by Paul R Daugherty; H James Wilson
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AI is radically transforming business. Are you ready?Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate.Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader's guide" with the five crucial principles required to become an AI-fueled business.Human + Machine provides the missing and much-needed management playbook for success in our new age of AI.BOOK PROCEEDS FOR THE AI GENERATIONThe authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.
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by Professor Rob Nederpelt; Professor Herman Geuvers
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A gentle introduction for graduate students and researchers in the art of formalizing mathematics on the basis of type theory.
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by Ethem Alpaydin; Francis Bach
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A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.
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by Brett Lantz
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Machine Learning with R - Second Edition
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by Ninghui Li; Min Lyu; Dong Su
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Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations.We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it.The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.
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by Petrucci, Laure
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by Frank Drewes; Carlos Martin-Vide; Bianca Truthe
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This book constitutes the refereed proceedings of the 11th International Conference on Language and Automata Theory and Applications, LATA 2017, held in Umeå, Sweden, in March 2017. The 31 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 73 submissions. The papers cover the following topics: algorithmic learning and semantics; automata and logics; combinatorics on words, compression, and pattern matching; complexity; finite automata; grammars, languages, and parsing; graphs and Petri Nets; non-classical automata; and pushdown automata and systems.
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by Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
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Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.
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by Gregory J. Chaitin
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Expounds Godel's incompleteness theorey using an information theoretic approach based on the size of computer programs."
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