times it needs to access slow resources. Output buffering can improve performance…
Script flattening is the process of combining multiple code files or modules into a single script. This is often done to simplify deployment, improve loading times, or make it harder to reverse-engineer code. By reducing the number of separate files, script flattening can help manage dependencies and ensure that all necessary code is included together.
Layer fusion is a technique used in machine learning and computer vision to combine multiple processing steps or layers into a single, more efficient operation. This process helps reduce the amount of computation and can speed up how quickly a model runs. It is especially useful when deploying models on devices with limited resources, such…
Model hardening refers to techniques and processes used to make machine learning models more secure and robust against attacks or misuse. This can involve training models to resist adversarial examples, protecting them from data poisoning, and ensuring they do not leak sensitive information. The goal is to make models reliable and trustworthy even in challenging…
Output shaping is a control technique used to reduce unwanted movements, such as vibrations or oscillations, in mechanical systems. It works by modifying the commands sent to motors or actuators so that they move smoothly without causing the system to shake or overshoot. This method is often used in robotics, manufacturing, and other areas where…
Quantum noise calibration is the process of measuring and adjusting for random fluctuations that affect quantum systems, such as quantum computers or sensors. These fluctuations, called quantum noise, can come from the environment or the measurement process itself. By calibrating for quantum noise, scientists can reduce errors and improve the accuracy of quantum experiments and…
Neural activation optimization is a process in artificial intelligence where the activity levels of neurons in a neural network are adjusted for better performance. This involves fine-tuning how much each neuron responds to inputs so that the entire network can learn more effectively and make accurate predictions. The goal is to find the best settings…