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Fattah Y: Causal Inference with Bayesian
Networks

Causal Inference with Bayesian Networks

Build Bayesian Networks and Causal Inference Models with R and Python

“Lively. . . . Rousing. . . . Prophecy—roving, intelligent, irreducibly idiosyncratic—can expand our sense of possibility, starting now.” —The New York Times Book Review Tech empires are the prophets of the modern day, and like the ancient oracles and medieval astrologers that preceded them, they're not in it for the common good—they're in it for power. Award-winning University of Oxford professor Carissa Véliz&nbsp;brilliantly argues why we must reclaim that power, and shows us how. “A masterpiece. . . . The most important book you will read for years.” —Roger McNamee, New York Times bestselling author of Zucked Longlisted for the Financial Times Business Book of the Year For thousands of years, oracles, seers, and astrologers advised leaders and commoners alike about the future. But predictions are often power plays in disguise, obfuscating accountability and stripping individuals of their agency. Today we face the same threat of powerful prophets but under a new facade: tech. Not only do modern predictions made by tech companies advise on war, industry, and marriages, but artificial intelligence also now determines whether we can get a loan, a job, an apartment, or an organ transplant. And when we cede ground to these predictions, we lose control of our own lives. Drawing on history’s cautionary tales and modern-day tech companies’ malfeasance—from surveillance and biased algorithms to a startling lack of accountability—Carissa Véliz demonstrates that big tech’s prophecies are just as shallow, dangerous, and unjust as their ancient counterparts’. What she uncovers in the process is chilling. Artificial intelligence is increasing risk in business and society while creating a false sense of security. In this incisive, witty, and bracingly original book, Véliz contends that the main promise of prediction is not knowledge of the future but domination over others. Powerful people use predictions to determine our future. Prophecy is an invitation to defy those orders and live life on our own terms.

Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI

Prophecy

De Gruyter Studium

Nach einer Einführung zum Aufbau von Rechnersystemen und zur Darstellung von Informationen folgt ein Einstieg in die Programmierung mit der Sprache Python. Dabei werden grundsätzliche Prinzipien von Programmiersprachen erläutert, darunter Schleifen, Rekursion, imperative, funktionale und objektorientierte Programmierkonzepte. Einige konkrete Projekte werden in Python realisiert, so etwa zur Datenbeschaffung im Internet und deren Aufbereitung oder zum Umgang mit diversen Sensoren und zur Steuerung externer Geräte mit dem Raspberry-Pi. Dem Objektorientierten Programmieren und insbesondere der Programmiersprache Java ist ein eigenes Kapitel gewidmet. Diese Sprache und ihre Infrastruktur unterstützen besonders die professionelle Entwicklung großer Projekte. Auch die neuesten Konzepte von Java (Lambdas, Ströme und Funktionale) werden anschaulich erläutert. Das letzte Kapitel behandelt klassische Algorithmen und Datenstrukturen: Such- und Sortieralgorithmen, Listen, Bäume, Graphen, Maps, und diverse andere Datentypen zum effi zienten Speichern, Wiederauffi nden und Transformieren von Daten. Diese werden mit ihren Vor- und Nachteilen und anhand von Java-Programmen dargestellt. Der zweite Band ist technischen Themen gewidmet - insbesondere der Rechnerarchitektur, Betriebssystemen, Rechnernetzen und speziell dem Internet. Der dritte und letzte Band der Buchreihe Informatik ist der Theoretischen Informatik gewidmet. Das Buch richtet sich an alle Einsteiger, die sich ernsthaft mit Informatik beschäftigen wollen, sei es zum Selbststudium oder zur Begleitung von Vorlesungen. In den folgenden Bänden dieses Buches werden die Themen, Rechnerarchitektur, Betriebssysteme, Rechnernetze, Internet, Compilerbau und Theoretische Informatik vertieft. Prof. Dr. Heinz-Peter Gumm ist Professor für Theoretische Informatik in Marburg. Nach dem Studium in Darmstadt und Winnipeg (Kanada) von 1970 bis 1975 und der Habilitation 1981 folgten Professuren in Hawaii, Kalifornien und New York. Seine Forschungsgebiete sind Formale Methoden, Allgemeine Algebren und Coalgebren. Prof. Dr. Manfred Sommer ist emeritierter Professor für Praktische Informatik in Marburg. Nach dem Studium in Göttingen und München von 1964 bis 1969, war er Assistent am ersten Informatik-Institut in Deutschland an der TU München. Es folgten zehn Jahre bei Siemens in München und von 1984 bis 2014 war er Informatik-Professor in Marburg.

Programmierung, Algorithmen und Datenstrukturen

Informatik

Software Studies

How the original ELIZA chatbot transformed ideas about AI and society’s response to them. As we reach the 60th anniversary of ELIZA’s public debut, Inventing ELIZA offers the first comprehensive critical analysis of Joseph Weizenbaum’s groundbreaking chatbot system through the lens of critical code studies. Drawing on extensive archival research at MIT, Stanford, and UCLA, this book presents the rediscovered original source code of ELIZA alongside previously unseen scripts that had been missing for decades, revealing a far more sophisticated system than previously documented. Sarah Ciston, David Berry, Anthony Hay, Mark Marino, Peter Millican, Arthur Schwarz, Jeff Shrager, and Peggy Weil trace ELIZA’s development (1965–1968), revealing that Weizenbaum created a chatbot within a conversational programming environment with previously unknown innovations well ahead of its time. Through close reading of both code and paratexts, the book reconstructs ELIZA’s conceptual evolution and situates it within the historical context of early AI development. The book’s website, https://findingeliza.org, includes a faithful recreation of the first chatbot and news about continued research.

How the First Chatbot Shaped the Future of AI

Inventing ELIZA

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

From Conceptual Algorithms to Real-World Applications

Methods of Nonsmooth Optimization in Stochastic Programming

Longlisted for the Financial Times Business Book of the Year 2026'Superb' Philip Pullman 'Revelatory' George SaundersIt presented itself as a form of futurism. But the future envisioned by the architects of the Artificial State looked, chillingly, like the very worst of the past...We live in the age of data. Over the past century, rapidly evolving technologies have transformed every aspect of our society, in troubling and seemingly unstoppable ways. Political campaigns have been reduced to attention-mining algorithms, multinational corporations control public discourse, and the era of the liberal nation-state seems to be coming to an end, replaced by the rule of artificial intelligence. Today, power is maintained through mystification: machines, we are told, know what humans cannot. Yet nothing about any of this was, or is, inevitable.In this astounding account of how technology has corroded democracy around the world, prizewinning historian Jill Lepore traces the rise and fall of what she calls the 'Artificial State'. Examining everything from robot rights and Elon Musk's origins to what data centres cost wildlife, Lepore offers a history - and a perspective - wholly missing from the debate.History, Lepore demonstrates, is driven not by machines but by individuals, societies, cultures and ideas. The architects of the Artificial State had no theory of governance, nor did they have any interest in the rule of law, or any capacity for restraint. They drew their ideas not from science, but from science fiction. Yet everything destructive that they have done can be undone. By illuminating the origins of the Artificial State, Lepore reveals how we can escape it - helping us to imagine, and fight for, a different future.

The Rise and Fall of the Artificial State

AI Optimism for the Mildly Alarmed

Don't Panic

Today our world is under the watchful and tireless eye of computer vision, with cam­eras and monitors tracing our every move. Furthermore, generative AI is now able to render a synthetic world indistinguishable from reality for us to explore. Trevor Paglen goes in search of the ways and means of understanding this new visual universe. Instead of asking what these technologies "say" about the world, he teaches us to ask what they "do" and where such images come from. Exploring the esoteric worlds of psyops, UFO imagery, magicians, and public relation gurus, Paglen shows that this appar­ently alien realm is more human, but much stranger, than we imagine.

Images After AI

How to See Like a Machine

Progress in IS

Challenges, Strategies and Trends

Understanding AI in Cybersecurity and Secure AI

How do we live alongside machines that are sometimes smarter than us? We are entering a new era. AI systems can already outperform humans in some domains while failing spectacularly in others. They can act as tutors, collaborators, researchers, strategists, and creators...yet they can also produce falsehoods, generate convincing nonsense, and fail unpredictably. The challenge is no longer simply adopting new technology. It is learning how to think, work, and live in a world where machines are sometimes smarter than us. In Co-Existence, Ethan Mollick—a Wharton professor and author of the widely read One Useful Thing newsletter—argues that the future of AI will not be defined by replacement, but by interaction. Drawing on years of research, experimentation, and frontline experience with rapidly evolving AI systems, Mollick shows readers how to: • work productively with AI while retaining judgment and autonomy; • distinguish where human insight matters most; • avoid becoming overly dependent on systems designed to persuade and imitate; • adapt creatively as AI reshapes education, business, and everyday life; and • build a durable human advantage in a world of intelligent machines. Clear-eyed, practical, and written with warmth and humor, Co-Existence is your guide to navigating the next phase of the AI revolution.

The Next Phase of AI

Co-Existence

'Sarah is one of the few people who really understands how AI is changing the character of work already and what it means for all of us' David Runciman'Original and enlightening... Not many books about the labour market make you laugh and bring tears to your eyes' Emma Duncan, The Times From award-winning Financial Times journalist Sarah O'Connor, a deeply reported investigation into how AI is transforming our working lives in unpredictable ways A tsunami of change, we are told, is sweeping the economy, as robots and AI threaten to take over tasks done by humans. But while we worry that we're robotizing our work, what if the bigger risk is that we're robotizing ourselves? When prize-winning Financial Times journalist Sarah O'Connor set out to investigate what was happening on the ground, she met people who weren't necessarily losing their jobs to machines, but who felt they were losing something, nonetheless. Because the quantity of work is not the only thing at stake in times of rapid technological change. So is its quality. From TV subtitle translators reduced to editing AI output to warehouse workers surrounded by robots and graduates interviewed by machines, O'Connor found stories of work becoming more intense, more lonely, less creative, less human.But she also investigated hopeful instances of work being made better, safer and more enjoyable - stories in which people have been able to make the machines work for them, rather than the other way around.Her reporting shows that the way our tools change our work - and ourselves - is shaped by power, design, culture, institutions and ideas. As a result, the outcome is not pre-determined but must be contested by us all. Inspired by stories from nineteenth-century English cotton mills to twenty-first century Swedish mines, We Are Not Machines reveals how we can fight for work which is more respectful of our limits, and more worthy of our minds.

The Fight for the Future of Work

We Are Not Machines

This book offers an in-depth exploration of artificial intelligence (AI) in human resource development (HRD). AI's transformative impact on work, learning, and organizational systems places HRD at the core of this challenge. Addressing the challenge, the book is structured into four parts. The initial section scrutinizes and frames the foundations for this challenge. The subsequent part examines the governance and ethical considerations, while the third part focuses on the design of AI-enabled HRD systems and processes. Part Four examined the human dimension, including workforce development and leadership.&nbsp;&nbsp;Overall, the chapters offer a comprehensive perspective of the critical issues around AI and HRD, linking individuals, organizations, and society. It will advance scholarship in this emerging area of research and address debates within the field among HRD scholars and scholar-practitioners about the role of AI in HRD.Jessica Li 
 is a Professor of Human Resource Development in the Department of Education Policy, Organization &amp; Leadership, College of Education at the University of Illinois, Urbana-Champaign, USA.
&nbsp;

 Amin Alizadeh&nbsp;
 is an Assistant Professor of Human Capital Development in the School of Leadership at the University of Southern Mississippi, USA.
&nbsp;

 Khalil Dirani&nbsp;
 is a Professor of Human Resource Department and Department Head at the Department of Educational Administration and Human Resource Development at Texas A&amp;M University, USA.

Transforming and Redesigning the Process

Artificial Intelligence in Human Resource Development

Causal Inference with Bayesian Networks

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