About: Collaborative intelligence is a research topic. Over the lifetime, 195 publications have been published within this topic receiving 2644 citations.
TL;DR: Neurosurgeon, a lightweight scheduler to automatically partition DNN computation between mobile devices and datacenters at the granularity of neural network layers is designed, finding that a fine-grained, layer-level computation partitioning strategy based on the data and computation variations of each layer within a DNN has significant latency and energy advantages over the status quo approach.
Abstract: The computation for today's intelligent personal assistants such as Apple Siri, Google Now, and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires significant amounts of data to be sent to the cloud over the wireless network and puts significant computational pressure on the datacenter. However, as the computational resources in mobile devices become more powerful and energy efficient, questions arise as to whether this cloud-only processing is desirable moving forward, and what are the implications of pushing some or all of this compute to the mobile devices on the edge.In this paper, we examine the status quo approach of cloud-only processing and investigate computation partitioning strategies that effectively leverage both the cycles in the cloud and on the mobile device to achieve low latency, low energy consumption, and high datacenter throughput for this class of intelligent applications. Our study uses 8 intelligent applications spanning computer vision, speech, and natural language domains, all employing state-of-the-art Deep Neural Networks (DNNs) as the core machine learning technique. We find that given the characteristics of DNN algorithms, a fine-grained, layer-level computation partitioning strategy based on the data and computation variations of each layer within a DNN has significant latency and energy advantages over the status quo approach.Using this insight, we design Neurosurgeon, a lightweight scheduler to automatically partition DNN computation between mobile devices and datacenters at the granularity of neural network layers. Neurosurgeon does not require per-application profiling. It adapts to various DNN architectures, hardware platforms, wireless networks, and server load levels, intelligently partitioning computation for best latency or best mobile energy. We evaluate Neurosurgeon on a state-of-the-art mobile development platform and show that it improves end-to-end latency by 3.1X on average and up to 40.7X, reduces mobile energy consumption by 59.5% on average and up to 94.7%, and improves datacenter throughput by 1.5X on average and up to 6.7X.
TL;DR: How AI affects the B2B sales funnel is described, key sales tasks are described, specific contributions AI can bring are explained, and the role humans play are clarified.
TL;DR: A framework is created based on four major components of crowdsourcing: the task that is outsourced, the crowd which carries out the task, the crowdsourcing process, and the outcome evaluation to support various phases of managerial decision-making and problem solving.
Abstract: Crowdsourcing can be viewed as a method of distributing work to a large number of workers (the crowd) both inside and outside of an organization, for the purpose of improving decision making, completing cumbersome tasks, or co-creation of designs and other projects. Of the various applications of crowdsourcing, the one investigated in this paper is to support various phases of managerial decision-making and problem solving. To examine the research issues related to such support, we created a framework based on four major components of crowdsourcing: the task that is outsourced, the crowd which carries out the task, the crowdsourcing process, and the outcome evaluation. Each component is examined from the managerial, behavioral, and information technology aspects. This framework enables us to organize existing literature and identify key research issues. Suggested topics for future research are described.
TL;DR: In this paper, J. Richard Hackman identifies six conditions necessary for any team to succeed: seeting up a well-defined, stable, interdependent unit; getting the right people on the team; defining a compelling purpose; establishing clear norms of conduct; creating a supportive organizational context; and providing team-focused coaching.
Abstract: Shows what's needed to lead teams in one of the most demanding settings imaginable: intelligence work
- Illustrated with actual stories drawn from the author's direct experience as an intelligence consultant and researcher
- Both a vital tool for intelligence professionals and a penetrating analysis of what makes teams work in any organization
Intelligence professionals are popularly viewed as solo operators--most famously as the lone wolf "secret agents" of fiction. But, particularly today, doing intelligence is mostly about teamwork--the volume, complexity, and global nature of the work demand collaboration acroos a diversity of people, disciplines, and organizations.
And yet teams in the intelligence community face formidable challenges. Needed information may be classified and difficult or impossible to obtain, and ultimate goals are sometimes covert--concealed from the very people working to achieve them. They bureaucracy is immense and complex, and the extraordinary demands of the work lead to high turnover and frequent transfers. As a result, teams often devolve into wheel-spinning, contentious assemblies that get nothing done--or do the wrong things.
But there is also good news. J. Richard Hackman draws on his unparalleled decade of experience as a researcher on and consultant to the intelligence community, as well as his pioneering work on teams, to show how intelligence leaders can create an environment where teamwork flourishes. Hackman identifies six conditions necessary for any team to succeed: seeting up a well-defined, stable, interdependent unit; getting the right people on the team; defining a compelling purpose; establishing clear norms of conduct; creating a supportive organizational context; and providing team-focused coaching. He uses concrete examples to show how each of these conditions helps teams accomplish their missions in the unusual and always demanding context of intelligence work.
Although written with intelligence, defense, crisis management, and law enforcement professionals in mind, the book contains lessons that can be applied to any organization--these necessary conditions are universal. Collaborative Intelligence is a vital resource for the intelligence community and a fascinating look inside that community for outsiders.
TL;DR: A conceptual framework for collaborative artificial intelligence in marketing is developed, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and Consumers.