Bottleneck Breakthrough is a tough-talking, no-nonsense guide to helping you bust through the "bottlenecks" that might be holding your business back. You'll learn how to utilize six different levers to design a customized bottleneck breakthrough plan that will improve all areas of your business.

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

Based on author's thesis (doctoral--Oxford University, 2009) under title: Opportunity pluralism.

"This book explains how to use the Theory of Constraints to improve patient flow in a hospital, medical office, urgent care center, or clinic"--

A comprehensive instruction guide to blues slide guitar styles. Contains over 25 accurate transcriptions of authentic bottleneck blues tunes by such masters as Son House, Robert Johnson, Charlie Patton and many more.

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.