Bernard Schiele – författare

Common sense is the endless frontier in the development of artificial intelligence, but what exactly is common sense, can we replicate it in algorithmic form, and if we can – should we?

Bauer, Schiele and their contributors from a range of disciplines analyse the nature of common sense, and the consequent challenges of incorporating into artificial intelligence models. They look at different ways we might understand common sense and which of these ways are simulated within computer algorithms. These include sensory integration, self-evident truths, rhetorical common places, and mutuality and intentionality of actors within a moral community. How far are these possible features within and of machines? Approaching from a range of perspectives including Sociology, Political Science, Media and Culture, Psychology and Computer Science, the contributors lay out key questions, practical challenges and "common sense" concerns underlying the incorporation of common sense within machine learning algorithms for simulating intelligence, socialising robots, self-driving vehicles, personnel selection, reading, automatic text analysis, and text production.

A valuable resource for students and scholars of Science–Technology–Society Studies, Sociologists, Psychologists, Media and Culture Studies, human–computer interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning.

Common sense is the endless frontier in the development of artificial intelligence, but what exactly is common sense, can we replicate it in algorithmic form, and if we can – should we?

Bauer, Schiele and their contributors from a range of disciplines analyse the nature of common sense, and the consequent challenges of incorporating into artificial intelligence models. They look at different ways we might understand common sense and which of these ways are simulated within computer algorithms. These include sensory integration, self-evident truths, rhetorical common places, and mutuality and intentionality of actors within a moral community. How far are these possible features within and of machines? Approaching from a range of perspectives including Sociology, Political Science, Media and Culture, Psychology and Computer Science, the contributors lay out key questions, practical challenges and "common sense" concerns underlying the incorporation of common sense within machine learning algorithms for simulating intelligence, socialising robots, self-driving vehicles, personnel selection, reading, automatic text analysis, and text production.

A valuable resource for students and scholars of Science–Technology–Society Studies, Sociologists, Psychologists, Media and Culture Studies, human–computer interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning.

Science communication, as a multidisciplinary field, has developed remarkably in recent years. It is now a distinct and exceedingly dynamic science that melds theoretical approaches with practical experience. Formerly well-established theoretical models now seem out of step with the social reality of the sciences, and the previously clear-cut delineations and interacting domains between cultural fields have blurred. Communicating Science in Social Contexts examines that shift, which itself depicts a profound recomposition of knowledge fields, activities and dissemination practices, and the value accorded to science and technology.

Communicating Science in Social Contexts is the product of long-term effort that would not have been possible without the research and expertise of the Public Communication of Science and Technology (PCST) Network and the editors. For nearly 20 years, this informal, international network has been organizing events and forums for discussion of the public communication of science.