Part 1

I feel that complexity is a major problem facing contemporary AGI.  It is
true, that for most human reasoning we do not need to figure out
complicated problems precisely in order to take the first steps toward
competency but so far AGI has not been able to get very far beyond the
narrow-AI barrier.

I am going to start with a text-based AGI program.  I agree that more kinds
of IO modalities would make an effective AGI program better.  However, I am
not aware of any evidence that sensory-based AGI or multi-modal sensory
based AGI or robotic based AGI has been able to achieve something greater
than other efforts. The core of AGI is not going to be found in the
peripherals.  And it is clear that starting with complicated IO accessories
would make AGI programming more difficult.  It seems obvious that IO is
necessary for AI/AGI and this abstraction is a probably more appropriate
basis for the requirements of AGI.

My AGI program is going to be based on discreet references. I feel that the
argument that only neural networks are able to learn or are able to
incorporate different kinds of data objects into an associative field is
not accurate. I do, however, feel that more attention needs to be paid to
concept integration.  And I think that many of us recognize that a good AGI
model is going to create an internal reference model that is a kind of
network.  The discreet reference model more easily allows the program to
retain the components of an agglomeration in a way in which the traditional
neural network does not.  This means that it is more likely that the parts
of an associative agglomeration can be detected.  On the other hand, since
the program will develop its own internal data objects, these might be
formed in such a way so that the original parts might be difficult to
detect. With a more conscious effort to better understand concept
integration I think that the discreet conceptual network model will prove
itself fairly easily.

I am going to use weighted reasoning and probability but only to a limited
extent.



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AGI
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