The key to creating AGI is that general learning is not enough without
some meta-level awareness of what is being learning, That
meta-awareness does not have to be sophisticated but it has to be able
to integrate a learned fact in with other information in such a way to
allow the AGI program to have some awareness about the issues
surrounding the learning.  This is going to create a kind of judgment
about what it is learning and how that knowledge may fit into other
related knowledge and how that knowledge might be used to continue
learning with, at least a little, help from inner direction and
insight.  You might string some Neural Networks and other Machine
Learning methods together to create what I would consider a crude
simple judgment, but if the program cannot make any of these
connections for itself it will not be capable of any autonomous AGI
reasoning.  I guess this is a new idea and people do not get what I am
talking about. While an AGI program needs a number of other
significant features this analysis does help me to understand that it
is possible to develop and study simple AGI sub-programs.  - Jim
Bromer

On Thu, Aug 15, 2013 at 9:44 PM, Jim Bromer <[email protected]> wrote:
> One other thing.  Some people have come up with elaborate plans for an
> AGI program without realizing that they were just too complicated.
> And even if they were able to implement their ideas the program would
> be so complicated that it would quickly get bogged down. The problem
> with scalability not only keeps AGI programs from progressing, it
> keeps them from reaching the first step.
>
> While my theory of Conceptual Types is impossible to implement as a
> pure abstraction, my focus right now is on gaining simplicity within
> the confines of a (potential) AGI system.  And that point of view is
> what makes this theory of a simple AGI subprogram (or
> pseudo-subprogram) interesting. - Jim Bromer


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