Most programs operate on a higher level of rules which operate on the data
that is encountered. This higher level program is a kind of general
abstraction. A program can create new rules at higher abstraction that
could operate on the rules at a lower level. (This characterization is
relative but I think it is useful. Programming languages do this but the
essential part of the program produced is created by a person) This would
allow for greater individuation which is necessary in order to work with
the data in decompressed form because the data would be too extensive for
the program to handle it in decompressed form. This individuation would
mean that the data could not be decompressed without the higher level
abstractions (or sub programs) that each individual run would produce.
These higher level sub-programs would be part of the data the program would
create but I am trying to emphasize the idea that the program would create
sub-programs based on the data that it was encountering and compressing.
(My idea of working with SAT is that it would transform statements in
traditional Boolean Logical form into another form which would also be
compressed.)
So creating individuated sub programs based on the data that was being
compressed (or transformed from one compressed form to another) might be
useful in devising ways to compress the data.
This kind of program would use generalizations as components both to
directly represent the data in compressed form and to create sub-programs
that could operate on the compressed data.

Jim Bromer

On Mon, Jun 5, 2017 at 10:04 PM, Jim Bromer <[email protected]> wrote:

> I realized that the encoding example was not a very good one because a
> compression method has to include a way to decompress the data to produce
> the reference object. Suppose you used generalizations which either
> referred to elaborate data objects or which could be elaborated more fully
> based on context then that might be used in a system that could operate (in
> some way) on the compressed data without decompressing it. If the
> compression data/operations method was able to create specialized
> compression systems based on the data encountered, then different threads
> or computers would develop individuated data systems. Different threads or
> computers might then be able to talk to each other in order to provide
> information about what they encountered without fully decompressing the
> data. They would have to have conversations to explain themselves. Suppose
> that they used some common basis of reference to start with, but then
> developed individuated systems. And further suppose that the compression
> method was like a language. While their conversation would represent a
> partial elaboration of the data that they had acquired, they would not have
> to fully decompress the information they had in order to explain the
> information they acquired to the other computer in spite of the fact that
> their records of information were compressed in different ways.
>
> This idea of being able to operate on compressed representations without
> having to fully decompress them might explain why my visual recognition
> memory is good enough even though my visual recollection is very
> unsophisticated (in spite of my knowing a lot about painting.)
>
> Jim Bromer
>
> On Mon, Jun 5, 2017 at 6:24 PM, Mike Archbold <[email protected]> wrote:
>
>> It seems like everything is "compressed" some way usually in computer
>> processing.  Going back to my banking days we'd have a debit to
>> general ledger, but what this represents is somebody walking around
>> and filling out a ticket, perhaps talking to someone else, but all the
>> software sees is the end result which could be viewed as a kind of
>> compression of the entire transaction.  "I went to Europe" is a
>> compression of the trip down to a single proposition.  But I think
>> what you are asking is:  can programming be done in generalizations,
>> and if so, how can that be formalized?
>>
>> On 6/5/17, Jim Bromer <[email protected]> wrote:
>> > An encoding is almost always a compression method. The data encoding is
>> > referring to some kind of object or event which can be described more
>> > fully. So anytime we devise or use an encoding and a system of
>> operations
>> > that can act on those encoded references we are effectively developing a
>> > compression system that can act on some kind of compressed data without
>> > fully (or excessively) decompressing it.
>> >
>> > So the basis for this kind of thing is well established.
>> >
>> > Jim Bromer
>> >
>> > On Mon, Jun 5, 2017 at 6:31 AM, Jim Bromer <[email protected]> wrote:
>> >
>> >> I realized that traditional logic is a system of compression which
>> allows
>> >> for some computations that can be run without fully decompressing the
>> >> data.
>> >> However, at certain steps at some (relatively low) levels of complexity
>> >> the
>> >> data has to be decompressed (to a great degree). So this example proves
>> >> the
>> >> system is feasible and it is not completely based on binary addition or
>> >> multiplication methods (which are also examples of compression systems
>> >> which can operate on compressed data without decompression.) I did not
>> >> want
>> >> to use binary arithmetic as an example because computers were designed
>> >> around those principles.
>> >>
>> >> So since an example is easy to find, this proves that the methodology
>> can
>> >> be studied as a separate branch of computer science. The question then
>> is
>> >> whether other, more powerful systems, can be developed.
>> >>
>> >> Jim Bromer
>> >>
>> >> On Sun, Jun 4, 2017 at 4:42 PM, Jim Bromer <[email protected]>
>> wrote:
>> >>
>> >>> The Halting Problem shows that the results of programs (programmable
>> >>> logic) cannot be completely computed - for every possible program -
>> >>> without
>> >>> running the program. (The Gödel Incompleteness Theorem shows that
>> there
>> >>> are -some- comprehendible logical problems that could not even be
>> >>> theoretically resolved programmatically.)
>> >>>
>> >>> But there are some programs that can be computationally processed so
>> >>> that
>> >>> a system of results can be produced more quickly than actually running
>> >>> the
>> >>> program.
>> >>>
>> >>> The significance of this came to me after I started criticizing the
>> >>> proposition that an advanced representational compression could
>> >>> be sufficient to produce AGI at this time. The problem is that
>> >>> representational compressions have to go through stages of
>> decompression
>> >>> and recompression in order to do any computation on the data, and
>> given
>> >>> the
>> >>> degree of compression that would be needed for AGI that would make the
>> >>> system way too slow.
>> >>>
>> >>> While logical computation is a simple process using binary
>> >>> representations of simple logical states for each literal (logical
>> >>> variable), the problem is that logical Satisfiability statements are
>> >>> (most
>> >>> familiarly) compressions of multiple logical states. A logical
>> statement
>> >>> is
>> >>> (typically) a compression of a system of individual 'solutions'. So
>> >>> what would be needed would be a computational method that can act
>> >>> efficiently on a wide variety of logical solutions. In other words we
>> >>> need
>> >>> a compression method which can do computations on the compressed
>> >>> representations without (excessively) decompressing them for each
>> >>> computation. I started thinking about this project from the view that
>> my
>> >>> goal is not to make p=np but to try to develop a methodology that
>> might
>> >>> one
>> >>> day be more efficient than methods that we currently use.
>> >>>
>> >>> I started thinking that it might be possible to create compression
>> >>> representations that acted from different levels of abstraction. These
>> >>> levels of abstraction might be thought of as programs. I started
>> >>> thinking
>> >>> about Turing's Halting Problem and I realized that while you cannot
>> find
>> >>> a
>> >>> shortcut to the completion state of every possible computer program,
>> >>> you can for some kinds of programs. The results of a system that I
>> >>> am imagining could be decompressed (at the end of the analytical
>> >>> computational stages) to produce solutions using the
>> >>> levels-of-abstraction
>> >>> sub-program. but the system could run through the computational steps
>> >>> without actually running that sub-program. The compressed
>> >>> representations
>> >>> would not have to be excessively decompressed in order to run a
>> >>> computation
>> >>> on them.
>> >>>
>> >>> I am all but certain that an example is feasible (although I do not
>> have
>> >>> one right now).
>> >>>
>> >>> And incidentally, an advancement that shows that a compression system
>> >>> might be accompanied by an effective computational method that can act
>> >>> on
>> >>> the compressed representations without fully decompressing them might
>> be
>> >>> interesting to some people. The system does not have to beat Turing at
>> >>> run-around-the-house chess or to prove Gödel wrong from under a table
>> at
>> >>> a
>> >>> week-long Oktoberfest or be dependent on winning a million dollars, it
>> >>> only
>> >>> has to be interesting, incrementally improvable and salient to the
>> study
>> >>> of
>> >>> logic.
>> >>> Jim Bromer
>> >>>
>> >>
>> >>
>> >
>> >
>> >
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