A
database is a collection of information for some particular purpose or
over some particular field. In statistics, every single category or option must be defined or limited in quantitative terms, given an exact definition of what information we need to measure, and later, the results of these measurements must be added to the database.
In
Impossible Probability the measurement of any quality of a subject or option is
the transformation of a qualitative property into a factor of mathematical operations, which can be converted into positive data, for the description, inference, or prediction of the phenomena studied in synthetic sciences.
The definition of
any data in quantitative terms is a warranty as well, which is defined in a
positive way. Data is positive when its acceptance is independent of any
ideological or political difference between different schools or paradigms.
Once we have a
general definition of the database through the theory of Impossible Probability, it is time to give a definition of the database in the Artificial Research by Application, that, generally speaking, would be a collection of: information, taxonomies, classifications, files,
categorizations, events, facts, phenomenon, characteristics of individuals and
populations (physically, behavioural, psychologically), or any other thing,
even any tool to use later; necessary in any artificial research processes made
by the Artificial Research by Application, which through the information collected
in its database, is going to be able to automatize the formation of empirical hypothesis within the synthetic science or synthetic academic field for what it
has been created, and able therefore to criticize rationally the empirical hypothesis in order to make further rational decisions.
If
the application finds some category, event, or phenomenon without a high
percentage of similarity with those ones stored in the database, this new one must then be integrated into the database as a new one.
This process of including new categories, events, or phenomena in the database constitutes an auto-replication process, as the application can improve its components—particularly the database—without human intervention.
As
first stage in the formation process of any Artificial Intelligence, either specific or global, for artificial research or any other purpose, a good
definition of the concept of database is necessary according to the purpose for which it is
going to be designed.
In
order to have a clear idea about what kind of database is going to be
necessary in Specific Artificial Intelligence for Artificial Research by Application, firstly is important to clarify what different models of database
are going to be developed, taking the theory of Impossible Probability as a
theoretical model, applied to Artificial Intelligence, Specific or Global. And
among all possible databases, the exact purpose of the database in Specific
Artificial Intelligence for Artificial Research by Application.
In
Impossible Probability is necessary to distinguish, at least, among
these models of databases:
-
Database in Artificial Research by Application in a Specific Artificial
Intelligence. In those Specific Artificial Intelligences for artificial
research in synthetic sciences or synthetic academic fields where the
artificial research is done by Artificial Research by Application, the database should include all information, data, taxonomies, classifications, files,
categorizations, events, facts, phenomena, or characteristics of individuals or
populations, or any other thing, even, tools if its necessary, that are going
to be needed in the investigations with this technology. This is the model developed
in this post.
- A
unified database of Artificial Research by Application. In the future, after
the completion of the unification process of all Specific Artificial Intelligence for Artificial Research by Application into a Global Artificial Intelligence. Once the creation of a Global Artificial Intelligence has
finished the completion of a global database, in addition to this global database it is going to be necessary the process of integration of the rest of specific databases of the rest of Specific Artificial Intelligences for artificial research in synthetic sciences and synthetic academic fields, into the GlobalArtificial Intelligence. This integration process should have different phases. In the first one the unification of all Specific Artificial Intelligences for ArtificialResearch by Application, and after that, the integration of all of them into
the Global Artificial Intelligence. The unification process should finish with
the integration of all these specific databases by application in only one, a unified database of Artificial Research by Application. Upon it, all the Specific
Artificial Intelligences for Artificial Research by Application, from any
synthetic science or synthetic academic field, could work together on only
one unified database for Artificial Research by Application, the Unified Application. All Specific
Artificial Intelligences for Artificial Research by Application, in any
synthetic science or synthetic field, could match the measurements taken from
real objects with the categories, events, and phenomena included in the unified database, forming and contrasting hypotheses at the same time from the unified
database. And in case a new category, event, or phenomenon should be
included, introducing improvements across the unified database at the same
time. One unified database for all Specific Artificial Intelligence for
Artificial Research by Application, whose hypothesis and decisions could
automatically be integrated into the Global Artificial Intelligence.
-
The database of Specific Artificial Intelligence for Artificial Research by
Deduction in any synthetic science or synthetic academic field. In this case, the
database is going to be a matrix, where each Specific Artificial Intelligence
for Artificial Research by Deduction of each synthetic science or academic
field must have its own matrix. The matrix should be formed by the definition
of every factor, subject, option, category or any key point relevant to the
research, that later on, in the second stage, the replication stage, is going
to be measured constantly, receiving a flow of continuous measurements at any
time. So, in the second stage, replication, this Specific Artificial
Intelligence for Artificial Research by Deduction would fill directly the
matrix with the flow of measurements related to every factor, subject, option,
category, etc., storing all the information in the memory. And upon the measurements,
by statistical methods, the automatic identification of high levels of correlations, identifying probabilities of cause and effect, and stochastic relations, which could be
formulated automatically as a hypothesis, that later could be criticised
rationally.
-
The database of the Global Artificial Intelligence for Artificial Research by Deduction. A gigantic global database, a global matrix, firstly perhaps over a
nation as an experiment that later could be extended over a whole continent,
the entire planet, finishing with the inclusion of information that could come
from the whole universe. Every single piece of information included must be defined in very exact and quantitative terms,
allowing the necessary measurements constantly all the time, getting a flow of
measurements at any time, stored continually in the memory, which, by
statistics methods, and studying the evolution of the flow, the Global
Artificial Intelligence could find high levels of correlations among factors,
subjects, options, categories, or probable causes and effects, and stochastic
relations. Upon the results, the formation of the empirical hypothesis contrasted
later with the critical reason, in order to make further rational decisions.
Among
all of these models of database developed under the
Artificial Intelligence theory in Impossible Probability, the database object of this post is the first one: the database in Artificial
Research by Application in a Specific Artificial Intelligence.
In
other posts, I had set out some examples of Artificial Research by
Application in Specific Artificial Intelligence for medical problems or
astronomy. In this one, I will expose some others for different purposes,
focusing on the explanation of how the database in this model of artificial
research could work
The
first example is applied to mineralogy, and the second one is to the physics of
particles.
The
first example would be a model of Specific Artificial Intelligence for
Artificial Research by Application in Mineralogy. Firstly, the database would
be an exact and full taxonomy of all kinds of minerals, rocks, pebbles, and every
single kind of mineral, rock, pebbles, would be a category described in
quantitative terms: from its possible chemical composition, resistance,
hardness, to a definition of the colour in quantitative terms, or any possible
other quality described in quantitative terms able to help in the artificial identification of any rock,
mineral, pebble, from the reality, the synthetic world.
The
description of every mineral, rock, or pebble in the database would be a
category (in this case, the subject). Any mineral, rock, or pebble found in the synthetic world, the reality,
would be a real object.
In
the second stage of Artificial Research by Application, the replication of
the research skills necessary for a scientific investigation, given the
measurements of any real object, in this case, mineral, rock, or pebble collected
from the real world, the Specific Artificial Intelligence could compare the
measurements taken from the real object with the quantitative descriptions of
every category in the database.
As
a result of this comparison, the Artificial Research by Application should give
a list of categories with the highest percentage of similarity between their
quantitative descriptions and the measurements from the real object, categories
as empirical hypotheses about the very nature of the real object taken from reality. Empirical Hypotheses that, if after rational criticism, are true,
become rational hypotheses, forming part, at the same time, universally and provisionally,
of the rational truth.
In
case the real object found in reality, the synthetic world, does not
match with any category in the database, the hypothesis must be formulated under
the idea that this object belongs to a new category that must be included in
the database. This automatic improvement in the database in the Specific
Artificial Intelligence for Artificial Research in mineralogy must be
considered as an auto-replication, improving its own database. One
way, among others, of auto-replication, which means auto-improvement, and
in much more developed models of auto-enhancement.
In
this first example of the database in a Specific Artificial Intelligence for
Artificial Research by Application in mineralogy, as application, the database would be a
collection of categories (in this specific example, each category from the database corresponds to each kind of mineral, rock, pebble, taken from the mineral
taxonomy). As a second stage of replication, upon the measurements of a real object, the comparison of the
empirical measurements from the real object and the quantitative description of
every category included in the database: that or those categories or categories
with the highest percentage of similarity between its quantitative description
with the quantitative measurements from the real object, is the category or are
the categories taken as an empirical hypothesis about the very nature of the real
object.
Using
the same database, within the same application for Artificial Research by
Application, the same Specific Artificial Intelligence could work in thousands
of locations at the same time.
Thousands
of robotic devices specialised in mineralogy, linked all together to the same
Specific Artificial Intelligence, with the same Artificial Research by
Application in mineralogy, using the same database: in thousands of locations
around the Earth, or in space exploration on board of artificial satellites or
spaceships, robotic devices specialised in mineralogy working in thousands of
planets, satellites, asteroids, or comets, or any other celestial body; could
allow us to have a deep knowledge about this specific science beyond Earth
and our human limits, and what is really important, reducing constantly
the margin of error through decisions more and more rationals.
This
Specific Artificial Intelligence, specialised in mineralogy through Artificial
Research by Application, could make thousands of thousands of hypotheses at the
same time, making thousands of thousands of rational contrasts of hypotheses, using
thousands and thousands of robotic devices taking measurements and preparing
samples for further rational contrasts and rational decisions, wherever we
would like to know the mineral structure of geological formations across the
Earth or the entire universe.
And,
at any time that any robotic device finds any real object that does not match anything in the database, it could then be possible the automatic
inclusion of new discoveries in the database, after rational contrast. New
discoveries are available automatically for any other robotic device.
This
would mean that thousands of thousands of auto-replications through
improvements in the database, adding new categories would be able to be possible at
the same time. Under this supposition, the database could be replicated every minute or every second. The increment of knowledge in synthetic sciences and synthetic
academic fields, thanks to the automation of scientific research, would give us
a very powerful tool in order to have incredible knowledge at a very high
speed.
The
second example of the database under such a Specific Artificial Intelligence for
Artificial Research by Application is its possible application to particle
physics.
The
first stage of such a Specific Artificial Intelligence would be the creation of
a database including a list of categories in quantitative terms for each kind
of particle. Secondly, the replication process of all research skills for the
formation and rational contrastation of empirical hypotheses: measuring any
property of the real object, in this case, the measurement of the properties of
any particle, and studying the similarities between empirical measurements from
real particles and the quantitative description of every category. Those
categories with a high level of similarity between them and a particle or group
of particles can be formulated as possible empirical hypotheses about the
nature of a particle or group of particles. Once the hypothesis is formulated,
over a sample of particles according to the hypothesis, the hypothesis is
rationally contrasted, and if it is true, within a rational margin of error, it is
rationally accepted.
In
case the particle found would have not matched with any category in the
database, under the hypothesis that this kind of particle Is a new one,
automatically, the artificial research should proceed to the collection of a
sample of particles like this one in order to contrast, within a margin of
error, that it is a new kind of particle not included yet in the database, so
this new one should be included as a new category in the database, describing
the new category according to the measurements taken from this new kind of particle
as quantitative terms of description. The auto-replication of the database
could be done automatically, something really important if thousands of robotic
devices in thousands of laboratories and accelerators of particles across the
world, or even astrophysics telescopes, would be working with the same database, in the same application of Artificial Research by Application, in the
same Specific Artificial Intelligence.
The
way in which the database would work is the same as the first example in
mineralogy. Given a list of categories in quantitative terms as a database,
the replication of rational processes should allow the Artificial Research by
Application the possibility that, after the measurement of the properties of a
real object, the contrast between the measurements and the categories, and that
or those categories with high similarity could be formulated as a possible
hypothesis in order to contrast them. In case that after the measurements, the
new object does not match with any category, the inclusion of the new object as
a new category considering the measurements as the quantitative description of
this new category. Inclusion that should be done after further contrastations
with samples or particles like this one that could ensure that it is a new kind of
category and not a mistake during the measurement process.
It
is very important every time that some new real object does not match with any
category in the database to discard that this has not happened as a result of a mistake in the measurement process, or for any other reason, such as robotic
problems or mechanical problems in the robotic device which has taken the
measurements. If gathering samples of real objects like the new one, all of
them reveal the same characteristics, within the lowest margin of error, only then,
automatically, the new category should be included in the database as a new category. According to
the general quantitative properties common to all the samples collected from this specific new object.
Using the same Specific Artificial Intelligence for Artificial Research by
Application in any synthetic science or synthetic academic field, thousands of thousands of robotics devices at the same time could work together online. So at the same
time, it would be possible that the same Specific Artificial Intelligence for
Artificial Research by Application could do as much research simultaneously
as robotic devices could work with the same Specific Artificial Intelligence
for Artificial Research in any synthetic science or synthetic academic field.
What that means is that thousands of thousands of hypotheses could be formulated
and rationally contrasted at the same time, making thousands of thousands of
further rational decisions at same time, at the very same time that every
minute, or every second, or even in a inferior period of time, the database in
a Specific Artificial Intelligence for Artificial Research by Application in a
synthetic science or synthetic academic field could be auto-replicated by itself through improvements in the database, including new categories based on new real objects found that would have
not matched with the current ones.
The
importance of the database in Artificial Research by Application is the fact
that the same value that the previous bibliographical revision has in any human
investigation in order to know what kind of advancements have been made in the
field where the investigation is going to be done, in Artificial Research by
Application all this previous work is made directly by the database because all new discoveries are already included automatically in the database, and after
inclusion, ready and available for any other robotic device
working with the same Specific Artificial Intelligence for Artificial Research
by Application in any synthetic science or synthetic academic field where this application is useful.
If
all discoveries in any synthetic science or synthetic academic field are
completely available and ready to be used for other robotic devices, or any
human scientists, or even human scientists could share their new discoveries directly
into the database, followings investigations in the same synthetic field or
synthetic academic field, would be easier to do, because all the new
advancements and discoveries are completely ready and available automatically,
through an automatic system of auto-replication that could improve, or even
enhance, the database, every minute, or even every second.
The
database in any specific synthetic science or synthetic academic field could
be renewed constantly, including continually, without pause, new discoveries and
advancements. The automation of all sciences, starting first with the synthetic
sciences, could bring a permanent and automatic scientific revolution.
Rubén García Pedraza, London 10th of February of 2018
Reviewed 1 August 2019, Madrid
Reviewed 1 August 2019, Madrid
Reviewed 8 August 2023, Madrid
Reviewed 3 May 2025, London, Leytostone
