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Navarro R: Work Reimagined

Work Reimagined

The AI Era

Every organization has two versions of itself. There's the documented one - the processes, the org charts, the goals and values that make up the official story. Then there's the way things actually work. The workarounds people build because the real process doesn't fit. The spreadsheets no one asked for but everyone depends on. The conversations where someone finally says what they actually need.

That second version has always been there. It's how businesses actually run. And AI is about to walk right into the middle of it.

An Inbox Between Us is not a framework, a productivity system, or a technology roadmap. David Dean draws on nearly 20 years working inside complex organizations - close to the real decisions, workarounds, and human behavior that determine how work actually gets done - to examine what AI makes visible when it enters that environment. AI doesn't understand context, politics, or consequence. It processes what it's given. But because it operates at scale and proximity to daily work, it surfaces patterns that were previously scattered, easy to rationalize, or buried across everyday business activities.

What becomes visible is often uncomfortable. But it's also an opportunity to self-realize - as a person and as an organization.

Inside this book, you'll find:Why organizations routinely misunderstand how their own work gets done - and what AI reveals about the gap
How people creatively adapt around broken processes, and why those adaptations matter more than most leaders realize
Why AI solutions fail when they're built on the official story instead of the real one
What it means to build trust with AI the same way you'd build trust with a new team member - gradually, with guardrails you control
Why the people doing the undocumented work every day are the most important part of this entire equation



This book is for business leaders and for every working person who's been handed AI and told to figure it out, and wants a clearer, more human way to understand what it actually means for how they work.

An Inbox Between Us

AN INSTANT NATIONAL BESTSELLER “Evenhanded and insightful.”&nbsp; —Publisher’s Weekly (starred review) “An accessible, often fascinating primer.... Essential, illuminating reading.” —Kirkus From the author of the New York Times bestseller The Digital Doctor comes an engaging, clear-eyed, and ultimately hopeful examination of healthcare’s efforts to embrace generative artificial intelligence. In A Giant Leap, physician and thought leader Robert Wachter navigates between hype and skepticism to make a compelling case for AI’s power to transform healthcare. He argues that, in a system buckling under the weight of bureaucratic pressures, soaring costs, and clinician burnout, AI doesn’t need to be perfect—it only needs to be better. Drawing on extensive research and more than 100 interviews with pioneers across medicine, technology, policy, and business, Wachter shows how AI is already entering hospitals and clinics to draft notes, field patient questions, recommend treatments, interpret images, and guide surgeries. He unflinchingly confronts risks like hallucinations, biases, and misinformation, while revealing how AI can now match, and sometimes surpass, physicians in areas ranging from diagnosis to empathy. But this isn’t simply a technology story. It’s about the human choices that will determine whether AI becomes healthcare’s salvation or another source of harm and frustration.&nbsp; Blending clinical insight, vivid storytelling, and journalistic precision, A Giant Leap offers an indispensable roadmap for healthcare leaders, clinicians, and patients. It&nbsp;is a vibrant and timely account of how AI is changing what it means to care—and be cared for—in this age of astonishing technology.

How AI Is Transforming Healthcare and What That Means for Our Future

A Giant Leap

This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)—from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level—an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:
<ul>
<li>Pretraining - Building general linguistic and semantic understanding.

</li><li>Midtraining - Expanding domain-specific capabilities and adaptability.

</li><li>Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.

</li><li>Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.



The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.
By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.
What You’ll Learn
<ul>
<li>How to configure and optimize your development environment using PyTorch

</li><li>The mechanics of tokenization, embeddings, normalization, and attention mechanisms.

</li><li>How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.

</li><li>How to integrate custom CUDA kernels to accelerate transformer computations.

</li><li>The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.

</li><li>Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.

</li><li>How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.





Who this book is for:
Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.
</li></ul></li></ul>

Design, Train, and Deploy LLMs with PyTorch

Building Large Language Models from Scratch

A New York Times and Sunday Times Bestseller Winner of the National Book Critics Circle Award 2025 Longlisted for the 2025 Financial Times &amp; Schroders Business Book of the Year Shortlisted for Foyles Non Fiction Book of the Year 2025 An eye-opening account of the tech arms race shaping out planet, from an award-winning journalist and AI insider to the world of Sam Altman and OpenAI When longtime AI expert and journalist Karen Hao first began covering OpenAI in 2019, she thought they were the good guys. Founded as a nonprofit with safety enshrined as its core mission, it was meant, its leader Sam Altman told us, to act as a check against more purely market forces. But the core truth of this massively disruptive sector is that it requires an unprecedented amount of proprietary resources: the 'compute' power of scarce high-end chips, the sheer volume of data that needs to be amassed at scale, the humans on the ground 'cleaning it up' for sweatshop wages throughout the Global South, and a truly alarming spike in the need for energy and water underlying everything. We have entered a new, ominous age of empire with OpenAI setting a breakneck pace, as a small group of the most valuable companies in human history try to chase it down. In exhilarating prose and with unparalleled access to those closest to Sam Altman, Hao recounts the meteoric rise of OpenAI and shows us the sinister impact that this industry is having on society.

Inside the reckless race for total domination

Empire of AI

Energy, Agriculture and Healthcare are key areas which address the development of the nation. Technology plays a vital role in the development of Energy, Agriculture and Healthcare. Development in sustainable Energy technologies will fulfill energy requirements for the long run. Technologies in agriculture will enable farmers to increase yield. Technology in healthcare will improve medical facilities. In today's world technology related to automation, computing, data analysis, sensor, AI/ML, and IoT drives growth in the Energy, Agriculture and Healthcare sector. There is enormous potential to explore, utilize and upgrade technologies in energy, agriculture and healthcare sector. This motivates to organize this conference which provides platform for researchers to exchange/showcase their research findings in the field of energy, agriculture and healthcare. We intend to attract high quality submission by following rigorous review process.There are conferences organized in recent times on advanced technologies independently focusing on energy, agriculture and healthcare. However our conference provides a single and integrated platform for researchers working in the area of energy, agriculture and healthcare with focus on use of IT tools in these area. Our conference is specifically focused on research in the area of Energy, agriculture and healthcare. The conference provides a platform for young researchers and Ph.D. scholars to showcase/share their research findings and get peer reviews and inputs to go further meticulously in their research area. Our conference will be organized in the hybrid mode, which will encourage researchers all over the world to participate and interact with peer researchers. Researchers will get exposure to best practices in their research area. We will be following the peer review process with a plagiarism check to maintain the originality and quality of the research paper. Under each track, researchers will be encouraged by awarding best paper.

Technologies for Energy, Agriculture, and Healthcare

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems. <ul><li>Integrates a rich array of machine learning methods into causal modeling frameworks</li><li>Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis</li><li>Features a breadth of real-world examples from recent papers</li><li>Includes coding implementation in Python, R, and Stata</li></ul>

Applied Microeconometrics

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.
The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. 
What You Will Learn

<li>A practical introduction to Machine Learning and Generative AI, tailored for engineers

</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.

</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops

</li><li>Understand different Agentic AI frameworks and their applications.

</li><li>Integrate agentic systems into real-world applications using hands-on coding examples

</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments




Who This Book Is For
Primary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. 
AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.
 


</li>

Architecting Goal-Driven Systems

Agentic AI for Engineers

Learning AutoML is your practical guide to applying automated machine learning in real-world environments. Whether you're a data scientist, ML engineer, or AI researcher, this book helps you move beyond experimentation to build and deploy high-performing models with less manual tuning and more automation. Using AutoGluon as a primary toolkit, you'll learn how to build, evaluate, and deploy AutoML models that reduce complexity and accelerate innovation. Author Kerem Tomak shares insights on how to integrate models into end-to-end deployment workflows using popular tools like Kubeflow, MLflow, and Airflow, while exploring cross-platform approaches with Vertex AI, SageMaker Autopilot, Azure AutoML, Auto-sklearn, and H2O.ai. Real-world case studies highlight applications across finance, healthcare, and retail, while chapters on ethics, governance, and agentic AI help future-proof your knowledge. <ul> <li>Build AutoML pipelines for tabular, text, image, and time series data</li> <li>Deploy models with fast, scalable workflows using MLOps best practices</li> <li>Compare and navigate today's leading AutoML platforms</li> <li>Interpret model results and make informed decisions with explainability tools</li> <li>Explore how AutoML leads into next-gen agentic AI systems</li> </ul>

Automating ML Pipelines with Autogluon, Leading Frameworks, and Real-World Integration

Learning Automl

AI Optimism for the Mildly Alarmed

Don't Panic

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

How AI can positively impact so many aspects of our daily lives, from health and wellness to work, education, and home life. Artificial intelligence (AI) is a powerful general-purpose technology that is reshaping the modern economy, but misperceptions about AI stand in the way of harnessing it for the betterment of humanity. In Thrive, Ravi Bapna and Anindya Ghose counter the backlash by showcasing how AI is positively influencing the aspects of our daily lives that we care about most: our health and wellness, relationships, education, the workplace, and domestic life. In the process the authors help explain the underlying technology and give people the agency they need to shape the debate around how we should regulate AI to maximize its benefits and minimize its risks.

Maximizing Well-Being in the Age of AI

Thrive

We're living through a transformation as profound as electricity or the printing press, yet AI often feels more like hype than help. With clear language, vivid illustrations, and humor, veteran AI researcher Sairam Sundaresan unpacks machine learning, natural language, computer vision, and recommender systems, showing how they drive the tech we use daily. You'll learn how AI works, where it appears, and how to make smarter choices about its role in your life and work. In two parts, the book moves from core concepts to real applications while debunking myths and stripping away buzzwords.

An Illustrated Introduction

AI for the Rest of Us

Work Reimagined

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