LLM Systems Engineering: Training and Building Large Language Models – Engineering AI Models Through Fine-Tuning, Continued P

Pick

The AI That Changed Everything

Cole Peterson

Short, plain language, for anyone who is tired of being the last one in the room getting the point about AI.

The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions

Geoff Woods

"The AI-Driven Leader" by Geoff Woods equips leaders with practical strategies to integrate artificial intelligence into their decision-making processes. The book explores how AI can augment human judgment by analyzing vast datasets, identifying patterns, and predicting future outcomes, enabling more informed and agile choices. Woods emphasizes the importance of understanding AI's capabilities and limitations to effectively leverage its power for improved organizational performance and competitive advantage.

Co-Intelligence: Living and Working with AI

Josh Tyrangiel

This book explores the evolving relationship between humans and artificial intelligence, moving beyond simple utility to consider how we can foster genuine collaboration. Tyrangiel argues for a "co-intelligent" future where AI acts as a partner, augmenting human capabilities and fostering creativity rather than replacing them. The work delves into practical strategies for navigating this new landscape in both personal and professional spheres, emphasizing the importance of adapting our skills and perspectives to harness AI's full potential.

AI for Good: How Real People Are Using Artificial Intelligence to Fix Things That Matter

Josh Tyrangiel

"AI for Good" by Josh Tyrangiel showcases how individuals and organizations are leveraging artificial intelligence to address critical global challenges. The book highlights practical applications in fields like healthcare, environmental protection, and social justice, demonstrating AI's potential to drive positive change. Tyrangiel presents compelling case studies of AI's impact, offering a hopeful perspective on its role in solving complex societal issues.

Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare

Sebastian Mallaby

This book chronicles the improbable journey of Marine Colonel Gregory Boyington's team as they spearheaded Project Maven, a groundbreaking initiative to integrate artificial intelligence into drone warfare. Mallaby details the complex challenges of overcoming bureaucratic inertia and technical hurdles to equip military drones with AI for target identification. The narrative highlights the ethical and strategic implications of this nascent era of AI-driven conflict, showcasing the human ingenuity behind a significant technological leap.

The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence

Sebastian Mallaby

Sebastian Mallaby's *The Infinity Machine* chronicles the ambitious journey of Demis Hassabis and his company DeepMind as they strive to create artificial general intelligence (AGI). The book details the scientific breakthroughs, intense competition, and ethical considerations surrounding DeepMind's pursuit of an AI capable of human-level reasoning and problem-solving across diverse domains. It explores the potential societal implications of achieving superintelligence and the complex challenges of guiding such a powerful technology responsibly.

I Am Not a Robot: My Year Using AI to Do (Almost) Everything

Donella H. Meadows

This insightful memoir chronicles a year-long experiment where the author, Donella H. Meadows, attempted to delegate a significant portion of her daily tasks and responsibilities to artificial intelligence. Meadows meticulously documents her experiences, exploring the practical applications, surprising limitations, and evolving relationship between human agency and AI-driven assistance across various facets of her life. The narrative offers a compelling examination of the potential and pitfalls of integrating AI into everyday routines, prompting reflection on work, creativity, and the very definition of human productivity.

AI Engineering: Building Applications with Foundation Models

Chip Huyen

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All

Eliezer Yudkowsky, Nate Soares, Rafe Beckley, Little

INSTANT NEW YORK TIMES BESTSELLER | The New Yorker's Best Books of 2025 | A 2025 Booklist Editors' Choice Pick The scramble to create superhuman AI has put us on the path to extinction—but it’s not too late to change course, as two of the field’s earliest researchers explain in this clarion call for humanity. "May prove to be the most important book of our time.”—Tim Urban, Wait But Why In 2023, hundreds of AI luminaries signed an open letter warning that artificial intelligence poses a serious risk of human extinction. Since then, the AI race has only intensified. Companies and countries are rushing to build machines that will be smarter than any person. And the world is devastatingly unprepared for what would come next. For decades, two signatories of that letter—Eliezer Yudkowsky and Nate Soares—have studied how smarter-than-human intelligences will think, behave, and pursue their objectives. Their research says that sufficiently smart AIs will develop goals of their own that put them in conflict with us—and that if it comes to conflict, an artificial superintelligence would crush us. The contest wouldn’t even be close. How could a machine superintelligence wipe out our entire species? Why would it want to? Would it want anything at all? In this urgent book, Yudkowsky and Soares walk through the theory and the evidence, present one possible extinction scenario, and explain what it would take for humanity to survive. The world is racing to build something truly new under the sun. And if anyone builds it, everyone dies. “The best no-nonsense, simple explanation of the AI risk problem I've ever read.”—Yishan Wong, Former CEO of Reddit