My research goal is to create machine learning algorithms that are interpretable to humans, that can understand human strengths and weaknesses, and can help humans improve themselves.  In this talk, I'll discuss how we accomplish this through a trivia game called quiz bowl. These questions are written so that they can be interrupted by someone who knows more about the answer; that is, harder clues are at the start of the question and easier clues are at the end of the question: a player must decide when it has enough information to "buzz in". Our system to answer quiz bowl questions depends on two parts: a system to identify the answer to questions and to determine when to buzz.  We discuss how deep averaging networks---fast neural bag of words models---can help us answer questions quickly using diverse training data (previous questions, raw text of novels, Wikipedia pages) to determine the right answer and how deep reinforcement learning can help us determine when to buzz.

More importantly, however, this setting also helps us build systems to adapt in cooperation and competition with humans.  In competition, we are also able to understand the skill sets of our competitors to adjust our strategy to optimize our performance against players using a deep mixture of experts opponent model.  The game of quiz bowl also allows opportunities to better understand interpretability in deep learning models to *help* human players perform better with machine cooperation.  This cooperation helps us with a related task, simultaneous machine translation.

Finally, I'll discuss opportunities for broader participation through open human-computer competitions:

http://hcqa.boydgraber.org/