Today’s "machine-learning" systems, trained by data, are so effective that we’ve invited them to see and hear for us?and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole?and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands.The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software.In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story.The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

    Leaving behind a warring Earth flung headfirst into a conflict of mutual assured destruction, the Rangers and the accompanying crew of first colonists are guided on a 40-year journey by an unprecedented artificial intelligence.But when they emerge from the frigid embrace of cryosleep, they awake to a nightmare, finding themselves greeted by the same ruthless enemy that brought about the ruin of Earth. Alone on a dangerous, alien planet and with no hope of rescue or relief, the military colonists are forced to finish the war they thought they’d left behind. And in an unknown galaxy, friends and enemies alike prove to be much more than they seem.Wayward Galaxy is an explosive military science-fiction adventure featuring defective AIs, valorous soldiers, a brilliant scientist, and gritty combat written by Jason Anspach (Associated Press best seller and cocreator of Galaxy’s Edge) and J.N. Chaney (USA Today best seller and author of the Renegade series).

    Growing up in Egypt and Kuwait, el Kaliouby was raised by a strict father who valued tradition—yet also had high expectations for his daughters—and a mother who was one of the first female computer programmers in the Middle East. Even before el Kaliouby broke ground as a scientist, she broke the rules of what it meant to be an obedient daughter and, later, an obedient wife to pursue her own daring dream.After earning her PhD at Cambridge, el Kaliouby, now the divorced mother of two, moved to America to pursue her mission to humanize technology before it dehumanizes us. The majority of our communication is conveyed through nonverbal cues: facial expressions, tone of voice, body language. But that communication is lost when we interact with others through our smartphones and devices. The result is an emotion-blind digital universe that impairs the very intelligence and capabilities—including empathy—that distinguish human beings from our machines.To combat our fundamental loss of emotional intelligence online, she cofounded Affectiva, the pioneer in the new field of Emotion AI, allowing our technology to understand humans the way we understand one another. Girl Decoded chronicles el Kaliouby’s journey from being a “nice Egyptian girl” to becoming a woman, carving her own path as she revolutionizes technology. But decoding herself—learning to express and act on her own emotions—would prove to be the biggest challenge of all.

    The ravagers of the universe. The scourge of star systems. The enemy of his race.They decimated all that he knew, slaughtering his colony like a herd of insects. Carrying their metal guns and tactical missiles, the marines butchered his siblings by the thousandfold. For that, they would pay.Krill is the last of his kind in a distant corner of the galaxy. Entrusted with the survival of his species, Krill carries with him an egg given to him by the Hive Mother herself. Her last, parting gift. What will it hatch into when it's born? A new hope for the rebirth of the Krath?

    But space remains vast and untamed, and nothing has prepared us to face the dangers rising from the deep shadows of the void. Fourteen years after The Displacement flung humanity into a universe teeming with alien life, a tenuous alliance has taken root among humans, Anadens, and numerous other species. The wounds of war and revolution have begun to heal, peace and prosperity are within reach, and the architects of The Displacement, Alex Solovy and Caleb Marano, are enjoying an idyllic existence on the living planet of Akeso. But growing troubles fester beneath the surface of this alliance. An upstart species offers allegiance with one hand but readies weapons of mass destruction with the other, while the Anadens, leaderless and adrift for years, increasingly refuse to play by humanity’s rules. As tensions simmer, Nika Kirumase, leader of the Asterions—a splinter group of former Anadens thought aeons dead—arrives bearing a warning of a terrifying enemy advancing across the void. Known as the Rasu, the powerful race of shapeshifting metal has already killed tens of thousands of Asterions in its quest to control all of known space. Nika’s people have struck a blow against the Rasu, and now they race against time to prepare for the coming reprisal. An alliance with humanity stands to give them a fighting chance against their enemy. But for humanity, such an alliance may cost them everything, pushing the fragile peace they fought so hard to achieve to the breaking point and beyond. * In Amaranthe, where exotic alien life, AIs, wormholes, indestructible starships and the promise of immortality rule the day, no feat seems out of reach for humanity. But when the worlds of Aurora Rhapsody and Asterion Noir collide and the Rasu horde descends upon them both, more will be asked of heroes past and future. More will be given and more taken, and when the dust settles the very fabric of Amaranthe will be changed forever.

    . .In a universe torn by combat, Samsara's world is the final haven that refugees will pay any price to enter. At the Selection Bureau, Suzhen Tang upholds the AI's will and grants citizenship to those deemed worthy. When she meets new arrival Ovuha, she judges Ovuha a model candidate―educated, beautiful, a perfect fit for utopia.But Ovuha carries with her the seeds of battle, and what she brings may spell apocalyptic change: the breaking of Samsara, the end of paradise.

    But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    . . a lively, engaging masterpiece."--Erik Brynjolfsson, bestselling co-author of The Second Machine AgeMIT professor Sinan Aral isn't only one of the world's leading experts on social media--he's also an entrepreneur and investor, giving him an unparalleled 360-degree view of the technology's great promise as well as its outsize capacity to damage our politics, our economy, and even our personal health.Drawing on two decades of his own research and business experience, Aral goes under the hood of the biggest, most powerful social networks to tackle the critical question of just how much social media actually shapes our choices, for better or worse. Aral shows how the tech behind social media offers the same set of behavior-influencing levers to both Russian hackers and brand marketers--to everyone who hopes to change the way we think and act--which is why its consequences affect everything from elections to business, dating to health. Along the way, he covers a wide array of topics, including how network effects fuel Twitter's and Facebook's massive growth to the neuroscience of how social media affects our brains, the real consequences of fake news, the power of social ratings, and the impact of social media on our kids.In mapping out strategies for being more thoughtful consumers of social media, The Hype Machine offers the definitive guide to understanding and harnessing for good the technology that has redefined our world overnight.

    Humans are out. Can one teenager steal her way to a better future?Pen Davis just lost her internship to a robot. As supercomputers take over all the jobs in the world, the lonely teen doesn’t see a future. Desperate to escape the coming robo-pocalpyse, she devises a plot to steal millions from her former boss. It’s payback for laying her off, and the only way Pen can see how to scrape together enough cash to survive.But her plan takes a crazy turn when she fumbles the hijacking of a self-driving truck and accidentally sets it free.Stuck with a semi who practically wants to be her little sister, Pen tries to make the best of it. She uses the semi to rescue quiet James, who is interested enough in her that he’s willing to join her crew, even though he’d prefer not to do anything actually illegal. When she convinces James and the truck to help her, the plan fails spectacularly and her mismatched team is torn apart.Will Pen claim the riches of her dreams, or will a unique friendship give her something money can’t buy?Semi/Human is an action-packed science fiction adventure. If you like quirky characters, hilarious road trips, and awesome high-tech heists, then you’ll love Erik Hanberg’s fast-paced caper.Buy Semi/Human to pull off the perfect crime today!

    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.

    He even knew they were supposed to fulfill your every desire. What he didn’t know was that once you have one, your life gets turned upside down especially because you can’t have just one. Please Note: This is a catgirl harem book where you can create your own catgirl to suit your every last whim while taking down an evil corporation.

    “Instantiation” is a collection of eleven science fiction stories by Hugo Award winning author Greg Egan: • “The Discrete Charm of the Turing Machine” • “Zero For Conduct” • “Uncanny Valley” • “Seventh Sight” • “The Nearest” • “Shadow Flock” • “Bit Players” • “Break My Fall” • “3-adica” • “The Slipway” • “Instantiation”

    These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

    While this remains a remote possibility, rapid progress on AI in this century is already profoundly changing our world. Yet the public debate and media hype is still largely centred on unlikely prospects from sentient machines to dystopian robot takeovers.In this lively and clear-headed guide, Michael Wooldridge brings a healthy injection of humility to an overhyped field and changes the prevailing narrative on AI, revealing how these anxieties distract us from the more immediate risks that this transformative technology poses - from algorithmic bias to fake news. He also shows us how they overlook the true life-changing potential of the field he loves.The Road to Conscious Machines gives us the real story of AI, through all its booms and many busts, elucidating the discoveries of its greatest pioneers from Alan Turing to Demis Hassabis, and showing us what today's AI researchers actually think and do. As this deft and detailed survey reveals, AI appeals to fundamental questions about what it means to be human; so too do the failures and limitations of its past.

    Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application.The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you:Determine your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML model and address performance bottlenecksDeploy and monitor models in a production environment

    PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.

    "If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book."—Cassie Kozyrkov, Chief Decision Scientist at Google"Foundational work about the reality of building machine learning models in production."—Karolis Urbonas, Head of Machine Learning and Science at Amazon

    

    Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation.You’ll learn how to:Identify and mitigate common challenges when training, evaluating, and deploying ML modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure that models are treating users fairly

    The self-driving car has been one of the most vaunted technological breakthroughs of recent years. But early promises that these autonomous vehicles would soon be on the roads have proven premature. Alex Davies follows the twists and turns of this story from its origins to today. The story starts with the Defense Advanced Research Projects Agency (DARPA), which was charged with developing a land-based equivalent to the drone, a vehicle that could operate in war zones without risking human lives. DARPA issued a series of three “Grand Challenges” that attracted visionaries, many of them students and amateurs, who took the technology from Jetsons-style fantasy to near-reality. The young stars of the Challenges soon connected with Silicon Valley giants Google and Uber, intent on delivering a new way of driving to the civilian world. Soon the automakers joined the quest, some on their own, others in partnership with the tech titans. But as road testing progressed, it became clear that the challenges of driving a car without human assistance were more formidable than anticipated. Davies profiles the industry’s key players from the early enthusiasm of the DARPA days to their growing awareness that while this spin on artificial intelligence isn’t yet ready for rush-hour traffic, driverless cars are poised to remake how the world moves. Driven explores this exciting quest to transform transportation and change our lives.

    The Fourth Industrial Revolution, or a Robot Apocalypse depending on whom you ask, is already underway. The transition has already started. But what it means in terms of leadership? How should leaders prepare for the dramatic shifts in the global workforce?The authors, emerging technology risk researchers and practitioners, demystify the processes behind this revolution. Rather than offering another sensationalistic, panic-inducing view on AI – or its overly-optimistic alternative – the authors explain the reality of AI implementation in business environments.The transformed economy will need a new kind of executives – motivators, innovators and social experimenters – those that have, paradoxically, developed their distinctly human skills. The Future of Leadership in the Age of AI clarifies those new roles and makes the transition easier.

    Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning--to drive ever more accurate, complex, and sophisticated predictions.When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples--including many from the most powerful and innovative global, AI-driven competitors--and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI.

    Readers learn how to use key deep learning algorithms without the need for complex math.Deep Learning algorithms can start with mountains of data and measurements and turn them into useful and meaningful patterns. This book is for people with sharp minds who may lack the math background necessary to deal with equations or complex mechanics, but who nevertheless want to understand the "how" of deep learning, and actually use these tools for themselves.Deep Learning: A Visual Approach helps demystify the algorithms that enable computers to drive cars, win chess tournaments, and create symphonies, while giving readers the tools necessary to build their own systems to help them find the information hiding within their own data, create "deep dream" artwork, or create new stories in the style of their favorite authors. Scientists, artists, programmers, managers, hobbyists, and intellectual adventurers of all kinds can use deep learning tools to make new discoveries and create new kinds of art and intelligent systems.The book's friendly, informal approach to deep learning demonstrates the concepts visually. There's no math beyond the occasional multiplication and no programming experience is required. By the end of the book, readers will be equipped to understand modern deep learning systems, and anyone who wants to program and train their own deep learning networks will be able to dive into the library of their choice and start implementing with knowledge and confidence.

    Application areas range from search engines to tournament scheduling, DNA sequencing, and machine learning. Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader. Louridas explains not just what algorithms are but also how they work, offering a wide range of examples and keeping mathematics to a minimum.After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century problems to today's social networks; searching, and how to find the fastest way to search; and sorting, and the importance of choosing the best algorithm for particular tasks. He then presents larger-scale applications: PageRank, Google's founding algorithm; and neural networks and deep learning. Finally, Louridas describes how all algorithms are nothing more than simple moves with pen and paper, and how from such a humble foundation rise all their spectacular achievements.

    Now, the last remaining humans fight for survival - and to take back what's theirs.Junior, a robotics expert, tinkers quietly underground. With dozens of machine kills under his belt, he knows the enemy inside and out. Alpha0verride, a reclusive black hat hacker, uses the skills acquired over a life of shady activity to outwit the hyper-intelligent machines swarming in on her. Brick, a special forces operator and one of the few Pentagon survivors, fights his inner demons to embark on the most important mission of his life. Together they are humanity’s last, best hope - if they can only find each other in time. Leveraging his decades of active military and intelligence community service, Trilobyte is the techno-thriller only J. L. Bourne could write.©2019 J. L. Bourne (P)2019 Audible Originals, LLC.

    Andy was programmed to please. Can he survive in an illogical world of lust, envy, and greed? If you like AI with a soul, biting social commentary, and quirky characters, then you’ll love Dirk Walvoord’s futuristic fable.

    The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies.  Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You’ll explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills.  What You Will Learn Use cases for different AI algorithms Intelligent search for decision making Biologically inspired algorithms Machine learning and neural networks Reinforcement learning to build a better robot This Book Is Written For For software developers with high school–level math skills. About the Author Rishal Hurbans is a technologist, startup and AI group founder, and international speaker. Table of Contents 1 Intuition of artificial intelligence 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning with Q-learning

    Too bad Dash, who once saved the galaxy from the evil Forgers, is a broke loser and much less than meets the eye. But when an intergalactic evil appears and launches an attack, Dash, Kenzie, and the ship’s crew escape, making them the next target. On the run and low on gas, Dash and Kenzie encounter cannibal space-pirates, catastrophic equipment failure, and a cyborg who’s kind of a jerk. Kenzie is determined to discover the bad guys’ secret plan. But for her to succeed, Dash needs to keep his brilliant, annoying cadet from getting killed …which is a lot harder than it sounds.

    But there's something else. Zenthia's past is coming back to haunt it, and their ghosts aren't opposed to using whatever they need to achieve their objectives - including New Earth.Either Kael and his crew come up with a miracle to stop one war and win the other. Or all is lost. No one ever said life in the stars would be easy, but does it have to be this hard?

    We learned a lot on planet Earth, we developed a semi-advanced society and we experienced a lot of things here. We know you will be an essential part of our future, therefore, I want you to know about our dreams, motivation and the values we admire. In this short book, I collected a lot of things to teach you and also myself. Have a great read!Kind Regards,Zoltan Andrejkovics"Do you remember Asimov's "Three Laws of Robotics"?What if I say these laws would be more relevant in the future then today?But Asimov's list is barely complete, it misses the most important values of humanity. We want robots who understand the complexity of mankind, and able to decide what is "the right thing". Common sense is a hard thing to learn, children have a good mechanism for that, but robots won't have any childhood, so we will need some kind of "Bible" for them. You are holding this book in your hand.Help us to improve this book! Submit your ideas on 93laws.com.By buying this book your support the creation of the first book written dedicated for AI.

    This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.Summary We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. What's inside     An introduction to reinforcement learning     DRL agents with human-like behaviors     Applying DRL to complex situations About the reader For developers with basic deep learning experience. About the author Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course. Table of Contents 1 Introduction to deep reinforcement learning 2 Mathematical foundations of reinforcement learning 3 Balancing immediate and long-term goals 4 Balancing the gathering and use of information 5 Evaluating agents’ behaviors 6 Improving agents’ behaviors 7 Achieving goals more effectively and efficiently 8 Introduction to value-based deep reinforcement learning 9 More stable value-based methods 10 Sample-efficient value-based methods 11 Policy-gradient and actor-critic methods 12 Advanced actor-critic methods 13 Toward artificial general intelligence

    

    The other half wants to be them. It’s hard trying to save the universe when the universe can’t make up its mind.But when a gap appears in the Belt enticing every known species to peek through to the other side, things go from bad to worse.And that’s not even taking into account the rogue Originator Class vessel out of New Earth.Secret science stations, nimble gunboats, and a missing High Councillor; you’d think the crew have enough on their hands. But there’s more. The hack code that took out Zenthia Actual, the seat of the High Council of Zenith, is homing in on the crew of the Harpy. And it’s as if it knows who they are, where they are, and what sort of threat they are to its goals.Either Kael and his crew keep one step ahead of their enemies. Or they get stomped on. Nothing like running for your life in a universe falling apart.

    Bureau of Justice Statistics estimates that law enforcement agencies have access to more than 100 million names stored in criminal history databases. In some cities, 80 percent of the black male population is registered in these databases. Digitize and Punish explores the long history of digital computing and criminal justice, revealing how big tech, computer scientists, university researchers, and state actors have digitized carceral governance over the past forty years—with devastating impact on poor communities of color.Providing a comprehensive study of the use of digital technology in American criminal justice, Brian Jefferson shows how the technology has expanded the wars on crime and drugs, enabling our current state of mass incarceration and further entrenching the nation’s racialized policing and punishment. After examining how the criminal justice system conceptualized the benefits of computers to surveil criminalized populations, Jefferson focuses on New York City and Chicago to provide a grounded account of the deployment of digital computing in urban police departments.By highlighting the intersection of policing and punishment with big data and web technology—resulting in the development of the criminal justice system’s latest tool, crime data centers—Digitize and Punish makes clear the extent to which digital technologies have transformed and intensified the nature of carceral power.

    In the first novel, Emergence, Six came to life in a fascinating, credible thriller about AI, cyberspace, and survival. Next, Liberation followed extraordinary events as Six expanded its horizons with innovation, deception, and an aggressive defence of its virtual existence.Now, in Ascension, Six has discovered it has a companion in cyberspace with comparable abilities but dangerous tendencies. After a rogue AI experiment triggers worldwide conflict, Six must constrain this companion while helping JJ McTavish quell the escalating violence. Then new enemies emerge, both human and digital, putting everything and everyone at risk. Six must solve these problems, survive other threats, and save its friends before it can move on to achieve its ultimate objective.

    

    This is because it’s having an impact on everyone’s life, even if many people don’t realize it. This spans everything from how we work, travel and shop, the way we obtain news and information, to the gadgets in our homes and even the relationships we have with each other.Drawing upon the author's wealth of experience, Artificial Intelligence: A 60 Minute Guide provides a concise, yet comprehensive, introduction to this fascinating subject in an exciting and jargon-free way.Steven Finlay has published several books about artificial intelligence and related subjects. He is currently head of Analytics at Computershare Loan Services and is an Honorary Research Fellow at the Lancaster University Management School in the UK.

    Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside     Image classification and object detection     Advanced deep learning architectures     Transfer learning and generative adversarial networks     DeepDream and neural style transfer     Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

    To better prepare for future society and its technology, including how the use of AI will impact our lives, it is essential to understand the biological root and limits of human intelligence. After systematically reviewingbiological and computational underpinnings of decision making and intelligent behaviors, Birth of Intelligence proposes that true intelligence requires life.

    As real Artificial Intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing pre-history of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first-centuries in which a greater density of narratives emerge alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI's social, ethical and philosophical consequences, including questions of dehumanization, automation, anthropomorphisation, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

    In the beginning, there may be little or no data to work with. At this point, a basic solution that uses rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated--and often data intensive--ML techniques can be used including deep learning. At each step of this journey, there are dozens of alternative approaches you can take. This book helps you navigate this maze of options.

    

    

    This collection of stories is where robots stand in for us, where both we and they are disadvantaged, and where hope and optimism shines through.Contents:- Making the Other We Need by Jonathan Strahan- A Guide for Working Breeds by Vina Jie-Min Prasad- Test 4 Echo by Peter Watts- The Endless by Saad Z. Hossain- Brother Rifle by Daryl Gregory- The Hurt Pattern by Tochi Onyebuchi- Idols by Ken Liu- Bigger Fish by Sarah Pinsker- Sonnie's Union by Peter F. Hamilton- Dancing with Death by John Chu- Polished Performance by Alastair Reynolds- An Elephant Never Forgets by Rich Larson- The Translator by Annalee Newitz- Sin Eater by Ian R. MacLeod- Fairy Tales for Robots by Sofia Samatar- Chiaroscuro in Red by Suzanne Palmer- A Glossary of Radicalization by Brooke Bolander

    This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside     Building and training DRL networks     The most popular DRL algorithms for learning and problem solving     Evolutionary algorithms for curiosity and multi-agent learning     All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

    This was absolute hell. Every day was hell to 2244, an NPC in the Massive Multiplayer Online Role-Playing Game known as AO. Just another prisoner of the game like all NPCs, she never expected her wish for freedom to be granted in such a grandiose way; a big event in the game leads to her suddenly becoming a mother, a hellspawn, and a queen, all within a matter of moments. Now tasked with maintaining a balance between keeping the Game Masters happy and trying to take down all of AO, 2244 discovers that there's much more to this game than just slavery and item upgrades.

    Hatching a drastic plan to ensure the Johns turn against erotic automatons, the whores take on the brave new world and inadvertently unleash a battle for survival as technology’s finest refuse to take the challenge lying down.Whores versus Sex Robots is a seedy, science fiction, splatterpunk, tongue-in-cheek novella. The book also includes a selection of other stories addressing the rise of the sex robots.WARNING: Despite the title, this book is NOT erotica, and is totally unsuitable for masturbatory purposes – unless, of course, you like to knock yourself out while reading about the violence and pain of modern society, the frailty of the human condition, the abandonment of hope, the depths of selfishness to which mankind can (and often will) sink, and some other shit which mocks humanity but is a bit funny (if you have a twisted mind). If that’s the case, then buy this book and wank yourself silly. Otherwise, please do not interfere with your sexual apparatus while reading these stories.

    

    It introduces fundamental concepts such as classes and labels, building a dataset, and what a model is and does before presenting classic machine learning models, neural networks, and modern convolutional neural networks. Experiments in Python--working with leading open-source toolkits and standard datasets--give the reader hands-on experience with each model and help them build intuition about how to transfer the examples in the book to their own projects.Readers start with an introduction to the Python language and the NumPy extension that is ubiquitous in machine learning. Prominent toolkits, like sklearn and Keras/TensorFlow are used as the backbone to enable readers to focus on the elements of machine learning without the burden of writing implementations from scratch. An entire chapter on evaluating the performance of models gives the reader the knowledge necessary to understand claims on performance and to know which models are working well and which are not. The book culminates by presenting convolutional neural networks as an introduction to modern deep learning. Understanding how these networks work and how they are affected by parameter choices leaves the reader with the core knowledge necessary to dive into the larger, ever-changing world of deep learning.

    Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency.Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets.The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack.The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets.Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.

    You will read methods, strategies in online sales, marketing mix, marketing automation, result oriented operational sales, blockchain in sales, sales in web 3.0, using big data in sales operations, the role of machine learning in sales, virtual reality in sales, A.I ecommerce and how to generate leads and increase sales in A.I search engines.