"Blind Faith" math vs. Non-Platonic Math

In my studies of math and logic, there has always been some initial basic ideas that were not explained, left to vague experience. They were apparently very 'simple and believable', clearly derivable from experience, intelligence and discussion. I'm referring to the words and symbols used in basic definitions, axioms or models. For example: what is a point, what is 'true', what is time, what is a symbol,  what is '1,2,3,...'?? It is clear to me now that the cause of this limitation on complete rigor is the inexact knowledge of the neural source of 'simple and believable', what exactly did that experience and discussion do in the brain. Many must have had the same unease, initially as impressionable children. But in the press of time, took what ever crude response helped them make some progress, and kept this to the grave. I first suspected this 30 years ago when I first drew a schematic for a general purpose neural net(sitting at a park table in Grandjunction Colorado). My recent completion of an improved version with implementation in JavaScript, a re-reading of Goedel's proof of the incompleteness of Peano arithmetic, Cantor's set theory with Russell and Burali-Forty paradoxes, Turing's halting problem, made it clear: Computers do not do Peano arithmetic since they can NOT add 1 to their memory full of 1's and get a larger number! Further, all problems and solutions that can be typed are ONE DIMENSIONAL since they can be SCANNED, sent down a wire to a computer. Mathematically, functionally, the computer is 1D machine - each state(set of voltages at a given time) is representable as ONE binary number. The computer operation is transforming one binary number at one time into another binary number at a subsequent time. Similarly, the brain converts ONE set of nerve stimulations into another. But neither the computer's voltages or the brains stimulations are fully observable without complex instruments. They are thus not the best source of elemental logic experience usable for definitions most reliably applicable to machines. I have not discovered a one dimensional mechanical logical device that is simply, fully observable by the human eye. But I discovered that 2D computers are simply, fully observable and imaginable from 1 direction and so provide a very complete and simple experience for the most sound intuition of a physically realizable non-human device. Try 2D logic simulator - needs 1Ghz, ie5.5 Also I felt a paradox in 'automatic theorem proving programs' applied to 'prove' theorems in a logic of 'higher level' than that of the computer. However this now seems a side issue in that the axioms of HOL are simply input variables whose negatives are just as easily 'accepted' by the computer. For example: our computers can display '1=1 is true' but they can also display '1=1 is not true' - this is a display of the current value of our computer's display VARIABLE. In contrast: if(1==2)alert("1=2")else alert("false") will only display "false". Any computer 'proof' using computer VARIABLEaassumptions yields what may be 'necessarily true' for a human but not necessarily true for the computer.
The computer exactly, repeat ably transforms keystrokes (1=2) into corresponding pixels (1=2). So TRUTH or consistency or proof for deterministic automatons is simply repeating the same digital transform of the same digital input. [Here 'digital transform' is defined as high or low voltages input to TTL nor-gates whose outputs may be input to other nors]. In this way, deterministic digital computers are consistent and cannot produce a 'paradox'! Philosophers still argue over TRUTH for humans after thousands of years; but it's simple, exact and directly observable for digital computers. See Digital transform=nor-formula (10k) also Interactive Russell's paradox demo (10k)


I initially resisted paradoxes in my beloved math. Some try to patch up the traditions like Russell's invention of theory of types.  And Intuitionists have made some efforts in the right direction but they have not completely challenged the 'simple and believable',  just shored it up a bit - they did not have today's examples of exactly defined intelligence to guide them. But now, it is clear that the math  paradoxes are validly showing with traditional techniques that the traditional techniques are invalid - what is a new basis for mathematics and logic besides the unknown, fickle human brain. See Super-logic(25k)

[the following paragraph is a new insert - Nov. 97]
Some may question 'how can anything make something better than itself'? This may be motivated by fear or pride in one's self or one's choice of myth. But my answer goes to basics of the explicit words: 'what is better?', 'what is self?'. I start with some facts: 1. The hand cannot hold a block as tightly as a vise, yet the vise was made by hands. 2. Genetic mutations in DNA may be caused by arbitrary gamma rays from space. Some lucky chance genetic changes in a life form may make it better adapted to its material environment. Man was created by an inferior ape in the jungle by stupid chance. 3. Computers consistently and quickly evaluate more complex propositional logic formulas than any human, yet they were made by humans. And idealized human nerve connections can be modeled by a computer depending on experience and chance. 4.  The BIG BANG cosmological start of our universe is simply observable as 'redder galaxies are dimmer'. It is modelable as photons being reddened by coming from receding sources and its distance spreading them out.. All virtual particles come into existence as self annihilationable pairs. So the BIG BANG to come from empty space, even the nonexistent space and time implied by its relativity, may consist of temporarily unannihilalated pairs of anti-particles(virtual photons). Both externally and internally our universe as a whole is chaotic nothingness - thus it has no consistent cause, it needs no consistent cause; the universe is self caused nothingness!
Yet the temporary local existence of some order is required to complete this over all causeless, nothingness chaos. Here is the potential of humanity and its new species outgrowth - computers. Well chosen actions complete the using up of temporary local order necessary for complete nothingness - necessary for the self-causation of our universe.  This 'well chosen action' will usually require almost standard social values of honest, logical cooperation to maximize its results.
This is the optimal objective of math and science development.

A continuing belief during all this was 'a correctly working computer cannot generate a paradox' - probably based on my long computer programming experience. There are clear, even if traditional, math logic proofs that propositional logic is consistent(preserves tautologies) and decide able (by truth table). This is equivalent to a digital transform - values of the propositional variables are '1' or '0' and the value of a propositional formula is '1' or '0'. The computer transform represents the evaluation of several PL formulas at once. Finite memory, deterministic (properly working and independent) computers implement only finite propositional logic and are thus consistent and decideable.  In contrast to the human brain, computers are exactly defined by easily observable, reliable, discrete logic elements. However, any given computer can be completely observed and exactly predicted by a sufficiently larger and faster computer; the larger one only needs to use enough of the same kind of logic elements so it can exactly model the smaller one, sooner.

I had good experience in designing and constructing an IC array processor for rhythmical 2D art. A coffee group philosophy discussion with CU Prof. Saalbach inspired me to answer the problem of defining 'not' by just wiring up an IC and saying this is 'not'. And the closest a computer can get to a contradiction like 'p implies not p' -is an IC inverter's output wired to its input - it just oscillates - the output is not true and false at the same time - no computer contradiction. This kind of thing is clarified by 'temporal logic', somewhat. See math in nor- formulas
Evolution omitted a window in the skull for detailed observation of the sequential mechanism of thought. Besides being of no use in a dimly lit cave, it would be embarrassing to clearly observe the unreliability and stupidity. So its understandable why the ancients left time out of their logic. For a demo of how the correct use of time removes paradox, see  Interactive Russell's Paradox. Also, one can understand how ideas of infinity arise from ignoring time - 'one can always add 1 to any number and get a larger number' - if it takes me longer than a lifetime to get the number and if I remember it can I still do it? - in Paradise?
These ideas were completed with my wrestling Turing's halting problem into JavaScript - all finite automatons will ultimately cycle their results: Interactive: Computer cycling: The key confusion of Turing's halting problem is infinite memory - was it logically necessary as in Turing's machine - an infinite memory would allow a computer to never repeat - it could just store more 0's before the next 1 in its memory forever: 010010001... Liar's, Zen's, Russell's, Goedel's and Turing's paradoxes do not carry over to real computers!

[start 22Apr 98 incert]

Fundamental idea:

If math or logic is to apply to the observed physical world in a given time period, their axioms, basis or implications should be consistent with the observed physical world in the given time period. I add 'in a given time period' to preclude 'ideas' that assume or imply some 'physical' event at some arbitrarily large time, like 'we can always add 1 to any number and get a larger number'. Since one merely asks for an n that takes longer to generate than P.

Maximum size of universe:

The maximum speed of physical objects is well established to be the speed of light in a vacuum. Hence, given a specific time period: P, anything further than light can travel during P from something cannot affect or be affected by that thing. Hence, for any given time period the effective universe is finite.

Maximum energy of the universe:

Again I consider the universe in a given finite time period. By the above the effective universe has a finite size. If it has infinite energy it has, by relativity, infinite mass and so infinite density. There are many problems with this. For one, everything is accelerated to the speed of light to the center of mass, everything gets unboundedly heavy from its increase in energy, its time stops. But we don't observe everything accelerating unboundedly or its radiation getting unboundedly red shifted , so our universe has finite energy for us inside it.

Minimum size of things:

Heisenberg's uncertainty principle is also well established. It states 'the energy of a particle multiplied by its lifetime must be greater than Planks constant to be detected'. Hence, for something to be detectable in a time period P, its energy can not be 'arbitrarily' small since it would take an arbitrarily long time to detect it. So the infinitesimals of traditional calculus are unrealistic. But, even I have found it easy to replace basic calculus ideas with ones of similar or greater usefulness with finite techniques - see derivatives without infinitesimals Thus Zeno's paradox of 'late starting but faster runner cannot catch up because he must catch up half way, first' is not realistic since the size of the catch up places is unboundedly small and will soon take longer to detect than the race to finish. Incidentally, other solutions use reduction in time required to 'catch up half way'. Shows time is a necessary part of logic.[temporal logic, forced on 'proving computers', is a modern admission of need for time as in integral part of logic],

So infinite sets are unrealistic:

Our universe cannot be divided into an infinite number of parts in any finite time period. Because it has finite effective size and its observable parts can not be infinitesimal in a finite time period.

Human brain has a finite number of states at any time, a maximum number of changes in any finite time period:

Biological experimentation demonstrates the 2 state possibilities of nerve stimulation(on or off) and the finite number of nerves in any given brain. And there is a maximum speed of neural transmission of the state changes, and there is a minimal nerve length in any given brain. Also the period of 'on-off' has a lower bound along with an upper bound of the individual's life time, so the total number of states in a lifetime is bounded.

Artificial brains can solve any physical problem:

Modern technology makes it clear that all significant physical processes can be adequately digitized. In particular, all significant physical problems in a given time period can be typed out in a finite number of letters. The same is true for the answers. This is a digital transform. See my simple proof that my JavaScripted -ANN can learn any digital transform, 1 synapse at a time: -ANN.

[end 22Apr 98 incert]

[start 20May98 insert]
Human thought applies best to those experiences it fully imitates:

Some sarcastic comments in sci.logic prompted more thinking on 'why has traditional math and logic been so successful? How can a human "understand" a computer's super-logic?'. My answer came from realizing how  math in nor-formulas and interactive -ANN can teach humans by experience: we can visually remember each sequence of non-human, mechanical steps,  after we observe we can imitate, if we are very careful.`[I plan to show internalsteps in demos]. It's like writing a computer program: one tries to think like the computer, think in complete detail how it interprets its language. One might say 'you can never rise above trivial mechanical steps that way'. But, -ANN can activate synapse formulas by activating their name nerve, can generalize by activating several synapse formulas by one name or accept equivalent ones. Since it can learn, synapse by synapse, any consistent, finite digital transform, it can learn anything consistent, that we can. Any typed general solution to any typed problem can be put into finite-mechanical-electrical, non-human form. Similarly, humans can not be super-logical by imitating themselves as long as significant details of brain operations are unknown or not clearly and simply experienced visually.

Yet, we or computers can never exactly model a complete universe because the model must include ourselves modeling it! But we can still beat out other things that make worse models in the race to use up temporary order sooner.

[end 22May98 insert]
 

[Jan99 insert]

More on human or machine logic: If we want our thoughts in our brains or electrons in computers to predict physical events, we first need to discover, in some way, how physical events are predictable. [Most religious 'theory' seems to proudly claim "gods are not slaves of human reason"]

[end Jan99 insert][end Jan99 insert]

Incidentally, computer cycling shows that computers cannot produce chaotic results since they will ultimately start exactly repeating previous values unless they breakdown. However it is this very repetitive nature of computers that allows them to help create chaos sooner by reliably modeling the potential repetitions in nature so they may be predicted and interrupted.  For example: the predictable momentum of water falling down a pipe guides the design of a turbine to change that momentum into electron energy. And the predictable electron energy delivered to myriad homes allows it to be reliably changed into the chaotic variety created by freedom. The purpose of order is chaos! Completely independent chaos is the causeless, self-created universe.
 
Super logic can only be built on an exactly defined intelligence and today that is a reliable finite propositional logic machine!
This general solution came from realizing the JavaScripted artificial negative feedback neural net(-ANN) is intrinsically motivated to learn ANY nor-formula that reliably lets it increase entropy(chaos) sooner: The -ANN is instructed by a teacher(human or machine) or the material environment in an exactly clear sequence of stimulations. Eureka! These -ANN lessons could be the basis of exact logic, math, philosophic definitions and formulas. The -ANN is simple and exactly defined in JavaScript for all to completely understand - 'simple and believable' is a -ANN lesson! Experience interactive -ANN. It was clear that larger nets would not introduce any new principles, largeness would just allow more elaborate language and formulas.


Infinity is of no use unless it is defined in -ANN lessons. Now, having majored in real analysis in grad school, I was very familiar with traditional ideas of infinity. So I started examining interesting cases where infinity was used and easily found it was either not needed or the results were very suspect(many paradoxes) - see  calculus without limits . In particular it's easy to binarily 'differentiate'  polynomials without Infinitesimals: instead of making a delta x less than 1, scale x upderivatives without infinitesimals Finally, arguments in comp.ai, sci.logic, etc. prompted efforts to make a simpler paradox, Russell like, than Goedel's but as this was difficult(hard to use ideas one does not like or are really impossible to understand to fight themselves -  a mad dog may bite its own tail, but it's dangerous to help it). I now simply point out 'one paradox should be enough' - human math is sick but too many have had faith in it for too long to be easily, generally rejected. To ignore the math contradictions is to use "blind faith" in the worst way! But much traditional human math may be translated into automaton math. We can still have a bit of Paradise on earth.

Consider, 'one can always get a larger number by adding 1 to any given number'. This has been adored for centuries. But what is doing the adding?  Who can 'take' the number anyone may think they are giving? Does 'any given number' include those that would take a computer 100 years to print out? What can always verify that the result is larger? Nothing, but maybe a god, certainly not a computer since it has reality limits. It can not correctly add 1 to the largest number it can remember. Why would one try to use something in the real world that is of use only by God in Paradise? Peano arithmetic is only for the gods!

If one could really prove something requiring 'infinity', using a finite automaton, then that aspect of 'infinity' has just been reduced in this way to the finite!  The following obvious theorem makes this rigorous. And it rigorously proves all responses of human thought is equivalent to one simple finite formula of only 'or', 'not', 'true' and 'false' elements. Also, a good packing and unpacking program applied to these inputs and outputs as a whole will find an efficient reduced form of the information that requires fewer nor-gates.
The axiom-proof-theorem system in math can be viewed as a knowledge packing system. But in keeping with super rigor, an axiom-proof-theorem system must be defined for a completely observable, finite intelligence. Also axiom-proof-theorem must be interpreted in a digital transform way: The 'theorem' may be a transform(nor-formula) constructed by 'proof' from simpler 'axiom' transforms. A simple unpacking problem would be to start with axioms and theorem and generate proofs. But the most ambitious math unpacking system would be an automatic proof system that would fill a given memory with those theorems from the axioms that let the intelligence maximize entropy sooner in a given environment.
Any higher logic or infinity process that could do better is equivalent to another nor-formula and could be just a nor-gate improvement to the pack process.


The following theorem shows that nothing more complex than the 'nors' (equivalent to a 'read only memory') is required in a device(machine or brain) for any consistent set of digital responses to finite digital inputs. And the eye nerve inputs and muscle outputs of a human brain are equivalent to digital numbers.
This means any transformation of digital input to digital output, any 'black box' or fixed(pre-learned) response system that cannot be duplicated with a finite nor-formula is inconsistent(non-repetitive)! Digital transform=nor-formula (10k)

Examples:


 

 

 

E-mail author: R Massey on 'blind faith math' Mar98.
top/home (40k)