GitHub Is Bad for AI: Solving ML Reproducibility - DZone AI

There is a crisis in machine learning that is preventing the field from progressing as fast as it could. It stems from a broader predicament surrounding reproducibility that impacts scientific research in general. A Nature survey of 1,500 scientists revealed that 70% of researchers have tried and failed to reproduce another scientist's experiments, and over 50% have failed to reproduce their own work. Reproducibility, also called replicability, is a core principle of the scientific method and helps ensure the results of a given study aren't a one-off occurrence but instead represent a replicable observation. In computer science, reproducibility has a more narrow definition: Any results should be documented by making all data and code available so that the computations can be executed again with the same results.