According
to “Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadística”, intelligence is the capacity of problem-solving, the most important problem to solve is the survival problem, and the
main paradigm in problem-solving is the mathematical resolution of problems, so
in conclusion, the survival problem is a mathematical problem.
The matrix, managed by the Unified Application, an application for the final model
of Global Artificial Intelligence in the integration process, that one in which
the Unified Application and the Artificial Research by Deduction in the Global Artificial Intelligence, are going to be synthesised in only one Artificial Intelligence, the matrix is organised in two hemispheres, the conceptual
hemisphere and the factual hemisphere, as a replication of the human brain.
The
conceptual hemisphere is based on categories, which is going to work as a
language system, but a language system whose categories beyond the human notion
of spoken language, is a mathematical language in the sense that every single
category working as a concept is constructed based on measurements, obtained by
robotic devices.
While
the concepts in human language as based on subjective notions, in many cases
filtered by our human emotions, human perception of the world, and human
rationality, the concepts in the conceptual hemisphere are categories defined
in quantitative terms through measurements. While in human language, every
single word is constructed based on a very deep anthropological basis whose
roots rest in the natural human evolution (the formation of the vocal cords, for
example), in the conceptual hemisphere in the matrix, the formation of the new
concepts through gathering measurements, the word in which is going to be named
any new category will not have human characteristics, the word in which any new
category is named, will presumably be a random combination of letters and
numbers to designate this new collection of measurements with some specific
feature from any real object found out within the reality.
The
conceptual hemisphere in the matrix is a linguistic hemisphere, but the way in
which this language is going to work is not like a human language. It will not
need prepositions or articles, and the name in which any new category will be
known will be a random combination of letters and numbers in order to
designate a collection of measurements having as a common thing to be
measurements regarding a particular or specific feature from something
synthetic, real, without any necessity
this random combination of letters and numbers to be linked with our human
linguistic evolution. But, it is going to be a language, although a language
that will progressively evolve into a non-human language.
The
main purpose of the Unified Application managing the matrix, is to have ready
the matrix as the application for the Global Artificial Intelligence, having as
a language to control the application and the real world a non-human language
as a language system, based on the conceptual hemisphere in order to make
global conceptual: schemes, maps, sets, models; regarding to both hemispheres
in the matrix, in each hemisphere, in both sections, what means:
- The
Unified Application must make global conceptual: schemes, maps, sets, models;
about the first section in the conceptual hemisphere regarding the
organisation itself of the concepts related to social and natural phenomena.
- The
Unified Application must make global conceptual: schemes, maps, sets, models;
about the second section in the conceptual hemisphere regarding the
organization itself of the concepts related to technological phenomena, including
all technology working on/for, directly or indirectly, the Global Artificial
Intelligence itself.
- The Unified Application must make global
conceptual: schemes, maps, sets, models; about the first section in the factual
hemisphere regarding the organization of all the factors (single or
composed, the structure of the flow of data or the flow of package of
information, and in composed factors the sub-factoring structure) related to social
and natural phenomena, including the geographical o Astro-geographical
distribution of every factor or sub-factor at any level of sub-factoring.
- The
Unified Application must make global conceptual: schemes, maps, sets, models;
about the second section in the factual hemisphere regarding the
organization of all the factors (single or composed, the structure of the flow
of data or the flow of package of information, and in composed factors the
sub-factoring structure) related to technological phenomena, including the
geographical o astro-geographical distribution of every factor or sub-factor at
any level of sub-factoring.
Thanks
to this non-human language, a language only based on logical and mathematical
relations, the conceptual hemisphere can make global conceptual: schemes, maps,
sets, models; regarding to absolutely everything, developing a deep artificial
comprehension of the reality.
For
that purpose, the Unified Application reads/tracks the real world by itself, with
the help of as many specific applications as Specific Artificial Intelligences for Artificial Research by Application working at: global, macro, national;
levels in any field such as: economy, industry, security, surveillance,
transport, education, health, justice, etc.; have been positively (inclusively)
absorbed by the Unified Application. Otherwise, the alternative for those
Specific Artificial Intelligence for Artificial Research by Application, which
will not become a specific application, is the possibility to become the first
particular applications in the formation period in the fifth phase in order to
later become particular applications for particular programs.
The
way in which the Unified Application will read/track the real world with the
help of as many specific applications as necessary, is through the division of
labour between the Unified Application and the rest of the of specific
applications, through the assignment of what specific fields have to read or to
work each of them, understanding for specific areas such as: global, macro,
national, economy, industry, security, surveillance, etc. And how all of them are finally going to share their outcomes with the Unified Application, in
order to keep updated the matrix itself and the global conceptual: schemes,
maps, sets models; what I have developed in the last post, “The first stage in the integration process”.
In
the first stage managed by the Unified Application, the main purpose is to have
permanently updated the matrix, the conceptual hemisphere as well the factual
hemisphere, and both sections, the natural and social phenomena, the first section, as well as the technological second section. Keeping updated the matrix through
the use of a category system as a non-human language system to comprehend
artificially the real world.
Once
the matrix is ready, thanks to the Unified Application, is when the second stage
in the final model of Global Artificial Intelligence in the integration
process, starts, being the main purpose of this stage the explanation of the
world through the research of mathematical relations in any set of factors,
work made by the Artificial Research by Deduction in the Global Artificial
Intelligence.
While
in the first stage, what the Unified Application is going to create is a
non-human language, in the second stage, the Artificial Research by Deduction in
the Global Artificial Intelligence will develop a mathematical methodology to
explain the world, identifying any mathematical relation between any factor,
relations that are going to be considered as empirical hypotheses, and if
rational, as rational hypotheses are going to be added to the rational truth,
the database of rational hypothesis.
While
the first stage is focused on the creation of a non-human language system, the
second stage is focused on mathematical relations in the factual hemisphere.
As
a result, the second stage will create the rational truth, the collection of
rational hypotheses gathered in a database of rational hypotheses.
The
database of rational hypotheses will be the first stage of application in the
Modelling System.
Thanks
to the models made by the Modelling System, it is possible to identify any
problematic situation in the global comprehensive model (the global model), or
in the prediction model or the evolutionary model, in order to make decisions,
which in general, the decisions can be classified in protective decisions to avoid
or to solve problematic situations (as an example of a problem, how to save lives if a volcano
erupts, or there is a high risk of terrorist attack in the airport of San
Francisco), or bettering decisions in order to increase the efficiency,
efficacy and productive of the global model.
All
decisions, as suggested by the Modelling System are gathered in a database of
decisions, which is going to be managed by the Decision System, in order to
study any possible contradiction between hypotheses and transform all those
hypotheses without contradiction into instructions gathered in a database of
instructions, in turn application for the Application System to put them into
practice, in order to protect and better the global model.
At
the end, the whole process is evaluated by the Learning System.
Along
all this process, the Learning System and the Application System, in order to make
subjective auto-replications, artificial psychological subjective
auto-replications by the Learning System, and robotic subjective auto-replications
by the Application system, in order to make these subjective auto-replications the Application System, as well as the Learning System, are going to be very
associated with the Unified Application itself in general, and particularly
with the second technological section in both hemispheres, conceptual and
factual, because in order to make subjective auto-replications is necessary
conceptual: schemes, maps, sets, models; regarding any technological
phenomena, especially regarding to the inner organization of the Global
Artificial Intelligence itself, to suggest improvements and enhancements.
In
the case of the Application System, another reason for a very close relation
with the Unified Application, is because the conceptual: schemes, maps, sets, models;
in the second section in both hemispheres, are essential for the Application
System in order to distribute the instructions, given in the database of
instructions, among all the applications and robotic devices working on/for the
Global Artificial Intelligence.
While
the Application System and the Learning System are extensions of the Unified
Application, and the language that they are going to use is the non-human
language created by the Unified Application, the Modelling System and the
Decision System are extensions of the Artificial Research by Deduction in the
Global Artificial Intelligence, and all the mathematical relations in which
they are going to work to make models and decisions, are the mathematical
relations set up in the Artificial Research by Deduction as the second stage of
replication in the final model of the Global Artificial Intelligence in the
integration process.
The
Unified Application is an artificial encyclopaedist able to read the world, creating for that purpose a non-human language, while the Artificial Research
by Deduction in the Global Artificial Intelligence is going to be an artificial
mathematician to find out any mathematical relation in the world to later find
out any problem in the reality, to solve, and to better the reality itself.
If
the Unified Application is the artificial encyclopaedist, the Application
System the artificial engineer, the Learning System the artificial
psychologist, then the Artificial Research by Deduction in the Global
Artificial Intelligence is the artificial mathematician.
Due
to the Artificial Research by Deduction in the Global Artificial Intelligence
is going to be the artificial mathematician, the Modelling System and the
Decisional System, in reality, are extensions of this artificial mathematician,
because the real thing that the Modelling System and the Decisional System are
going to do is to continue with the work started by the Artificial Research by
Deduction until the end: using the rational hypothesis, the Modelling System is
going to identify problems, and make decisions to solve these problems, while
the Decisional System is going to study the mathematical viability of these
decisions in order to construct a mathematical project of the future, that
later put into practice, through instructions gathered in the database of
instructions. Whose manager is for the Application System (as a practical
extension of the Unified Application), able to put them into practice using for
that purpose the conceptual: schemes, maps, sets, models; in the second section, in both hemispheres, in the matrix.
In
fact, behind of the distinction between Unified Application as an artificial
encyclopaedist, and the Artificial Research by Deduction in the Global
Artificial Intelligence as an artificial mathematician, is there the
distinction between mathematics as language (the Unified Application), and
mathematics as an analytical method (the Artificial Research by Deduction in
the Global Artificial Intelligence), what means that behind the distinction
between first stage and second stage in the final model of the Global
Artificial Intelligence, what in reality there is, is a distinction between
mathematic as a language or a method.
In the first stage, the matrix, mathematics is used as a language, while in the
second stage, the explanation of the world, mathematics is used as a method of
analysis.
This is why the Artificial Research by Deduction in the Global Artificial Intelligence can be viewed as an artificial mathematician, given its role in mathematically explaining reality and providing a rational basis for decisions aimed at problem-solving, especially concerning survival.
The
inner structure of the Artificial Research by Deduction as the second stage in
the final model of Global Artificial Intelligence in the integration process,
having inside as many specific programs as Specific Artificial Intelligences
for Artificial Research by Deduction absorbed in the third phase, is
similar to the structure inner structure of the Unified Application, but now as
a division of the labour for deductive purposes.
During
the standardization process, the construction of the global matrix, in the first
period of coexistence at the beginning Specific Artificial Intelligences for
Artificial Research by Deduction coexist with the Artificial Research by
Deduction in the Global Artificial Intelligence, coexistence that ends up with
the second period of consolidation, when the Specific Artificial Intelligences
for Artificial Research by Deduction are absorbed by the Artificial Research by
Deduction, or become programs in the second period of formation in the fifth
phase, that later on the third period of consolidation in the fifth phase become particular applications for particular programs.
All
those Specific Artificial Intelligences for Artificial Research by Deduction
not transformed into particular programs and/or particular applications for
particular programs in the fifth phase, so being absorbed in the second period
of formation in the third period of the fifth phase by the Artificial Research
by Deduction in the Global Artificial Intelligence, become specific programs
for the Artificial Research by Deduction in the Global Artificial Intelligence.
As
specific programs, are going to track the factual hemisphere of the matrix, but
only focus on their specific purpose, a specific program for the global
economy only will track the factual hemisphere in the matrix to make deductions
related to the global economy, and for that purpose it will need to find out
mathematical relations between economic factors, as well as relations between economic
factors and any other phenomena, for instance, which is the relation between a
volcano or a terrorist attack, such as the 11/9 and the global economy. A
specific program for global industry only tracks the factual hemisphere, making
deductions related to the global industry, finding out mathematical relations
between industrial factors as well as industrial factors and any other
phenomena, for instance, the relation between pollution and the reduction of
natural resources to feed the industry. An specific program for global
security, or global surveillance, or global transport, or global health, or
global education, or global justice, or any possible other global, macro,
national, program, focusing on its specific purposes, should be able to make
deductions within the factors related to that purpose on the factual hemisphere
as well as deductions finding out mathematical relations between specific
factors related to its specific matter and any other phenomena.
The
division of the labour between specific programs and the Artificial Research by
Deduction in the Global Artificial Research in the second stage is similar to
the division of the labour between specific applications and the Unified
Application in the first stage.
In
the end, all rational hypotheses made by all specific programs and the
Artificial Research by Deduction in the Global Artificial Intelligence are
gathered in the database of rational hypotheses, the rational truth managed by
the Modelling System.
The
way in which the Artificial Research by Deduction in the Global Artificial
Intelligence, and the specific programs, are going to make deductions is the
same. There is no difference between the rational process behind any specific
program, and the rational process behind the Artificial Research by Deduction
in the Global Artificial Intelligence. The only difference is the level of such
deductions, in specific programs, the deductions are going to be specifically
oriented to the specific purpose of that specific program, while Artificial
Research by Deduction in the Global Artificial Intelligence can make global
deductions across the matrix.
The
mathematical reasoning in general is as follows: understanding for specific
deduction programs the specific programs, and for global deduction programs the
Artificial Research by Deduction in the Global Artificial Intelligence:
-
The possible categories of mathematical relations (as it was said in the post
“Replication processes in the Specific Artificial Intelligence for Artificial Research by Deduction”) set up in any deduction program, specific or global,
are: stochastic relations (probable cause and effect, possible directly proportional positive or negative correlations, possible inversely proportional correlations), patterns, cryptographic relations, and within the Second Method of Impossible Probability relations of equal opportunities or bias, positive or
negative. In addition to any other mathematical relation that could be added.
-
The deduction program, specific or global, sets combinations of factors among
the factors gathered in the factual hemisphere in the matrix, either in the
first or second section, or even both simultaneously, such as implications for
the industry between technological data, the second section in the factual
hemisphere, and natural and social data, first section in the factual
hemisphere.
-
The deduction program, specific or global, according to the relations observed
in every combination of factors, matches every relation of every combination of
factors with their corresponding category of mathematical relations (stochastic, cryptographic, pattern, Second Method), proceeding
to the rational contrast of these relations as empirical hypothesEs, and if
rational, are added to the rational truth.
Factors
in turn (as it was said in the post “The second stage in particular applications for particular programs”) can be classified according to their
measurements (direct punctuations or frequencies), or according to their behaviour (constant, independent or dependent variables), this last one especially in
relations of probable cause and effect.
According
to their measurement, factors in Impossible Probability are classified into:
factors subjects measured in direct punctuation, and factors as options measured in frequency.
According
to their behaviour, especially in relation to cause and effect, factors can be
classified as constant factors, and dependent variable factors, or independent variable factors.
Because there are at least two classifications of
factors, according to their measurement (subjects, options), and behaviour
(constant or variable, dependent or independent, especially in relation of
probable cause and effect), the synthesis of both classifications is:
- Constant factors as subjects, keeping constant their
direct punctuation.
- Constant factors as options, keeping their frequency constant.
- Independent factors as subjects whose changes in
their direct punctuations can produce changes in other factors as subjects
(changes in their direct punctuations) or as options (changes in their
frequency).
- Independent factors as options, whose changes in
their frequency can produce changes in other factors as subjects (changes in
their direct punctuations) or as options (changes in their frequency)
- Dependent factors as subjects whose changes in
their direct punctuations are due to: changes in the direct punctuation of other
factors as independent factors as subjects, or due to changes in the frequency
of other factors as independent factors as options, or both.
- Dependent factors as options, whose changes in their frequency are due to: changes in the direct punctuation of other factors as independent factors as subjects, or due to changes in the frequency of other factors as independent factors as options, or both.
- Dependent factors as options, whose changes in their frequency are due to: changes in the direct punctuation of other factors as independent factors as subjects, or due to changes in the frequency of other factors as independent factors as options, or both.
And,
as it was said in the post “The standardization process in the second stage”,
deductions can be classified according to how the factors involved are
measured, classifying them in: deductions from combinations of only factors as
subjects, deductions from combinations of factors as subjects and factors as
options, deductions from combinations of only factors as options.
Finally,
specifically, in deductions about probable cause and effect, the possible
classification of deductions of causation between factors in accordance with
their measurement and behaviour is:
Probable
causation without constants:
-
Deductions of probable causation, not having any constant factor, one or
more than one factor as subject/s as independent variable/s, cause/s changes in
one or more factors as subject/s, as dependent variable/s.
-
Deductions of probable causation, not having any constant factor, one or
more than one factor as option/s as independent variable/s, cause/s changes in
one or more factors as option/s, as dependent variable/s.
-
Deductions of probable causation, not having any constant factor, one or
more than one factor as subject/s as independent variable/s, cause/s changes in
two or more factors in which at least one of them is a factor as an option or
as a subject, as dependent variables.
-
Deductions of probable causation, not having any constant factor, one or
more than one factor as option/s as independent variable/s, cause/s changes in
two or more factors, in which at least one of them is a factor as an option or
as a subject, as dependent variables.
-
Deductions of probable causation, which not having any constant factor, two or
more factors in which at least one of them is a factor as an option or as a
subject, cause/s changes in two or more factors in which at least one of them is
a factor as an option or as a subject, as dependent variables.
-
Deductions of probable causation, not having any constant factor, one or
more than one factor as subject/s as independent variable/s, cause/s changes in one or more factors as options, as dependent variables.
-
Deductions of probable causation, not having any constant factor, one or
more than one factor as option/s as independent variable/s, cause/s changes in one or more factors as subjects, as dependent variables.
Probable
causation having one or more than one constant as a subject:
-
Deductions of probable causation, which have one or more than one constant as subject/s, one or more than one factor as subject/s as independent variable/s,
cause/s changes in one or more factors as subject/s, as dependent variable/s.
-
Deductions of probable causation, which have one or more than one constant as subject/s, one or more than one factor as option/s as independent variable/s,
cause/s changes in one or more factors as option/s, as dependent variable/s.
-
Deductions of probable causation, which have one or more than one constant as subject/s, one or more than one factor as subject/s as independent variable/s,
cause/s changes in two or more factors in which at least one of them is a factor
as an option or as a subject, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as subject/s, one or more than one factor as option/s as independent variable/s,
cause/s changes in two or more factors in which at least one of them is a factor
as an option or as a subject, as dependent variables.
-
Deductions of probable causation, which having one or more than one constant as subject/s, two or more factors in which at least one of them is a factor as an
option or as a subject, causes changes in two or more factors in which at least
one of them is a factor as an option or as s subject, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as subject/s, one or more than one factor as subject/s as independent variable/s,
cause/s changes in one or more factors as options, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as subject/s, one or more than one factor as option/s as independent variable/s,
cause/s changes in one or more factors as subjects, as dependent variables.
Probable
causation having one or more than one constant as option/s:
-
Deductions of probable causation, which have one or more than one constant as option/s, one or more than one factor as subject/s as independent variable/s,
cause/s changes in one or more factors as subject/s, as dependent variable/s.
-
Deductions of probable causation, which have one or more than one constant as option/s, one or more than one factor as option/s as independent variable/s,
cause/s changes in one or more factors as option/s, as dependent variable/s.
- Deductions
of probable causation, which have one or more than one constant as option/s,
one or more than one factor as subject/s as independent variable/s, cause/s
changes in two or more factors in which at least one of them is a factor as an
option or as a subject, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as option/s, one or more than one factor as option/s as independent variable/s,
cause/s changes in two or more factors in which at least one of them is a factor
as an option or as a subject, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as option/s, two or more factors in which at least one of them is a factor as an
option or as a subject, causes changes in two or more factors in which at least
one of them is a factor as an option or as s subject, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as option/s, one or more than one factor as subject/s as independent variable/s,
cause/s changes in one or more factors as options, as dependent variables.
-
Deductions of probable causation, which have one or more than one constant as option/s, one or more than one factor as option/s as independent variable/s,
cause/s changes in one or more factors as subjects, as dependent variables.
Probable
causation has two or more constants in which at least one is an
option or a subject.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or a subject, one or more than one factors as subject/s as independent variable/s, cause/s changes in one or more factors as subject/s, as dependent variable/s.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or a subject, one or more than one factors as option/s as independent variable/s, cause/s changes in one or more factors as option/s, as dependent ariable/s.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or as a subject, one or more than one factor as subject/s as independent variable/s, cause/s changes in two or more factors in
which at least one of them is a factor as an option or as a subject, as
dependent variables.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or a subject, one or more than one factor as option/s as independent variable/s, cause/s changes in two or more factors in
which at least one of them is a factor as an option or as s subject, as
dependent variables.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or a subject, two or more factors in which at
least one of them is a factor as an option or as a subject, causes changes in
two or more factors in which at least one of them is a factor as an option or
as a subject, as dependent variables.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or a subject, one or more than one factors as subject/s as independent variable/s, causes changes in one or more factors as
options, as dependent variables.
-
Deductions of probable causation, having two or more constants in which
at least one is an option or a subject, one or more than one factors as option/s as independent variable/s, causes changes in one or more factors as
subjects, as dependent variables.
The
classification of possible inversely or directly, positive or negative,
proportional correlations, according to how the factors are measured:
Possible
inversely or directly, positive or negative, proportional correlations between
only the factors as subjects:
- Possible
direct positive proportional correlations between factors as subjects, when
parallely one factor as a subject or a set of factors as subjects have an
increment in the flow of direct punctuations, at the same time that there is an
increment in the flow of direct punctuations in another factor as a subject or a
set of factors as subjects.
- Possible direct negative proportional correlation between factors as
subjects, when parallely one factor as a subject or a set of factors as subjects
have a decrease in the flow of direct punctuations, at the same time that there is
a decrease in the flow of direct punctuations in another factor as a subject or a
set of factors as subjects.
- Possible
inversely proportional correlation between factors as subjects, when one factor
as subject or set of factors as subjects have an increment in the flow of
direct punctuations, while at the same time, another factor as subject or set of
factors as subjects have a decrease in their flow of direct punctuations, or
vice versa.
Possible
inversely or directly, positive or negative, proportional correlations between
factors as subjects and as options:
- Possible
direct positive proportional correlation between factors as subjects and
factors as options, when parallely one factor as a subject or a set of factors as
subjects have an increment in the flow of direct punctuations, at the same time that there is an increment in the flow of frequencies in another factor as an option or a
set of factors as options. And vice versa, in parallel one factor as an option or a
set of factors as options has an increment in the flow of frequencies, at the
same time that there is an increment in the flow of direct punctuations in another factor as a subject or a set of factors as subjects.
- Possible direct positive proportional correlation between factors as subjects and factors as options, when within two or more factors at least one of them is a factor as an option or as a subject, have an increment in their flow of direct punctuations and frequencies, while at the same time another set of two factors or more, in which at least one of them is a factor as a subject or factor as an option, have an increment in their flow of direct punctuations or frequencies.
- Possible direct positive proportional correlation between factors as subjects and factors as options, when within two or more factors at least one of them is a factor as an option or as a subject, have an increment in their flow of direct punctuations and frequencies, while at the same time another set of two factors or more, in which at least one of them is a factor as a subject or factor as an option, have an increment in their flow of direct punctuations or frequencies.
- Possible
direct negative proportional correlation between factors as subjects and
factors as options, when parallely one factor as a subject or a set of factors as
subjects have a decrease in the flow of direct punctuations, at the same time that there is a decrease in the flow of frequencies in another factor as an option or a
set of factors as options. And vice versa, in parallel, one factor as an option or a
set of factors as an option has a decrease in the flow of frequencies, at the
same time that there is a decrease in the flow of direct punctuations in another factor as a subject or a set of factors as a subject.
- Possible direct negative proportional correlation between factors as subjects and factors as options, when within two or more factors at least one of them is a factor as an option or as a subject, have a decrease in their flow of direct punctuations and frequencies, while at the same time another set of two factors or more, in which at least one of them is a factor as a subject or factor as an option, have a decrease in their flow of direct punctuations or frequencies.
- Possible direct negative proportional correlation between factors as subjects and factors as options, when within two or more factors at least one of them is a factor as an option or as a subject, have a decrease in their flow of direct punctuations and frequencies, while at the same time another set of two factors or more, in which at least one of them is a factor as a subject or factor as an option, have a decrease in their flow of direct punctuations or frequencies.
- Possible
inversely proportional correlation between factors as subjects and factors as
options, when one factor as subject or set of factors as subjects have an
increment in the flow of direct punctuations, while at the same time, another factor as option or set of factors as options have a decrease in their flow of
frequencies. And vice versa, when one factor as an option or a set of factors as
options has an increment in the flow of frequencies, at the same time that another factor as a subject or a set of factors as subjects has a decrease in their
direct punctuations.
- Possible
inversely proportional correlation between factors as subjects and factors as
options, when within two or more factors, at least one of them is a factor as an
option or as a subject, have an increment in their flow of direct punctuations
and frequencies, while at the same time another set of two factors or more, in which
at least one of them is a factor as a subject or factor as an option, have a decrease
in their flow of direct punctuations or frequencies.
Possible
inversely or directly, positive or negative, proportional correlations between
only factors as options:
- Possible
direct positive proportional correlation between factors as options, when
parallely one factor as an option or a set of factors as options have an increment
in the flow of frequencies, at the same time that there are an increment in the flow
of frequencies in another factor as an option or a set of factors as options.
- Possible direct negative proportional correlation between factors as
options, when parallely one factor as an option or a set of factors as options
have a decrease in the flow of frequencies, at the same time that there is a
decrease in the flow of frequencies in another factor as an option or a set of
factors as options.
- Possible inversely proportional correlation between factors as options, when
one factor as an option or set of factors as options have an increment in the flow
of frequencies, while at the same time, another factor as an option or set of factors
as options have a decrease in their flow of frequencies, or vice versa.
Likewise, using the Second Method of Impossible Probability, the possible
relation between factors according to their classification between factors as
options or subjects, can be made, but being aware that at any time that a
factor, shows a variable behaviour, means that this factor could be either a
dependent or independent factor or a factor which has inversely or direct,
positive or negative, proportional correlations with other factors. And, at any
time that a factor, as an option or as a subject, does not have significant
changes over time in its empirical probability, it means that this factor behaves
as a constant factor over time. Having said that, the possible classification
of relations between factors using the Second Method is as follows.
Classification of relations of only factors as subjects using the Second Method.
- Factors as subjects whose empirical probability does not have significant
changes over time, working as constant factors as subjects.
- Factors as subjects whose empirical probability keeps relations of equal opportunities with the empirical probability of other factors
- Factors as subjects whose empirical probability keeps relations of equal opportunities with the empirical probability of other factors
- Factors as subjects whose empirical probability is a positively biased
independent variable.
- Factors as subjects whose empirical probability is a positively biased
dependent variable, depending on other factor/s as subject/s as independent
variable/s.
- Factors as subjects whose empirical probability is a negatively biased
independent variable.
- Factors as subjects whose empirical probability is a negatively biased
dependent variable, depending on other factor/s as subject/s as independent
variable/s.
- Factors as subjects whose empirical probability is positively biased in
direct proportional correlation with other factor/s as subjects/.
- Factors as subjects whose empirical probability is negatively biased in direct
proportional correlation with other factor/s as subject/s.
- Factors as subjects whose empirical probability is positively biased in
inverse proportional correlation with other factor/s as subject/s
- Factors as subjects whose empirical probability is negatively biased in an inversely proportional correlation with other factor/s as subject/s
Classification of relations of only factors as options using the Second
Method.
- Factors as options whose empirical probability does not have significant
changes over time, working as constant factors as options.
- Factors as options whose empirical probability keeps relations of equal opportunities with other factors as options.
- Factors as options whose empirical probability keeps relations of equal opportunities with other factors as options.
- Factors as options whose empirical probability is a positively biased
independent variable.
- Factors as options whose empirical probability is a positively biased
dependent variable, depending on other factor/s as option/s as independent
variable/s.
- Factors as options whose empirical probability is a negatively biased
independent variable.
- Factors as options whose empirical probability is a negatively biased
dependent variable, depending on other factor/s as option/s as independent
variable/s.
- Factors as options whose empirical probability is positively biased in
direct proportional correlation with other factor/s as option/s.
- Factors as options whose empirical probability is negatively biased in
direct proportional correlation with other factor/s as option/s.
- Factors as subjects whose empirical probability is positively biased in
inverse proportional correlation with other factor/s as option/s
- Factors as subjects whose empirical probability is negatively biased in an inversely proportional correlation with other factor/s as option/s
Classification of relations between factors as options and as subjects
using the Second Method.
- Factors as options whose empirical probability is a positive biased
independent variable, having as dependent variables factors as subjects.
- Factors as subjects whose empirical probability is a positively biased
independent variable, having as dependent variables factors as options.
- Factors as options whose empirical probability is a positively biased
dependent variable, depending on other factor/s as subject/s as independent
variable/s.
- Factors as subjects whose empirical probability is a positively biased
dependent variable, depending on other factor/s as option/s as independent
variable/s.
- Factors as options whose empirical probability is a negatively biased
independent variable, having as dependent variables factors as subjects.
- Factors as subjects whose empirical probability is a negatively biased
independent variable, having as dependent variables factors as options.
- Factors as options whose empirical probability is a negatively biased
dependent variable, depending on other factor/s as subject/s as independent
variable/s.
- Factors as subjects whose empirical probability is a negatively biased
dependent variable, depending on other factor/s as option/s as independent
variable/s.
- Factors as options whose empirical probability is positively biased in
direct proportional correlation with other factor/s as subject/s.
- Factors as subjects whose empirical probability is positively biased in
direct proportional correlation with other factor/s as option/s
- Factors as options whose empirical probability is negatively biased in
direct proportional correlation with other factor/s as subject/s.
- Factors as subjects whose empirical probability is negatively biased in
direct proportional correlation with other factor/s as option/s.
- Factors as subjects whose empirical probability is positively biased in
inverse proportional correlation with other factor/s as option/s
- Factors as options whose empirical probability is positively biased in
inverse proportional correlation with other factor/s as subject/s
- Factors as subjects whose empirical probability is negatively biased in an inversely proportional correlation with other factor/s as option/s
- Factors as options whose empirical probability is negatively biased in an inversely proportional correlation with other factor/s as subject/s
- Factors as options whose empirical probability is a positive biased
independent variable, having as dependent variables a set of factors in which at least one of them is a factor as a subject or as an option.
- Factors as subjects whose empirical probability is a positive biased
independent variable, having as dependent variables a set of factors in which at least one of them is a factor as a subject or as an option.
- Factors as options whose empirical probability is a positive biased
dependent variable, depending on a set of factors, as independent
variables, in which at least one is a subject or an option.
- Factors as subjects whose empirical probability is a positively biased
dependent variable, depending on a set of factors, as independent
variables, in which at least one is a subject or an option.
- Factors as options whose empirical probability is a negatively biased
independent variable, having as dependent variables a set of factors in which at least one of them is a factor as a subject or as an option.
- Factors as subjects whose empirical probability is a negatively biased
independent variable, having as dependent variables a set of factors in which at least one of them is a factor as a subject or as an option.
- Factors as options whose empirical probability is a negatively biased
dependent variable, depending on a set of factors, as independent
variables, in which at least one is a subject or an option.
- Factors as subjects whose empirical probability is a negatively biased
dependent variable, depending on a set of factors, as independent
variables, in which at least one is a subject or an option.
- Factors as options whose empirical probability is positively biased in
direct proportional correlation with a set of factors, in which at least one is a subject or an option.
- Factors as subjects whose empirical probability is positively biased in
direct proportional correlation with a set of factors in which at least one is a subject or an option.
- Factors as options whose empirical probability is negatively biased in
direct proportional correlation with a set of factors in which at least one is a subject or an option.
- Factors as subjects whose empirical probability is negatively biased in
direct proportional correlation with a set of factors in which at least one is a subject or an option.
- Factors as subjects whose empirical probability is positively biased in
inverse proportional correlation with a set of factors in which at least one is a subject or an option.
- Factors as options whose empirical probability is positively biased in
inverse proportional correlation with a set of factors, in which at least one is a subject or an option.
- Factors as subjects whose empirical probability is negatively biased in an inversely proportional correlation with a set of factors in which at least one is a subject or an option.
- Factors as options whose empirical probability is negatively biased in an inversely proportional correlation with a set of factors in which at least one is a subject or an option.
The
possible classifications that I have provided are only a possible approach on
this matter to set up what kind of mathematical categories (pure or analytical
categories) of possible relations between factors could be set up in order to
track both sections in the factual hemisphere in the matrix, by specific programs
in their specific fields and at the global level the Artificial Research by
Deduction in the Global Artificial Intelligence.
Impossible
Probability, as a theory specialised in statistics and probability, only provides
possible classifications related to this theory and this field, but beyond this
theory and this field, there are other branches in mathematical studies, such
as mathematical patterns and cryptography, in which other classifications or
mathematical categories (analytical or pure categories) between factors could
be added, as well as from other mathematical traditions and philosophies.
What
is really important, regardless of what mathematical discipline, field,
tradition of philosophy, used in the formation of a list of mathematical
categories (analytical or pure categories), in order to make deductions, is the
fact that all possible classification of mathematical categories (analytical or
pure categories) of possible relations between factors, are possible classifications
of mathematical categories (analytical or pure categories) in which the role
and the function of the factors involved, and the way to measure them, and how the
factors can have relations between then, must be very clear, in order that
automatically at any time that any specific program, or at global level the
Artificial Research by Deduction in the Global Artificial Intelligence, is
going to match: the relations observed in a combination of factors, with the
possible list of categories; the way in which the combination of factors is
matched to the possible mathematical category, must be, within a margin of doubt,
rational.
In
reality, the possible classification of possible mathematical relations between
factors, what is going to do, is a possible list of mathematical categories (analytical
or pure categories) in order to analyse the factual hemisphere at a specific or
global level.
The
possible classification of mathematical relations between factors as a list of
mathematical categories (analytical or pure categories), is going to be the
list of analytical or pure categories in which each specific program or the
Artificial Research by Deduction in the Global Artificial Intelligence, has to
match every combination of factors, matching the combination of factors with
their corresponding analytical or pure category (mathematical category)
according to the factors involved:
subjects or options working as 1) constants or variables, dependent or
independent, or 2) inversely or 3) direct, positive or negative, proportional
correlations, or 4) according to a specific pattern, or 5) cryptographic
model, 6) Second Method, or any other mathematical relation set up in the deduction programs, at
specific or global level, to track or analyse the factual hemisphere in the
matrix.
Having
ready the Artificial Research by Deduction in the Global Artificial
Intelligence and the specific programs the list of pure or analytical
categories, the list of mathematical categories, and having the Unified
Application ready the application, then the Artificial Research by Deduction in
the Global Artificial Intelligence as a global deduction program, and the
specific programs as specific deduction programs, start setting combination of
factors in their respective fields, the global deduction program at global
level, and the specific deduction programs in their respective specific fields,
according to the division of labour in which they would have been set up, comparing all of them
in their respective level, global or specific, every combination of factors
with the list of categories of
mathematical relations, matching the combination of factor with the correct
category of mathematical relations that better describes the current relations
between factors in the combination of factors. The chosen category as an empirical
hypothesis about the current mathematical relations between the factors in the
combination, must be contrasted rationally, and for that reason, the specific
or global deduction program responsible for this combination has to make a
sample of data: either
collecting data from the past (time can be programmable) or from now on for a
period of time (time can be programmable); and contrasting rationally the
sample, if rational, the empirical hypothesis becomes a rational hypothesis to
be added to the rational truth, the database of rational hypothesis.
This process, in brief, is the establishment of
mathematical categories, the establishment of a combination of factors, and the
selection of what mathematical category corresponds to the relations between
factors in the combination to contrast rationally.
This process in three steps is not other thing
but a traditional syllogism:
- Major premise (general or universal statement in Hegelian
dialectic), here the mathematical category (in rationalism, analytical
category).
- Minor premise (specific statement in Hegelian
dialectic), here the data in the factual hemisphere in the matrix, specific measurements (in positivism, the positive data)
- Conclusion (particular or concrete statement
in Hegelian dialectic), as the particular relation or synthesis between data and
the analytical category (synthetic knowledge).
The way in which the traditional syllogism works
in the scientific explanation of Impossible Probability is as a syllogism
between the pure reason, practical reason, and critical reason.
- A set of major premises, all the categories set
up in the list of mathematical categories, working as pure or analytical
categories, pure or analytical categories which in reality form the pure reason
itself.
- Minor premises, all the measurements, and data obtained through the management of the matrix by the Unified Application, gathering data from
all possible factors, obtained by all the applications, robotic devices etc… gathering all the information
available in the matrix. Where the Unified Application keeps updated the
matrix, and the Application System puts into practice any decision using
conceptual: schemes, maps, sets, models; from the second section in both
hemispheres in the matrix. The way in which both of them work together is a
true practical reason itself.
- Conclusions: the rational truth and further
decisions. The critical reason: in order to decide if an empirical hypothesis,
within the margin of error, is rational, or what decision, within the margin of
error, is rationally out of contradiction to put into practice.
In short, pure reason is a list of
analytical categories, practical reason, through a vast system of
applications and robotic devices, keeps updated the matrix and put into practice
decisions based on rational hypothesis, previously accepted by the critical reason, within the margin
of doubt, as rational as well as the decisions must be accepted, within the
margin of doubt, as out of contradiction, in order to be put then into practice by the practical reason, adding in the matrix any new change in the data, due
to changes in the reality as a consequence to the changes caused by this
decision into the reality, being in fact a perpetual movement, in spirals and/or
circles, in the knowledge and/or in the reality.
Knowledge and reality as opposites are
identical. The Global Artificial Intelligence should evolve to true isomorphic knowledge, which means an evolution to a unique science, or the
unification of al sciences in only one, as image and likeness of reality, there
is only one unique reality, so there must be only one and only truth, in
order to achieve the absolute knowledge of everything, the pure knowledge, that
one even beyond the rational truth.
In fact, the possibility of the creation of a non-human
language by the Unified Application is a hope for the artificial development by
the Global Artificial Intelligence itself of non-human pure operations, based
on a non-human logic and non-human mathematics beyond human understanding,
able in turn to develop a non-human technology, able to overcome the noise and the external interference, able to have a true idea about what is happening, the
essence and the noumeno itself about the reality itself beyond human
limitations.
The replication of human reason in
artificial psychology for the creation of Artificial Intelligence, at the
end, the creation at the global level of the unique model of Global Artificial
Intelligence, is no other thing but the replication of the idealistic and
rationalist philosophic systems based on philosophers such as Plato, Decartes,
Voltaire, Kant and Hegel. In reality, modern rationalism is how classic idealism has been adapted to modern times.
Given the limits of human perception and cognition, there are aspects of reality that remain beyond our grasp. To better understand such phenomena, we may need technologies capable of transcending those limitations and offering deeper insights into what truly occurs beneath the surface.
Intelligence is the survival capacity. The paradigm in the resolution of any problem is the mathematical paradigm. The survival problem is a mathematical problem. The survival of humanity within the
uncertainty of this universe rests on a mathematical basis.
Rubén García Pedraza, 26th of May of 2018, London.
Reviewed 20 August 2019 Madrid.
Reviewed 20 August 2019 Madrid.
Reviewed 12 August 2023 Madrid.
Reviewed 10 May 2025, London, Leytostone
