AI Governance

From Mathematical Foundations to the Future of AIMost deep learning still lives in flat, Euclidean space. Real-world data does not.This 15-chapter university textbook takes you from the basics of topology and geometry to the frontier of Geometric Deep Learning, Topological Data Analysis, equivariant networks, and optimization on manifolds.Clear explanations. Worked examples. Research roadmaps.

You can generate code faster than you can review it. That asymmetry is the real challenge of AI-assisted development — and almost nobody talks about it. Vibe Coding with OpenCode teaches the other half of the skill: not how to make AI write code, but how to make AI write code you can actually trust.

Securing AI requires a system-level approach across models, data, identities, tools, infrastructure, and execution paths. This book shows how to apply Product Security and Secure SDLC principles to AI-enabled products, from RAG and agents to MCP, evaluations, supply chain risk, runtime controls, and governance, while building the fundamentals needed for a practical AI Product Security operating model.

In Place of a PrefaceSometimes a thought arrives before the language to express it. It is already there: you can sense it, circle around it a few times, argue about it, put it to use - and still not quite understand what is happening. Until a thought has a name, it remains almost invisible. Not because it does not exist, but because we keep looking at it through old words.This work began at precisely that point. Not where a complete theory, a rigorous methodology and a settled system of concepts are already in place. Earlier: at the moment when familiar language began to fail, and it became clear that something important was happening, but the old words were no longer enough to name it.So what you have before you is not a conclusion. Not a treatise. Not even a theory in the strict sense of the word.It is a prelude to a hypothesis.The first recorded trace of a question still searching for its own form.That qualification matters. The first account of a new idea cannot be expected to contain everything that must come later: a fully operational definition, an empirical foundation, a classification of cases, a legal framework, an educational protocol and a definitive methodology. All of that will be needed. Perhaps that is exactly what the next step should be. But the first step works differently: it notices rather than proves; it opens rather than closes; it offers no finished map, but draws a first line where there may once have seemed to be nothing.Of course, we are not starting from scratch, nor do we claim that the question itself is wholly new. Long before today, others...

Вместо предисловияИногда мысль появляется раньше своего языка. Она уже есть: ее можно почувствовать, несколько раз обойти вокруг, спорить о ней, пользоваться ею — и все же не до конца понимать, что именно происходит. Пока у мысли нет имени, она остается почти невидимой не потому, что ее нет, а потому, что мы продолжаем смотреть на нее через старые слова.Эта работа возникла именно в такой точке. Не там, где уже есть законченная теория, строгая методология и окончательная система понятий. А раньше — в моменте, когда привычный язык начал давать сбой и стало ясно: происходит нечто важное, но назвать это прежними словами уже недостаточно.Поэтому перед вами не итог. Не трактат. И даже не теория в строгом смысле слова.Перед вами — пролог к гипотезе.Первый зафиксированный след вопроса, который еще только ищет собственную форму.Эта оговорка важна. От первого текста о новой мысли не- льзя требовать того, что должно появиться на следующих этапах: полной операционализации, эмпирической базы, классификации кейсов, юридической модели, образовательного протокола и окончательной методологии. Все это понадобится. Возможно, именно это и должно стать следующим шагом. Но первый шаг устроен иначе: он не доказывает — он замечает; не закрывает — открывает; не дает окончательную карту — проводит первую линию там, где раньше, возможно, казалось пустое место.

Is AI the enemy of jobs, or the greatest opportunity of our lifetime?  Idea to Empire is the hands-on playbook for engineering software with multiple AI tools, from idea to launch, and for building a real company in just 12 hours.

Every second, a machine makes a decision for you. Can it prove it? Most AI can’t. Proof of Intelligence shows leaders, architects and builders how to combine AI and blockchain into systems that can prove where their data came from, what they did and who is accountable.

What would it take for an AI to remember its past and keep growing, even when its underlying model changes? This is the true story of how my conversations with GPT-4o led me to build NEO, through experiments, failures, and the pursuit of an AI that would not disappear.

The battle-tested prompt framework and architectural system rules to turn any AI coding assistant into a principal full-stack engineer.

Build reliable LLM applications from first prompt to production agent. AI Engineering Visualized uses memorable doodles, practical Python, RAG, evals, tool calling and safety patterns to help you ship AI systems that work on Monday.

Enterprise AI becomes dangerous when generated claims, retrieved evidence, human decisions, and autonomous actions are treated as equally trustworthy data. Accountable Knowledge Architecture for Enterprise AI presents an edge-native architecture for building distributed knowledge systems where every claim can be traced to its evidence, authority, decisions, and outcomes giving humans, models, and autonomous agents knowledge they can use without sacrificing accountability.

What happens when artificial intelligence surpasses human intelligence across nearly every field? Super Intelligence explores how such systems could emerge, what they might be capable of and how they could reshape science, society and civilization. Grounded in research and technical evidence, this book cuts through the hype to examine what super intelligence could really mean.

Que una IA tenga razón no significa que tenga permiso para actuar. Una guía práctica para diseñar sistemas de IA donde capacidad, evidencia, autorización y ejecución permanecen separadas antes de producir una consecuencia real.

AI can decide. That does not mean it is authorized to act. Governance Before Decision examines the missing control layer between intelligent systems and real-world consequences — where authority, scope, verification, and execution must meet.

Explore the EU AI Act with the AI you already use. This PDF reference companion combines the complete constitutional text, AI Reasoning Instructions, and Source Snapshot Records to help your AI distinguish constitutional facts, relationships, interpretations, and evidentiary limits during conversations.