This book looks at how America designed and built the fastest chips and maintained its lead as the #1 superpower. Now, America's edge is slipping, undermined by competitors in Taiwan, Korea, Europe, and, above all, China. Today, as Chris Miller reveals, China, which spends more money each year importing chips than it spends importing oil, is pouring billions into a chip-building initiative to catch up to the US. At stake is America's military superiority and economic prosperity.

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How do you know what might have happened, had you done things differently? I this book, Robert Osazuwa Ness gives insights on how to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers, and provides a practical introduction to building AI models that can reason about causality.

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In this book Anil Ananthaswamy explains the fundamental math behind machine learning-powered AI, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. Ananthaswamy goes on to suggest intriguing links between artificial and natural intelligence. Might the same math underpin them both? He also investigates the transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.

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From the creator of Dilbert, this is an updated second edition of Scott Adam's guidebook to spotting and avoiding loserthink - the  sneaky mental habits trapping victims in their own bubbles of reality. The premise of the book is that if you've been on social media lately, or turned on your TV, you may have noticed a lot of dumb ideas floating around, such as "We can tell the difference between evidence and coincidences," and "The simplest explanation is usually true."

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In this book. subtitled "A Friendly Approach to Modern Functional Programming, Type Theory, and Artificial Intelligence", Anton Antich uses playful metaphors and examples to help teach Haskell through imagination, building on math without relying on imperative crutches or technical complexity. Readers will use math to build completely different Typed Functional patterns from the ground up and understand the link between building mathematics through yypes and constructing Haskell as a programming language. 

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This book introduces mathematical techniques, key algorithms, and Python implementations for building machine learning models for unannotated data. It bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. Vaibhav Verdhan introduces hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic business decisions.

<ASIN: 1617298727>

Subtitled "A mainframe in my living room", this book is about the PiDP-10 kit and the three operating systems that you can run on it. Andrew Barron acknowledges that very few people have the room, the money, or the time to restore a full-size PDP-10 as they were large installations that required a big climate-controlled room. However, he points out that due to the work by Bob Supnik, the original developer of the SimH emulator, enthusiasts can download an emulator onto a Raspberry Pi and run an accurate simulation of the DEC PDP-10 system.

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In this book Susanne Kaiser looks at how to design and build adaptive systems that can handle change. By combining Wardley Mapping, Domain-Driven Design, and Team Topologies, this book offers a comprehensive toolset for organizations to anticipate change. Drawing from historical examples of companies that failed to adapt, Kaiser emphasizes that optimization requires treating organizations as socio-technical systems where social and technical aspects are aligned and designed together.

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This guide to Swift has been updated for Swift 6.2. Jon Hoffman covers advanced concepts and techniques, such as concurrency, memory management, and Generics, all essential for creating high-performance applications. This edition covers Swift 6.2’s newest features alongside advanced programming topics such as reflection, concurrency, and Generics.

<ASIN: 1836203691>

This book, with the subtitle "Why Human Creativity Still Wins in the AI Revolution" looks at the past and future of creativity, showing why flaws, grit, and the human voice still matter in an age of machine-made everything. Micah Voraritskul traces the arc from the printing press to generative AI and reveals how each wave of innovation reshapes what we create, how we connect, and what we decide is worth keeping. With stories, cultural insight, and sharp reflection, Voraritskul argues that the human voice — with all its texture and imperfection — remains the most powerful force we have.

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This book introduces the statistical and quantitative methods that will help programmers go beyond “gut feeling” for tasks like predicting stock prices or assessing quality control, with examples using the rich tools of the Python ecosystem. Gary Sutton blends the theory behind these statistical techniques with practical Python-based applications, offering structured, reproducible, and defensible methods for tackling complex decisions.

<ASIN: 1633436055>

This book aims to make stats approachable by combining clear explanations with practical applications. Joseph Schmuller shows how to download and use R and RStudio - two free, open-source tools - to learn statistics concepts, create graphs, test hypotheses, and draw meaningful conclusions. The book starts with the basics of statistics and R, and goes on to show how to calculate descriptive statistics, and use inferential statistics to test hypotheses. Then, visualize it all with graphs and charts.

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