They say “Experience is the best teacher, and the worst experiences teach the best lessons”. For humans, learning from experiences involves acquiring knowledge from failures and using this to avoid mistakes in the future.
In the context of technology, this concept is known as feedback look. One area where the subject of feedback loop is fast growing in importance is Artificial Intelligence, specifically generative AI. Why generative AI?
Generative AI has become increasingly powerful over the years, with products like ChatGPT featuring over 900 plugins. We owe some of this growth to the feedback loop. However, the power generative AI holds today has an estimated 45 percent of the US population using it, and this popularity means ordinary feedback loops now pose a threat to the integrity of AI training.
If your organization is developing or planning to develop AI solutions that will largely depend on feedback, such as an AI knowledge base, then you need to understand the iconic role that a feedback role will play in these solutions.
So, what is it about, and why should you care? We answer this important question.
What is a Feedback Loop?
A feedback loop relates to the use of current outputs to optimize future outputs. It involves utilizing outputs to generate better inputs and, hence, better results from a system.
It is similar to using customer reviews or complaints (outputs) to generate better business strategies (better inputs). These improved strategies are then used to improve strategies to generate better business results (optimized outputs). However, in IT systems like the generative AI ones, there’s more to learn about the feedback loop.
The feedback loop generally involves four stages — input creation, input capturing, input analysis, and decision.
Input Creation: The first stage is the creation of an input from a previous output. This is the input that is used in the feedback loop.
Input Capturing: Essentially means saving the input for future reference. Specifically to be used for identifying trends, diagnoses, evaluating performance, and controlling tuning, among others.
Input Analysis: The third stage is where improvements emerge. Inputs are thoroughly evaluated to determine if they satisfy the expected results.
Decision: Here, if needed, new inputs are created based on loopholes/failures found within the old input (previous output). The decision is typically geared towards accurately.
In addition to the stages, there are two types of feedback loops, depending on how the system works.
The first is the positive feedback loop, which involves using positive results to reinforce or validate how a system works.
The second is the negative feedback loop, which involves analyzing outputs for discrepancies. The findings are used to change/improve system workings for better results.
How do feedback loops apply in the context of generative AI?
Feedback Loops and Generative AI
Generative AI, as you may know, is AI technology designed to create synthetic content. This content is either in the form of text, audio, video, or simply synthetic data.
In the context of the feedback loop, the synthetic content created by generative AI is what we call our output. This output is used to improve future results from the AI model. In other words the outputs become the inputs for the loop. The AI outputs are analyzed to optimize training data and algorithmic parameters. The goal is to improve the quality of future synthetic content generation.
The negative feedback loop in AI is where inadequacies in AI outputs, like a poor interpretation of user intent by an LLM or inaccuracy from an image generation model, are identified. Inadequacies are then used to improve the quality of training data, optimize model parameters, and perfect algorithms. The positive feedback loop involves using accurate outputs to identify and reinforce optimal AI model operation. The more positive loops occur, the stricter the AI parameters become.
New model outputs are put into the same AI optimization process, and this goes on and on. This is why it is called a AI feedback loop. Sadly, regardless of the benefits of utilizing the AI feedback loop in model optimization, there is an emerging risk that we’ll talk about later.
Model collapse: A threat to the AI feedback loop
Model collapse describes a situation where the quality of model outputs drifts away from the true states of reality. It is a condition built up over time where model performance deteriorates gradually due to corruption by sub-par training data.
In the perspective of generative AI, this is happening because the content that is generated by the Large Language model largely finds its way to the avenues that these very models source their training data from.
The internet, for example, is a massive source of the training information that these models are using for training. But increasingly, a lot of content is being generated by these models and this content is being published online. It means that over time, the models will start training in their own generated content. What this means is that a time may come where there will be no more original content available to the models for training. They’ll be training on AI-generated content, meaning the AI feedback loop will be unhelpful, and this could lead to a chaotic collapse.
There is already a huge discussion that is going on around this subject at a larger scale, including some initial research by institutions such as the Cornel University, who are already sounding the alarm.
For you as a business looking to benefit from generative AI, this is something you need to take seriously into account. The solution lies in the best practices outlined here.
Best practices for AI feedback loop excellence
There are many ways organizations like yours can prevent their AI systems from experiencing model collapse. The more effective of these include;
Specially retaining original training datasets. These datasets are periodically used to retrain AI models to protect human content against catastrophic forgetting.
Retraining models with new human content, where real and synthetic content are differentiated by tags to ensure accuracy
Employing subject-matter experts (SMEs) as reviewers to protect against manipulative data corruption
AI feedback loops, just like in the business environment, improve the quality of generative AI outputs. However, the threat of model collapse means we must take care not to spoil a great party that is just starting.
This is especially important today when most organizations are moving to build in-house AI solutions such as AI knowledge base. Thankfully, there are best practices in managing optimal AI feedback loops, and these majorly revolve around one factor — high-quality training data.
In addition to periodically utilizing old and new original datasets, you can go the extra step to create quality benchmarks for training data. This ensures that the use of synthetic data within the positive feedback loop remains continuously reviewed against human-like quality standards.