Java Virtual Machine

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

Millions of children visit virtual worlds every day. In such virtual play spaces asHabbo Hotel, Toontown, and Whyville, kids chat with friends from school, meet new people, constructavatars, and earn

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook f

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

In the twenty-first-century digital world, virtual goods are sold for real money.Digital game players happily pay for avatars, power-ups, and other game items. But behind everyvirtual sale, there is a

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

How a virtual journalist in the virtual world of online gaming landed on the real-world front page of the New York Times and how his virtual newspaper chronicled the emergence of the next generation

In From Technological to Virtual Art, respected historian of art and technology Frank Popper traces the development of immersive, interactive new media art from its historical antecedents through tod

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

Although many people view virtual reality as a totally new phenomenon, it has its foundations in an unrecognized history of immersive images. Indeed, the search for illusionary visual space can be tra

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictiv

Machine learning -- programming computers to learn from data -- has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transactio

"Machine art" is neither a movement nor a genre, but encompasses diverse ways in which artists engage with technical systems. In this book, Andreas Broeckmann examines a

Today's Web-enabled deluge of electronic data calls for automated methods of dataanalysis. Machine learning provides these, developing methods that can automatically detect patternsin data and then us

The interplay between optimization and machine learning is one of the most importantdevelopments in modern computational science. Optimization formulations and methods are proving tobe vital in design

World of Warcraft is more than a game. There is no ultimate goal,no winning hand, no princess to be rescued. WoW is an immersive virtual world inwhich characters must cope in a dangerous environment,

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past d

In The Government Machine, Jon Agar traces the mechanization of government work in the United Kingdom from the nineteenth to the early twenty-first century. He argues that this transformation has bee

Provides a unified, coherent account of machine interaction at the level of the machine designers (the society of designers) and the level of the machine interaction itself (the resulting artificial

Machine Models of Music brings together representative models ranging from Mozart's "Musical Dice Game" to a classic article by Marvin Minsky and current research to illustrate the rich impact that ar

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk a

As the power of computing has grown over the past few decades, the field of machinelearning has advanced rapidly in both theory and practice. Machine learning methods are usuallybased on the assumptio

Le Corbusier, who famously called a house “a machine for living,” was fascinated—even obsessed—by another kind of machine, the automobile. His writings were strewn with refere

Today government agencies not only have official Web sites but also sponsor moderated chats, blogs, digital video clips, online tutorials, videogames, and virtual tours of national landmarks. Sophist

The ever-expanding capacities of computing offer new narrative possibilities for virtual worlds. Yet vast narratives—featuring an ongoing and intricately developed storyline, many characters, a

Machine learning develops intelligent computer systems that are able to generalizefrom previously seen examples. A new domain of machine learning, in which the prediction mustsatisfy the additional co

Is the Internet a vast arena of unrestricted communication and freely exchanged information or a regulated, highly structured virtual bureaucracy? In Protocol Alexander Galloway argues that the foundi

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

How science is opening up the mysteries of the heart, revealing the poetry in motion within the machine.Your heart is a miracle in motion, a marvel of construction unsurpassed by any human-made creation. It beats 100,000 times every day―if you were to live to 100, that would be more than 3 billion beats across your lifespan. Despite decades of effort in labs all over the world, we have not yet been able to replicate the heart’s perfect engineering. But, as Sian Harding shows us in The Exquisite Machine, new scientific developments are opening up the mysteries of the heart. And this explosion of new science―ultrafast imaging, gene editing, stem cells, artificial intelligence, and advanced sub-light microscopy―has crucial, real-world consequences for health and well-being. Harding―a world leader in cardiac research―explores the relation between the emotions and heart function, reporting that the heart not only responds to our emotions, it creates them as well. The condition known as Brok

A textbook that uses a hands-on approach to teach principles of programming languages, with Java as the implementation language.This introductory textbook teaches the principles of programming languages by using an experiential learning style, in which students learn about language features by realizing those features in a pedagogical compiler. Students use Java―the most commonly used programming language in the first two years of the computer science curriculum―as the implementation language throughout. The book also discusses a range of emerging topics in programming languages missing from existing textbooks, including concurrency, Big Data, and event-driven programming. The goal is to prepare students to design, implement, analyze, and understand both domain-specific and general-purpose programming languages. The book first develops basic concepts in languages, including means of computation using primitive values, means of combination such as variable definition and functions, an

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learnin

How new biomedical technologies—from prenatal testing to gene-editing techniques—require us to imagine who counts as human and what it means to belong.From next-generation prenatal tests, to virtual c

The North American power grid has been called the world's largest machine. The grid connects nearly every living soul on the continent; Americans rely utterly on the miracle of electrification. In thi

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduct

In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a s

This is the story of how the xerographic copier, or "Xerox machine," became a creative medium for artists and activists during the last few decades of the twentieth century. Paper jams, mang

This book begins with the building of a house, and the building of a company while building the house. It expands to look at the ideas found in various rooms, some of which expanded into virtual space