Promotions - Choice's Outstanding Academic Titles 2019 - Science & Technology — Health Sciences — Women's & Gender Studies

Jennings, well known in the field of AI, looks at key AI issues humanity will face in the coming decades. One of his main themes is that AI-based technologies will be unpredictable and risky, a position he supports with extensive examples and research by leading scientists. Like many other high-impact technologies, AI will be impossible to control. Even if such control were possible, scientists do not know how to implement limiting mechanisms. For example, the implementation of ethical systems is notoriously difficult. Jennings discusses in detail work funded by governments of many countries to strengthen artificial intelligence research. The AI clock is ticking increasingly fast, and the existential AI crisis major governments face is undeniable. Jennings discusses how AI impacts economies, military organizations, business, and education. Many possible scenarios are dystopian. According to some visionaries, the AI explosion that will create unimaginable powers--aka the singularity event--will happen at the end of the next decade. On the plus side, one of the brightest areas of AI application will be in education, where smart algorithms will figure out how to teach most effectively. Both important and highly entertaining, this volume will be valuable to anyone interested in current and future political and technological issues.Summing Up: Highly recommended. All readers.

Big Data Analytics provides a broad, pragmatic guide to big data applications and practices. This book stands apart from many other recent publications on big data in two primary ways. First, it is designed for the beginning or aspiring data scientist, perhaps a student or entry-level professional. Most recent publications on this topic focus on theory or ethics issues. These books have little use for those seeking to develop marketable data science skills. Big Data Analytics provides what is sorely missing: robust explanation of data science theory, technical expertise, and applications of concepts. The second distinction of this title is that is not software-program specific. Many of the more technical big data titles focus on how to perform specific tasks using a single platform. This title instead focuses on the concepts behind the analysis, both strengths and weaknesses, and how to apply this knowledge in the field. This focus also extends this title's relevance, because it will not become outdated as software programs and platforms update. With 14 self-contained chapters, this title could serve well as a text in an introductory data science course.Summing Up: Highly recommended. Advanced undergraduates through faculty and professionals.

Software languages are an important component in computer science education, and yet it is hard to provide a comprehensive and methodologically sound approach as more languages and associated concepts are continuously invented. With Software Languages, Lammel (Univ. of Koblenz-Landau, Germany) succeeds in providing such an approach. The book's 12 chapters cover a wealth of information by adopting a formal point of view that supplies the rigor needed for such an extensive treatment while offering plenty of self-learning opportunities through exercises and additional references. Diagrams and pictures enhance the presentation of most material, and the judicious use of color is appropriate and useful. This book is also useful as the first step in a more in-depth exploration of specific topics, such as metaprogramming, as it provides the references needed for a comprehensive literature review. Although the author claims that this book can be used without an instructor, this can likely only be accomplished by experienced computer scientists or software engineers, especially given that no solutions to the exercises are provided. For the appropriate audience, however, Software Languages represents a formidable learning tool.Summing Up: Highly recommended. Advanced undergraduates through faculty and professionals.

In The Software Arts, Warren Sacks--a media theorist, software designer, and artist at the University of California, Santa Cruz--draws on science and technology studies (STS), media studies, and the history of science and the humanities to argue that software should be considered an art, not a science, because it is the result of a lengthy "translation" (in the sense of STS scholar Bruno Latour) from the old liberal arts of logic, rhetoric, and grammar to the present-day rule of computer algorithms. The book is always thought provoking, especially in regard to understanding machine learning and such well-known figures as Alan Turing and Noam Chomsky. But it can be frustrating when the long history of the liberal arts pushes software off the stage for too long. In arguing for the existence of a new "computational episteme," Sack makes such memorable statements as, "When many of our social, cultural, economic, and political institutions are configured around software, I say we live under 'computational conditions,' or, more simply, that our life is a 'digital life'" (p. 22). A valuable read for undergraduate and graduate students in the humanities and in computer science.Summing Up: Highly recommended. Undergraduate and graduate students.