The post-industrial society’s dimensions would include the spread of a knowledge class, the change from goods to services and the role of women. All of these would be dependent on the expansion of services in the economic sector and an increasing dependence on science as the means of innovating and organizing technological change.Bell prophetically stated in The Coming of the Post-Industrial Society that we should expect “… new premises and new powers, new constraints and new questions—with the difference that these are now on a scale that had never been previously imagined in world history.”

    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.

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    It draws our attention away from the bright shiny objects of the new colonialism through elucidating the social, material and political dimensions of Artificial Intelligence.”—Geoffrey C. Bowker, University of California, Irvine What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased racial, gender, and economic inequality. Drawing on more than a decade of research, award‑winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind “automated” services, to the data AI collects from us.    Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

    You think you are special, unique, and that whatever it is that you are doing is impossible to replace. You are wrong. As we speak, millions of algorithms created by computer scientists are frantically running on servers all over the world, with one sole purpose: do whatever humans can do, but better.That is the argument for a phenomenon called technological unemployment, one that is pervading modern society. But is that really the case? Or is it just a futuristic fantasy? What will become of us in the coming years, and what can we do to prevent a catastrophic collapse of society?Robots Will Steal Your Job, But That's OK: how to survive the economic collapse and be happy explores the impact of technological advances on our lives, what it means to be happy, and provides suggestions on how to avoid a systemic collapse.

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    He argues that the anthropocene - the age in which humans acquired planetary-scale technologies - is, after 300 years, coming to an end. A new age - the novacene - has already begun.New beings will emerge from existing artificial intelligence systems. They will think 10,000 times faster than we do and they will regard us as we now regard plants - as desperately slow acting and thinking creatures. But this will not be the cruel, violent machine takeover of the planet imagined by sci-fi writers and film-makers. These hyper-intelligent beings will be as dependent on the health of the planet as we are. They will need the planetary cooling system of Gaia to defend them from the increasing heat of the sun as much as we do. And Gaia depends on organic life. We will be partners in this project.It is crucial, Lovelock argues, that the intelligence of Earth survives and prospers. He does not think there are intelligent aliens, so we are the only beings capable of understanding the cosmos. Maybe, he speculates, the novacene could even be the beginning of a process that will finally lead to intelligence suffusing the entire cosmos. At the age 100, James Lovelock has produced the most important and compelling work of his life.

    Yet the reality of the cloud is embodied in thousands of massive data centers, any one of which can use as much electricity as a midsized town. Even all these data centers are only one small part of the cloud. Behind that cloud-shaped icon on our screens is a whole universe of technologies and cultural norms, all working to keep us from noticing their existence. In this book, Tung-Hui Hu examines the gap between the real and the virtual in our understanding of the cloud. Hu shows that the cloud grew out of such older networks as railroad tracks, sewer lines, and television circuits. He describes key moments in the prehistory of the cloud, from the game "Spacewar" as exemplar of time-sharing computers to Cold War bunkers that were later reused as data centers. Countering the popular perception of a new "cloudlike" political power that is dispersed and immaterial, Hu argues that the cloud grafts digital technologies onto older ways of exerting power over a population. But because we invest the cloud with cultural fantasies about security and participation, we fail to recognize its militarized origins and ideology. Moving between the materiality of the technology itself and its cultural rhetoric, Hu's account offers a set of new tools for rethinking the contemporary digital environment.

    Advocated here are—among other things—universal conscription, the full acknowledgment of envy and resentment as a fundamental challenge to any communist society, and the acceptance that the division between work and leisure cannot be overcome. To create a new world, we must first change the way we envision the world. Jameson’s text is ideally placed to trigger a debate on the alternatives to global capitalism. In addition to Jameson’s essay, the volume includes responses from philosophers and political and cultural analysts, as well as an epilogue from Jameson himself.Many will be appalled at what they will encounter in these pages—there will be blood! But perhaps one has to spill such (ideological) blood to give the Left a chance.

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    China delivered a bold message when it released a national plan to dominate all aspects of AI across the planet. Within weeks, Russia's Vladimir Putin raised the stakes by declaring AI the future for all humankind, and proclaiming that, "Whoever becomes the leader in this sphere will become the ruler of the world."The race was on. Consistent with their unique national agendas, countries throughout the world began plotting their paths and hurrying their pace. Now, not long after, the race has become a sprint.Despite everything at risk, for most of us AI remains shrouded by a cloud of mystery and misunderstanding. Hidden behind complex technical terms and confused even further by extravagant depictions in science fiction, the realities of AI and its profound implications are hard to decipher, but no less crucial to understand.In T-Minus AI: Humanity's Countdown to Artificial Intelligence and the New Pursuit of Global Power, author Michael Kanaan explains the realities of AI from a human-oriented perspective that's easy to comprehend. A recognized national expert and the U.S. Air Force's first Chairperson for Artificial Intelligence, Kanaan weaves a compelling new view on our history of innovation and technology to masterfully explain what each of us should know about modern computing, AI, and machine learning.Kanaan also illuminates the global implications of AI by highlighting the cultural and national vulnerabilities already exposed and the pressing issues now squarely on the table. AI has already become China's all-purpose tool to impose authoritarian influence around the world. Russia, playing catch up, is weaponizing AI through its military systems and now infamous, aggressive efforts to disrupt democracy by whatever disinformation means possible.America and like-minded nations are awakening to these new realities, and the paths they're electing to follow echo loudly, in most cases, the political foundations and moral imperatives upon which they were formed.As we march toward a future far different than ever imagined, T-Minus AI is fascinating and critically well-timed. It leaves the fiction behind, paints the alarming implications of AI for what they actually are, and calls for unified action to protect fundamental human rights and dignities for all.

    Since then, that share has plummeted to where it was in 1900. As Richard Baldwin explains, this reversal of fortune reflects a new age of globalisation that is drastically different from the old.In the 1800s, globalisation leaped forward when steam power and international peace lowered the costs of moving goods across borders. This triggered a self-fueling cycle of industrial agglomeration and growth that propelled today's rich nations to dominance. That was the Great Divergence. The new globalisation is driven by information technology, which has radically reduced the cost of moving ideas across borders. This has made it practical for multinational firms to move labor-intensive work to developing nations. But to keep the whole manufacturing process in sync, the firms also shipped their marketing, managerial, and technical know-how abroad along with the offshored jobs. The new possibility of combining high tech with low wages propelled the rapid industrialisation of a handful of developing nations, the simultaneous deindustrialisation of developed nations, and a commodity supercycle that is only now petering out. The result is today's Great Convergence.Because globalisation is now driven by fast-paced technological change and the fragmentation of production, its impact is more sudden, more selective, more unpredictable, and more uncontrollable. As The Great Convergence shows, the new globalisation presents rich and developing nations alike with unprecedented policy challenges in their efforts to maintain reliable growth and social cohesion.

    Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

    Meritocracy. The possibility of overnight success. What's not to love about Silicon Valley?These days, it's hard to be unambiguously optimistic about the growth-at-all-costs ethos of the tech industry. Public opinion is souring in the wake of revelations about Cambridge Analytica, Theranos, and the workplace conditions of Amazon warehouse workers or Uber. We're starting to see the cracks in the edifice, as we realise that the wealth that the tech industry is so good at creating is neither sustainable nor always desirable. Abolish Silicon Valley is both a heartfelt personal story about the wasteful inequality and unsubstantiated lies of Silicon Valley, and a rallying call to engage in the radical politics needed to upend the status quo. Going beyond the idiosyncrasies of the individual founders and companies that characterise the industry today, Liu delves into the structural factors of the economy that led to Silicon Valley in its current form, and links them to the economy at large. Ultimately, she proposes a more radical way of developing technology, where innovation is conducted for the benefit of society at large, and not merely to enrich a select few.

    AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” —Steven D. Levitt, bestselling co-author of Freakonomics From leading data scientists Nick Polson and James Scott, what everyone needs to know to understand how artificial intelligence is changing the world and how we can use this knowledge to make better decisions in our own lives. Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the 21st century in the same way that the Industrial Revolution remade the world in the 19th century. AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language—but in an unconventional way, anchored in stories rather than equations. You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability, and better thinking. Along the way, you'll see how these same ideas are playing out in the modern age of big data and intelligent machines—and how these technologies will soon help you to overcome some of your built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.