Hoffman aptly demonstrates the mysterious constructive powers of our eye-brain machines using lots of simple drawings and diagrams to illustrate basic rules of the visual road. Many of the examples are familiar optical illusions--perspective-confounding cubes, a few lines that add up to a more complex shape than seems right. Hoffman also takes a cue from Oliver Sacks, employing anecdotes about people with various specific visual malfunctions to both further his mechanical explanation of visual intelligence and drive home how important this little-understood aspect of cognition can be in our lives. An especially intriguing example involves a boy, blind from birth, who is surgically given the power to see. At first, he is completely unable to visually distinguish objects familiar by touch, such as the cat and the dog. Other poignant examples show clearly how image construction is normally linked to our emotional well-being and sense of place. Visual Intelligence is a fascinating, confounding look (as it were) at an aspect of human physiology and psychology that very few of us think about much at all. --Therese Littleton

    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.

    On the cusp of decoding brain signals that govern motor skills, they are developing miraculous technologies to enable paraplegics and wounded soldiers to move prosthetic limbs, and the rest of us to manipulate computers and other objects through thought alone. These fiercely competitive scientists are vying for Defense Department and venture capital funding, prestige, and great wealth. Part life-altering cure, part science fiction, part military dream, these cutting-edge brain-computer interfaces promise to improve lives—but also hold the potential to augment soldiers' combat capabilities. In The Brain Electric, Malcolm Gay follows the dramatic emergence of these technologies, taking us behind the scenes into the operating rooms, start-ups, and research labs where the future is unfolding. With access to many of the field's top scientists, Gay illuminates this extraordinary race—where science, medicine, profit, and war converge—for the first time. But this isn't just a story about technology. At the heart of this research is a group of brave, vulnerable patient-volunteers whose lives are given new meaning through participating in these experiments. The Brain Electric asks us to rethink our relationship to technology, our bodies, even consciousness itself—challenging our assumptions about what it means to be human.

    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.

    Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.Explore the importance of storytelling with a simple visualization exerciseLearn how color conveys information that our brains recognize before we're fully aware of itDiscover how the books we buy and the people we associate with reveal clues to our deeper selvesRecognize a method to the madness of air travel with a visualization of civilian air trafficFind out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include:Nick Bilton, Michael E. Driscoll, Jonathan Feinberg, Danyel Fisher, Jessica Hagy, Gregor Hochmuth, Todd Holloway, Noah Iliinsky, Eddie Jabbour, Valdean Klump, Aaron Koblin, Robert Kosara, Valdis Krebs, JoAnn Kuchera-Morin et al., Andrew Odewahn, Adam Perer, Anders Persson, Maximilian Schich, Matthias Shapiro, Julie Steele, Moritz Stefaner, Jer Thorp, Fernanda Viegas, Martin Wattenberg, and Michael Young.

    Some of these species can move and eat, see, reproduce, and die. Some behave like birds or ants. One such life form may turn out to be our best weapon in the war against AIDS.What these species have in common is that they exist inside computers, their DNA is digital, and they have come into being not through God's agency but through the efforts of a generation of scientists who seek to create life in silico.But even as it introduces us to these brilliant heretics and unravels the intricacies of their work. Artificial Life examines its subject's dizzying philosophical implications: Is a self-replicating computer program any less alive than a flu virus? Are carbon-and-water-based entities merely part of the continuum of living things? And is it possible that one day "a-life" will look back at human beings and dismiss us as an evolutionary way station -- or, worse still, a dead end?

    Each chapter of An Introduction to Brain and Behavior explores a specific question asked by working neuroscientists and students (i.e. "Why do we have a brain?"  "How is the nervous system organized?").  This approach brings coherence to a vast subject, and helps students understand what information is important as their study of brain and behavior progresses chapter to chapter.

    This text provides network administrators with a reference for implementing and maintaining sound security policies

    It discusses neuroanatomy, cell and molecular mechanisms and signaling through a cognitive approach to behaviour. It features an expanded treatment of the nervous system, neurological and psychiatric diseases and perception.

    Our brave new technologies offer incredible opportunities for work and play. But at what price?   Now renowned neuroscientist Susan Greenfield—known in the United Kingdom for challenging entrenched conventional views—brings together a range of scientific studies, news events, and cultural criticism to create an incisive snapshot of “the global now.” Disputing the assumption that our technologies are harmless tools, Greenfield explores whether incessant exposure to social media sites, search engines, and videogames is capable of rewiring our brains, and whether the minds of people born before and after the advent of the Internet differ.   Stressing the impact on Digital Natives—those who’ve never known a world without the Internet—Greenfield exposes how neuronal networking may be affected by unprecedented bombardments of audiovisual stimuli, how gaming can shape a chemical landscape in the brain similar to that in gambling addicts, how surfing the Net risks placing a premium on information rather than on deep knowledge and understanding, and how excessive use of social networking sites limits the maturation of empathy and identity.   But Mind Change also delves into the potential benefits of our digital lifestyle. Sifting through the cocktail of not only threat but opportunity these technologies afford, Greenfield explores how gaming enhances vision and motor control, how touch tablets aid students with developmental disabilities, and how political “clicktivism” foments positive change.   In a world where adults spend ten hours a day online, and where tablets are the common means by which children learn and play, Mind Change reveals as never before the complex physiological, social, and cultural ramifications of living in the digital age. A book that will be to the Internet what An Inconvenient Truth was to global warming, Mind Change is provocative, alarming, and a call to action to ensure a future in which technology fosters—not frustrates—deep thinking, creativity, and true fulfillment.

    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.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Techniques covered include state- and goal-based behavior, inter-agent communication, individual and group steering behaviors, team AI, graph theory, search, path planning and optimization, triggers, scripting, scripted finite state machines, perceptual modeling, goal evaluation, goal arbitration, and fuzzy logic.