The Future of Human In The Loop

Since the 1980's, human/machine interactions, and human-in-the-loop (HTL) scenarios in particular, have been systematically studied. It was often predicted that with an increase in automation, less human-machine interaction would be needed over time. Human input is still relied upon for most common forms of AI/ML training, and often even more human insight is required than ever before. As AI/ML evolves and baseline accuracy of models improves, the type of human interaction required will change from creation of generalized ground truth from scratch, to human review of the worst-performing ML predictions in order to improve and fine-tune models iteratively and cost-effectively. Deep learning algorithms thrive on labeled data and can be improved progressively if more training data is added over time.