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Martin D: Bears & Mosquitoes

Bears & Mosquitoes

Building Trusted AI from the Inside Out - A Field Guide to AI That Earns Its Keep

AI and quantum computing are two disruptive forces about to push financial services and the banking industry over the edge, demanding an unprecedented transformation. They are not fleeting trends but fundamental disruptors that will reshape the core of banking and finance. Authored by a senior Swiss banking technology executive, this practical guide helps board members and senior leaders turn disruption into a disciplined strategic agenda. It offers a rare insider perspective and a roadmap for navigating these seismic shifts. Bridging the gap between technological innovation and practical application, this book equips leaders with the tools to address the challenges and opportunities posed by AI and quantum computing. It features a thorough review of the latest business opportunities and threats associated with these technologies, alongside foresight-based strategic decision frameworks to guide prioritisation. Real-world case studies and an online companion with 125 AI use cases tailored for banks provide actionable insights. It also introduces an original AI Adoption Maturity Model and a Hybrid Enterprise Reference Architecture to ensure safe integration of internal systems with external frontier models. Finally, it provides two detailed multi-year roadmaps, offering a clear path to harness AI's potential and to prepare for a post-quantum future through crypto agility.Rather than offering abstract predictions or generic solutions, this book presents financial-services leaders with the frameworks, choices, and implementation path they need to act with confidence at a moment when waiting is no longer an option.

Evolutions at the Bleeding Edge

How AI and Quantum Computing Will Make or Break the Bank

Most AI governance is built around the wrong object.

Enterprises watch the model. They should be governing the crossing-the moment-generated language asks to become institutional reality.

The Bounding Gap exposes the substitutions that pass for AI safety: observation for control, behavior modification for enforcement, explanation for proof, and human review after the decisive movement has already begun. It relocates governance from the model itself to the boundary where generated possibility becomes institutional consequence.

Through a series of scientific inversions, the book develops a new architecture of authority. Gödel shows why a system cannot certify itself. Turing is inverted to ask not whether machines can imitate human judgment but whether their outputs possess standing to enter institutional life. Wilson's correlation geometry explains why modern AI cannot be governed through the old imagination of local logic and linear control. Hooke and Payne-Gaposchkin reveal why observation without the correct interpretive structure remains blind.

These arguments converge on one claim:

Authority cannot live inside the generative field. It must be external, deterministic, and enforceable before consequence becomes real.

The book also names the danger created by speed. Synthesis entropy and governance dilation describe what happens when machine-generated states multiply faster than institutions can establish admissibility. A rationale can now be produced in milliseconds, while responsibility, evidence, and authority remain unresolved.

For enterprise architects, Chief Compliance Officers, General Counsels, Chief Risk Officers, AI founders, and regulators, The Bounding Gap offers more than a critique of current governance. It identifies the missing boundary and shows why observation, monitoring, alignment, and review cannot substitute for executable refusal.

The future of institutional AI will belong to those who can govern the crossing before machine-generated possibility becomes consequence.

Why Institutions Must Govern Before AI Becomes Consequence

THE BOUNDING GAP

Wiley Finance

The handbook to unleash the power of AI in Finance with guidance to pragmatically managing AI Risk Are you ready to harness the transformative potential of artificial intelligence and machine learning in finance while effectively managing the associated risks? "AI and Risk Management in Finance: A Practitioner's Handbook" by Sri Krishnamurthy, CFA, is your indispensable guide. Tailored for financial professionals, this comprehensive handbook delivers practical insights, case studies, and frameworks to navigate the complex landscape of AI and ML in the financial sector. This pragmatic handbook, developed primarily for professionals involved in designing, developing, and deploying AI models in the financial industry explores cutting-edge technologies, regulatory landscapes, and pragmatic frameworks. From generative AI to robust risk management strategies, this handbook equips you with the tools you need to validate models, ensure compliance, and operationalize AI risk management effectively. Leveraging lessons from QuantUniversity's AI and Machine Learning Risk Management program offered in 18 countries, this book focuses on enabling practitioners apply the state-of-the-art AI and machine learning techniques factoring unique risks specific to the financial services domain. Whether you're a seasoned professional or a newcomer, this book offers invaluable insights for harnessing the power of AI responsibly in the financial industry. Highlights of the Book: <ul> <li>Machine Learning and AI in Financial Services: Dive into the fundamentals of ML and AI, exploring various applications within the financial industry through real-world case studies.</li> <li>Generative AI and LLMs in Finance: Understand the emerging landscape, key players, and advanced concepts like prompt engineering and fine-tuning with practical examples.</li> <li>Risk Management for AI/ML Applications: Learn about current and emerging regulatory landscapes, AI risk management frameworks, and legislative efforts shaping the field.</li> <li>Data Governance Issues: Address critical aspects of data quality, privacy, security, and governance, with actionable frameworks and illustrative case studies.</li> <li>Model and System Governance: Gain insights into validating machine learning models, quantifying model risk, and ensuring robust system governance with MLOps and model monitoring.</li> <li>Key Risk Assessment Facets: Focus on documentation, explainability, fairness, security, and reliability with detailed case studies.</li> <li>Verification and Validation: Distinguish between verification and validation and learn frameworks for effective AI application validation.</li> <li>Algorithmic Auditing: Conduct comprehensive algorithmic audits with detailed processes and real-world examples.</li> <li>Operationalizing AI Risk Management: Discover ten essential strategies for implementing AI risk management practices effectively in your organization.</li> </ul> Prepare for the future of finance with expert advice and actionable strategies. Don't miss out on this comprehensive guide-your roadmap to navigating AI and risk management in the financial sector.

A Practitioner's Guide to Machine Learning, Generative AI, Agentic Systems, and Responsible Deployment

AI and Risk Management in Finance

What Leaders Must Keep When AI Takes the Rest

RetAIn

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.
Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.
You Will:
<ul>
<li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li>
<li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li>
<li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li>
</ul>
&nbsp;
This book is for : Software Architects &amp; System Designers
&nbsp;

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.You Will:<ul><li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li><li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li><li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li></ul>&nbsp;This book is for : Software Architects &amp; System Designers&nbsp;

Architecting Scalable, Ethical, and Trustworthy Systems for Enterprise

Agentic AI Systems in Practice

Getting a large language model to load on your own hardware takes an afternoon. Keeping it serving real traffic, at a cost you can predict, is a different job, and it is the one this book covers. It does so by building one system and growing it across all fifteen chapters.
You begin with an 8B model on a single card, the kind you may already own or can rent for well under a dollar an hour. From there the book adds what real traffic needs: a choice of inference engine, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves to models that do not fit on one card, placing a 70B deployment across GPUs chosen for the bandwidth between them as much as for their memory, and buying that capacity from several providers at once.
In the final part you serve a 753-billion-parameter sparse model on eight rented H200s, measure what each session costs as concurrency rises, and find the point where a larger batch stops making sessions cheaper. That result explains how commercial inference providers price what they sell, and when self-hosting is cheaper than their APIs.
Costs come from real rented-GPU runs, and the book says which numbers are measurements and which are predictions. The control plane is open source and written in Go, so you can run every step yourself.
You Will:
<ul>
<li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li>
<li>Learn how inference uses a GPU, covering KV cache growth, quantization formats and what they cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li>
<li>Work out per-session cost from real runs as batch size and context length grow, and find the point where self-hosting stops beating a commercial API.</li>
</ul>
This book is for:
Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Running a large language model on hardware you control is no longer exotic, but the gap between a model that loads and a service that holds up under load is where most self-hosting efforts stall. This book closes that gap by building the thing, one working system that grows across all fifteen chapters.You begin with an 8B model on a single card, the kind of hardware you can rent for pennies an hour or already own. From there the book adds what real traffic demands: an inference engine chosen on its merits, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves past what one card can hold, placing a 70B deployment across a pool of GPUs selected on the network between them rather than the sum of their memory, and shopping for that capacity across several providers at once.The final part goes to the frontier. You will serve a 753-billion-parameter sparse model on eight rented H200s, then measure what it costs per session as concurrency climbs, and find the point where a larger batch stops making each session cheaper. That result explains how commercial inference providers price what they sell, and it tells you when self-hosting wins and when it does not.Costs are measured rather than estimated throughout. Every figure in the final part is regenerated from a committed data file, and the book says plainly which numbers are measurements and which are predictions. The complete control plane is available as an open-source companion repository, written in Go, so you can run what you read.You Will:<ul><li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li><li>Learn how inference actually consumes a GPU, covering KV cache growth, quantization formats and their real cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li><li>Measure the economics from real runs, working out per-session cost as batch size and context length grow, and identifying the point where self-hosting stops beating a commercial API.</li></ul>This book is for:Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Building and Operating Production AI Systems from a Single GPU to Multi-Node Frontier Models

Self-Hosted LLM Inference

Non-human identities now outnumber people, and someone must sign for what the machines do.
Defensible Identity Architecture is a professional reference by Professor Kai London. It covers non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE and workload identity, ITDR and cyber-insurance posture.
Written for boards, executives, CISOs, engineers, risk and compliance leaders and practitioners who need clear, defensible guidance they can put to work, the book combines explanation with practical frameworks, checklists and decision aids.
Key topics include: non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE, workload identity, ITDR, cyber-insurance posture.

Defensible Identity Architecture

Put AI to work to achieve your marketing goals An estimated 88% of marketers already use AI daily, and they're seeing real results in content creation, brand awareness, and sales. Marketing with AI For Dummies gives you the skills to simplify your workload, automate tasks, and make smarter marketing decisions. You'll learn to create personalized marketing, generate emails, and develop creative assets in record time - all with AI doing the heavy lifting. This edition answers your questions to Agentic AI workflows, answer engine optimization, large language models, and synthetic personas. You'll also get guidance on AI ethics and current regulations, plus platform-specific AI features and the modern SEO landscape. Whether you're generating content or building personalized customer experiences, this book has you covered. Inside this book: <ul> <li>Learn how Agentic AI reshapes campaign execution and customer engagement through autonomous workflows</li> <li>Discover answer engine optimization and how to stay visible in AI-generated search results</li> <li>Explore large language models, including embeddings, fine-tuning, and retrieval-augmented generation for marketing</li> <li>Build synthetic personas for research, creative testing, and targeted message development</li> <li>Get up to speed on AI-mediated commerce and how AI influences recommendations and purchasing</li> </ul> Whether you're a seasoned marketer looking to level up your AI skills or someone who handles marketing as part of a bigger role, this Dummies guide makes it easy to get started. From content creation to predictive analytics, you'll find friendly, practical advice you can use to put AI to work for you right now.

Marketing with AI For Dummies

Build a resilient, high-performing organization by integrating agentic AI into strategy, operations, and decision-making. Building an Agentic Organization is a strategic guide for senior technology, AI, and business leaders responsible for delivering value from agentic AI while managing its risks. As organizations move toward human and AI collaboration at scale, leaders must balance innovation with governance, adoption, and long-term economic impact.Written by experienced AI executive Geoffrey Schaefer, this book provides a comprehensive framework for designing an agentic organization by examining strategy, governance, and economics. It explores how to define the purpose of agentic AI, align it with business objectives, and create the structures needed to support safe, scalable adoption. Addressing both opportunity and risk, it equips leaders to navigate workforce concerns, reputational exposure, and the evolving role of AI agents in enterprise environments.You'll learn how to: - Define a clear strategy for integrating agentic AI - Establish governance frameworks to manage risk and accountability - Drive workforce adoption and address resistance to AI collaboration - Assess economic value and impact of agentic AI systems - Manage reputational and operational risks from AI agent behaviorFeaturing real-world examples from Leidos, JPMorgan Chase, SY Partners, and OMA, this book provides a practical roadmap for embedding agentic AI into the fabric of your organization while protecting value, trust, and long-term performance.Themes include: agentic AI, AI strategy, AI governance, AI transformation, enterprise AI, risk management

The Strategy, Governance, and Economics of Agentic AI

Building an Agentic Organization

Build an AI security operating model that manages risk, strengthens governance and enables AI to scale securely. AI Risk Management is a practical guide for AI risk owners, GRC managers, AI governance managers and technology risk professionals responsible for managing the security risks of AI.Written by security expert Rafah Knight, this book shows how to make AI security operational, measurable and embedded into existing business processes. It provides practical guidance on establishing ownership and accountability for AI risk, designing effective governance structures, implementing policies and controls and managing third-party AI vendor exposure.Drawing on real-world examples from financial services, retail, the public sector and healthcare, learn how to: - Build an effective AI security operating model - Establish clear ownership and accountability for AI risk - Design AI governance policies, controls and assurance processes - Measure AI security and operational risks effectively - Manage third-party AI vendor exposure at scaleAI Security gives you the practical structures and controls to operationalize AI assurance, strengthen AI governance and support secure AI adoption at scale.Themes include: AI security, AI risk management, AI governance, GRC, AI assurance, third-party AI risk

How to Scale AI Securely

AI Risk Management

AI in Practice

Useful & Usable

Bears & Mosquitoes

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