Download or read book Algorithms Automation and News written by Neil Thurman and published by Routledge. This book was released on 2021-05-18 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the growing importance of algorithms and automation—including emerging forms of artificial intelligence—in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on ‘reporting algorithms’, such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner. The chapters in this book were originally published as a special issue of Digital Journalism.
Download or read book Algorithms Automation and News written by Neil Thurman and published by Routledge. This book was released on 2021-05-18 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the growing importance of algorithms and automation—including emerging forms of artificial intelligence—in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on ‘reporting algorithms’, such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner. The chapters in this book were originally published as a special issue of Digital Journalism.
Download or read book Automating the News written by Nicholas Diakopoulos and published by Harvard University Press. This book was released on 2019-06-10 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. Nicholas Diakopoulos explains the present and future of a world in which algorithms have changed how the news is created, disseminated, and received, and he shows why journalists—and their values—are at little risk of being replaced.
Download or read book Automating the News written by Nicholas Diakopoulos and published by . This book was released on 2019 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. Nicholas Diakopoulos explains the present and future of a world in which algorithms have changed how the news is created, disseminated, and received, and he shows why journalists--and their values--are at little risk of being replaced.
Download or read book Automate This written by Christopher Steiner and published by Penguin. This book was released on 2012-08-30 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rousing story of the last gasp of human agency and how today’s best and brightest minds are endeavoring to put an end to it. It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected. In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge. The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans. The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What happens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others? Who knows—maybe there’s a bot learning to do your job this minute.
Download or read book Automating Inequality written by Virginia Eubanks and published by St. Martin's Press. This book was released on 2018-01-23 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Algorithms and Autonomy written by Alan Rubel and published by Cambridge University Press. This book was released on 2021-05-20 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines how algorithms in criminal justice, education, housing, elections and beyond affect autonomy, freedom, and democracy. This title is also available as Open Access on Cambridge Core.
Download or read book Tech Giants Artificial Intelligence and the Future of Journalism written by Jason Paul Whittaker and published by Routledge. This book was released on 2019-02-11 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the impact of the "Big Five" technology companies – Apple, Alphabet/Google, Amazon, Facebook and Microsoft – on journalism and the media industries. It looks at the current role of algorithms and artificial intelligence in curating how we consume media and their increasing influence on the production of the news. Exploring the changes that the technology industry and automation have made in the past decade to the production, distribution and consumption of news globally, the book considers what happens to journalism once it is produced and enters the media ecosystems of the internet tech giants – and the impact of social media and AI on such things as fake news in the post-truth age. The audience for this book are students and researchers working in the field of digital media, and journalism studies or media studies more generally. It will also be useful to those who are looking for extended case studies of the role taken by tech giants such as Facebook and Google in the fake news scandal, or the role of Jeff Bezos in transforming The Washington Post. The Open Access version of this book, available at https://doi.org/10.4324/9781351013758, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
Download or read book The Ethical Algorithm written by Michael Kearns and published by Oxford University Press. This book was released on 2019-10-04 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Automating Open Source Intelligence written by Robert Layton and published by Syngress. This book was released on 2015-12-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data. Presents a coherent set of methods and processes for automating OSINT Focuses on algorithms and applications allowing the practitioner to get up and running quickly Includes fully developed case studies on the digital underground and predicting crime through OSINT Discusses the ethical considerations when using publicly available online data
Download or read book Automated Media written by Mark Andrejevic and published by Routledge. This book was released on 2019-09-24 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this era of pervasive automation, Mark Andrejevic provides an original framework for tracing the logical trajectory of automated media and their social, political, and cultural consequences. This book explores the cascading logic of automation, which develops from the information collection process through to data processing and, finally, automated decision making. It argues that pervasive digital monitoring combines with algorithmic decision making and machine learning to create new forms of power and control that pose challenges to democratic forms of accountability and individual autonomy alike. Andrejevic provides an overview of the implications of these developments for the fate of human experience, describing the "bias of automation" through the logics of pre-emption, operationalism, and "framelessness." Automated Media is a fascinating and groundbreaking new volume: a must-read for students and researchers of critical media studies interested in the intersections of media, technology, and the digital economy.
Download or read book The Cambridge Handbook of the Law of Algorithms written by Woodrow Barfield and published by Cambridge University Press. This book was released on 2020-11-05 with total page 1327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental building block of artificial intelligence - and, increasingly, society - but our legal institutions have largely failed to recognize or respond to this reality. The Cambridge Handbook of the Law of Algorithms, which features contributions from US, EU, and Asian legal scholars, discusses the specific challenges algorithms pose not only to current law, but also - as algorithms replace people as decision makers - to the foundations of society itself. The work includes wide coverage of the law as it relates to algorithms, with chapters analyzing how human biases have crept into algorithmic decision-making about who receives housing or credit, the length of sentences for defendants convicted of crimes, and many other decisions that impact constitutionally protected groups. Other issues covered in the work include the impact of algorithms on the law of free speech, intellectual property, and commercial and human rights law.
Download or read book Algorithms and Law written by Martin Ebers and published by Cambridge University Press. This book was released on 2020-07-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploring issues from big-data to robotics, this volume is the first to comprehensively examine the regulatory implications of AI technology.
Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Download or read book After the Digital Tornado written by Kevin Werbach and published by Cambridge University Press. This book was released on 2020-07-23 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks powered by algorithms are pervasive. Major contemporary technology trends - Internet of Things, Big Data, Digital Platform Power, Blockchain, and the Algorithmic Society - are manifestations of this phenomenon. The internet, which once seemed an unambiguous benefit to society, is now the basis for invasions of privacy, massive concentrations of power, and wide-scale manipulation. The algorithmic networked world poses deep questions about power, freedom, fairness, and human agency. The influential 1997 Federal Communications Commission whitepaper “Digital Tornado” hailed the “endless spiral of connectivity” that would transform society, and today, little remains untouched by digital connectivity. Yet fundamental questions remain unresolved, and even more serious challenges have emerged. This important collection, which offers a reckoning and a foretelling, features leading technology scholars who explain the legal, business, ethical, technical, and public policy challenges of building pervasive networks and algorithms for the benefit of humanity. This title is also available as Open Access on Cambridge Core.
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Download or read book Digital Journalism Drones and Automation written by Cate Dowd and published by Oxford University Press, USA. This book was released on 2020 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: ""The next generation of systems and practices in journalism will require knowledge beyond online editing techniques, aggregation, social media flow and assumptions about fake news. The profession may also want to aim for ethical practices in journalism to be embedded in algorithms for new systems. Engagement in an early design phase may also be useful for scoping reforms for online and social media legislation. However, these pursuits require higher levels of understanding about backend data and online systems, and development of formal vocabulary for journalism concepts and practices. This new domain knowledge should also be expressed in ontological models, informed by participatory approaches. Some problems to be addressed include editorial control issues and fair distribution of news stories and other challenges of data and online systems. Problematic issues should also include the lack of transparency in corporate data sharing arrangements. The semantic language for future systems for journalism will be distinctly different from the vocabulary and classifications used for online news tags. It will also need to distinguish the vocabulary for social media things in context of journalism. Most importantly, the design of new systems will need participatory and semantic design methods that can support the need for high-level knowledge of data and semantic search methods. The influence of social media partnerships in news and backend data sharing are other problem areas. Data via integrated media systems in news organisations flows onto cloud servers where it is processed with a myriad of methods. These hubs are for the new generation of data sharing, where large volumes of data are sorted and processed at accelerated speeds, for a range of purposes. Cloud servers are now literally the highest levels of digital convergence, other than legislation, and the latter is lagging. This is where data is shared for advertising, social media benefits and other domain purposes. Integrated media systems bring benefits for global networked news media organisations, but they also enable more monetisation of data via cloud servers. ""--
