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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

Can democracies combat political disinformation, foreign interference in elections and manipulative political expression without undermining free speech?This book is the first to demonstrate how democracies can regulate social media communication causing democratic harm, including political disinformation, consistently with the right to freedom of speech.Through a rigorous analysis of new laws, including the UK's Online Safety Act and National Security Act, as well as the EU's Digital Services Act and AI Act, the book illustrates how and why states must respond to democratic harm on social media platforms. In doing so, the book explores the types of social media communication that cause democratic harm and develops a novel theory of democratic harm that looks beyond negative electoral consequences and focuses on the preconditions necessary for democratic legitimacy in relation to freedom of expression.Defending Democracy in The Digital Age provides pragmatic solutions to the complex challenge of balancing free speech and addressing social media communication causing democratic harm. Policymakers, scholars, judges and legal practitioners interested in democracy and freedom of speech will benefit from the findings made by this book. In the digital age, when powerful social media companies facilitate the spread of harmful communications that erode the function of our democracies, this book provides a necessary and timely intervention in debates on the safeguarding of democracy and the protection of free speech.

Regulating Democratic Harm on Social Media Platforms

Defending Democracy in The Digital Age

No magic. No shortcuts.&nbsp;No runtime to blame. You can ask an AI to explain how vtables work in x86. It will give you something that sounds right. What it won’t give you is what Windows actually expects the vtable to look like, why method dispatch behaves the way it does at the instruction level, or what breaks when you deviate from convention. This volume of The Art of 64-Bit Assembly closes the gap between a plausible explanation and genuine understanding. Every chapter takes a construct you’ve used in C++, Python, or Rust, strips away the runtime, and rebuilds it from scratch in MASM, running under Windows. Objects, exceptions, closures, coroutines, concurrency: Each is dissected at the instruction level, with every decision made visible and explicit. What you’ll build: <ul><li>Object-oriented programs in MASM: vtables, method dispatch, and inheritance, from scratch by hand</li><li>Windows structured exception handling (SEH) installed and managed at the instruction level</li><li>Thunks, closures, and iterators that behave like higher-order functions</li><li>Coroutines, generators, and fibers without resorting to HLL code</li><li>Concurrent programs with real synchronization primitives, directly from assembly</li><li>Unicode string handling done correctly, at the level where most code gets it wrong</li><li>Domain-specific macro languages inside MASM, built from first principles</li></ul> If you already know assembly and want to stop taking the hard parts on faith, this is the book.

Machine-Level OOP, Exceptions, and Concurrency

The Art of 64-Bit Assembly, Volume 2

A writing professor asks her students to share a childhood memory. They are stranded: all they can recall are family videos, photos resurfaced by an algorithm--moments already framed before they became memories at all.For the last one hundred years, technology has shaped not only how we communicate but how we experience time itself. But today the amassing of big data, the speed of problem solving, and the ubiquity of smartphones and AI threaten to upend our relationship to space and time. The past returns to us only in curated feeds and automatic reminders: an unceasing, untethered stream of content. Might we, in retaking control of our narrative threads, regain agency over our hours, our days, and the stories of our lives?Claire Messud's urgent manifesto argues that we must recapture our futures from the digital forces that consume us, and reclaim what makes us most human.

Reclaiming the Stories of Our Lives

Time, Now

AI can write the code. You still need to know what it does. You’re already good at Excel. But you’re tired of the copy/paste, the helper columns, and the brittle formulas that break when someone adds a row. You want automation, and whether you write the code yourself or let AI generate it, you need to understand what’s actually running your business processes. Automate Excel with Python teaches you to build real workflows, step-by-step. You’ll read messy workbooks into pandas dataframes, filter and reshape data without helper columns, merge sources without silent VLOOKUP failures, and export polished results with formatting intact. A capstone chapter ties it together: import a multi-tab workbook, generate exception reports, and email the results—all from one script you run with a single click. You’ll learn how to: <ul><li>Read existing Excel files you already use into Python, even the messy ones</li><li>Replace daily copy/paste routines with reusable scripts</li><li>Merge and match data across sources with auditable results</li><li>Handle dates, times, and the edge cases that break formulas</li><li>Export from Python to Excel with column widths, number formats, and frozen panes intact</li><li>Build workflows that run daily without babysitting</li></ul> AI can write the code. This book makes sure you’re the one in control of it.

A Practical Guide for Nonprogrammers

Automate Excel with Python

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

Stablecoins have emerged as a significant innovation in the digital finance landscape, bridging the stability of traditional currencies and the efficiency of blockchain-based transactions. Designed to maintain a stable value through asset backing, algorithmic mechanisms, or hybrid models, stablecoins may improve payment systems, cross-border transfers, and financial inclusion. However, their growing adoption also raises concerns on liquidity, reserve management, operational resilience, market integrity, and systemic financial risk. Effective governance frameworks, including regulatory oversight, risk management practices, and accountability mechanisms, maintain user confidence and monetary stability. As stablecoins increase in usage, understanding their design principles, associated risks, and governance structures may ensure their safe integration into the global financial system. Design, Risk, and Governance of Stablecoins as Monetary Instruments provides a systematic and accessible analysis of how stablecoins are designed, how they operate in practice, the risks they pose, and the governance and regulatory frameworks that shape their use. This book explores how stablecoins interact with existing financial systems, payment infrastructures, and monetary policy frameworks. Covering topics such as auditing, cryptocurrency, and financial systems, this book is a valuable resource for engineers, investors, entrepreneurs, policymakers, researchers, and scientists.

Design, Risk, and Governance of Stablecoins as Monetary Instruments

A sweeping roadmap for business leaders in the AI age, when every organization must rethink how it operates, competes, and creates value—from AI legend and New York Times bestselling author of AI Superpowers and AI 2041 Kai-Fu Lee “Kai-Fu Lee is among our most prescient thinkers and writers…. Powerful.”—Satya Nadella, Microsoft CEO Two years ago, the best AI agents could autonomously complete tasks that took a human expert roughly six minutes to complete. Today, top AI agents across nearly all white-collar industries can complete the workday of the smartest humans in less time than a coffee break—at a fraction of the cost.  What happens when the most expensive thing that companies buy—human thinking—becomes practically free? This is the single most consequential question facing business leaders for a generation, and many are struggling with how to answer it. AI is not simply another technology cycle but a fundamental transformation in the economics of business. The answer is not to dabble. It is to become AI Native. Kai-Fu Lee has spent the past four decades pioneering AI in business from nearly every vantage point: as a researcher, senior executive at Apple, Microsoft, and Google, and now a CEO himself. Few thinkers can match his combination of technical expertise and business acumen.  In AI Native, he draws from this deep well of experience to offer a radical new roadmap for company leaders to become AI Native. With punchy, insightful guidance, Lee dispels the lies CEOs are being told about AI, offers a clear roadmap for how to re-structure teams with powerful agentic AI at the center of the enterprise, and reveals the power and potential of using AI to tap into the hidden trove of knowledge inside companies. Those that get it right will flourish in the AI age. AI Native is the only book leaders need to navigate the defining challenge of our time.

The Mandate to Transform Your Company

AI Native

The Fight for the Future of Work

We Are Not Machines

Dance with the Dragon “A must-read for any reverse engineer.” ­—Max “Libra” Kersten Developed through more than a decade of research within the NSA, Ghidra was created to address some of the most challenging reverse engineering problems faced by the US government. With its open source release, this formerly restricted tool suite is now freely available to cybersecurity practitioners, researchers, and students worldwide. The Ghidra Book provides a clear, practical guide to understanding and using this powerful platform. In addition to introducing core reverse engineering techniques for software and malware analysis, the book explains Ghidra’s key components, features, and support for extensibility and collaborative analysis. Beginning with the fundamentals and progressing to more advanced workflows, you’ll learn how to use Ghidra effectively and adapt it to new challenges. You’ll learn how to: <ul><li>Navigate and interpret a disassembly</li><li>Use Ghidra’s built-in decompiler to expedite analysis</li><li>Analyze unfamiliar and obfuscated binaries</li><li>Extend Ghidra to recognize new data types</li><li>Build custom analyzers, loaders, and processor support modules</li><li>Script Ghidra tasks to automate analysis tasks</li><li>Set up a collaborative reverse engineering environment</li></ul> This fully revised second edition reflects the modern Ghidra platform as it is used in practice, with updated tooling, improved workflows, and hands-on real-world examples. Designed for beginners and experienced users alike, The Ghidra Book prepares you to tackle real reverse engineering problems with confidence. New to this edition: <ul><li>Behavioral analysis with BSim</li><li>Full Python 3 support via PyGhidra</li><li>Enhanced debugging and graphing tools</li><li>Modern container-based deployment</li></ul>

The Definitive Guide

The Ghidra Book, 2nd Edition

A timely, practical guide to AI—its strengths, weaknesses, and real-world applications—for business professionals and policymakers. Artificial intelligence, or AI, can recognize a pattern from any set of data it is given, which is what makes it such an extraordinarily powerful tool. But because not all patterns are authentic or reliable, AI's pattern-finding superpower can lead to spurious patterns—and to disastrous results for business and government entities that rely on them. Hence the conundrum at the heart of AI: its greatest strength can also be its greatest weakness. Targeting the businessperson who needs to know how to use AI profitably and responsibly, Caleb Briggs and Rex Briggs offer in this book a foundational understanding of AI that is easy to grasp yet thorough enough to be used effectively.

Harnessing the Power of AI for Your Organization—Profitably and Safely

The AI Conundrum

The Omicron Forex Trading Manual is a literate and focused explanation for non-specialists of Forex strategies for automated (algorithmic) trading.

The manual is also a guide to the essential general requirements of Forex trading which all traders must master. These include the ability to think in probabilities, to maintain discipline (which the automated strategies are designed to assist with) and master the psychology of Forex Trading. It improves the Forex trading learning process. It shows how discipline can be maintained. It also explains how the software does back-testing and it demonstrates why algorithms are important.

You need automation in your Forex trading to:Significantly improve the Forex trading learning process.
Carry out research and do back-testing.
Help you impose discipline and appreciate the need for it.
Allow for risk reduction through diversification in currency pairs, which would be onerous and error prone if attempted manually.
Take over certain repetitive functions that are more appropriate for computers to handle.


You do not want to use automation in your Forex trading to "Set and forget" and leave to run unsupervised on a live account. This way lies potential disaster.

The Omicron Forex Trading Manual contains an introduction to the automated strategies and does not include the software routines themselves.

The Omicron Forex Trading Manual

Persuasion, Datafication, and Privacy

The Algorithmic Child Consumer

Causal Inference with Bayesian Networks

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