From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. Nicholas Diakopoulos explains the present and future of a world in which algorithms have changed how the news is created, disseminated, and received, and he shows why journalists—and their values—are at little risk of being replaced.
First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.
Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.
In Cloud Ethics Louise Amoore examines how machine learning algorithms are transforming the ethics and politics of contemporary society. Conceptualizing algorithms as ethicopolitical entities that are entangled with the data attributes of people, Amoore outlines how algorithms give incomplete accounts of themselves, learn through relationships with human practices, and exist in the world in ways that exceed their source code. In these ways, algorithms and their relations to people cannot be understood by simply examining their code, nor can ethics be encoded into algorithms. Instead, Amoore locates the ethical responsibility of algorithms in the conditions of partiality and opacity that haunt both human and algorithmic decisions. To this end, she proposes what she calls cloud ethics—an approach to holding algorithms accountable by engaging with the social and technical conditions under which they emerge and operate.
Software has become a key component of contemporary life and algorithmic techniques that rank, classify, or recommend anything that fits into digital form are everywhere. This book approaches the field of information ordering conceptually as well as historically. Building on the philosophy of Gilbert Simondon and the cultural techniques tradition, it first examines the constructive and cumulative character of software and shows how software-making constantly draws on large reservoirs of existing knowledge and techniques. It then reconstructs the historical trajectories of a series of algorithmic techniques that have indeed become the building blocks for contemporary practices of ordering. Developed in opposition to centuries of library tradition, coordinate indexing, text processing, machine learning, and network algorithms instantiate dynamic, perspectivist, and interested forms of arranging information, ideas, or people. Embedded in technical infrastructures and economic logics, these techniques have become engines of order that transform the spaces they act upon.
"The many diseases that are endemic in most of the developing nations of the world (and that may also affect travelers to these regions) are, at world levels, the most important sources of morbidity that affect the entire human race. The change in morbidity patterns in the more developed nations should not be permitted to blind the more affluent countries to the implications of this simple statement. Thus, direct and useful guides are needed to assure efficient and economical diagnosis and treatment of those infections that are endemic to the less affluent two-thirds of the earth.
"The algorithms in this book have been developed by Drs. Warren and Mahmoud, as the result of a systematic effort to produce such guides. The book is presented as another in the series "Studies in Infectious Disease Research" and is a most welcome addition, certain to supply a major and hitherto inadequately fulfilled need."—from the Foreword, by Edward H. Kass, M. D., Ph.D
Machine learning algorithms and artificial intelligence influence many aspects of life today and have gained an aura of objectivity and infallibility. The use of these tools introduces a new level of risk and complexity in policy. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems.
When we look at the cultural public sphere through the lens of digitalization, a paradoxical picture emerges. In some ways, the digital age seems to have brought the goals of the Enlightenment to their fullest fruition, giving us boundless and instantaneous access to every kind of knowledge and art. But the internet and its platforms also frequently bring chaos, immersing us in a sphere of often unverified information whose scope is unimaginable. This book takes a tour through the current debates on digital culture, bringing together a wide array of perspectives from aesthetic theory, cultural studies, electronic media, and the arts.
Analysis of Algorithms is the fourth in a series of collected works by world-renowned computer scientist Donald Knuth. This volume is devoted to an important subfield of Computer Science that Knuth founded in the 1960s and still considers his main life's work. This field, to which he gave the name Analysis of Algorithms, deals with quantitative studies of computer techniques, leading to methods for understanding and predicting the efficiency of computer programs. Analysis of Algorithms, which has grown to be a thriving international discipline, is the unifying theme underlying Knuth's well known book The Art of Computer Programming. More than 30 of the fundamental papers that helped to shape this field are reprinted and updated in the present collection, together with historical material that has not previously been published. Although many ideas come and go in the rapidly changing world of computer science, the basic concepts and techniques of algorithmic analysis will remain important as long as computers are used.