Machine learning has a dirty secret. Despite all the buzzwords thrown around and the wonders that have been accomplished leveraging this technology, there\u2019s a very human element at the root of every application. Machine learning\u2019s secret is that to train a model, a real flesh and blood person had to tag, label, describe, or otherwise interact with massive amounts of data so that a model can be trained.<\/p>\n
For a good reason, the developing of this data is considered to be unengaging and unrewarding work. In HBO\u2019s show Silicon Valley where the grunt work of the tech industry is a running gag, Erlich, one of the show\u2019s main characters, attempts to trick a Stanford introductory computer science class into labeling millions of images of food with which to train his \u201cSee Food\u201d application. The application couldn\u2019t be built without the data, and the effort required to build up a meaningful dataset was something that was below his character\u2019s station as a titan of the technology industry. It\u2019s a commentary on a problem unique to machine learning. The data required to train the model is invaluable, it\u2019s extremely difficult to get, and yet it\u2019s considered lowly work.<\/p>\n