Stephen Robertson

I found it a delight to read. The author is not trying to write yet another book on the history of computer developments but rather to show that those developments rely on a long history of humans creating solutions to problems that arose as they became more and more sophisticated in their treatment of concepts of information and its manipulation. In many ways it resembles a work of philosophy more than a technical history, but relies on explaining that technical history to make his points. Michael R. Williams, Department of Computer Sciences, University of Calgary The idea that the digital age has revolutionized our day-to-day experience of the world is nothing new, and has been amply recognized by cultural historians. In contrast, Stephen Robertson’s BC: Before Computers is a work which questions the idea that the mid-twentieth century saw a single moment of rupture. It is about all the things that we had to learn, invent, and understand – all the ways we had to evolve our thinking – before we could enter the information technology revolution of the second half of the twentieth century. Its focus ranges from the beginnings of data processing, right back to such originary forms of human technology as the development of writing systems, gathering a whole history of revolutionary moments in the development of information technologies into a single, although not linear narrative. Treading the line between philosophy and technical history, Robertson draws on his extensive technical knowledge to produce a text which is both thought-provoking and accessible to a wide range of readers. The book is wide in scope, exploring the development of technologies in such diverse areas as cryptography, visual art and music, and the postal system. Through all this, it does not simply aim to tell the story of computer developments but to show that those developments rely on a long history of humans creating technologies for increasingly sophisticated methods of manipulating information. Through a clear structure and engaging style, it brings together a wealth of informative and conceptual explorations into the history of human technologies, and avoids assumptions about any prior knowledge on the part of the reader. As such the expert and the general reader alike will find it of interest.

B C, Before Computers: On Information Technology from Writing to the Age of Digital Data

Shane White

Stephen Garton

Graham White

The most ubiquitous feature of Harlem life between the world wars was the game of "numbers." Thousands of wagers, usually of a dime or less, would be placed on a daily number derived from U.S. bank statistics. The rewards of "hitting the number," a 600-to-1 payoff, tempted the ordinary men and women of the Black Metropolis with the chimera of the good life. This book tells the story of this illegal form of gambling and the central role it played in the lives of African Americans who flooded into Harlem in the wake of World War I. For a dozen years the "numbers game" was one of America's rare black-owned businesses, turning over tens of millions of dollars every year. The most successful "bankers" were known as Black Kings and Queens, and they lived royally. Yet the very success of "bankers" like Stephanie St. Clair and Casper Holstein attracted Dutch Schultz, Lucky Luciano, and organized crime to the game. By the late 1930s, most of the profits were being siphoned out of Harlem.

Gambling in Harlem Between the Wars

Playing the Numbers

Hugo Zaragoza

The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970-80s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account structure and link-graph information. Again, this has led to one of the most successful web-search and corporate-search algorithms, BM25F. The Probabilistic Relevance Framework: BM25 and Beyond presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25, BM25F. Besides presenting a full derivation of the PRF ranking algorithms, it provides many insights about document retrieval in general, and points to many open challenges in this area. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimization for models with free parameters. The Probabilistic Relevance Framework: BM25 and Beyond is self-contained and accessible to anyone with basic knowledge of probability and inference

Stephen Robertson

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