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Everything Explained That Is Explainable is the audacious, utterly improbable story of the publication of the Eleventh Edition of the legendary Encyclopædia Britannica. It is the tale of a young American entrepreneur who rescued a dying publication with the help of a floundering newspaper, and in so doing produced a series of books that forever changed the face of publishing. Thanks to the efforts of 1,500 contributors, among them a young staff of university graduates as well as some of the most distinguished names of the day, the Eleventh Edition combined scholarship and readability in a way no previous encyclopedia had (or ever has again). Denis Boyles’s work of cultural history pulls back the curtain on the 44-million-word testament to the age of reason that has profoundly shaped the way we see the world.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Drawn from the cutting-edge frontiers of science, This Explains Everything will revolutionize your understanding of the world. What is your favorite deep, elegant, or beautiful explanation? This is the question John Brockman, publisher of Edge.org ("The world's smartest website"—The Guardian), posed to the world's most influential minds. Flowing from the horizons of physics, economics, psychology, neuroscience, and more, This Explains Everything presents 150 of the most surprising and brilliant theories of the way of our minds, societies, and universe work. Jared Diamond on biological electricity • Nassim Nicholas Taleb on positive stress • Steven Pinker on the deep genetic roots of human conflict • Richard Dawkins on pattern recognition • Nobel Prize-winning physicist Frank Wilczek on simplicity • Lisa Randall on the Higgs mechanism • BRIAN Eno on the limits of intuition • Richard Thaler on the power of commitment • V. S. Ramachandran on the "neural code" of consciousness • Nobel Prize winner ERIC KANDEL on the power of psychotherapy • Mihaly Csikszentmihalyi on "Lord Acton's Dictum" • Lawrence M. Krauss on the unification of electricity and magnetism • plus contributions by Martin J. Rees • Kevin Kelly • Clay Shirky • Daniel C. Dennett • Sherry Turkle • Philip Zimbardo • Lee Smolin • Rebecca Newberger Goldstein • Seth Lloyd • Stewart Brand • George Dyson • Matt Ridley
The National Book Critics Circle Award–winning author delivers a collection of essays that serve as the perfect “antidote to mansplaining” (The Stranger). In her comic, scathing essay “Men Explain Things to Me,” Rebecca Solnit took on what often goes wrong in conversations between men and women. She wrote about men who wrongly assume they know things and wrongly assume women don’t, about why this arises, and how this aspect of the gender wars works, airing some of her own hilariously awful encounters. She ends on a serious note— because the ultimate problem is the silencing of women who have something to say, including those saying things like, “He’s trying to kill me!” This book features that now-classic essay with six perfect complements, including an examination of the great feminist writer Virginia Woolf’s embrace of mystery, of not knowing, of doubt and ambiguity, a highly original inquiry into marriage equality, and a terrifying survey of the scope of contemporary violence against women. “In this series of personal but unsentimental essays, Solnit gives succinct shorthand to a familiar female experience that before had gone unarticulated, perhaps even unrecognized.” —The New York Times “Essential feminist reading.” —The New Republic “This slim book hums with power and wit.” —Boston Globe “Solnit tackles big themes of gender and power in these accessible essays. Honest and full of wit, this is an integral read that furthers the conversation on feminism and contemporary society.” —San Francisco Chronicle “Essential.” —Marketplace “Feminist, frequently funny, unflinchingly honest and often scathing in its conclusions.” —Salon
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
A masterful commentary on the history of science from the Greeks to modern times, by Nobel Prize-winning physicist Steven Weinberg—a thought-provoking and important book by one of the most distinguished scientists and intellectuals of our time. In this rich, irreverent, and compelling history, Nobel Prize-winning physicist Steven Weinberg takes us across centuries from ancient Miletus to medieval Baghdad and Oxford, from Plato’s Academy and the Museum of Alexandria to the cathedral school of Chartres and the Royal Society of London. He shows that the scientists of ancient and medieval times not only did not understand what we understand about the world—they did not understand what there is to understand, or how to understand it. Yet over the centuries, through the struggle to solve such mysteries as the curious backward movement of the planets and the rise and fall of the tides, the modern discipline of science eventually emerged. Along the way, Weinberg examines historic clashes and collaborations between science and the competing spheres of religion, technology, poetry, mathematics, and philosophy. An illuminating exploration of the way we consider and analyze the world around us, To Explain the World is a sweeping, ambitious account of how difficult it was to discover the goals and methods of modern science, and the impact of this discovery on human knowledge and development.
Greatness is a journey. It's a trip. It's a time thing. You don't get to be great; you become great. You don't get to be a great athlete, president, teacher, doctor, or Christian. You become great. Success doesn't come to you; you have to go after success. Your ship will never come in; you have to swim out to it. Great people read, study, learn, practice, and work hard. We are made in the image of greatness. Say yes to becoming a great Christian. That's what this book is about. Learn to Live 2: The Image of Greatness.
Why doesn't all this cognitive processing go on "in the dark," without any consciousness at all? In this book philosophers, physicists, psychologists, neurophysiologists, computer scientists, and others address this central topic in the growing discipline of consciousness studies. At the 1994 landmark conference "Toward a Scientific Basis for Consciousness", philosopher David Chalmers distinguished between the "easy" problems and the "hard" problem of consciousness research. According to Chalmers, the easy problems are to explain cognitive functions such as discrimination, integration, and the control of behavior; the hard problem is to explain why these functions should be associated with phenomenal experience. Why doesnt all this cognitive processing go on "in the dark", without any consciousness at all? In this book, philosophers, physicists, psychologists, neurophysiologists, computer scientists, and others address this central topic in the growing discipline of consciousness studies. Some take issue with Chalmers' distinction, arguing that the hard problem is a non-problem, or that the explanatory gap is too wide to be bridged. Others offer alternative suggestions as to how the problem might be solved, whether through cognitive science, fundamental physics, empirical phenomenology, or with theories that take consciousness as irreducible. Contributors Bernard J. Baars, Douglas J. Bilodeau, David Chalmers, Patricia S. Churchland, Thomas Clark, C. J. S. Clarke, Francis Crick, Daniel C. Dennett, Stuart Hameroff, Valerie Hardcastle, David Hodgson, Piet Hut, Christof Koch, Benjamin Libet, E. J. Lowe, Bruce MacLennan, Colin McGinn, Eugene Mills, Kieron OHara, Roger Penrose, Mark C. Price, William S. Robinson, Gregg Rosenberg, Tom Scott, William Seager, Jonathan Shear, Roger N. Shepard, Henry Stapp, Francisco J. Varela, Max Velmans, Richard Warner