<ul><li>Generative artificial intelligence, such as Generative Adversarial Networks (GANs), has been used to amplify data for scientific analysis, allowing for data generation in reduced computing time.</li><li>The process of data amplification, which violates the principle of getting information for free, can result in a gain factor greater than one while keeping the information content unchanged.</li><li>This study presents a mathematical bound for data amplification, dependent on the number of generated and training events, and determines conditions for ensuring this bound.</li><li>While the resolution of variables in amplified data is not improved, the increase in sample size can improve statistical significance.</li></ul>

An information theoretic limit to data amplification

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