The Adoption Machine

MAY 2014. The Irish public woke to the horrific discovery of a mass grave containing the remains of most 800 babies in the ‘Angels’ Plot’ of Tuam’s Mother and Baby Home. What followed would rock the last vestiges of Catholic Ireland, enrage an increasingly secularised nation, and lead to a Commission of Inquiry. In The Adoption Machine, Paul Jude Redmond, Chairperson of the Coalition of Mother and Baby Homes Survivors, who himself was born in the Castlepollard Home, candidly reveals the shocking history of one of the worst abuses of Church power since the foundation of the Irish State. From Bessboro, Castlepollard, and Sean Ross Abbey to St. Patrick’s and Tuam, a dark shadow was cast by the collusion between Church and State in the systematic repression of women and the wilful neglect of illegitimate babies, resulting in the deaths of thousands. It was Paul’s exhaustive research that widened the global media’s attention to all the homes and revealed Tuam as just the tip of the iceberg of the horrors that lay beneath. He further reveals the vast profits generated by selling babies to wealthy adoptive parents, and details how infants were volunteered to a pharmaceutical company for drug trials without the consent of their natural mothers. Interwoven throughout is Paul’s poignant and deeply personal journey of discovery as he attempts to find his own natural mother. The Adoption Machine exposes this dark history of Ireland’s shameful and secret past, and the efforts to bring it into the light. It is a history from which there is no turning away.

A New York Times Notable Book The shocking truth about postwar adoption in America, told through the bittersweet story of one teenager, the son she was forced to relinquish, and their search to find each other. “[T]his book about the past might foreshadow a coming shift in the future… ‘I don’t think any legislators in those states who are anti-abortion are actually thinking, “Oh, great, these single women are gonna raise more children.” No, their hope is that those children will be placed for adoption. But is that the reality? I doubt it.’”[says Glaser]” -Mother Jones During the Baby Boom in 1960s America, women were encouraged to stay home and raise large families, but sex and childbirth were taboo subjects. Premarital sex was common, but birth control was hard to get and abortion was illegal. In 1961, sixteen-year-old Margaret Erle fell in love and became pregnant. Her enraged family sent her to a maternity home, where social workers threatened her with jail until she signed away her parental rights. Her son vanished, his whereabouts and new identity known only to an adoption agency that would never share the slightest detail about his fate. The adoption business was founded on secrecy and lies. American Baby lays out how a lucrative and exploitative industry removed children from their birth mothers and placed them with hopeful families, fabricating stories about infants' origins and destinations, then closing the door firmly between the parties forever. Adoption agencies and other organizations that purported to help pregnant women struck unethical deals with doctors and researchers for pseudoscientific "assessments," and shamed millions of women into surrendering their children. The identities of many who were adopted or who surrendered a child in the postwar decades are still locked in sealed files. Gabrielle Glaser dramatically illustrates in Margaret and David’s tale--one they share with millions of Americans—a story of loss, love, and the search for identity.

"This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity"--

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.

Originally published in 1989 this book gives an overview of the empirical work on new technology objectives, together with an analysis of management strategies for adoption at the corporate, technological and people levels. It also reviews previous work on the extent to which staff at different levels, and from different specialism, are involved in decision-making, as well as the adoption process more generally. The book looks at different approaches to analysing organizational contexts and provides a framework for studying the stages of the adoption process. The book includes case studies - two in financial services and two in engineering contexts.

“The Knowledge Machine is the most stunningly illuminating book of the last several decades regarding the all-important scientific enterprise.” —Rebecca Newberger Goldstein, author of Plato at the Googleplex A paradigm-shifting work, The Knowledge Machine revolutionizes our understanding of the origins and structure of science. • Why is science so powerful? • Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe? In a groundbreaking work that blends science, philosophy, and history, leading philosopher of science Michael Strevens answers these challenging questions, showing how science came about only once thinkers stumbled upon the astonishing idea that scientific breakthroughs could be accomplished by breaking the rules of logical argument. Like such classic works as Karl Popper’s The Logic of Scientific Discovery and Thomas Kuhn’s The Structure of Scientific Revolutions, The Knowledge Machine grapples with the meaning and origins of science, using a plethora of vivid historical examples to demonstrate that scientists willfully ignore religion, theoretical beauty, and even philosophy to embrace a constricted code of argument whose very narrowness channels unprecedented energy into empirical observation and experimentation. Strevens calls this scientific code the iron rule of explanation, and reveals the way in which the rule, precisely because it is unreasonably close-minded, overcomes individual prejudices to lead humanity inexorably toward the secrets of nature. “With a mixture of philosophical and historical argument, and written in an engrossing style” (Alan Ryan), The Knowledge Machine provides captivating portraits of some of the greatest luminaries in science’s history, including Isaac Newton, the chief architect of modern science and its foundational theories of motion and gravitation; William Whewell, perhaps the greatest philosopher-scientist of the early nineteenth century; and Murray Gell-Mann, discoverer of the quark. Today, Strevens argues, in the face of threats from a changing climate and global pandemics, the idiosyncratic but highly effective scientific knowledge machine must be protected from politicians, commercial interests, and even scientists themselves who seek to open it up, to make it less narrow and more rational—and thus to undermine its devotedly empirical search for truth. Rich with illuminating and often delightfully quirky illustrations, The Knowledge Machine, written in a winningly accessible style that belies the import of its revisionist and groundbreaking concepts, radically reframes much of what we thought we knew about the origins of the modern world.

AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.