An apparently contradictory yet radically urgent collection of texts tracing the genealogy of a controversial current in contemporary philosophy. Accelerationism is the name of a contemporary political heresy: the insistence that the only radical political response to capitalism is not to protest, disrupt, critique, or détourne it, but to accelerate and exacerbate its uprooting, alienating, decoding, abstractive tendencies. #Accelerate presents a genealogy of accelerationism, tracking the impulse through 90s UK darkside cyberculture and the theory-fictions of Nick Land, Sadie Plant, Iain Grant, and CCRU, across the cultural underground of the 80s (rave, acid house, SF cinema) and back to its sources in delirious post-68 ferment, in texts whose searing nihilistic jouissance would later be disavowed by their authors and the marxist and academic establishment alike. On either side of this central sequence, the book includes texts by Marx that call attention to his own “Prometheanism,” and key works from recent years document the recent extraordinary emergence of new accelerationisms steeled against the onslaughts of neoliberal capitalist realism, and retooled for the twenty-first century. At the forefront of the energetic contemporary debate around this disputed, problematic term, #Accelerate activates a historical conversation about futurality, technology, politics, enjoyment, and capital. This is a legacy shot through with contradictions, yet urgently galvanized today by the poverty of “reasonable” contemporary political alternatives.

A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.

We are told our lives are too fast, subject to the accelerating demand that we innovate more, work more, enjoy more, produce more, and consume more. That’s one familiar story. Another, stranger, story is told here: of those who think we haven’t gone fast enough. Instead of rejecting the increasing tempo of capitalist production they argue that we should embrace and accelerate it. Rejecting this conclusion, /Malign Velocities/ tracks this 'accelerationism' as the symptom of the misery and pain of labour under capitalism. Retracing a series of historical moments of accelerationism - the Italian Futurism; communist accelerationism after the Russian Revolution; the 'cyberpunk phuturism' of the ’90s and ’00s; the unconscious fantasies of our integration with machines; the apocalyptic accelerationism of the post-2008 moment of crisis; and the terminal moment of negative accelerationism - suggests the pleasures and pains of speed signal the need to disengage, negate, and develop a new politics that truly challenges the supposed pleasures of speed.

If Newton lived in these dark days he would probably reformulate his laws following the formula of Accelerationism. By nature, manifestos are linked to time, build upon a time. For Alex Williams and Nick Srnicek, time is a raw material that can only be released from its obligations and contracts by means of acceleration. - Gato Negro Ediciones.

A pocket color manifesto for a new futuristic feminism Injustice should not simply be accepted as “the way things are.” This is the starting point for The Xenofeminist Manifesto, a radical attempt to articulate a feminism fit for the twenty-first century. Unafraid of exploring the potentials of technology, both its tyrannical and emancipatory possibilities, the manifesto seeks to uproot forces of repression that have come to seem inevitable—from the family, to the body, to the idea of gender itself. If nature is unjust, change nature!

Reason, like information, wants to be free.

The first authoritative and comprehensive survey of the originsand current state of transhumanist thinking The rapid pace of emerging technologies is playing anincreasingly important role in overcoming fundamental humanlimitations. Featuring core writings by seminal thinkers in thespeculative possibilities of the posthuman condition, essaysaddress key philosophical arguments for and against humanenhancement, explore the inevitability of life extension, andconsider possible solutions to the growing issues of social andethical implications and concerns. Edited by the internationallyacclaimed founders of the philosophy and social movement oftranshumanism, The Transhumanist Reader is an indispensableguide to our current state of knowledge of the quest to expand thefrontiers of human nature.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.