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Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?

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arxiv 2303.01255 v1 pith:ZGIN3T3M submitted 2023-02-17 cs.CV

Gonzalo Martínez , Lauren Watson , Pedro Reviriego , José Alberto Hernández , Marc Juarez , Rik Sarkar This is my paper
classification cs.CV
keywords datagenerativetoolsinternetwillgeneratedimagesmassive
verification ladder T0 review T1 audit T2 compute T3 formal
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In the span of a few months, generative Artificial Intelligence (AI) tools that can generate realistic images or text have taken the Internet by storm, making them one of the technologies with fastest adoption ever. Some of these generative AI tools such as DALL-E, MidJourney, or ChatGPT have gained wide public notoriety. Interestingly, these tools are possible because of the massive amount of data (text and images) available on the Internet. The tools are trained on massive data sets that are scraped from Internet sites. And now, these generative AI tools are creating massive amounts of new data that are being fed into the Internet. Therefore, future versions of generative AI tools will be trained with Internet data that is a mix of original and AI-generated data. As time goes on, a mixture of original data and data generated by different versions of AI tools will populate the Internet. This raises a few intriguing questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve with the new data sets or degenerate? Will evolution introduce biases in subsequent generations of generative AI tools? In this document, we explore these questions and report some very initial simulation results using a simple image-generation AI tool. These results suggest that the quality of the generated images degrades as more AI-generated data is used for training thus suggesting that generative AI may degenerate. Although these results are preliminary and cannot be generalised without further study, they serve to illustrate the potential issues of the interaction between generative AI and the Internet.

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Cited by 3 Pith papers

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  1. Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training

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  3. LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs

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    Under a shared retrieval-augmented memory, multiple LLMs' outputs converge to near-identical semantic answers, and the analogous Gaussian mixture system is proven to collapse.

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