DARPA's plan to speed machine learning development -- GCN

The Defense Advanced Research Projects Agency wants to make the process of training machine-learning models more efficient. Currently, training a ML model requires the test data be labeled, which requires humans identify an an image or specific phrases in text that the algorithm should learn to recognize. The more labeled data the system can review, the more complete its training and the better the eventual results. Amassing enough clean, consistently labeled data to train an algorithm is expensive and time consuming. If, for example, a company wants to analyze user reviews of its products, it will need "at least 90,000 reviews to build a model that performs adequately," according to AltexSoft, a software R&D engineering firm.