He traces AI's origins in ancient myth, through literary classics like Frankenstein, to today's sci-fi blockbusters, arguing that a fascination with AI is hardwired into the human psyche. He explains AI's history, technology, and potential; its manifestations in intelligent machines; its connections to neurology and consciousness, as well as—perhaps most tellingly—what AI reveals about us as human beings.In Our Own Image argues that we are on the brink of a fourth industrial revolution—poised to enter the age of Artificial Intelligence as science fiction becomes science fact. Ultimately, Zarkadakis observes, the fate of AI has profound implications for the future of science and humanity itself.

    Turing Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be "computable," creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing's own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.

    This book makes extensive use of Dee's library and annotations to clarify this mystery by providing a detailed analysis of these conversations. Professor Harkness contextualizes Dee's angel conversations within the natural, philosophical, religious, and social contexts of his time, arguing that the conversations represent a continuing development of John Dee's earlier concerns and interests. This book will appeal to those with an interest in the history of science, students of religion, and everyone who approaches the new millennium with a wary eye.

    Russell's classic The Principles of Mathematics sets forth his landmark thesis that mathematics and logic are identical―that what is commonly called mathematics is simply later deductions from logical premises.His ideas have had a profound influence on twentieth-century work on logic and the foundations of mathematics.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    It focuses mainly on the study and use of language and Languages of the world, the phonetic structure and change, syntax, Morphology, written records, Dialect Geography, Fluctuation in the Frequency of Forms, Dialect, Applications and Outlook, etc.

    This happens automatically when following agood, simple set of guidelines.*They improve development speed, because the programmer doesn't need toalways make decisions starting from first principles.*They enhance teamwork by eliminating needless debates on inconsequentialissues and by making it easy for teammates to read and maintain each other'scode.The coding standards introduced by this book are a collection of guidelines forwriting high-quality C++ code.***They are the distilled conclusions of a rich collective experience of the C++community. Until now, this body of knowledge has been available only asfolklore or spread in bits and pieces throughout books.

    The collected teachings of The Golden One.Dauntless: The Wild Hunt Edition (2021) is a greatly updated version of this book.

    Now at your fingertips. The Mahabharata is the world's longest epic. It contains within it numerous fables, anecdotes and pieces of practical wisdom that make up what we today call Indian culture.Two thousand tales from this ocean of Vedic literature are now being retold for your reading pleasure. Crisp, comprehensive, contextual.In this volume you will find stories such as:- The three disciples of Dhaumya the sage- The strange adventures of Uttanka- The tortoise that fought with an elephant over years by a lake- How Sesha came to become the supporter of Mother Earth- Who the second Indra was and how he was 'defeated'And many more. Whether you're a casual reader of mythology or a die-hard fanatic, this is a must-read.

    His uniqueness lies not only in the fact that at the young age of 19 and a half, he travelled to snow clad Himalayas from Kerala, and there he met and lived for several years with a ‘real-time’ yogi, Babaji, but also that he should undertake such an unusual and adventurous exploration, given his non-Hindu birth and antecedents.The metamorphosis of Mumtaz Ali Khan into Sri ‘M’, a yogi with profound knowledge of the Upanishads and deep personal insights, born of first hand experiences with higher levels of consciousness is indeed a fascinating story.The bonus for those interested in the secrets of yoga, meditation and sankhyan metaphysics is that Sri ‘M’ is still living and easily reachable. He leads a normal life, married with two children, wears no special robes and conducts himself without pomp or paraphernalia.Someone who met him recently said, “I expected a flashy godman and instead I saw a jean clad gentleman with a smile of his face, ready to discuss my problems. In five minutes flat, I said to myself, this is no ordinary man. The peace and tranquility that enters your system is tangible”.

    We want to know! We want to know the purpose of our life, why we are here on earth, where we came from, whether there is a God, who this God is, whether there is an afterlife, and so much more! These most basic and fundamental questions are hopefully in the forefront of our minds. And if they are not, it's never too late to start! This book offers some of the answers to these questions. It offers the answers found is our Creed. At first, the Creed can seem dry and unimpressive. It can even seem confusing and overly academic. But when properly understood, the Creed holds the answer to the questions we so deeply seek.

    At a glance, it bears few similarities to any place we know and inhabit. But upon closer examination, the differences between this complex virtual reality and our own might not be as vast as we think. In “How You Play the Game,” author and philosopher Charlie Huenemann looks philosophically at the game of Minecraft (“What is the point of this game? How does one win? Well, this depends on what you want to do”) and grapples with the ethical conundrums, existential crises and moral responsibilities of the virtual realm. From the Overworld to the Ender Dragon, Huenemann offers an entertaining, insightful and often hilarious examination of Minecraft and the strange worlds—both virtual and not—surrounding it.Charlie Huenemann is a Professor of Philosophy at Utah State University. He writes for 3quarksdaily, and has published several books on the history of philosophy.Cover design by Adil Dara.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.