The pursuit of artificial intelligence has been a highly active
domain of research for decades, yielding exciting scientific
insights and productive new technologies. In terms of generating
intelligence, however, this pursuit has yielded only limited
success. This book explores the hypothesis that adaptive growth is
a means of moving forward. By emulating the biological process of
development, we can incorporate desirable characteristics of
natural neural systems into engineered designs and thus move closer
towards the creation of brain-like systems. The particular focus is
on how to design artificial neural networks for engineering
tasks.
The book consists of contributions from 18 researchers, ranging
from detailed reviews of recent domains by senior scientists, to
exciting new contributions representing the state of the art in
machine learning research. The book begins with broad overviews of
artificial neurogenesis and bio-inspired machine learning, suitable
both as an introduction to the domains and as a reference for
experts. Several contributions provide perspectives and future
hypotheses on recent highly successful trains of research,
including deep learning, the Hyper NEAT model of developmental
neural network design, and a simulation of the visual cortex. Other
contributions cover recent advances in the design of bio-inspired
artificial neural networks, including the creation of machines for
classification, the behavioural control of virtual agents, the
design of virtual multi-component robots and morphologies and the
creation of flexible intelligence. Throughout, the contributors
share their vast expertise on the means and benefits of creating
brain-like machines.
This book is appropriate for advanced students and practitioners
of artificial intelligence and machine learning.
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