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.
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Que são estas fórmulas matemáticas, base da Inteligência Artificial? Como impõem regras e condutas sociais — nunca debatidas e sempre a serviço do poder econômico? É possível inverter seu sentido?
A palavra “algoritmo” foi criada pelo matemático persa Muhammad ibs Musa al-Khwarizmi. Entre suas muitas inovações, o trabalho de al-Khwarizmi levou à criação da álgebra e do avançado sistema numeral hindo-árabe que usamos hoje. É a tradução para o latim do nome de al-Khwarizmi para “Algoritmi” – combinada numa mistura etimológica com a palavra grega para número (ἀριθμός, pronunciada “are-eeth-mos”) que resulta em “algoritmo”.
Leia também: A silenciosa ditadura do algoritmo
Provides recent advances in the fields of big data analysis.
The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.
Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.
Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You’ll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you’ll learn specific technologies like Hadoop, Storm, and NoSQL databases.
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.
Regardless of the reason for internet use during class, it is clear that students are not experiencing the oft-touted benefits of laptop use in class. They spend minimal time accessing supplemental course material or surfing the web for content related to the ongoing lecture, and these activities do not appear to enhance course performance. Although students may use the internet to download slides and take notes, related research shows that taking notes by hand is more effective than doing so with a laptop. Thus, there seems to be little upside to laptop use in class, while there is clearly a downside. Students are distracting themselves for significant periods of class time by using laptops to surf social media sites, visit chat rooms, watch videos, and play games, and these activities harm the learning process. Furthermore, related research suggests that multitasking laptop users also distract their classmates, as peers with a direct view of those laptops suffer academically. Perhaps it is time for students to consider going “old school,” and adding one more item to their shopping wish lists: a good old fashioned spiral notebook.
This challenging collection of essays deals with the impact of evolving information technologies on human mental life and, indeed, on the nature and organization of human culture as a store of information-processing techniques. What topic could be more relevant to our swiftly changing contemporary world? For we are blessed, besotted, and threatened by such technologies and preoccupied by their uses. Some are seemingly benign, as when the Internet broadens the horizons of the young, or when computers take the Dickensian drudgery out of bookkeeping. Some are more worrying in their impact, as when one speculates whether information technology may promote imperialism by widening the gap between informationally adept military powers and word-of-mouth local insurgents. This volume is principally (though not exclusively) about the former, about changes in thinking, feeling, and relating to each other created by the current information revolution. But it goes beyond its influences on individual mental activity to consider how the new technologies might alter the cultures and the economies that come to rely on them.
Democratic institutions today look much as they have done for decades, if not centuries. The Houses of Parliament, the US Congress, and some of the West’s oldest parliaments are largely untouched by successive waves of new technology. We still live in a world where debates require speakers to be physically present, there is little use of digital information and data sharing during parliamentary sessions, and where UK MPs vote by walking through corridors. The UK Parliament building, in particular, is conspicuous for the absence of screens, good internet connectivity and the other IT infrastructure which would enable a 21st century working environment comparable to the offices of almost any modern business.
At the same time almost every other sphere of life – finance, tourism, shopping, work and our social relationships – has been dramatically transformed by the rise of new information and communication tools, particularly social media or by the opportunities opened through increased access to and use of data, or novel approaches to solving problems, such as via crowdsourcing or the rise of the sharing economy.
In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.
Only a few years ago mobile was thought to be a realm solely populated by Millennials. But this is rapidly changing, and the new mobile landscape is ripe for application across all demographics. Millennials, those ages 18-34, only a few years ago had the largest cell phone use by far, and showed a willingness to ditch traditional internet-accessible devices such as desktops and laptops for the newer handheld technology. As a result it was thought that the use of mobile learning was suited as a specialized tool to reach those who had excluded themselves from traditional forms of learning and considered them outdated. But the new mobile landscape, and thus the mobile learning environment, is ripe for application across all demographics.