Craig Venter became the first to successfully create synthetic life putting humankind at the threshold of the most important and exciting phase of biological research, one that will enable us to actually write the genetic code for designing new species to help us adapt and evolve for long-term survival. The science of synthetic genomics will have a profound impact on human existence, including chemical and energy generation, health, clean water and food production, environmental control, and possibly even our evolution. In "Life at the Speed of Light," Venter presents a fascinating and authoritative study of this emerging field from the inside detailing its origins, current challenges and controversies, and projected effects on our lives. This scientific frontier provides an opportunity to ponder anew the age-old question What is life? and examine what we really mean by playing God. " Life at the Speed of Light "is a landmark work, written by a visionary at the dawn of a new era of biological engineering."

    As she sped through the pages, she became enthralled by the intense drama behind the competition to discover the code of life. Even though her high school counselor told her girls didn’t become scientists, she decided she would.Driven by a passion to understand how nature works and to turn discoveries into inventions, she would help to make what the book’s author, James Watson, told her was the most important biological advance since his co-discovery of the structure of DNA. She and her collaborators turned ​a curiosity ​of nature into an invention that will transform the human race: an easy-to-use tool that can edit DNA. Known as CRISPR, it opened a brave new world of medical miracles and moral questions. The development of CRISPR and the race to create vaccines for coronavirus will hasten our transition to the next great innovation revolution. The past half-century has been a digital age, based on the microchip, computer, and internet. Now we are entering a life-science revolution. Children who study digital coding will be joined by those who study genetic code. Should we use our new evolution-hacking powers to make us less susceptible to viruses? What a wonderful boon that would be! And what about preventing depression? Hmmm…Should we allow parents, if they can afford it, to enhance the height or muscles or IQ of their kids? After helping to discover CRISPR, Doudna became a leader in wrestling with these moral issues and, with her collaborator Emmanuelle Charpentier, won the Nobel Prize in 2020.

    For centuries, such fears have been misplaced, and many economists maintain that they remain so today. But as Daniel Susskind demonstrates, this time really is different. Breakthroughs in artificial intelligence mean that all kinds of jobs are increasingly at risk.Drawing on almost a decade of research in the field, Susskind argues that machines no longer need to think like us in order to outperform us, as was once widely believed. As a result, more and more tasks that used to be far beyond the capability of computers – from diagnosing illnesses to drafting legal contracts, from writing news reports to composing music – are coming within their reach. The threat of technological unemployment is now real.This is not necessarily a bad thing, Susskind emphasizes. Technological progress could bring about unprecedented prosperity, solving one of humanity’s oldest problems: how to make sure that everyone has enough to live on. The challenges will be to distribute this prosperity fairly, to constrain the burgeoning power of Big Tech, and to provide meaning in a world where work is no longer the center of our lives. Perceptive, pragmatic, and ultimately hopeful, A World Without Work shows the way.

    We will soon be able to meet and exceed the basic needs of every man, woman and child on the planet. Abundance for all is within our grasp. This bold, contrarian view, backed up by exhaustive research, introduces our near-term future, where exponentially growing technologies and three other powerful forces are conspiring to better the lives of billions. An antidote to pessimism by tech entrepreneur turned philanthropist, Peter H. Diamandis and award-winning science writer Steven Kotler. Since the dawn of humanity, a privileged few have lived in stark contrast to the hardscrabble majority. Conventional wisdom says this gap cannot be closed. But it is closing—fast. The authors document how four forces—exponential technologies, the DIY innovator, the Technophilanthropist, and the Rising Billion—are conspiring to solve our biggest problems. Abundance establishes hard targets for change and lays out a strategic roadmap for governments, industry and entrepreneurs, giving us plenty of reason for optimism.Examining human need by category—water, food, energy, healthcare, education, freedom—Diamandis and Kotler introduce dozens of innovators making great strides in each area: Larry Page, Steven Hawking, Dean Kamen, Daniel Kahneman, Elon Musk, Bill Joy, Stewart Brand, Jeff Skoll, Ray Kurzweil, Ratan Tata, Craig Venter, among many, many others.

    In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development: digitization--the coding of information, computation, and control in binary form, ones and zeros; the convergence of multiple streams of techniques, devices, and machines, yielding more than the sum of their parts; the steady advance of electronic technology, as characterized famously by "Moore's Law"; and the human-machine interface. Ceruzzi guides us through computing history, telling how a Bell Labs mathematician coined the word "digital" in 1942 (to describe a high-speed method of calculating used in anti-aircraft devices), and recounting the development of the punch card (for use in the 1890 U.S. Census). He describes the ENIAC, built for scientific and military applications; the UNIVAC, the first general purpose computer; and ARPANET, the Internet's precursor. Ceruzzi's account traces the world-changing evolution of the computer from a room-size ensemble of machinery to a "minicomputer" to a desktop computer to a pocket-sized smart phone. He describes the development of the silicon chip, which could store ever-increasing amounts of data and enabled ever-decreasing device size. He visits that hotbed of innovation, Silicon Valley, and brings the story up to the present with the Internet, the World Wide Web, and social networking.

    Can small artists still thrive in the Internet era? Can giant record labels avoid alienating their audiences? This is a book about the pitfalls and the opportunities that creative industries (and individuals) are confronting today — about how the old models have failed or found new footing, and about what might soon replace them. An essential read for anyone with a stake in the future of the arts, Information Doesn’t Want to Be Free offers a vivid guide to the ways creativity and the Internet interact today, and to what might be coming next.

    The topic everyone in the world should be talking about.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Then he wrote a groundbreaking operating system and distributed it via the Internet -- for free. Today Torvalds is an international folk hero. And his creation LINUX is used by over 12 million people as well as by companies such as IBM.Now, in a narrative that zips along with the speed of e-mail, Torvalds gives a history of his renegade software while candidly revealing the quirky mind of a genius. The result is an engrossing portrayal of a man with a revolutionary vision, who challenges our values and may change our world.

    Readers will find excerpts from bestsellers such as Douglas R. Hofstadter's Gödel, Escher, Bach, Francis Crick's Life Itself, Loren Eiseley's The Immense Journey, Daniel Dennett's Darwin's Dangerous Idea, and Rachel Carson's The Sea Around Us. There are classic essays ranging from J.B.S. Haldane's "On Being the Right Size" and Garrett Hardin's "The Tragedy of the Commons" to Alan Turing's "Computing Machinery and Intelligence" and Albert Einstein's famed New York Times article on "Relativity." And readers will also discover lesser-known but engaging pieces such as Lewis Thomas's "Seven Wonders of Science," J. Robert Oppenheimer on "War and Physicists," and Freeman Dyson's memoir of studying under Hans Bethe.A must-read volume for all science buffs, The Oxford Book of Modern Science Writing is a rich and vibrant anthology that captures the poetry and excitement of scientific thought and discovery.One of New Scientist's Editor's Picks for 2008.

    citizen recognized its ominous potential. Armed with clear evidence of computer espionage, he began a highly personal quest to expose a hidden network of spies that threatened national security. But would the authorities back him up? Cliff Stoll's dramatic firsthand account is "a computer-age detective story, instantly fascinating [and] astonishingly gripping" (Smithsonian). Cliff Stoll was an astronomer turned systems manager at Lawrence Berkeley Lab when a 75-cent accounting error alerted him to the presence of an unauthorized user on his system. The hacker's code name was "Hunter" -- a mysterious invader who managed to break into U.S. computer systems and steal sensitive military and security information. Stoll began a one-man hunt of his own: spying on the spy. It was a dangerous game of deception, broken codes, satellites, and missile bases -- a one-man sting operation that finally gained the attention of the CIA...and ultimately trapped an international spy ring fueled by cash, cocaine, and the KGB.

    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

    As society stands on the cusp of unprecedented change, Jerry Kaplan unpacks the latest advances in robotics, machine learning, and perception powering systems that rival or exceed human capabilities. Driverless cars, robotic helpers, and intelligent agents that promote our interests have the potential to usher in a new age of affluence and leisure — but as Kaplan warns, the transition may be protracted and brutal unless we address the two great scourges of the modern developed world: volatile labor markets and income inequality. He proposes innovative, free-market adjustments to our economic system and social policies to avoid an extended period of social turmoil. His timely and accessible analysis of the promise and perils of artificial intelligence is a must-read for business leaders and policy makers on both sides of the aisle.

    In the beginning was Josh Levine, an idealistic programming genius who dreamed of wresting control of the market from the big exchanges that, again and again, gave the giant institutions an advantage over the little guy. Levine created a computerized trading hub named Island where small traders swapped stocks, and over time his invention morphed into a global electronic stock market that sent trillions in capital through a vast jungle of fiber-optic cables. By then, the market that Levine had sought to fix had turned upside down, birthing secretive exchanges called dark pools and a new species of trading machines that could think, and that seemed, ominously, to be slipping the control of their human masters. Dark Pools is the fascinating story of how global markets have been hijacked by trading robots--many so self-directed that humans can't predict what they'll do next.

    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.