DeveloperWeek 2018

AI and machine learning technologies have made substantial progress over the last two decades and as a result have made their way into a wide array of applications. With this widespread adoption has come a demand for systems that can imitate human intelligence. Neural networks have allowed us to construct extremely complex systems while tuning key parameters to achieve commercially usable intelligence. While tuning these algorithms is important to progress, the data that is used to train these models is equally important. Human-in-the-loop is emerging as a critical component of AI offering a source of clean data that can be used to improve AI technologies. HITL utilizes human judgement to augment data that is used to train deep networks in different industries. From autonomous cars to content recommendations, companies are quickly adopting HITL to improve their AI capabilities. In this session we will discuss the importance of data in training deep networks. We will cover conventional methods of sourcing data, why HITL is essential for large scale AI technologies, and how it can be used.