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Author: Denis Boyles
Publisher: Vintage
ISBN: 0307389782
Category : History
Languages : en
Pages : 466
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Book Description
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.
Author: Denis Boyles
Publisher: Vintage
ISBN: 0307389782
Category : History
Languages : en
Pages : 466
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Book Description
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.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
Author: John Brockman
Publisher: Harper Collins
ISBN: 0062230182
Category : Science
Languages : en
Pages : 429
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Book Description
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
Author: Wojciech Samek
Publisher: Springer Nature
ISBN: 3030289540
Category : Computers
Languages : en
Pages : 435
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Book Description
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Author: Jerry Davidson Wheatley
Publisher:
ISBN: 9780970316103
Category : Philosophy
Languages : en
Pages : 810
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Book Description
This book describes how understanding the structure of reality leads to the Theory of Everything Equation. The equation unifies the forces of nature and enables the merging of relativity with quantum theory. The book explains the big bang theory and everything else.
Author: Luca Longo
Publisher: Springer Nature
ISBN: 3031638034
Category :
Languages : en
Pages : 480
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Book Description
Author: Ursula Renz
Publisher: Oxford University Press
ISBN: 0199350175
Category : Philosophy
Languages : en
Pages : 329
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Book Description
This book reconstructs Spinoza's theory of the human mind against the backdrop of the twofold notion that subjective experience is explainable and that its successful explanation is of ethical relevance, because it makes us wiser, freer, and happier. Doing so, the book defends a realist rationalist interpretation of Spinoza's approach which does not entail commitment to an ontological reduction of subjective experience to mere intelligibility. In contrast to a long-standing tradition of Hegelian reading of Spinoza's Ethics, it thus defends the notion that the experience of finite subjects is fully real.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 1892
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Book Description
Author: Denis Rothman
Publisher: Packt Publishing Ltd
ISBN: 1800202768
Category : Computers
Languages : en
Pages : 455
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Book Description
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
Author: Patricia Auspos
Publisher: Open Book Publishers
ISBN: 1800648383
Category : Social Science
Languages : en
Pages : 424
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Book Description
This rich history illuminates the lives and partnerships of five married couples – two British, three American – whose unions defied the conventions of their time and anticipated social changes that were to come in the ensuing century. In all five marriages, both husband and wife enjoyed thriving professional lives: a shocking circumstance at a time when wealthy white married women were not supposed to have careers, and career women were not supposed to marry. Patricia Auspos examines what we can learn from the relationships of the Palmers, the Youngs, the Parsons, the Webbs, and the Mitchells, exploring the implications of their experiences for our understanding of the history of gender equality and of professional work. In expert and lucid fashion, Auspos draws out the interconnections between the institutions of marriage and professional life at a time when both were undergoing critical changes, by looking specifically at how a pioneering generation tried to combine the two. Based on extensive archival research and drawing on mostly unpublished letters, journals, pocket diaries, poetry, and autobiographical writings, Breaking Conventions tells the intimate stories of five path-breaking marriages and the social dynamics they confronted and revealed. This book will appeal to scholars, students, and anyone interested in women’s studies, gender studies, masculinity studies, histories of women in the professions, and the history of marriage.
