Abstract
An analytical review of the models and risks in the researcher’s reproduction system in the scientific specialty “1.2.1. Artificial Intelligence and Machine Learning” is presented. The issues of graduate school management and regulatory barriers in the training of young scientists are considered. Successful practices for defending a PhD thesis at leading national research universities have been identified and categorized. The justifications for the need to protect a PhD thesis by machine learning engineers are given. Proposals for changes to the scientific model of postgraduate studies and for AI augmentation of scientific research have been summarized, which help overcome risks in assigning qualification based on the textual results of scientific work.
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