The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.

The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.

Learn the psychological constrictions of attention, perception, memory, disposition, motivation, and social influence that determine whether customers will be receptive to your digital innovations. Bottlenecks: Aligning UX Design with User Psychology fills a need for entrepreneurs, designers, and marketing professionals in the application of foundational psychology to user-experience design. The first generation of books on the topic focused on web pages and cognitive psychology. This book covers apps, social media, in-car infotainment, and multiplayer video games, and it explores the crucial roles played by behaviorism, development, personality, and social psychology. Author David Evans is an experimental psychology Ph.D. and senior manager of consumer research at Microsoft who recounts high-stakes case studies in which behavioral theory aligned digital designs with the bottlenecks in human nature to the benefit of users and businesses alike. Innova tors in design and students of psychology will learn: The psychological processes determining users’ perception of, engagement with, and recommendation of digital innovations Examples of interfaces before and after simple psychological alignments that vastly enhanced their effectiveness Strategies for marketing and product development in an age of social media and behavioral targeting Hypotheses for research that both academics and enterprises can perform to better meet users’ needs Who This Book Is For Designers and entrepreneurs will use this book to give their innovations an edge on what are increasingly competitive platforms such as apps, bots, in-car apps, augmented reality content. Usability researchers and market researchers will leverage it to enhance their consulting and reporting. Students and lecturers in psychology departments will want it to help land employment in the private sector. Praise “Bottlenecks’ is a tight and eminently actionable read for business leaders in startups and enterprises alike. Evans gives us a rich sense of key psychological processes and even richer examples of them in action.” - Nir Eyal, Author of Hooked: How to Build Habit-Forming Products “Clients frequently ask our UX researchers and designers for deeper truths about why certain designs work and others fail. Bottlenecks offers practical explanations and evidence based on the idea that human cognition did not begin with the digital age.” - John Dirks, UX Director and Partner, Blink UX “Bottlenecks brings together two very important aspects of user experience design: understanding users and translating this into business impact. A must-read for anyone who wants to learn both.” - Josh Lamar, Sr. UX Lead, Microsoft Outlook

Bottlenecks introduces a powerful new way of understanding equal opportunity. Rather than literal equalization, Joseph Fishkin argues that Americans ought to aim to broaden the range of opportunities open to people, at every stage in life, to pursue different paths. This approach has significant implications for public policy and antidiscrimination law.

This volume, in conjunction with the two volumes CICS 0002 and LNAI 4682, constitutes the refereed proceedings of the Third International Conference on Intelligent Computing held in Qingdao, China, in August 2007. The 139 full papers published here were carefully reviewed and selected from among 2,875 submissions. Collectively, these papers represent some of the most important findings and insights into the field of intelligent computing.

This book is an ideal and practical resource on the potential impact Artificial Intelligence (AI) can have in space sciences and applications. AI for Space Application presents a hands-on approach to browse in the subject and to learning how to do. AI is not yet fully accepted as a pervasive technology in space applications because they are often mission-critical and the cost of space equipment and modules raises skepticism on any practical use and reliability. However, it is evident that its potential impact on many aspects is dramatic. Starting from either actual or experimental realizations, the book accompanies the reader through such fascinating subjects like space exploration, autonomous navigation and landing, rover control and guidance on rough surfaces, image analysis automation for planet or star classification, and for space debris avoidance without human intervention. This kind of approach may facilitate further investigations on the same or similar subjects, as the future of space explorations is going toward adopting AI. The intended audience of the book are researchers from academia and space industries and practitioners in related start-ups.

This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.