A Specific Artificial Intelligence for Artificial Research by Deduction is that Artificial
Intelligence specifically designed for artificial research in one synthetic science, synthetic academic field, or activity, which deduces, and applies, in a database, statistical and probabilistic methods, in order to identify possible stochastic relations,
that taken as empirical hypotheses, after rational contrast if they are accepted
as rational, are objects of single and comprehensive virtual models
Synthetic
sciences, in Impossible Probability, are the empirical sciences, and synthetic
academic fields are those areas of an academic scientific
investigation taken as empirical
objects for academic studies. Another case is the possible use of interdisciplinary or multidisciplinary synthetic studies as a synthetic
academic field.
This
distinction is important. One change in the last century is the possibility of doing research beyond the traditional sciences. For instance, the examples in the next posts I will use to explain what is Specific Artificial
Intelligence for Artificial Research by Deduction will be tectonics,
climatology, transport, and gravity. So, in the end, what I will have developed
is a Specific Artificial Intelligence for Artificial Research by Deduction in
Tectonics, another one only for Climatology, another one only for transport,
and another one only for gravity.
Tectonics,
Climatology, and gravity are traditional investigation objects. Still, there is
no synthetic science specialized in transport, but if I check any academic database, there are a lot of very recent investigations about transport. It is not
itself a synthetic science but is a synthetic academic field, which for this
purpose, can use multidisciplinary and interdisciplinary approaches: from the history of transport to engineering and the social use of transport.
The
main difference between multidisciplinary or interdisciplinary studies as
synthetic academic fields done by a Specific Artificial Intelligence for Artificial
Research, by Application or Deduction, or multidisciplinary and
interdisciplinary studies done by Global Artificial Intelligence, is the fact
that, while Specific Artificial Intelligence is able to work with different
categories or factors, from different synthetic sciences or synthetic academic
fields, depending on the Artificial Research, by Application or Deduction,
categories or factors previously inserted in the database, a Global Artificial
Intelligence is able to do multidisciplinary and interdisciplinary studies
crossing permanently absolutely all factors included in the database, the
global matrix.
Once
I have defined what is Specific Artificial Intelligence for Artificial
Research by Deduction, and explained how to use it in synthetic sciences and
synthetic academic fields, I would like to explain why I will start with the application
of this model of artificial research in a Specific Artificial Intelligence
instead of the application directly in a Global Artificial Intelligence, which
is going to be developed after the completion of all posts regarding the application
of this model of artificial research in Specific Artificial Intelligence.
For
the construction of a global matrix, and all the necessary replications, and auto-replication
models in Global Artificial Research, are necessary previously, as an
experiment. The experimentation of this technology in models of Specific
Artificial Intelligence, after successful results, will be suitable to be
applied in the Global Artificial Intelligence.
This doesn’t mean we can’t begin early steps toward Global Artificial Intelligence. It simply means all deduction methods must first be tested in Specific Artificial Intelligences. This only means that at least all
the applications, replications, and auto-replications, in Artificial Research by
Deduction to put into practice in Global Artificial Intelligence,
previously must be experimented with in Specific Artificial Intelligences. But,
while these experiments are completed, the very first steps in any Global
Artificial Intelligence: all the agreements necessary between national
agencies, or continental agencies, to merge all their databases into only one, which
later must be transformed in the global matrix.
Once
all the databases from absolutely everything, without restriction, are integrated
into only one database, the next step is to start working on how to transform
all this information into quantitative factors in a global database whose flow
of data can be updated permanently: the final formation of the global matrix.
The
formation of a global matrix within the Global Artificial Intelligence is not
so different to the formation of any specific matrix in any Specific Artificial
Intelligence for Artificial Research by Deduction in any synthetic science or
synthetic academic fields. The only thing that is different is the amount of
data to integrate in the global matrix, much more than in any other Specific
Artificial Intelligence.
For
that reason, for the facilitation of any
work in the formation of a Global Artificial Intelligence, is going to be
necessary Specific Artificial Intelligences for Artificial Engineering: the
Artificial Designer of Intelligence, and the Intelligence Robotic Mechanic;
artificial assistants useful during the formation of the Global Artificial
Intelligence.
Basically, the Artificial Designer of Intelligence is: firstly, an application which
consists of a database including all kinds of models of Artificial Intelligence (including
models of applications, replications, and auto-replications). Secondly, replication
skills and tools to create, fix, or enhance any Artificial Intelligence, taking
as models the ones stored in its database, and finally, auto-replication, the
improvement of its own knowledge using artificial learning strategies, every
time that finds something new, such as a new problem to fix, or even a new
Artificial Intelligence. For instance, an Artificial Designer of Intelligence
from one country or continent that, by artificial learning, learns new models of
Artificial Intelligence from other countries or continents, decomposing or
trying to fix that other Artificial Intelligence.
The
Intelligent Robotic Mechanic is the same: as an application, a database with
all robotic models of any kind, as replication the necessary skills and tools
to create, fix, and enhance, any robotic device, as auto-replication, the ability to
learn by artificial learning anything new and useful for the creation of new
robots, to fix new robots, or enhance the current robots.
In
the same way that a Specific Artificial Intelligence for Artificial Research
by Application for medical problems taken as an application, its database with a list
of categories of medical problems should be able to cure any disease, the
Artificial Designer of Intelligence and the Intelligence Robotic Mechanic must
be able to fix any Artificial Intelligence or robotic problem, even at a global
level. But, at the same time, having the skills and tools necessary to fix
any Artificial Intelligence or robotic problem means that they must be
able to create new Artificial Intelligence and new robotic devices, and, by
artificial learning, learn how to construct new models, and how to enhance
the current ones.
In the
very first step for the creation of a Global Artificial Intelligence, the very
first database, much more than a global matrix, is likely to be only a gigantic
database, where all agencies at the national or continental level are going to share
all their information. But later, as long as the experimentation in the
construction of a specific matrix for Specific Artificial Intelligence, using
Artificial Research by Deduction in different synthetic sciences, synthetic
academic fields, or activities, having the results in these experiments, the transformation of the
first gigantic database, within the first Global Artificial Intelligence, into
a global matrix for Artificial Research by Deduction at global level, will be
easy, and additionally, by that time, good results in Specific Artificial
Intelligence for Artificial Engineering: the Designer of Intelligence and
Intelligent Robotic Mechanic; will be achieved, excellent assistants for the creation
of the first global matrix.
Having
explained why, I will start the explanations in Artificial Research by Deduction
in different examples of Specific Artificial Intelligences, something that I
will do in the following posts, explaining every single step in the formation of
Artificial Research by Deduction. In this post, what I really want is to show a
general glance at how Artificial Research by Deduction in Specific
Artificial Intelligence works.
Following
the three general stages in any Artificial Intelligence, Specific or Global,
firstly, the application, the database. In Artificial Research by
Deduction, the database must have the shape of a matrix, and the only thing
that the matrix in the database has, is only an exact definition
in quantitative terms of every factor to study. But, in Impossible Probability
this construction of the database must understand that there are two different types of universes: a universe of subjects and options, and the universe of options. Something really important in Impossible Probability, the Second Method.
The
Second Method of Impossible Probability distinguishes between universes of
subjects and options and a universe of options. One difference is: in universes
of subjects and options the treatment of any direct punctuation of any subject is
like the treatment of any frequency of any option, so the empirical probability
of any subject is the direct punctuation of any subject divided by the sum of
all the direct punctuations, like the empirical probability of any option is
the frequency of this option divided by the total frequency.
For
that reason, the definition of empirical probability in the Second Method of
Impossible Probability is: the direct punctuation or frequency of any subject
or option divided by the sum of all the direct punctuations or frequencies from
all subjects and options.
This
does not mean that I only propose the use of the Second Method as a main
statistical and probabilistic method for deduction in Artificial Research for
Deduction. As I have always said, the Second Method of Impossible Probability
must be used in combination with traditional statistics and traditional
probability, is the reason why I called my new method a Second Method because the Second Method of Impossible Probability is only the second one after
the first one, the first one always is the traditional statistics and the
traditional probability.
But,
independently of any Artificial Intelligence, Specific or Global, for
Artificial Research by Deduction, use any possible method available. What is
really important is the distinction between direct punctuation and frequency.
The measurement of some factors is made through direct punctuations through
measurement tools, for instance, thermometers in climatology. Others are measured
using frequency, for instance, the number of bicycles, motorcycles, cars, buses,
trains, aeroplanes, ships, and spaceships, working at the same time in a country or
continent, or the number of people using a transport at the same time in the same
country or in the same continent.
So,
regardless of what kind of statistical method is used, it Is necessary to have the
necessary statistical and probabilistic methods for any kind of measurement. And
even the possibility of using one method in which the research with direct punctuations
and frequencies, all together in the same matrix, will be done using a common
method. The possibility to use the same method, for any kind of information,
from direct punctuations to frequency, in the same way, and that method is the Second
Method of Impossible Probability.
One
advantage, among others, that the Second Method of Impossible Probability has in
Artificial Research by Deduction in any Artificial Intelligence, Global or
Specific, is the possibility of treatment of any kind of flow of data, direct
punctuations or frequency, in the matrix, global or specific, in the same way.
The
design of the matrix in Specific Artificial Intelligence for Artificial
Research by Deduction that only works with frequency must be created, defining
every factor in this specific matrix as an option that later on, in the
replication stage, the robotic devices can fill every file of every factor as a
probabilistic option with its correspondent flow of frequencies.
The
design of the matrix in Specific Artificial Intelligence for Artificial
Research by Deduction that only works with direct punctuations must be created, defining every factor in this specific matrix as a subject that later on, in
the replication stage, the robotic devices can fill every file of every factor
as a statistical subject with its correspondent flow of direct punctuations.
The
design of a global or specific, matrix in any Artificial Intelligence, Global
or Specific, for Artificial Research by Deduction, that works with frequencies
and direct punctuations simultaneously, must be created, defining every factor
as a probabilistic and statistical subject or option, that later on, in the replication
stage, the robotic devices can fill every file of every factor as a probabilistic
and statistical subject or option with its correspondent flow of direct
punctuations and frequencies.
In
the second stage, replication, once the robotic devices have added the flow of information
into the matrix, global or specific, in any Artificial Intelligence, global or
specific, for Artificial Research by deduction, the information is treated by
replication processes according to the very nature of the data.
That
flow of data that is registered in the matrix as a flow of frequencies is
studied using traditional probability and traditional statistics, as well as the
possible use of the Second Method.
That
flow that is registered in the matrix as a flow of direct punctuations is studied
using traditional statistics as well as the possible use of the Second Method.
And
in that flow of data registered in the matrix, in which some information is
information through direct punctuations, and some information is information
through frequencies, the flow of direct punctuations can be treated as direct
punctuations, as well as the flow of frequencies, can be treated as frequencies,
but another option: the treatment of all kind of information, regardless of
their nature, direct punctuations or frequencies, in the same way, as empirical
probabilities, the Second Method of Impossible Probability.
There
must be a moment when all information must be treated in the same way. Maybe the Second Method of Impossible Probability is not perfect, but at least it is a good starting point in that direction.
The
most important challenge and question of mathematical research in a global
matrix using the Second Method is what I call the paradigm of the sample of zeros, when absolutely everything is nearly to come to nothing. It is in this
notion of nothingness that the investigation using the Second Method of
Impossible Probability in a global matrix is going to be one of the most
important aspects to study. Another reason for the consideration of
Impossible Probability as a model of Logic Nihilism.
As
long as the race for the Global Artificial Intelligence is only starting, and
there are a lot of questions, details, and aspects, to study, I am completely sure
that, taken as a possible model, the Second Method of Impossible Probability,
among others, in the future will be elaborated more powerful statistical and
probabilistic methods, and the next experiments in Artificial Research, by
Application and Deduction, Specific or Global, among others models of
Artificial Research that are going to appear (these ones that I propose are only
my personal contribution) are going to be in fact laboratories of mathematical
and logical research.
Once
the matrix has been filled with the flow of data, direct punctuations or
frequencies, depending on the nature of the factors to study, is time to deduce
correlations and possible causes and effects, or any other possible stochastic relation among the factors included in the matrix.
Using
replication processes over the matrix, the Artificial Intelligence must be able
to identify at least the next possible stochastic relations:
1) When,
at least significantly, two or more factors increase their measurements in
the flow of data at the same time. Possible directly proportional positive correlations.
2)
When, at least significantly, two or more factors decrease their measurements
in the flow of data at the same time. Possible directly proportional negative correlations.
3)
When, at least significantly, one or more factors increase or decrease measurements
in the flow of data at the same time, while other or others decrease or
increase their measurements at the same time. Possible inversely proportional
correlations.
4)
When, at least significantly, after changes in the measurements in one or
more factors, other or other factors has or have changes in their measurements.
Possible causes and effects.
After
the identification of at least one of these possible stochastic relations:
directly proportional positive correlations, directly proportional negative correlations,
inversely proportional correlations, possible probable causes and effects. The
possible stochastic relation is automatically treated as an empirical hypothesis, and depending on the nature of the empirical hypothesis, after
taking a sample of flow, from the past records of the flow of data in the
matrix or waiting some time and gathering new flow of data in the matrix, for
every factor involved in the possible stochastic relation, then the Artificial
Intelligence will contrast rationally the empirical hypothesis using the
samples collected, and depending on the nature of the information, if direct punctuations
or frequency, the Artificial Intelligence chooses what probabilistic or
statistical methods are more suitable for the current investigation. After the
rational contrast, if the empirical hypothesis is accepted as rational, then the
Artificial Intelligence will make a single model of this stochastic relation,
now considered as a rational stochastic relation, and later, the integration of
the single virtual model within the comprehensive virtual model, where are integrated
all the single models formed by rational stochastic relations founded for this
Artificial Intelligence.
As
I have said, this is my personal contribution, but I am sure there are many other
possible uses of this matrix for many other mathematical and logical
investigations. One promising future use of this matrix is in detecting mathematical patterns or applying cryptographic methods, opening new frontiers in AI research. But in order to prepare the global matrix, it is first necessary to conduct further investigation in any specific matrix using Specific
Artificial Intelligences for Artificial Research by Deduction.
Finally,
the auto-replication stage, among many other ways in which any Artificial
Intelligence, Global or Specific, for Artificial Research, by Application or
Deduction, can auto-improve and auto-enhance itself, one of the most important
is the permanent auto-improvement through the rational hypothesis discovered.
In Artificial Research by Deduction, the auto-improvement, through the
rational hypothesis founded, is going to operate directly over the
comprehensive virtual model, that model within the Artificial Intelligence,
Global or Specific, made by the integration of all the single virtual models
from all empirical hypotheses accepted as rational.
Rubén García Pedraza, London 14th of February of 2018
Reviewed 5 August 2019, Madrid
Reviewed 5 August 2019, Madrid
Reviewed 8 August 2023, Madrid
