Human Machine Reconfigurations

Publisher description

A compelling case for the re-examination of interface design models is presented by this text's assertion that human behavior is not taken into account in the planning model generally favored by artificial intelligence.

machines stems from the consumer, the product, or the interplay between the two. --

Medicine has recently discovered spectacular tools for human enhancement. Yet to date, it has failed to use them well, in part because of ethical objections. Meanwhile, covert attempts flourish to enhance with steroids, mind-enhancing drugs, and cosmetic surgery—all largely unstudied scientifically. The little success to date has been sporadic and financed privately. In How to Build a Better Human, prominent bioethicist Gregory E. Pence argues that people, if we are careful and ethical, can use genetics, biotechnology, and medicine to improve ourselves, and that we should publicly study what people are doing covertly. Pence believes that we need to transcend the two common frame stories of bioethics: bioconservative alarmism and uncritical enthusiasm, and that bioethics should become part of the solution—not the problem—in making better humans.

Many animals, including humans, acquire valuable skills and knowledge by copying others. Scientists refer to this as social learning. It is one of the most exciting and rapidly developing areas of behavioral research and sits at the interface of many academic disciplines, including biology, experimental psychology, economics, and cognitive neuroscience. Social Learning provides a comprehensive, practical guide to the research methods of this important emerging field. William Hoppitt and Kevin N. Lala define the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. They present techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social learning strategies, and introduce readers to mathematical methods and models used in the study of cultural evolution. This book is an indispensable tool for researchers and an essential primer for students. Provides a comprehensive, practical guide to social learning research Combines theoretical and empirical approaches Describes techniques for the laboratory and the field Covers social learning mechanisms and strategies, statistical modeling techniques for field data, mathematical modeling of cultural evolution, and more

An argument that—despite dramatic advances in the field—artificial intelligence is nowhere near developing systems that are genuinely intelligent. In this provocative book, Brian Cantwell Smith argues that artificial intelligence is nowhere near developing systems that are genuinely intelligent. Second wave AI, machine learning, even visions of third-wave AI: none will lead to human-level intelligence and judgment, which have been honed over millennia. Recent advances in AI may be of epochal significance, but human intelligence is of a different order than even the most powerful calculative ability enabled by new computational capacities. Smith calls this AI ability “reckoning,” and argues that it does not lead to full human judgment—dispassionate, deliberative thought grounded in ethical commitment and responsible action. Taking judgment as the ultimate goal of intelligence, Smith examines the history of AI from its first-wave origins (“good old-fashioned AI,” or GOFAI) to such celebrated second-wave approaches as machine learning, paying particular attention to recent advances that have led to excitement, anxiety, and debate. He considers each AI technology's underlying assumptions, the conceptions of intelligence targeted at each stage, and the successes achieved so far. Smith unpacks the notion of intelligence itself—what sort humans have, and what sort AI aims at. Smith worries that, impressed by AI's reckoning prowess, we will shift our expectations of human intelligence. What we should do, he argues, is learn to use AI for the reckoning tasks at which it excels while we strengthen our commitment to judgment, ethics, and the world.

Computer science as an engineering discipline has been spectacularly successful. Yet it is also a philosophical enterprise in the way it represents the world and creates and manipulates models of reality, people, and action. In this book, Paul Dourish addresses the philosophical bases of human-computer interaction. He looks at how what he calls "embodied interaction"—an approach to interacting with software systems that emphasizes skilled, engaged practice rather than disembodied rationality—reflects the phenomenological approaches of Martin Heidegger, Ludwig Wittgenstein, and other twentieth-century philosophers. The phenomenological tradition emphasizes the primacy of natural practice over abstract cognition in everyday activity. Dourish shows how this perspective can shed light on the foundational underpinnings of current research on embodied interaction. He looks in particular at how tangible and social approaches to interaction are related, how they can be used to analyze and understand embodied interaction, and how they could affect the design of future interactive systems.

If you are one of the many who have read about and heard about virtual worlds but do not really understand what a virtual world is, or even how to use appropriate terminology when discussing them, then this is the book for you."--Jacket.

This study shows that the brain has at least ten intelligences, some emotional, and some behavioural. Using stories and simple exercises, it teaches how to access and orchestrate all ten intelligences, discovering new skills in the process.

Ranging from abstract theory to practical design solutions, this book provides the reader with the understandings needed to design and run cutting edge experiments.