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The ink and stylus tablets discovered at the Roman fort of Vindolanda are a unique resource for scholars of ancient history. However, the stylus tablets in particular are extremely difficult to read. This book details the development of what appears to be the first system constructed to aid experts in the process of reading an ancient document, exploring the extent to which techniques from artificial intelligence can be used to develop a system that could aid historians in reading the stylus texts. Using knowledge elicitation techniques (borrowed from artificial intelligence and engineering science), a model is proposed for how experts construct a reading of a text. A prototype system is presented that can read in image data and produce realistic and plausible textual interpretations of the writing that appears on the documents. Incorporating knowledge elicited from experts working on the texts, and utilizing image processing techniques developed in engineering science to analyze the stylus tablets, the book includes a corpora of letter forms generated from the Vindolanda text corpus, and a detailed description of the architecture of the system. This research presents the first stages towards developing a cognitive visual system that can propagate realistic interpretations from image data.

The mathematical genius Alan Turing (1912-1954) was one of the greatest scientists and thinkers of the 20th century. Now well known for his crucial wartime role in breaking the ENIGMA code, he was the first to conceive of the fundamental principle of the modern computer — the idea of controlling a computing machine's operations by means of coded instructions, stored in the machine's ‘memory’. In 1945, Turing drew up his revolutionary design for an electronic computing machine — his Automatic Computing Engine (‘ACE’). A pilot model of the ACE ran its first programme in 1950 and the production version, the ‘DEUCE’, went on to become a cornerstone of the fledgling British computer industry. The first ‘personal’ computer was based on Turing's ACE. This book describes Turing's struggle to build the modern computer. It contains first-hand accounts by Turing and by the pioneers of computing who worked with him. The book describes the hardware and software of the ACE and contains chapters describing Turing's path-breaking research in the fields of Artificial Intelligence (AI) and Artificial Life (A-Life).

The hope that we might one day upload our minds into robots and, eventually, cyberspace has become commonplace and now affects life across a broad spectrum of western culture. Popular science books on robotics and artificial intelligence (AI) by Hans Moravec, Ray Kurzweil, and others argue that one day advances in robotics, AI and neurobiology will enable us to copy our conscious selves into machines, which will take over the cosmos and live eternally in a perfect world of supremely intelligent Mind. Such views borrow from the apocalyptic traditions of Judaism and Christianity and influence the politics of research grants, life in online virtual reality environments, and conversations within philosophical, legal and theological circles. Apocalyptic AI is important to scientific research because it promotes public and private funding for robotics and AI. In addition, residents of the online world Second Life have adopted it as a worldview that gives meaning to their activities and many already wish to live in Second Life or a similar environment forever, just as Moravec and Kurzweil promise they will. Finally, several of the claims of Apocalyptic AI have become a serious topic of debate for philosophers of mind, legal scholars and theologians. The successful integration of religion, science and technology in Apocalyptic AI creates a powerful worldview with considerable influence in modern life and challenges many of our long held assumptions about the relationship between religion and science.

The human‐built environment is increasingly being populated by artificial agents that, through artificial intelligence (AI), are capable of acting autonomously. The software controlling these autonomous systems is, to‐date, “ethically blind” in the sense that the decision‐making capabilities of such systems does not involve any explicit moral reasoning. The title Moral Machines: Teaching Robots Right from Wrong refers to the need for these increasingly autonomous systems (robots and software bots) to become capable of factoring ethical and moral considerations into their decision making. The new field of inquiry directed at the development of artificial moral agents is referred to by a number of names including machine morality, machine ethics, roboethics, or artificial morality. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems.

Although computational models of cognition have become very popular, these models are relatively limited in their coverage of cognition—they usually only emphasize problem solving and reasoning, or treat perception and motivation as isolated modules. The first architecture to cover cognition more broadly is the Psi theory, developed by Dietrich Dörner. By integrating motivation and emotion with perception and reasoning, and including grounded neuro-symbolic representations, the Psi contributes significantly to an integrated understanding of the mind. It provides a conceptual framework that highlights the relationships between perception and memory, language and mental representation, reasoning and motivation, emotion and cognition, autonomy and social behavior. So far, the Psi theory's origin in psychology, its methodology, and its lack of documentation have limited its impact. This book adapts the Psi theory to cognitive science and artificial intelligence, by elucidating both its theoretical and technical frameworks, and clarifying its contribution to how we have come to understand cognition.

In this book, Susan Brenner analyzes the complex and evolving interactions between law and technology and provides a thorough and detailed account of the law in technology at the beginning of the 21st century. She draws upon recent technological advances, evaluating how developing technologies may alter how humans interact with each other and with their environment. She analyzes the development of technology as shifting from one of “use” to one of “interaction,” and argues that this interchange requires us to reconceptualize our approach to legal rules, which were originally designed to prevent the “misuse” of older technologies. Brenner argues that as technologies continue to evolve, the laws targeting the relationship between humans and technology must become, and should remain, neutral. She explains how older technologies rely on human implementation, but new, “smart” technologies are intelligent and autonomous, in varying degrees. This, she notes, will eventually lead to the ultimate progression in our relationship with technology: the fusion of human physiology and technology. Law in an Era of “Smart” Technology provides a detailed, historically-grounded analysis of why our traditional relationship with technology is evolving in ways that require a corresponding shift in our law.

Recent advances in artificial intelligence (AI) highlight its potential to affect productivity, growth, inequality, market power, innovation, and employment. In September 2017, the National Bureau of Economic Research held its first conference on the Economics of AI in Toronto. The purpose of the conference and associated volume is to set the research agenda for economists working on AI. The focus of the volume is on the economic impact of machine learning, a branch of computational statistics that has driven the recent excitement around AI. The volume also highlights key questions on the economic impact of robotics and automation, as well as the potential economic consequences of a still-hypothetical artificial general intelligence. The volume covers four broad themes: AI as a general purpose technology; the relationship between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. In highlighting these themes, the volume provides several frameworks for understanding the economic impact of AI. In doing so, it identifies a number of key open research questions in a variety of research areas including productivity, growth, decision-making, jobs, inequality, market structure, privacy, trade, liability, political economy, econometrics, behavioral economics, and innovation.