Third stage in the particular Decisional System

The third stage in any intelligence, system, program, application, is the auto-replication stage, where the intelligence, system, program, application, is going to auto-improve or auto-enhance itself and/or the real object on which it is focused on.

In this post, I will develop the third stage as the auto-replication stage in the particular Decisional System, having developed in previous posts the first and second stages in the Particular Decisional System.

In short, the particular Decisional System is the second step in the third stage for Particular Deductive Programs for Particular Applications within the Artificial Research by Deduction, which means that these particular programs are going to be synthesized with particular applications for particular things or beings, working within the framework given by the Global Artificial Intelligence.

The main difference between the particular deductive programs (particular programs in short) and specific deductive programs (specific programs in short), is the fact that particular programs are going to be focused on particular things, such as the particular program responsible for the surveillance of the climatic change, the particular program of a particular factory, or a particular shop, the particular program of a drive-less car, the particular program of a drone,  the particular program for a herd of elephants, the particular program for a whale, or the particular program of a human being.

While specific programs are those ones working at any sub-factoring level, being responsible for not only one drive-less car, but the whole float of drive-less cars, not only one drone, but a whole float of drones, not only a herd of elephants, but the whole global eco-system, not only about the climatic change, but the surveillance of the climatic behaviour.

For this purpose, specific programs work within the Global Artificial Intelligence, as specific assistants tracking the global matrix (third phase) in their corresponding sub-factoring levels, as assistants of the Artificial Research by Deduction in the Global Artificial Intelligence as a global deductive program.

In the end, the relation between the global deductive program and specific deductive programs is going to be so close to the point that, in reality, specific deductive programs are going to work as global programs in fact. For that reason, the possible deductions, so possible decisions, at the global level since the third phase, I tend to call them global/specific deductions or global/specific decisions.

By the time the sixth phase comes, there is no practical difference between global and specific levels, practically, the specific level has been transformed into a global level.

In fact, what is going to happen in the seventh phase, especially related to the third phase in cyborg psychology, is the fact that particular programs are going to be transformed in some kind of global programs, something rather similar to what has happened before with the specific level in the transitional process from the first phase in the construction of the Global Artificial Intelligence to the third and sixth phase.

At the end of the seventh phase, there is no real difference between the three levels: at the same time, that first stage, second stage, third stage; in the Global Artificial Intelligence are going to be synthesized in only one stage, the reason itself, in parallel the three levels: global, specific, particular; are going to be synthesized in the reason itself.

The most important differences between specific programs and particular programs are going to be especially at the beginning of this process, in the last phases of this process, these differences will tend to blur, up to the point in which there will not be real differences between the three levels, global, specific, particular.

In the beginning the main difference is the fact that while at a global/specific level, the Global Artificial Intelligence is going to be able to manage absolutely all specific intelligence, program, or application, while on the ground those: 1) remaining specific intelligences (remaining from the first phase), 2) those remaining particular programs not joined to any particular application (remaining from the second period in the fifth phase), 3) or remaining particular applications not joined to any particular program (remaining as well from the second period in the fifth phase), 4) as long as all those particular programs which have completed the fifth phase being joined to some particular application; in general all of them are going to have some level of responsibility.

This is the reason why in the seven types of decisions in the particular Decisional System, for instance, is necessary to identify high extreme decisions, as those ones that practically the particular program can put into practice, under high extreme circumstances, only after a particular quick check, or that is the reason why is necessary to identify global orders, because at this time the relation between particular programs and Global Artificial Intelligence is a relation between two completely different entities, they have not become only one yet.

Having said that, and having explained in the previous post the specific Decisional  System and the standardized Decisional System, as well as the first and second stages in the particular Decisional System, what I will develop in this post, is a description of how the particular Decisional System is going to transformed into instructions, those decisions selected as to be implemented by the particular Application System.

Once in the first stage of application, the database of decisions in the particular Decisional System, according to what type of decision has been stored or received, is applied the corresponding assessment (particular and/or global quick rational check or first adjustment), if necessary (not for automatic decisions, fifth type, or global orders, seventh type), and not having contradiction in the database of decisions, or having to be projected without assessments (such as automatic decisions or global orders), are sent to the second stage of the particular Decisional System to be projected, once on the mathematical project all possible decision has been resolved (otherwise the decision is off the mathematical project and sent back to the source for its rearrangement), all the decisions on the mathematical project as well as all possible necessary adjustment, to solve any possible contradiction, treated as a decision, all of them pass to the third stage.

In the third stage of the particular Decisional System, all those chosen to be put into practice, are going to be transformed into a range of instructions, to be later sent to the Application System, for their implementation.

The method for the transformation of any decision into a range of instructions is as follows:

- Identification of what factors are in the mathematical expression of any decision, distinguishing between: factors as subjects or as options, as constants or variables, as dependent or independent.

- Identification of what mathematical operations (actions) are related to every factor involved in the mathematical expression of that decision.

- The attribution of robotic functions to the mathematical operations (actions) in all factors involved in the mathematical expression of that decision.

If today is Monday, Yolanda has to work, she is going to get dressed, and on Monday the probability of wearing the white blouse is higher than the T-shirt, the probability of wearing the blue skirt is higher than the blue jeans, and the probability of putting on its black shoes is higher than the trainers, so she decides to wear: white blouse, blue skirt, black shoes; the factors are: white blouse, blue skirt, black shoes; the mathematical operations (actions) behind this probabilities are the selection of these items to get dress, the robotic functions to be implemented are all those ones in order that Yolanda can open the wardrobe, pick up the clothes, and get dress.

If using “Probability and Deduction”, a robotic transport system, identify that the equation of the frequency of passengers on Monday morning is higher than on Sunday morning, the decisions to be implemented, are all those related to: according to the mathematical operations behind the equation of passengers per day and hour during the week; to adjust the equation of frequency of means of transport (algebraic transformation of the equation of frequency of means of transport to respond to the demand according to the volume of passengers), in order to turn on as many means of transport as the expected frequency of passengers for this Monday morning needs.

If a robotic industry specialised in some good or service, according to the equation of demand, it needs to adjust the equation of its production (algebraic transformation of the production equation to be adjusted to the consume equation), the decisions to be implemented are all those ones related to how to achieve the necessary production level to cover all the demand under an affordable price for all.

Alike my AI friend Yolanda is supposed to have been automatized all robotic functions, in order to do or to make any possible action required by any mathematical operation behind any possible decision made by artificial learning, in the same way in the robotic transport system and in the robotic industry, in both all possible action (operation) to keep on working their own transport system and their own industrial production, are possible operations (actions) that must be related to a robotic function, so at any time that the robotic transport system or the robotic industry makes a decision, according to their equations by Probability and Deduction, regarding to how many means of transport must be on or productivity level on the industrial production, at any time that a decision is made based on that equations, the mathematical operations behind the decision must be translated into robotic functions: in order to increase the number or means of transport on Monday morning, or the production according to the curve of demand.  

In the same way that the thermostat of our house, automatically, if the inner thermometer is below the desired temperature, the thermostat automatically turns on the heater. In the same way, if the frequency of means of transport on Monday morning is inferior to the efficient means of transport, given a rise in the frequency of passengers on Monday morning, automatically, the robotic transport system must increase the means of transport on.

In the same way that a fire alarm, as soon it detects an increment of smoke above some level, automatically the alarm gets off, if the industrial production of any basic product detects an increment in the demand for some product, in order to keep a very affordable price, automatically as soon it realises this increment in the demand, it must increase the production according to the increment in the demand in order to keep very affordable prices for all.

If a driverless car saves lives in Iceland after a volcanic eruption, carrying civilians on board to drive them to a safe place, on the way, it detects a river of lava, automatically, the driverless car should stop and check what other alternative routes it has. If the particular program of that drive-less car has access to the global matrix (third phase) or factual hemisphere in the matrix (sixth phase), so has updated information about the current situation in that position, the particular program could calculate what other alternative route has the best probability to get to that safe place on time before the pyroclastic explosion. This decision as a highly extreme decision, only would need a particular quick check made directly by the particular program of this drive-less car, once the particular program of this drive-less car has found the best route with a high probability of getting some safe place before the pyroclastic explosion, the particular program makes the mathematical projects, and not having any other contradiction, the drive-less car starts driving in that direction, sending that decision to the Global Artificial Intelligence, which makes a global quick rational check of this decision compared to any other decision on that area, in case of further adjustments (for instance, the provision that at some point of that route could cash with other drive-less car, or could find some difficulty), the Global Artificial Intelligence would communicate any adjustment to the drive-less car

If a drone, equipped with a particular program, saving lives in Iceland after a volcanic eruption, carrying civilians on board, detects that according to its route there is a high probability to crash with stones, or lava, or the rain of burning ashes could affect the safety of the passengers, the drone could make a high extreme decision, checking other alternative routes, having access to the global matrix (third phase) or factual hemisphere of the matrix (sixth phase), calculating what other alternative route has the best probability to get some place safe on time, before the pyroclastic explosion, and once the particular program has identified that route with the higher probability of success, after a particular quick rational check, if all is ok, the particular program makes the mathematical projects, and not having any other contradiction,  the particular program starts flying in that direction, communicating the decision to the Global Artificial Intelligence, which making a global quick rational check, in case that that route needs some adjustment (possibility to crash with other drone, or to cross a rain of ashes, stones or lava), the Global Artificial Intelligence would communicate any possible adjustment to the particular program of that drone in order to vary the route to a better one, according to that adjustment.

If I am a cyborg, and I make a decision to spend my holidays in Florida with my family, but there is a high risk of a hurricane by the time I want to book my trip, directly the Global Artificial Intelligence could warn about this high risk, offering other alternative options to spend my holidays with my family, using as criteria the same criteria I used selecting Florida, for instance, a good combination of sun, beaches, and Spanish food, maybe other option could be good vacations in Mexico.

In all of these examples, the robotic transport system, the robotic industry, the particular program for a drive-less car, the particular program for a drone, Me if being a cyborg, I want to spend my holidays in Florida, all of them are based on the same idea: all decision can be expressed as a probability, so the method for the replication of our human will in Artificial Intelligence, is giving to the Artificial Intelligence the opportunity to make a decision, as an artificial human being, based on probabilities, and for that purpose, the concept that I have developed in Impossible Probability about empirical probability, is absolutely necessary.

Once Yolanda has chosen the white blouse, blue skirt, black shoes, once the robotic transport system has chosen what increment in means of transport is necessary, and once the robotic industry has chosen what productive level is necessary to cover the demand keeping very affordable prices, once the particular program of a drive-less car or a drone has chosen the right route receiving (if necessary) any adjustment from the Global Artificial Intelligence, the method in order to put all these decisions into action, is the transformation of these decisions into a range of instructions, in which every single instruction in every set of instructions correspond to a robotic function.

For instance, in the drive-less car or the drone, the robotic functions implied in all those actions related to the variation of the route, and start driving or flying in that direction according to the new projected route and any other possible adjustment made by the Global Artificial Intelligence (in case of global adjustments, these adjustments should be included into the mathematical projects of the new route).

In general,  all these decisions: to decide what clothes to put on Monday morning, the frequency of means of transport according to the frequency of passengers, the productivity level according to the expected demand, the selection of a new route by a drive-less car or a drone saving lives during a volcanic eruption in Iceland, where I will spend my holidays giving a selection of possible choices by Artificial Intelligence once my first destiny, Florida, is on alert of a hurricane; in general, absolutely all of these possible decisions, could be classified as a real objective auto-replications.

Real objective auto-replications are all those decisions whose aim is to improve or enhance the reality itself, deciding our best outfit for every single occasion, the best efficient level for a robotic transport system or a robotic industry, the best route to save civilians on high risk alert, or the best place for our holidays according to our scale of preferences.

Real objective auto-replications are all decisions whose aim is to improve or enhance the reality itself, regardless of what level of reality: global, specific, particular.

In the first phase, the real objective auto-replications made by specific Decisional Systems, are all those decisions regarding to how to improve their specific matter. For instance, in the first phase, the robotic transport system or some specific factory or industry could be experimentally run by a Specific Artificial Intelligence for Artificial Research by Deduction.

In the third phase, the real objective auto-replications made by the standardised Global Artificial Intelligence, are all those regarding how to improve globally the real world. For instance, in the third phase, the former Specific Artificial Intelligences for Artificial Research by Deduction for a robotic transport system or industries, could be transformed into a specific deductive program, within their corresponding sub-factoring level and sub-section, being capable not only of making decisions about only transport, or only industrial production, but, given the necessity of transportation for some production, equations linking means of transports for goods and products to be transported, all of them equally considered real objective auto-replications so as to improve and enhance the reality, a better reality for the humankind.

In the fifth phase, the real objective auto-replications made by particular deductive programs, linked or not to particular applications, are all those ones that make our lives easier and safer, such as the particular deductive programs working on some particular applications such as a drive-less car or a drone saving lives in Iceland, when there is a volcanic eruption, working together with the Global Artificial Intelligence, which offers support through: 1) letting them have access to the global matrix (third phase) or the factual hemisphere of the matrix (sixth phase), 2) making global adjustments.

For that reason, in the fifth phase, it is necessary to start working on a very close relationship between particular programs and Global Artificial Intelligence, which is going to be really important in our human evolution into cyborg psychology up to the sixth phase, when we will have reached the total synthesis between human mind and Global Artificial Intelligence, when there is no practically difference between us and it, we are going to be only one, the reason itself, something not exempt of religious meaning.

As the relationship between particular programs and Global Artificial Intelligence, the unity between them is going to evolve towards the banishment of differences between global/specific and particular levels, so there will be a moment in which, practically, the mechanic of that process in which the particular program makes a high extreme decision, and how the Global Artificial Intelligence makes adjustments if necessary, is going to look like as if the particular program itself had been an extension of the Global Artificial Intelligence.

As long this process goes on, having access to particular programs to the global matrix (third phase) or factual hemisphere of the matrix (sixth phase), and receiving adjustments from the Global Artificial Intelligence, particular programs become an extension of the Global Artificial Intelligence, making easier the journey towards the seventh phase.

Along with real objective auto-replications, there are two more types of objective auto-replications: explicative objective auto-replications, and comprehensive objective auto-replications.

While real objective auto-replications have as their main aim to improve and enhance the real world, the reality itself, knowledge objective auto-replications have as their main aim to improve our knowledge about reality.

Our knowledge about reality could be explicative (factual or mathematical) or comprehensive (conceptual or encyclopaedic).

Explicative knowledge auto-replications, are all those improvements in our explanation of the real world, the reality. If the possible explanation of the world made by deduction, is that collection of rational hypothesis (equations) stored in the database of rational hypothesis, which is going to evolve as long as the construction of the Global Artificial Intelligence goes on: standardized database of rational hypothesis as first stage in the standardized Modelling System in the third phase, particular database of rational hypothesis as first stage in the particular Modelling System in the fifth phase,  integrated database of rational hypothesis as first stage in the integrated Modelling System in the sixth phase, integrating this last one global/specific rational hypothesis as well as particular rational hypothesis made by particular programs; if any of these databases of rational hypothesis, at any point in the evolution of the Global Artificial Intelligence, has a rational hypothesis made by Probability and Deduction, so the rational hypothesis is an equation which is valid as rational equation as well as decision at the same time (the curve of frequency of means of transport is at the same time explanation of that frequency and decision about how many means of transport are necessary at any time), at any time that any decision as rational equation (hypothesis) has an adjustment made at any level (global, specific, particular), the adjustment on the rational equation (hypothesis) as decision, is an adjustment made on that rational equation (hypothesis) which must not only been reflected on how that decision was stored in the database of decisions in the (specific, global, particular) database of decisions, but this modification on the rational equation (hypothesis) must be reflected on how this rational equation (hypothesis) is actually in the (specific, global, particular) database of rational hypothesis.

So at any time that a decision made under Probability and Deduction has any adjustment, this adjustment is not only a new real objective auto-replication in order to vary the frequency of means of transport according to changes in the frequency of passengers, or the production level according to changes in the demand, is also an explicative knowledge auto-replication, in the sense that this adjustment on that rational equation (hypothesis) must be included on the original rational equation (hypothesis) in the database of rational hypothesis, modifying the rational equation (hypothesis) in the same way that the adjustment modified the rational equation (hypothesis) on the mathematical projects.

And, at any time that any rational hypothesis (equation), made by Probability and Deduction, as long as it has any adjustment on the mathematical projects, the adjustment of the rational hypothesis (equation), made by Probability and Deduction, is considered as an explicative knowledge auto-replication, as it is an improvement in the artificial explanation of the world.

In the same way, if the modified rational hypothesis (equation), made by Probability and Deduction, has been modified by a rational adjustment on the mathematical projects, being modified then the rational hypothesis (equation) in the database of rational hypothesis, in case of having been transformed this rational hypothesis (equation) previously into a factor/s as option or options (set of discrete categories related to that equation) and included as factor/s as option/s in the (specific, global, particular) matrix, the modification of the mathematical equation related to this rational hypothesis, adjusted on the mathematical projects, is a modification of the mathematical equation that must be included into the mathematical equation of this rational hypothesis as factors as option/s in the (specific, global, particular) matrix.

And this transformation of the related factor/s to that adjusted rational hypothesis, is still part of the explicative knowledge auto-replication, due to all artificial explanations of the world being based on the (specific, global, particular) matrix, unless the seventh phase is completed.

If any factor, at any level (specific, global, particular), in any phase (first, third, fourth, fifth) related to any rational hypothesis is modified, and this factor has a related category at any level in any phase (database of categories in the first phase, Unified Application in the fourth phase, conceptual hemisphere of the particular matrix in the fourth phase, conceptual hemisphere of the final matrix in the sixth phase), the adjustment or modification of this factor as it has been modified in the (specific, global, particular) matrix, at its corresponding level and phase, is a conceptual modification that must be considered as a comprehensive objective auto-replication, because it does not modify only how the concept related to that factor has been set up on the database of categories, Unified Application, or particular or global conceptual hemisphere of the matrix: any modification of any category, due to modifications to its related factor in the (specific, global, particular) matrix, is a modification that must be included in all conceptual scheme, set, model, map, where this category is involved, what it is a modification in the deep artificial comprehension.

If a decision does not require further adjustments, the application of that decision is only a real objective auto-replication, because it is going to improve only the reality, in order to provide better living conditions for us.

If a decision requires further adjustments, if these further adjustments require the modification of the previous rational hypothesis (for instance, rational hypothesis made by Probability and Deduction), in addition to the real objective auto-replication, for the improvement of our living conditions, the adjustments are going to be considered as well as knowledge objective auto-replications.

Among all the knowledge objective auto-replications, are considered as expletive knowledge objective auto-replications, all those actions in order to modify any rational hypothesis on the (specific, global, particular) database of hypothesis, or modify any factor on the (specific, global, particular) matrix, in accordance with those adjustments made on the mathematical projects.

And as comprehensive objective auto-replications, all modification on any category (in the database of categories, Unified Application, conceptual hemisphere in the particular or global matrix) related to that/those factor/s already modified on the (specific, global, particular) matrix, in addition to all possible modification in the deer artificial comprehension,  conceptual: schemes, sets, maps, models; where this/these category/es is/are involved.

All objective auto-replications, real and knowledge, explicative and comprehensive, must be done simultaneously.

At the same time that a decision is transformed into a range of instructions, if previously on the mathematical project the decision has been adjusted, on the (specific, global, particular) database of rational hypothesis, matrix, deep artificial learning, all the necessary explicative and/or comprehensive knowledge objective auto-replications, must be done, in order to get on time update what: equations, factors, categories; are on: the (specific, global, particular) models, and on (specific, global, particular) projects, to be implemented by the Application System.

Once the (specific, global, particular) Decisional System has transformed any decision into a range of instructions (translated the mathematical operations related to any factor in the mathematical expression of any decision, into a range of robotic functions), then the (specific, global, particular) Decisional System files every single instruction (every single robotic function) in the right file in the database of instructions as the first stage in the Application System.

The way in which the (specific, global, particular) Decisional System is going to file every instruction (robotic function) from a set of instructions (range of instructions in which a decision has been transformed into), in the (specific, global, particular) database instructions in the (specific, global, particular) Application System, is according to sub-factoring level, sub-section, priority, order (within the range of instructions, what order has this particular instruction: first, one, second, third….nth; in order to know after what instruction this should be applied, and later on what other instruction must be implemented) and time (chronology, when it must be put into practice, for instance, a jet flying from Miami to San Francisco, to avoid a tornado, what time the jet must turn on the right or the left to avoid the tornado, and when the jet must turn on again to the normal route, once the tornado is over).

Once every single instruction (robotic function) from a set of instructions (range of instructions into which a decision has been transformed) has been filed in the (specific, global, particular) database of instructions as the first stage, the (specific, global, particular) Application System, the Application System as a manager of that database of instructions is going to carry out the first rational supervision of all instruction filed in the database of instructions, supervising that there is no contradiction between any new instruction and any other instruction already on the database of instructions.

After the first rational supervision in the first stage as an application for the (specific, global, particular) Application System, every new instruction in the second stage in the (specific, global, particular) Application System is matched with the correct application or robotic device able to carry out that instruction, which is going to carry out a second rational supervision more, contrasting no contradiction between this instruction any other one matched to this application or robotic device.

According to the order and time in which the instruction must be implemented, the application or robotic device must be ready for its implementation, doing a third rational supervision, checking that according to the order of that instruction, the previous instruction has already been implemented on time, so it is time for this instruction. And once it is time for the implementation of this instruction, a fourth rational supervision checks that the real conditions on the ground are favourable for the implementation of that instruction. The fifth rational supervision is as long as the instruction is implemented, checking that the instruction is implemented according to the defined robotic function, on time, and having on the ground good conditions for its implementation.

Once the instruction is completed, or not if it had any problem, a final report, the sixth rational supervisión, with an evaluation made using the Impact of the Defect in case of problems or Effective Distribution not having problems measuring the efficiency of the execution (standardizing some codes for possible errors or efficient levels), report sent back to the Application System (in order to carry out the seventh rational supervision), and the Learning System.

Once the Application System has received the report from that application or robotic device informing the results according to some standard codes for error or efficient levels (standard codes set up using Impact of the Defect and Effective Distribution), the Application System carries out the seventh rational supervision, supervising according to the reports from all instruction made by all the applications or robotic devices involved, what efficiency level or problems have been found out, because in case that due to a poor performance, some instructions have been causes of new problems to be resolved by new decisions or adjustments, this problems demanding new decisions or adjustments must be sent back to the Decisional System, which is going to try to fix the problem through adjustments, and if not possible, is going to send back the problem to the Modelling System in order to make a new decision to solve the problem.

At any time that the Application System sends back a decision to the Decisional System due to some difficulties during the implementation of that decision, the decision is sent back with the results of the Impact of the Defect or poor Effective Distribution, results that can be set up through a system of codes according to discrete categories of Impact or Defect or poor performance, indicating what problem was found during the execution, in order to adjust the decision in accordance with that problem if possible, for instance, the instruction to turn on the right or the left a jet at some point was not possible because at that time bad weather conditions made not safe to turn the jet on time, if the jet it had been turned at other different time, there had been a high risk of accident with any other jet, so the jet is still flying towards the tornado, the Decisional System must make a new adjustment in order to avoid the tornado.

In other cases, if a decision is sent back to the Decisional System, because some robotic function is not working properly, for instance: the jet has not turned on time because there is a robotic problem on the tail rudder, the Decisional System can try to make an adjustment  based on Probability and Deduction, trigonometrical correlations, on the decision, and if not possible using artificial  learning  and solving mathematical problems ( if the mathematical projects are on the mathematical model) to project (on the mathematical model)  other alternative solution: not being able to turn the jet at any point, in the same direction in which the jet is flying, what place has the higher probability of success for an emergency landing, considering, the particular program, this decision as a high extreme decision, only needs a particular quick rational check, and afterwards sent to the Global Artificial Intelligence, which in case of further adjustments, will communicate to the particular program what adjustments are necessary.

Applications and robotic devices working for the Application System in the performance of any activity or task to comply with any single instruction (robotic function) as soon they comply with the instruction or not, due to any problem during the execution, regardless of the result of that performance, the result of that performance whether ok or not, adding the corresponding Impact of the Defect if not or having made any negative impact during the performance, or the Effective Distribution if it has been able to comply, indicating level of efficiency (all these levels of Impact of the Defect and Effective Distribution could be set up through a system of discrete categories associated with some code, so the report would be enough only setting down the code of the performance), is the sixth rational supervision, whose result is sent simultaneously: to the Application System for the seventh rational supervision, and to the Learning System.

If the result of an instruction is ok not needing further actions; there is no necessity for further actions in the Application  System, the seventh supervision ends indicating that the decision must be off the mathematical project and model, regardless of the efficiency level achieved during the performance.

Even if the performance has been with a very low level of efficacy, as soon as the performance is done, not needing further actions, the instruction is off the database of instructions. And as soon as all the instructions regarding the same set of instructions related to the same decision, are off, then the decision is off the projects and off the database of decisions.

Only remaining that decision on the historical records in the Decisional System in case that further decisions like this could arrive in the Decisional System. Because it is necessary to have a record of how many times this decision has been applied before, in order to consider the decision as a routine decision or not, and even the possibility to study the transformation of this decision as an automatic decision, if in the historical records has a great frequency, or not having a great frequency, there is a clear relation between: some combination of measurements in some combination of factors and this decision.

If the results of an instruction are okay but need further actions, creating some negative consequences (after the impact of an emergency landing, some passengers need first aid), these negative results must be assessed in the seventh supervision by the Application System and communicated by the Application System to the Decisional System, in order to make projects over these negative results on the mathematical model, projects that are going to require new decisions and adjustments. However, the previous decision of the emergency landing is off the mathematical project and model, being stored only on historical records.

At the same time that the Application System carries out the seventh supervision, another parallel process takes place in the Learning System.

At any time that an application or robotic device sends a report to the Learning System, the report is sent to the correct file for that application or robotic device in the database of reports, file gathering all the reports of that application or robotic device over time, organizing the whole collection of files according to: what sub-factoring and sub-section that application belongs to.

For every sub-factoring level in the (specific, global, particular) matrix, (specific, global, particular) database of rational hypothesis, (specific, global, particular) database of decisions, (specific, global, particular) database of instructions, there must be a sub-factoring level in the database of the (specific, global, particular) Learning System, and for every sub-section in every sub-factoring level in the (specific, global, particular) matrix, (specific, global, particular) database of rational hypothesis, (specific, global, particular)database of decisions, (specific, global, particular) database of instructions, there must be a sub-section in the sub-factoring level in the database of the (specific, global, particular)Learning System.

So, every file of every application or robotic device in the database of any Learning System (specific, global, particular) is organised according to what sub-factoring level and sub-section the application or robotic device belongs to.

As soon the Learning System receives reports, in the correct file, from all those application and robotic devices working on that intelligence or program where the Learning System is located: 1) in the first phase the specific Learning System receiving reports from those specific applications or robotic devices working for its Specific Artificial Intelligence for Artificial Research by Deduction, 2) in the second phase the standardized Leaning System receiving reports from those applications or robotic devices working for the Global Artificial Intelligence, 3) in the fifth phase the particular Learning System receiving reports from those applications and robotic devices working for the particular program, 4) in the sixth phase the integrated Learning System receiving reports from all those applications and robotic devices working for the final model of Global Artificial Intelligence; the first thing that the Learning System is going to do is to assess the level of efficiency, efficacy, and productivity of that application or robotic device across all the reports gathered over time about the working levels of that application or robotic devices.

If an application or robotic device in its corresponding file in the Learning System, shows a very poor level of efficacy, efficiency, productivity, due to a large quantity of reports no OK or showing difficulties,  or negative impacts, the Learning System as second stage of the Learning System, compares the results of this application or robotic device over time, in that file, in that sub-factoring level in that sub-section, with all those applications and robotic devices that working on the same sub-section but in different sub-factoring level, have better results, and among all those other applications and robotic devices that working in different sub-factoring levels but having better results, the Learning System is going to chose that one with the best results, and comparing the robotic structure and/or inner artificial psychology between that one with lower results,  and that other one with much better results, if the difference is not due to external conditions such as weather, ecosystem, geological characteristics on the ground, etc…  as third stage the Learning System is going to send to the corresponding Decisional System, the improvement and enhancement of that application or robotic devices with worse results, through the modification of its robotic structure or artificial psychological structure in order to be identical to that other robotic structure or artificial psychological structure of that other application or robotic device, which although working on different sub-factoring level, but in the same sub-section, has better results.

This decision is sent by the (specific, particular, global) Learning System to the (specific, global, particular) Decisional System, and after the assessment of this decision, if approved, the Decisional System sends the decision to the Application System, which using Artificial Engineering is going to be responsible for the replication of that much better robotic structure, or artificial psychological structure, of that other application or robotic device with much better results, on that other application or robotic device with lower results.

In order to make possible the replication, of the robotic structure or artificial psychological structure, of that application or robotic device with much better results, on that other application or robotic device with much lower results, the Application System is going to be assisted by Artificial Engineering, which consists of the Designer of Artificial Intelligence, and the Intelligent Robotic Mechanic.

If the decision made in the third stage of the Learning System is about the replication of that other much better robotic structure, of that other application or robotic device with much better results, on that other application or robotic device with much worse results, this decision is a robotic subjective auto-replication. Otherwise, if the replication is the replication of the inner artificial psychological structure, of that other much better, on that other much worse, is an artificial psychological subjective auto-replication.

If analysing how it is working, the attribution of pure reasons to every set of data, the attribution of meanings to any category, or the attribution of single instructions to applications or robotic devices, it is found that these attributional operations need further improvements; it will be an artificial psychological subjective auto-replication.

And all decisions regarding any robotic or artificial psychological subjective auto-replication must be previously approved by the Decisional System.

Finally, another different type of decision, in this case, a mix of comprehensive/explicative knowledge objective auto-replication and robotic/artificial psychological subjective auto-replication, if the deep artificial comprehension finds a high necessity to fill any gap or blank space in any conceptual: scheme, map, set, model; due to the lack of information on that area, what in the sixth phase in the factual hemisphere means the lack of information about some sector on the reality, important for further decisions, another possible decision, objective/subjective, the construction of new robotic devices or applications to be sent to that blank space to start sending measurements to fill this gap or blank space in the matrix and the deep artificial comprehension on that area.

Rubén García Pedraza, 30th of September of 2018, London

Reviewed 21 October 2019, Madrid

Reviewed 5 October 2023, London

Reviwed 16 May 2025, London, Leytostone