Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet

• URL: http://arxiv.org/abs/2306.06130v1
• Date: Thu, 8 Jun 2023 11:14:51 GMT
• Title: Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
• Authors: Gonzalo Mart\'inez, Lauren Watson, Pedro Reviriego, Jos\'e Alberto Hern\'andez, Marc Juarez, Rik Sarkar
• Abstract summary: generative AI tools can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. Future versions of generative AI tools will be trained with a mix of human-created and AI-generated content. This interaction raises many questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data?
• Score: 6.62688326060372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already contributing to the data that is available to train future models. Therefore, future versions of generative AI tools will be trained with a mix of human-created and AI-generated content, causing a potential feedback loop between generative AI and public data repositories. This interaction raises many questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve and improve with the new data sets or on the contrary will they degrade? Will evolution introduce biases or reduce diversity in subsequent generations of generative AI tools? What are the societal implications of the possible degradation of these models? Can we mitigate the effects of this feedback loop? In this document, we explore the effect of this interaction and report some initial results using simple diffusion models trained with various image datasets. Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.

Related papers

• Synthetic data: How could it be used for infectious disease research? [0.16752458252726457]
  Concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence in fields such as cybercrime. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models.
  arXiv  Detail & Related papers  (2024-07-03T17:13:04Z)
• The Generative AI Paradox: "What It Can Create, It May Not Understand" [81.89252713236746]
  Recent wave of generative AI has sparked excitement and concern over potentially superhuman levels of artificial intelligence. At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans. This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make?
  arXiv  Detail & Related papers  (2023-10-31T18:07:07Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• AI for the Generation and Testing of Ideas Towards an AI Supported Knowledge Development Environment [2.0305676256390934]
  We discuss how generative AI can boost idea generation by eliminating human bias. We also describe how search can verify facts, logic, and context. This paper introduces a system for knowledge workers, Generate And Search Test, enabling individuals to efficiently create solutions.
  arXiv  Detail & Related papers  (2023-07-17T22:17:40Z)
• DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection [57.51313366337142]
  There has been growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery and data poisoning. We introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection.
  arXiv  Detail & Related papers  (2023-06-02T05:11:27Z)
• Constructing Dreams using Generative AI [23.344751807278044]
  Generative AI tools introduce new and accessible forms of media creation for youth. They raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. We facilitated students' generative AI learning through expression of their imagined future identities.
  arXiv  Detail & Related papers  (2023-05-19T21:56:12Z)
• Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images [66.20578637253831]
  There is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content.
  arXiv  Detail & Related papers  (2023-04-25T17:51:59Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation? [6.62688326060372]
  generative AI tools that can generate realistic images or text have taken the Internet by storm. Future versions of generative AI tools will be trained with Internet data that is a mix of original and AI-generated data. 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?
  arXiv  Detail & Related papers  (2023-02-17T17:39:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.