Human-Machine Shared Contexts considers the foundations, metrics,
and applications of human-machine systems. Editors and authors
debate whether machines, humans, and systems should speak only to
each other, only to humans, or to both and how. The book
establishes the meaning and operation of "shared contexts" between
humans and machines; it also explores how human-machine systems
affect targeted audiences (researchers, machines, robots, users)
and society, as well as future ecosystems composed of humans and
machines. This book explores how user interventions may improve the
context for autonomous machines operating in unfamiliar
environments or when experiencing unanticipated events; how
autonomous machines can be taught to explain contexts by reasoning,
inferences, or causality, and decisions to humans relying on
intuition; and for mutual context, how these machines may
interdependently affect human awareness, teams and society, and how
these "machines" may be affected in turn. In short, can context be
mutually constructed and shared between machines and humans? The
editors are interested in whether shared context follows when
machines begin to think, or, like humans, develop subjective states
that allow them to monitor and report on their interpretations of
reality, forcing scientists to rethink the general model of human
social behavior. If dependence on machine learning continues or
grows, the public will also be interested in what happens to
context shared by users, teams of humans and machines, or society
when these machines malfunction. As scientists and engineers "think
through this change in human terms," the ultimate goal is for AI to
advance the performance of autonomous machines and teams of humans
and machines for the betterment of society wherever these machines
interact with humans or other machines. This book will be essential
reading for professional, industrial, and military computer
scientists and engineers; machine learning (ML) and artificial
intelligence (AI) scientists and engineers, especially those
engaged in research on autonomy, computational context, and
human-machine shared contexts; advanced robotics scientists and
engineers; scientists working with or interested in data issues for
autonomous systems such as with the use of scarce data for training
and operations with and without user interventions; social
psychologists, scientists and physical research scientists pursuing
models of shared context; modelers of the internet of things (IOT);
systems of systems scientists and engineers and economists;
scientists and engineers working with agent-based models (ABMs);
policy specialists concerned with the impact of AI and ML on
society and civilization; network scientists and engineers; applied
mathematicians (e.g., holon theory, information theory);
computational linguists; and blockchain scientists and engineers.
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