Species And Machines

This book offers a re-examination of the relationship between humans and nature with a new methodology: by examining our entanglement with machines. Using central ideas of critical theory, it uncovers the suppression of nature through technology, tools and engines. It focuses on the ways in which human social forms have actively subjugated and destroyed other species in order to enhance their own social power and accumulation, leading to a new Anthropocene epoch in which human intervention is signalled in the geological record. Beginning with an account of the interactions between humans and other species, the book moves on to explore the hidden history of Marx and his obsession with machines, as well as new attempts to rethink a Marxist ecology, before proceeding to examine the manner in which technologies were used to suppress and destroy one particular species - the Whale of what we call the Cetacean Holocaust. Following this, there are analyses of the emergence of the ‘human encampments’ of the cities and the rise of mobile, locomotive cultures, and consideration of the relationship between machines of memory, and the ‘capturing’ of nature. A radical rethinking of classical social theory that develops new ways of thinking about ecological catastrophe and nature, this book will appeal to scholars of social theory and environmental sociology.

Argues that humanity can be seen as a case of mistaken identity.

A multidisciplinary introduction to the field of computational creativity, analyzing the impact of advanced generative technologies on art and music. As algorithms get smarter, what role will computers play in the creation of music, art, and other cultural artifacts? Will they be able to create such things from the ground up, and will such creations be meaningful? In Beyond the Creative Species, Oliver Bown offers a multidisciplinary examination of computational creativity, analyzing the impact of advanced generative technologies on art and music. Drawing on a wide range of disciplines, including artificial intelligence and machine learning, design, social theory, the psychology of creativity, and creative practice research, Bown argues that to understand computational creativity, we must not only consider what computationally creative algorithms actually do, but also examine creative artistic activity itself.

"Written in a lively and entertaining style, this little book, which deals with topics such as 'personhood,' animal rights, and artificial intelligence . . . makes some rather difficult philosophical points clear in an unpedantic fashion." -- M E Winston, Trenton State College

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.