Abstract
People understand many domains more deeply than today's machine learning systems. Having a good representation for a problem is crucial to the success of intelligent systems. In this talk, we discuss recent work and future opportunities for how humans can aid machine learning algorithms. Beyond simply labeling data, the crowd can help uncover the latent representation behind a problem. We discuss recent work on eliciting features using active learning as well as other aspects of crowdsourcing and machine learning, such as how crowdsourcing can help generate data, raise questions, and assist in more complex AI tasks.