A SCHOLARLY BOOK REPOSITORY
This Charleston Briefing seeks to provide some answers to these very important questions, offering both general essays on AI and more specific essays on AI in scholarly publishing, academic libraries, and AI in information discovery and knowledge building. The essays will help publishers, librarians, and researchers better understand the actual impact of AI on libraries and publishing so that they can respond to the potentially transformative impact of AI in a measured and knowledgeable manner.
Charleston Briefings: Trending Topics for Information Professionals is a thought-provoking series of brief books concerning innovation in the sphere of libraries, publishing, and technology in scholarly communication. The briefings, growing out of the vital conversations characteristic of the Charleston Conference and Against the Grain, will offer valuable insights into the trends shaping our professional lives and the institutions in which we work.
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.
This volume provides an update on the state of the art in the development and application of broad-coverage declarative grammars built on sound linguistic foundations - the 'deep' processing paradigm - and presents several aspects of an international research effort to produce comprehensive, re-usable grammars and efficient technology for parsing and generating with such grammars.
Contributors:
Daron Acemoglu, Massachusetts Institute of Technology
Philippe Aghion, Collège de France
Ajay Agrawal, University of Toronto
Susan Athey, Stanford University
James Bessen, Boston University School of Law
Erik Brynjolfsson, MIT Sloan School of Management
Colin F. Camerer, California Institute of Technology
Judith Chevalier, Yale School of Management
Iain M. Cockburn, Boston University
Tyler Cowen, George Mason University
Jason Furman, Harvard Kennedy School
Patrick Francois, University of British Columbia
Alberto Galasso, University of Toronto
Joshua Gans, University of Toronto
Avi Goldfarb, University of Toronto
Austan Goolsbee, University of Chicago Booth School of Business
Rebecca Henderson, Harvard Business School
Ginger Zhe Jin, University of Maryland
Benjamin F. Jones, Northwestern University
Charles I. Jones, Stanford University
Daniel Kahneman, Princeton University
Anton Korinek, Johns Hopkins University
Mara Lederman, University of Toronto
Hong Luo, Harvard Business School
John McHale, National University of Ireland
Paul R. Milgrom, Stanford University
Matthew Mitchell, University of Toronto
Alexander Oettl, Georgia Institute of Technology
Andrea Prat, Columbia Business School
Manav Raj, New York University
Pascual Restrepo, Boston University
Daniel Rock, MIT Sloan School of Management
Jeffrey D. Sachs, Columbia University
Robert Seamans, New York University
Scott Stern, MIT Sloan School of Management
Betsey Stevenson, University of Michigan
Joseph E. Stiglitz. Columbia University
Chad Syverson, University of Chicago Booth School of Business
Matt Taddy, University of Chicago Booth School of Business
Steven Tadelis, University of California, Berkeley
Manuel Trajtenberg, Tel Aviv University
Daniel Trefler, University of Toronto
Catherine Tucker, MIT Sloan School of Management
Hal Varian, University of California, Berkeley
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.
In Experimenting the Human, G Douglas Barrett argues that experimental music speaks to the contemporary posthuman, a condition in which science and technology decenter human agency amid the uneven temporality of postwar global capitalism. Time moves forward for some during this period, while it seems to stand still or even move backward for others. Some say we’re already posthuman, while others endure the extended consequences of never having been considered fully human in the first place. Experimental music reflects on this state, Barrett contends, through its interdisciplinary involvements in postwar science, technology, and art movements.
Rather than pursuing the human's beyond, experimental music addresses the social and technological conditions that support such a pursuit. Barrett locates this tendency of experimentalism throughout its historical entanglements with cybernetics, and in his intimate analysis of Alvin Lucier’s neurofeedback music, Pamela Z’s BodySynth performances, Nam June Paik’s musical robotics, Pauline Oliveros’s experiments with radio astronomy, and work by Laetitia Sonami, Yasunao Tone, and Jerry Hunt. Through a unique meeting of music studies, media theory, and art history, Experimenting the Human provides fresh insights into what it means to be human.
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.
Holographic Reduced Representations (HRRs) are introduced here to model how the brain distributes each piece of information among thousands of neurons. It had been previously thought that the grammatical structure of a language cannot be encoded practically in a distributed representation, but HRRs can overcome the problems of earlier proposals. Thus this work has implications for psychology, neuroscience, linguistics, and computer science, and engineering.
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.
Inhuman Power explores the relationship between Marxist theory and AI through three approaches, each using the lens of a different Marxist theoretical concept. While the idea of widespread AI tends to be celebrated as much as questioned, a deeper analysis of its reach and potential produces a more complex and disturbing picture than has been identified. Inhuman Power argues that on its current trajectory, AI is likely to render humanity obsolete and that the only way to prevent it is a communist revolution.
In The Inspiration Machine, Eitan Y. Wilf explores the transformative potentials that digital technology opens up for creative practice through three ethnographic cases, two with jazz musicians and one with a group of poets. At times dissatisfied with the limitations of human creativity, these artists do not turn to computerized algorithms merely to execute their preconceived ideas. Rather, they approach them as creative partners, delegating to them different degrees of agentive control and artistic decision-making in the hopes of finding inspiration in their output and thereby expanding their own creative horizons.
The algorithms these artists develop and use, however, remain rooted in and haunted by the specific social predicaments and human shortfalls that they were intended to overcome. Experiments in the digital thus hold an important lesson: although Wilf’s interlocutors returned from their adventures with computational creativity with modified, novel, and enriched capacities and predilections, they also gained a renewed appreciation for, and at times a desire to re-inhabit, non-digital creativity. In examining the potentials and pitfalls of seemingly autonomous digital technologies in the realm of art, Wilf shows that computational solutions to the real or imagined insufficiencies of human practice are best developed in relation to, rather than away from, the social and cultural contexts that gave rise to those insufficiencies, in the first place.
This concise and authoritative work offers the latest guidance on journalism ethics for students and media professionals and will help empower news consumers to make informed decisions about the trustworthiness of their sources of information. It offers advice on all aspects of journalism ethics including accuracy and seeking the truth, representation of women, LGBTQ coverage, climate change, mental health, use of images, conflict reporting, elections, and how to use artificial intelligence. The author brings a unique perspective and depth of knowledge to the complex challenges facing journalists and news consumers in this era of fake news, disinformation, and artificial intelligence.
The authors develop the notion of an instructable robot—one which derives its intelligence in part from interaction with humans. Since verbal interaction with a robot requires a natural language semantics, the authors propose a natural-model semantics which they then apply to the interpretation of robot commands. Two experimental projects are described which provide natural-language interfaces to robotic aids for the physically disabled. The authors discuss the specific challenges posed by the interpretation of "stop" commands and the interpretation of spatial prepositions.
The authors also examine the use of explicit verbal instruction to teach a robot new procedures, propose ways a robot can learn from corrective commands containing qualitative spatial expressions, and discuss the machine-learning of a natural language use to instruct a robot in the performance of simple physical tasks. Two chapters focus on probabilistic techniques in learning.
Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians well before the computer age: How do you draw the line between computational rules and the complexities of making systems comprehensible to people? By attending to this question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day.
Here Jeffrey M. Binder offers a compelling tour of four visions of universal computation that addressed this issue in very different ways: G. W. Leibniz’s calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole’s nineteenth-century logic system; and the early programming language ALGOL, short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. Machine learning, in its increasing dependence on words, erodes the line between technical and everyday language, revealing the urgent stakes underlying this boundary.
The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction.
“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.
Early artificial intelligence movements such as cybernetics, information theory, and the Turing test define ways of seeming—rather than being—human. Using these theories of verisimilitude to read Victorian novelists such as Elizabeth Gaskell, Margaret Oliphant, Anthony Trollope, Thomas Hardy, and Henry James, Seeming Human argues that mechanicity has been perceived as anti-realist because it is the element that we least want to identify as human. Because AI produces human-like intelligence, it makes clear that we must actually turn to machines in order to understand what makes realist characters seem so human.
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.
Artificial Intelligence fuels both enthusiasm and panic. Technologists are inclined to give their creations leeway, pretend they’re animated beings, and consider them efficient. As users, we may complain when these technologies don’t obey, or worry about their influence on our choices and our livelihoods. And yet, we also yearn for their convenience, see ourselves reflected in them, and treat them as something entirely new. But when we overestimate the automation of these tools, award-winning author Antonio A. Casilli argues, we fail to recognize how our fellow humans are essential to their efficiency. The danger is not that robots will take our jobs, but that humans will have to do theirs.
In this bracing and powerful book, Casilli uses up-to-the-minute research to show how today’s technologies, including AI, continue to exploit human labor—even ours. He connects the diverse activities of today’s tech laborers: platform workers, like Uber drivers and Airbnb hosts; “micro workers,” including those performing atomized tasks like data entry on Amazon Mechanical Turk; and the rest of us, as we evaluate text or images to show we’re not robots, react to Facebook posts, or approve or improve the output of generative AI. As Casilli shows us, algorithms, search engines, and voice assistants wouldn’t function without unpaid or underpaid human contributions. Further, he warns that if we fail to recognize this human work, we risk a dark future for all human labor.
Waiting for Robots urges us to move beyond the simplistic notion that machines are intelligent and autonomous. As the proverbial Godot, robots are the bearers of a messianic promise that is always postponed. Instead of bringing prosperity for all, they discipline the workforce, so we don’t dream of a world without drudgery and exploitation. Casilli’s eye-opening book makes clear that most “automation” requires human labor—and likely always will—shedding new light on today’s consequences and tomorrow’s threats of failing to recognize and compensate the “click workers” of today.
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