Model lag refers to the delay between when a machine learning model is trained and when it is actually used to make predictions. This gap means the model might not reflect the latest data or trends, which can reduce its accuracy. Model lag is especially important in fast-changing environments where new information quickly becomes available.
Batch pacing is a method used to control the speed and timing at which groups of tasks, jobs or items are processed in a system. It helps ensure that resources are used efficiently and prevents bottlenecks by spacing out the workload over time. Batch pacing is often used in manufacturing, software processing, and online advertising…
Model memory refers to the way an artificial intelligence model stores and uses information from previous interactions or data. It helps the model remember important details, context, or patterns so it can make better predictions or provide more relevant responses. Model memory can be short-term, like recalling the last few conversation turns, or long-term, like…
Output depth refers to the number of bits used to represent each individual value in digital output, such as in images, audio, or video. It determines how many distinct values or shades can be displayed or recorded. For example, higher output depth in an image means more subtle colour differences can be shown, resulting in…
Repetition avoidance means taking steps to prevent the same information, actions, or patterns from happening multiple times unnecessarily. This concept can be applied in writing, programming, music, and daily routines to make things clearer, more efficient, and less boring. The goal is to keep content or actions fresh and engaging, while also saving time and…
Retry reasoning is a process where a system or program decides whether to try an action again after it fails. Instead of simply repeating the same step blindly, the system analyses why the failure happened and chooses the best way to proceed. This approach helps to avoid repeating mistakes and increases the chances of eventual…
Chain triggering is a process where one event or action automatically causes another event to happen, creating a sequence or chain of responses. It is often used in systems, software, or machinery to automate tasks and reduce manual intervention. This method can help ensure that complex operations happen smoothly and in the correct order.
Staging models are frameworks that describe how a process, condition, or disease progresses through different phases or stages over time. They help to organise information, predict outcomes, and guide decisions by breaking down complex progressions into understandable steps. These models are commonly used in medicine, psychology, education, and project management to track changes and plan…
Output batching is a technique where multiple pieces of output data are grouped together and sent or processed at the same time, instead of handling each item individually. This can make systems more efficient by reducing the number of separate actions needed. It is commonly used in computing, machine learning, and data processing to improve…
Epoch reduction is a technique used in machine learning and artificial intelligence where the number of times a model passes through the entire training dataset, called epochs, is decreased. This approach is often used to speed up the training process or to prevent the model from overfitting, which can happen if the model learns the…