Data Science Office
As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. In this study, we present the first analysis of AI “aging”: the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle. Indicated are potential causes of temporal degradation, and suggest approaches to detecting aging and reducing its impact.
Read the article here: https://lnkd.in/eFht96mv
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