Crowdsourcing offers a powerful new paradigm for online work. However, real world tasks are often interdependent, requiring a big picture view of the difference pieces involved. Existing crowdsourcing approaches that support such tasks -- ranging from Wikipedia to flash teams -- are bottlenecked by relying on a small number of individuals to maintain the big picture. In this paper, we explore the idea that a computational system can scaffold an emerging interdependent, big picture view entirely through the small contributions of individuals, each of whom sees only a part of the whole. To investigate the viability, strengths, and weaknesses of this approach we instantiate the idea in a prototype system for accomplishing distributed information synthesis and evaluate its output across a variety of topics. We also contribute a set of design patterns that may be informative for other systems aimed at supporting big picture thinking in small pieces.

As in-vehicle automation becomes increasingly prevalent and capable, there will be more opportunity for vehicle drivers to delegate control to automated systems. as well as increased ability for automated systems to intervene to increase road safety. With the decline in how much a driver must be engaged, two problems arise: driver disengagement and reduced ability to act when necessary; and also a likely decrease in active driving, which may reduce the engagement a driver can have for the purpose of enjoyment. As vehicles become more intelligent, they need to work collaboratively with human drivers, in the frame of a joint-cognitive system in order to both extend and backstop human capabilities to optimize safety, comfort, and engagement.