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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

Build intelligent applications—no data science degree required. Your boss is pitching new AI features. Your team is buzzing about MCP servers. Job postings are asking for AI experience with RAG, vector databases, fine-tuning, and agents. You can feel the excitement. You see the potential. You may be wondering how to get started in AI without a data science degree. You’re in the right place. The Developer’s Guide to AI gives working developers a practical path through the terminology, tools, and implementation patterns that matter. It shows you how to build with AI using the tools you already know: JavaScript, Python, APIs, SDKs, and databases. By the end of this book, you’ll know how to: <ul><li>Call LLM APIs and stream intelligent responses directly to your UI.</li><li>Engineer prompts that produce reliable, production-ready results.</li><li>Build RAG pipelines using vector databases to give AI access to your private data.</li><li>Fine-tune models with LoRA for specialized tasks like classification.</li><li>Deploy AI agents using tool-calling and the Model Context Protocol (MCP) to reason and act inside real workflows.</li></ul> LLMs, RAG, LoRA, MCP, embeddings, and agents are not just intimidating buzzwords. They are the building blocks for the next generation of software. Grab your code editor, bring your engineering instincts, and let’s build what’s next!

The Developer's Guide to AI

A USA Today Bestseller. A dramatic new future of work in which managers assemble exactly the expertise they need—within minutes. 2026 PROSE Award Winner: Business, Finance, and Management "If you’ve ever wished you could assemble your version of The Avengers at work, this book will help you make it happen.” —Adam Grant, #1 New York Times-bestselling author of Think Again and host of the podcast Re:Thinking Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement, too; some of those experts give a second opinion or a moment of brainstorming, whereas others join as full-fledged team members for a sustained collaborative effort. This is the future promised by flash teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations. Drawing on original research and industry examples, this book will help readers to: <ul><li>Recognize and source needed expertise within minutes</li><li>Anticipate and plan team designs with AI</li><li>Redesign team structures on the fly as projects evolve</li></ul> Industries are already being transformed by this new approach to teaming. Flash Teams arms leaders, managers, and entrepreneurs with the tools they need to accomplish their goals with confidence, speed, and agility.

Leading the Future of AI-Enhanced, On-Demand Work

Flash Teams

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

From the New York Times bestselling authors of Abundance, Bold, and The Future Is Faster Than You Think comes a bold exploration of what it means to stay human in a world where technology has granted us godlike power. 'Timely and important' Eric Schmidt 'A manual for optimizing our destiny' Ray Kurzweil 'An extraordinary blueprint for hope' Mo Gawdat In 1968, Stewart Brand declared: 'We are as gods - and we might as well get good at it.' Half a century later, that prophecy has come true. We can rewrite genes, edit embryos, build artificial minds, extend life, and terraform worlds. The old miracles - omniscience, omnipresence, even resurrection - are becoming standard operating procedure. But the real question isn't whether humanity can play god. It's whether we can do it wisely. In this book, the bestselling authors of Abundance and Bold return with a sweeping exploration of our species' next great transformation. Blending hard science with vivid storytelling, Peter Diamandis and Steven Kotler chart humanity's ascent from scarcity to superabundance - and the psychological, ethical, and existential challenges that come with it. Across breakthroughs in AI, robotics, genetics, longevity, and consciousness research, they reveal a paradox at the heart of progress: as our external power expands, our inner resilience must evolve to match. Abundance without meaning leads to collapse. Intelligence without wisdom leads to extinction. To thrive in a world of everything, everywhere, all the time, we must learn to wield our godlike powers with humility, creativity, and flow. Equal parts warning and invitation, We Are As Gods is a map for flourishing in the exponential century. Because the future won't be built by those who fear what's coming, but by those who know how to turn chaos into creation. Abundance is here. Are you ready?

A Survival Guide for the Age of Abundance

We Are As Gods

De Gruyter Studium

Nach einer allgemeinen Diskussion formaler Sprachen, deren Beschreibungen und Grenzfällen der Erkennbarkeit werden die regulären Sprachen behandelt, welche in der lexikalischen Defi nition von Programmiersprachen ihre wichtigste Anwendung finden sowie die kontextfreien Sprachen, mit denen man die Syntax von Programmiersprachen definiert. Aus theoretischer Sicht befriedigend ist die eindeutige Entsprechung zwischen Sprachbeschreibung und Spracherkennung - den regulären Sprachen entsprechen die endlichen Automaten und den kontextfreien Sprachen die Stackmaschinen. Weitere Stufen der Chomsky-Hierarchie werden nur kurz behandelt, da sie in der Praxis von geringerer Bedeutung sind. Stattdessen zeigt ein eigenes Kapitel zum Thema Compilerbau weitere Techniken auf, die aus einer Sprachbeschreibung einen Parser, also das komplette 'front-end' eines Compilers, entstehen lassen. Der Begriff des 'Algorithmus' wird anhand verschiedener Maschinenmodelle erklärt und bestätigt wird auch die Churchsche These, dass jede vernünftige Defi nition von 'Berechenbarkeit' auf die gleiche Klasse von Funktionen führt. Die Grenzen des algorithmisch Machbaren werden anhand des Halteproblems und des Satzes von Rice klar abgesteckt. Das abschließende Kapitel zur Komplexitätstheorie erkundet unter den lösbaren Problemen die Grenze zwischen denen, die mit einem vertretbaren (polynomiellen) Aufwand lösbar sind und solchen, deren Lösung nicht wesentlich effi zienter ist, als ein systematisches Ausprobieren von Lösungskandidaten. Dieses Kapitel führt den Leser zu dem bekanntesten noch ungelösten Problem der Theoretischen Informatik: P = NP? Der erste Band der Informatik erklärt die grundlegenden Konzepte: Programmierung, Algorithmen und Datenstrukturen. Der zweite Band ist technischen Themen gewidmet - insbesondere der Rechnerarchitektur, Betriebssystemen, Rechnernetzen und speziell dem Internet. 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.

Formale Sprachen, Compilerbau, Berechenbarkeit und Komplexität

Informatik

A cutting-edge, interdisciplinary exploration of new media - this book does not dumb ideas down, but writes with complexity and nuance, now with new pedagogy to consolidate students' learning.

Understanding New Media

Toward a deeper understanding of minds, whether natural or artificial, and of how they are studied and built across AI, AGI, and cognitive science. Minds are functional entities that enable intelligent behavior. In On Minds, Paul Rosenbloom offers reflections and perspectives on the study of minds arising from a multi-decade career focused largely on cognitive architectures—hypotheses concerning the fixed structures and processes that yield a mind, whether natural or artificial. Cognitive architectures must be integrative in accommodating the full range of capabilities required of a mind. They must also be general, in applying across many, if not all, problems and domains. This breadth of investigation, across a sequence of three such architectures—XAPS, Soar, and Sigma—and over many years, has inspired a set of reflections on both cognitive architectures and three core disciplines within which such work occurs—artificial intelligence (AI), artificial general intelligence (AGI), and cognitive science. The book’s reflections are of three types. The first explores four concepts critical to understanding minds—architectures, symbols, intelligence, and theories—to provide new perspectives and insights on them. The second rethinks the structure of the space of technologies that underlies work on minds, yielding multidimensional maps that clarify their similarities and differences plus detailed insights into the tradeoffs that exist across both individual technologies and integrated systems—whether symbolic, neural, or hybrid—built from them. The third traces the author’s decades of work on architectures—including cognitive architectures, the Common Model of Cognition, and the nature and structure of computing as a great scientific domain—interwoven with personal reflections and anecdotes.

Reflections of a Cognitive Architect

On Minds

'Engaging, insightful, panoramic' Mustafa Suleyman, CEO of Microsoft AI &amp; Cofounder of DeepMind 'An eye-opening exploration of a revolution unfolding before our eyes' New York Journal of Books Stunning advances in digital technology have introduced a new wave of human-like AI systems. Chatbots like ChatGPT, Claude, and Gemini are already reshaping economies, challenge democracies, and reshaping society in unpredictable ways. And soon, these AI systems could make autonomous decisions on their users' behalf, transforming everything we do. Understanding how they work is crucial. Can AI systems think, know, and understand?Could they manipulate or deceive you, and if so, what might they make you do? Whose interests do they represent? When will they be able to move beyond words and take action in the real world? Neuroscientist and AI researcher Christopher Summerfield explores these questions, charting AI's evolution from early ideas in the seventeenth century to today's deep neural networks. His book is the most accessible, up-to-date, and authoritative exploration of this radical technology. With an understanding of AI's inner workings, we can address the existential question of our age: can we look forward to a technological utopia, or are we writing ourselves out of history? 'As a leading authority...Summerfield is perfectly situated to explore the meaning and implications of these machines that are so uncannily like - and unlike - ourselves' Brian Christian, co-author of Algorithms to Live by 'You might choose to be alarmed, excited, or indifferent to LLMs, but you should read Chris's book before you decide' Mike Woolridge, author of The Road to Conscious Machines

How AI Learned to Talk and What It Means

These Strange New Minds

How AI is transforming pricing decisions, and what opportunities, challenges, and risks these transformations bring. Pricing in the Age of AI explores the evolution of pricing decisions, with a particular emphasis on recent advancements. In the age of big data and AI, pricing strategies have undergone a dramatic transformation. For instance, in e-commerce, prices can now be adjusted every few seconds, tailored to individual users or personas, and optimized with precision. This transformation has the potential to disrupt pricing decisions on an unprecedented scale. Author Maxime Cohen begins by tracing the history and importance of pricing decisions. He focuses on the transformative changes of the past decade, highlighting how big data and AI have upended traditional pricing strategies. He also examines the psychological and behavioral tactics employed to influence consumer behavior through pricing, as well as how recent advancements in AI—and generative AI—are revolutionizing pricing practices, enabling managers to automate, optimize, and personalize decisions at scale. Finally, he discusses the challenges and risks associated with these innovations, including ethical concerns such as anticompetitive practices, consumer protection, and discrimination issues.

Pricing in the Age of AI

'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

Audits are no longer random-and compliance is no longer manual. Today, artificial intelligence determines which businesses get audited, how deeply investigators dig, and whether a review escalates into penalties or quietly disappears.

AI Audit Defense &amp; Risk Reduction reveals how modern enforcement systems actually work-and how smart businesses use the same AI logic to stay protected, credible, and invisible to unnecessary scrutiny.

This book is not about fear, loopholes, or hiding income. It's about designing a business that algorithms trust.
Inside, you'll discover how AI-driven tax authorities evaluate risk, how audit scores are calculated, and how to proactively shape your financial data so your business remains statistically clean, defensible, and low-priority for investigation-year after year.
In this book, you'll learn how to:Understand how AI selects businesses for IRS audits and regulatory reviews
Identify the hidden financial patterns that silently increase audit risk
Use AI to calculate and monitor your business's audit risk score
Build a complete digital audit trail that satisfies both humans and machines
Detect compliance red flags in real time-before filings are submitted
Respond to audit inquiries strategically without escalating scrutiny
Turn past audits into intelligence that lowers future risk
Design long-term compliance systems that create "audit immunity"
Make defensible financial decisions that remain explainable years later
Transform compliance from a liability into a competitive advantage


Who this book is for:Business owners and entrepreneurs
Investors and real estate professionals
Consultants, agencies, and service firms
Online businesses and multi-entity operators
Anyone who wants to grow without triggering regulatory attention



If you want to stop worrying about audits, understand how enforcement algorithms really work, and build a business that thrives under constant observation, AI Audit Defense &amp; Risk Reduction gives you the framework, mindset, and systems to do exactly that.
In the age of AI-driven enforcement, the safest businesses aren't the smallest or quietest-they're the smartest.

How Businesses Avoid Costly IRS Investigations

AI Audit Defense & Risk Reduction

How to Make Sense of a Complex Future

The AI Paradox

Causal Inference with Bayesian Networks

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