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|Author||: Cathy O'Neil|
|Editor||: Broadway Books|
Longlisted for the National Book Award New York Times Bestseller A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort r sum s, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change. -- Longlist for National Book Award (Non-Fiction) -- Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology) -- Kirkus, Best Books of 2016 -- New York Times, 100 Notable Books of 2016 (Non-Fiction) -- The Guardian, Best Books of 2016 -- WBUR's "On Point," Best Books of 2016: Staff Picks -- Boston Globe, Best Books of 2016, Non-Fiction
|Author||: Cathy O'Neil,Rachel Schutt|
|Editor||: "O'Reilly Media, Inc."|
Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
|Author||: Michael Kearns,Aaron Roth|
|Editor||: Oxford University Press|
Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps. Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.
|Author||: Peter Woit|
|Editor||: Random House|
Not Even Wrong is a fascinating exploration of our attempts to come to grips with perhaps the most intellectually demanding puzzle of all: how does the universe work at its most fundamnetal level? The book begins with an historical survey of the experimental and theoretical developments that led to the creation of the phenomenally successful 'Standard Model' of particle physics around 1975. Despite its successes, the Standard Model does not answer all the key questions and physicists continuing search for answers led to the development of superstring theory. However, after twenty years, superstring theory has failed to advance beyond the Standard Model. The absence of experimental evidence is at the core of this controversial situation which means that it is impossible to prove that superstring theory is either right or wrong. To date, only the arguments of the theory's advocates have received much publicity. Not Even Wrong provides readers with another side of the story.
|Author||: Hannah Fry|
|Editor||: Black Swan|
'One of the best books yet written on data and algorithms. . .deserves a place on the bestseller charts.' (The Times) You are accused of a crime. Who would you rather determined your fate - a human or an algorithm? An algorithm is more consistent and less prone to error of judgement. Yet a human can look you in the eye before passing sentence. Welcome to the age of the algorithm, the story of a not-too-distant future where machines rule supreme, making important decisions - in healthcare, transport, finance, security, what we watch, where we go even who we send to prison. So how much should we rely on them? What kind of future do we want? Hannah Fry takes us on a tour of the good, the bad and the downright ugly of the algorithms that surround us. In Hello World she lifts the lid on their inner workings, demonstrates their power, exposes their limitations, and examines whether they really are an improvement on the humans they are replacing. A BBC RADIO 4- BOOK OF THE WEEK SHORTLISTED FOR THE 2018 BAILLIE GIFFORD PRIZE AND 2018 ROYAL SOCIETY SCIENCE BOOK PRIZE
|Author||: R. Everett Langford|
|Editor||: John Wiley & Sons|
Crucial information on nuclear, chemical, and biological weapons From the diseased animal carcass hurled over the wall of a besieged castle to the nuclear suitcase bomb carried by a clandestine operative, the threat of unconventional weapons has always been a feature of warfare. Today's danger comes mainly from the potential use of nuclear, biological, and chemical (NBC) weapons of mass destruction (WMD) by international terrorists or rogue states. False alarms and misinformation about these weapons have abounded in the jittery post-9/11 atmosphere. To understand and deal with the actual threat posed requires basing response plans, policy, and reporting on actual facts. Introduction to Weapons of Mass Destruction separates fact from fiction about NBC weaponry by providing clear, technically precise information. For each family of weapon, coverage in this handbook includes: * History and background information * Agent types and delivery mechanisms * Effects of exposure * Protection * Safe storage and handling * Decontamination * Medical treatments Drawing from a broad array of military, scientific, and safety resources, this text offers both accessibility to the general public and accuracy and depth for professional emergency responders. Additional resources include a bibliography of references and a list of addresses and telephone numbers of federal and military agencies and professional organizations of interest. With full coverage of WMDs, from high-tech, genetically modified organisms to rudimentary radiological "dirty bombs," Introduction to Weapons of Mass Destruction is an essential reference for understanding and responding to these dangerous warfare agents.
|Author||: Matt Parker|
#1 INTERNATIONAL BESTSELLER AN ADAM SAVAGE BOOK CLUB PICK The book-length answer to anyone who ever put their hand up in math class and asked, “When am I ever going to use this in the real world?” “Fun, informative, and relentlessly entertaining, Humble Pi is a charming and very readable guide to some of humanity's all-time greatest miscalculations—that also gives you permission to feel a little better about some of your own mistakes.” —Ryan North, author of How to Invent Everything Our whole world is built on math, from the code running a website to the equations enabling the design of skyscrapers and bridges. Most of the time this math works quietly behind the scenes . . . until it doesn’t. All sorts of seemingly innocuous mathematical mistakes can have significant consequences. Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean. Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.
|Author||: Safiya Umoja Noble|
|Editor||: NYU Press|
A revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for “black girls”—what will you find? “Big Booty” and other sexually explicit terms are likely to come up as top search terms. But, if you type in “white girls,” the results are radically different. The suggested porn sites and un-moderated discussions about “why black women are so sassy” or “why black women are so angry” presents a disturbing portrait of black womanhood in modern society. In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color. Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance—operating as a source for email, a major vehicle for primary and secondary school learning, and beyond—understanding and reversing these disquieting trends and discriminatory practices is of utmost importance. An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.
|Author||: Patricia Roberts-Miller|
|Editor||: The Experiment|
A clear-eyed guide to demagoguery—and how we can defeat it What is demagoguery? Some demagogues are easy to spot: They rise to power through pandering, charisma, and prejudice. But, as professor Patricia Roberts-Miller explains, a demagogue is anyone who reduces all questions to us vs. them. Why is it dangerous? Demagoguery is democracy’s greatest threat. It erodes rational debate, so that intelligent policymaking grinds to a halt. The idea that we never fall for it—that all the blame lies with them—is equally dangerous. How can we stop it? Demagogues follow predictable patterns in what they say and do to gain power. The key to resisting demagoguery is to name it when you see it—and to know where it leads.
|Author||: Jordan Ellenberg|
The columnist for Slate's popular "Do the Math" celebrates the logical, illuminating nature of math in today's world, sharing in accessible language mathematical approaches that demystify complex and everyday problems.
|Author||: Linda Tirado|
One of the Best 5 Books of 2014 — Esquire "I’ve been waiting for this book for a long time. Well, not this book, because I never imagined that the book I was waiting for would be so devastatingly smart and funny, so consistently entertaining and unflinchingly on target. In fact, I would like to have written it myself – if, that is, I had lived Linda Tirado’s life and extracted all the hard lessons she has learned. I am the author of Nickel and Dimed, which tells the story of my own brief attempt, as a semi-undercover journalist, to survive on low-wage retail and service jobs. Tirado is the real thing." —from the foreword by Barbara Ehrenreich, New York Times bestselling author of Nickel and Dimed We in America have certain ideas of what it means to be poor. Linda Tirado, in her signature brutally honest yet personable voice, takes all of these preconceived notions and smashes them to bits. She articulates not only what it is to be working poor in America (yes, you can be poor and live in a house and have a job, even two), but what poverty is truly like—on all levels. Frankly and boldly, Tirado discusses openly how she went from lower-middle class, to sometimes middle class, to poor and everything in between, and in doing so reveals why “poor people don’t always behave the way middle-class America thinks they should.”
|Author||: Matt Parker|
|Editor||: Doubleday Canada|
Mathematics made mouth-watering. Things to Make and Do in the Fourth Dimension is an alternative math class. How can math help you choose a second-hand car? Why is a text message like a Sudoku? How much fun can you have with a barcode? Matt Parker explains that math is difficult because it's one of the few subjects that requires us to train our brains to think in an entirely new way, and to confront things with no direct analogy in everyday life--imaginary numbers, snowflakes that only exist in 196884 dimensions, and objects beyond infinity--and shows us why it's worth the effort. Starting with basic arithmetic and geometry, Things To Make and Do teaches us the math we never got to enjoy at school. Each chapter is structured around activities and thought experiments: we are invited to make a calculator out of dominoes, find out why wrapping oranges in plastic wrap is a good way to learn about higher dimensions, and discover what soap bubbles have to teach us about calculus. A series of incremental and hugely entertaining steps take us all the way from simple algebra to the most exotic and fascinating ideas in mathematics: Klein bottles, higher dimensional topology and the many different species of infinity, via unimaginably small pizza slices, Mobius strips and a thorough examination of The Sausage Conjecture. This lively, funny, and deeply intelligent book teaches math in a fun, interactive manner rather than by rote learning and exercises. You'll not look at the number 37 the same way again. And you just might take part in Mobius strip craftwork.
|Author||: Richard Evan Schwartz|
|Editor||: American Mathematical Soc.|
In the American Mathematical Society's first-ever book for kids (and kids at heart), mathematician and author Richard Evan Schwartz leads math lovers of all ages on an innovative and strikingly illustrated journey through the infinite number system. By means of engaging, imaginative visuals and endearing narration, Schwartz manages the monumental task of presenting the complex concept of Big Numbers in fresh and relatable ways. The book begins with small, easily observable numbers before building up to truly gigantic ones, like a nonillion, a tredecillion, a googol, and even ones too huge for names! Any person, regardless of age, can benefit from reading this book. Readers will find themselves returning to its pages for a very long time, perpetually learning from and growing with the narrative as their knowledge deepens. Really Big Numbers is a wonderful enrichment for any math education program and is enthusiastically recommended to every teacher, parent and grandparent, student, child, or other individual interested in exploring the vast universe of numbers.
|Author||: Was Rahman|
|Editor||: Sage Publications Pvt. Limited|
Was Rahman′s AI and Machine Learning achieves that rare balance of making a difficult and complex topic accessible to non-specialists, without dumbing down. He starts with an enlightening and entertaining explanation of what artificial intelligence (AI) is and how it works. This includes often-overlooked fundamentals like what we actually mean by ′intelligence′, artificial or otherwise. Rahman brings his explanations to life with lucid and, at times, surprising examples of AI already in use around us. He takes these back to first principles, deftly avoiding any need to understand the maths or computing involved. This allows him to demystify what the technology is really doing and show us that much of it is reassuringly mundane, despite the hype. This distinctive approach comes into its own when examining the challenges and risks of AI. It allows the author to remove the drama and fear of sensationalized headlines and doom-laden movie plots. In their place, he offers an insightful analysis of how the major issues surface, what options we have for addressing them and why some dilemmas may prove intractable. A must-read to understand the reality and implications of AI beyond the hype!
|Author||: Adrian Mackenzie|
|Editor||: MIT Press|
If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.
|Author||: Lee Mayer,Emily Motayed|
|Editor||: Ten Speed Press|
From the co-founders of Havenly comes “a perfect read for anyone looking to infuse more personality and style into their space—on their own time and budget, and in their own unique way” (Rachel Zoe). “Not only do Lee and Emily unpack all their tips for creating a space that looks as good as it feels, but they do it in a way that is made for real-life application.”—Bobby Berk, design expert and host of Netflix’s Queer Eye Interior design can be daunting, and as a result, many of us never even attempt to design our own homes. In Design the Home You Love, Havenly founders Lee Mayer and Emily Motayed break down the ambiguous world of home design. First you learn how to identify your own style (whether you’re a fan of Parisian Modern or California Casual) and then how to incorporate furniture that matches your style and fits your budget. Design the Home You Love takes you step-by-step and room-by-room through each part of the house to help you fulfill your home’s potential. Whether you’re looking to give your home a complete makeover, spruce up your rental apartment, or merely take your living room from blah to fab, Lee and Emily bring fresh ideas, advice, and inspiration to the table. Illustrated with eye-catching photography and livable inspiration from real-life clients, this is the interior design book that finally makes it possible for us all to achieve our design goals.
|Author||: Virginia Eubanks|
|Editor||: St. Martin's Press|
WINNER: The 2018 McGannon Center Book Prize and shortlisted for the Goddard Riverside Stephan Russo Book Prize for Social Justice The New York Times Book Review: "Riveting." Naomi Klein: "This book is downright scary." Ethan Zuckerman, MIT: "Should be required reading." Dorothy Roberts, author of Killing the Black Body: "A must-read." Astra Taylor, author of The People's Platform: "The single most important book about technology you will read this year." Cory Doctorow: "Indispensable." A powerful investigative look at data-based discrimination—and how technology affects civil and human rights and economic equity The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years—because a new computer system interprets any mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.
|Author||: Brian Christian|
|Editor||: W. W. Norton & Company|
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.
|Author||: Jan Friso Groote,Mohammad Reza Mousavi|
|Editor||: MIT Press|
Rigorous theory and real-world applications for modeling and analysis of the behavior of complex communicating computer systems Complex communicating computer systems—computers connected by data networks and in constant communication with their environments—do not always behave as expected. This book introduces behavioral modeling, a rigorous approach to behavioral specification and verification of concurrent and distributed systems. It is among the very few techniques capable of modeling systems interaction at a level of abstraction sufficient for the interaction to be understood and analyzed. Offering both a mathematically grounded theory and real-world applications, the book is suitable for classroom use and as a reference for system architects. The book covers the foundation of behavioral modeling using process algebra, transition systems, abstract data types, and modal logics. Exercises and examples augment the theoretical discussion. The book introduces a modeling language, mCRL2, that enables concise descriptions of even the most intricate distributed algorithms and protocols. Using behavioral axioms and such proof methods as confluence, cones, and foci, readers will learn how to prove such algorithms equal to their specifications. Specifications in mCRL2 can be simulated, visualized, or verified against their requirements. An extensive mCRL2 toolset for mechanically verifying the requirements is freely available online; this toolset has been successfully used to design and analyze industrial software that ranges from healthcare applications to particle accelerators at CERN. Appendixes offer material on equations and notation as well as exercise solutions.
|Author||: Jaron Lanier|
|Editor||: Simon and Schuster|
Evaluates the negative impact of digital network technologies on the economy and particularly the middle class, citing challenges to employment and personal wealth while exploring the potential of a new information economy.