Shoshana Zuboff, named big brother

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As local media institutions collapse and news deserts sprout up across the country, the US is facing a profound journalism crisis. Meanwhile, continuous revelations about the role that major media outlets–from Facebook to Fox News–play in the spread of misinformation have exposed deep pathologies in American communication systems. Despite these threats to democracy, policy responses have been woefully inadequate. In Democracy Without Journalism? Victor Pickard argues that we’re overlooking the core roots of the crisis. By uncovering degradations caused by run-amok commercialism, he brings into focus the historical antecedents, market failures, and policy inaction that led to the implosion of commercial journalism and the proliferation of misinformation through both social media and mainstream news. The problem isn’t just the loss of journalism or irresponsibility of Facebook, but the very structure upon which our profit-driven media system is built. The rise of a

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This book will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization. This book provides the following benefits: Organizations know they need to leverage AI but they need the described proven roadmap to enable this journey. This book identifies common pitfalls that businesses run into when adopting AI and describes how to avoid them. Enables organizations to get a handle on their data (one of their most valuable assets) which is typically not well organized and scattered throughout different parts of the business. Describes, at a high level, how to build and manage AI models which is different than traditional application code practices. Covers the challenges and best practices of using AI at scale in a production environment. Applies automated testing methodologies to AI models to ensure quality improves with each iteration

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This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms. To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big Data algorithms, and yet do not require tedious calculations or a very deep mathematical background

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