A revealing genealogy of image-recognition techniques and technologies
Today’s most advanced neural networks and sophisticated image-analysis methods come from 1950s and ’60s Cold War culture—and many biases and ways of understanding the world from that era persist along with them. Aerial surveillance and reconnaissance shaped all of the technologies that we now refer to as computer vision, including facial recognition. The Birth of Computer Vision uncovers these histories and finds connections between the algorithms, people, and politics at the core of automating perception today.
James E. Dobson reveals how new forms of computerized surveillance systems, high-tech policing, and automated decision-making systems have become entangled, functioning together as a new technological apparatus of social control. Tracing the development of a series of important computer-vision algorithms, he uncovers the ideas, worrisome military origins, and lingering goals reproduced within the code and the products based on it, examining how they became linked to one another and repurposed for domestic and commercial uses. Dobson includes analysis of the Shakey Project, which produced the first semi-autonomous robot, and the impact of student protest in the early 1970s at Stanford University, as well as recovering the computer vision–related aspects of Frank Rosenblatt’s Perceptron as the crucial link between machine learning and computer vision.
Motivated by the ongoing use of these major algorithms and methods, The Birth of Computer Vision chronicles the foundations of computer vision and artificial intelligence, its major transformations, and the questionable legacy of its origins.
Cover alt text: Two overlapping circles in cream and violet, with black background. Top is a printed circuit with camera eye; below a person at a 1977 computer.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system.
In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI.
The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Since antiquity, philosophers and engineers have tried to take life’s measure by reproducing it. Aiming to reenact Creation, at least in part, these experimenters have hoped to understand the links between body and spirit, matter and mind, mechanism and consciousness. Genesis Redux examines moments from this centuries-long experimental tradition: efforts to simulate life in machinery, to synthesize life out of material parts, and to understand living beings by comparison with inanimate mechanisms.
Jessica Riskin collects seventeen essays from distinguished scholars in several fields. These studies offer an unexpected and far-reaching result: attempts to create artificial life have rarely been driven by an impulse to reduce life and mind to machinery. On the contrary, designers of synthetic creatures have generally assumed a role for something nonmechanical. The history of artificial life is thus also a history of theories of soul and intellect.
Taking a historical approach to a modern quandary, Genesis Redux is essential reading for historians and philosophers of science and technology, scientists and engineers working in artificial life and intelligence, and anyone engaged in evaluating these world-changing projects.
An in-depth assessment of innovations in military information technology informs hypothetical outcomes for artificial intelligence adaptations
In the coming decades, artificial intelligence (AI) could revolutionize the way humans wage war. The military organizations that best innovate and adapt to this AI revolution will likely gain significant advantages over their rivals. To this end, great powers such as the United States, China, and Russia are already investing in novel sensing, reasoning, and learning technologies that will alter how militaries plan and fight. The resulting transformation could fundamentally change the character of war.
In Information in War, Benjamin Jensen, Christopher Whyte, and Scott Cuomo provide a deeper understanding of the AI revolution by exploring the relationship between information, organizational dynamics, and military power. The authors analyze how militaries adjust to new information communication technology historically to identify opportunities, risks, and obstacles that will almost certainly confront modern defense organizations as they pursue AI pathways to the future. Information in War builds on these historical cases to frame four alternative future scenarios exploring what the AI revolution could look like in the US military by 2040.
“Exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it.”
—John Horgan
“If you want to know about AI, read this book…It shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence.”
—Peter Thiel
Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. A computer scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to reveal why this is a profound mistake.
AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don’t correlate data sets. We make conjectures, informed by context and experience. And we haven’t a clue how to program that kind of intuitive reasoning, which lies at the heart of common sense. Futurists insist AI will soon eclipse the capacities of the most gifted mind, but Larson shows how far we are from superintelligence—and what it would take to get there.
“Larson worries that we’re making two mistakes at once, defining human intelligence down while overestimating what AI is likely to achieve…Another concern is learned passivity: our tendency to assume that AI will solve problems and our failure, as a result, to cultivate human ingenuity.”
—David A. Shaywitz, Wall Street Journal
“A convincing case that artificial general intelligence—machine-based intelligence that matches our own—is beyond the capacity of algorithmic machine learning because there is a mismatch between how humans and machines know what they know.”
—Sue Halpern, New York Review of Books
A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practices
Neural Networks proposes to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same name. If so-called machine learning comprises a statistical approach to pattern extraction, then neural networks can be defined as a biologically inspired model that relies on probabilistically weighted neuron-like units to identify such patterns. Far from signaling the ultimate convergence of human and machine intelligence, however, neural networks highlight the technologization of neurophysiology that characterizes virtually all strands of neuroscientific and AI research of the past century. Taking this traffic as its starting point, this volume explores how cognition came to be constructed as essentially computational in nature, to the point of underwriting a technologized view of human biology, psychology, and sociability, and how countermovements provide resources for thinking otherwise.
Cryptology, the mathematical and technical science of ciphers and codes, and philology, the humanistic study of natural or human languages, are typically understood as separate domains of activity. But Brian Lennon contends that these two domains, both concerned with authentication of text, should be viewed as contiguous. He argues that computing’s humanistic applications are as historically important as its mathematical and technical ones. What is more, these humanistic uses, no less than cryptological ones, are marked and constrained by the priorities of security and military institutions devoted to fighting wars and decoding intelligence.
Lennon’s history encompasses the first documented techniques for the statistical analysis of text, early experiments in mechanized literary analysis, electromechanical and electronic code-breaking and machine translation, early literary data processing, the computational philology of late twentieth-century humanities computing, and early twenty-first-century digital humanities. Throughout, Passwords makes clear the continuity between cryptology and philology, showing how the same practices flourish in literary study and in conditions of war.
Lennon emphasizes the convergence of cryptology and philology in the modern digital password. Like philologists, hackers use computational methods to break open the secrets coded in text. One of their preferred tools is the dictionary, that preeminent product of the philologist’s scholarly labor, which supplies the raw material for computational processing of natural language. Thus does the historic overlap of cryptology and philology persist in an artifact of computing—passwords—that many of us use every day.
Most human thinking is thoroughly informed by context but, until recently, theories of reasoning have concentrated on abstract rules and generalities that make no reference to this crucial factor. Perspectives on Contexts brings together essays from leading cognitive scientists to forge a vigorous interdisciplinary understanding of the contextual phenomenon. Applicable to human and machine cognition in philosophy, artificial intelligence, and psychology, this volume is essential to the current renaissance in thinking about context.
Decisions about war have always been made by humans, but now intelligent machines are on the cusp of changing things – with dramatic consequences for international affairs. This book explores the evolutionary origins of human strategy, and makes a provocative argument that Artificial Intelligence will radically transform the nature of war by changing the psychological basis of decision-making about violence.
Strategy, Evolution, and War is a cautionary preview of how Artificial Intelligence (AI) will revolutionize strategy more than any development in the last three thousand years of military history. Kenneth Payne describes strategy as an evolved package of conscious and unconscious behaviors with roots in our primate ancestry. Our minds were shaped by the need to think about warfare—a constant threat for early humans. As a result, we developed a sophisticated and strategic intelligence.
The implications of AI are profound because they depart radically from the biological basis of human intelligence. Rather than being just another tool of war, AI will dramatically speed up decision making and use very different cognitive processes, including when deciding to launch an attack, or escalate violence. AI will change the essence of strategy, the organization of armed forces, and the international order.
This book is a fascinating examination of the psychology of strategy-making from prehistoric times, through the ancient world, and into the modern age.
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