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Perhaps a discussion between the knowledge scientists will occur here, over
the next few weeks.


Ben said (with great insight):

"Any mind has got to be able to transform data into information, and to
ontologies based on the information."


As a simplification of what the biological system does, particularly higher
animal brain systems, the mind does transform "data" into information and
then does organize this information through the experience of awareness.
Autopoiesis (Maturana and Varela) is the clearest statement of this process.
(Already one has some difficulty understanding exactly what "data" is in the
context of human experience.  But the analogy is a positive start towards
architecturing a many to many (M2M) communication device.  )


Real human experience does exist, the question is what is it  !

The architecture for the mind is perhaps stratified in the sense that I have
been specifying in (for example):

in which open loops (G. Edelman would call this re-entrant processing)
establish (what Pribram calls) feed forward processing. These open loop
processes "must" pass through epistemic gaps (such as we see in the
Heisenburg gap or in the gap between the physics of individual particle
trajectories and chemical pressure etc.  The Russians often called this the
"S- (or entropy theorem)).  The biology is very much involved, as are cross
scale (non-algorithmic phenomenon involved in emergence and dissolution of
wholes greater than the sum of their parts).  This is the "new"
non-reductionist science.

The issue of aggregating data into the invariance (even invariance that is
broken up into discontinuous parts - such as passages in complex human
dialog) is treated in the notions of categorical abstraction, and the claim
that I make is that the human memory systems (Tulving and Schactner)
functions in exactly this way.  Top down expectancy then inhibits what is a
competition of emergent "phase coherences" that ride the underlying physics
occurring in the brain-mind system (Levine, Prueitt, Pribram).

Hameroff and his circles of scholars may be scoffed at by the AI camp, but
the phenomenon that Stu is exposing is real physical phenomenon - as is
human consciousness. This scoffing is an indication of the fact that
scientific reductionism is in fact a type of religion with tenets that are
reinforced using social pressure.  This scoffing is NOT science.

Don Mitchell and I developed (2000-2002) a system that has categorical
abstraction (even on small datasets) and has a means to observe and control
various "event chemistries" and so provides a transformation of data into
information, and provides for a human vetting of this information space into
that part of the information which is meaning ful in context under the
control of the human or human community.

I believe that this work by Prueitt and Mitchell is the most advanced
knowledge technology on the planet at this point - but it is not seen
because the markets look at a naked emperor and claims that it is dressed:

The scoffing is part of the social pressure to not just look and see.

It would be a shame to leave gF/cA/eC behind to work on architecture that is
not as fully tuned into the difference between a natural system and a formal

But the gF/cA/eC innovation is too far away from the funding stream.  At
least, so far, one can not bring this up onto the attentional horizon of the

But the issues of "impedance mismatch" (Dr Ruth David - CIA technology)
between human factors and computer science remain no matter how ignored.


The architecture of many to many communication requires that a transform of
data be made that merges and resolves scope and viewpoint issues, and then
presents these issues to humans in an open loop architecture.

One such architecture is the following that I proposed in 1998

other exists.

However, the human factors issues in these types of architectures are often
forgotten, and there are good reasons for this.  One of these good reasons
is that the problem is not so very well understood.  But the procurement
system is also broken in exactly this regards.

My NIMA proposal directly addressed this issue of "human in the loop", and
we almost won the right to build a new type of knowledge technology where
the human is an essential part of the "system".

But we are on to something else now.  And there are lessons learned.

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