The
collaboration between Specific Artificial Intelligences for Artificial Researchby Application and Specific Artificial Intelligences for Artificial Research by Deduction, corresponds to the second phase for the construction of the Global Artificial Intelligence, and this collaboration means the collaboration between
Artificial Research by Application and Artificial Research by Deduction, as
well as within the different types of Artificial Research by Application the
possibility of collaboration between Heuristic and Mixed Artificial Research by
Application and Productive Artificial Research by Application.
In
the end, the main objective of the second phase is to start the process of
collaboration between different intelligences as a propedic to initiate in
following phases the most intimate interconnection between different
intelligences, getting ready everything for the union of particular programs and particular applications in the fifth phase, as to experiment what later
will be the integration process at global level.
The
Modelling System, categorical in by Application or deductive in by Deduction,
is in each type of intelligence the first step of the third stage, and the
Modelling System consists of the modelling of real objects to make decisions
upon the models.
Before
starting the second phase of collaboration, is necessarily that previously in
the previous phase, the first phase for the construction of specific intelligences,
by Application or by Deduction, the intelligences which are going to initiate
the experimentation period of the second phase, these intelligences have been
able to move on to the generalization period in the first phase leaving behind
the experimentation period.
Once
the intelligences designated for the first experiments in the collaboration
process between intelligences have been able to move from the experimentation
period in the first phase to the generalisation period in the first phase,
these intelligences are ready for the next period of collaboration between
these intelligences.
The
experiments of collaboration between specific intelligences might be reduced to
two types of experiments:
- Collaboration
between Specific Artificial Intelligence for Artificial Research by Deduction
and Specific Artificial Intelligence for Artificial Research by Application.
This type of experiment could be subdivided into three different types of
sub-experiments: collaboration between by Deduction and Heuristic Artificial
Research by Application, between by Deduction and Productive Artificial
Research by Application, and by Deduction and Mixed Artificial Research by
Application.
-
Collaboration between different types of Specific Artificial Intelligences for
Artificial Research by Application, distinguishing at least these sub-types of
collaboration: between Heuristic and Productive ( some of them destined to experiment how to create Mixed Artificial
Research by Application), between Heuristic and Mixed, between Productive and
Mixed, between heuristic and heuristic, between productive and productive, between Mixed and Mixed.
Having
in mind always that the main difference between by Deduction and by Application
is the fact that in by Deduction the first stage is a matrix of data, by
Application a conceptual database of categories, the second stage by Deduction
consists of the attribution of pure reasons (equations) to sets of data, the
second stage by Application consists of the attribution of categories to real objects.
And
having always in mind that the main difference between Heuristic studies by
Application and Productive studies by Application, is the purpose (heuristic or
productive), and in case that any category does not reach the matching point,
then in heuristic studies is necessary to set up a new attribution (the sample
of measurements from the real object becomes a new category to add to the
conceptual database of categories as a new category), in productive studies is
necessary utilitarian attributions (taking as utilitarian attribution the
category with the highest percentage of similarity accepting a wider margin of error).
The
importance of the experiments of collaboration between heuristic and productive
studies is the possibility to improve Mixed intelligences upon the results.
The
collaboration between intelligences in the second phase could be described in
two different types of collaboration: categorical/factual collaboration (when
by Deduction and by Application can share categories and factors
interchangeably between them), robotic collaboration (when sharing robotic
devices in common).
categorical/factual
collaboration means that existing factors in a matrix, or new factors added to
a matrix due to the possibility to transform rational hypothesis into factors,
these factors could be set up as conceptual categories in conceptual database
of categories, especially when the factors to transform into categories are
factors as options, or a set of factors able to be transformed into a set of discrete categories.
At
any time that any factor from a matrix, as first stage by Deduction, is
suitable for the transformation into a category or set of discrete categories,
because the factor is a factor as option or the system used to take
measurements is possible to be transformed into discrete categories, these
categories can be included in the conceptual database of categories in by
Application.
In
the same way, existing categories or new categories within the conceptual
database of categories, as first stage by Application, suitable for the
transformation into a factor as option or a set of discrete categories working
as factors as options in a matrix, as soon these categories can work as
factors, as options or discrete categories, these factors can be added to a
matrix working by Deduction.
The
possibility to inter-change factors and categories between a specific matrix as the first stage of a Specific Artificial Intelligence by Deduction, and a
conceptual database of categories as the first stage of a Specific Artificial
Intelligence by Application, is the foundation of the starting point for the
collaboration between these two different types of intelligences.
In
the same way, different intelligences by Application: heuristic, productive,
mixed; if these intelligences are able to share categories from their
conceptual databases of categories, this process is the starting point for the
collaboration between these intelligences, and as long as heuristic
intelligences by Application and mixed intelligences by Application could be
able to make new attributions setting up new categories for new real objects,
the possibility of sharing these new categories found out by heuristic or mixed
intelligences by Application with the rest of intelligences, including the
possibility to share new attributions with productive intelligences and
deductive intelligences, in the same way that deductive intelligences can share
new rational hypothesis transformed into factors, as options or discrete
categories, with the rest of intelligences by Application: heuristic, productive,
mixed.
In
the categorical/factual collaboration between different intelligences,
distinguishing as main types of collaboration firstly between Deduction and
Application including its different types (heuristic, productive, mixed),
secondly the collaboration between the
different types of Applications (heuristic, productive, mixed), there are at
least two different processes that will need a deep investigation during the
first period of experimentation in the second phase, these two different
processes are:
-
The criteria to decide what categories/factors are suitable to share between
different intelligences, and when and how to share categories/factors.
-
How to process the inclusion of a new category/factor in the specific
conceptual database or specific matrix.
The
criteria to decide what categories and factors are suitable to share between
specific intelligences, must be defined during the experimentation. It is
difficult beforehand to say which is going to be the mechanism, due to the
huge differences that will have the different intelligences caused by their
different purposes and processes.
In
general what is desirable is the possibility that different intelligences
working on the same specific science, discipline, activity, could share
absolutely all new category or factor, for instance, an Specific Artificial
Intelligence for Heuristic Artificial Research by Application in climate, at
any time that finds out a new type of atmospheric event, the possibility to
make a new attribution transforming the measurements of this new atmospheric
event as a new category, and automatically, once the new category has been set
up in the conceptual database of categories in this intelligence by
Application, the category should be shared as soon as possible with the related
Specific Artificial Intelligence for Artificial Research by Deduction, in order
to transform this category into a factor as option, counting the frequency of
its occurrence, or as a subject measuring the intensity of the event, or the
possibility to transform this category into a set of discrete categories
identifying different levels of intensity for this category within the specific
matrix.
And
vice versa, if a Specific Artificial Intelligence for Artificial Research by
Deduction finds out a new rational hypothesis about how the behaviour of any
atmospheric event has a strong relation of causation with another one, for
instance, some specific hurricanes in the Caribbean sea could be related to
some specific stream of air in the Pacific, the transformation of this event
into a category to be included in the related Specific Artificial Intelligence
for Artificial Research by Application.
In
the end, what should be the final goal of the categorical/factual
collaboration is the possibility that as many factors as possible, or even
absolutely all factors, within a specific matrix, could be transformed into
categories within the conceptual database of categories in the related
intelligences by Application.
The
transformation of factors into categories should depend on what type of factor
is, as an option or as a subject, a factor as option is transformable into a
conceptual category, a factor as a subject could be transformable into a set of
discrete categories, at the end both types of categories, conceptual categories
as options, sets of discrete categories, are part of the conceptual database
of categories.
And
vice versa, the possibility that all category within the conceptual database of
categories as first stage by Application could be transformed into factors in
the related intelligence by Deduction, transforming every conceptual category
as a factor as option, and possible sets of discrete categories as a set of
factors as options or a factor as subject filed with the direct measurements
got by the artificial sensor.
The
process of transformation of all categories into a factor, and all factors into
categories, at the end will facilitate the integration process. By the time the third phase takes place, practically all the factors in the global
matrix have their related category in the Unified Application. As long as all the
categories in the Unified Application and all the factors in the global matrix are interconnected, the more the categories and factors are linked, the easier later the integration process will be, creating the first global brain, able to
manage the world.
This
long journey to the unknown starts with very few steps, as for instance, how
to start the collaboration process in the first stage by Application and by
Deduction.
Regarding the first process expressed before: the criteria to decide what
categories/factors are suitable to share between different intelligences, and when and how to
share categories/factors; the answer should be: all categories
and factors should be shared between intelligences, starting this process sharing related
categories/factors between specific intelligences working on the same specific
science, discipline, activity, ending up this process in the absolute communication
and sharing of absolutely all categories and factors at global level by the
time that the integration process takes place.
Regarding to the second process: how to process the inclusion of a new category/factor in
the specific conceptual database or specific matrix; the answer is: the process
to include any new category or factor coming from another different
intelligence, including the new category or factor within the first stage as
application stage, is considering the inclusion alike any other change in the
application as first stage by Deduction or by Application.
In
the same way that in a specific matrix, there can be changes in the factors due to:
inclusion of new factors due to the transformation of a new rational hypothesis
into a factor (as option or as subject), modification of any existing factor
due to changes in rational hypothesis caused by the solution of contradictions
(modification of rational hypothesis and factors related due to solutions of
contradictions in the rational checks in the deductive Modelling System, in the
quick check or rational adjustments in the deductive Decisional System, in the
supervisions in the deductive Application System), elimination of factors due
to the elimination of rational hypothesis because are not valid any longer.
In
the same way that a specific matrix as the first stage by deduction can have
changes due to: inclusion of new factors, modification of factors due to
modification of the rational hypothesis, elimination of factors due to elimination
of the rational hypothesis not valid any longer.
In
the same way, the possible changes in a specific conceptual database of
categories as the first stage by Application are: inclusion of new categories,
modification of categories, elimination of categories.
The
inclusion of new categories in a conceptual database of categories as first
stage by Application is due to new attributions made by an intelligence itself
as comprehensive knowledge objective auto-replications in heuristic or mixed intelligences,
or new categories due to the collaboration process of any intelligence by
application (heuristic, productive, mixed) with another related intelligence, this
related intelligences could be an intelligence by Application or by Deduction.
If
the new category/ies to include in the conceptual database of categories is as
a result of the collaboration process between an intelligence by Application
and another one by Deduction, the new category/ies to include in the conceptual
database of categories is in fact a factor, as option or as subject, within
the matrix of that other intelligence by Deduction, factor as option or as
subject transformed into a conceptual category or a set of discrete categories.
When
the new category is a result of the collaboration process between two
intelligences by application, the new category to include is in fact a new
attribution made by another different heuristic or mixed application, shared
with that other application, which could be heuristic, productive, or
mixed.
An
example of collaboration between two different applications, one heuristic the
other one heuristic or productive, an heuristic application studying botany,
finds out a new plant whose chemical qualities could be important in medicine,
the new discovery as a new category, from a heuristic application in Botany, is
a new category suitable to be shared with another heuristic or mixed
application working in medical research, for instance, producing medicines.
Once
the first phase developing the first Specific Artificial Intelligences is able
to overcome the experimentation period moving on to the generalization period,
for all those specific intelligences within the generalization period, starts a
new second phase of experimentation, but now researching how these new specific
intelligences can interact each other, whose first initiative should be the
collaboration at first stage, categorical/factual collaboration, a
collaboration that must include all possible relations of collaboration between
intelligences.
Along
with the inclusion of new categories within the conceptual database of
categories as first stage by Application, as a result of new attributions in
heuristic or mixed applications, or due to the collaboration process including
categories/factors coming from other intelligences, by Application or by
Deduction, other changes that the database of categories can have are changes
due to the modification or elimination of categories, and the main responsible
for these changes is the categorical Learning System responsible for the
categorical critiques.
There
are four categorical critiques: the first objective categorical critique
criticizing the attribution of categories and real objects, the second
decisional categorical critique
criticizing the distribution of decisions as attribution of set of decisions to
set of qualities, the third
instructional categorical critique criticizing the attribution of robotic
functions (instructions) to decisions, the fourth robotic critique criticizing
the attribution of robotic devices to robotic functions.
The
main difference between the categorical critiques by Application and the
rational critiques by Deduction, is the fact that categorical critiques are
those critiques to attributional processes depending on the categorical
attribution or attributional process of categories made in the second stage by
Application, so all the critical process has as a root the category attributed
in the second stage by Application, which is going to determine the rest of
processes related to that real object, from the attribution of decisions,
instructions, to the attribution of robotic devices.
While
categorical critiques by Application rest on the categorical attribution, the
attributional process of categories to real objects in the second stage by
Application, rational critiques by Deduction rest on the rational attribution
in the second stage by Deduction where to match sets of data and pure reasons
(equations).
In
the same way that the first rational critique in the second stage by Deduction
has as purpose to identify if wrong
attributions of data to a pure reason, is due to a problem in the formulae of
the pure reason, analysing if the number of rational decisions rejected in the
first rational check in the rational truth, the database of rational
hypothesis, due to further contradictions with other rational hypothesis, are
caused by wrong formulae in a pure reason, so as to make modifications in that
pure reason after the analysis of the common error in all the rational
hypothesis attributed to that pure reason: changing the formulae to fix it, or
adapting the formulae to the type of data normally attributed, making further
amendments in the way in which is modelled later on the deductive Modelling
System.
In
the same way the first objective categorical critique must have as main aim to
identify if the number of wrong attributions to a category is equal to or
greater than a critical reason, and if it is, to analyse the common error in
all these wrong attributions as to make as many modifications as necessary in
the category to fix it or adapt it to the type of real object in which is
normally attributed.
The
responsible for the first rational critique criticising the pure reasons by
Deduction is the deductive Learning System by Deduction, and the responsible
for the first objective categorical critique is the categorical Learning System
by Application. The most important consequence of any change in any category as
proposed by the first objective categorical Learning System is the possibility
to make amendments in the quantitative description of the qualities associated
to a category as it is defined in the conceptual database of categories, so any
change in any quantitative quality in the definition of a category in the
conceptual database by Application, is a change in the conceptual database by
Application, with further consequences in the categorical Modelling System as I
will analyse.
In
addition to new attributions, or new categories/factors due to the
collaboration process, and the possibility of modifications in categories due
to the first objective categorical modification, the third reason for possible
changes in the conceptual database of categories, as the first stage by Application, is due to the elimination of categories.
Reasons
able to justify the elimination of categories could be, for instance that after
the analysis made by the first objective categorical critique, criticizing the
categorical process in the second stage by Application, could find out that two
or more categories are overlapped or are redundant, causing the elimination of
those categories redundant or overlapped. Another reason for the elimination of
a category within the conceptual database of categories, the disappearance from
the real world of all real objects related to some category. For instance, as long
the animal species are disappearing, the elimination of the categories related
to those extinct species from the conceptual database of zoology. The record of
the existence of this species is kept in the records of the attributions, or on
the models recorded, but as long this species will not exist any longer, the
category of this species should be eliminated from the current zoology, due to
is not present any more on the real models of the world, unless these extinct
species could be cloned or recreated by artificial genetics, but in this case,
the new category related to these clones or species created by artificial
genetics are categories related to these clones or artificial species, not the
original one.
In
essence, the changes that a conceptual database of categories can have are
changes due to the inclusion of new categories, the modification of existing categories, and the elimination of categories. The way to process the inclusion of new
categories due to the collaboration process within the conceptual database of
categories, is considering this addition like another change in the conceptual
database of categories, carrying out the same processes for the inclusion of
these categories product of the collaboration process within the conceptual
database of categories alike any other inclusion in heuristic or mixed
intelligences due to comprehensive knowledge objective auto-replications, being
aware that in case of productive applications, because these do not perform new
attributions, what these intelligences will do is to include new categories from
other intelligences applying the same process of inclusion of new categories
within the database alike any other heuristic or mixed application.
Modifications
in the conceptual database of categories as first stage by Application will
have further consequences in the following stages, starting with the second
stage by Application where the new processes of attribution of categories to real
objects, will be done over the changes done in the conceptual database of
categories, what means that, once it has been done the addition of a new
category/factor within the database, or a category has been modified, or a
category has been deleted, in following processes of attribution after these
changes in the database, will be attributional processes according to the
update conceptual database of categories, having following attributional
processes the option to attribute real objects to the new added category, or
the option to attribute real objects to a modified category, not having any
more the option to attribute a deleted category to another real object.
In
the same way, the update of the conceptual database of categories as first
stage by Application, will have further consequences in the third stage by Application,
starting with the consequences that this update will have in the categorical Modelling System, whose
first stage is the conceptual scheme, the second stage the conceptual/logical
sets to make models to locate on the map, and as third stage the distribution
of decisions according to the model on the map based on the categorical
attribution of that category to that object.
In
this series of posts that I have started about the third stage by Application, and
within the third stage by Application analysing firstly the categorical
Modelling System as first step within the third stage, having analysed in
previous posts the specific categorical Modelling System, the first step in the
third stage in the first phase of intelligences by Application, as long as
these intelligences have been consolidated, moving from the first phase to the
second phase of collaboration, what I will analyse is how the
categorical/factual collaboration will affect the first stage and second stage
of the specific categorical Modelling System, and the robotic collaboration the
third stage of the specific categorical Modelling System, being dedicated this
post to how the collaboration process affect the first stage of the specific
categorical Modelling System.
Till
now, what I have explained is how the addition of new categories/factors to the
conceptual database of categories could be understood as an update of this
database, so the treatment of these changes in the database should be
considered in the same way as any other change in the database, what means that
the addition of new categories/factors due to the collaboration process could
be considered as an update of the database in the same way as to any other
addition due to new attributions, or any other change in the database due to
amendments of any category or the elimination of any category.
All
update of the conceptual database of categories will have consequences in the
rest of stages, in the second stage following attributional processes will be
done according to the update, in the third stage further consequences in every
step, and every stage of every step, starting with the conceptual scheme as
first stage of the first step, the categorical Modelling System.
The
conceptual scheme is the first stage of the categorical Modelling System, the
way that it works is as follows: once the second stage by Application has
matched a real object with a category within the database as first stage, the
object is filed in the conceptual scheme by the second stage, being the
conceptual scheme the first stage of the categorical Modelling System, and the
way in which the object is filed in the conceptual scheme is placing the second
stage the object in that place where the attributed category is in the
conceptual scheme.
As
I have explained in the last post, one possibility for the organization of the
conceptual scheme is the organization of the conceptual scheme in a
sub-abstraction system, in which depending on the level of
abstraction/generalization of a category, the category could be located in a
sub-abstraction system moving from the most abstract and general categories to
the most particular or concrete
categories, according to the abstraction level the category could be located in
different levels.
For
instance, the category of single-seat automobile is a category belonging to
another more abstract or general idea, the concept of automobile, which
includes the category of single-seat automobile, and the concept of automobile
is a concept belonging to another more abstract or general idea, such as the
idea of means of transport.
The
organisation of categories from the most abstract/general categories to the
most particular/concrete categories, in fact, is the creation of a sub-setting,
which later will be useful for the creation of sub-settings of decisions
related to sub-settings of categories in the third stage of the categorical
Modelling System, in addition to possible sub-settings of decisions related to
other logical sub-settings and sub-factoring levels according to locations on
the map.
The
organization of the conceptual scheme according to an abstract/general
sub-setting system, in short a sub-abstraction system, classifying the
categories in the conceptual scheme according to their level of abstraction or
general categories, classifying the categories from the most abstract/general
categories to the most particular/concrete, is a sub-abstraction organization
like a Russian Dolls system, which could be completed and combined with the
possibility to set up vectors between categories based on common qualities,
what in the end will form conceptual/logical sets based on common qualities
between categories, what will facilitate the attribution of decision as third
stage of the categorical Modelling System, setting sets of decisions for every
logical/conceptual set with some quality in common, in addition to sets of
decisions per abstract/general set, and sets of decisions according to
locations on the map.
As
long as the setting of sets of decisions associated with different level of
abstraction/general category on the conceptual scheme, sets of decisions
associated to logical/conceptual sets on the conceptual scheme based on common
qualities, sets of decisions associated with locations on the map, the setting
of these sets of decisions will later make the attributional process of
decisions to an object as easy as to analyse by Venn diagram, in what
abstract/general sets, logical/conceptual sets, map sets, could be placed a
real object, as to analyse what sets of decisions are applicable.
In
this process the first stage of the categorical Modelling System as conceptual
scheme, will be very important because all the information related to the
sub-abstraction level, and what logical/conceptual sets are applicable, is
information already gathered in the conceptual scheme, according to where the
real object was placed on the conceptual scheme based on the categorical
attribution made in the second stage by Application.
As
soon as the second stage by Application files a real object in the place of the conceptual
scheme corresponding to the attributed category, the sub-abstraction level
attributed to that real object corresponds to the sub-abstraction level of that
place where the category is in the conceptual scheme.
In
addition to the sub-abstraction level, every place of every category in the
conceptual scheme has as many vectors as necessary linking this place of this
category with any other placer of any other category having in common some
quality, every single link connecting one category to another single category
is a single vector, every single vector has a weight of importance, and in the
end, the total number of vectors of any place of any category will be equal to
the total number of categories which have some connection with that place of that
category, having every single link assigned weight of importance.
The
vector weight of a category is equal to the total number of vectors starting
from this place, connecting this place to other places. Every vector only links
two places that have in common some quality of their categories, not needing all the
vectors to have the same quality in common; it could be different qualities.
The
weight of importance per average of a category is equal to the sum of the
weight of importance of all the vectors in this category divided by the
number of vectors.
In
full attributions, unless the full attribution was made having 100% of
percentage of similarity between the real object and the category, if the full
attribution did not reach the 100% of similarity, but was made within the
rational margin of error, due to the margin of error accepted, the real object
may not share the total number of vectors of the category attributed, needing
to compare if the vector weight of this attribution is equal to or greater than
a critical reason to be accepted, in full attributions, otherwise it should be
rejected or accepted as utilitarian attribution accepting a wider margin of
error.
The
greater the margin of error accepted is, the wider the difference between the
vectors associated with a category and the vectors associated with a real object, due to external vectors.
External
vectors are all the vectors staring from a real object, linking the real object
with other places in the conceptual schemes not linked to the place where the
real object has been placed, because this link is associated with a quality not present in the category attributed to
that object, being a quality in the real object explainable for the margin of
error. The wider the margin of error is, the larger the number of external
vectors is.
As
long a real object has more and more external vectors, there might be more and
more discrepancies between the normal set of decisions related to the category
attributed, and the sets of decisions related to the external vectors, what
could cause further contradictions, to be found out in the fifth rational
check, when checking that the attribution of decisions to real objects is done
correctly within a margin of error, as to send the decisions to the categorical
Decisional System.
Meantime,
the first rational check in the conceptual database of categories, could be
able to identify any contradiction between the real object placed in this
category, and any other object placed in this category, in order to analyse if
within a rational error, critical reason, the real object keeps the harmony
respect to the rest of the real objects placed in this category.
Later
on the second rational check in the second stage checking the
logical/conceptual sets assigned to this object, should be able to find any
contradiction due to contradictions between logical/conceptual sets related to
internal vectors, logical/conceptual sets where the object could be included
due to the category attributed, and external vectors, logical/conceptual sets due
to the acceptance of the rational margin of error, if full attribution, or due
to the acceptance of a wider margin of error in case of utilitarian
attributions.
In
the categorical/factual collaboration, sharing categories/factors, is necessary
to understand that when a productive intelligence by Application borrows any
new category or factor, from another different intelligence by Application or
Deduction, the new category/factor could be used by the productive intelligence
to make utilitarian attributions with wider margin of error if necessary, for
instance, the creation of a new category of vegetable by artificial genetics,
if trying the cultivation of this new category in another moon or planet, the
attribution was made assuming a wider margin of error, utilitarian attribution,
what this intelligence is doing is to use a new category obtained by the
collaboration process to make utilitarian attributions.
In
any intelligence by Application: heuristic, productive, mixed; as soon a new
category/factor is included in the conceptual database of categories as first
stage by Application, automatically the new category/factor must be placed in
the conceptual scheme as first stage of the categorical Modelling System,
setting automatically all possible vector linking the place of this category in
the conceptual scheme with other places of other categories in the conceptual
scheme, measuring the weight of the importance of every vector, the important
of every single link between this new category/factor and the other category
involved in the single link, having as weigh of importance per average the
average of the sum of the weights of importance divided between the vector
weight, the number of vectors.
For instance, in a family tree, the category
related to grandfathers and grandmothers could have a wider level of
abstraction or generalisation, as these concepts are able to include within the
concepts of father, mother, son, daughter, grandson, granddaughter. This means
that the concept of grandfather and the concept of grandmother have a larger
vector number than the category of son or the category of daughter.
As a son I only have vectors with my father, mother, siblings, grandfather, and
grandmother. But my grandfather has vectors with his own grandfather and his own
grandmother, his own father and his own mother, his wife, his siblings, his
sons and daughters, his grandsons and granddaughters.
Because
the vector weight of the category grandfather or grandmother is higher than the
vector weight of son or daughter, it is possible to say that the level of
abstraction/general meaning of the category of grandfather or grandmother is
higher than the level of abstraction/general meaning of the category of son or
daughter.
The
larger the vector weight is, the more abstract/general the meaning of a
category is. This means that when a new category/factor is added to the
conceptual database of categories as the first stage by Application, automatically
should be set up the place for this new category/factor in the conceptual
scheme as first stage of the categorical Modelling System, as first step in the
third stage by Application.
The
way to place a new category/factor in the conceptual scheme should be made
automatically after the analysis of the vector weight of the new category/factor,
the larger the vector weight is, the more abstract/general the place for this
category should be placed in the conceptual scheme, being the vector weight the
possible number of possible individual links connecting this category to other
single categories, every single connection of this category to another single
category is a vector, and the larger the number of single connection is, much bigger the vector weight is, so the more abstract/general the category is
to be placed in the conceptual scheme.
In
correlation with the vector weight, it is necessary to set up the weight of
importance of every vector, according to some criteria. If the criteria is the weight of the information, in a family tree the foundation of all the information is
genetics, so the higher is the vector number the larger the information is, for
instance, in a family tree not only the grandfather or grandmother will have
the larger vector number, at the same time the grandfather and the grandmother
are the origin of all the genetic information of that family, the genetic
information between the father and the grandfather will be more similar than
the genetic information shared between the grandfather and the son, so the
weight of importance of the vector linking the father and the grandfather should
have more weight than the vector linking the son and the father. And if the
criteria is information, the vector linking the father and the grandfather is
not so important as the vector linking the father and the grandmother, and the
vector linking the mother and the grandmother should be the largest vector in
importance, and the vector linking the mother and the daughter, if less
important than the vector mother-grandmother, more important than the vector
son-mother, unless the vector son-mother should be more important than the
vector son-father.
In
addition to the automatic creation of the place for every new category in the
conceptual scheme, is possible to set up further logical/conceptual sets, as
for instance, if we have to set up the conceptual scheme of the mammals, not
only we have to make a family tree of the mammals according to the different
animal species among the mammals, is necessary to set up logical/conceptual
links between different qualities of the mammals, as for instance, what mammals
only eat vegetables, or only eat meat, or vegetables and meat, what mammals are
adapted to very low temperatures or very high temperatures, what mammals are predators
and what mammals live in flocks, sets of mammals for continent, clime, genetics
etc…
At
any time that a new category/factor is added to the conceptual database of
categories as first stage by Application, automatically the category/factor
must be placed in the conceptual scheme as first stage of the categorical
Modelling System, placing the new category/factor in the corresponding
abstract/general level within the scheme according to the vector number,
calculating the weight of importance of every vector, obtaining the weight of
importance per average, and addition to include automatically the new
category/factor within those other sets of categories due to common qualities.
The
sets used to attribute the right place in the conceptual scheme according to
vector weight will be called conceptual/logical sets; the sets where a category
could be included due to common qualities will be called only quality sets.
For
instance, in a taxonomy of zoology, humans belong to the conceptual/logical
set of the hominids, but in terms of intelligence, humans and dolphins could be
set up in the same quality set related to high animal intelligence. Or, for
instance, humans and elephants could be included in the quality set related to
high animal memory.
While
Dolphins and humans belong to the quality set of high animal intelligence, and
elephants and humans are included in the set of high animal memory, not all
the hominids can be included in these sets. Although some chimpanzees and
other monkeys, along with humans, could be included in the quality set of animals
able to use tools.
The
taxonomy of zoology, like the family tree, draws a wide range of
conceptual/logical relations, based on vector weight, and every vector itself
linking two different species of animals like two different places in the
scheme, has different weight of importance, in fact is possible that some
species of rats as possible origin of mammals after the disappearance of the
dinosaurs, could have the most important weight, due to the high percentage of
similarity between our genetics and the genetics of that species of rats, but
beyond the importance due to the information carried on the category, and the vector
number of the category, the categorical analysis of every single new
category/factor included in the conceptual scheme, must carry out an exhaustive
analysis of all the common qualities between this category and any other one as
to include the new category/factor within the corresponding quality set of that
common quality.
At
the end of the third stage, the decision will depend on the analysis of
logical/conceptual sets, quality sets, sets to be associated with sets of
decisions, and sets of decisions associated with the location of the model in
the map.
Once
the new category/factor has been included in the conceptual database of
categories as first stage by Application, and the new category/factor has been
placed in the conceptual scheme as first stage of the categorical Modelling
System, according to vector weight assigning to every single vector its weight
of importance according to weight of information comprehended within the
vector, the second stage by Application should be able to match real objects
corresponding to this new category/factor with this new category/factor, and
able to place the new real object matched to this new category/factor within
the place corresponding to this new category/factor within the conceptual
scheme.
As
soon as the real object has been filed by the second stage in the place of this
new category/factor, the first stage of the Modelling System should carry out
the first categorical check.
The
first categorical check of the first
object filed in the new place for a new category/factor will not carry out any
check about the harmony between the sample of measurements of this object and
any other already placed in this category, because there is no other object
placed before in that place, because this new object placed in this new
category, is the first one to be placed in the new place for the new category
in the conceptual scheme.
Only
applicable for the first object filed in a new place of a new category in the
conceptual scheme, the first categorical check only will ensure that within the
margin of error accepted, the weight of the internal vectors of this object,
understanding for internal vectors: those possible vectors linking the object
with any other category in the conceptual scheme as a result to link qualities
of this object, in harmony with the qualities of the category attributed,
linking the object with categories which already have links with the category
attributed. In other words, the internal vectors of a real object attributed to
a category in the conceptual scheme are vectors shared between the real object
and the category, linking the category and the real object with those other
categories with conceptual/logical relations with that category and that
vector. In opposition external vectors of an object are vectors linking that
object with some categories, being vectors not shared by the category
attributed to that object, because the qualities in which this relations are
based, are not qualities within the category, but only related to the object
belonging to the margin of error in which the object was attributed to that
category.
If
the vector weight of internal vectors of the object reaches a percentage of
similarity with respect to the vector weight of the category, equal to or greater
than a critical reason, the object is accepted within the category in the
conceptual scheme, otherwise should be rejected, unless it is accepted as a
utilitarian attribution. This criticism within the first categorical check will be
called vector critic, the vector critic consists of the comparison of the
percentage of similarity between the vector weight of the category in the
conceptual scheme, and the vector weight of internal vectors of an object
attributed to that category, understanding for internal vectors all those
vectors of the object, linking the
object with other categories, in common with the vectors of the category
attributed linking that category with other ones in the conceptual scheme. The
vectors to include in the vector critic as part of the first categorical check are conceptual/logical vectors as well as quality vectors. All vectors, either
conceptual/logical or quality, should be included in the criticism of the vector; at the
end, the essence of the meaning of a category is given in the conceptual
database by the quantification of the qualities, in the conceptual scheme by
all the vectors associated with it.
In
addition to the contrast of the vector weight, another contrast to do in the first
categorical check is to contrast if the weight of importance per average of the
internal vectors of the object is similar to the weight of importance per
average of the category, if the importance of both is very similar, according
to the margin of error, the object is accepted, otherwise should be rejected
unless is accepted as an utilitarian attribution.
The
vector critic and the importance critic should be accompanied by a third
critic, as long as this place for this new category, is filed with more
objects, the third critic in this check should be the harmony critic,
criticizing if the samples of measurements of every new object added to this
new place attributed to this new category, keep rational levels of harmony
within a margin of error, in other words, there is a high similarity, within a
critical reason, between the measurements of every new object added to this
place in the conceptual scheme, and all the objects added before to this place,
ensuring that per average the quantitative qualities of all new object added to
this category are within the quantitative qualities of the objects filed in this pace in the conceptual scheme.
In
this way, the first categorical check in the conceptual scheme should be:
-
The vector critic: the number of vectors, as links between the category
attributed and other categories, between the real object and other categories,
shared by the object and the category, is within the critical reason,
understanding these shared vectors as internal vectors. Any other external
relation not included in the vectors of the category, between the object and
any other category not associated with the category attributed, is considered an external vector.
-
The importance critique: the importance per average of the internal vectors of
the object is within the critical reason in harmony with the weight of
importance of the vectors of the category attributed.
-
The harmony critique: the quantitative qualities shared by the object with the
category, in general, are, within the critical reason, in harmony with the
average of quantitative qualities of all the objects filed in this category.
The
first categorical check consisting of the vector critic, the importance critic,
and the harmony critic, will ensure that the object has been placed in the
right category in the conceptual scheme upon the attribution made in the second
stage by Application, but at any time will check the conceptual/logical sets,
or the quality sets, is in the second categorical check in the second stage of
the categorical Modelling System, where the relation between the object and the
sets will be analysed, checking possible contradictions between
logical/conceptual sets and quality sets, in order to make later the conceptual
model without any contradiction, distinguishing between single model and
comprehensive model, but even the possibility to distinguish between, single or
comprehensive, evolution model and, single or comprehensive, predictive model,
and once the models are done, checking in the third categorical check the
absence of contradictions, is time to locate the models on the map carrying out
the fourth categorical check. But all these processes are part of the second
stage of the categorical Modelling System, where in the next post will be
analysed the effects of the categorical/factual collaboration in the models.
In
this post, analysing the categorical/factual collaboration in the first stage
of the categorical Modelling System, what is important to highlight is the fact
that in the end, the way to process any new category/factor as a consequence of
the collaboration process in the second phase, is in the same way as any other
update due inclusion, modification, elimination, of any category.
In
the same way that new attributions are going to set up new categories in the
conceptual database of categories as first stage in heuristic or mixed
applications, in the same way the additions of new categories due to the
collaboration process should be treated in the same way, adding the shared category in the
conceptual database as first stage by Application, and creating in the
conceptual scheme as first stage of the categorical Modelling System the place
where to locate this new category, place located in the conceptual scheme in
the right abstraction/general level, according to the vector weight, assigning
the weight of importance to every vector according to information comprehended
in the vector.
Once
the new category has been added to the conceptual database and set up the place
for the new category in the conceptual scheme, once the second stage by
Application starts attributing this category to any object, as soon the object
is filed in the place of this category in the conceptual scheme is possible to
carry out the vector critic, the importance critic, and the harmony critic
(except for the first object added to this place).
The
addition of new attributions or new categories/factors due to the collaboration
process is only an update of the conceptual database of categories as first
stage by Application, in the same way that other update is the modification of
a category as a result of the first objective categorical critique carried
out by the categorical Learning System, or the elimination of any category due
to this category is not going to exist anymore.
At
any time that there is an update in the conceptual database, there must be an update on the conceptual scheme. If the addition of new attributions or new
categories/factors due to the collaboration process, means the creation of new
places in the conceptual scheme, and the creation of new vectors between this
new category and the rest of existing categories, in the same way at any time
that a category is modified in the conceptual database, modification made by
the categorical Learning System based on the first categorical critique, if
this modification means the incorporation of new qualities for the category
modified, or this modification means the elimination of some qualities in the
category modified, or this modification means the modification of the
quantitative qualities of the category modified, in harmony with the
modifications of the category in the database: inclusion or elimination of
qualities in the category, or modification of the quantities associated with
the qualities of the category; in harmony with this modifications in the database,
should be done the modifications in the conceptual scheme.
If
the modification of a category in the database means the inclusion of new
qualities, associated with some quantities of quality, within the category
modified, then the corresponding modification should be reflected in the
conceptual scheme updating the vector weight according to the consequences of
the addition of the new qualities, what it could mean a greater vector number
as to have more level of abstraction as to upgrade to an upper level of
abstraction in the conceptual scheme, in addition to the new weight of
importance for this category as a result to include the new vectors, measuring
the importance of every new vector according to the amount of information comprehended
in the vector.
As
a result, not only the category modified can change the vector weight and the
weight of importance, information weight, as a result, all the categories not
linked before to his category, but now linked to this category, will have a new
vector, what it can make further changes in their respective vector weight, in
addition to changes in the average weight of importance, information weight, as
to make these other categories upgrade as well.
Any
modification in any category, as long as it can affect other categories, could
provoke a chain reaction of changes, changing not only the category modified,
but all those categories able to have new vectors with the category modified.
In
the same way but in opposite direction, if a category is deleted, the place for
this category is not going to exist anymore, and all the vectors associated
between the deleted vector and any other one, are going to be deleted as well,
so the categories affected by the elimination of that category, will have a reduce vector weight, having a change in
the weight of their importance, a change in their information weight.
If
a category is updated because of modifications in the quantitative qualities,
this change could create new vectors according to the new quantities of the
quality, or eliminate previous vectors associated with the old quantity of this
quality, or changes in the weight of information in some vectors if the
information was related to the quantity of that quality.
In
any case, what is important to highlight is the fact that the addition of new
categories/factors due to the collaboration between by Application and by
Deduction, or the collaboration between different intelligences by Application, is a process to be considered as an update of the conceptual database
of categories as first stage by Application, demanding the corresponding update
of the conceptual scheme as first stage of the categorical Modelling System,
whose consequences in the second and third stage in the categorical Modelling
System could be analysed as changes due to an update of the database, what
in fact it is, because at the end of the process, all databases of categories
and matrices should be altogether in the same application, the first stage of
the integrated Global Artificial Intelligence.
Rubén García Pedraza, 26 January 2020, London
Reviewed 18 May 2025, London, Leytostone
