Suchergebnisse

Intro -- Half Title Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- List of Illustrations -- Preface -- Acknowledgments -- 1. Introduction -- 2. American Military Chaplaincy, 1607-1860 -- 3. Progress and Foretaste, 1861-1865 -- 4. Nadir and Turnaround, 1866-1898 -- 5. Transition and Integration, 1899-1917 -- 6. Bureaucratization and Autonomy, 1917-1920 -- 7. A Bureaucratized Profession -- Notes -- Bibliographical Essay -- Index.

"This book provides both an outline and a critique of neo-Gramscian international relations theory, from a Marxist perspective"--

The last hundred years have seen a monumental rise in the power and capability of machines to perform intelligent tasks in the stead of previously human operators. This rise is not expected to slow down any time soon and what this means for society and humanity as a whole remains to be seen. The overwhelming notion is that with the right goals in mind, the growing influence of machines on our every day tasks will enable humanity to give more attention to the truly groundbreaking challenges that we all face together. This will usher in a new age of human machine collaboration in which humans and machines may work side by side to achieve greater heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of intelligent systems come to the fore in complex systems where the interaction between humans and machines can be made seamless, and it is this goal of symbiosis between human and machine that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven methods have come to the fore and now represent the state-of-the-art in many different fields. Alongside the shift from rule-based towards data-driven methods we have also seen a shift in how humans interact with these technologies. Human computer interaction is changing in response to data-driven methods and new techniques must be developed to enable the same symbiosis between man and machine for data-driven methods as for previous formula-driven technology. We address five key challenges which need to be overcome for data-driven human-in-the-loop computing to reach maturity. These are (1) the 'Categorisation Challenge' where we examine existing work and form a taxonomy of the different methods being utilised for data-driven human-in-the-loop computing; (2) the 'Confidence Challenge', where data-driven methods must communicate interpretable beliefs in how confident their predictions are; (3) the 'Complexity Challenge' where the aim of reasoned communication becomes increasingly important as the complexity of tasks and methods to solve also increases; (4) the 'Classification Challenge' in which we look at how complex methods can be separated in order to provide greater reasoning in complex classification tasks; and finally (5) the 'Curation Challenge' where we challenge the assumptions around bottleneck creation for the development of supervised learning methods. ; Open Access

The Transaction Cost Economy In May 2015, I was on a Brexit discussion panel with a Conservative local councillor and a former Cabinet Minister. It was not the most edifying experience given the audience was dominated by Leave.EU supporters who on being informed that Brexit would lead to increased transaction costs derisively responded: "Wot's Dat?" This response showed a more profound problem that went beyond the 2016 Referendum campaign. That is, the general lack of understanding of how modern economies work, that appears to afflict not only large numbers of the public but many of their political representatives and media commentators.