Reproducibility Issues Hinder Machine Learning Progress Androidheadlines.com

Reproducibility, the factor that allows other scientists to reproduce an experiment's results by reproducing its procedure, is largely absent in machine learning and artificial intelligence development, and given the scale and scope of the field, it's starting to become a real issue. One of the biggest pieces of the puzzle is being able to record and factor in small changes, such as GPU driver updates in mid-job, or changes to the data set during a training run by an outside source. A very large number of factors can affect an AI research project's journey from conception to fruition, and without being able to reproduce all of these factors, AI researchers are essentially unable to reproduce one another's work. This harms collaboration and piggyback development, two of the more basic tenets of publishable scientific research, in a field that would benefit greatly from having no issues in those areas. To paint as simple a picture as possible, imagine a data scientist wanting to set up a simple AI program that searches for and sorts images of blue jays from nature photos.