Reducing the Data Cost of Machine Learning with AI: A Case Study

The past several years have seen a strong push toward using Deep Learning systems–Neural Networks with multiple hidden layers. Deep Learning is now used in many machine learning applications and provides leading performance on numerous benchmark tasks. However, this increase in performance requires very large datasets for training. From a practitioner prospective, the model that performs best in benchmark tasks may be too data intensive to be adapted to practical application. We describe a behavior recognition problem that was solved using a sequence-based Deep Learning system and then reimplemented using a more knowledge-driven sequence matching approach due to data constraints. We contrast the two approaches and the data required to achieve sufficient performance and flexibility.