Most AI/AGI methods of learning or of figuring things out are either
based on applying a method on a particular (typically narrow) kind of
situation or on using a crude method to adapt to a narrow kind of
situation.  Using reason-based-reasoning I expect to be able to
discover new possibilities that are not used in mainstream AI/AGI
methods as far as I can tell.  Of course sometimes the nature of the
aspects (or parts) of a situation can be appreciated by simple
association but in those cases those kinds of associations (or
correlations) can then be considered to be (part of) the reason we can
explain familiar observations.  We probably noticed that we could see
an airplane in the sky before we could hear it before we were able to
understand the explanation.  But the difficulty of insightfully
understanding the relations of noticing things and then discovering an
explanation then applying the explanation and confirming it through
similar situations (including other kinds of observations) is the key
to intelligence.  Finding thorough discussions of this kind of
situation in AI discussions has been pretty thin for many years
because the initial attempts to develop computational methods of
integrating observations and ideas failed in the early years of AI.
So now instead of recognizing the relation that observation plays in
forming and generalizing superficial theory-like descriptions and then
trying to discover the details of how the theory-like descriptions can
then be integrated with explanations and how the explanations can be
confirmed (to some extent) through a variety of other observations
which can almost isolate the central effect, the discussions on groups
like this get lost on some banality like the question of whether
robotics is necessary for AGI or not.

It is my insight into this that gives me so much confidence.  Writing
an AGI program is going to be complicated.  That is a challenge.  But
that just takes time.

Jim Bromer


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AGI
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