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The following review appeared in the March 2016 issue of CHOICE. The review is for your internal use only. Please review our Permission and Reprints Guidelines or email permissions@ala-choice.org.
Science & Technology
Information & Computer Science
Despite the somewhat pretentious title, “Artificial Superintelligence,” this is a very interesting book. Crammed into some 200 pages, index included, the book tries to establish a method of measuring progress in artificial intelligence (AI) by creating an AI analogy to the work of Stephen Cook and others in computational complexity. Specifically, the book introduces the author's concepts of AI-complete and AI-hard as analogies to the computational complexity categories of NP-complete and NP-hard. Yampolskiy (Univ. of Louisville) makes his case in just ten chapters. Chapter 1 introduces the topic of AI-Completeness. Chapters 2 through 8 elaborate the details of the author's vision of superintelligences. Chapter 9, “Efficiency Theory: A Unifying Theory for Information, Computation, and Intelligence,” brings together the diversity of issues presented in the earlier chapters and does a good job of unifying the book. Yampolskiy presents his thoughts on AI's future in the final chapter. Each chapter includes an impressive collection of references, and the text has a healthy index. In general, this work should interest researchers in both AI and computational complexity. Readers may also wish to consult Nick Bostrom's Superintelligence (CH, Mar'15, 52-3620).
--J. Beidler, University of Scranton