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Leading economists and policymakers consider what economic tools are most effective in reversing the rise in inequality.Economic inequality is the defining issue of our time. In the United States, the wealth share of the top 1% has risen from 25% in the late 1970s to around 40% today. The percentage of children earning more than their parents has fallen from 90% in the 1940s to around 50% today. In Combating Inequality, leading economists, many of them current or former policymakers, bring good news: we have the tools to reverse the rise in inequality. In their discussions, they consider which of these tools are the most effective at doing so.The contributors express widespread agreement that we need to aim policies at economic inequality itself; deregulation and economic stimulus will not do the job. No longer does anyone ask, in relation to expanded social programs, “Can we pay for it?” And most believe that US taxes will have to rise—although they debate whether the progressivity should focus on the revenue side or the expenditure side, through broad-based taxes like the VAT or through a wealth tax aimed at the very top of the income scale. They also consider the philosophical aspects of inequality—whether it is bad in itself or because of its consequences; the risks and benefits of more radical interventions to change the nature of production and trade; and future policy directions.ContributorsDaron Acemoglu, Philippe Aghion, Danielle Allen, Ben Ansell, David Autor, Sheri Berman, Marianne Bertrand, Olivier Blanchard, Lucas Chancel, William Darity Jr., Peter Diamond, Christian Dustmann, David T. Ellwood, Richard Freeman, Caroline Freund, Jason Furman, Hilary Hoynes, Lawrence F. Katz, Wojciech Kopczuk, N. Gregory Mankiw, Nolan McCarty, Dani Rodrik, Jesse Rothstein, Emmanuel Saez, T. M. Scanlon, Heidi Shierholz, Tharman Shanmugaratnam, Stefanie Stantcheva, Michael Stynes, Laura D'Andrea Tyson, Philippe Van Parijs, Gabriel Zucman

How people judge humans and machines differently, in scenarios involving natural disasters, labor displacement, policing, privacy, algorithmic bias, and more.How would you feel about losing your job to a machine? How about a tsunami alert system that fails? Would you react differently to acts of discrimination depending on whether they were carried out by a machine or by a human? What about public surveillance? How Humans Judge Machines compares people's reactions to actions performed by humans and machines. Using data collected in dozens of experiments, this book reveals the biases that permeate human-machine interactions. Are there conditions in which we judge machines unfairly? Is our judgment of machines affected by the moral dimensions of a scenario? Is our judgment of machine correlated with demographic factors such as education or gender? César Hidalgo and colleagues use hard science to take on these pressing technological questions. Using randomized experiments, they create revealing counterfactuals and build statistical models to explain how people judge artificial intelligence and whether they do it fairly. Through original research, How Humans Judge Machines bring us one step closer tounderstanding the ethical consequences of AI.

A reconceptualization of origins research that exploits a modern understanding of non-covalent molecular forces that stabilize living prokaryotic cells.Scientific research into the origins of life remains exploratory and speculative. Science has no definitive answer to the biggest questions--"What is life?" and "How did life begin on earth?" In this book, Jan Spitzer reconceptualizes origins research by exploiting a modern understanding of non-covalent molecular forces and covalent bond formation--a physicochemical approach propounded originally by Linus Pauling and Max Delbrück. Spitzer develops the Pauling-Delbrück premise as a physicochemical jigsaw puzzle that identifies key stages in life's emergence, from the formation of first oceans, tidal sediments, and proto-biofilms to progenotes, proto-cells and the first cellular organisms.

Rules for building formal models that use fast-and-frugal heuristics, extending the psychological study of classification to the real world of uncertainty.This book focuses on classification--allocating objects into categories--"in the wild," in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning.

An essential guide for teaching and learning computational art and design: exercises, assignments, interviews, and more than 170 illustrations of creative work.This book is an essential resource for art educators and practitioners who want to explore code as a creative medium, and serves as a guide for computer scientists transitioning from STEM to STEAM in their syllabi or practice. It provides a collection of classic creative coding prompts and assignments, accompanied by annotated examples of both classic and contemporary projects, and more than 170 illustrations of creative work, and features a set of interviews with leading educators. Picking up where standard programming guides leave off, the authors highlight alternative programming pedagogies suitable for the art- and design-oriented classroom, including teaching approaches, resources, and community support structures.

An engaging introduction to human and animal movement seen through the lens of mechanics.How do Olympic sprinters run so fast? Why do astronauts adopt a bounding gait on the moon? How do running shoes improve performance while preventing injuries? This engaging and generously illustrated book answers these questions by examining human and animal movement through the lens of mechanics. The authors present simple conceptual models to study walking and running and apply mechanical principles to a range of interesting examples. They explore the biology of how movement is produced, examining the structure of a muscle down to its microscopic force-generating motors. Drawing on their deep expertise, the authors describe how to create simulations that provide insight into muscle coordination during walking and running, suggest treatments to improve function following injury, and help design devices that enhance human performance.Throughout, the book emphasizes established principles that provide a foundation for understanding movement. It also describes innovations in computer simulation, mobile motion monitoring, wearable robotics, and other technologies that build on these fundamentals. The book is suitable for use as a textbook by students and researchers studying human and animal movement. It is equally valuable for clinicians, roboticists, engineers, sports scientists, designers, computer scientists, and others who want to understand the biomechanics of movement.

Detailed answer keys to all 140 self-assessment exercises and solutions to the 173 odd-numbered end-of-chapter exercises in Intermediate Microeconomic Theory.This book accompanies Ana Espinola-Arredondo and Felix Muñoz-Garcia's Intermediate Microeconomic Theory: Tools and Step-by-Step Examples, offering detailed answer keys to all 140 self-assessment exercises and solutions to the 173 odd-numbered end-of-chapter exercises. It provides readable step-by-step explanations and algebra support, enabling students to approach similar exercises on their own, emphasizing the economic intuition behind mathematical results.

The development of psychiatry in the Middle East, viewed through the history of one of the first modern mental hospitals in the region.ʿAsfuriyyeh (formally, the Lebanon Hospital for the Insane) was founded by a Swiss Quaker missionary in 1896, one of the first modern psychiatric hospitals in the Middle East. It closed its doors in 1982, a victim of Lebanon's brutal fifteen-year civil war. In this book, Joelle Abi-Rached uses the rise and fall of ʿAsfuriyyeh as a lens through which to examine the development of modern psychiatric theory and practice in the region as well as the sociopolitical history of modern Lebanon.

An up-to-date overview of the field of connectomics, introducing concepts and mechanisms underlying brain network change at different stages.The human brain undergoes massive changes during its development, from early childhood and the teenage years to adulthood and old age. Across a wide range of species, from C. elegans and fruit flies to mice, monkeys, and humans, information about brain connectivity (connectomes) at different stages is now becoming available. New approaches in network neuroscience can be used to analyze the topological, spatial, and dynamical organization of such connectomes. In Changing Connectomes, Marcus Kaiser provides an up-to-date overview of the field of connectomics and introduces concepts and mechanisms underlying brain network changes during evolution and development. Drawing on a range of results from experimental, clinical, and computational studies, Kaiser describes changes during healthy brain maturation and during brain network disorders (including such neurodevelopmental conditions as schizophrenia and depression), brain injury, and neurodegenerative disorders including dementia. He argues that brain stimulation is an area where understanding connectome development could help in assessing long-term effects of interventions. Changing Connectomes is a suitable starting point for researchers who are new to the field of connectomics, and also for researchers who are interested in the link between brain network organization and brain and cognitive development in health and disease. Matlab/Octave code examples available at the MIT Press website will allow computational neuroscience researchers to understand and extend the shown mechanisms of connectome development.

A comprehensive and integrated introduction to the phenomena and theories of perceptual learning, focusing on the visual domain.Practice or training in perceptual tasks improves the quality of perceptual performance, often by a substantial amount. This improvement is called perceptual learning (in contrast to learning in the cognitive or motor domains), and it has become an active area of research of both theoretical and practical significance. This book offers a comprehensive introduction to the phenomena and theories of perceptual learning, focusing on the visual domain. Perceptual Learning explores the tradeoff between the competing goals of system stability and system adaptability, signal and noise, retuning and reweighting, and top-down versus bottom-down processes. It examines and evaluates existing research and potential future directions, including evidence from behavior, physiology, and brain imaging, and existing perceptual learning applications, with a focus on important theories and computational models. It also compares visual learning to learning in other perceptual domains, and considers the application of visual training methods in the development of perceptual expertise and education as well as in remediation for limiting visual conditions. It provides an integrated treatment of the subject for students and researchers and for practitioners who want to incorporate perceptual learning into their practice.Practice or training in perceptual tasks improves the quality of perceptual performance, often by a substantial amount. This improvement is called perceptual learning, in contrast with learning in the cognitive or motor domains. Perceptual learning has been a very active area of research of both theoretical and practical interest. Research on perceptual learning is of theoretical significance in illuminating plasticity in adult perceptual systems, and in understanding the limitations of human information processing and how to improve them. It is of practical significance as a potential method for the development of perceptual expertise in the normal population, for its potential in advancing development and supporting healthy aging, and for noninvasive amelioration of deficits in challenged populations by training. Perceptual learning has become an increasingly important topic in biomedical research. Practitioners in this area include science disciplines such as psychology, neuroscience, computer sciences, and optometry, and developers in applied areas of learning game design, cognitive development and aging, and military and biomedical applications. Commercial development of training products, protocols, and games is a multi-billion dollar industry. Perceptual learning provides the basis for many of the developments in these areas.This book is written for anyone who wants to understand the phenomena and theories of perceptual learning or to apply the technology of perceptual learning to the development of training methods and products. Our aim is to provide an introduction to those researchers and students just entering this exciting field, to provide a comprehensive and integrated treatment of the phenomena and the theories of perceptual learning for active perceptual learning researchers, and to describe and develop the basic techniques and principles for readers who want to successfully incorporate perceptual learning into applied developments. The book considers the special challenges of perceptual learning that balance the competing goals of system stability and system adaptability. It provides a systematic treatment of the major phenomena and models in perceptual learning, the determinants of successful learning and of specificity and transfer. The book provides a cohesive consideration of the broad range of perceptual learning through the theoretical framework of incremental learning of reweighting evidence that supports successful task performance. It provides a detailed analysis of the mechanisms by which perceptual learning improves perceptual limitations, the relationship of perceptual learning and the critical period of development, and the semi-supervised modes of learning that dominate perceptual learning. It considers limitations and constraints on learning multiple tasks and stimuli simultaneously, the implications of training at high or low levels of performance accuracy, and the importance of feedback to perceptual learning. The basis of perceptual learning in physiology is discussed along with the relationship of visual perceptual learning to learning in other sensory domains. The book considers the applications of perceptual learning in the development of expertise, in education and gaming, in training during development and aging, and applications to remediation of mental health and vision disorders. Finally, it applies the phenomena and models of perceptual learning to considerations of optimizing training.

The creative collaborations of engineers, artists, scientists, and curators over the past fifty years.Artwork as opposed to experiment? Engineer versus artist? We often see two different cultural realms separated by impervious walls. But some fifty years ago, the borders between technology and art began to be breached. In this book, W. Patrick McCray shows how in this era, artists eagerly collaborated with engineers and scientists to explore new technologies and create visually and sonically compelling multimedia works. This art emerged from corporate laboratories, artists' studios, publishing houses, art galleries, and university campuses. Many of the biggest stars of the art world—Robert Rauschenberg, Yvonne Rainer, Andy Warhol, Carolee Schneemann, and John Cage—participated, but the technologists who contributed essential expertise and aesthetic input often went unrecognized.Coming from diverse personal backgrounds, this roster of engineers and scientists includes Frank J. Malina, the American rocket pioneer-turned-kinetic artist who launched the art-science journal Leonardo, and Swedish-born engineer Billy Klüver, who established the group Experiments in Art and Technology (E.A.T). At schools ranging from MIT to Caltech, engineers engaged with such figures as artist Gyorgy Kepes and celebrity curator Maurice Tuchman. Today, we are in the midst of a new surge of corporate and academic promotion of projects and programs combining art, technology, and science. Making Art Work reveals how artists and technologists have continually constructed new communities in which they exercise imagination, display creative expertise, and pursue commercial innovation.

Why a new approach is needed in the quest for general artificial intelligence.Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent, nor likely.Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence. Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.