""The Brain's Hidden Algorithms"" explores the fascinating intersection of neuroscience and artificial intelligence, unveiling the complex mechanisms of our minds and their implications for AI development. This groundbreaking book delves into three key areas: decoding neural networks, applying brain-inspired algorithms to AI, and examining the ethical consequences of merging human and artificial intelligence. The book's central argument proposes that by unraveling the intricate algorithms underlying human cognition, we can revolutionize AI while gaining unprecedented insights into our own minds. It guides readers through the latest research in mapping neural networks and demonstrates how these discoveries are being applied to create more advanced AI systems. Drawing on cutting-edge studies and interviews with leading experts, the author presents a compelling case for the symbiotic relationship between neuroscience and AI. What sets this book apart is its unique approach to synthesizing complex scientific concepts with philosophical questions about intelligence and consciousness. By balancing academic rigor with accessible language and engaging analogies, it offers valuable insights for both experts and general readers interested in the future of technology and human cognition. The book challenges readers to reconsider their understanding of human intelligence and the potential of AI, paving the way for a new era of discovery at the intersection of mind and machine.

'Algorithms to Live By' looks at the simple, precise algorithms that computers use to solve the complex 'human' problems that we face, and discovers what they can tell us about the nature and origin of the mind.

The rousing story of the last gasp of human agency and how today’s best and brightest minds are endeavoring to put an end to it. It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected. In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge. The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans. The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What hap­pens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others? Who knows—maybe there’s a bot learning to do your job this minute.

Every day, corporations are connecting the dots about our personal behavior—silently scrutinizing clues left behind by our work habits and Internet use. The data compiled and portraits created are incredibly detailed, to the point of being invasive. But who connects the dots about what firms are doing with this information? The Black Box Society argues that we all need to be able to do so—and to set limits on how big data affects our lives. Hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only scratched the surface of this troubling behavior. Frank Pasquale exposes how powerful interests abuse secrecy for profit and explains ways to rein them in. Demanding transparency is only the first step. An intelligible society would assure that key decisions of its most important firms are fair, nondiscriminatory, and open to criticism. Silicon Valley and Wall Street need to accept as much accountability as they impose on others.

“If online dating can blunt the emotional pain of separation, if adults can afford to be increasingly demanding about what they want from a relationship, the effect of online dating seems positive. But what if it’s also the case that the prospect of finding an ever more compatible mate with the click of a mouse means a future of relationship instability, a paradox of choice that keeps us chasing the illusive bunny around the dating track?” It’s the mother of all search problems: how to find a spouse, a mate, a date. The escalating marriage age and declin­ing marriage rate mean we’re spending a greater portion of our lives unattached, searching for love well into our thirties and forties. It’s no wonder that a third of America’s 90 million singles are turning to dating Web sites. Once considered the realm of the lonely and desperate, sites like eHarmony, Match, OkCupid, and Plenty of Fish have been embraced by pretty much every demographic. Thanks to the increasingly efficient algorithms that power these sites, dating has been transformed from a daunting transaction based on scarcity to one in which the possibilities are almost endless. Now anyone—young, old, straight, gay, and even married—can search for exactly what they want, connect with more people, and get more information about those people than ever before. As journalist Dan Slater shows, online dating is changing society in more profound ways than we imagine. He explores how these new technologies, by altering our perception of what’s possible, are reconditioning our feelings about commitment and challenging the traditional paradigm of adult life. Like the sexual revolution of the 1960s and ’70s, the digital revolution is forcing us to ask new questions about what constitutes “normal”: Why should we settle for someone who falls short of our expectations if there are thousands of other options just a click away? Can commitment thrive in a world of unlimited choice? Can chemistry really be quantified by math geeks? As one of Slater’s subjects wonders, “What’s the etiquette here?” Blending history, psychology, and interviews with site creators and users, Slater takes readers behind the scenes of a fascinating business. Dating sites capitalize on our quest for love, but how do their creators’ ideas about profits, morality, and the nature of desire shape the virtual worlds they’ve created for us? Should we trust an industry whose revenue model benefits from our avoiding monogamy? Documenting the untold story of the online-dating industry’s rise from ignominy to ubiquity—beginning with its early days as “computer dating” at Harvard in 1965—Slater offers a lively, entertaining, and thought provoking account of how we have, for better and worse, embraced technology in the most intimate aspect of our lives.

The anatomy and physiology of the basal ganglia and their relation to brain and behavior, disorders and therapies, and philosophy of mind and moral values. The main task of the basal ganglia—a group of subcortical nuclei, located at the base of the brain—is to optimize and execute our automatic behavior. In this book, Hagai Bergman analyzes the anatomy and physiology of the basal ganglia, discussing their relation to brain and behavior, to disorders and therapies, and even to moral values. Drawing on his forty years of studying the basal ganglia, Bergman presents new information on physiology and computational models, Parkinson’s disease and other ganglia-related disorders, and such therapies as deep brain stimulation. Focusing on studies of nonhuman primates and human basal ganglia and relying on system physiology and in vivo extra-cellular recording techniques, Bergman first describes the major brain structures that constitute the basal ganglia, the morphology of their cellular elements, their synaptic connectivity and their physiological function in health and disease. He discusses the computational physiology of the healthy basal ganglia, describing four generations of computational models, and then traces the computational physiology of basal ganglia–related disorders and their treatments, including Parkinson’s disease and its pharmacological and surgical therapies. Finally, Bergman considers the implications of these findings for such moral concerns as free will. Explaining this leap into domains rarely explored in neuroscientific accounts, Bergman writes that the longer he studies the basal ganglia, the more he is convinced that they are truly the base of both brain and mind.

From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—"a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.

In this book, a variety of algoritbms are described that may be of interest to everyone who writes software for 3D-graphics. It is a book that haB been written for programmers at an intermediate level as well aB for experienced software engineers who simply want to have some particular functions at their disposal, without having to think too much about details like special cases or optimization for speed. The programming language we use is C, and that has many advantages, because it makes the code both portable and efficient. Nevertheless, it should be possible to adapt the ideas to other high-level programming languages. The reader should have a reasonable knowledge of C, because sophisticated pro grams with economical storage household and fast sections cannot be written without the use of pointers. You will find that in the long run it is just aB easy to work with pointer variables as with multiple arrays . .Aß the title of the book implies, we will not deal with algorithms that are very computation-intensive such as ray tracing or the radiosity method. Furthermore, objects will always be (closed or not closed) polyhedra, which consist of a certain number of polygons.

A groundbreaking narrative on the urgency of ethically designed AI and a guidebook to reimagining life in the era of intelligent technology. The Age of Intelligent Machines is upon us, and we are at a reflection point. The proliferation of fast–moving technologies, including forms of artificial intelligence akin to a new species, will cause us to confront profound questions about ourselves. The era of human intellectual superiority is ending, and we need to plan for this monumental shift. A Human Algorithm: How Artificial Intelligence Is Redefining Who We Are examines the immense impact intelligent technology will have on humanity. These machines, while challenging our personal beliefs and our socioeconomic world order, also have the potential to transform our health and well–being, alleviate poverty and suffering, and reveal the mysteries of intelligence and consciousness. International human rights attorney Flynn Coleman deftly argues that it is critical that we instill values, ethics, and morals into our robots, algorithms, and other forms of AI. Equally important, we need to develop and implement laws, policies, and oversight mechanisms to protect us from tech’s insidious threats. To realize AI’s transcendent potential, Coleman advocates for inviting a diverse group of voices to participate in designing our intelligent machines and using our moral imagination to ensure that human rights, empathy, and equity are core principles of emerging technologies. Ultimately, A Human Algorithm is a clarion call for building a more humane future and moving conscientiously into a new frontier of our own design. “[Coleman] argues that the algorithms of machine learning––if they are instilled with human ethics and values––could bring about a new era of enlightenment.” —San Francisco Chronicle

This book is Part II of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part II contains Chapters 4 through 6 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.