Machine learning builds models of the world using training data
from the application domain and prior knowledge about the problem.
The models are later applied to future data in order to estimate
the current state of the world. An implied assumption is that the
future is stochastically similar to the past. The approach fails
when the system encounters situations that are not anticipated from
the past experience. In contrast, successful natural organisms
identify new unanticipated stimuli and situations and frequently
generate appropriate responses.
The observation described above lead to the initiation of the
DIRAC EC project in 2006. In 2010 a workshop was held, aimed to
bring together researchers and students from different disciplines
in order to present and discuss new approaches for identifying and
reacting to unexpected events in information-rich environments.
This book includes a summary of the achievements of the DIRAC
project in chapter 1, and a collection of the papers presented in
this workshop in the remaining parts."
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