Breaking Down the Risks of Inbreeding in Generative AI: Avoiding Model Collapse

If a Generative AI model is trained on data generated by Generative AI, eventually it could lead to AI Model Collapse.

As AI-generated content floods the internet, experts warn of a phenomenon called “model collapse,” which could lead to AI producing low-quality outputs and even pose a threat to the AI technology that produced it in the first place.

If AI is trained only on content generated by other AI, it could result in “inbred mutant” responses, here is how:

The Phenomenon: Researchers have found that large language models like ChatGPT may potentially be trained on other AI-generated content, leading to lower-quality outputs as models become more widely trained on “synthetic data” instead of human-made content.

The Terminology: Different researchers have coined terms like “Model Autography Disorder” and “Habsburg AI” to describe the self-consuming loop of AI training itself on content generated by other AI, resulting in exaggerated, grotesque features in the responses.

The Implications: This could make it difficult to pinpoint the original source of information an AI model is trained on, leading to media companies limiting or paywalling their content to prevent misuse, creating a “dark ages of public information.”

The Counterargument: Some tech experts, like Saurabh Baji from Cohere, believe that human guidance is still critical to the success and quality of AI-generated models and that the rise of AI-generated content will only make human-crafted content more valuable.

The rise of AI-generated content and the potential for “model collapse” and AI inbreeding presents a significant challenge for the future of online information.

As AI-generated content becomes more prevalent, it is crucial to consider the implications and develop strategies to ensure the quality and accuracy of the information we consume and produce.

What are your thoughts on AI-generated content creating issues for Generative AI down the road?

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