This book brings together contributions to the Fourth Artificial Life Workshop, held at the Massachusetts Institute of Technology in the summer of 1994.

Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Argues that treating people and artificial intelligence differently under the law results in unexpected and harmful outcomes for social welfare.

Communication and artificial intelligence (AI) are closely related. It is communication – particularly interpersonal conversational interaction – that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic.

An account of the creation of new forms of life and intelligence in cybernetics, artificial life, and artificial intelligence that analyzes both the similarities and the differences among these sciences in actualizing life.The Allure of Machinic Life

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.

Examining the construction, manipulation and re-definition of life in contemporary technoscientific culture, this book aims to re-focus concern on the ethics rather than on the 'nature' of artificial life.

In addition to presenting the latest work in the field, Artificial Life V includes a retrospective and prospective look at both artificial and natural life with the aim of refining the methods and approaches discovered so far into viable, practical tools for the pursuit of science and engineering goals. May 16-18, 1996 · Nara, Japan Despite all the successes in computer engineering, adaptive computation, bottom-up AI, and robotics, Artificial Life must not become simply a one-way bridge, borrowing biological principles to enhance our engineering efforts in the construction of life-as-it-could-be. We must ensure that we give back to biology in kind, by developing tools and methods that will be of real value in the effort to understand life-as-it-is. Artificial Life V marks a decade since Christopher Langton organized the first workshop on artificial life--a decade characterized by the exploration of new possibilities and techniques as researchers have sought to understand, through synthetic experiments, the organizing principles underlying the dynamics (usually the nonlinear dynamics) of living systems. In addition to presenting the latest work in the field, Artificial Life V includes a retrospective and prospective look at both artificial and natural life with the aim of refining the methods and approaches discovered so far into viable, practical tools for the pursuit of science and engineering goals. Complex Adaptive Systems series

The term "artificial life" describes research into synthetic systems that possess some of the essential properties of life. This interdisciplinary field includes biologists, computer scientists, physicists, chemists, geneticists, and others. Artificial life may be viewed as an attempt to understand high-level behavior from low-level rules—for example, how the simple interactions between ants and their environment lead to complex trail-following behavior. An understanding of such relationships in particular systems can suggest novel solutions to complex real-world problems such as disease prevention, stock-market prediction, and data mining on the Internet. Since their inception in 1987, the Artificial Life meetings have grown from small workshops to truly international conferences, reflecting the field's increasing appeal to researchers in all areas of science.

An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.