Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?
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ID: 283245
2023
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Abstract
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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| Authors | Gonzalo Martínez; Lauren Watson; Pedro Reviriego; José Alberto Hernández; Marc Juarez; Rik Sarkar |
| Journal | arXiv |
| Year | 2023 |
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