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some thoughts I considered for book 3 of possibility thinking explorations in logic and thought [Dec. 3rd, 2007|09:31 am]
These are some thoughts that I considered for book 3 of
possibility thinking explorations in logic and thought and
many of them are probably flawed so the burden of understanding
lies entirely on the reader and gossip is not allowed.
This is an unfinished writing and I disclaim all liability.
Copyright 2/1/2005 Justin Coslor
Hierarchical Number Theory: Graph Theory Conversions Looking for
patterns in this: Prime odd and even cardinality on the natural number
system (*See diagram). First I listed out the prime numbers all in a
row, separated by commas. Then above them I drew connecting arcs over
top of every other odd prime (of the ordering of primes). Over top of
those I drew an arc over every two of those arcs, sequentially. Then
over top of every sequential pair of those arcs I drew another arc, and
so on. Then I did the same thing below the listing of the numbers, but
this time starting with every other even prime.
Then I sequentially listed out whole lot of natural numbers and did
the same thing to them down below them, except I put both every other
even and every other odd hierarchical ordering of arcs over top of one
another, down below the listing of the natural number system.
Then over top of the that listing of the natural number system I
transposed the hierarchical arc structures from the prime number system;
putting both every other even prime and every other odd prime
hierarchically on top of each other, as I previously described. *Now I
must note that in all of these, in the center of every arc I drew a line
going straight up or down to the center number for that arc. (See
In another example, I took the data, and spread out the numbers all
over the page in an optimal layout, where no no hierarchical lines cross
each other, but the numbers act as nodal terminals where the
hierarchical arches sprout out of. (See Diagram) This made a very
beautiful picture which was very similar to a hypercube that has been
unfolded onto a 2D surface. Graph Theory might be able to be applied to
hierarchical representations that have been re-aligned in this manner,
and in that way axioms from Graph Theory might be able to be translated
into Hierarchical Number Theory.
The center-poles are very significant because when I transposed the
prime number structures onto the natural number system there is a
central non-prime even natural number in the very center directly
between the center-poles of the sequential arc structures of the every
other even prime and every other odd prime of the same hierarchical
level and group number. The incredibly amazing thing is that when
dealing with very large prime numbers, those prime numbers can be
further reduced by representing them as an offset equation of the
central number plus or minus an offset number. The beauty of is, that
the since the central numbers aren't prime, they can be reduced in
parallel as the composite of some prime numbers, that when multiplied
together total that central number; and those prime composite numbers
can be further reduced in parallel by representing each one as their
central number (just like I previously described) plus or minus some
offset number, and so on and so on until you are dealing with very
managably small numbers in a massively parallel computation. The offset
numbers can be similarly crunched down to practically nothing as well.
This very well may solve a large class of N-P completeness problems!!!
Hurray! It could be extremely valuable in encryption, decryption,
heuristics, pattern recognition, random number testing, testing for
primality in the search for new primes, several branches of mathematics
and other hard sciences can benefit from it as well. I discovered pretty
much independently, just playing around with numbers in a coffee shop
one day on 1/31/2005, and elaborated on 2/1/2005, and it was on 2/4/2005
when describing it to a friend who wishes to remain anonymous that I
realized this nifty prime-number crunching technique, a few days after
talking with the Carnegie Mellon University Logic and Computation Grad
Student Seth Casana, actually it was then that I realized that prime
numbers could be represented as an offset equation, and then I figured
out how to reduce the offset equations to sets of smaller and smaller
offset equations. I was showing Seth the diagrams I had drawn and the
patterns in them. He commented that it looked like a Friege lattice or
something. I think After I pointed out the existance of central numbers
in the diagrams Seth told me that sometimes people represent prime
numbers as an offset, and that all he could think of was that they could
be some kind of offset or something. He's a total genius. He's
graduating this year with a thesis on complexity theory and the
philosophy of science. He made a bunch of Flash animations that teach
people epistemology. Copyright 2/1/2005 Justin Coslor Rough draft typed
3/19/2005. This is an entirely new way to perceive of number systems.
It's a way to perceive of them hierarchically. Many mathematical
patterns may ready become apparent for number theorists as larger and
larger maps in this format are drawn and computed. Hopefully some will
be in the prime number system, as perceived through a variety of other
numbering systems and forms of cardinality. (See photos.) Copyright
3/25/2004 Justin Coslor Hierarchical Number Theory Applied to Graph
When every-other-number numerical hierarchies are converted into
dependency charts and then those dependency charts are generalized and
pattern matched to graphs and partial graphs of problems, number theory
can apply to those problems because the hierarchies are based on the
number line of various cardinalities.
I had fun at Go Club yesterday, and while I was at the gym I thought
of another math invention. It was great. I figured out how to convert a
graph into a numerical hierarchy which is based on the number line, so
number theory can apply to the graph, and do so by pattern matching the
graph to the various graphs that are generated by converting numerical
hierarchical representations of the number line into dependency charts.
I don't know if that will make sense without seeing the diagrams, but
it's something like that. The exciting part is that almost any thing,
concept, game, or situation can be represented as a graph, and now, a
bunch of patterns can be translated into being able to apply to them.
Copyright 1/31/2005 Justin Coslor Odd and Even Prime Cardinality First
twenty primes: 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 37, 41, 43, 47, 53,
59, 61, 67, 71, 73. ------------------- *See the photo of the digram I
drew on the original page. What properties and relations are there
between the odd primes? First ten odd primes: 2, 5, 11, 17, 23, 37, 43,
53, 61, 71. First five odd odd primes: 2, 11, 23, 43, 61. First five odd
even primes: 5, 17, 37, 53, 71. First ten even primes: 3, 7, 13, 19, 29,
41, 47, 59, 67, 73. First five even even primes: 7, 19, 41, 59, 73.
First five even odd primes: 3, 13, 29, 47, 67.
-------------------------- prime^(odd^4) = prime^(odd)^(odd)^(odd)^(odd)
= 2, 61, . . prime^(odd^3) = prime^(odd)^(odd)^(odd) = 2, 23, 43, 61, .
. . prime^(odd^2) = prime^(odd)^(odd) = 2, 11, 23, 43, 61, . . .
prime^(odd) = prime^(odd) = 2, 5, 11, 17, 23, 37, 43, 53, 61, 71, . . .
prime^(odd)^(even) = 5, 17, 37, 53, 71, . . . prime^(even)^(odd) = 3,
13, 29, 47, 67, . . . ---------------------------------- Copyright
6/10/2005 Justin Coslor HOPS: Hierarchical Offset Prefixes
For counting hierarchically, prefix each set by the following
variables: parity, level, and group (group starting number). Then use
that group starting number as the starting position, and count up to the
number from zero placed at that starting position for representation of
a number prior to HOP computation. I need to develop a calculation
method for that representation.
Have a high-level index which lists all of the group starting
numbers for one of the highest rows, then the rest of the number's group
numbers can be derived for any given level above or below it. All
calculations should access this index.
If I was to look for the pattern "55" in a string of numbers, for
example, I might search linearly and copy all two-digit locations that
start with a "5" into a file, along with the memory address of each,
then throw out all instances that don't contain a "5" as the second
digit. That's one common way to search. But for addresses with a log of
digits, such as extremely large numbers, this is impractical and it's
much easier to do hierarchical level math to check for matches. The
simplest way to do it is a hierarchical parity check + level check +
group # check before proceeding to check both parities of every subgroup
on level 1 of that the offset number. The offset begins at zero at the
end of the prefix's group number, and a micro-hierarchy is built out of
that offset. For large numbers, this is much faster than using big
numbers for everything. Example: Imagine the number 123,456,789 on the
number line. We'll call it "N". N = 9 digits in decimal, and many more
digits in binary. In HOP notation, N = parity.level.group.offset. If I
had a comprehensive index of all the group numbers for a bunch of the
levels I could generate a prefix for this # N, and then I'd only have to
work with a tiny number that is the difference between the closest
highest group and the original number, because chances are the numbers I
apply it to are also offset by that prefix or a nearby prefix. The great
part about hierarchical offset prefixes is that it makes every number
very close to every other number because you just have to jump around
from level to level (vertically) and by group to group (horizontally).
I'll need to ask a programmer to make me a program that generates an
index of group numbers on each level, and the program should also be
able to do conversions between decimal numbers and hierarchical offset
prefixes (HOPs). That way there are only four simple equations necessary
to add, subtract, multiply, divide any two HOP numbers: just perform the
proper conversions between the HOPs' parity, levels, groups, and
Parity conversions are simple, level conversions are just dealing
with powers of 2, group conversions are just multiples of 2 + 1, and
offset conversions just deal with regular mathematics using small
numbers. Copyright 7/7/2005 Justin Coslor Prime Breakdown Lookup Tables
Make a lookup table of all of the prime numbers in level 1
level.group.offset notation, and calculate values for N levels up from
there for each prime in that same level.group.offset notation using the
level 1 database. 2^n = distance between prime 2^(n + m) and prime 2^(n
+ (m + 1)).
Center numbers are generated by picking another prime on that same
level somehow (I'm not positive how yet), and the number in-between them
is the center number. Center number factoring can be done repeatedly so
that, for example, if you wanted to multiply a million digit number by a
million digit number, you could spread that out into several thousand
small number calculations, and in that way primes can be factored using
center numbers + their offsets.
Also, prime number divisor checking can be done laterally in
parallel by representing each divisor in level.group.offset notation and
then converting the computation into a set of parallel processed center
number prime breakdown calculations, which would be significantly faster
than doing traditional divisor checking, especially for very large
divisors, assuming you have a parallel processor computer at your
disposal, or do distributed computing, and do multiprocessing/multi-
threading on each processor as well. Copyright 10/7/2004 Justin Coslor
Prime divisor-checking in parallel processing pattern search. *I assume
that people have always known this information. Prime Numbers are not:
1. Even --> Add all even numbers to the reject filter. 2. Divisible by
other prime numbers --> Try dividing all numbers on the potentially
prime list by all known primes. 3. Multiples of other prime numbers -->
Parallel process: Map out in parallel multiples of known primes up to a
certain range for the scope of the search field, and add those to the
reject filter for that search scope. When you try to divide numbers on
the potentially prime list, all of those divisions can be done in
parallel where each prime divisor is granted its own process, and
multiple numbers on the potentially prime list for that search scope
(actually all of the potentials) could be divisor-checked in parallel,
where every number on the potentially prime list is granted its own
complete set off parallel processes, where each set contains a separate
parallel process for every known prime. So for less than half of the
numbers in the search scope will initially qualify to make it onto the
potentially prime list for divisor checking. And all of the potentially
prime numbers will need to have their divisor check processes augmented
as more primes are discovered in the search scope. The Sieve of
Eratosthenes says that the search scope is in the range of n^2, where n
is the largest known prime. Multiple search scopes can be running
concurrently as well, and smaller divisor checks will always finish much
sooner than the larger ones (sequentially) for all numbers not already
filtered out. 12/24/2004 Justin Coslor Look for Ways to Merge Prime
Number Perception Algorithms I don't yet understand how the Riemann Zeta
Function works, but it might be compatible with some of the mathematics
I came up with for prime numbers (sequential prime number word list
heuristics, active filtering techniques, and every other number
groupings on the primes and on the natural number system). Maybe there
are lots of other prime number perception algorithms that can also be
used in conjunction with my algorithms. ??? -------------- Try applying
my algorithm for greatly simplifying the representation of large prime
numbers to the Riemann Zeta function. My algorithm reduces the
complexity of the patterns between sequential prime numbers to a fixed
five variable word for each pair of sequential primes, and there are
only 81 possible words in all. So as a result of fixing the pattern
representation language to only look for certain qualities that are in
every sequential prime relationship, rather than having infinite
possibilities and not knowing what to look for, patterns will emerge
after not to long into the computer runtime. These patterns can then be
used to predict the range of the scope of future undiscovered prime
numbers, which simplifies the search for the next prime dramatically,
but even more important than that is that my algorithm reduces the
cardinality complexity (the representation) of each prime number
significantly for all primes past a certain point, so in essence, this
language I've invented is a whole new number system, but I'm not sure
how to run computations on it. . .though it can be used with a search
engine as a cataloging method for dealing with extremely large numbers.
My algorithm is in this format: The Nth prime (in relation to the prime
that came before it) = the prime number nearest to [the midpoint of the
Nth prime, whether it be in the upper half or the lower half] : in
relation to the remainder of that "near-midpoint-prime" when subtracted
from the Nth prime. The biggest part always gets listed to the left of
the smaller part (with a ratio sign separating them), and if for the N-
1th prime if the prime prime part got listed on one side and in the next
if it's on the opposite side we take note of that. Next we find the
difference in the two parts and note if it is positive or negative, even
or odd, and lastly we compare it to the N-1th difference to see if it is
up, down, the same, or if N-1's difference is greater than 1 and N's
difference is 1 then we say it has been "reset". If the difference jumps
from 1 to a larger difference in N's difference we say it's "undo
reset". Also, the difference is the absolute value of the
"near-midpoint-prime" minus the remaining amount between it and the Nth
prime. Now each of these qualities can be represented by one letter and
placed in one of four sequential places (categories) to make a four
character word. Numbers could even be used instead of characters, but
that might confuse people (though not computers). *******************
"Prime Sequence Matcher" (to be made into software) *******************
This whole method is Copyright 10/25/2004 Justin Coslor, or even sooner
(most likely 10/17/2004, since that's when it occurred to me. I thought
of this idea to help the whole world and therefore must copyright it to
ensure that nobody hordes or misuses it. The algorithms behind this
method that I have invented are free for academic use by all United
Nations member nations, for fair good intent only towards everyone.
---------------------------------- Download a list of the first 10,000
prime numbers from the Internet, and consider formating it in EMACS to
look something like this: 12 23 35 47 5 11 6 13 . . . 10,000 ____ and
name that file primelist.txt ----------------------- Write a computer
program in C or Java called "PrimeSequenceMatcher" that generates a file
called "primerelations.txt" in the following format based on
calculations done on each of line of the file "primelist.txt".
primelist.txt->PrimeSequenceMatcher->primerelations.txt file:
primerelations.txt 2 3 2:1 diff 1 left, pos, odd, same 3 5 3:2 diff 1
left, pos, even, up 4 7 5:2 diff 3 left, pos, even, same 5 11 7:4 diff 3
LR, neg, even, down(or reset) 6 13 6:5 diff 1 right, pos, even, up(or
undo reset) 7 17 10:7 diff 3 . . . N __ __:__ diff __ For the C program
see pg. 241 to 251 of Kernigan and Ritchie's book, "The C Programming
Language", for functions that might be useful in the program. See the
scans of my journal entries from 10/17/2004, 10/18/2004, and 10/24/2004
for details on the process (*Note, there may be a few errors, and the
paperwork is sort of sloppy for those dates...), and turn it into an
efficient explicit algorithm. **2/22/2005 Update: I wrote out the gist
of the algorithms for the software in my 10/26/2004 journal entry. The
point of the generating the file primerelations.txt is to run the file
through pattern searching algorithms, and build a relational database,
because since the language of the primes's representation in my method
is severely limited, patterns might emerge. Nobody knows whether or not
the patterns will be consistent in predicting the range that the next
primes will be in, but I hope that they will, and it's worth doing the
experiment since that would be a remarkable tool to have discovered. The
patterns may reveal in some cases which is larger: the
nearest-to-midpoint prime or it's corresponding additive part. Where the
sum equals the prime. That would tell you a general range of where the
next prime isn't at. Also the patterns may in some cases have a
predictable "diff" value, which would be immensely valuable in knowing,
so that you can compare it to the values of the prime that came before
it, which would give a fairly close prediction of where the next prime
may lye. By looking at the pattern of the ordering of sentences, we can
possibly tell which side of the ratio sign the nearest-to-midpoint prime
of the next prime we are looking for lies on (and thus know whether it
is in the upper half or the lower half of the search scope). The search
scope for the next prime number is in the range of the largest known
prime squared. We might also be able to in some cases determine how far
from the absolute value of the difference between the nearest-to-
midpoint prime and the prime number we are looking for, that the prime
number that we are looking for is. Copyright 10/26/2004 to 10/27/2004
Justin Coslor I hereby release this idea under The GNU Public License
Agreement (GPL). ************************* Prime Sequence Matcher
Algorithm ************************* (This algorithm is to be turned into
software. See previous journal entries that are related.) Concept
conceived of originally on 10/17/2004 by Justin Coslor Trends in these
sequential prime relation sentences might emerge as lists of these
sentences are formed and parsed for all, or a large chunk of, the known
primes. ------------------------------- The following definitions are
important to know in order to understand the algorithm: nmp = the prime
number nearest to the midpoint of "the Nth prime we are representing
divided by 2" aptnmp = adjacent part of the nmp = prime number we are
representing minus nmp prime/2 = (nmp+aptnmp)/2 = the midpoint of the
prime nmp = (2 * midpoint) - aptnmp aptnmp = (2 * midpoint) - nmp prime
= 2 * midpoint We take notice of whether nmp is greater than, equal to,
or less than aptnmp. diff = |nmp - aptnmp| N prime = nmp:aptnmp or
aptnmp:nmp, diff = |nmp - aptnmp|
| a | b | c | d |
| left | pos | even | up |
| right | neg | odd | down |
| LR | null | | same |
| RL | | | reset |
| | | | undoreset |
Each possible word can be abbreviated as a symbolic character or
symbolic digit, so the sentence is shortened to the size of a four
character word or four digit number. *Note: "a" only = "same" when prime
= 2 (.....that is, when N = 1) **Note: If "c" ever = "same", then N is
not prime, so halt. "abcd" has less than or equal to 100 possible
sequential prime relation sentences (SPRS)'s, since the representation
is limited by the algorithms listed below. Generate a list of SPRS's for
all known primes and do pattern matching/search algorithms to look for
trends that will limit the search scope. The algorithms might even
include SPRS orderings recursively. --------------------------------
Here are the rules that govern abcd: If nmp > aptnmp, then a = left. If
nmp < aptnmp, then a = right. If nmp = aptnmp, then a = same. If N - 1's
"a" = left, and N's "a" = right, then set N's "a" = LR. If N - 1's "a" =
right, and N's "a" = left, then set N's a = RL. If N's nmp - (N - 1)'s
nmp > 0, then b = pos. If N's nmp - (N - 1)'s nmp < 0, then b = neg. If
C = same, then b = null. Meaning, if N's nmp - (N-1)'s nmp = 0, then b=
null. If N's nmp - (N-1)'s nmp is an even integer, then c = even. If N's
nmp - (N - 1)'s nmp is an odd integer, then c = odd. If N's diff > (N -
1)'s diff, then d = up. If N's diff < (N - 1)'s diff, then d = down. If
N's diff = (N-1)'s diff, then d = same. If (N - 1)'s diff > 1 and N's
diff = 1, then d = reset. If (N - 1)'s diff = 1 and N's diff > 1, then d
= undoreset. [......But maybe when (N - 1)'s diff and N's diff = either
1 or 3, then d would also = up, or d = down.] If a = left or RL, then N
prime = nmp:aptnmp, diff = |nmp - aptnmp| If a = right or LR, then N
prime = aptnmp:nmp, diff = |nmp - aptnmp| If a = same, then N prime =
nmp:nmp, diff = |nmp - aptnmp|, but only when N prime = N.
----------------------------------- Copyright 10/24/2004 Justin Coslor
Prime number patterns based on a ratio balance of the largest
near-midpoint prime number and the non-prime combinations of factors in
the remainder: An overlay of symmetries describe prime number patterns
based on a ratio balance of the largest near midpoint prime number and
the non-prime combinations of factors in the remainder. This is to cut
down the search space for the next prime number, by guessing at what
range to search the prime in first, using this data.
For instance, we might describe the prime number 67 geometrically by
layering the prime number 31 under the remainder 36, which has the
modulo binary symmetry equivalency of the pattern 2*2*3*3. We always put
the largest number on top in our description, regardless of whether it
is prime or non-prime, because this ordering will be of importance in
our sentence description of that prime.
We describe the sentence in relation to how we described the prime
number that came before it. For instance, we described 61 as 61=31:2*3*5
ratio (the larger composite always goes on the left of the ratio symbol,
because it will be important to note which side the prime number ends up
on), difference of 1 (difference shows how far from the center the
near-mid prime lies. 31-30=1), right->left (this changing of sides is
important to note because it describes which side of the midpoint of the
prime that the nearest-to-midpoint prime lies on or has moved to, in
terms of the ratio symbol) odd same (this describes whether the
nearest-to-midpoint primes of two prime numbers have a difference that
is even, odd, or if they have the same nearest-to-midpoint primes.)
67=2*2*3*3:31 ratio, difference of 5, left->right same undo last reset.
By looking at the pattern in the sentence descriptions (180 possible
sentences), we can tell which side of the ratio sign that the next
prime's nearest-to-midpoint prime lies on, which tells you which half of
the search scope the next prime lies in, which might cut the
computational task in finding the next finding that next prime number in
half or more. A computer program to generate these sentences can be
written for doing the pattern matching. In the prime number 67 example,
the part that says "same", refers to whether the nearest-to- midpoint
primes of two prime numbers have a difference that is even, odd, or if
they have the same nearest-to-midpoint primes. I threw in the "reset to
1" thing just because it probably occurs a lot, then there's also the
infamous "undo-from-last-reset" which it brings the difference from 1
back to where it was previously at. Copyright 10/5/2004 Justin Coslor
Prime Numbers in Geometry continued . . . Modulo Binary I think that if
prime numbers can be expressed geometrically as ratios there might be a
geometric shortcut to determining if a number is prime or maybe
non-prime. Prime numbers can be represented symmetrically, but not with
colored partitions. (*See diagrams.) Here's a new kind of binary code
that I invented, based on the method of partitioning a circle and
alternately coloring and grouping the equiangled symmetrical partitions
of non-prime partition sections. (*Note, since prime numbers don't have
symmetrical equiangled partitions, use the center-number + offset
converted into modulo binary (see my 2/4/2005 idea and the 2/1/2005
diagram I drew for prime odd and even cardinality and data compression
on the prime numbers)). Modulo binary: *Based on geometric symmetry
ratios. **I may not have been very consistent with my numbering scheme
here, but you should be in final draft version. 1=1 2=11 3=111 4=1010
5=11111 6=110110 or 101010 7=1111111 8=10101010 or 11101110 9=110110110
10=1010101010 11=11111111111 12=110110110110 13=1111111111111
14=10101010101010 15=10110,10110,10110 16=1010,1010,1010,1010 Find a
better way of doing this that might incorporate my prime center number +
offset representation of the primes and non-primes. This is an entirely
new way of counting, so try to make it scalable, and calculatable.
Secondary Levels of Modulo Binary: (*This is just experimental. . .I
based these secondary levels on the first level numbers that are
multiples of these.) 0=00 1=1 2=10 3=110 4=2+2=1010 5=10110 6=3+3=110110
or 111000 or 101101 7= 8=4+4=10101010 9=3+3+3=110110110 10=1010101010
11= 12=3+3+3+3=110110110110 13= 14=10101010101010101010
15=5+5+5=101101011010110 16=4+4+4+4=1010101010101010 Draw a 49 section
and 56 section circle, and look for symmetries to figure out how best to
represent the number 7 in the secondary layer of modulo binary. There
needs to be a stop bit too. Maybe 00 or something, and always start
numbers with a 1. The numbers on through ten should be sufficient for
converting partially from base 10. Where calculations would still be
done in base 10, but using modulo binary representations of each digit.
For encryption obfuscation and stuff. It seems that for even numbers,
the half-circle symmetries rotate between 0,0 across the circle for
numbers that are odd when divided by two, and the numbers that are odd
when divided by two have alternate-half 0,0 symmetry. But numbers that
are prime when divided by two have middle- across 0,1 symmetry.
Copyright 9/30/2004 Justin Coslor Prime Numbers in Geometry *Turn this
idea into a Design Science paper entitled "Patterns in prime composite
partition coloring structures". In the paper, relate these discoveries
to the periodic table. (All prime numbers can be represented as unique
symmetries in Geometry.) 1/1 = 0 division lines 1/2 = 1 division lines
1/3 = 3 division lines 1/4 = 2 division lines 1/5 = 5 division lines 1/6
= 5 division lines = one 1/2 division line and two 1/3 division lines on
each half circle. 1/7 = 7 division lines 1/8 = 4 division lines 1/9 =
_____ division lines . . . Or maybe count by partition sections rather
than division lines. . . How do I write an algorithm or computer program
that counts how many division lines there are in a symmetrically
equiangled partitioning of a circle, where if two division lines that
meet in the middle (as all division lines do) form a straight line they
would only count as one line and not two? Generate a sequential list of
values to find their number of division lines, and see if there is any
pattern in the non-prime division line numbers (i.e. 1/4, 1/6, 1/8, 1/9,
1/10, 1/12, ...) that might be able to be related to the process of
determining or discovering which divisions are prime, or the sequence of
the prime numbers (1/2, 1/3, 1/5, 1/7, 1/11, 1/13, 1/17, ...). 10/5/2004
Justin Coslor As it turns out, there is a pattern in the non-prime
division lines that partition a circle. The equiangled symmetry
partition patterns look like stacks of prime composites layered on top
of one another like the Tower of Hanoi computer game, where each layer's
non-prime symmetry pattern can be colored using it's own colors in an
on-off configuration around the circle (See diagrams.). Prime layers
can't be colored in an on-off pattern symmetrically if the partitions
remain equiangled, because there would be two adjacent partitions
somewhere in the circle of the same color, and that's not symmetrical.
Copyright 7/25/2005 Justin Coslor Geometry of the Numberline: Pictograms
and Polygons. (See diagrams)
Obtain a list of sequential prime numbers. Then draw a pictogram
chart for each number on graph paper, with the base 10 digits 1 through
10 on the Y-axis, and on the X-axis of each pictogram the first column
is the 1's column, the second column is the 10's column, the third
columns is the 100's column, etc. Then plot the points for each digit of
the prime number you're representing, and connect the lines
sequentially. That pictogram is then the exact unique base-10
geometrical representation of that particular prime number (and it can
be done for non-prime numbers too). Another way to make the pictogram
for a number is to plot the points as described, but then connect the
points to form a maximum surface area polygon, because when you do that,
that unique polygon exactly describes that particular number when it's
listed in its original orientation. inside the base-10 graph paper
border that uses the minimum amount of X-axis boxes necessary to convey
the picture, and pictograms are always bordered on the canvas 10 boxes
high in base 10. Other bases can be used too for different sets of
pictograms. What does the pictogram for a given number look like in
other bases? We can connect the dots to make a polygon too, that is
exactly the specific representation in its proper orientation of that
particular unique number represented in that base. Also I wonder what
the pictograms and polygon pictograms look like when represented in
polar coordinates?
These pictogram patterns might show up a lot in nature and artwork,
and it'd be interesting to do a mathematical study of photos and
artwork, where each polygon that matches gets bordered by the border of
it's particular matching pictogram polygon in whatever base it happens
to be in, and pictures might be representable as layers of these
numerical pictograms, spread out all over the canvas overlapping and
all, and maybe partially hidden for some. You could in that way make a
coordinate system in which to calculate the positions and layerings of
the numerical pictograms that show up within the border of the photo or
frame of the artwork, and it could even be a form of steganometry when
intentionally layered into photos and artwork, for cryptography and art.
Summing multiple vertexes of numerical polygon pictograms could also
be used as a technique that would be useful for surjectively distorting
sums of large numbers. That too might have applications in cryptography
and computer vector artwork.
See the diagram of the base 10 polar coordinate pictogram
representation of the number 13,063. With polar notation, as with
Cartesian Coordinate System notation of the pictograms, it's important
to note where the reference point is, and what base it's in, and whether
it's on a polar coordinate system or Cartesian Coordinate System. In
polar coordinates, you need to know where the center point is in
relation to the polygon. . .no I'm wrong, it can be calculated s long as
no vertexes lie in a line. In all polygon representations, the edge
needs to touch all vertexes. Copyright 7/27/2005 Justin Coslor Combining
level.group.offset hierarchical representation with base N pictogram
representation of numbers (See diagrams)
level.group offset notation is (baseN^level)*group+offset Pictogram
notation is as described previously.
If you take the pictogram shape out of context and orient it
differently it could mean a lot of different things, but if you know the
orientation (you can calculate the spacing of the vertexes in different
orientations to find the correct orientation, but you know must also
know what base the number is in to begin with) then you can decipher
what number the polygon represents. You must know what the base is
because it could be of an enormous base. . .you must also know an anchor
point for lining it up with the XY border of the number line context in
that base because it could be a number shape floating inside a enormous
base for all anyone knows, with that anchor point. Also, multiple
numbers on the same straight line can be confusing unless they are
clearly marked as vertexes. If multiple polygons are intersecting, then
they could represent a matrix equation of all of those numbers. Or if
there are three or four polygons connected to each other by a line or a
single vertex, then the three pictograms might represent the three or
four parts of a large or small level.group.offset number in a particular
base. Pictograms connected in level.group offset notation would still
need to be independently rotated into their correct orientation, and
you'd need to know their anchor points and base, but you could very
simply represent an unfathomably enormous number that way in just a tiny
little drawing. Also, numbers might represent words in a dictionary or
letters of an alphabet. This is literally the most concise way to
represent unfathomably enormous numbers that possibly anyone has ever
imagined. Ever. You could write a computer program that would draw and
randomize these drawings as a translation from a dictionary/language set
and word processor document. They could decoded in the reverse process
by people who know the anchor point keys and base keys for each polygon.
You can make the drawings as a subtle off-white color blended into the
white part of the background of a picture, and transmit enormous
documents as a single tiny little picture that just needs some
calculating and keys to decode. Different polygon pictograms, which each
could represent a string of numbers, which can be partitioned into
sections that each represents a word or character, could each be drawn
in a different color. So polygons that are in different colors and
different layers in a haphazard stack, could be organized, where the
color of multiple polygons, means they are part of the same document
string, and the layering of the polygons indicates the order that the
documents are to be read in. Copyright 7/28/2005 Justin Coslor Optimal
Data Compression: Geometric Numberline Pictograms
If each polygon is represented using a different color, you don't
even need to draw the lines that connect the vertexes, so that you can
cram as many polygons as possible onto the canvas. In each polygon, the
number of vertexes is the number of digits in whatever base it's being
represented in. Large bases will mean larger image dimensions, but will
allow for really small representations of large numbers. Ideally one
should only use a particular color on one polygon once. For optimal
representation, one should represent each number in a base that is as
close to the number of digits in that base as possible. If you always do
that, then you won't have to know what base the polygon is represented
in to begin with (because it can be calculated). However, you will still
need to know the starting vertex or another anchor point to figure out
which orientation the polygon is to be perceived of in. On polar
coordinate polygon pictograms, you will just need to know the center
point and a reference point such as where the zero mark is, as well as
what base the polygon is represented in (in most cases). Hierarchical
level.group.offset data compression techniques or other data compression
techniques can also be used. Copyright 7/24/2005 Justin Coslor Prime
Inversion Charts (See diagram) Make a conversion list of the sequential
prime numbers, where each number (prime 1 through the N'th prime) is
inverted so that the least significant digit is now the most significant
digit, and the most significant digit is now the least significant digit
(ones column stays in the ones column, but the 10's column gets put in
the 10ths column on the other side of the decimal point, same with
hundreds, etc.). So you have a graph that goes from 0 through 10 on the
Y-axis, and 0 through N along the X axis, and you just plot the points
for prime 1 through the N'th prime and connect the dots sequentially.
Also, you can convert this into a binary string by making it so that if
any prime is higher up on the Y-axis than the prime before it, it
becomes a 1, and if it is less than the prime before it, it becomes a 0.
Then you can look for patterns in that. I noticed many recurring binary
string patterns in that sequence, as well as many pallendrome string
patterns in that representation (and I only looked at the first couple
of numbers, so there might be something to it). 10/8/2004 Justin Coslor
Classical Algebra (textbook notes) Pg. 157 of Classical Algebra fourth
edition says: The Prime Number Theorem: In the interval of 1 through X,
there are about X/LOGeX primes in this interval. P=X/LOGeX scope: (1,X)
or something. The book claims that they cannot factor 200 digit primes
yet. In 1999 Nayan Hajratwala found a record new prime 2^6972593 - 1
with his PC. It's a Mersenne Prime over 2 million digits long. This book
deals a lot with encryption. I believe that nothing is 100% secure
except for the potential for a delay. On pg. 39 it says "There is no
known efficient procedure for finding prime numbers." On pg. 157 it
directly contradicts that statement by saying: "There are efficient
methods for finding very large prime numbers." The process I described
in my 10/7/2004 journal entryis like the sieve of Eratosthenes, except
my method goes a step farther in making a continuously augmented filter
list of divisor multiplicants not to bother checking, while
simultaneously running the Sieve of Eratosthenes in a massive
synchronously parallel computational process. Prime numbers are useful
for use in pattern search algorithms that operate in abductive and
deductive reasoning engines (systems), which can be used to explore and
grow and help solve problems and provide new opportunities and to invent
things and do science simulations far beyond human capability. (Pg. 40)
Theorem: An integer x>1 is either prime or contains a prime factor
<=sqrt(x). Proof: x=ab where a and b are positive integers between 1 and
x. Since P is the smallest prime factor, a>=p, b>=p and x=ab>=p^2. Hence
p<=sqrt(x). Example: If x=10 a=2 and b=5. p=3 p^2=9 so 10=2*5>=9. So
factors of x are within the scope of (2, sqrt(x)) or else it's prime.
a^2>=b^2. x^2>=p^4. x^2/p^4=big. Try converting Fermat's Little Theorem
and other corollaries into geometry symmetries and modulo binary format.
The propositions in Modern Algebra about modulo might only hold for two-
dimensional arithmetic, but if you add a 3rd dimension the rotations are
countable as periods on a spiral, which when viewed from a perpendicular
side-view looks like a 2-dimensional waveform. 9/26/2004 Justin Coslor
Privacy True privacy may not be possible, but the best that we can hope
for is a long enough delay in recognition of observations to have enough
time and patience to put things intot the perspective of a more
understanding context. Copyright 9/17/2004 Justin Coslor A Simple,
Concise, Encryption Syntax. This can be one layer of an encryption, that
can be the foundation of a concise syntax. *Important: The example does
not do this, but in practice, if you plan on using this kind of
encryption more than once, then be sure to generate a random unique
binary string for each letter of the alphabet, and make each X digits
long. Then generate a random binary string that is N times as long as
the length of your message to be sent, and append unique sequential
pieces (of equal length) of this random binary string to the right of
each character's binary representation. The remote parts should have
lots of securely acquired random unique alphabet/random binary string
pairs, such as on a DVD that twas delivered by hand. In long messages,
never use the same alphabet's character(s) more than once but rotate to
the next binary character representation on the DVD sequentially. Here's
the example alphabet (note that you can of course choose your own
alphabetic representation as long as it is logically consistent): a
010101 b 011001 c 011101 d 100001 e 100101 f 101001 g 110001 h 110101 i
111001 --------- j 010110 k 011010 l 011110 m 100010 n 100110 o 101010 p
110010 q 110110 r 111010 --------- s 010111 t 011011 u 011111 v 100011 w
100111 x 101011 y 110011 z 110111 space 111011 ------------------------
EXAMPLE: "peace brother" can be encoded like this using that particular
------------------------ 2/18/2005 Update by Justin Coslor Well, I
forgot how to break my own code. Imagine that! I think it had something
to do with making up a random string that was of a length that is
divisible by the number of letters in the alphabet, yet is of equal
bit-length to the bit-translated message, so that you know how long the
message is, and you know how many bits it takes to represent each
character in the alphabet. Then systematically mix in the random bits
with the bits in the encoded message. In my alphabet I used 27
characters that were each six bits in length; and in my example, my
message was 13 characters long, 11 of which were unique. I seriously
have no idea what I was thinking when I wrote this example, but at least
my alphabet I do understand, and it's pretty concise, and sufficiently
obscured for some purposes. Copyright 6/30/2005 Justin Coslor Automatic
Systems (See Diagram) There is 2D, and there are 3D snapshots
represented in 2D, and there is the model-theory approach of making
graphs and flowcharts, but why not add dimensional metrics to graph
diagrams to represent systems more accurately?
--------------------------------- Atomic Elements -> Mixing pot ->
Distillation/Recombination: A->B->C->D->E -> State Machine Output
Display (Active Graphing = real-time) -> Output Parsing and calculation
of refinements (Empirical) -> Set of contextually adaptive relations:
R1->A, R2->B, R3->C, R4->D, R5->E. -------------------------------------
Copyright 5/11/2005 Justin Coslor How to combine sequences: Draw a set
of Cartesian coordinate system axis, and on the x axis mark off the
points for one sequence, and on the y axis mark off the points for the
sequence you want to combine with it (and if you have three sequences
you want to combine, mark off the third sequence on the z-axis. ...for
more than 3 sequences, use linear algebra). Next draw a box between the
origin and the first point on each sequence; then calculate the length
of the diagonal. Then do the same for the next point in each sequence
and calculate the length of the diagonal. Eventually you will have a
unique sequence that is representative of all of the different sequences
that you combined into one in this manner. For instance, you could
generate a sequence that is the combination of the prime numbers and the
Fibonacci Sequence. In fact, the prime numbers might be a combination of
two or more other sequences in this manner, for all I know. 1/4/2005
Justin Coslor Notes from the book "Connections: The Geometric Bridge
Between Art and Science" + some ideas.
In a meeting with Nehru in India in 1958 he said "The problem of a
comprehensive design science is to isolate specific instances of the
pattern of a general, cosmic energy system and turn these to human use."
The topic of design science was started by architect, designer, and
inventor Buckminster Fuller. The chemical physicist Arthur Loeb, who
considers design science to be the grammar of space. Buy that book, as
well as the book "The Undecidable" by Martin Davis.
Chemist Istvan Hergittai edited two large books on symmetry. He also
edits the journals "symmetry" and "space structures" where I could
submit my paper on the geometry of prime numbers and patterns in
composite partition coloring structures. *Also, send it to Physical
Science Review to solicit scientific applications of my discovery. Send
it to some math journals too. Again, the paper I want to write is called
"Patterns in prime composite partition coloring structures", and it will
be based on that journal entry I had about symmetrically dividing up a
circle into partitions, then labeling the alternating patterns in the
symmetries using individual colors for each primary pattern in the
stack, similar to that game "The Tower of Hanoi". Study the writings of
Thales (Teacher of Pythagoras), who is known as the father of Greek
mathematics, astronomy, and philosophy, and who visited Egypt to learn
its secrets [Turnbull, 1961 "The Great Mathematicians], [Gorman, 1979
Pythagoras - A Life] ---------------------------- Connections page 11.
Figure 1.7 The Ptolemaic scale based on the primes 2, 3, and 5. C=1,
D=8/9, E=4/5, F=3/4, G=2/3, A=3/5, B=8/15, C=1/2.
------------------------- Figure 1.6 The Pythagorean scale derived from
the primes 2 and 3: C=1, space=8/9, D=8/9, space=8/9, E=64/81,
space=243/256, F=3/4, space=8/9, G=2/3, space=8/9, A=16/27, space=8/9,
B=128/243, space=243/256, C'=1/2, space=8/9, D'=4/9, space=8/9,
E'=32/81, space=243/256, F'=3/8, space=8/9, G'=1/3, space=8/9, A'=8/27,
space=8/9, B'=64/243, space=243/256, C"=1/4. ----------------- *1/4/2005
Someday try writing an electronic music song that makes vivid use of
parallel mathematical algorithms based on the prime numbers, actually
come to think of it, this concept was presented in an episode of Star
Trek Voyager. ---------------------------- 8/26/2004 Justin Coslor Notes
(pg. 1) These are my notes on three papers contributed to the MIT
Encyclopedias of Cognitive Science by Wilfried Sieg in July 1997: Report
CMU-PHIL-79, Philosophy, Methodology, Logic. Pittsburgh, Pennsylvania
15213-3890. - Formal Systems - Church Turing Thesis - Godel's Theorems
-------------------------------- Notes on Wilfried Sieg's "Properties of
Formal Systems" paper: Euclid's Elements -> axiomatic-deductive method.
Formal Systems = "Mechanical" regimentation of the inference steps along
with only syntactic statements described in a precise symbolic language
and a logical calculus, both of which must be recursive (by the
Church-Turing Thesis). Meaning Formal Systems use just the syntax of
symbolic word statements (not their meaning), recursive logical
calculus, and recursive symbolic definitions of each word.
Frege in 1879: "a symbolic language (with relations and
quantifiers)" + an adequate logical calculus -> the means for the
completely formal representation of mathematical proofs. Fregean frame
-> mathematical logic ->Whitehead & Russell's "Principia Mathematica" ->
metamathematical perspective <- Hilbert's "Grundlagen der Geometrie"
1899 *metamathematical perspective -> Hilbert& Bernays "Die Prizipien
der Mathematik" lectures 1917- 1918 -> first order logic = central
language + made a suitable logical calculus. Questions raised:
Completeness, consistency, decidability. Still active. Lots of progress
has been made in these areas since then. **Hilbert & Bernays "Die
Prizipien der Mathematik" lectures 1917-1918 -> mathematical logic.
Kinds of completeness: Quasi-empirical completeness of Zermelo Fraenkel
set theory, syntactic completeness of formal theories, and semantic
completeness = all statements true in all models. - Sentential logic
proved complete by Hilbert and Bernays (1918) and Post (1921). - First
order logic proved complete by Godel (1930). "If every finite subset of
a system has a model, so does the systems." But first order logic has
some non-standard models.
Hilbert's Entsheidungsproblem proved undecidable by Church & Turing.
It was the decision problem for first order logic. So the "decision
problem" proved undecidable, but it lead to recursion theoretic
complexity of sets, which lead to classification of 1. arithmetical, 2.
hyper-arithmetical, and 3. analytical hierarchies. It later lead to
computational complexity classes. So they couldn't prove what could be
decided in first order logic, but they could classify the complexity of
modes of computation using first order logic. ---In first order logic,
one can classify the empirical and computational complexity of syntactic
configurations whose formulas and proofs are effectively decidable by a
Turing Machine. I'm not positive about this next part. ...but, such
syntactic configurations (aka software that eventually halts) are
considered to be formed systems. In other words, ,one cannot classify
the empirical and computational complexity of software that never halts
(or hasn't halted), using first order logic. The Entsheidungsproblem
(First order logic Decision Problem) resulted in model theory, proof
theory, and computability thoery. It required "effective methods" of
decision making to be precisely defined. Or rather, it required
effective methods of characterizing what could or couldn't be decided in
first-order logic.
The proof of the completeness theorem resulted in the relativity of
"being countable" which in turn resulted in the Skolem paradox. ***I
believe that paradoxes only occur when the context of a logic is
incomplete or when it's foundations scope is not broad enough.
Semantic arguments in geometry yielded "Relative Consistency
Proofs". Hilbert used "finitist means" to establish the consistency of
formal systems. Ackerman, von Neumann, and Herbrand used a very
restricted induction principle to establish the consistency of number
theory. Modern proof theory used "constructivist" means to prove
significant parts of analysis. Insights have been gained into the
"normal form" of proofs in sequent and natural deduction calculi. So
they all wanted to map the spectrum of unbreakable reason. Godel firmly
believed that the term "formal system' or 'formalism' should never be
used for anything but software that halts.
------------------------------------- 9/1/2004 Justin Coslor Notes on
Wilfried Sieg's "Church-Turing Thesis" paper:
Church re-defined the term "effective calculable function" (of
positive integers) with the mathematically precise term "recursive
function". Kleen used the term "recursive" in "Introduction to
Metamathematics, in 1952. Turing independently suggested identifying
"effectively calculable functions" as functions whose values can be
computed (mechanically) using a Turing Machine.Turing & Church's theses
were, in effect, equivalent, and so jointly they are referred to as the
Church-Turing Thesis. Metamathematics takes formally presented theories
as objects of mathematical study (Hilbert 1904), and it's been pursued
since the 1920's, which led to precisely characterizing the class of
effective procedures, which led to the Entsheidungsproblem, which was
solved negatively relative to recursion (****but what about for
non-recursive systems?). Metamathematics also led to Godel's
Incompleteness Theorems (1931), which apply to all formal systems, like
type theory of Principia Mathematica or Zermalo-Fraenkel Set Theory,
etc. Effective Computability: So it seems like they all wanted
infallable systtems (formal systems), and the were convinced that the
way to get there required a precise definition of effective
calculability. Church and Kleen thought it was equivalent to
lambda-definability, and later prove that lambda-definability is
equivalent to recursiveness (1935-1936).
Turing thought effective calculability could be defined as anything
that can be calculated on a Turing Machine (1936). Godel defined the
concept of a (general) recursive function using an equational calculus,
but was not convinced that all effectively calculable functions would
fall under it. Post (*my favorite definition...*) in 1936 made a model
that is strikingly similar to Turing's, but didn't provide any analysis
in support of the generality of his model. But Post did suggest
verifying formal theories by investigating ever wider formulations and
reducing them to his basic formulation. He considered this method of
identifying/defining effectively calculable functions as a working
Post's method is strikingly similar to my friend Andrew J.
Dougherty's thesis of artificial intelligence, which is that at a
certain point, the compactness of a set of functions is maximized
through optimization and at that point, the complexity of their
informational content plateaus, unless you keep adding new functions. So
his solution to Artificial Intelligence is to assimilate all of the
known useful functions in the world, and optimize them to the plateau
point of complexity (put the information in lowest terms), and to then
use that condensed information set/tool in exploring for new functions
to add, so that the rich depth of the problem solving and information
seeking technology can continually improve past any plateau points.
(in 1939) Hilbert and Bernays showed that deductively formalized
functions require that their proof predicates to be primitive recursive.
Such "reconable" functions are recursive and can be evaluated in a very
restricted number of theoretic formalism. Godel emphasized that
provability and definability depend on the formalism considered. Godel
also emphasized that recursiveness or computability have an absoluteness
property not shared by provability or definability, and other
metamathematical notions.
My theory is a bottom-up approach for pattern discovery and adaptive
reconceptualization between the domains of different contexts, and can
provide the theoretical framework for abductive reaasoning, necessary
for the application of my friend Andrew J. Dougherty's thesis. Perhaps
my theories could be abductively formalized? My theories do not require
empiricism (deduction), to produce new elements that are
primitive-recursive to produce new elements that are primitive-recursive
(circular-reasoning-based/symbolic/repetition-based) predicates to be
used in building and calculating statements and structures, that can add
new information. To me, "meaning" implies having an "appreciation" for
the information and functions and relations, at least in part; and that
this "appreciation" is obtained through recognition of the information
(and functions' and relations') utility or relative utility via use or
simulation experience within partially- defined contexts. I say
"partially-defined" contexts because by Godel's Incompleteness Theorems,
an all-encompassing ultimate context cannot be completely defined since
the definition itself (and it's definer would have to be part of that
context, which isn't possible because it would have to be infinitely
recursive and thus never fully representable.
Turing invented a mechanical method for operating symbolically. His
invention's concepts provided the mechanical means for running
simulations. Andrew J. Dougherty and I have created the concepts for
mechanically creating new simulations to run until all possible
simulations that can be created in good intention, that are helpful and
fair for all, exceeds the number of such programs that can be possibly
used in all of existence, in all time frames forever, God willing.
Turing was a uniter not a divider and he demanded immediate
recognizability of symbolic configurations, so that basic computation
steps need not be further subdivided. *But there are limitations in
taking input at face value. Sieg in 19944, inspired by Turing's 1936
paper formulated the following boundedness conditions and locality
limitations of computors: (B.1) there is a fixed bound for the number of
symbolic configurations a computor can immediately recognize; (B.2)
there is a fixed bound for the number of a computor's internal states
that need to be taken into account; -- therefore he can carry out only
finitely many different operations. These operations are restricted by
the following locality conditions: (L.1) only elements of observed
configurations can be changed. (L.2) the computor can shift his
attention from one symbolic configuration to another only if the second
is within a bounded distance from the first. *Humans are capable of more
than just mechanical processes. ---------------------------------- Notes
on Wilfried Sieg's "Godel's Theorems" paper: Kurt Godel established a
number of absolutely essential facts: - completeness of first order
logic - relative consistency of the axiom of choice - generalized
continuum hypothesis - (And relevant to the foundations of mathematics:)
*His two Incompleteness Theorems (a.k.a. Godel's Theorems.
In the early 20th century dramatic development of logic in the
context of deep problems in the foundations in mathematics provided for
the first time the means to reflect mathematical practice in formal
theories. 1. - One question asked was: "Is there a formal theory such
that mathematical truth is co- extensive with provability in that
theory?" (Possibly... See Russell's type theory P of Principia
Mathematica and axiomatic set theory as formulated by Zermelo...) - From
Hilbert's research around 1920 another question emerged: 2. "Is the
consistency of mathematics in its formalized presentation provable by
restricted mathematical, so-called finitist means? *To summarize
informally: 1. Is truth co-extensive with provability? 2. Is consistency
provable by finitist means? Godel proved the second question to be
negative for the case of formalizably finitist means. Godel's
Incompleteness theorems: - If P is consistent (thus recursive), then
there is a sentence sigma in the language of P, such that neither sigma
nor its negation not-sigma is provable in P. Sigma is thus independent
of P. (Is sigma the dohnut hole of reason that fits into the center of
the circular reasoning (into the center of, but independent from the
recursion)?) - If P is consistent, then cons, the statement in the
language of P that expresses the consistency of P, is not provable in P.
Actually Godel's second theorem claims the unprovability of that second
(meta) mathematical meaningful statement noted on pg. 7. Godel's first
incompleteness theorem's purpose is to actually demonstrate that some
syntactically true statements can be semantically false. He possibly did
this to show that formal theories are not adequate by themselves to
fully describe true knowledge, at least with knowledge that is
represented by numbers, that is. It illustrates how it is possible to
lie with numbers. In other words, syntax and semantics are mutually
exclusive, and Godel's second Incompleteness Theorem demonstrates that.
In other words the symbolically representative nature of language makes
it possible to lie and misinterpret.
Godel liked to explain how every consistently formal system that
contains a certain amount of number theory can be rigorously proven to
contain undecidably arithmetical propositions, including proving that
the consistency of systems within such a system is non-demonstratable;
and that this can all be proven using a Turing Machine.
Godel thought "the human mind (even within the realm of pure
mathematics) infinitely surpasses the power of any finite machine."
**But what about massively parallel Quantum supercomputers? Keep in mind
the boundary and limitation conditions that Sieg noted in his
Church-Turing Thesis paper of dimensional minds in relatable
timelines... (Computors). 8/26/2004 Justin Coslor Concepts that I'll
need to study to better understand logic and computation: Readings:
Euclid's Elements Principia Mathematica Completeness: quasi-empirical
completeness, syntactic completeness, semantic completeness consistency
decidability recursion theoretic complexity of sets classification
hierarchies computational complexity classes modes of computation model
theory proof theory computability theory relative consistency proofs
consistency of formal systems consistency of number theory modern proof
theory constructivist proofs semantic arguments in geometry analysis
sequent and natural deduction calculi recursive functions
Metamathematics Type Theory Zermelo-Fraenkel Set Theory effective
computability Lambda-definability investigating ever-wider formulations
primitive recursive proof predicates provability and definability
meaning: [11/11/2004 Justin Coslor -- Meaning depends on goal-subjective
relative utility. In other words, Experience leading up to perspective
filters and perspective relational association buffers.] utility and
relative utility simulation deductively formalized functions boundedness
conditions locality limitations formalizably finitist means choice,
continuum, foundations syntax & semantics incompleteness undecidable
arithmetical propositions hierarchies: arithmetical, hyper-arithmetical
(is hyper-arithmetical where all of the nodes' relations are able to be
retranslated to the perspective of any particular node?), and analytical
hierarchies hierarchical complexity computational complexity Graph
Theory Knowledge Representation Epistemology Pattern Search,
Recognition, Storage, and retrieval Appreciation
This is an unfinished writing and I disclaim all liability.