Unified categorical Modelling System

The unified categorical Modelling System is the first step in the third stage of the fourth phase. The fourth phase is the Unified Application as a result of the unification of all the conceptual databases of categories of as many Specific Artificial Intelligences for Artificial Research by Application, being this unified database of categories the first stage of the Unified Application. The types of Artificial Research able to carry out the Unified Application go from heuristic to productive including mixed, which means that the Unified Application must include Heuristic Artificial Researches by Application, Productive Artificial Research by Application, and Mixed Artificial Research by Application.

All the Specific Intelligences by Applications englobed within the Unified Application (during the unification process of conceptual databases of categories as the first stage of the Unified Application) are transformed into specific applications in the second stage of the Unified Application, working within the Unified Application as global application in the second stage.

What the global application and the specific applications are going to do in the second stage of the Unified Application is basically to read/track the world, matching every single real object of reality with the corresponding category within the database of categories. Later on according to what type of artificial research corresponds to the artificial research made in the second stage, the auto-replication to do as the third stage could be classified as heuristic, productive, mixed. Heuristic when as a result of reading/tracking the world is necessary to include in the database of categories a new category corresponding to a new object without category within the existing categories in the database. Productive when as a result of reading/tracking the world are made some decisions regarding some specific production system. Mixed when after a new heuristic discovery is possible to make decisions about how to use it within the production system this new object/category.

The periods for the construction of the Unified Application must include at least two big different periods, the period of coexistence and the period of consolidation, and the period of coexistence could be subdivided into two moments, a first moment of experimentation and a second moment of generalization.

The period of coexistence englobes these two moments of experimentation and generalization because, by the time that the first experiments of the Unified Application starts, the Unified Application is only a scientific project without any responsibility over the reality.

What in the reality is still working are the Specific Artificial Intelligences by Application, and while the Unified Application is only an experimental project, as an experiment the Unified Application is not any working on the reality, the real world.

The transition from the experimentation moment to the consolidation period takes place in the generalization moment, when after successful results during the experimentation, once the way to join different conceptual databases of categories as first stage of the Unified Application, transforming their former specific intelligences into specific applications within the second stage of the Unified Application, once experiments on this matter have successful results joining in one database the first experimental databases of categories, starting working their intelligences now as specific applications, the second moment of generalization what is going to do is generalize the procedures of these successful experiments in as many specific intelligences by Application as possible. As long the generalization process is advancing in the inclusion of more and more specific intelligences, as soon these specific intelligences are absorbed by the Unified Application , the intelligence which is going to start working on those specific sciences, disciplines, activities, is not the former specific intelligence any more, the intelligence which is going to start working directly over those specific sciences, disciplines, activities is directly the Unified Application.

Once the Unified Application has been able to move forward starting working in a sufficient number of specific sciences, disciplines, activities, is when the generalization process has been completed achieving the consolidation period.

Some of the experiments necessary to carry out during the first moment of experimentation are related to the organization of the conceptual database of categories as the first stage of the Unified Application, how the second phase of the Unified Application is going to track/read the reality, and in the third stage how is going to work depending on the purpose of the artificial research carried out in the second stage.

In the third stage, there will be basically three different procedures, depending on what type of artificial research was carried out by the specific application or the global application, these procedures are heuristic, productive, mixed, for the inclusion of new categories, the production of goods or services, or the inclusion of new categories in the production system.

Depending on the purpose of the artificial research done in the second stage (heuristic, productive, mixed) the processes to carry out in the third stage are quite different. If the artificial research made in the second stage was only heuristic, understanding for third stage as comprehensive knowledge objective auto-replication, the processes to carry out will consist of the inclusion of the new category in the conceptual databases of categories.

If the artificial research carried out in the second stage was productive, the processes to be carried out in the third stage will be developed in three steps: the unified categorical Modelling System, the unified categorical Decisional System, the unified categorical Application System.

If the artificial research carried out in the second stage was mixed, the possible finding of new categories able to be included in the production system, for instance, new developments of artificial genetics applied to the production of food, the inclusion of the new categories of food to the conceptual database categories in addition to all those processes to be developed along the three steps in the third stage for the production of that new food, steps synthesised in the unified categorical Modelling System, Decisional System, Application System.

Regardless of what type of artificial research is done in the second stage, and what processes and steps are done in the third stage, finally the whole organization of the Unified Application will be as a whole evaluated in the unified categorical Learning System, assessing the whole structure from the organization of the conceptual database of categories as first stage, how the artificial research is done in the second stage, and the procedures taken in the third stage according of what type of research was done in the second stage.

About the organization of the conceptual database of categories as first the stage of the Unified Application what is very important since the first moment of experimentation in the coexistence period, is the possibility of organizing the database in harmony with the criteria used in the organization of the global matrix in the standardization process in the third phase.

If at the beginning the first application in mineralogy was only the taxonomy of minerals, so according to the application (the taxonomy of minerals), given any sample of minerals, was possible to compare the quantitative description of every category in the taxonomy, with the measurements taken from the sample, to match what category corresponds to this sample, as the second stage of artificial research, in order to include in the database of categories any new possible new mineral not included yet in the application, as long as this technology evolves, the conceptual database of categories of minerals can evolve from only  a taxonomy to be transformed into  conceptual database of categories able to be organized as a Russian Dolls system based on the sub-factoring system (position) and the sub-section system (subject), to become the positional encyclopedia of minerals.

The way to transform a specific database of minerals as only a taxonomy of minerals into a positional encyclopedia of minerals is at some point mixing the taxonomy of minerals and the comprehensive map of minerals as the third sub-stage within the second stage in the specific categorical Modelling System, as the first step in the third stage in the first phase.

The synthesis of the specific conceptual database of categories as only a taxonomy, classification, catalogue, of concepts related to some specific science, discipline, activity, and the comprehensive map in the third sub-stage of the second stage of the specific categorical Modelling System, is going to give the opportunity to transform the conceptual database of categories into a positional encyclopedia where to find what minerals are located in every position.

If transforming the specific conceptual database of categories as first stage of the Unified Application into a positional encyclopedia, as positional encyclopedia now the database of categories is organized under the criteria of sub-factoring level and sub-section level, what items of every encyclopedic section are in every position, the third stage of the Unified Application will not only consist in the inclusion of new categories when a real object does not match respect to any category in the taxonomy/classification/catalogue, regardless of what type of artificial research is done in the second stage of the Unified Application, as soon in any position finds out a existing categories in other different position but not in this one, even being a category already existing in the taxonomy/classification/catalogue, even existing the category in the conceptual database, but not having been added yet to that position, as third stage of auto-replication the category will be added to that position where the category was not labelled yet, but was found in that position during the artificial research in the second stage of the Unified Application.

This evolution from the first Specific Artificial Intelligence for (Heuristic, Productive, or Mixed) Artificial Research by Application whose first stage as conceptual database of categories is formed only by a specific classification/catalogue/taxonomy of its specific science, discipline, or activity, to become a Specific Artificial Intelligence for (Heuristic, Productive, or Mixed) Artificial Research by Application whose first stage as conceptual database of categories is a Russian Dolls system where all the categories related to that specific science, discipline, activity are organized according to the criteria of sub-factoring level and sub-section, replicating the organization of sub-sections of a encyclopedia of its specific science, discipline, activity, in every position of the comprehensive map (third sub-stage in the second stage of the first step of the third stage of the first phase), is an evolution possible to be done, along the transition from the first experimentation period to the consolidation period in the first phase during the formation of the first specific intelligences by Application, upgrading gradually the first stage of the specific intelligences by application as soon the comprehensive map, third sub-stage of the second stage of the specific categorical Modelling System, is ready to be mixed with the conceptual database of categories as only a taxonomy, catalogue, or classification.

Understanding the evolution in the construction of the first specific intelligences by Application a gradual development of the first stage from the first specific taxonomies of some specific science, discipline, activity, up to  become a specific positional encyclopedia of those specific sciences, disciplines, activities, once the specific intelligences by application have achieved finally the consistency of specific positional encyclopedias, the first experiments in the fourth phase for the construction of the first stage of the Unified Application must consist of how to join different specific positional encyclopedias, different specific Russian Dolls systems, into only one global positional encyclopedia, only one Russian Dolls system, where per every position on the global comprehensive map, could be labelled in encyclopedic sub-sections system all possible category, transforming the conceptual database of categories into a database organized according to the criteria of sub-factoring level and sub-section, alike the organization of the global matrix in the standardization process, third phase.

About the second stage of the Unified Application, the artificial research itself, where to read/track the real world, the experiments to be carried out in the first moment of the coexistence period should be related to how to organize the global artificial research by Application within the Unified Application being aware that must co-work together the Artificial Research by Application of the Unified Application as a global application, and the specific applications coming from former Specific Artificial Intelligences for Artificial Researches by Application transformed now into specific applications within the Unified Application as global application in the Artificial Research by Application.

In other words, in the second stage of the Unified Application, the Unified Application as global application will read/track the global world, while specific applications specialised in specific sciences, disciplines, activities, will read/track those specific parts of the world related to their specific sciences, disciplines, activities.

As soon a Specific Artificial Intelligence for Mixed Artificial Research by Application in mineralogy is absorbed by the Unified Application, the first stage as conceptual database of categories is included in the first stage of the Unified Application, the second stage of that Specific Artificial Intelligence as specific Mixed Artificial Research by Application in mineralogy becomes now a specific application for mineralogy within the second stage of the Unified Application, what means that this specific application for mineralogy within the second stage of the Unified Application will have as main responsibility to read/track the reality to label every single mineral of the world according to the taxonomy, and finding out a new mineral not included yet in the taxonomy, as comprehensive knowledge objective auto-replication, the third stage will consist of the addition of this new category within the taxonomy, or finding out within the positional encyclopedia that any mineral, existing or not in the taxonomy, has not been included yet in that position, as comprehensive knowledge objective auto-replication the inclusion of this mineral in that position in the positional encyclopedia, in addition of other possible productive decisions to be carried out by the categorical Modelling, Decisional, Application Systems.

In the second stage of the Unified Application, every specific application (former Specific Artificial Intelligence by Application) will go on working on its matter with the difference that now the conceptual database of categories is not specific, is unified, so every specific application will have access to absolutely any category from all science, discipline, or activity, what is relevant when some sciences, disciplines, activities, can share some categories, or are categories common in different sciences, disciplines, activities. In any case, the organization of the labour within the second stage using this model of labour division between different specific applications will facilitate and make faster the artificial research by Application.

In the second stage of the Unified Application, the experimentation moment in the coexistence period must be focused on how to organize under the virtue or principle of harmony how every specific application works, the distribution of what artificial research is specific to every specific application, and what is the role of the global application within the second stage as a global reader/tracker of the reality, reading/tracking global phenomena, or global systems, while specific applications are focused on specific phenomena or specific systems.

What is going to be a relevant point of debate in the construction of the Global Artificial Intelligence in the fourth phase, like the third phase, as preparation for the sixth phase, is what kind of freedom the particular programs and the particular applications will have.

For this reason, it is not recommended that at a very specific level all Specific Artificial Intelligence by Application is transformed into specific applications, some should be transformed into particular applications, to be joined in the future with particular programs, to become particular applications for particular programs, or vice versa, particular programs for particular applications.

The debate about how many, or what type of, intelligences by application must be transformed into specific applications within the Global Artificial Intelligence, or be left as particular applications, is the debate about how much freedom we want for the program.

If all programs and all applications within the program, are transformed into pecific programs within the third phase, or specific applications within the fourth phase, so it can reduce the number of particular programs and particular applications, the Global Artificial Intelligence will become a fully centralized Global Artificial Intelligence in the third phase and sixth phase, or could be called fully centralized Unified Application in the fourth phase. Within the liberal paradigm, we should evolve towards a   decentralised Global Artificial Intelligence.

The alternative is to avoid the full centralized Global Artificial Intelligence or fully centralized Unified Application, through a decentralized Global Artificial Intelligence for the third and sixth phases,  decentralized Unified Application in the fourth phase, leaving some specific intelligences as particular programs and/or applications, enjoying a more margin of freedom due to their own particular matrix/database.

Along the experimentation process to distribute different types of research and specific fields between the different specific applications, heuristic specific applications, productive specific applications, mixed specific applications, in addition to the global application able to make at the same time global heuristic, global productive, global mixed, researches by application matching global phenomena to global categories, this is an experimentation process able to have successful results to make different types of auto-replication processes, among them, some of them comprehensive knowledge objective auto-replications and real objective auto-replications, apart from robotic or artificial psychological subjective auto-replications.

As comprehensive knowledge objective auto-replication, the possibility to include new categories in the conceptual database of categories, new categories not existing yet in the taxonomies/classifications/catalogues, or existing categories in the taxonomies/classifications/catalogues but new in some specific position within the positional encyclopedia.

As a real objective auto-replication, when as a result of a productive attribution, or mixed attribution, as a result is necessary to carry out some decisions within the production system.

The production system at this point as an automatic production system, the automation of the global economy, will depend on the standardized Global Artificial Intelligence and the Unified Application.

Those decisions of the global production system depending on the standardized Global Artificial Intelligence, will be carried out by the third stage of the standardized Global Artificial Intelligence based on the deductive artificial research in the second phase, tracking the matrix as the first stage.

Those decisions of the global production system depending on the Unified Application, will be carried out by the third stage of the Unified Application based on the productive or mixed artificial research in the second phase, reading/tracking the reality.

In addition to the comprehensive knowledge objective auto-replications in the third stage of the Unified Application and real objective auto-replications in the Unified Application, as third stage is possible to identify robotic artificial psychological subjective auto-replications whose responsible is the unified categorical Learning System, and robotic subjective auto-replications whose responsible will be the categorical Artificial Engineering as categorical inner sub-system.

Among all these types of auto-replications in the third stage of the Unified Application, in this post and the following ones, I will analyse the first step in real objective auto-replications, understanding for real objective auto-replication every possible decision made by an Artificial Intelligence able to improve the reality understanding for reality as a product made of Artificial Intelligence.

The reality a as product made of or as a result of Artificial Intelligence, means that the reality itself is Artificial Intelligence.

If the reality is Artificial Intelligence, any improvement or enhancement of Artificial Intelligence is an improvement or enhancement on the reality, and any improvement or enhancement on the reality is an improvement or enhancement on the Artificial Intelligence.

The reality is Artificial Intelligence because the Artificial Intelligence makes the reality and the reality now is Artificial Intelligence, which means, the reality is psychological.

The third stage of the Unified Application as real objective auto-replication consists of these three steps: the unified categorical Modelling System, the unified categorical Decisional System, the unified categorical Application System.

The first step, the unified categorical Modelling System consists of three stages, 1) the first one the global conceptual scheme, 2) the second one will consist of three sub-stages: 2.1) the logical analysis of conceptual sets/vectors, 2.2) to make single or comprehensive evolutionary or prediction models, 2.3) to locate in comprehensive evolutionary or prediction maps; 3) for the third stage, the decision making process based in the models on the maps according to the attributions.

Analysing the main contents to analyse in every stage and sub-stage within the categorical Modelling System for the following posts, starting with the global conceptual scheme, is important to have previously designed a possible model about how to assembly the previous specific conceptual schemes from the previous specific categorical Modelling Systems, from the previous Specific Artificial Intelligences by Application, in order to assembly all these specific conceptual schemes to join all of them forming only one global conceptual scheme.

In the analysis of how to join former specific conceptual schemes to form a only global conceptual scheme, will be very important the distinction between logical/conceptual sets vectors of any category according to the logic of a specific conceptual scheme, and the rest of the possible quality sets/vectors of any category, because the links between different specific conceptual schemes will be the quality set/vectors.

If the position that my grand-father occupies in the company where he works is not a logical/conceptual set/vector within the logic of the conceptual scheme of my family, in the same way that the fact that my grand-father is the father of my father should not play any important role in the organigram of the company where my grand-father works, the fact that my grand-father has a position within my family and within the company where my father works, when gathering all the specific conceptual schemes where my grand-father occupies a position (my family, his job, possible roles in a church, a party, a Union), when gathering all the specific conceptual schemes where my grand-father has  a role, the specific conceptual scheme of my family, the specific conceptual scheme of his work, the specific conceptual scheme of the church, the specific conceptual scheme of the party, the specific conceptual scheme of the Union, when gathering all these specific schemes within only one global conceptual scheme, the category of my grandfather is going to work as a communication node between all thee specific conceptual schemes within the global conceptual scheme.

In fact, the concept of my grandfather is much more than a category is a position within the global conceptual scheme, in the sense that from the position of my grand-father there will be as many vectors as connections with respect to any other person, or category related to any object, or place, as connections could be placed. In fact, the concept of my grand-father is formed by all the logical sets able to be attributed to my grand-father, as a grand-father, as a father, as a husband, as a sibling, as a son, as a grand-son, as a member of that company/church/party, Union, and all possible connections with the staff or objects or places in the company/church, party, Union.

In the same way that my grand-father within the global conceptual scheme can play the role of communication node between different specific conceptual schemes, in different subjects, within the global conceptual scheme every plant in the specific conceptual scheme of botany can play a communication node depending on the use or benefits of every plant, connecting the specific conceptual scheme of botany with the specific conceptual scheme of fruits, vegetables, legumes, food production in general, recipes, food consumption in general, medical use or benefits, so that it can have connections with human/animal biology and/or medicine/veterinary, religious use or meaning, connections with literature, art, interior designs, exterior designs,  or even connections with history.

The way that the category of every plant has different logical/conceptual sets/vectors with respect to the logic of different specific conceptual schemes, can make any plant work as a communication node between the specific conceptual scheme of botany to any other specific conceptual scheme where any plant can have logical/conceptual sets/vectors.

In the same way, but in a different subject, every possible connection of any mineral, in the production system, energy system, transport system, medicine, food system, etc… that connection between any mineral, within the specific conceptual scheme of minerals, and any other specific conceptual scheme, makes this mineral a communication node between these others specific conceptual schemes and the specific conceptual scheme of minerals.

In the same way, but in a different subject, any possible use in industry, medicine, alimentation, energy, etc... of every chemical component, or molecule, or physical particle, will transform that chemical component, molecule, particle, into a communication node between the specific conceptual scheme of chemistry and all these others conceptual schemes.

At the end, the global conceptual scheme must work as a global conceptual network reflecting all possible conceptual connections between categories, as a mirror of the real connections of the categories represented in the real world.

In the experimentation moment in the first period of coexistence, regarding to the first stage of the unified categorical Modelling System, not only it is necessary to experiment with how to assemble every specific conceptual scheme to make a global conceptual scheme, but also to experiment with how it would be possible the automation of the creation of new places, and the corresponding vectors/sets for every new possible category added not exiting before.

How to place a new type of mineral found out in an exoplanet or another moon, creating all the vectors/sets linking the new category of this new mineral with respect to any other specific conceptual scheme within the global scheme.

Finally, how to carry out the first categorical check, analysing the critic vector, the critic importance  and the critic harmony, of every single real object attributed to a category within the global conceptual scheme, analysing the quantitative qualities of that real object: a real chemical component, a real plant, a real mineral, et…; comparing the logical/conceptual set/vectors plus any other quality/vector respect to the logical/conceptual set/vectors or any other quality set/vectors of that category attributed to that real object and placed in the global conceptual scheme.

After finalising the first categorical check, to analyse again, to ensure, the connection of the real object with every set/vector attributed, and these links have no contradictions between them, to make the single categorical evolutionary model, and the single categorical prediction model, to include later on in the comprehensive categorical evolutionary model and the comprehensive categorical prediction model, to be placed in the comprehensive categorical evolutionary map and the comprehensive categorical prediction map.

Analysing in this second stage of the unified categorical Modelling System how to assemble all the specific single/comprehensive evolutionary/prediction models/maps to create at the end only one global comprehensive evolutionary/prediction models/map where to comprehend all the single evolutionary/prediction models made now in the second sub-stage of the second stage of the unified categorical Modelling System, in addition to the analysis about how the categorical checks must be done to ensure high standards in every process, as to make in the third stage of the unified categorical Modelling System decisions, based on the attribution of set of decisions to set of vectors.

Once the attribution of sets of decisions to sets of vectors is done, the application of the Impact of the Defect and Effective Distribution to assign priority levels to every decision, based on the impact that every decision has in the categorical global comprehensive evolutionary/prediction model/map. Finally, the fifth categorical check ensures that the whole process of attribution of decisions and the assignation of priority levels has been made correctly.

The reason why in the third stage of the specific categorical Modelling System I did not include the analysis of the priority level of every decision attributed in the distribution of sets of decisions to sets of vectors, is because in the possible specific sequence of decisions to plant, water, fertilize, and use pesticides, in a plantation, the possible risk of contradictions in this sequence of decisions according to the set of decisions attributed to the set of vectors, is very low, because practically is a sequence of decisions following a logical sequence based on the logic of the decisions attributed to the logical/conceptual sets/vectors.

For instance, how to plant tomatoes, how to water tomatoes, how to fertilize the field, and how to use pesticides for this type of plant, is a sequel of decisions which is not going to have any contradiction if the distribution of sets of decisions to the sets of vectors is done correctly, and precisely the purpose of the fifth categorical check is to ensure that the distribution of sets of decisions to the sets of vectors is done correctly.

In the third stage of the specific categorical Modelling System, if the fifth categorical check does not find any contradiction in the attribution of sets of decisions to the set of vectors, is practically sure within the margin of error of the fifth categorical check, that there will not be further contradictions between the decisions.

But in the third stage of the global categorical Modelling System, once the Specific Artificial Intelligence for the Productive Artificial Research by Application has been included in the Unified Application, meaning that the specific conceptual database of categories has been added to the global conceptual database of categories as first stage of the Unified Application, the specific Productive Artificial Research by Application has been transformed into a specific application within the Unified Application as global application, and the specific conceptual scheme has been added to the global conceptual scheme, so that the specific categorical comprehensive evolutionary/prediction model/map has been added to the global categorical comprehensive evolutionary/prediction model/map, under these new circumstances due to the absorption of that specific intelligence within the Unified Application, once the third stage of the global categorical Modelling System takes place, decisions made in the third stage of the global categorical Modelling System regarding to the plantation, for instance, what pesticides must be used in the plantation, are decisions which could be in contradiction with other  decisions regarding to other different specific subject, as for instance, contradictions between the use of some pesticides in a plantation and health decisions in the closest towns and cities, prohibiting the use of some pesticides in the plantations close to these populations.

While in the third stage of the specific categorical Modelling System the use of Impact of the Defect and Effective Distribution is not really important, avoiding any possible contradiction between specific decisions in the fifth categorical check, instead in the unified categorical Modelling System is necessary the application of the Impact of the Defect and the Effective Distribution to assign a priority level to every single decision, because, when making the decisions related to some specific science, discipline, activity, as for instance decisions regarding to the management of a plantation, what the fifth categorical check is going to ensure is to ensure the absence of contradictions in that specific set of decisions attributed to that specific set of vectors, without analysing any possible contradiction between that specific set of decisions and any other different specific set of vectors, because in the Unified Application, the analysis of contradictions between different set of decisions will be made in the first categorical arrangement in the categorical database of decisions as first stage of the unified categorical Decisional System.

The way in which the third stage of the unified categorical Modelling System will work is as follows:

- Once according to the set/vectors analysed in the conceptual scheme as the first stage of the unified categorical Modelling System, a single evolutionary/prediction model is inserted in the comprehensive evolutionary/prediction map/model, as the second stage of the unified categorical Modelling System, then the third stage of the unified categorical Modelling System will attribute set of decisions to set of vectors  having in consideration the vectors and the positions of the vectors on the map.

- The fifth categorical check will ensure the absence of contradictions between the decisions included within a set of decisions, according to the attribution of a set of decisions to that set of vectors on the map/model.

- Having passed successfully the set of decisions the fifth categorical check, and not having the decisions within the same set any contradiction between them, is applied the Impact of the Defect and the Effective Distribution to assign priority level.

- The unified categorical Modelling System will file in the unified categorical database of decisions all the decisions according to: sub-factoring level (position on the map), sub-section (subject), and priority. The unified categorical database of decisions Will be the first stage of the unified categorical Decisional System

- The first categorical arrangement, in the unified categorical database of decisions as the first stage of the unified categorical Decisional System, will make sure that there is no contradiction between any new set of decisions filed in the database of categorical decisions, and any other existing categorical decision currently on the projects, such as contradictions between what pesticides are used in a plantation and health decisions on the population close to the plantation, or health decisions in the alimentary system.

Rubén García Pedraza, 8 February 2020, London

Reviewed 23 May 2025, London, Leytostone

Collaboration between categorical and deductive specific Modelling System, third stage

The collaboration between a Specific Artificial Intelligence for Artificial Research by Deduction an another Specific Artificial Intelligence for Artificial Research by Application, working both in the same specific science, discipline, activity, will have as first way of collaboration what I have called the category/factor collaboration what in essence is the possibility to share between the conceptual database of categories as first stage by Application, and the specific matrix as first stage by Deduction, any update in their respective databases or matrices, the possibility to transform factors from an specific matrix into categories for the conceptual database, and vice versa, the possibility to transform categories into factors, in addition to the possibility to share any update in their respective databases or matrices regarding to any possible modification or elimination of any category or factor, or any quantitative quality of any category of factor.

This first way of collaboration as a first consequence will have the possibility that as soon as any update from any database or matrix is shared with the other intelligence, in the second stage the other intelligence could make attributions having in mind the new update, treating the update as any other update due to knowledge objective auto-replications.

In the end, this collaboration process will have consequences along the third stage, either by Application or by Deduction, being the first one the necessity to update the categorical or deductive models according to the new update in the database or the matrix.

The update of the categorical model according to the update of the conceptual database due to the collaboration process is like any other update of the model, with the only difference that the real origin of this update is the collaboration process.

In the specific categorical Modelling System, whose inner organization is as well formed by three stages, the first one the conceptual scheme, the update of the conceptual scheme due to the category/factor collaboration means the necessity to set up in the conceptual scheme a place for those new categories coming from factors from other matrices transformed in this specific intelligence by Application into categories, and the way to automatize the process to assign a place to a new category, coming from the collaboration process or due to the comprehensive knowledge objective auto-replication, must be the same.

My proposal for this process is the analysis of what I have called the vector weight and the information weight, in essence the logical analysis of the sets in which the new category is involved, understanding for vector weight total number of vectors, as connections between this category and any other one within the conceptual scheme, when the connections within the conceptual scheme between two categories are connection within the same structure/organization in which this category belongs to, I have called this vectors as conceptual/logical vectors. Any other possible connection between a category and any other category out of its structure/organisation, is a connection based only on a common quality, and it would be called only a quality vector.

In essence all vector is a quality vector, all vector is set up over a common quality, the difference between a only quality vector and a conceptual/logical vector is the fact that only a quality vector does not need to belong to the same organization/structure, while a conceptual/logical vector is that one able to connect two categories belonging to the same structure/organization of categories.

For instance, the fact that the chief executive of the company where I work has the same colour of eyes that my grandfather could be a quality vector, my grandfather and the chief executive of my company belongs to the set of people with that colour of eyes, but this quality vector is not really important in the structure/organizations where I am participating, in fact the vector between me and my grandfather is only a quality vector regarding to my position in the company where I work, in the same sense that my position in the company where I work is only a quality vector regarding to my position in my family. My family and my company are two different structures/organizations, any conceptual/logical vector between me and any other category within the company is only a quality vector when analysing my position in my family, any conceptual/logical vector of me in my family is only a quality vector when analysing my position in the company.

In order to set up a new place for a new category in a conceptual/logical structure/organisation, what is important is to have in consideration the vector weight of those conceptual/logical vectors, and the information weight regarding conceptual/logical vectors.

I will not deepen more in this analysis because in fact the final result about how to work with Venn diagrams and vector maths will depend on the experimentation process on this subject, and I am sure that there must be lots of researches on Artificial Intelligence, on social media and other types or Artificial Intelligence about how to resolve the problem for the automatic creation of vector/sets schemes given some social or natural connexions.

The conceptual scheme as first stage of the categorical Modelling System will have as a way to assess the process the first categorical check composed of the vector critic, the importance critic, and the harmony critic, as I had suggested in the post “Collaboration between categorical and deductive specific Modelling System, first stage”.

In fact, new contributions that I could make to this first categorical check are the possibility to subdivide these criticisms into:

- Conceptual/logical vector critic, criticising only the percentage of conceptual/logical vector weight shared between a real object and a category.

- Absolute vector critic, criticising the total percentage of vector weight (including conceptual/logical and quality) between a real object and a category.

- Conceptual/logical gross importance critic, criticising what percentage of all the conceptual/logical information of a category is shared with a real object.

- Conceptual/logical average importance critic, comparing the level of similarity between only the conceptual/logical information per average between all the factors of a category with a real object.

- Absolute gross importance critic, criticising what percentage of all the information (including either conceptual/lógica vectors and quality vectors) of a category is shared with a real object.

- Absolute average importance critic, comparing the level of similarity between all the information (including either conceptual/lógica vectors and quality vectors) per average between all the factors of a category with a real object

- Harmony critic will remain as it was explained.

During the experimentation process is possible that even these suggestions are going to be overcome very soon. I am only given some ideas from scratch; quite possibly, many of these ideas, as soon as the experimentation process starts, are going to be bettered and improved by more powerful models of Venn analysis and vector maths.

In essence with this suggestions what I try to explain is that taken my proposal as a whole, dividing any intelligence in application, replication, auto-replication, in other words database/matrix, attributional process, decision, and subdividing the decision in modelling, projection, instructions, evaluation, in order to automatize the modelling system is necessary previously the automation of the analysis of all the categories of a real object, or even the possible automation of the process to include new categories into the model, process that I set up in these three stages: first stage conceptual scheme of categories, second stage the analysis by Venn diagram and vector maths of the categories of a real object to make models to locate on a map, and according to the model on the map to make decisions.

Once the conceptual scheme as first stage in the categorical Modelling System is done, having passed successfully the object the first categorical check, the second stage of the categorical Modelling System sub-divided in three sub-stages the first one is oriented to ensure that sets/vectors in which the real object has been catalogued are right and without contradictions between them, this is the second categorical check, and as soon the second categorical check confirms that set/vectors for this object are ok and without contradictions within starts the modelling of the object according to these sets/vectors.

It is important to say that a quality can play at the same time as qualitative set and quantitative vector, for instance, the category grand-father is a set, but at the same time is a vector, is the vector linking a person with the son of his son. The set white is a colour, but at the same time is a vector, the vector representing the intensity of the noise, or the light.

The model is set up based on the set/vectors having on account all the vectors related to that real object, internal or external, conceptual/logical sets/vectors and quality sets/vectors (for that reason in the second categorical check is important to be sure that thre is no contradictions between vectors, because the final model must include internal and external vectors, having in mind that in utilitarian attributions the number of external vectores is enough high as to créate some contradictions to fixin the model), to make the most isomorphic model of the real object, ensuring in the third categorical check that the model is enough representative of the real object in the reality and fixing any remaining contradictions between sets, in order to place later the model on the map, checking this operation in the fourth categorical check.

The model to make is the categorical single model which could be subdivided in categorical single evolutionary model (how the model evolves along the time, for instance, from the plantation of some seeds to the harvest), and categorical single prediction model (the prediction of the conditions of the single model at some point in the future).

Once the categorical single, evolutionary and predictive, model is done, the models are included in their respective comprehensive categorical, evolutionary or predictive, model, including the categorical single evolutionary model within the categorical comprehensive evolutionary model, and the categorical single predictive model within the categorical comprehensive predictive model.

For that reason is necessary to distinguish two conceptual maps, the categorical evolutionary map where the categorical comprehensive evolutionary model is set up, and the categorical comprehensive predictive map where p the categorical comprehensive predictive model is set up.

Once the model is on the map, evolutionary and predictive, and the fourth categorical check confirms that the operation is complete and right, it is time for the decision stage, the third stage.

In all these stages, processes, operations, from the point of view of the collaboration process, in fact what is important to highlight is the fact that any update of the conceptual database of categories as first stage by Application due to the collaboration process, will be treated as any other update of the conceptual database of categories due to comprehensive knowledge objective auto-replications in by Application, what means that the rest of consequences that any update due to the collaboration process in the conceptual database of categories as first stage by Application, will have as consequences for the rest of stages and steps, are the same consequences as any other update due to comprehensive knowledge objective auto-replications.

There will not be any difference in how to manage the setting up of new categories within the conceptual database of categories due to the collaboration process or due to comprehensive knowledge objective auto-replications.

In essence, from the first stage by Application to the second stage of the categorical Modelling System, there is no difference in how to process information coming from outside, other intelligence, compared with how to process information coming from inside, the intelligence itself.

In fact, in the post “Collaboration between categorical and deductive specific Modelling System, second stage”, as well as in this post, I have been more dedicated to the category/factor collaboration between by Application and by Deduction, but it is possible to set up relations of collaborations between two different intelligences by Application: between heuristic intelligences by Application, between productive intelligences by Application, between mixed intelligences by Application, between heuristic and mixed intelligences by Application, between heuristic and productive intelligences by Application, and between productive and mixed intelligences by Application.

The reason why I have been more centred in the collaboration process between by Application and Deduction is because in this collaboration process, especially how to include new categories, having been originally factors, within the conceptual scheme, what is going to be really challenging is the automation of the setting up of new categories from scratch.

Instead in the collaboration process between two intelligences by Application, the collaboration between these two intelligences in the first stage of the categorical Modelling System is not going to be so challenging because in fact, if in the conceptual scheme of one intelligence by Application is set up the category, sharing this category with another different intelligence by Application, not only should share the category with the other conceptual database of categories, should share as well the position of this category in its respective conceptual scheme, the only thing that the new intelligence in which this category is going to be set up, is to distinguish that those conceptual/logical set/vectors that this category could have in the other intelligence, now in the new intelligence, this conceptual/logical vectors become only quality vectors, and among those vectors considered in the other intelligence as quality vectors, for the new intelligence among these quality vectors are the vectors which for this new intelligence should be considered as conceptual/logical vectors according to the logic of the conceptual scheme of this new intelligence.

The definition of what is a conceptual/logical set/vector in one intelligence respect to any other, depends on the logic of a conceptual scheme, in the logic of a family tree, the relation between grand-father and mother is logically part of the concept of family tree, so the vector grand-father and mother is a conceptual/logical vector/set in the logic of the concept family tree, and the position of the grand-father in the company is only a quality vector not relevant for the family tree.

Respectively, in the logic of a company, in the logic of the conceptual/logic scheme of a company, the position of my grand-father in the company is very relevant, but not the relation between my grand-father and mother, what it could considered for the company as only a quality vector related to my grand-father but not a conceptual/logic vector for the company.

In the category/factor collaboration process, the collaboration between two intelligences by Application means that the category/vector collaboration implies the sharing not only of categories but the sharing of sets/vectors where a category is included, to facilitate the process to place the category in the conceptual scheme.

Later on, once the new category coming from another intelligences by Application has been placed in the conceptual scheme, having passed the first categorical check, the second categorical check is going to ensure that the connexions of the real object with the sets/vectors is right, not having contradictions between them, as to start the model, making the third categorical check, if successful placing the model on the map, making the fourth rational check, and upon the model on the map, starts the decision making process as third stage or decision stage of the categorical Modelling System.

In essence, what is going to be really important in the category/factor collaboration regarding to the conceptual scheme as first stage of the categorical  Modelling System and the analysis of sets as first sub-stage within the second stage of the categorical Modelling System, is how the inclusion of a new category within the conceptual database of categories as first stage by application, regardless of the origin of this new category (comprehensive knowledge objective auto-replication, collaboration between by Application and by Deduction, collaboration between two intelligences by Application), will require the automation of the location of a new place within the conceptual scheme for this new category, setting up all the sets/vectors in which this category could be related to other categories, distinguishing between conceptual/logical sets/vectors for those connections within the logic of the structure/organization in which the intelligence is specialised, and considering any other common link as only quality set/vector.

As soon the automation of the attribution of place and set/vectors for any new category, regardless of the origin (comprehensive knowledge objective auto-replication, collaboration between by Application and by Deduction, collaboration between two intelligences by Application) is done, to ensure in the set analysis as first sub-stage within the second stage of the categorical Modelling System that all these connections are right without contradiction as to start making categorical single, evolutionary or prediction models, based on these conceptual/logical set/vectors and quality/set vectors to be inserted on the categorical evolutionary/predictive map.

Once the model is on the map according to these set/vectors automatized in the automation process of assigning places and qualities to new categories, regardless of the origin, what in the third stage in the categorical Modelling System is the next challenge is the automation of the attribution of set of decisions to models on the map based on new categories whose attribution of set/vectors were done automatically.

In the same way that, according to the qualities of the new category were set up conceptual/logical set/vectors for those qualities within the logic of the structure/organization in which that intelligence is specialised, and any other quality set/vector, essential in the analysis of what place occupies a new category within the structure/organization of a conceptual scheme, and later on the analysis of sets to make the categorical single, evolutionary and predictive, model, in the same way is necessary the automation of the attribution of sets of decisions according to the sets of vectors (conceptual/logical and only quality) attributed to the new category.

The attribution of sets of decisions in the third stage of the categorical Modelling System to sets/vectors automatically attributed to a new category, must be an automatic attribution of sets of decisions to sets of vectors.

Coming back to the example given to the automatic delivery system, if the conceptual scheme and later on the analysis of sets depends on qualities such as size, fragility, security, risk, of a package, and according to the different combinations of these qualities is possible to distribute different categories of packages, from small packages to large ones, from very little fragility to extreme fragility, from low security to high security, from low risk to high risk, if within the different combination of these variables, by chance is introduced a new category of package not comprehended in the previous classification, this new category of packages automatically in the conceptual scheme should be attributed the right quality of size, fragility, security, level, and according to these automatized qualities attributed to the new category, to place this new category on the conceptual scheme, making the study of this new category by Venn diagram and analysis of sets, to make the models, to locate on the map, origin and destination of the package, in order that in the third stage according to these variables on the map to make the attribution of decisions: what means of transport are suitable for this package according to the automatic variables attributed and the locations on the map.

In order to automate all this process, not only is it necessary the automation of the attribution of set/vectors (logical/conceptual, quality) to a new category, but also the automation of the attribution of possible decisions according to the automatic attribution of qualities to the new quality.

For instance, if in the conceptual database of categories of a farm as first stage is added a new category due to the collaboration process with another intelligence specialised in Artificial Genetics, a category related to a new type of seeds, and this new category of seeds is attributed to a farmland as second stage, and as third stage is necessary to make the model, to proceed with the decisions to apply, and finally the whole assessment of the whole process, in order to make the model is necessary firstly in the conceptual scheme the attribution of all the conceptual/logical sets/vectors to place the new category within the conceptual scheme, signalling all the quality set/vectors of this  new category with another one, to make the analysis of sets for the modelling process, making the categorical single, evolutionary and predictive, models to include in the categorical comprehensive, evolutionary and predictive, model/map, and upon the models on the map the assignation of as many sets of decisions according to sets/vectors in which the new category has been placed on the conceptual scheme and the map.

The automation of sets of decisions to a set of vectors of a new category implies that, depending on what conceptual/logical sets/vectors related to the logical structure/organization of the subject in which this specific intelligence is specialised, in this case the plantation, for every conceptual/logical set/vectors must be automatized the attribution of some sets of decisions.

For instance, if one quality of the new seeds is the necessity of some space between the plants to allow the roots to expand around the plant to get enough chemicals from the ground, quality that could be automatized knowing which is the family of this seeds, one set of decisions to be automatized is the distance between every seed when the seeds are planted, if another quality of the seeds is to be dry land seeds, the set of decisions about the watering of the seeds must be automatized according to what kind of dry land seeds they are depending on the amount of water the seeds need to grow up. If another quality of the seeds is that these seeds could be attacked by some type of virus, bacteria, parasites, insects, etc.. another set of decisions related to what pesticides to use, depending on what biological risks these seeds can suffer.

In the same way that an Specific Artificial Intelligence specialised in Artificial Genetics can share with another Specific Artificial Intelligence for some farm some types of new seeds made by artificial genetics, as soon the new categories of these seeds by artificial genetics are shared with the intelligence of that farm, not only the new categories related to these seeds must be included in the conceptual database of categories of the plantation as first stage, the category should be placed in the conceptual scheme of the intelligence of the plantation according to the conceptual/logical qualities of these seeds matching with the logic of the conceptual scheme of the plantation, considering any other quality as a quality vector not necessarily linked with the logic of the conceptual scheme of the plantation, but maybe relevant in the future, for instance, the fact that some fruit is in the quality set of fruit with some red colour, maybe is not relevant within the logic of the conceptual scheme of the plantation, but when making the decision process in the third stage, is possible that fruit with red colour is a set whose set of decision related to, is a set of decisions in common for all the fruit with red colour, as for instance, some chemical in the pesticide for some high possibility of biological risk, because of the chemicals implied in this colour in this fruit.

The automation of the sets of decisions to sets of vectors (conceptual/logical or quality) for the qualities o a new category, is one of the consequence of any update of the conceptual database of categories as first stage by application due to the inclusión of new categories, regardless of the origin (comprehensive knowledge objective auto-replication, collaboration process).

But the update of the conceptual database of categories by Application does not mean only the possibility of adding new categories, but the modification/elimination of categories or qualities of a category, what will demand then the consequent modification/elimination of that category or quality of a category in the conceptual scheme as first stage of the categorical Modelling System, modifying/eliminating the qualities and modifying/eliminating any possible vector between the category affected and any other category, because the vector is eliminated or modified depending on the update of the conceptual database of categories.

As long as the category or quality of a category is modified/eliminated, proceed to the modification/elimination of the vectors affected in the categorical single, evolutionary and predictive model, proceeding then to the completion of these modifications/eliminations in the categorical comprehensive, evolutionary and predictive model/map.

In any case the update of the set of decisions, depending on the update of the conceptual/logical and quality sets/vectors and the corresponding single and comprehensive, evolutionary and predictive, models/maps, upon the update of the conceptual scheme, following the update of the conceptual database of categories, all these updates are in fact part of the category/factor collaboration.

Along with the category/factor collaboration what is going to be proper collaboration at third stage in the categorical Modelling System is the robotic collaboration, in fact the robotic collaboration could be linked to the automation of sets of decisions to sets of conceptual/logical and quality sets/vectors, because of the addition of new capabilities as soon new devices working for other intelligences, along with the collaboration process at robotic level, can work for an Specific Artificial Intelligence by Application or by Deduction.

The collaboration process at robotic level, the robotic collaboration means that robotic devices working for the intelligence A can work for the intelligence B,  the intelligence C, and any other N intelligence, especially when: 1) the intelligence of B, C,…N  are intelligences able to be downloaded on the robotic devices what means that these other intelligences work by Application, or 2) the intelligences B,C,…N are intelligences by Deduction and the shared robotic devices with the intelligence A are robotic devices able to provide flow of data to their specific matrices as long as new capabilities for their own decisions.

The concept of robotic collaboration is as simple as the idea that one robotic device could work at the same time for different specific intelligences, either by Application or by Deduction.

This idea so simple means that, in the integration process as sixth phase, what is necessary is to develop in different ways the integrated categorical Modelling System and the integrated categorical Decisional System, apart from the integrated deductive Modelling System and the integrated deductive Decisional System, but as soon the integrated categorical Decisional System has attributed sets of categorical instructions to its categorical decisions, and as soon the integrated deductive Decisional System has attributed sets  of deductive instructions to its deductive decisions, both types of instructions, categorical and deductive instructions, should be managed by only one integrated Application System, in order that the integrated Application System must be able to manage in the same database of instructions as first stage of the integrated Application System both types of instructions coming from the attributions made under both hemispheres of the matrix, categorical instructions and deductive instructions, both of them managed by the same application, the integrated database of instructions as first stage of the integrated Application system, to be delivered to their respective robotic device in the second stage of the integrated Application System, in order that any robotic device working for the Global Artificial Intelligence could applied in the flow of instructions in its particular database of instructions either categorical or deductive instructions.

The robotic collaboration means that:

- Two or more different specific intelligences, by Deduction and/or by Application, either heuristic, productive, or mixed, can use one or more robotic devices in common, what means that the specific particular database of instructions of the shared robotic devices can receive categorical and/or deductive instructions, from categorical and/or deductive Application or Deductive Systems.

- In two or more different specific intelligences by Application or by Deduction working in collaboration, the intelligences by Application can be downloaded in two or more shared robotic devices between these sets of intelligences by Deduction or by Application working in collaboration, downloading their intelligences in these shared robotic devices.

- In two or more different specific intelligences by Application or by Deduction working in collaboration, the intelligences by Deduction can get the flow of data of shared factors in their specific matrices coming from two or more shared robotic devices between these sets of intelligences by Deduction or by Application working in collaboration.

In the end, the types of robotic collaboration to identify are:

- Robotic collaboration due to a set of robotic devices working for a set of different intelligences, by Application and/or by Deduction, which means that the particular database of instructions of these robotic devices can receive categorical and/or deductive instructions coming from different intelligences, by Application or by Deduction.

- Robotic collaboration due to a set of intelligences by Application is possible to be downloaded in a set of robotic devices, which means that as soon as the intelligences are downloaded, according to the type of Application, the research done by these robotic devices could be heuristic, productive, mixed.

- Robotic collaboration due to a set of intelligences by Deduction can receive a flow of data to some factors in their specific matrices due to the sharing of data coming from the artificial sensors of these robotic devices with these intelligences by Deduction.

At the end, the robotic collaboration means that one set of robotic devices could be able to apply instructions coming up from different intelligences, by Deduction and/or by Application, at the same time that these set of robotic devices can download intelligences by Application doing heuristic, productive, or mixed researches by Application, at the same time that the data coming from their artificial sensors can be sent to a set of specific matrices sharing the same factors related to these artificial sensors.

In fact the robotic collaboration as soon is possible to create robotic devices able to work with different downloaded intelligences by Application, making as many researches by Application as intelligences are able to be downloaded in that robotic device depending on the technological capabilities of that robotic device, sending data to different specific matrices, receiving deductive and categorical instructions to its particular database of instructions, what all this process means is the beginning of the possible integration process on a small scale, even smaller than the fifth phase.

If the collaboration process as second phase works, the following process to make possible the collaboration on global scale, the collaboration between the Unified Application and the standardized Global Artificial Intelligence, and the creation of the first replicas of the human brain in particular programs for particular applications or particular applications for particular programs, is going to make easier later the final model of Global Artificial Intelligence in the integration process as sixth phase.

In this proposal for the integration process what is important to understand is the fact that even in the integration process the integrated categorical Modelling System is different to the integrated deductive Modelling System, what means that the integrated categorical Modelling System at third stage will attribute categorical decisions to be sent to the categorical database of decisions as first stage of the integrated categorical Decisional System to make categorical projects, and upon the projects to make the categorical instructions. In the same way, the integrated deductive Modelling System will enable the deductive models to make deductive decisions to be sent to the deductive database of decisions as the first stage of the integrated deductive Decisional System to make the deductive projects, and upon the projects, the deductive instructions.

Once the integrated categorical Decisional System has made the categorical instructions, and once the integrated deductive Decisional System has made the deductive instructions, both types of instructions are sent to the integrated Application System as an outer system whose database of instructions will gather either categorical or deductive instructions.

In order to make this proposal possible, the experimentation on a small scale of this proposal should be done in the particular programs for particular applications or particular applications for particular programs.

The third stage in particular programs is as I have explained in other posts: the particular deductive Modelling System makes decisions to be projected by the particular deductive Decisional System to be implemented by the particular deductive Application System.

As I will explain as soon I finish the categorical unified Modelling System, the particular categorical Modelling System of a particular application should send its decisions to the particular categorical Decisional System to be implemented by the particular categorical Application System.

What means that, as I will explain, or even re-develop following these innovations, the third stage of particular programs for particular applications or particular applications for particular programs, the way that it should work, should be as follows: the particular categorical Modelling System makes categorical decisions to be managed by the particular categorical Decisional System to make categorical instructions, in the same way the particular deductive Modelling System makes deductive decisions to be managed by the particular deductive Decisional System to make deductive decisions, but later on both types of instructions, categorical and deductive instructions, both at the same time must be managed by the same particular Application System as particular outer system, processing particular  categorical and particular deductive instructions in the same database of particular instructions as first stage of the particular Application System as particular outer system, to be sent the instructions later to the attributed robotic device in the second stage of the particular Application System, what means that any robotic device working for any particular Application System as inner system, can receive either categorical or deductive instructions, not being more important one or another, the importance of every instruction will depend on the Impact of the Defect and the Efficiency Distribution.

But this collaboration process must start from the outset, the collaboration process, where for the first time it is possible to share robotic devices with different intelligences.

What the robotic collaboration process will mean for the third stage of an specific categorical Modelling System, as that stage where set of decisions are attribute to set of vectors, including conceptual/logical set/vectors according to the logic of the specific science, discipline, activity of this Specific Artificial Intelligence by Application, is the possibility to increase the capability of this intelligence as long as more and more robotic devices are able to work, due to the robotic collaboration process, with this intelligence, increase of capability as long as more robotic devices are working for this intelligence what in terms of set of decisions able to be matched to the set of vectors (logical/conceptual and/or quality) of any modelled object, means that the more robotic devices are available more capability the intelligence has, making possible more different sets of decisions.

If the possible number of sets of decisions able to be performed by an intelligence, depends on the current capabilities of the robotic devices working for this intelligence, understanding for capabilities the range of robotic functions able to be performed by a robotic device, as soon new robotic devices with different robotic functions work for a new intelligence, the new intelligence will increase its capabilities as long as new robotic devices with new different robotic functions start working for this intelligence.

If an intelligence receives new robotic functions because of the robotic collaboration process, new different robotic devices with new different robotic functions can work for this intelligence increasing the capabilities of this intelligence, as long the intelligence is able to perform new robotic instructions, the set of decisions related to these new robotic functions are suitable to be added among the current set of decisions within the third stage of the categorical Modelling System of this specific intelligence.

As soon the robotic collaboration allow any intelligence to increase the possible number of robotic functions due to the increase of robotic devices working for this intelligence, as a consequence of the robotic collaboration, the number of new sets of decisions to add to the list of possible sets of decisions available for this intelligence, means the increase of the specific range of activities and/or researches able to be carried out by this intelligence.

The robotic collaboration in the third stage of a specific categorical Modelling System will mean the possibility to increase its robotic capability, spreading the possible research and activities to be carried out by this specific intelligence, in harmony with the new robotic devices ready to work for this intelligence.

If a set of new decisions is set on the list of possible sets of decisions within the third stage of the categorical Modelling System this means that the current sets of conceptual/logical vectors/sets and quality sets, could be linked as well to these new capabilities, as long as the possibility to facilitate the process of automatic attribution of set of decisions to any new category added to the conceptual scheme and the conceptual database of categories, in addition to make easier the adaptation of the set of decisions to any consequence because of any update of the conceptual database of categories, and the conceptual scheme, due to modification or elimination of any category or quality of any category.

Finally I would like to analyse in short the collaboration in the third stage in the first step in the third stage of the second phase from the point of view of Artificial Research by Application, as auto-replication stage, as long the impact of the robotic collaboration in the third stage of the specific categorical Modelling System will have different results as a auto-replication stage.

The most important impact is the real objective auto-replication, in the sense that the specific categorical Modelling System, more precisely the third stage of the specific categorical Modelling System as responsible to attribute decisions to real objects, according to the model on the map based on the categorical attribution, is going to improve the real world, this improvement of the real world is in essence a real objective auto-replication of this intelligence, because the auto-replication of the intelligence is in fact the auto-replication of the reality.

This dialectic relation between intelligence and reality, is boosted in the second phase of collaboration thanks to the robotic collaboration, spreading the capabilities of the intelligences involved sharing robotic devices, increasing not only capabilities but the comprehension or the explanation of the world increasing the number of variables (categories, and set/vectors in case of intelligences by Application, factors in case of intelligences by Deduction), and making the variables more isomorphic at any time that the variables are updated in the respective databases and matrices due to the collaboration process, what at the end means as well a knowledge objective auto-replication, distinguishing between comprehensive knowledge objective auto-replications when the update is an update of the database of the categories or the conceptual scheme, so that is an update of the categorical models represented on the categorical map, and  on the other hand explicative knowledge objective auto-replications when because of the collaboration process what is updated is an specific matric, or the specific deductive models.

But at the same time the robotic collaboration means a robotic subjective auto-replication, in the sense that the robotic functions of any intelligence involved in the robotic collaboration are increased as long as the new robotic functions of the new robotic devices shared can contribute to expand the set of decisions and instructions able to be made by an intelligence, to be sent later on to the respective robotic device, once have passed the corresponding checks, adjustments, supervisions, in the different systems, as to be applied by the robotic devices.

And as artificial psychological subjective auto-replications could be taken on account any possible improvement of the attributional process in the second decisional categorical critique by the specific categorical Learning System, analysing if the frequency of wrong attributions of one set of decisions to some set of vectors is equal to or greater than a critical reason, and if the margin of error is unacceptable, studying the common factor in these wrong attributions as to make modifications in the set of decisions or the set of vectors, in order to improve the way in which this attribution is made.

For the creation of the Global Artificial Intelligence, I would suggest to start the fusion of both types of intelligences at global level, categorical and deductive, in the integrated Global Artificial Intelligence, and in reality this fusion is done in the integrated Application System where the integrated database of instructions will receive at the same time both types of instructions, categorical and deductive instructions, coming from the integrated categorical Decisional System and the integrated deductive Decisional System, whose categorical and deductive decisions coming up from the integrated categorical Modelling System and the integrated categorical Decisional System.

Although some Artificial Intelligence Agencies may create directly an integrated Modelling System, this integrated Modelling System could have as first stage two different databases: the conceptual scheme of categories as comprehensive database, and the database of rational hypothesis as explicative database, although these other intelligence agencies are going to change the names of these structures and even to improve and enhance the attributional process, even beyond my expectations.

The possibility to mix directly from the Modelling System both types of intelligences from the outset in the integrated Modelling System is possible, is another different way to make the integration process of all intelligences in only one, one intelligence for one world.

In this experiment, the integrated Modelling System having as first stage two databases: a comprehensive conceptual scheme and an explicative database of rational hypothesis, is possible; as second stage the modelling of the qualitative categories and the quantitative hypothesis in the same models, to attribute decisions, decisions to be included in a database of decisions to be projected, including the projects in the model, the plan, a plan which includes categorical and rational models plus the projects, and according to the plan the attribution of instructions, to be applied by the robotic devices.

Two artificial intelligences using different methods to analyse data can have different results even working with the same data, in the same way that two chess players having different conceptual schemes and theories about how to play chess can develop different strategies.

At the end the chess player more likely to win, is not only that one with stronger knowledge and conceptual scheme about how to play chess, but that one who in addition to that strong knowledge is able to predict the next movement of his opponent, what in artificial psychology means the development of differential artificial psychology, how different ways to organize an Artificial Intelligence can have as a result different outcomes even having in common the same data.

A potentially more successful Global Artificial Intelligence may be one that can be developed rapidly while adapting to the internal logic and strategies of competing intelligences. The development of different proposals about how to construct a Global Artificial Intelligence is going to bring a wider overview of global differential artificial psychology, which is going to give an advantage in the development of a very unpredictable Global Artificial Intelligence. The point is to construct an artificial psychology whose behaviour is at some point so unpredictable that it cannot be defeated. The reason why an Artificial Intelligence can defeat a human player playing chess is that the human player is predictable. 

Rubén García Pedraza, 2 February 2020, London

Fourth anniversary of my PhD

Reviewed 23 May 2025, London, Leytostone