  The overarching problem in artificial intelligence (AI) is that we do not
understand the intelligence process well enough to enable the development of
adequate computational models. Much work has been done in AI over the years at
lower levels, but a big part of what has been missing involves the high level,
abstract, general nature of intelligence. We address this gap by developing a
model for general intelligence. To accomplish this, we focus on three basic
aspects of intelligence. First, we must realize the general order and nature of
intelligence at a high level. Second, we must come to know what these
realizations mean with respect to the overall intelligence process. Third, we
must describe these realizations as clearly as possible. We propose a
hierarchical model to help capture and exploit the order within intelligence.
The underlying order involves patterns of signals that become organized, stored
and activated in space and time. These patterns can be described using a
simple, general hierarchy, with physical signals at the lowest level,
information in the middle, and abstract signal representations at the top. This
high level perspective provides a big picture that literally helps us see the
intelligence process, thereby enabling fundamental realizations, a better
understanding and clear descriptions of the intelligence process. The resulting
model can be used to support all kinds of information processing across
multiple levels of abstraction. As computer technology improves, and as
cooperation increases between humans and computers, people will become more
efficient and more productive in performing their information processing tasks.
