The AI Feedback Loop: Researchers Warn of ‘Model Collapse’ | National Crowdfunding & Fintech Association of Canada

VentureBeat | Carl Franzen | Jun 12, 2023

Researchers from the UK and Canada have found that as AI-generated content proliferates on the internet, AI models that train on this content develop irreversible defects.

• Rapid Deterioration of Quality:
  • As AI models train on content generated by other AI models, they tend to lose touch with the original human-created data distribution.
  • Mistakes in generated data compound rapidly, leading to a significant deterioration in the quality and accuracy of the AI models’ output. This can lead to erroneous and monotonous responses. This phenomenon is termed ‘model collapse,’ where the AI models over time fail to understand the true underlying data distribution and misperceive reality due to compounding mistakes in the generated data.

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• Distortion of Minority Data:
  • AI models tend to overfit popular data and misrepresent less popular data.
  • In successive training cycles, the characteristics of minority data are progressively distorted and eventually lost.
  • This could have serious implications, including potential discrimination based on gender, ethnicity, or other sensitive attributes.
• Increasing Value of Human-Created Content:
  • The researchers posit that human-generated content will become increasingly valuable, especially as a source of pristine training data for AI.
  • In a future filled with AI-generated content, ensuring that AI models have access to human-created data will be essential for maintaining their integrity and performance.

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