I wondered about whether artificial intelligence (AI) will eventually reach a point where the quality of the results it produces will begin to decrease. Eventually, there may be more AI-generated content on the internet than high-quality human content on which the GPT is trained. As the system starts to learn by scraping its own content, there is a possibility that the quality of its output will decrease over time.

This issue is similar to the photocopy problem, where the quality of an image or text deteriorates with each successive copy. It’s an interesting question to consider as AI becomes more prevalent in our lives and the amount of AI-generated content increases.

It’s worth considering whether GPT will face a similar issue as the photocopy problem. Why not ask the ChatGPT itself what it thinks about this? So…

PROMPT: “Will the human-perceived quality of AI results eventually reach a peak inflection point, where the output quality starts to decrease because there is now more AI-produced content available to train the models than there is good quality human content?”

REPLY: It is possible the human-perceived quality of its results may reach a peak inflection point. However, it’s important to note that the quality of its output depends on the quality of data used to train its models. Although there’s an increasing amount of AI-produced content available to train models, it doesn’t necessarily mean that the quality of the output will decrease. In fact, the availability of more data could potentially lead to more accurate and robust models, provided that the data is of high quality and diverse enough to capture the full range of human experience and knowledge.

Furthermore, it is possible to incorporate human feedback and expertise into the training process, which can help ensure that we align the models with human preferences and values.

GPT uses user feedback to help train itself, but I don’t know how that feedback is incorporated into the model or the underlying mechanisms of the thumbs up and thumbs down system. However, I could potentially give a thumbs up or thumbs down response, which means that while there may be challenges associated with the increasing amount of artificially produced content, it is not a foregone conclusion that the quality of the output will decline as a result. It will depend on the ongoing refinement of the models and the data used to train them, as well as the incorporation of human feedback and expertise.

PROMPT: “What would happen if humans stopped providing feedback and experience?”

REPLY: “It could potentially have a negative impact on the quality of AI-generated content. This is because AI models rely heavily on training data and feedback from humans is a crucial part of this process. Without human feedback, AI models may struggle to accurately capture the full range of human experiences and preferences, which can result in less relevant or useful content and may even lead to biases or inaccuracies in the output.”

Ultimately, if there is no human feedback in the system to nudge it in the right direction, there is a good chance it might reach an inflection point where the human perceived quality just starts to decrease. It is worth noting that humans are unlikely to completely stop providing feedback and experience. As long as humans continue to interact with AI-generated content and products, they will inevitably provide some degree of feedback and input. Moreover, there are also ways to incorporate feedback and experience into AI models without relying on direct human input. For example, some AI models use reinforcement learning, which allows the model to learn from its own actions and receive feedback in the form of rewards or penalties. Unsupervised learning techniques can allow AI models to learn from large amounts of unstructured data, which can help capture a broader range of human experience.

It’s important to note that disabling feedback for an AI model would typically be done for a specific reason, such as concerns around bias or privacy. In such cases, there may be alternative methods for improving AI models, such as using diverse and representative training data or employing techniques like adversarial training or interpretability and analysis to identify and address potential biases. While the ability to learn from feedback is an important component of AI development, there are other ways to improve AI models if it’s deemed too risky to continue with feedback.

The most interesting thing here is that even the system itself tends to agree that there is some merit to the idea that this could happen. Of course, the time frames here are quite long-term, since there is not a lot of AI content for it to learn from currently. However, as more content is generated and made available on the web and elsewhere, it will be used as training data.

Is there some parallel to DNA mutation and evolution? I bet it takes less time for us to figure out.