Re: hi everyone, i had s.ome questions about holographic information

cool thanks for your reply.. a great deal of comprehensible info
there..

i've been working with an algorithm that looks at a binary sequence
and converts it into the different bases it can represent as digits.
as you mentioned, it takes a lot of computational resources processor-
wise to scan and pan and clip the various sections. i've been really
interested in the results of parametric curves over this chaotic
pattern representation, as only a triplet of pre-offset, range, and
post-offset need to be stored for each base representation, with the
rest able to be binary in representation.
i've not come across any algorithms like it, but as i can just see it
being able to be computationally available with today's multiparallel
graphics processors in the consumer space, i wonder what other
algorithms have been constructed, perhaps in antiquity, and
disregarded simply because it was too much computationally to
organize.
I see things in terms of the potential dimensions of change, and then
attempt to tap into the flux to gain a measure of order, even under
supremely chaotic patterns, such as the aforementioned base
representations on a stream.
Even under highly entropic processes, there is still a symmetry to the
directions of change at each moment.
To have a stream type compression with advanced algorithms (even
extremely computationally expensive algorithms that are, i think the
term is, NP hard, where you have to check each and every case), i see
it like a chessboard..

on one hand you have the black white black white layout, and then at
the other end the all whites together all blacks together.. two
extremely ordered states, with the entropy as the shifting between
those two states. I percieve a lossless method where you have the most
highly advanced realization of the symmetries of the decompositions
between the two states into each other, and 'freeze frame' the data
whenever it gets close enough to represent at either end that the
amount of information needed to correct, losslessly, is less than the
cost of encoding the whole entropic set.

it is in this sense that i mentioned the pigeonhole principle and the
counting argument as of no consequence, as the data size of the
compressor is not looking at N files of M bits, but rather one file of
potentially infinite bits being tested, in maximal flux, at each bit.

since in this analogy the data set size is only limited by the memory
and computation, and eventually there is a congruence in the data due
to the chaotic shifting at each individual bit.

so i'm in the middle of trying to figure out one of the overlay
principles.. if i have that chessboard, of many squares, and i say in
the encoder that the two states are whiteblackwhiteblackwhite or
whitewhitewhiteblackblackblack, am i really, if i were to say use
parametric curves of various degrees, just adding those states to the
algorithm? classically i'd say yes, but with regards to a most-
disordered representation at each stage, perhaps it's adding more
degrees of freedom to potentially match up with, as there's no way to
do the wav/text style directed algorithm, but is rather waiting for a
specific probablistic congruence to appear in an arbitrary set.

specifically, as that probability is not zero, and potential chaotic
shifting increases, the probability increases linearly, thus there are
indeed more of the congruences with larger and larger data sets.

does this make any sense to anyone else? looking for the patterns
inside the base representations of a digit stream rather than having a
contextual layer overtop saying 'text' 'wav'?


thanks,
chris.

.

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