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Michael Anton Budd's THE SCULPTURE MACHINE traces the tension between the modern world and the classical interpretation of physicality as influenced by technological forces of industry and revolution. This insightful work illustrates how ideas about bodies influence the building of identities in concert with the construction of a larger consumer culture. Illustrations. Copyright © Libri GmbH. All rights reserved.
An investigation of artists' engagement with technical systems, tracing art historical lineages that connect works of different periods. “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 variety of twentieth- and early twenty-first-century artworks that articulate people's relationships with machines. In the course of his investigation, Broeckmann traces historical lineages that connect art of different periods, looking for continuities that link works from the end of the century to developments in the 1950s and 1960s and to works by avant-garde artists in the 1910s and 1920s. An art historical perspective, he argues, might change our views of recent works that seem to be driven by new media technologies but that in fact continue a century-old artistic exploration. Broeckmann investigates critical aspects of machine aesthetics that characterized machine art until the 1960s and then turns to specific domains of artistic engagement with technology: algorithms and machine autonomy, looking in particular at the work of the Canadian artist David Rokeby; vision and image, and the advent of technical imaging; and the human body, using the work of the Australian artist Stelarc as an entry point to art that couples the machine to the body, mechanically or cybernetically. Finally, Broeckmann argues that systems thinking and ecology have brought about a fundamental shift in the meaning of technology, which has brought with it a rethinking of human subjectivity. He examines a range of artworks, including those by the Japanese artist Seiko Mikami, whose work exemplifies the shift.
In 1934, New York’s Museum of Modern Art staged a major exhibition of ball bearings, airplane propellers, pots and pans, cocktail tumblers, petri dishes, protractors, and other machine parts and products. The exhibition, titled Machine Art, explored these ordinary objects as works of modern art, teaching museumgoers about the nature of beauty and value in the era of mass production. Telling the story of this extraordinarily popular but controversial show, Jennifer Jane Marshall examines its history and the relationship between the museum’s director, Alfred H. Barr Jr., and its curator, Philip Johnson, who oversaw it. She situates the show within the tumultuous climate of the interwar period and the Great Depression, considering how these unadorned objects served as a response to timely debates over photography, abstract art, the end of the American gold standard, and John Dewey’s insight that how a person experiences things depends on the context in which they are encountered. An engaging investigation of interwar American modernism, Machine Art, 1934 reveals how even simple things can serve as a defense against uncertainty.
An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.
An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans. Today's computers are composing music that sounds “more Bach than Bach,” turning photographs into paintings in the style of Van Gogh's Starry Night, and even writing screenplays. But are computers truly creative—or are they merely tools to be used by musicians, artists, and writers? In this book, Arthur I. Miller takes us on a tour of creativity in the age of machines. Miller, an authority on creativity, identifies the key factors essential to the creative process, from “the need for introspection” to “the ability to discover the key problem.” He talks to people on the cutting edge of artificial intelligence, encountering computers that mimic the brain and machines that have defeated champions in chess, Jeopardy!, and Go. In the central part of the book, Miller explores the riches of computer-created art, introducing us to artists and computer scientists who have, among much else, unleashed an artificial neural network to create a nightmarish, multi-eyed dog-cat; taught AI to imagine; developed a robot that paints; created algorithms for poetry; and produced the world's first computer-composed musical, Beyond the Fence, staged by Android Lloyd Webber and friends. But, Miller writes, in order to be truly creative, machines will need to step into the world. He probes the nature of consciousness and speaks to researchers trying to develop emotions and consciousness in computers. Miller argues that computers can already be as creative as humans—and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature.
This is not your run-of-the-mill fitness book. Developed by world-renowned gluteal expert Bret Contreras, Strong Curves offers an extensive fitness and nutrition guide for women seeking to improve their physique, function, strength, and mobility. Contreras spent the last eighteen years researching and field-testing the best methods for building better butts and shapelier bodies. In Strong Curves, he offers the programs that have proven effective time and time again with his clients, allowing you to develop lean muscle, rounded glutes, and greater confidence. Each page is packed with information decoding the female anatomy, providing a better understanding as to why most fitness programs fail to help women reach their goals. With a comprehensive nutritional guide and over 200 strength exercises, this book gets women off the treadmill and furnishes their drive to achieve strength, power, and sexy curves from head to toe. Although the glutes are the largest and most powerful muscle group in the human body, they often go dormant due to lifestyle choices, leading to a flat, saggy bum. Strong Curves is the cure.
Machine Art and Other Writings documents the wide proportions of Pounds's polemic against the abstractions of modernism and reveals the extent to which he was at odds with the metaphysical assumptions of his time. The volume, edited by Ardizzone, is the result of years of systematic and intensive study of Pound's manuscripts, including glosses from the texts of his personal library.
Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the book, you’ll learn how to implement a range of powerful ML techniques, starting with the k-Nearest Neighbors (k-NN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more. With the aid of real datasets, you’ll delve into regression models through the use of a bike-sharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. You’ll also find expert tips for avoiding common problems, like handling “dirty” or unbalanced data, and how to troubleshoot pitfalls. You’ll also explore: How to deal with large datasets and techniques for dimension reduction Details on how the Bias-Variance Trade-off plays out in specific ML methods Models based on linear relationships, including ridge and LASSO regression Real-world image and text classification and how to handle time series data Machine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, you’ll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use. Requirements: A basic understanding of graphs and charts and familiarity with the R programming language
During his lifetime Brion Gysin (1916-1986) inspired an array of artists, writers, poets and musicians, notably the Beat Generation. Since his death Gysin's own work has only increased in popularity, yet his radical approach to art defies categorization. Dream Machine is the first detailed study of Gysin's œuvre in both art-historical and contemporary contexts. A devotee of invention, Gysin created paintings, drawings, photo-collages, installations, poetry and sound experiments. He produced the cut-up collage novel The Third Mind (1965) with William Burroughs, and with Ian Sommerville developed the Dreamachine (1961), a kinetic sculpture designed to induce visions by playing flickering light on the closed eyes of the viewer. This exciting new book, featuring incisive texts, a photo essay, and appreciations by contemporary artists, captures the remarkable daring of an artistic visionary.