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With Empire of AI, Hao is fundamentally shaping many people’s perceptions and understanding of the company at the center of the AI revolution.” —TIME Magazine, “TIME100 AI 2025”“Excellent and deeply reported.” —Tim Wu, The New York Times“Startling and intensely researched . . . an essential account of how OpenAI and ChatGPT came to be and the catastrophic places they will likely take us.” —Vulture“Hao’s reporting inside OpenAI is exceptional, and she’s persuasive in her argument that the public should focus less on A.I.’s putative ‘sentience’ and more on its implications for labor and the environment.” —Benjamin Wallace-Wells, New YorkerFrom a brilliant longtime AI insider with intimate access to the world of Sam Altman's OpenAI from the beginning, an eye-opening account of arguably the most fateful tech arms race in history, reshaping the planet in real time, from the cockpit of the company that is driving the frenzyWhen AI expert and investigative journalist Karen Hao first began covering OpenAI in 2019, she thought they were the good guys. 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At the head of the pack with its ChatGPT breakthrough, how would OpenAI resist such temptations?Spoiler alert: it didn’t. Armed with Microsoft’s billions, OpenAI is setting a breakneck pace, chased by a small group of the most valuable companies in human history—toward what end, not even they can define. All this time, Hao has maintained her deep sourcing within the company and the industry, and so she was in intimate contact with the story that shocked the entire tech industry—Altman’s sudden firing and triumphant return. The behind-the-scenes story of what happened, told here in full for the first time, is revelatory of who the people controlling this technology really are. But this isn’t just the story of a single company, however fascinating it is. The g forces pressing down on the people of OpenAI are deforming the judgment of everyone else too—as such forces do. Naked power finds the ideology to cloak itself; no one thinks they’re the bad guy. But in the meantime, as Hao shows through intrepid reporting on the ground around the world, the enormous wheels of extraction grind on. By drawing on the viewpoints of Silicon Valley engineers, Kenyan data laborers, and Chilean water activists, Hao presents the fullest picture of AI and its impact we’ve seen to date, alongside a trenchant analysis of where things are headed. An astonishing eyewitness view from both up in the command capsule of the new economy and down where the real suffering happens, Empire of AI pierces the veil of the industry defining our era.   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What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of the new and familiar is the most fulfilling? These may seem like uniquely human quandaries, but they are not. Computers, like us, confront limited space and time, so computer scientists have been grappling with similar problems for decades. And the solutions they’ve found have much to teach us.In a dazzlingly interdisciplinary work, Brian Christian and Tom Griffiths show how algorithms developed for computers also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one’s inbox to peering into the future, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.   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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. 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They now influence discoveries in chemistry, biology and physics - the study of genomes, extra-solar planets, even the intricacies of quantum systems.We are living through a revolution in artificial intelligence that is not slowing down. This major shift is based on simple mathematics, some of which goes back centuries: linear algebra and calculus, the stuff of eighteenth-century mathematics. Indeed by the mid-1850s, a lot of the groundwork was all done. It took the development of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today. 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This book offers a clear and structured approach to help you tackle a wide range of GenAI system design questions. It provides a practical framework and real-world examples to make learning these concepts easier.This book complements our ML System Design Interview book. While the first book focuses on topics such as search and recommendation systems, this one centers on generative systems, with detailed examples and explanations to help you understand how GenAI systems are built in practice.What’s inside?An insider’s perspective on what interviewers are truly looking for and why.A 7-step framework to help you tackle GenAI system design interview questions.10 real-world GenAI system design questions with in-depth solutions.280+ diagrams to demystify complex GenAI systems.   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It can be used as a reference manual by those readers in the computer Science industry. The book has around 21 chapters and covers recursion and Backtracking, linked lists, stacks, queues, trees, priority queue and heaps, disjoint sets ADT, graph algorithms, sorting, searching, selection algorithms [Medians], symbol tables, hashing, string algorithms, algorithms design techniques, greedy algorithms, divide and conquer algorithms, dynamic programming, complexity classes, and other Miscellaneous concepts. Data structures and algorithms made easy in Java: data structure and algorithmic puzzles by Narasimha karumanchi was published in 2011, and it is coded in Java language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in C\/C++. in short, this book provides solutions to various complex data structures and algorithmic problems. What is unique. Our main objective isn't to propose theorems and proofs about antibodies and algorithms. We took the direct route and solved problems of varying complexities. That is, each problem corresponds to multiple solutions with different complexities. In other words, we enumerated possible solutions. With this approach, even when a new question arises, we offer a choice of different solution strategies based on your priorities. Topics covered: introduction recursion and Backtracking linked lists stacks queues trees priority queue and heaps disjoint sets ADT graph algorithms sorting searching selection algorithms [Medians] symbol tables hashing string algorithms algorithms design techniques greedy algorithms divide and conquer algorithms dynamic programming complexity classes Miscellaneous concepts target audience? These books prepare readers for interviews, exams, and campus work. Language? All code was written in Java. If you are using C\/C++, please search for \"data structures and algorithms made easy\". 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Within two decades, aspects of daily human life will be unrecognizable. AI will generate unprecedented wealth, revolutionize medicine and education through human-machine symbiosis, and create brand new forms of communication and entertainment. However, AI will also challenge the organizing principles of our economic and social order and bring new risks in the form of autonomous weapons and smart technology that inherits human bias. AI is at a tipping point, and people need to wake up-both to AI's radiant pathways and its existential perils for life as we know it.In this provocative, utterly original work of \"scientific fiction,\" Kai-Fu Lee, the former president of Google China and bestselling author of AI Superpowers, joins forces with celebrated novelist Chen Qiufan to imagine our AI world in 2041 in ten gripping short stories.Gazing toward a not-so-distant horizon, AI 2041 offers urgent insights into our collective future and reminds us that we are the authors of our own destiny.   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With so much knowledge scattered across online resources, it's easy to feel lost or stressed about where to start. Over the past seven and half years, I've focused on gathering and distilling the most important concepts, algorithms, and models into one comprehensive and accessible resource. This book is the result of this effort. My goal is to make learning both simple and fast, the two pillars of effective education today. To make even the most complex ideas memorable, I've included comics and humorous examples throughout the book with the hope that readers not only understand the material but also retain it long after they've finished reading.About the Author:Reza Rawassizadeh is an Associate Professor of Computer Science at Boston University with over a decade of experience in academic research and industrial projects. His scholarly contributions span digital health, ubiquitous technologies, resource-efficient computing, and on-device machine learning. 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Ray Kurzweil is the inventor of the most innovative and compelling technology of our era, an international authority on artificial intelligence, and one of our greatest living visionaries. Now he offers a framework for envisioning the twenty-first century--an age in which the marriage of human sensitivity and artificial intelligence fundamentally alters and improves the way we live. Kurzweil's prophetic blueprint for the future takes us through the advances that inexorably result in computers exceeding the memory capacity and computational ability of the human brain by the year 2020 (with human-level capabilities not far behind); in relationships with automated personalities who will be our teachers, companions, and lovers; and in information fed straight into our brains along direct neural pathways. 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You’ll also find four new chapters on database integration, speech recognition, and audio and video editing, as well as 16 new programming projects and expanded coverage of developer techniques like creating command line programs.Don’t spend your time on work a well-trained monkey could do. Even if you’ve never written a line of code, you can pass off that grunt work to your computer. Learn how in Automate the Boring Stuff with Python.   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Learn Python the Easy Way with No Prior Experience!Key Features include: Comprehensive coverage of Python basicsIdeal for beginners and career changersReal-world applications and case studiesPractical examples and quizzes for reinforcementGet ready to program with Python- one of the most in-demand skills of this decade! If you are interested in learning programming, but not sure where to start, Python Essentials You Always Wanted to Know is your guide to learning Python, one of the most versatile and beginner-friendly programming languages. This book is designed with absolute beginners in mind, focusing on clear explanations and practical examples rather than technical jargon. Regardless of skill level, there's something here for everyone, from the basics of programming logic to more advanced topics like object-oriented programming and error handling. 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Whether you are an experienced data scientist or just starting, this edition delivers practical insights and clear explanations of essential concepts like regression, classification, clustering, deep learning, and time series forecasting.This edition introduces two new chapters: \"Mastering GenAI and LLMs\" and \"Understanding GANs for Generative AI with a Hands-on Project\", which provide deep dives into large language models and generative adversarial networks (GANs). With hands-on Python code snippets and real-world project examples, the book bridges the gap between theory and application, offering you the tools to apply machine learning techniques effectively.Additional highlights include performance evaluation methods, data preprocessing techniques, feature engineering, and a quick reference appendix for tuning machine learning models. 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