A new preprint on arXiv tackles a meta-problem in machine learning: how to choose the right classification algorithm for a given task. The authors describe this as "artificial intelligence for artificial intelligence," where one AI method helps solve a problem in AI itself. In this case, they propose using reinforcement learning to select among classification algorithms for predicting non-alcoholic fatty liver (NAFLD).
The idea is that instead of manually testing several classifiers, a reinforcement learning agent can learn which algorithm works best for the prediction task. This is a form of automated model selection, a growing area of interest in applied machine learning.
However, the abstract is brief and does not include details about the dataset, the compared classifiers, or any performance metrics. As a result, the practical effectiveness of the approach cannot be assessed from the announcement alone. The full paper would be needed to evaluate whether the reinforcement learning–based selection actually improves prediction accuracy over conventional baselines.