Walmart. Coca-Cola. BP. Toyota. The world economy runs on the profits of transnational corporations. Politicians need their backing. Non-profit organizations rely on their philanthropy. People look to their brands for meaning. And their power continues to rise. Can these companies, as so many are now hoping, provide the solutions to end the mounting global environmental crisis? Absolutely, the CEOs of big business are telling us: the commitment to corporate social responsibility will ensure it happens voluntarily. Peter Dauvergne challenges this claim, arguing instead that corporations are still doing far more to destroy than protect our planet. Trusting big business to lead sustainability is, he cautions, unwise — perhaps even catastrophic. Planetary sustainability will require reining in the power of big business, starting now.

Climate change seems to be an insurmountable problem. Political solutions have so far had little impact. Some scientists are now advocating the so-called 'Plan B', a more direct way of reducing the rate of future warming by reflecting more sunlight back to space, creating a thermostat in the sky. In this book, Mike Hulme argues against this kind of hubristic techno-fix. Drawing upon a distinguished career studying the science, politics and ethics of climate change, he shows why using science to fix the global climate is undesirable, ungovernable and unattainable. Science and technology should instead serve the more pragmatic goals of increasing societal resilience to weather risks, improving regional air quality and driving forward an energy technology transition. Seeking to reset the planet’s thermostat is not the answer.

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

We live in a world that is increasingly difficult to understand. It is not just changing: it is metamorphosing. Change implies that some things change but other things remain the same capitalism changes, but some aspects of capitalism remain as they always were. Metamorphosis implies a much more radical transformation in which the old certainties of modern society are falling away and something quite new is emerging. To grasp this metamorphosis of the world it is necessary to explore the new beginnings, to focus on what is emerging from the old and seek to grasp future structures and norms in the turmoil of the present. Take climate change: much of the debate about climate change has focused on whether or not it is really happening, and if it is, what we can do to stop or contain it. But this emphasis on solutions blinds us to the fact that climate change is an agent of metamorphosis. It has already altered our way of being in the world the way we live in the world, think about the world and seek to act upon the world through our actions and politics. Rising sea levels are creating new landscapes of inequality drawing new world maps whose key lines are not traditional boundaries between nation-states but elevations above sea level. It is creating an entirely different way of conceptualizing the world and our chances of survival within it. The theory of metamorphosis goes beyond theory of world risk society: it is not about the negative side effects of goods but the positive side effects of bads. They produce normative horizons of common goods and propel us beyond the national frame towards a cosmopolitan outlook.

What if our civilization were to collapse? Not many centuries into the future, but in our own lifetimes? Most people recognize that we face huge challenges today, from climate change and its potentially catastrophic consequences to a plethora of socio-political problems, but we find it hard to face up to the very real possibility that these crises could produce a collapse of our entire civilization. Yet we now have a great deal of evidence to suggest that we are up against growing systemic instabilities that pose a serious threat to the capacity of human populations to maintain themselves in a sustainable environment. In this important book, Pablo Servigne and Raphaël Stevens confront these issues head-on. They examine the scientific evidence and show how its findings, often presented in a detached and abstract way, are connected to people’s ordinary experiences – joining the dots, as it were, between the Anthropocene and our everyday lives. In so doing they provide a valuable guide that will help everyone make sense of the new and potentially catastrophic situation in which we now find ourselves. Today, utopia has changed sides: it is the utopians who believe that everything can continue as before, while realists put their energy into making a transition and building local resilience. Collapse is the horizon of our generation. But collapse is not the end – it’s the beginning of our future. We will reinvent new ways of living in the world and being attentive to ourselves, to other human beings and to all our fellow creatures.

A series of case studies that look at how people can adapt to climate change.

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.