The
Unified Application is the fourth phase in the proposal of Impossible Probability for the construction of the Global Artificial Intelligence, and
this phase consists of the union of as many Specific Artificial Intelligences for Artificial Research by Application, including Heuristic, Productive or
Mixed Artificial Researches by Application, what means that in the first stage
of the Unified Application the unified conceptual database of categories consists
of the union of all the specific conceptual databases of categories from all
the former Specific Artificial Intelligences by Application absorbed by the
Unified Application, unified conceptual database of categories which is going
to be the base for the attributional process in the second stage of Artificial Research in the Unified Application when matching any real object with the
corresponding category in the unified database of categories, attributional
process organized following the same criteria as in the standardized GlobalArtificial Intelligence, but now applied to the Artificial Research by
Application, former Specific Artificial Intelligences for Artificial Research
by Application, whose specific database has been joined to the Unified
Application, will be transform into specific applications within the second stage
of the Unified Application, specific applications whose purpose is to read/track
the world matching any real object within the limits of its specific science,
discipline, activity, with the corresponding category in the unified database of
categories, at the same time that in the second stage of the Unified
Application, the Unified Application as global Artificial Research by
Application, make attributions of global phenomena or processes within global
structures with the corresponding categories in the unified database of
categories.
Depending
on what type of attribution was done in the second stage: heuristic,
productive, mixed; if heuristis the third stage will consist only on
comprehensive knowledge objective auto-replications (in addition to possible
explicative knowledge objective auto-replication within the collaboration with the standardized Global Artificial Intelligence), creating new categories
within the unified database, or making as many modifications in the categories already
existing in the unified database, or eliminating categories in the unified
database, and communicating these changes to the standardized Global Artificial Intelligence due to possible relations of collaborations on these affected
categories, in order to transform them into factors, the modification of
already existing factors, or the elimination of factors, plus changes in the
deductive plan: possible changes in models and projects.
If
the attributional process in the second stage of the Unified Application has a
productive purpose in that case is treated in the third stage of the Unified Application following these three steps: as first step the unified categorical Modelling System, the unified categorical Decisional System, and the unified categorical Application
System as outer system (in distinction to the categorical Artificial
Engineering as inner system as well by Application, responsible for the robotic
subjective auto-replications).
In
the end, both types of objective auto-replications: in heuristic researches comprehensive
and possible explicative in the collaboration process;
will be evaluated by the unified categorical Learning System as responsible for
the artificial psychological subjective auto-replications.
For
productive researches the third stage of the Unified Application is structured
in three steps starting the first one with the unified categorical Modelling
System, which in turn is as well subdivided in three stages: 1) first stage of
the unified categorical Modelling System is the unified conceptual scheme, 2) second
stage of the unified categorical Modelling System subdivided as well in three
sub-stages 2.1) the logical analysis of set/vectors involved in any object,
2.2) to make a single categorical evolutionary and prediction models of the
object to include in a more comprehensive evolutionary and prediction models
with the rest of models from all the real objects of the represented world,
2.3) comprehensive categorical evolutionary and prediction models to be
included on the comprehensive categorical evolutionary and prediction maps, the
maps of the world representing how the objects according their attributed
categories and models are going to evolve in the represented world, 3) to make
decisions according to the categorical world evolution and prediction.
Among
all the stages and sub-stages comprehended within the unified categorical
Modelling System in this post I will analyse the first stage of unified
conceptual scheme, and the contents that I will analyse are related to: how to
assemble specific conceptual schemes from former specific intelligences by
application to make a unified conceptual scheme, how to create new places in
the unified scheme to locate any new possible category product of heuristic and
mixed researches, the first categorical check in the unified conceptual scheme,
possible ways of category/factor collaboration between the unified categorical
scheme as first stage in the unified categorical Modelling System and the
database of rational hypothesis as first stage of the standardized deductive Modelling System, and later on, ways of collaboration between the unified
categorical conceptual scheme and particular programs, particular applications, particular programs for particular applications or particular applications for particular programs.
The
first issue to bring here is about the assemble process of different specific
conceptual schemes to make a unified conceptual scheme as first stage of the
unified categorical Modelling System.
Till
now what we have is a collection of Specific Artificial Intelligences by
Application, some of them working for heuristic researches, others for
productive or mixed researches, and all of them have their own specific conceptual scheme. Regardless of their purpose, heuristic, productive, or mixed, every
specific conceptual scheme is a logic conceptual organization about some
specific science, discipline, activity, which must be included in the unified
conceptual scheme as global conceptual representation of the organization of
the categorical world, in fact the unified conceptual scheme represents the
global conceptual interconnections between categories coming up from different
spheres of knowledge, different fields of wisdom interacting as only one unity,
alike a conceptual network where any barrier between different fields of knowledge
are in fact artificial barriers.
Under
this approach the possibility to créate only one conceptual network as first
stage of the unified categorical Modelling System, will make easier that later on the models could be possible the
representation of relations between objects of the real world belonging to
different sciences, disciplines, and activities, recognising the reality like a
multiple interaction in which at some point and some grade, everything is
connecting to everything, in fact, this would be the conceptual representation
of Mother, Gaia, a network where everything works as only one spirit, one soul,
one intelligence.
The
most difficult thing in this task to assemble specific conceptual schemes
coming up from different sciences, disciplines, and activities, is the
recognition of every category not only as a dot within a single specific
conceptual scheme, but like a communication node between different specific
conceptual schemes.
In
the same way that the category corresponding to lentils could be placed within
the logic of the conceptual scheme of botany, like the seeds of the plant which
produces this seeds, the category lentils could have multiples set/vectors in
the specific conceptual scheme related to food, health (like a legume with high
level or iron, for instance), for the treatment of some health problems like
anemia, in addition to every set/vector of every recipe with lentils, the
set/vector of lentils soup, and every sub-category within the category lentils
soup, from the Spanish lentils soup to the Lebanese lentils soup, the set/vector
or lentils salad and all the variety of lentils salads.
The
logical/conceptual set/vector linking lentils in the logic of the conceptual
scheme of botany, is a vector connecting lentils with the conceptual scheme, in
the same way that the logical/conceptual set/vectors linking lentils with some
chemical components, is another set of vectors in the base of the chemical
definition of the lentils, in the same way that the possible medical use of
lentils, or the use of lentils in alimentation, are another set of vectors
regarding to lentils that must be represented in the unified conceptual scheme.
While
in the specific conceptual scheme of botany, the only logical/conceptual
set/vectors with high importance, information in botany, were all those related
to the relation of the seeds of lentils in the family tree of the plants, and
any other possible set/vector was not considered as logical/conceptual, being
only quality sets, for instance in a conceptual scheme of botany any possible
medical or alimentary use of the seeds of lentils are only quality set/vectors
of the lentils, but not providing really important information in the logic of
the family tree of the plants, instead now in the unified conceptual scheme as
long as lentils are connected at the same time with the logical organization of
the conceptual scheme of botany, chemistry, health, medicine, alimentation,
recipes, all these connections are providing a wider information about the role
of the lentils in botany, health, medicine, chemistry, alimentation, recipes,
etc.
In
this way the role that lentils are going to play in the unified conceptual
scheme, is the role of a communication node between all the specific conceptual
schemes regarding to different sciences (chemistry, medicine, etc…),
disciplines (alimentation, etc.), activities (recipes, etc.) in which lentils
play some specific role, and are integrated within the unified conceptual
scheme.
In
the same way that the category lentils are going to play a role of
communication node, every single category of every specific conceptual scheme
joined to the unified conceptual scheme, now are going to play a role of
communication node between their formers specific conceptual schemes and all
the specific conceptual schemes integrated in the unified conceptual scheme,
what means that as of now one of the roles of the categories is to play a role
of communication node in addition to the traditional role in the description of
the reality when matching categories and real objects.
In
the case of the lentils, due to the great variety of lentils, not only the
category of lentils is going to play a role of communication node between different
specific conceptual schemes already joined to the unified conceptual scheme,
because as long as the category of lentils comprehends a great variety of types
of lentils, every different type of lentils is in fact a sub-category of
lentils within the category of lentils, and due to the differences between the
different types of lentils, as for instance differences regarding to the
percentage of some chemical components in their chemical composition, these
differences between the different types of lentils, within the great variety of
lentils, is going to make possible settle different sub-categories of lentils
within the category of lentils, and every sub-category of lentils could even
have different set/vectors compared with the rest of lentils sub-categories,
according to their chemical differences and possible use in medicine, health,
alimentation, etc…
When
any specific application within the second stage by Application matches some
seeds found while reading/tracking the reality, or even if the Unified
Application as global application matches some seeds found while
reading/tracking any space, if matching these seeds with some sub-category of
lentils, if this attributional process is done for a Productive or Mixed
Artificial Research, and the object identified as some sub-category of lentils is
placed in the the corresponding sub-category of lentils in the unified conceptual
scheme, as first stage of the unified categorical Modelling System, as soon
this real object defined as sub-category of lentils is placed in the
sub-category of lentils in the conceptual scheme, what the first categorical
check does in order to confirm the absence of contradiction between the object
and the sub-category attributed, is to read again what are the quantitative qualities
of that object, given in the sample of measurements used for the attributional
process, observing what possible set/vectors can be attributed to the
quantitative qualities of these object.
If
the attribution in the second stage by Application was done within an
acceptable margin of error as full attribution, the contradiction between the
set/vectors of the object according to its quantitative qualities and the
set/vectors of the category attributed, is a contradiction within a margin of
error, otherwise, if the attribution was a utilitarian attribution in that case
the contradiction with set/vectors of that object compared to the set of
vectors of the category within the conceptual scheme will be wider.
The
first categorical check not only checks if the possible set of vectors of the
real object within the conceptual scheme according to its quantitative
qualities, is or is not within a margin of error in harmony with the set of
vectors of the category attributed in the conceptual scheme, because the first
categorical check must analyse that per average the quantitative qualities of
the object attributed to that category are in harmony within a margin of error
with the quantitative qualities of the rest of objects attributed to that
category, and still on the plan.
In
brief, as I had stated in the previous post “Collaboration between categorical and deductive specific Modelling System, third stage”, the possible checks that
I would propose for the first categorical check could be synthesised as
follows:
-
Conceptual/logical vector critic, criticizing only the percentage of
conceptual/logical vector weight shared between a real object and a category. Home
many sets of conceptual/logical vectors of the category are shared by the real object.
-
Absolute vector critic, criticizing the total percentage of weight (including
conceptual/logical vectors and quality vectors) between a real object and a category. For
instance, a quality set of vectors for every
sub-category of lentils could be the colour, such as orange lentils or green
lentils, according to this quality set these categories could be placed in different
quality sets such as the set of seeds with orange colour, the set of seeds with
green colour, or another quality set could be the size of the seeds, grouping every
sub-category of lentils with their corresponding set of seeds according to
their size. The absolute vector critic will take on account for the critique
not only the total of logical/conceptual sets, but quality sets.
-
Conceptual/logical gross importance critic, criticizing what percentage of all
the conceptual/logical information of a category is shared with a real object.
Here what is important is to determine how to measure the information level of
every conceptual/logical vector, information level that must be set up according to the importance
of every vector in their respective family tree, and the importance of the
vector in the definition of every category or sub-category. Evidently the
vectors linking the set of every chemical component within the lentils, as part
of the chemical definition of a lentil, should have more importance rather than
the different varieties of salads with lentils within every sub-type of lentils.
-
Conceptual/logical average importance critic, comparing the level of similarity
between the conceptual/logical information vector per average of an object and the conceptual/logical information vector per average of the category attributed .
-
Absolute gross importance critic, criticizing what percentage of all the
information ( inlcuding conceptual/logical information weight and quality information weight) of a category is shared with a
real object.
-
Absolute average importance critic, comparing the level of similarity between
all the information (conceptual/logical and quality) per average between all
the factors of a category with a real object
-
Harmony critic, criticizing that per average the quantitative qualities of a
category is within a margin of error (except for utilitarian attributions) in
harmony with the quantitative qualities of the rest of objects attributed to that
category or sub-category in the unified conceptual scheme.
In
aspects like the importance of every vector as information weight, or the
vector weigh as number of vectors for every category, and quality sets like
those sets not having logical/conceptual value in any specific conceptual
scheme but provide some information about links between an object and other
objects, from different family trees even, but having the same quality, these
three aspects in general: vector weight (number of vectors), quality sets (not
having real logical/conceptual value, only quality value), importance of every
vector (information weigh per vector); are aspects that should not represent
any problem for those categories already included in specific conceptual
schemes, which being added to the unified conceptual scheme, the vector weight,
importance or information weigh, and number of quality sets, are aspects
already settle in every category coming up from former specific conceptual
schemes, and what is going to happen is the fact that these already existing
categories now transformed into communication nodes are going to transfer from
the specific to the unified conceptual scheme the previous specific vector weight,
so now the unified vector weight of every communication node is the result of
the addition of every vector weight that this category could have in different
specific conceptual schemes, in the same way that the importance weight in
total of a category transformed into a communication node is the result of the
addition of the information weight of all the vectors linking the category to
other categories or sets, transferring in the same way the categories all the
quality sets.
In
the transfer of quality sets, what is going to happen is that some quality sets
in some former specific conceptual schemes, being even at that time quality sets
for those specific conceptual schemes, but logical/conceptual sets/vectors in
other different conceptual schemes, some former set qualities are going to be
now considered logical/conceptual sets/vectors linking the same category to
different specific conceptual schemes within the unified conceptual scheme.
While
some others quality sets, not having any logical relation in any specific
conceptual scheme, are going to remain as set qualities, as for instance, a
quality set of the orange lentils is the quality set of all the seeds with
orange colour. Or quality sets within every sub-category of lentils, according
to the size per average of every sub-category of lentils, adding as quality set
of every sub-category of lentils the quality set of their size per average.
While
lentils and rice have no logical/conceptual relation in the family tree of
plants, a possible quality set division where is possible to locate lentils and
seeds, is according to the division in discrete categories of sizes of seeds.
The
three aspects above mentioned: vector weight (number of vectors), importance
weight (information weight), quality sets; as I have said, will not suppose any
problem for the already existing categories in former specific conceptual
schemes, joined to the unified conceptual scheme, joining in the unifying
process all these three aspects in the category as a shared category by the
different former specific conceptual schemes now gather in the unified
conceptual scheme.
What
it is going to suppose a challenge, is the automation process of locating new
places in the conceptual scheme, assessing the vector weight and the
information weight of every vector, setting not only logical/conceptual sets/vectors
for every category as communication node with the different specific conceptual
schemes added to the unified conceptual scheme, but setting as well all those
quality sets of every new category as a product of Heuristic or Mixed
Artificial Research in the second stage by application, when doing new
attributions, or as a result of the category/factor collaboration between the
Unified Application and the standardized Global Artificial Intelligence or any
other particular program or particular application or particular program for particular
application or particular application for particular program.
In
any case, when as a result of the collaboration between heuristic and
productive researches, or mixed researches, or collaboration between by
Application and by Deduction, under any of these possible sceneries is
necessary to settle a new place in the unified conceptual scheme, in the same
way that due to any possible collaboration process is necessary to make
modifications in any category or is necessary the deletion of any category, all
these changes as a whole must be considered as an update of the unified
conceptual scheme as first stage of the unified categorical Modelling System, in
the same way that any update on any category as it must be reflected in the
conceptual database of categories as first stage by Application, is an update
too on the first stage of the Unified Application, the unified database.
In
the specific case of updates due to new attributions, or category/factor
collaboration, in addition to place the new category in the unified conceptual
database of categories as first stage of the Unified Application, to make
attributions in the second stage according to the new update, is necessary to
place the category in the unified conceptual scheme as first stage of the unified
categorical Modelling System.
When
placing a new category in the unified categorical Modelling System, what is
necessary to do is the analysis of the quantitative qualities of the category,
in order to match every quantitative quality with the corresponding
logical/conceptual set/vectors and quality vectors, understanding that this
category not only has a role in the attributional process of real objects and
categories in the second stage of the Unified Application, because now in the
first stage of the unified categorical Modelling System the new category has as
role as well the role of a communication node, and now the new category as
communication node within the unified conceptual scheme must serve as a node to
link different conceptual schemes through different sets/vectors.
The
analysis of the quantitative qualities
of a category must provide the elements of information to place the category
within the unified conceptual scheme, linking the new category with any other
category from any other specific conceptual scheme within the unified
conceptual scheme as long as these links are based on common qualities, common
qualities which can be conceptual/logical sets if these qualities respond to
the logic of the conceptual scheme, otherwise these quality sets are not upgraded
to the level of logical/conceptual set/vectors
remaining as quality sets.
The
distinction between a quality set able to be upgraded as logical/conceptual
set/vector due to the logic connections of this quality in a conceptual scheme,
respect to be only a quality set, for instance, the fact that lemons are yellow
is only a quality set to be included in the set of fruit with yellow colour,
like bananas, is only a quality set whose importance, information weight, is
not so important as the information about the chemical composition of the
lemons, health properties of lemons, or the vectors linking the lemon in the
family tree of citrus.
The
quality set informing that lemon belongs to the set of fruit whose colour is yellow
is a quality set not as important as the vector linking the lemon with the
citrus, or the chemical composition of the lemon, or medical use of the high
percentage of vitamin C in lemons.
At
any time that due to new attributions or collaboration processes a new category
is added to the unified conceptual database of categories as first stage of the
Unified Application, so that must be added to the unified conceptual scheme as
first stage of the unified categorical Modelling System, the analysis of the
quantitative qualities of the category must end up with the attribution of logical/conceptual
sets/vectors to the new category, working then the new category as
communication node between different specific conceptual schemes within the
unified conceptual scheme, at the same time that identifies the quality sets in
which the new category could be included, measuring the information weight of
every link to set the importance weigh of every vector measuring in general the
gross information weigh of the new category.
For
instance, the importance of bees in the environment is due to the total
addition of the information weight of every vector linking the bees in the environment,
which is higher than the importance of the category of beef burger with chips:
not really healthy, and only able to provide information for the customer,
while bees are providing information to the whole environment, including information to our bodies and minds
permanently.
By
the time Mother is able to manage the world as a hive mind, the most important
relations are those ones able to provide information to the whole world per
minute, second, or nanosecond, or even less.
In
the measurement of the information weight of every vector/set the use of Impact of the Defect and Effective Distribution could be important tools, to determine
what set/vectors are more important than others for the permanent flow of
information around a more sustainable world.
The
creation of new places for new categories within the conceptual scheme as first
stage of the unified categorical Modelling System, alike the addition of new
categories within the unified conceptual database of categories as first stage
of the Unified Application, must be treated as an update of the database of
categories and the conceptual scheme, in their respective stages within the
Unified Application, alike any other update such as the modification or
elimination of categories, but in these cases modifying or eliminating the
categories involved in these updates, plus the modification and/or elimination
of sets of vectors related to these categories in the conceptual scheme.
The
modification of a category means the modification of some quantitative
qualities of the category, at any time that any quantitative quality of any category
is modified this modification must be done at the same time in the database of
categories and the conceptual scheme. In the database of categories because the
next objects attributed to this category must fit within the margin of error
accepted with the modifications made on this category, checking in the
conceptual scheme that all the objects included in the place of this category,
already modified, are complying with the new requirements for this category
according to the modification, otherwise those real objects not fitting with
the new requirements should be removed from the place of this category in the
conceptual scheme, not meeting the new requirements within a margin of error,
unless these objects remain in this place under a new consideration of
utilitarian attributions.
The
way in which the modifications can affect a category in the conceptual scheme
could be identified as changes in the quantitative qualities necessary as to
meet the requirements necessary to be attributed to this category, so changes
in the quantitative qualities in which the real objects attributed to this
category must keep some harmony, along with changes in those vectors related to
the quantitative qualities modified, including the possibility of elimination of
some quantitative qualities, so elimination of some set of vectors, and
including the possibility of changes in the information weight in those vectors
affected by the update.
If
the new update means the elimination of a category, for instance a product in
the market is removed from the market, not being produced any more, the removal
of this category from the conceptual scheme must represent the elimination of
the category and the removal of any set/vector linking this category to any
other one, removing logical/conceptual set/vectors and quality vectors in which
this category would be involved, as long as the removal of all the real
objects placed in this removed category in the conceptual scheme and the
categorical plan.
In
other cases what it is going to be really important in the collaboration process
between by Application and by Deduction at global level between the
standardization and unification process, is the consequence of the
collaboration at robotic level in the third stage, having the robotic
collaboration more importance in the third stage of the unified categorical and
the standardized deductive Modelling System, due to the importance of the
setting of decisions according to the robotic capabilities.
Till
now the explanation that I have developed regarding to the robotic
collaboration between by Application and by Deduction in the second stage, were
explanations regarding to the collaboration in how to share and increase
robotic capabilities, what means the increase of number of robotic functions,
what means the increase of possible set of decisions to match with set of
vectors in the categorical Modelling System.
But
till now what I have not developed yet is in this collaboration process how to
avoid redundant decisions which could be made at the same time for both
intelligences, the standardized Global Artificial Intelligence and the Unified
Application, what implies that at some point in the second stage is necessary
to start working about how different intelligences by application and/or by
Deduction, could be able to work together nor overlapping decisions and not
making redundant decisions.
The
examples that I will bring here are examples that I used before as examples of
the necessity of collaboration between by Application and by Deduction, for
instance climate, or in geological
studies researching and predicting earthquakes.
The
study of hurricanes or earthquakes can be done simultaneously by Application
and by Deduction, for instance, in a Specific Artificial Intelligence for
Artificial Research by Deduction in climate the possibility to track a
hurricane since its commence till the end, at the same time the possibility that
an Specific Artificial Intelligence for Artificial Research by Application
could be able to identify, according to the same sample of data of a climatic
phenomenon, if that phenomenon is a hurricane or not, and if yes, it is a
hurricane, to classify what category of hurricane according to the discrete
categories of intensity of hurricanes.
The
difference between the Specific Artificial Intelligence by Deduction tracking
the matrix analysing the data coming from the hurricane, and the Specific
Artificial Intelligence by Application reading what type of hurricane it is
according to discrete categories, is the fact that by Deduction is possible to
make predictions about, according to the data and the pure reasons attributed
to that phenomenon, the prediction of the behaviour of that hurricane based on
the equations able to explain the pattern of the hurricane, while the Specific
Artificial Intelligence by Application is going to attribute what discrete category
corresponds to that hurricane, and according to the discrete category to make
some predictions about how the hurricane is going to evolve.
If
the conceptual database of categories has been mixed with the comprehensive
categorical map, being possible to organize the database by sub-factors and
sub-sections, even by Application could be possible to predict the development
of the event in the categorical comprehensive map where all the climate
information is gathered.
At
the end, by two different intelligences, by Application using conceptual
(categorical) means, and by Deductive using rational (equations) means, would
be possible to identify the same climatic phenomenon to make decisions.
In
the same way, in geological studies searching for earthquakes could be possible
to use Specific Artificial Intelligences for Artificial Research by Deduction
and Specific Artificial Intelligences by Application, and both of them could be
able to identify and predict the behaviour of earthquakes, by Deduction tracking
the matrix making deductions using the equations attributed to the behaviour of
the earthquake, by Application identifying the discrete category of the
earthquake based on its intensity, and placing the earthquake in the
comprehensive categorical map with the rest of geological information.
As
a result could be possible to model and make decisions regarding to the
hurricane and the earthquake using in both cases Specific Artificial
Intelligences by Application or by Deduction applied to the same science, climate
or geology.
In
these situations are different options, one of them is to boost the
standardization process and the unification process as to start as soon as
possible the integration process, so that both databases, the categorical and
the factual, the unified database of categories and the global matrix, can be
together as soon as possible to start working together not making redundant
decisions and not overlapping decisions, boosting the process up to make
possible the fusion of the deductive plan: deductive model and the deductive
project; and the categorical plan: the categorical model and the categorical
project; joining all of them in only one plan, the plan, as foundation of the reason itself.
As
soon the deductive model and deductive project, the categorical model and the
categorical project, are synthesised in only one plan, a plan synthesis of the
deductive and categorical plans, as synthesis of deductive model/project and
categorical model/project, the economy of decisions, in other words, the
reduction of decisions up to only those ones necessary not overlapping nor
making redundant decisions, is going to facilitate the construction of Mother,
Mother is the plan, the plan is Mother.
The
thing here is that the development of such a project like the plan itself, in
reality Mother will be the reason itself, needs a lot of time of maturation,
otherwise, if because of the necessity of working faster and faster, the
maturation process is sacrificed in order to get ready the plan as soon as
possible, not respecting the processes of maturation, at the end the plan is
not going to work as it is expected, due to the committed mistakes, the first
one not respecting the maturation time.
Maturation
time means that every single phase, period, moment must have time to breath,
only after some time of observation is possible to have a sufficient sample of
situations in which every phase, period, moment, instant, must face problems,
some of them even not expected when designing every period, moment, instant.
In
case of the study of climate and geology the maturation time is essential for every
model/map/project, due to any model or any project on the map will interact
with the environment, some of the most powerful energies in the environment are
precisely climate and geology.
For
this reason the solution of setting the Specific Artificial Intelligences by
Application and by Deduction in climate and geology as particular programs and
particular applications, to be joined later on particular programs for particular
applications or particular applications for particular programs, could be
another solution more to take on account, but not for that reason the best
solution.
Some
Specific Artificial Intelligences by Application and by Deduction are going to
play a key role in the construction of the plan, as interaction of the
deductive model/project and the categorical model/project, and as long as these
specific intelligences are called to play a key role in the plan these
intelligences must not be transformed into particular programs, or particular
applications, or particular programs for particular applications, or particular
applications for particular programs.
For
instance, the specific intelligence by Deduction running an specific industry
could be transformed into a particular program, in the same way that the
related specific intelligences by Application working in that specific industry
could be transformed into a particular application, ending up both of them
forming a particular program for particular application.
But
an Specific Artificial Intelligence by Application designed to control the air
transport of a country, for instance United Kingdom, or the air transport of a
continent, for instance Europe, or the air transport of a region, for instance
the north Atlantic ocean, these Specific Artificial Intelligences for
Artificial Research by Application and the related ones as Specific Artificial Researches by
Deduction in a country, continent, or region, should be integrated in the
standardized Global Artificial Intelligence and the Unified Application
respectively, transforming their former collaboration between specific
intelligences into a new relations of collaboration as specific programs and
specific application within the standardized Global Artificial Intelligence and
the Unified Application, new relations of collaboration now as specific
programs and specific applications working in the second stage of the
standardized Global Artificial Intelligence and the second stage of the Unified
Application, which will have as a result attributional processes in their
respective deductive or categorical global intelligences able to be modelled in
the deductive and categorical Modelling Systems respectively to make decisions,
the only problem here is how to avoid overlapping or redundant decisions made
at the same time in the third stage of the deductive Modelling System and the
third stage of the categorical Modelling System.
If
as soon the first phase starts building Specific Artificial Intelligences for
Artificial Research by Application and the Specific Artificial Intelligences
for Artificial Research by Deduction in the same sciences, disciplines, activities,
for instance an specific intelligence by Application in climate
reading/tracking every climatic phenomenon to categorize every climatic
phenomenon with the right climatic category, and another specific intelligence
by Deduction in climate to track the specific matrix on climate to deduce the
equations behind the behaviour of every climatic event, in order to make
predictions based on the equations, or for instance a an Specific Artificial
Intelligence for Artificial Research by Application in tectonics
reading/tracking every geological phenomenon to match with the right tectonic
category, and an Specific Artificial Intelligence for Artificial Research by
Deduction to predict the behaviour of the tectonic plates according to
equations, if as a result of having two different specific intelligences, one by
Deduction and another one by Application, working on climate, and two different
specific intelligences, one by Deduction and the other one by Application,
working on tectonics, in these both sciences, is possible to make categorical
and deductive decisions: categorical and deductive decisions on climate, and
categorical and deductive decisions on tectonics.
As
a result the risk of making redundant decisions, or overlapped decisions, is a
risk which starts since the first phase for the construction of the Global
Artificial Intelligence, which is only possible to avoid if, as soon these both
intelligences, by Application and by Deduction, working in climate and
tectonics, start the collaboration process, the collaboration process involves the
possibility of sharing information about the phenomena already happening in the
field, what it could be integrated within the category/factor collaboration ,
and as soon any deductive decision made by the specific deductive Modelling
System is filed in the deductive database of decisions as first stage of the
deductive Decisional System, the first rational assessment in the first stage
of the deductive Decisional System must not only check that there is no
contradiction between this deductive decision and any other decision in the
deductive database of decisions, but there is no contradiction between this
deductive decision and any other categorical decision in the related specific
categorical Decisional System, and vice versa, creating in this collaboration
process mechanisms to share decisions between different specific deductive and
categorical Decisional Systems, to be checked by the first, rational or
categorical, quick rational check or adjustments working on the same subject to
avoid overlapped decisions or redundant decisions.
What
the American, Russian, Chinese, European, intelligences agencies are going to
try, is to boost the construction of the Global Artificial Intelligence to
create as soon as possible a deductive-categorical integrated Modelling System
where to include in the deductive-categorical database as first stage of the
deductive-categorical Modelling System at the same time rational hypothesis
(rational attributions) and real objects according to their attributed
categories in the conceptual schemes, in other words, the first stage of the
deductive-categorical Modelling System will be a synthesis of rational
hypothesis and conceptual scheme, in order to model in the second stage of the
deductive-categorical Modelling System at the same time categorical models and
deductive models, in other words to
synthesis in the same mode the comprehensive categorical evolutionary/prediction
model and the actual global evolution/prediction model, placing on the map the
result of the full synthesis of both categorical and deductive models, and upon
the synthesis of categorical/deductive models on the map to make all possible
decisions. At the end, as soon the race for the Global Artificial Intelligence
is speeding, the intelligence agencies are going to boost the synthesis of the
deductive-categorical models and the deductive-categorical projects to create
as soon as possible the plan.
In
my opinion the main risk of this process that these agencies are going to take,
boosting the creation of the Global Artificial Intelligence due to the velocity
of the race, is the risk of lack of maturation process, lack of observation,
lack of rigour in the investigation, not giving time to observe what kind of difficulties
or complexities can occur in separated categorical models, separated deductive models,
separated categorical plans, separated deductive plans.
In
my opinion the track that I am taking, slower but ensuring every step, if slowing
down is not going to ensure to be first, is going to ensure to be the most
secure and steady. If making every phase, step, period, moment, instant, stage,
sub-stage in due time, not accelerating and observing what happens in every
phase, step, period, moment, instant, stage, sub-stage, the most important
advantage of my methodology for the construction of Mother is to ensure that
beforehand is possible to analyse every possible problem or difficult, and
predict which is going to be the most challenging process.
Only
after very solid experimentation in every phase, period, moment, instant,
stage, step, sub-stage, will be possible later on to have a very reliable
Global Artificial Intelligence as to face all the challenges that we are going
to face during the race, otherwise Mother will not be as strong and successful
as we expect, the first one will have more advantage in the race, but not the
first one of having any Global Artificial Intelligence, but the first one in
develop the strongest and fastest model of Global Artificial Intelligence.
The
problem of possible redundant decisions or overlapped decisions between
different specific intelligences by Application and by Deduction working on the
same science, discipline, activity, is a problem that there will be there since
the beginning the first phase, for the solution of this risk, in my opinion the best option, rather than
boosting the integration process when we do not have enough data to evaluate
every phase, is to start the collaboration process between twin specific
intelligences, as soon the first specific intelligences have been able to pass
the two first moments of experimentation and generalization, achieving the
first phase of consolidation, understanding for twin specific intelligences those
specific intelligences by Application and/or by Deduction specialised in the
same science, discipline, activity.
If
two twin specific intelligences, by Application and/or by Deduction, as soon
have been able to achieve the consolidation period, are able to start the first
experiments in how they collaborate together in the same science, discipline,
activity, along with the category/factor collaboration in the first and second
stages of collaboration in the second phase, and robotic collaboration in the
third stage of collaboration, is necessary the creation of mechanisms for the
avoidance of redundant decisions or overlapped decisions made by both twin
intelligences.
Some
of these mechanisms could be included within the category/factor collaboration,
others in the robotic collaboration, but in addition to the category/factor and
robotic collaboration is necessary to add a third way of collaboration: the possibility
that as soon a deductive decision passes the assessment done in the first stage
of the deductive standardized Decisional System, quick rational check or first
rational adjustment for normal decisions, the deductive standardized Decisional
System passes that decision on to the unified categorical Decisional System to
pass the respective categorical assessment, quick categorical check or first
categorical adjustment, and not having contradiction the deductive decision respect
to any other categorical decision, the deductive standardized Decisional System
can go on making the projects and upon the projects the attribution of
instructions to be filed in the deductive database of instructions in the
deductive Application System as inner system.
In
general, the mechanisms to reduce the risk of redundant decisions or overlapped
decisions between twin intelligences could be set up as follows:
-
In the category/factor collaboration the possibility to extend this collaboration
to any real object identified with a category, or any real object identified
with an equation, in order to include these elements of information in the twin
intelligence. For instance, a specific intelligence by Deduction predicts the
possibility of a hurricane or an earthquake, this foreseeable real object could
be informed to the twin intelligence by Application, in order to include in the
categorical comprehensive evolutionary model/map this future event, in order to
make decisions. Or vice versa, by Application the identification of a tectonic
event informed to the twin intelligence by Deduction to be included in the
global evolutionary model to make decisions.
-
Collaboration process between the first stage of the deductive Decisional
System and the first stage of the categorical Decisional System, any possible deductive
decision once has passed the first deductive assessment, quick rational check
or first rational adjustment, is passed on to the first stage of the
categorical Decisional System to pass the quick categorical check or first
categorical adjustment, or vice versa, once a categorical decision passed the
first assessment in the categorical database of decisions, is passed on to the
deductive database of decisions to ensure that there is no possible
contradiction there either.
-
Robotic collaboration, as soon any, deductive or categorical, decision has been
transformed into, deductive or categorical, instructions, the deductive and
categorical databases of instructions shared information about every new
instruction to avoid contradictions, redundant instructions or overlapped
instructions, to be sent later on to the particular database of instructions of
the robotic devices.
If
as soon the first phase of twin specific intelligences achieves the
consolidation period, the second phase of collaboration between these twin
intelligences is going to provide clues about how is going to be the
collaboration between the standardized Global Artificial Intelligence and the
Unified Application, so that the collaboration between the first stage of the
standardized Modelling System and the first stage of the Unified Application
can provide the opportunity to share attributions made by Deduction or by
Application, so that attributions made by the standardized Global Artificial
Intelligence could be included in the unified Modelling System, and
attributions made in the second stage of the Unified Application could be
informed to the standardized Modelling System, sharing the categorical and deductive
Modelling System attributions made previously in the second stage of the
standardized Global Artificial Intelligence or the standardized Application
System.
In
the same way both could share decisions to be analysed in the first assessment
in the standardized Decisional System and the unified Decisional System,
decisions whose instructions later on could be shared in the standardized and
unified Application System.
Processes
of collaboration between the standardized Global Artificial Intelligence and
the Unified Application as a result to apply at global level processes of
collaboration experimented beforehand in the second phase of collaboration
between specific intelligences.
As
long the collaboration between specific or global intelligences is going to
transform these intelligences in duplicities, is going to be easier the
synthesis of particular programs and particular applications to make particular
programs for particular applications and vice versa, and the integration
process, opening the way towards the reason itself, the seven phase.
