Real rationality, of the sort studied by psychologists, social scientists, and mathematicians. The kind of rationality where you make good decisions, even when it's hard; where you reason well, even in the face of massive uncertainty; where you recognize and make full use of your fuzzy intuitions and emotions, rather than trying to discard them. In "Rationality: From AI to Zombies," Eliezer Yudkowsky explains the science underlying human irrationality with a mix of fables, argumentative essays, and personal vignettes. These eye-opening accounts of how the mind works (and how, all too often, it doesn't!) are then put to the test through some genuinely difficult puzzles: computer scientists' debates about the future of artificial intelligence (AI), physicists' debates about the relationship between the quantum and classical worlds, philosophers' debates about the metaphysics of zombies and the nature of morality, and many more. In the process, "Rationality: From AI to Zombies" delves into the human significance of correct reasoning more deeply than you'll find in any conventional textbook on cognitive science or philosophy of mind. A decision theorist and researcher at the Machine Intelligence Research Institute, Yudkowsky published earlier drafts of his writings to the websites Overcoming Bias and Less Wrong. "Rationality: From AI to Zombies" compiles six volumes of Yudkowsky's essays into a single electronic tome. Collectively, these sequences of linked essays serve as a rich and lively introduction to the science—and the art—of human rationality.

    The topic everyone in the world should be talking about.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    Without communication or resupply from Earth for the last fifteen years, the colony has but one chance to survive: restore the ancient starship Chimera and train a young crew to pilot her. The fate of the entire colony rests on the shoulders of a pair of misfits: Theo Puck, a sixteen-year-old hacker with a gift for speaking to machines, and fifteen-year-old Selena Samuelson, a brash but talented pilot with a dark secret in her past.To Theo, the Mandate to crew the Chimera seems like a game—one he isn’t invited to play. A brutal murder changes everything. Left with no choice, Theo has to complete the Selection training and make it aboard the Chimera or face terrible consequences.Selena wants to do what she does best—fly. Piloting her father’s ore trawler is the only life she’s known before a horrifying accident strands her aboard the Hydra, the station responsible for rebuilding the Chimera. Forced into the Mandate testing against her will, Selena encounters an unexpected ally, forever changing the way she sees the Chimera and herself.Forced to make brutal choices in order to survive, Theo and Selena’s fates intertwine. But behind the scenes, someone else sets into motion events that could destroy everything they’re fighting to protect.

    These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

    The planet looks benign but a small mix-up with "hostile life-forms" has the three surviving members of the team on the run. Drik, Max, and Laree seek shelter in a small cave, completely cut off from their ship and communications. But it turns out the cave is anything but small. Kodo, frozen in stasis for almost a thousand years, awakens to find everything he knew is gone. His fellow crewmembers are dead, his ship is a wreck, and his homeworld is gone. Only one other member of the crew survives, Kalaya, the Aurora's synthetic organism. Now, on top of it all, humans are on his ship! Humans, the universe's newest addition to deep space-faring races and a complete embarrassment to the galactic community, are on his ship. Could this get any worse?... yes it can! Follow Kodo, Laree, and friends on their mission to escape Fleece and the human military eager to capture their ship. A mission filled with danger, laughter, spiteful hamsters, and...Sentient BLT sandwiches? Game show hosts? Space crickets? This is a light science fiction novel, if you are looking for discussions of quantum theory; then this is not the book for you.

    Includes the Valkyrie Chronicles books, Seventy Two Hours, and Titans. Valkyrie Chronicles: Seventy Two Hours Upon news of the destruction of the Ragnarok civilization by the Frost Giants of Jotunheim. The Valkyrie, Kara, and the combined races must prevent the same fate from befalling Folkvangr. The invention of the Bifrost may hold the key to Valhalla's survival. Secrets are revealed and unexpected friendships are formed. Valkyrie Chronicles: Titans The Valkyrie and the Asgard must contend with an adversary unlike any they have faced before. Kara comes up against an enemy she does not know how to confront. Her brute force fighting skills are useless against this new enemy, and she must match wits with the Titan leader in a battle of wills. Surprises abound and awkward alliances are formed.

    Many important problems involve decision making under uncertainty--that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance.Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

    He's a jaded AI developer with a family who wants nothing to do with him, working for a stagnant tech company. In his spare time, he's also working on a secret project, combining the latest areas of research from his company into one forward-thinking prototype that he knows will change the world. But it must start with changing his own world.

    This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.

    Dorian returns to one of his former universities, having been summoned by his old friend, Adam Notting. Instead, he is greeted by BEAUTY: the product of Notting's research into artificial intelligence. But is BEAUTY all it seems to be? And what of the BEAST...? Note: The Confessions of Dorian Gray contains adult material and is not suitable for younger listeners.

    As a result of this perfect storm of changes, many organizations are struggling to stay relevant to customers, capitalize on opportunities in the marketplace, and attract top talent. Successful companies, on the other hand, are shifting to embrace four key capacities that will drive the future of business: digital, clear, fluid, and fast. Drawing on both cutting-edge case studies and research on Millennials in the workplace, you ll learn how to successfully apply these four capacities in your context to drive real business results, like more engaged employees, higher-value customers, greater strategic agility, and stronger, magnetic cultures. When Millennials Take Over delivers concrete, actionable advice you can use to set your company apart as a leader--rather than a follower. The only constant is change. These four capacities are the key to being able to keep up with the complexity, uncertainty and rapid shifts in our industries and our world. When Millennials Take Over is an intelligently practical guide to how you can build these capacities for your organization - starting NOW.

    Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970’s, the study of algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on a wealth of applications. There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect, and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks as central aspects of daily life presents both opportunities and challenges for theory.While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems. With this in mind we have written this book to cover the theory we expect to be useful in the next 40 years, just as an understanding of automata theory, algorithms, and related topics gave students an advantage in the last 40 years. One of the major changes is an increase in emphasis on probability, statistics, and numerical methods.Early drafts of the book have been used for both undergraduate and graduate courses. Background material needed for an undergraduate course has been put in the appendix. For this reason, the appendix has homework problems.

    Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.

    This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field. Written by two PhDs in nuclear engineering, this book includes practical examples drawn from a working knowledge of physics concepts. You’ll learn how to use the Python programming language to perform everything from collecting and analyzing data to building software and publishing your results. In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your findings, and collaborate efficiently; dive into software licenses, ownership, and copyright procedures

    The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing, and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.

    Before it collapses, it says only two words: "Twilight Sparkle..."Twilight fixes the mechanical mare and gives her a new directive: "Make friends." Now a robot must learn what it takes to be a good friend and, while she's at it, what it means to live...

    When the Ventura and its crew enter orbit for a scheduled planet survey, the ship activates an automated defense system protecting the planet. Although the Ventura is destroyed in the attack, Barcus alone survives the harrowing fall to the remote planet surface. He struggles to remain alive and sane, and to discover why everyone he knew and loved on the Ventura was deliberately murdered. Swinging between despair and fury, Barcus discovers that for every answer he obtains, there are more questions raised. Barcus is assisted by the Emergency Module, Em, his most useful tool. It is an Artificial Intelligence system contained in an all-terrain vehicle specifically designed to help him survive. Barcus soon finds himself in the middle of a planetary genocide of the local native population. He is unable to stand passively by as more people die, even if they are long lost colonists who fear "The Man From Earth" like children fear the monster under their bed. Will Barcus ever find his way home? Will he find out who is responsible? Will his rage just burn this world down? Or will he find his soul in the eyes of a starving, frightened woman?

    

    In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames – phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning) – underlie the emergence of cognition. Integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence, Downing provides a series of concrete examples of neurocognitive emergence. Doing so, he offers a new motivation for the expanded use of bio-inspired concepts in artificial intelligence (AI), in the subfield known as Bio-AI.One of Downing's central claims is that two key concepts from traditional AI, search and representation, are key to understanding emergent intelligence as well. He first offers introductory chapters on five core concepts: emergent phenomena, formal search processes, representational issues in Bio-AI, artificial neural networks (ANNs), and evolutionary algorithms (EAs). Intermediate chapters delve deeper into search, representation, and emergence in ANNs, EAs, and evolving brains. Finally, advanced chapters on evolving artificial neural networks and information-theoretic approaches to assessing emergence in neural systems synthesize earlier topics to provide some perspective, predictions, and pointers for the future of Bio-AI.

    Some would deny personhood to Dick, others who fear him would take him apart chip by chip.After he witnesses a bloody murder, Dick offers to assist the San Diego Police Department catch the killer. But when the search for the murderer turns up a second body, Dick’s Satisfaction Index plummets. He breaks company with the police and begins investigating the case on his own. As he follows the clues, Dick learns more and more about humans: how they live, how they love and how they murder. He will need that knowledge to overcome the killer who threatens to destroy Dick and everyone that Dick holds dear.

    An endless war. An implacable enemy. Nearly a millennium has passed since the mysterious invaders known as the Gray forced humanity to abandon the cradle of their birth. Spreading out among the stars, the wayward children of Earth are fiercely united under the ancient and enduring banner of the Sol Alliance as they struggle to survive and thrive in the cold darkness. For Alisa Pierce, fighting the Gray is all she knows and all she is. Aloof, efficient and focused only on total victory, her sole close companion and confidant is Rin, her artificial intelligence operator and partner. Alisa's cold distance from her fellow humans only served to draw her ever more closely to Rin. After the success of a deadly and difficult solo mission, Alisa and Rin transfer to the celebrated and elite 703rd Hyperspace Assault Wing. On the surface, Alisa's future seems a brilliant star rising, placed firmly on the heroine's path. All is not as it seems, and the fortunes of war can easily take a very dark turn. A devastating and tragic event erupts into a sinister conspiracy within the Alliance. As the line between ally and enemy is erased, Alisa and Rin will have no choice but to fight for their lives, their people and their love.ALSO CONTAINS AN ORIGINAL SHORT STORY BY K.J. RUSSELL Speculative fiction writer and cat-herder K.J. Russell, author of THE DUSTY MAN, presents a different perspective on the ancient conflict with the Gray. In his short story, "Destructive Interference: ISO 953," the Nightshade, one of the Alliance Fleet's seven cutting-edge stealth destroyers, scrambles to replace one of its sister vessels in an emergency redeployment. Surrounded on all sides by the enigmatic Gray who seem determined to deny humanity even the slightest foothold on the frontier, the Nightshade's crew will have to overcome impossible odds to complete the mission!

    If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics.In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection. The latter half of the report focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners.With this report, you will: Learn the stages involved when developing a machine-learning model for use in a software application Understand the metrics used for supervised learning models, including classification, regression, and ranking Walk through evaluation mechanisms, such as hold?out validation, cross-validation, and bootstrapping Explore hyperparameter tuning in detail, and discover why it’s so difficult Learn the pitfalls of A/B testing, and examine a promising alternative: multi-armed bandits Get suggestions for further reading, as well as useful software packages