Feedback loop

See also: Machine learning terms

Introduction

A feedback loop is a systemic mechanism in which an input is processed and an output produced. This output then serves as input for subsequent iterations of the process, creating an endless cycle. A hallmark feature of such a feedback loop is how one iteration's output influences subsequent ones - creating self-regulating cycles.

Feedback loops can be either positive or negative. In a positive feedback loop, each iteration's output reinforces that of its previous iteration and leads to exponential growth or decay. On the other hand, in a negative feedback loop, each iteration dampens its input for future iterations, leading to stability or convergence.

Feedback loops are ubiquitous in both natural and artificial systems, such as ecology, economics, and engineering. In machine learning applications, feedback loops help to enhance models' performance over time by providing valuable inputs for improvement.

Feedback loop in machine learning

In machine learning, feeback loop occurs when the model's predictions influence the training data for the same or other models. As an example, let's imagine YouTube recommendation models influencing the videos people watch which will influence the video future recommendation models.