In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very concept of the firm. AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value.
Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have constrained business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, drive massive scope increase, enabling companies to straddle industry boundaries, and enable powerful opportunities for learning--to drive ever more accurate, complex, and sophisticated predictions.
When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani:
Present a framework for rethinking business and operating models
Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition and altering the structure of our economy
Show how these collisions force traditional companies to change their operating models to drive scale, scope, and learning
Explain the risks involved in operating model transformation and how to overcome them
Describe the new challenges and responsibilities for the leaders of these firms
Packed with examples--including the most powerful and innovative global, AI-driven competitors--and based on research in hundreds of firms across many sectors, this is the essential guide for rethinking how your firm competes and operates in the era of AI.

For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence?
Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world-not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought.
A Thousand Brains heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word.

Two renowned investment advisors and authors of the bestseller The Great Reckoning bring to light both currents of disaster and the potential for prosperity and renewal in the face of radical changes in human history as we move into the next century. The Sovereign Individual details strategies necessary for adapting financially to the next phase of Western civilization.
Few observers of the late twentieth century have their fingers so presciently on the pulse of the global political and economic realignment ushering in the new millennium as do James Dale Davidson and Lord William Rees-Mogg. Their bold prediction of disaster on Wall Street in Blood in the Streets was borne out by Black Tuesday. In their ensuing bestsellar, The Great Reckoning, published just weeks before the coup attempt against Gorbachev, they analyzed the pending collapse of the Soviet Union and foretold the civil war in Yugoslavia and other events that have proved to be among the most searing developments of the past few years.
In The Sovereign Individual, Davidson and Rees-Mogg explore the greatest economic and political transition in centuries -- the shift from an industrial to an information-based society. This transition, which they have termed "the fourth stage of human society," will liberate individuals as never before, irrevocably altering the power of government. This outstanding book will replace false hopes and fictions with new understanding and clarified values.

The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology.
The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team.
A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app.

Refactoring is about improving the design of existing code. It is the process of changing a software system in such a way that it does not alter the external behavior of the code, yet improves its internal structure. With refactoring you can even take a bad design and rework it into a good one. This book offers a thorough discussion of the principles of refactoring, including where to spot opportunities for refactoring, and how to set up the required tests. There is also a catalog of more than 40 proven refactorings with details as to when and why to use the refactoring, step by step instructions for implementing it, and an example illustrating how it works The book is written using Java as its principle language, but the ideas are applicable to any OO language.
Features
The first update to a classic that is the standard reference for refactoring in over 15 years
Enhanced, web-based features including interactive examples, video snippets, code highlighting
Extensive use of JavaScript examples
New to This Edition
Martin Fowler is retaining everything that made Refactoring one of the world’s most popular and influential computer books, while updating with JavaScript examples throughout (to address many programmers’ current preferences) and reflecting today’s programming paradigms. The book’s catalog of individual refactorings has been updated, revised, and expanded.

Focusing on autonomous robotic applications, this cutting-edge resource offers a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. Engineers and researchers may find detailed coverage of Simultaneous Localization and Map Building (SLAM) - an area referred to as the "Holy Grail" of autonomous robotics research.

Testing is a key component of agile development. The widespread adoption of agile methods has brought the need for effective testing into the limelight, and agile projects have transformed the role of testers. Much of a tester's function, however, remains largely misunderstood. What is the true role of a tester? Do agile teams actually need members with QA backgrounds? What does it really mean to be an "agile tester?" Two of the industry's most experienced agile testing practitioners and consultants, Lisa Crispin and Janet Gregory, have teamed up to bring you the definitive answers to these questions and many others. In Agile Testing, Crispin and Gregory define agile testing and illustrate the tester's role with examples from real agile teams. They teach you how to use the agile testing quadrants to identify what testing is needed, who should do it, and what tools might help. The book chronicles an agile software development iteration from the viewpoint of a tester and explains the seven key success factors of agile testing. Readers will come away from this book understanding * How to get testers engaged in agile development * Where testers and QA managers fit on an agile team * What to look for when hiring an agile tester * How to transition from a traditional cycle to agile development * How to complete testing activities in short iterations * How to use tests to successfully guide development * How to overcome barriers to test automationThis book is a must for agile testers, agile teams, their managers, and their customers.

As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are now taking an interest in high-level software architecture patterns such as hexagonal/clean architecture, event-driven architecture, and strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn’t always straightforward.
With this practical guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity. Each pattern is illustrated with concrete examples in idiomatic Python that explain how to avoid some of the unnecessary verbosity of Java and C# syntax. You’ll learn how to implement each of these patterns in a Pythonic way.
Architectural design patterns include:
Dependency inversion, and its links to ports and adapters (hexagonal/clean architecture)
Domain-driven design’s distinction between entities, value objects, and aggregates
Repository and Unit of Work patterns for persistent storage
Events, commands, and the message bus
Command Query Responsibility Segregation (CQRS)
Event-driven architecture and reactive microservices

Automated testing is a cornerstone of agile development. An effective testing strategy will deliver new functionality more aggressively, accelerate user feedback, and improve quality. However, for many developers, creating effective automated tests is a unique and unfamiliar challenge. xUnit Test Patterns is the definitive guide to writing automated tests using xUnit, the most popular unit testing framework in use today. Agile coach and test automation expert Gerard Meszaros describes 68 proven patterns for making tests easier to write, understand, and maintain. He then shows you how to make them more robust and repeatable--and far more cost-effective. Loaded with information, this book feels like three books in one. The first part is a detailed tutorial on test automation that covers everything from test strategy to in-depth test coding. The second part, a catalog of 18 frequently encountered "test smells," provides trouble-shooting guidelines to help you determine the root cause of problems and the most applicable patterns. The third part contains detailed descriptions of each pattern, including refactoring instructions illustrated by extensive code samples in multiple programming languages. Topics covered include * Writing better tests--and writing them faster * The four phases of automated tests: fixture setup, exercising the system under test, result verification, and fixture teardown * Improving test coverage by isolating software from its environment using Test Stubs and Mock Objects * Designing software for greater testability * Using test "smells" (including code smells, behavior smells, and project smells) to spot problems and know when and how to eliminate them * Refactoring tests for greater simplicity, robustness, and execution speed This book will benefit developers, managers, and testers working with any agile or conventional development process, whether doing test-driven development or writing the tests last. While the patterns and smells are especially applicable to all members of the xUnit family, they also apply to next-generation behavior-driven development frameworks such as RSpec and JBehave and to other kinds of test automation tools, including recorded test tools and data-driven test tools such as Fit and FitNesse. Visual Summary of the Pattern Language Foreword Preface Acknowledgments Introduction Refactoring a Test PART I: The Narratives Chapter 1 A Brief Tour Chapter 2 Test Smells Chapter 3 Goals of Test Automation Chapter 4 Philosophy of Test Automation Chapter 5 Principles of Test Automation Chapter 6 Test Automation Strategy Chapter 7 xUnit Basics Chapter 8 Transient Fixture Management Chapter 9 Persistent Fixture Management Chapter 10 Result Verification Chapter 11 Using Test Doubles Chapter 12 Organizing Our Tests Chapter 13 Testing with Databases Chapter 14 A Roadmap to Effective Test Automation PART II: The Test Smells Chapter 15 Code Smells Chapter 16 Behavior Smells Chapter 17 Project Smells PART III: The Patterns Chapter 18 Test Strategy Patterns Chapter 19 xUnit Basics Patterns Chapter 20 Fixture Setup Patterns Chapter 21 Result Verification Patterns Chapter 22 Fixture Teardown Patterns Chapter 23 Test Double Patterns Chapter 24 Test Organization Patterns Chapter 25 Database Patterns Chapter 26 Design-for-Testability Patterns Chapter 27 Value Patterns PART IV: Appendixes Appendix A Test Refactorings Appendix B xUnit Terminology Appendix C xUnit Family Members Appendix D Tools Appendix E Goals and Principles Appendix F Smells, Aliases, and Causes Appendix G Patterns, Aliases, and Variations Glossary References Index

Practical Software Architecture Solutions from the Legendary Robert C. Martin (“Uncle Bob”)
By applying universal rules of software architecture, you can dramatically improve developer productivity throughout the life of any software system. Now, building upon the success of his best-selling books Clean Code and The Clean Coder, legendary software craftsman Robert C. Martin (“Uncle Bob”) reveals those rules and helps you apply them.
Martin’s Clean Architecture doesn’t merely present options. Drawing on over a half-century of experience in software environments of every imaginable type, Martin tells you what choices to make and why they are critical to your success. As you’ve come to expect from Uncle Bob, this book is packed with direct, no-nonsense solutions for the real challenges you’ll face—the ones that will make or break your projects.
Learn what software architects need to achieve—and core disciplines and practices for achieving it
Master essential software design principles for addressing function, component separation, and data management
See how programming paradigms impose discipline by restricting what developers can do
Understand what’s critically important and what’s merely a “detail”
Implement optimal, high-level structures for web, database, thick-client, console, and embedded applications
Define appropriate boundaries and layers, and organize components and services
See why designs and architectures go wrong, and how to prevent (or fix) these failures
Clean Architecture is essential reading for every current or aspiring software architect, systems analyst, system designer, and software manager—and for every programmer who must execute someone else’s designs.
Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.

A practical introduction to the robot operating system

“One of the most significant books in my life.” —Obie Fernandez, Author, The Rails Way
“Twenty years ago, the first edition of The Pragmatic Programmer completely changed the trajectory of my career. This new edition could do the same for yours.” —Mike Cohn, Author of Succeeding with Agile, Agile Estimating and Planning, and User Stories Applied
“. . . filled with practical advice, both technical and professional, that will serve you and your projects well for years to come.” —Andrea Goulet, CEO, Corgibytes, Founder, LegacyCode.Rocks
“. . . lightning does strike twice, and this book is proof.” —VM (Vicky) Brasseur, Director of Open Source Strategy, Juniper Networks
The Pragmatic Programmer is one of those rare tech books you’ll read, re-read, and read again over the years. Whether you’re new to the field or an experienced practitioner, you’ll come away with fresh insights each and every time.
Dave Thomas and Andy Hunt wrote the first edition of this influential book in 1999 to help their clients create better software and rediscover the joy of coding. These lessons have helped a generation of programmers examine the very essence of software development, independent of any particular language, framework, or methodology, and the Pragmatic philosophy has spawned hundreds of books, screencasts, and audio books, as well as thousands of careers and success stories.
Now, twenty years later, this new edition re-examines what it means to be a modern programmer. Topics range from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you’ll learn how to:
Fight software rot
Learn continuously
Avoid the trap of duplicating knowledge
Write flexible, dynamic, and adaptable code
Harness the power of basic tools
Avoid programming by coincidence
Learn real requirements
Solve the underlying problems of concurrent code
Guard against security vulnerabilities
Build teams of Pragmatic Programmers
Take responsibility for your work and career
Test ruthlessly and effectively, including property-based testing
Implement the Pragmatic Starter Kit
Delight your users
Written as a series of self-contained sections and filled with classic and fresh anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best approaches and major pitfalls of many different aspects of software development. Whether you’re a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you’ll quickly see improvements in personal productivity, accuracy, and job satisfaction. You’ll learn skills and develop habits and attitudes that form the foundation for long-term success in your career.
You’ll become a Pragmatic Programmer.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing.
Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.
Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets.
Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Did Alan Turing play a major role in the advent of the all-purpose computer? Did Turing Award winners, like Edsger W. Dijkstra, depend on his famous accomplishments?
In his well-researched book The Dawn of Software Engineering: from Turing to Dijkstra, Edgar G. Daylight deromanticizes Turing's & logic's role in the history of computing.
Dijkstra's pioneering work in compilers lies at the heart of modern-day computers. The book vividly describes how & why Dijkstra's ideas stood out among those of his contemporaries.
This book includes interviews with Turing Award winners Tony Hoare, Niklaus Wirth, Peter Naur, and Barbara Liskov.

At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. What Makes Us Smart makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory—in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the “stupid” errors of human cognition.
Embarking on a journey across psychology, neuroscience, computer science, linguistics, and economics, Gershman presents unifying principles that govern human intelligence. First, inductive bias: any system that makes inferences based on limited data must constrain its hypotheses in some way before observing data. Second, approximation bias: any system that makes inferences and decisions with limited resources must make approximations. Applying these principles to a range of computational errors made by humans, Gershman demonstrates that intelligent systems designed to meet these constraints yield characteristically human errors.
Examining how humans make intelligent and maladaptive decisions, What Makes Us Smart delves into the successes and failures of cognition.

The past few decades have seen an explosion of research on causal reasoning in philosophy, computer science, and statistics, as well as descriptive work in psychology. In Causation with a Human Face, James Woodward integrates these lines of research and argues for an understanding of how each can inform the other: normative ideas can suggest interesting experiments, while descriptive results can suggest important normative concepts. Woodward's overall framework builds on the interventionist treatment of causation that he developed in Making Things Happen. Normative ideas discussed include proposals about the role of invariant or stable relationships in successful causal reasoning and the notion of proportionality. He argues that these normative ideas are reflected in the causal judgments that people actually make as a descriptive matter.
Woodward also discusses the common philosophical practice-particularly salient in philosophical accounts of causation—of appealing to "intuitions" or "judgments about cases" in support of philosophical theses. He explores how, properly understood, such appeals are not different in principle from appeals to results from empirical research, and demonstrates how they may serve as a useful source of information about causal cognition.

The Rust programming language offers the rare and valuable combination of statically verified memory safety and low-level control. Imagine C++ but without dangling pointers, null pointer dereferences, leaks, or buffer overruns. With this practical guide, systems programmers will understand Rust’s rules clearly and economically. You’ll learn how to express programs that Rust can prove are free of a broad class of common errors.
Rust brings the benefits of an expressive modern type system to systems programming. Authors Jim Blandy and Jason Orendorff demonstrate how Rust’s features put programmers in control over memory consumption and processor use, combining predictable performance with memory safety and trustworthy concurrency.
You’ll learn:
How to write fast, safe, concurrent programs in Rust
Rust’s rules for managing memory efficiently, including ownership, borrowing, moves, and lifetimes
How to design interfaces that fit well into the Rust ecosystem
Rust’s all-purpose Cargo tool for building, testing, and managing Rust packages
High-level features like traits, generics, closures, and iterators that make Rust productive and flexible

When Peter Thiel and Max Levchin launched an online payment website in 1999, they hoped their service could improve the lives of millions around the globe. But when their start-up, PayPal, survived the dot.com crash only to find itself besieged by unimaginable challenges, that dream threatened to become a nightmare. PayPal's history – as told by former insider Eric Jackson – is an engrossing study of human struggle and perseverance against overwhelming odds. The entrepreneurs that Thiel and Levchin recruited to overhaul world currency markets first had to face some of the greatest trials ever thrown at a Silicon Valley company before they could make internet history. Business guru Tom Peters, author of "In Search of Excellence," called the hardcover edition of The PayPal Wars "a real page turner" that featured what he called "the best description of business strategy unfolding in a world changing at warp speed." This new paperback edition features updated material and even more insights on the state of internet commerce.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs--a nascent but quickly growing subset of graph representation learning.

(excerpts from my proposal for the book)
Anyone who has explored the history, technology, or theory of computers has likely encountered the concept of the Turing Machine. The Turing Machine is an imaginary — not even quite hypothetical — computer invented in 1936 by English mathematician Alan Turing (1912–1954) to help solve a question in mathematical logic. As a byproduct, Turing also founded the field of computability theory — the study of the abilities and limitations of digital computers.
Although the concept of the Turing Machine is well known, Turing’s original 1936 paper is only rarely read. This neglect may have something to do with the paper’s title — “On Computable Numbers, with an Application to the Entscheidungsproblem” — and perhaps the paper’s extensive use of a scary German gothic font. That’s too bad, because the paper is not only a fascinating read but a milestone in the history of computing and 20th century intellectual thought in general.
This book presents Turing’s original 36-page paper (and a follow-up 3-page correction) with background chapters and extensive annotations. Mathematical papers like Turing’s are often terse and cryptic. I have elaborated on many of Turing’s statements, clarified his discussions, and provided numerous examples.
Interwoven into the narrative are the highlights of Turing’s own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of “gross indecency,” and his early death by apparent suicide at the age of 41.
The book is divided into four parts: Parts I and II together are about 200 pages in length and cover the first 60% of Turing’s paper, encompassing the Turing Machine and computability topics. This part of the book is entirely self-contained and will be of primary interest to most readers.
Part III is a faster paced look at the remainder of Turing’s paper, which involves the implications for mathematical logic. Some readers might want to skip these chapters.
Part IV resumes the more "popular" presentation showing how the Turing Machine has become a vital tool in understanding the workings of human consciousness and the mechanisms of the universe.
Although I expect the primary readers of the book to be programmers, computer science majors, and other “techies,” I have tried my best to make the book accessible to the general reader. There is unavoidably much mathematics in the book, but I have tried to assume that the reader only has knowledge of high-school mathematics, and probably a foggy one at that.