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Temporal quality degradation in AI models.

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  • 1Monterrey Institute of Technology and Higher Education, Monterrey, Mexico.

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Artificial intelligence (AI) models degrade over time, a phenomenon termed AI aging. This study analyzes temporal degradation patterns across industries to identify causes and mitigation strategies.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • AI models are increasingly deployed in real-world applications.
  • Maintaining AI model quality over time is crucial for reliable performance.
  • Current machine learning models are static, relying on data from their training period.

Purpose of the Study:

  • To introduce and analyze the concept of AI aging, defined as AI model quality degradation over time.
  • To identify and describe temporal degradation patterns in AI models.
  • To differentiate AI aging from related concepts like data concept drift and continuous learning.

Main Methods:

  • Analysis of AI model performance using datasets from healthcare, transportation, finance, and weather.
  • Evaluation of four standard machine learning models.
  • Comparative analysis with data concept drift and continuous learning.

Main Results:

  • Identification and description of distinct temporal degradation patterns in AI models.
  • Demonstration of key differences between AI aging and other forms of model performance decline.
  • Exploration of potential causes for AI aging across various industries.

Conclusions:

  • AI aging is a significant, multifaceted challenge impacting AI model reliability.
  • Understanding temporal degradation patterns is essential for AI system maintenance.
  • Developing methods for detecting and mitigating AI aging is critical for long-term AI deployment.