Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling.--Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library JournalTHE UNTOLD TECH STORY OF OUR TIMEWhat does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create?With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.Long dismissed as a technology of the distant future, artificial intelligence was a project consigned to the fringes of the scientific community. Then two researchers changed everything. One was a sixty-four-year-old computer science professor who didn't drive and didn't fly because he could no longer sit down--but still made his way across North America for the moment that would define a new age of technology. The other was a thirty-six-year-old neuroscientist and chess prodigy who laid claim to being the greatest game player of all time before vowing to build a machine that could do anything the human brain could do.They took two very different paths to that lofty goal, and they disagreed on how quickly it would arrive. But both were soon drawn into the heart of the tech industry. Their ideas drove a new kind of arms race, spanning Google, Microsoft, Facebook, and OpenAI, a new lab founded by Silicon Valley kingpin Elon Musk. But some believed that China would beat them all to the finish line.Genius Makers dramatically presents the fierce conflict between national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice. Like a great Victorian novel, this world of eccentric, brilliant, often unimaginably yet suddenly wealthy characters draws you into the most profound moral questions we can ask. And like a great mystery, it presents the story and facts that lead to a core, vital question:How far will we let it go?

    In this richly illustrated book, accessible examples are used to show how information theory can be understood in terms of everyday games like '20 Questions', and the simple MatLab programs provided give hands-on experience of information theory in action. Written in a tutorial style, with a comprehensive glossary, this text represents an ideal primer for novices who wish to become familiar with the basic principles of information theory.Download chapter 1 from http://jim-stone.staff.shef.ac.uk/Boo...

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Its enable us to sense, measure, and understand aspects of our existence in ways never before possible. The Human Face of Big Data captures, in glorious photographs and moving essays, an extraordinary revolution sweeping, almost invisibly, through business, academia, government, healthcare, and everyday life. It's already enabling us to provide a healthier life for our children. To provide our seniors with independence while keeping them safe. To help us conserve precious resources like water and energy. To alert us to tiny changes in our health, weeks or years before we develop a life-threatening illness. To peer into our own individual genetic makeup. To create new forms of life.  And soon, as many predict, to re-engineer our own species. And we've barely scratched the surface . . . Over the past decade, Rick Smolan and Jennifer Erwitt, co-founders of Against All Odds Productions, have produced a series of ambitious global projects in collaboration with hundreds of the world's leading photographers, writers, and graphic designers. Their Day in the Life projects were credited for creating a mass market for large-format illustrated books (rare was the coffee table book without one).  Today their projects aim at sparking global conversations about emerging topics ranging from the Internet (24 Hours in Cyberspace), to Microprocessors (One Digital Day), to how the human race is learning to heal itself, (The Power to Heal) to the global water crisis (Blue Planet Run). This year Smolan and Erwitt dispatched photographers and writers in every corner of the globe to explore the world of “Big Data” and to determine if it truly does, as many in the field claim, represent a brand new toolset for humanity, helping address the biggest challenges facing our species. The book features 10 essays by noted writers:Introduction: OCEANS OF DATA by Dan GardnerChapter 1: REFLECTIONS IN A DIGITAL MIRROR by Juan Enriquez, CEO, BiotechnomomyChapter 2: OUR DATA OURSELVES by Kate Green, the EconomistChapter 3: QUANTIFYING MYSELF by AJ Jacobs, EsquireChapter 4: DARK DATA by Marc Goodman, Future Crime InstituteChapter 5:  THE SENTIENT SENSOR MESH by Susan Karlin, Fast CompanyChapter 6: TAKING THE PULSE OF THE PLANET by Esther Dyson, EDventureChapter 7: CITIZEN SCIENCE by Gareth Cook, the Boston GlobeChapter 8: A DEMOGRAPH OF ONE by Michael Malone, Forbes magazineChapter 9: THE ART OF DATA by Aaron Koblin, Google Artist in ResidenceChapter 10: DATA DRIVEN by Jonathan Harris, Cowbird The book will also feature stunning info graphics from NIGEL HOLMES.1) GOOGLING GOOGLE: all the ways Google uses Data to help humanity2) DATA IS THE NEW OIL3) THE WORLD ACCORDING TO TWITTER4) AUCTIONING EYEBALLS: The world of Internet advertising5) FACEBOOK: A Billion Friends

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Try out more than 1,000 commands to find and get software, monitor system health and security, and access network resources. Then, apply the skills you learn from this book to use and administer desktops and servers running Ubuntu, Debian, and KNOPPIX or any other Linux distribution.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    Is your company ready?If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them. In this book you'll learn how: Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights Blockchain has the potential to restructure the economy Drones and driverless vehicles are becoming essential tools 3-D printing is making new business models possible Augmented reality is transforming retail and manufacturing Smart speakers are redefining the rules of marketing Humans and machines are working together to reach new levels of productivity This collection of articles includes "Artificial Intelligence for the Real World," by Thomas H. Davenport and Rajeev Ronanki; "Stitch Fix's CEO on Selling Personal Style to the Mass Market," by Katrina Lake; "Algorithms Need Managers, Too," by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; "Marketing in the Age of Alexa," by Niraj Dawar; "Why Every Organization Needs an Augmented Reality Strategy," by Michael E. Porter and James E. Heppelmann; "Drones Go to Work," by Chris Anderson; "The Truth About Blockchain," by Marco Iansiti and Karim R. Lakhani; "The 3-D Printing Playbook," by Richard A. D’Aveni; "Collaborative Intelligence: Humans and AI Are Joining Forces," by H. James Wilson and Paul R. Daugherty; "When Your Boss Wears Metal Pants," by Walter Frick; and "Managing Our Hub Economy," by Marco Iansiti and Karim R. Lakhani.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    

     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.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.