Through analytics, companies can reduce overload, attrition, and other costs of collaboration -- and increase its rewards. No question, in a competitive global landscape, collaboration allows companies to serve exacting clients more seamlessly, respond more quickly to changing environments, and innovate more rapidly. But when an organization tries to boost collaboration by adopting a new formal structure, technology, or way of working, it often adds a steady stream of time- and energy-consuming interactions to an already relentless workload, diminishing instead of improving performance. Think about the consequences at an individual level: It's not unusual to feel as if we are just starting our work at 5 p.m., after the daily battery of demands has finally quieted down. Thanks to the plethora of technologies that keep us connected, increasingly integrated global operations, and the need for a multidisciplinary approach to deploying complex products and services, the problem has snowballed over the past decade, with collaborative time demands rising more than 50%. Most knowledge workers and leaders spend 85% or more of their time on email, in meetings, and on the phone.1 Employees struggle with increases in email volume, the proliferation of new collaborative tools, and expectations of fast replies to messages -- with deleterious effects on their quality of work and efficiency. Research tells us that simple distractions like checking a text message fragments our attention more than we realize, and more consuming distractions -- such as answering an email -- can cost us more than 20 minutes to fully regain our focus.2 Even though employees are acutely aware that they're suffering, most organizations don't recognize what's happening in the aggregate.

Our workdays are getting noisier. Industry research shows that employees at larger organizations use an average of 36 cloud services at work, including tools for productivity, project management, communication, and storage. This information overload is a key source of pain for people at work--and a prime opportunity to leverage the help of machine intelligence. When we talk about machine intelligence at Dropbox, we mean the whole range of applied machine learning, from simple linear classifiers to advanced deep learning networks. For many years we've been building a world-class machine learning team, improving our platform behind the scenes.

The construction of computer systems that are intelligent, collaborative problem-solving partners is an important goal for both the science of AI and its application. From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. In this address, it is argued that collaboration must be designed into systems from the start; it cannot be patched on. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented.

Children with complex health conditions require care from a large, diverse set of caregivers that includes parents and community support organizations as well as multiple types of medical professionals. Coordination of their care is essential for good outcomes, and  extensive  research has shown that the use of integrated, team-based care plans improves care coordination. Care plans, however, are rarely deployed in practice. This paper describes barriers to effective implementation of care plans in complex care revealed by a study of care providers treating such children. It draws on teamwork theories, identifying ways AI capabilities could enhance care plan use; describes the design of GoalKeeper, a system to support providers use of care plans; and describes  initial work toward information sharing algorithms for such systems.

This thesis aims to provide a foundation for designing computer agents able to work better with people and with other agents in heterogeneous groups. When agents work together on a collaborative activity, in addition to performing their share of the activity, they may be able to help one another and thus improve the collective utility. The thesis specifically focuses on investigating the question of how, when and what kinds of helpful behavior should emerge when agents collaborate, taking into account the costs of a helpful action. It considers collaborative activities that take place in settings in which there is uncertainty about agents' capabilities and about the state of the world. To ensure that helpful behavior improves the overall benefit of the collaboration, the thesis incorporates decision-theoretic mechanisms for managing helpful behavior into existing formalizations of collaborative activity. It provides an investigation of the way people perceive the usefulness of helpful actions when proposed by a computer agent. It proposes incentives for facilitating collaboration among self-interested agents. In addition to these theoretical and empirical contributions, my findings are applied to several real-life application domains with different characteristics.

People often act together with a shared purpose; they collaborate. Collaboration enables them to work more efficiently and to complete activities they could not accomplish individually. An increasing number of computer applications also require collaboration among various systems and people. Thus, a major challenge for AI researchers is to determine how to construct computer systems that are able to act effectively as partners in collaborative activity. Collaborative activity entails participants forming commitments to achieve the goals of the group activity and requires group decision making and group planning procedures. In addition, agents must be committed to supporting the activities of their fellow participants in support of the group activity. Furthermore, when conflicts arise (for example, from resource bounds), participants must weigh their commitments to various group activities against those for individual activities. This article briefly reviews the major features of one model of collaborative planning called SHARED-PLANS (Grosz and Kraus 1999, 1996). It describes several current efforts to develop collaborative planning agents and systems for human-computer communication based on this model. Finally, it discusses empirical research aimed at determining effective commitment strategies in the SHAREDPLANS context.

The construction of computer systems that are intelligent, collaborative problem-solving partners is an important goal for both the science of AI and its application. From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. In this address, it is argued that collaboration must be designed into systems from the start; it cannot be patched on. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented. It is further argued that research on, and the development of, collaborative systems should itself be a collaborative endeavor -- within AI, across subfields of computer science, and with researchers in other fields.