They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Consequently, organizations that understand these tools and know how to use them are benefiting at the expense of their rivals.Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies.This revised and fully updated edition contains several new sections and chapters, covering a broader set of topics than before, but retains the no-nonsense style of the original.Steven Finlay is a data scientist and author with more than 20 years’ experience of developing practical, business focused, analytical solutions. He holds a PhD in management science and is an honorary research fellow at Lancaster University in the UK.

    Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data--or anything in between--this guide makes it easier to understand and implement machine learning seamlessly.Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

    C. R. Licklider, whose visionary dream of a human-computer symbiosis transformed the course of modern science and led to the development of the personal computer. Reprint.

     Where are we? Who are we? Are our emotions, our beliefs, and our hopes and dreams ultimately meaningless out there in the void? Does human purpose and meaning fit into a scientific worldview?In short chapters filled with intriguing historical anecdotes, personal asides, and rigorous exposition, readers learn the difference between how the world works at the quantum level, the cosmic level, and the human level--and then how each connects to the other.  Carroll's presentation of the principles that have guided the scientific revolution from Darwin and Einstein to the origins of life, consciousness, and the universe is dazzlingly unique.Carroll shows how an avalanche of discoveries in the past few hundred years has changed our world and what really matters to us. Our lives are dwarfed like never before by the immensity of space and time, but they are redeemed by our capacity to comprehend it and give it meaning.The Big Picture is an unprecedented scientific worldview, a tour de force that will sit on shelves alongside the works of Stephen Hawking, Carl Sagan, Daniel Dennett, and E. O. Wilson for years to come.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    Tracy Kidder got a preview of this world in the late 1970s when he observed the engineers of Data General design and build a new 32-bit minicomputer in just one year. His thoughtful, prescient book, The Soul of a New Machine, tells stories of 35-year-old "veteran" engineers hiring recent college graduates and encouraging them to work harder and faster on complex and difficult projects, exploiting the youngsters' ignorance of normal scheduling processes while engendering a new kind of work ethic.These days, we are used to the "total commitment" philosophy of managing technical creation, but Kidder was surprised and even a little alarmed at the obsessions and compulsions he found. From in-house political struggles to workers being permitted to tease management to marathon 24-hour work sessions, The Soul of a New Machine explores concepts that already seem familiar, even old-hat, less than 20 years later. Kidder plainly admires his subjects; while he admits to hopeless confusion about their work, he finds their dedication heroic. The reader wonders, though, what will become of it all, now and in the future. —Rob Lightner

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

    More then seven thousand robotic systems are now in Iraq. Pilots in Nevada are remotely killing terrorists in Afghanistan. Scientists are debating just how smart - and how lethal - to make their current robotic prototypes. And many of the most renowned science fiction authors are secretly consulting for the Pentagon on the next generation.Blending historic evidence with interviews from the field, Singer vividly shows that as these technologies multiply, they will have profound effects on the front lines as well as on the politics back home. Moving humans off the battlefield makes wars easier to start, but more complex to fight. Replacing men with machines may save some lives, but will lower the morale and psychological barriers to killing. The "warrior ethos", which has long defined soldiers' identity, will erode, as will the laws of war that have governed military conflict for generations.While his analysis is unnerving, there's an irresistible gee-whiz quality to the innovations Singer uncovers. Wired for War travels from Iraq to see these robots in combat to the latter-day "skunk works" in America's suburbia, where tomorrow's technologies of war are quietly being designed. In Singer's hands, the future of war is as fascinating as it is frightening.

    . . But in fact, your data is your best defense against coercive marketing and intrusive government practices. Your data is nothing less than a superpower waiting to be harnessed.” —FROM THE INTRODUCTION In the past, the future was opaque—the territory of fortune-tellers, gurus, and dubious local TV weathermen. But thanks to recent advances in computing and the reams of data we create through smartphone and Internet use, prediction models for individual behavior grow smarter and more sophisticated by the day. Whom you should marry, whether you’ll commit a crime or fall victim to one, if you’ll contract a specific strain of flu—even your precise location at any given moment years into the future—are becoming easily accessible facts. The naked future is upon us, and the implications are staggering.Patrick Tucker draws on stories from health care to urban planning to online dating to reveal the shape of a future that’s ever more certain. In these pages you’ll meet scientists and inventors who can predict your behavior based on your friends’ Twitter updates. They are also hacking the New York City sewer system to predict environmental conditions, anticipating how much the weather a year from now will cost an individual farmer, figuring out the time of day you’re most likely to slip back into a bad habit, and guessing how well you’ll do on a test before you take it. You’ll learn how social networks like Facebook are using your data to turn you into an advertisement and why the winning formula for a blockbuster movie is more predictable than ever.The rise of big data and predictive analytics means that governments and corporations are becoming much more effective at accomplishing their goals and at much less cost. Tucker knows that’s not always a good thing. But he also shows how we’ve gained tremendous benefits that we have yet to fully realize.Thanks to the increased power of predictive science, we’ll be better able to stay healthy, invest our savings more wisely, learn faster and more efficiently, buy a house in the right neighborhood at the right time, avoid crime, thwart terrorists, and mitigate the consequences of natural disasters. What happens in a future that anticipates your every move? The surprising answer: we’ll live better as a result.

    Gilder says or writes is ever delivered at anything less than the fullest philosophical decibel... Mr. Gilder sounds less like a tech guru than a poet, and his words tumble out in a romantic cascade." “Google’s algorithms assume the world’s future is nothing more than the next moment in a random process. George Gilder shows how deep this assumption goes, what motivates people to make it, and why it’s wrong: the future depends on human action.” — Peter Thiel, founder of PayPal and Palantir Technologies and author of Zero to One: Notes on Startups, or How to Build the Future The Age of Google, built on big data and machine intelligence, has been an awesome era. But it’s coming to an end. In Life after Google, George Gilder—the peerless visionary of technology and culture—explains why Silicon Valley is suffering a nervous breakdown and what to expect as the post-Google age dawns. Google’s astonishing ability to “search and sort” attracts the entire world to its search engine and countless other goodies—videos, maps, email, calendars….And everything it offers is free, or so it seems. Instead of paying directly, users submit to advertising. The system of “aggregate and advertise” works—for a while—if you control an empire of data centers, but a market without prices strangles entrepreneurship and turns the Internet into a wasteland of ads. The crisis is not just economic. Even as advances in artificial intelligence induce delusions of omnipotence and transcendence, Silicon Valley has pretty much given up on security. The Internet firewalls supposedly protecting all those passwords and personal information have proved hopelessly permeable. The crisis cannot be solved within the current computer and network architecture. The future lies with the “cryptocosm”—the new architecture of the blockchain and its derivatives. Enabling cryptocurrencies such as bitcoin and ether, NEO and Hashgraph, it will provide the Internet a secure global payments system, ending the aggregate-and-advertise Age of Google. Silicon Valley, long dominated by a few giants, faces a “great unbundling,” which will disperse computer power and commerce and transform the economy and the Internet. Life after Google is almost here.   For fans of "Wealth and Poverty," "Knowledge and Power," and "The Scandal of Money."

    Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

     A young physics student starts a revolutionary new marketplace immune to State coercion; he ends up ordering hits on people because they might threaten his great experiment, and is jailed for life without parole. Fully automated contractual systems are proposed to make business and the law work better; the contracts people actually write are unregulated penny stock offerings whose fine print literally states that you are buying nothing of any value. The biggest crowdfunding in history attracts $150 million on the promise that it will embody “the steadfast iron will of unstoppable code”; upon release it is immediately hacked, and $50 million is stolen. How did we get here? David Gerard covers the origins and history of Bitcoin to the present day, the other cryptocurrencies it spawned including Ethereum, the ICO craze and the 2017 crypto bubble, and the attempts to apply blockchains and smart contracts to business. Plus a case study on blockchains in the music industry. Bitcoin and blockchains are not a technology story, but a psychology story. Remember: if it sounds too good to be true, it almost certainly is.

    Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.