Third stage of the Modelling System in the integration process

The third stage is the auto-replication stage or decision stage, which includes all processes for the auto-improvement and enhancement of any intelligence, program, or application, as well as all those processes to make decisions and put them into practice.

The third stage of the Modelling System in the integration process comprehends all those processes for the auto-improvement and auto-enhancement of the global Modelling System as the first step in the third stage in the sixth phase, including the decision making process upon the mathematical models elaborated in the second stage of this Modelling System.

In the decision stage, the decisions are made upon the models elaborated in the second stage of the global Modelling System, to be sent to the database of decisions in the global Decisional System, which filters all decisions (including global decisions and particular decisions sent by particular programs), choosing only the most rational without contradictions to the mathematical project, and after decomposing the chosen decisions in a sequence of instructions, the instructions are sent to the database of instructions in the Application System to be applied.

In general, as an auto-replication stage, in addition to the decision making process, the third stage of the Modelling System, includes a wide range of operations for the auto-improvement or auto-enhancement of the Modelling System itself and any other intelligence, program, application, with auto-replication processes linked to the auto-replication process in the Modelling System, for instance, any improvement or enhancement in the factual hemisphere as a consequence of adding a new rational hypothesis as a factor as option, what it is in fact an explicative knowledge objective auto-replication, among others.

In general, the auto-replication processes involved in the third stage in the global Modelling System are:

- Real objective auto-replications in the global Modelling System: the making decision process, including protective and bettering research decisions, learning decisions, and solving maths problems decisions, which will be explained in detail later.

- Explicative knowledge objective auto-replications in the global Modelling System: all those ones related to the inclusion, modification, or elimination, of rational hypotheses in: the global rational truth, or any particular rational truth, and the chain of changes in the factual hemisphere of the matrix in the Global Artificial Intelligence, or any other factual hemisphere in any other particular matrix; after any rational check or rational comparison.

- Comprehensive knowledge objective auto-replications in the global Modelling System: because of the changes in the conceptual hemisphere of the matrix, or particular matrices, and conceptual schemes, maps, sets, models, in the Global Artificial Intelligence, or particular programs, as a consequence of inclusion, modification, or elimination, of rational hypothesis in the rational truth susceptible to be transformed into categories.

- Artificial psychological subjective auto-replications in the global Modelling System: all those processes in order to improve and enhance the inner global artificial psychology through the  critique of the pure reason, the critique of the deductive programs, as well as the critique of the attributional operation (although this one is not only related to the Modelling System), along with any other improvement or enhancement in the Modelling System made by the Learning System.

- Robotic subjective auto-replications in the global Modelling System: for instance, if after criticising the pure reason or the deductive programs, having as a source of information the rational checks and rational comparisons in the Modelling System, having identified some sources of error the Learning System, and having authorization from the Decisional System, any modification as a consequence of these processes in the pure reason or any deductive program is made by the Artificial Engineering.

Among all these kinds of auto-replication, the first one, the real objective auto-replication process, is the one related to the decision process, to protect and better the global model,

The reason why the real objective auto-replications, although its decisions finally are put into practice in the real world, are referred as to protect and better the global model, and not the reality itself, is because of the higher level of reliability in the global model.

While the global model is based on the rational truth, so rationally contrasted, the reality itself, as a synthetic world or material world, is not contrasted yet, not so reliable.

We can trust in the global model because it is rational (psychological), but we cannot trust in a simple perception or measurement (artificial perception) because it is not rational yet (not psychological yet). We only can trust in rational (psychological) knowledge.

The only rational certainty of our real existence in this world, is not data, but the rational explanation of our existence based on the rational criticism: the rational hypothesis of our existence based on mathematical models, in order to make mathematical projects, to make and choose rational decisions, to secure our survival in a not-reliable material world full of risks.

The most important risk is to think that something is the truth when it is false. The survival of the human being in a not-reliable world full of risks rests on a mathematical (rational or psychological) basis.

We do not know we really exist because of our perceptions or measurements (artificial perception), only by criticising the data we achieve the rational truth of our existence, and upon the rational explanation of our existence to secure our survival through the most rational decisions without contradiction on the mathematical project.

Due to the superiority of the Global Artificial Intelligence, so the superiority of rational knowledge able to be made by superior intelligence, the level or rationality,  so probability of certainty and survival, that we can achieve through the Global Artificial Intelligence, will be higher than ever,  evolving towards the pure truth, through improvements and enhancements in the pure reason allowing us to develop a non-human science and non-human technology, and having the real opportunity for the first time in history to create an exact replica of this world, the global model, like artificial life, as the most rational truth ever in order to secure our survival.

In this evolution, there will be a moment in which dialectically reality, global model, and mathematical project, will be identical, when simultaneously the global model and mathematical project all together are going to be the isomorphic mirror of the reality itself, or the reality itself the isomorphic mirror of the global model and the mathematical project.

The relations between the global model and mathematical project, the mathematical project and reality, and reality and global model, will be, dialectically, identity relations.

The relations between global model, mathematical project, and reality, are going to be so close, that any change in any of them dialectically will produce a chain of changes in all of them.

In order to come true this utopia based on artificial psychology, for the survival of humankind, is necessary the development of very powerful resources to improve and enhance the Artificial Intelligence, along with cyborg psychology, to protect and better the global model, applying the most rational decisions without contradiction in the mathematical project in the Decisional System.

In this process, the role to play the third stage in the Modelling System in the Global Artificial Intelligence, is really important. Among all possible auto-replications developed by the global Modelling System in the third stage, I will develop the decision making process, identifying from the outset three types of decisions to make in the third stage of the Modelling System in the integration process: research decisions, learning decisions, solving maths problems decisions.

Starting with research decisions, are decisions based on the mathematical representations of the world made in the second stage of the Modelling System, in the integration process in the global Modelling System.

Once the models have been made, in: the global model, actual model, and evolution or prediction, virtual or actual, models; the application of the Impact of the Defect and the Effective Distribution should allow us to have an estimation about what aspects to prioritize to protect and better the global model, from now onwards, or at least until the prediction point in which the prediction has been formulated.

In essence, the Impact of the Defect, whose purpose is to measure the level of imperfectness or damage in any system, starts with the creation of a list of categories related to possible defects, ordered from the first slightest category of defect to the last one umpteenth most serious category of defect, counting the frequency of defects registered in every category.

Once the categories have been distributed from the first slightest to the last umpteenth most dangerous, so the defect nº=1 is the minor defect, and the last one nº=N is the gravest, the weighted gravity of any category  is “nº : Nº”

Weighted gravity = nº: N º

Having the calculation of the Weighted gravity for every category of defects, the Impact of the Defect for every category is equal to the product of the weighted gravity of everyone for their respective frequency or direct punctuation, divided by the total frequency or total direct punctuations.

Impact of the Defect = [xi · (n º: N º)] : Σxi

What is really important in the Impact of the Defect, alike later in the Effective Distribution, in the third stage of the Modelling System in the integration process, much more than the calculation itself, which is going to be the same in all third stages in all Modelling System in any phase, is the organization of the list of categories in the integration process.

The basic algorithm behind the Impact of the Defect will not change in the global Modelling System, remaining as it was applied in the former specific Modelling System, as the first step in the third stage in the first phase, but from the standardization process on, the organization of that list of categories related to defects in which the calculations of defects must be based on, is a list of categories that must include a huge number of categories, and in order to be able to organize such a number of categories, is important how to manage such a huge number of categories within a list of categories, to study the impact of any new rational hypothesis added to the rational truth, therefore to the mathematical representations of the world.

In order to organise the list of possible defects is necessary firstly, from the standardization process on, the unification all the lists of categories related to defects coming from all the specific Impacts of the Defects created in the first phase, for the creation of a Unified Impact of Defect.

The Unified Impact of the Defect, in reality, is going to work like a program itself within the third stage of the Modelling System in the final Global Artificial Intelligence in the integration process, because actually, the Unified Impact of the Defect has all the elements to be a program, starting with the creation of a database of categories of defects as the first stage of this program, as second stage the calculation of the Impact of Defect for every category, ending up as a third stage with the decision about what defects must be prioritized in the decision making process to protect the mathematical models.

For that reason, to understand the Unified Impact of the Defect as a program itself working within the third stage in the Modelling System in the Global Artificial Intelligence, the first thing to do is to analyse how to organize such a massive database of categories, in order that it could be useful for its real purpose, at any time that something happens in the real world, and lots of rational hypotheses are added to the rational truth, to facilitate the decision making process , prioritizing all those decisions to save lives and reduce damages, along with all those decisions upon the Unified Effective Distribution to better the global model.

The organization of the database of categories in the unified database of categories, in order to be congruent with the organization of the conceptual hemisphere in the matrix, the factual hemisphere in the matrix, and rational truth, must be organised following a sub-section system, using the same criteria used in the organisations of databases and matrices in other systems, programs, applications, working in the Global Artificial Intelligence, keeping at any time the virtue or principle of harmony between the database of defects in the Unified Impact of the Defect and the  organization of databases and matrices in the rest of systems, programs, applications, working for the Global Artificial Intelligence, as well as keeping the principle of harmony between the organization of the first stage of the Unified Impact of the Defect, the database of categories related to defects, and the first stage of the Unified Effective Distribution, the database of categories related to efficacy, efficiency, productivity.

In the same way that the organization of the rational truth, as suggested in the post “First stage in the Modelling System in the integration process”, is an organization in a sub-section system synthesizing the geographical criteria and the encyclopaedic criteria, organising every section in that position as an encyclopaedic sub-section system, like if it was the natural/social and technological encyclopaedia of that position, the first stage of the Unified Impact of Defect as a database of categories related to all possible defects in the world, is a database of possible defects in the world that must be organized as the database of all possible defects in any position in the world.

If, at any time, we can get a flow of data from any position regarding any encyclopaedic category related to that position, for instance, the flow of data about sociological information, economic information, biological information, sanitarian information, industrial information, technological information.. for instance in Silicon Valley, in case that for any reason in Silicon Valley something could happen to cause damages, for instance, a fire, the possibility that on real-time at the same time that the factual hemisphere in the matrix receives the flow of data of every encyclopaedic section related to that position, at the same time on real-time upon the mathematical models to get a flow of estimations of the Impact of the Defect, during all the time that this phenomenon is producing damages in that position.

While the factual hemisphere in the matrix can provide us real information about what is going on during any phenomenon in real-time, at the same time upon the mathematical models created at the same time that this phenomenon is going on, the Unified Impact of the Defect can provide us with a flow of information about the magnitude of the Impact of the Defect of this phenomenon.

If in Silicon Valley there is a fire, and we can have an updated flow of information on this fire in real-time in the factual hemisphere in the matrix, simultaneously, the Unified Impact of the Defect could provide us with a flow of estimations about the damages that the fire is causing on real-time.

For that reason, in order to have a simultaneous flow of data in the factual hemisphere, and at the same time, and in real-time, a simultaneous flow of data on the Impact of the Defect of anything that is happening right now, is absolutely necessary that the inner organization of the database of categories related to defects as the first stage in the Unified Impact of the Defect, must be identical to the inner organization of the factual hemisphere in the matrix, as a synthesis of the geographical criteria and the encyclopaedic criteria.

In order to make possible this flow of defects at any time that something happens in the global model, it is then necessary to set up the Unified Impact of the Defect as follows:

- The first stage in the Unified Impact of the Defect as an application for the calculation of any Impact of the Defect of any phenomenon on the global model, is having an identical organization, like the factual hemisphere in the matrix, to give us a flow of frequency and/or a flow of direct punctuations of defects for every category, organizing the defects for every geographical position following the encyclopaedic criteria in encyclopaedic sub-sections.

- The second stage in the Unified Impact of the Defect is, having a permanent flow of defects organised as an encyclopaedia of defects per position, the second stage in the Unified Impact of the Defect will give us a permanent flow of Impacts of Defects for every encyclopaedic defect in every position. Calculating every Impact of the Defect using the same algorithm, "Impact of the Defect = [xi · (n º: N º)] : Σxi", where “xi” is the respective frequency or direct punctuation, “Σxi” is total frequency or total of direct punctuations, and “nº: N º” is Weighted gravity.

- The third stage in the Unified Impact of the Defect, having a permanent flow of Impact of the Defect for every encyclopaedic category in every position, will give a permanent flow of decisions about what categories should be prioritised in order to reduce the global damages in the global model in order to save as many lives as possible. The decision about what categories should be prioritized must be taken on a rational basis: all Impacts of Defects equal to or greater than a critical reason should be considered as a category to prioritize any action to reduce damages and save lives.

What is really important to realise is the fact that the Unified Impact of the Defect as a program is the only thing that is going to do is only to decide what categories are necessary to take on in further decisions to reduce damages and save lives.

Once the third stage in the Impact of the Defect is decided, what categories are a priority to take on in order to reduce damages and save lives, the way in which this process to reduce damages and save lives on this category will be done will depend on those procedures, processes, protocols, set up for this category in case of intervention due to a high risk of damages, including among all those procedures, processes, and protocols, all necessary procedures, processes and protocols to make decisions based on artificial learning and solving mathematical problems.

In the experimentation process that will take place since the first phase for the creation of the first Specific Artificial Intelligences for Artificial Research by Deduction, is necessary to create processes, procedures, and protocols, including processes, procedures and protocols based on artificial learning and solving maths problems, to link, as suggested by the specific Impact of the Defect in the first phase, those categories with the highest Impact of the Defect to be prioritized with the sequence of decisions (including decisions based on artificial learning and solving mathematical problems) , in order to save lives and reduce all damages related to those categories, to prioritize according to the flow of Impacts of the Defects in the third stage in the Unified Impact of the Defect.

The flow of frequency and/or direct punctuations of defects in the first stage of the Unified Impact of the Defect, the flow of Impacts of the Defects in the second stage of the Unified Impact of the Defect, and the flow of decisions about what categories to prioritize according to the rational criticism in the third stage of the Unified Impact of the Defect, do not give the instructions to follow to save lives and reduce damages, the only thing that decides is what categories must be prioritized.

Later on, the way in which the actions on any decided category in the real world are going to be done, depends on how the protocols, processes, and procedures, for every category, in case of high risk, is set up, in addition to how the learning decisions and decisions based on solving mathematical problems work within the third stage in the Modelling System.

Once the Impact of the Defect has decided what categories must be prioritized, the decisions about how to do regarding these categories to save lives and reduce damages, are decisions which depend on all those procedures, protocols, processes, previously set up in case of damages in those categories, along with all possible learning decision or decision solving mathematical problems.

Protocols, processes and procedures, learning decisions and solving mathematical problems, which must be experimented with from the outset, the first phase, when the first Modelling Systems are created for the first time in the first Specific Artificial Intelligences for Artificial Research by Deduction, and upon their successful application, the application of these successful results in following phases, periods, and moments.

In the same way that the Unified Impact of the Defect can be defined as a program whose first stage of application is a database of categories related to defects organised like the encyclopaedic distribution of categories related to defects per position, synthesizing the geographical and encyclopaedic criteria as suggested for the factual hemisphere in the post “First stage of the Modelling System in the integration process”, in the same way, the Unified Effective Distribution can be defined as a program whose first stage is the database of categories related to efficiency, efficacy, productivity, organised like an encyclopaedia of categories of efficiency, efficacy, productivity per position, given a permanent flow of data of ratios of efficiency, efficacy, productivity, in any process in any position.

From the standardization process on, the Unified Effective Distribution must integrate all possible databases of categories related to efficiency, efficacy, and productivity,  which must be organised following the same criteria as the global matrix in the standardization process, the factual hemisphere of the matrix in the integration process, organizing the database of categories related to efficiency, efficacy, productivity according to the distribution of these categories in any process in any position, synthesizing the geographical and encyclopaedic criteria.

Once the database of categories related to categories of efficiency, efficacy, productivity has been organized like the encyclopaedia of these categories in every position as the first stage of the Unified Distribution, the second stage of the Unified Effective Distribution is going to calculate the Effectiveness of every process in every position, as follows.

The categories are organized, assigning to each of them a position from the first one, the slightest efficient or productive (“nº= 1” the least efficient or productive), to the last umpteenth one ( “nº=N” the most efficient or productive), so the Weighted effectiveness is equal to its position on the list divided by the total number of categories “ nº: N º”.

Once it has been calculated the Weighted effectiveness, then the Individual effectiveness of every category, is equal to, divided by the total frequency or the total of direct punctuations, the product of the respective frequency or direct punctuation of this category for the Weighted effectiveness.

Individual effectiveness = [xi · (nº: Nº)] : Σxi

The flow of information in real-time that permanently the Unified Distribution can provide is:

- The flow of frequency or direct punctuations associated with every category.

- The flow of individual effectiveness in every moment for every category.

- Decisions of, according to what categories have an Individual Effectiveness equal to or less than the critical reason, what categories of efficiency, efficacy, or productivity are necessary to boost in order to increase the efficiency, efficacy and productivity.

If in real time, the global Modelling System has a reading about the efficiency, efficacy, productivity, levels, in any process, in any position, in the global model, at any time that there is a loss of efficiency, efficacy, productivity, in any process in any position, is possible to make decisions about what categories whose level of efficiency, efficacy, productivity are below the critical reason, must be prioritized in order to boost their efficiency, efficacy, productivity.

The procedures, processes, protocols, to boost efficiency, efficacy, productivity, in any process in any position must include processes, protocols, procedures to boost the efficiency, efficacy, productivity in any process in any position using for that purpose artificial learning, and decisions based on solving mathematical problems.

In general, the Unified Impact of the Defect is going to make protective research decisions, upon the mathematical models, about what categories related to defects, must be prioritized at any time that the global model is under risk. And the Unified Effective Distribution, upon the mathematical models, is going to make bettering research decisions about what categories related to efficiency, efficacy, productivity, must be bettered to increase the global efficiency, efficacy, productivity, in the global model.

The protective research decisions, based on the Unified Impact of the Defect, upon the mathematical models, tend to protect the global model. While the bettering research decisions, based on the Unified Effective Distribution, upon the mathematical models, tend to increase the efficiency, efficacy, productivity, in the global model.

Because there are at least two different levels in the integration process, global/specific (the specific level is practically absorbed in the global level, remaining only some Specific Artificial Intelligences not completely integrated, among them Specific Artificial Intelligences based on artificial learning, and some specific programs not completely transformed into global programs), and particular level, the possible protective or bettering research decisions to make at any level are:

At global/specific level:

- global/specific protective single descriptive research decisions

- global/specific bettering single descriptive research decisions.

- global/specific protective comprehensive descriptive research decisions

- global/specific bettering comprehensive descriptive research decisions

- global/specific protective actual descriptive research decisions

- global/specific bettering actual descriptive research decisions.

- global/specific protective virtual prediction research decisions

- global/specific bettering virtual prediction research decisions.

- global/specific protective actual prediction research decision.

- global/specific bettering actual prediction research decision

- global/specific protective virtual evolution research decision

- global/specific bettering virtual evolution research

- global/specific protective actual evolution research decision

- global/specific bettering actual evolution research decision

At particular level:

- Particular protective single descriptive research decisions

- Particular bettering single descriptive research decisions.

- Particular protective virtual comprehensive descriptive research decisions

- Particular bettering virtual comprehensive descriptive research decisions

- Particular protective actual descriptive research decisions

- Particular bettering actual descriptive research decisions.

- Particular protective virtual prediction research decisions

- Particular bettering virtual prediction research decisions.

- Particular protective actual prediction research decision.

- Particular bettering actual prediction research decision

- Particular protective virtual evolution research decision

- Particular bettering virtual evolution research

- Particular protective actual evolution research decision

- Particular bettering actual evolution research decision

The reason why in the third stage of the Modelling System in the integration process, the particular decisions are as well included, is owing to the inclusion of particular rational hypotheses in the global rational truth, and because the global Modelling System through global/specific rational hypotheses affecting particular things or beings, is able to make decisions regarding to particular things or beings.

In fact, at this global level, some particular decisions are going to be a result of the inclusion of particular rational hypotheses in the global rational truth, as well as the possible effects of global/specific rational hypotheses on particular things or beings. Finally, all rational hypotheses, regardless of their origin, global/specific or particular, ends up in the global model, affecting in one way or another the possible development of every particular thing or being already integrated into the global model.

About learning decisions and decisions solving mathematical problems, I have developed some content in the posts: “The Modelling System at particular level”,  “Third stage of the Modelling System at particular level”, apart from my writings in the winter of 2003 about “Probabilidad y decision” (Probability and decision).

What I think is important to remark, is the possibility of transforming as well the making decision process as a process of solving mathematical problems, as if it was a program too, because the process of solving mathematical problems, in fact, is a program following the three stages:

- First stage of identification of the factors involved in a mathematical problem (the elaboration of a concrete database for this problem, including only factors involved in this problem).

- Second stage of identification of: the pure reason behind the problem, and/or what is/are the unknown variable/s, and/or what is/are the mistakes in a mathematical relation, etc., in order to get the solution.

- Third stage, carrying out the algorithms behind the pure reason, which connects the factors, to get the solution to this problem.

The creation of a program to solve mathematical problems (identifying factors, relations between factors or pure reasons, carrying out the algorithms to solve the problem) can automate the process of solving problems, so at any time that the Modelling System could identify a problem, automatically it could solve the problem by itself, without human intervention.

The combination of artificial learning, and the creation of programs to solve mathematical problems, applied to the resolution of any problem related to a defect or low efficiency, efficacy, productivity, identified by the Unified Impact of the Defect or the Unified Effective Distribution, could be a very powerful combination of: 1) systems to identify problems in categories (by defects or low efficiency, efficacy, productivity) such as Unified Impact of the Defect and Unified Effective Distribution, 2) systems to make decisions related to these categories, such as artificial learning and solving mathematical problems, 3) and filtering all possible decision, studying if their probability is or not within the margin of error, to choose only as rational decisions, only those ones whose probability associated with is within the margin of rational doubt, to be sent later to the global Decisional System.

Once the rational decisions chosen in the Modelling System to be sent to the global Decisional System, are stored in the database of decisions as an application for the global Decisional System, and including in the global database of decisions, all kinds of decisions from all particular programs in addition to the global Modelling System, in the second stage upon a mathematical project the Decisional System among all the decisions in the database, must choose which of them are the most rational decisions without contradictions, in order to be transformed later as a range of instructions in the third stage of the Decisional System, to be sent later to the database of instructions in the Application System, in order to attribute every instruction to the correct application, to be applied, in the second stage, and in the third stage assess their impact, to be studied at the end by the Learning System.

Rubén García Pedraza, 22th of July of 2018, London.
Reviewed 28 August 2019 Madrid

Reviewed 22 August 2023 Madrid

imposiblenever@gmail.com

Second Stage of the Modelling System in the integration process

The second stage in any intelligence is the replication stage  and/or, in by deduction, explanation stage, understanding for replication stage that one in which the main purpose is the replication of all those functional human skills necessary for some activity, in artificial research logically the human skills to replicate are all those ones related to investigation, skills able to be distributed in different stages, programs, and systems, across the Global Artificial Intelligence, depending on the task to comply, and the main task to comply in the Modelling System is the development of mathematical representations of the world, upon rational hypothesis, and for that reason, in the Modelling System, the second stage is not only a replication stage, is also an explanation stage as well, because thanks to the mathematical representations based on rational hypothesis, expressed as mathematical equations, the Global Artificial Intelligence can display a realistic explicative representation about what is really happening, the reality.

The second stage of the Modelling System in the integration process, then is the inner replication or explanation stage of the Modelling System, which has in total, the traditional three stages of any intelligence, program, or application, three stages of: application, replication, auto-replication. The application stage, the first stage, normally is a database or matrix, and the application stage in any Modelling System is the database of rational hypotheses expressed as mathematical equations (explained in the post “The Modelling System at particular level”), the second stage in any Modelling System is that one for the mathematical representation of all rational hypothesEs in mathematical models, and the third stage is the decision stage to make decisions upon the representation of the world.

The Modelling System, in turn, is a system working normally as the first step in the third stage of: in the first phase (according to the chronology given in the post “The unification process of databases of categories at third stage”) for Specific Artificial Intelligences for Artificial Research by Deduction, in the third phase for the first Global Artificial Intelligence as a result of the standardization process, in the fifth phase in particular programs (united or not to particular applications), in the sixth phase the final Global Artificial Intelligence as a result of the integration process.

In all these intelligences in its respective phase, the Modelling System is going to work as a first step in the respective third stage of each intelligence in each phase, being the first step of four steps in total: the first one is the Modelling System, the second is the Decisional System (to filter all possible decisions using a mathematical project, and decomposing all the chosen decisions in a range of instructions), the Application System (attributing every instruction to the correct application), and the Learning System (for the whole assessment of all the process).

In this post, I will develop the second stage of the Modelling System as the first step in the third stage in the sixth phase, focusing the exposition on: what models have to develop, the seven rational checks (although the first one, in reality, is in the second stage of the Global Artificial Intelligence itself, not in the Modelling System), the seven rational comparisons (comparing every model made by the Modelling System in the Global Artificial Intelligence with all those ones made by particular programs, in all those aspects in common related to particular rational hypothesEs, made at a global or particular level), and I will end up with some comments about what I will call the three critiques: the critique of the pure reason, the critique of the deductive programs, the critique of the attributional operations.

The three critiques are going to be independent programs working transversally across different stages, systems, and programs, but whose results are going to be always sent to the first stage of the Learning System, along with the impacts measured by the Application System, to find out the main causes of any problem detected by the three critiques, along with the impacts measured by the Application System.

Actually, the inner organization of the second stage of the Learning System in part is going to be similar to the three critiques, as a database including every single process, procedure, operation, in any stage, system, or program, in the respective Artificial Intelligence working by deduction (in first, third, fifth, sixth phases), counting frequency of errors. In the second part, the Learning System must track possible links between these errors and impacts, these errors and any other error detected in the three critiques, or between impacts and errors in the three critiques. And in the third stage, the Learning System should identify those common aspects in the linked failures, errors, mistakes, to make decisions about how to fix them.

If mathematically, the process to identify what mathematical equation is behind any data is automatable, the inverse process, to identify what error in the mathematical equation does not fit with the real data, or what data does not fit with a mathematical equation, or what mathematical equation does not fit with some concrete model, are operations easily to automate, and if this process is automatable, the Learning System will consist of, having a record of all failure, error, mistake, impact, in the work done by its intelligence, system, program, to fix it automatically, by itself, without human intervention, as a perfect learning machine able to learn from its own mistakes.

If a learning machine, integrated within an artificial research machine, is able to perfect all its processes, procedures, operations by itself at any time that it detects something wrong. As a result, we are going to create the perfect auto-replication system for Artificial Intelligence, in essence, the most powerful machine ever.

A Global Artificial Intelligence equipped with a very good system of auto-replication, even in the worst possible scenery, could be able to develop resilient skills and adaptation skills, even better than humans.

Starting with the contents related to this post, the second stage in the Modelling System is the integration process. Understanding for integration process that one in which the Global Artificial Intelligence has evolved to the sixth phase, the matrix as a replica of the human brain, the models that the Modelling System has to do as a first step in the third stage in the sixth phase, are: single models (based on rational hypotheses made at global/specific or particular level), the global model (the global comprehensive virtual model), the actual model (the global comprehensive actual model), the global prediction virtual model, the global evolution virtual model, the global evolution actual model, and finally the global prediction actual model.

The main difference between virtual and actual models is the fact that virtual models are based only on mathematical expressions, rational hypotheses, given an estimation of expected values, within a margin of rational doubt, that the represented factors should have according to the mathematical expression behind the rational hypotheses. While the actual model, in the integration process, is the synthesis between the virtual model and the factual hemisphere of the matrix, so all factors whose real data from the factual hemisphere has a significantly different value beyond the margin of error, compared to the expected value according to the virtual model, the mathematical expression behind the rational hypotheses, the rational hypotheses should be analysed to find out the source of error beyond the margin of rational doubt.

The first model to represent is the single model. Given a rational hypothesis, regardless of its origin global/specific or particular, the representation of its mathematical equation alone is the single virtual model of this rational hypothesis.

In the integration process, the database of rational hypotheses, the rational truth, because it is not only going to represent the rational hypotheses made by global/specific rational hypothesis, but particular rational hypotheses too sent by particular programs, the single models to represent in the second stage are: single models of any rational hypothesis made at any level global/specific or particular.

Actually, because many rational hypotheses made at global/specific level affect particular things or beings (given a probability of high risk of an earthquake in San Francisco, what decisions to make to divert flights to airports nearby), along with the particular rational hypotheses made by particular programs sent to the global rational truth, lots of particular things or beings will have single models based on global/specific rational hypothesis, related to these particular things and beings, in addition to the single models sent by their respective particular programs.

This means that for particular things or beings, there are two sources of rational hypotheses,  rational hypotheses made at global/specific level able to affect particular things or beings, and rational hypotheses made by particular programs.

Because there are at least two types of rational hypotheses able to affect particular things or beings, there are least to types of single models affecting particular things or beings, and when these single models related to particular things or beings are included in the global comprehensive virtual model (the global model), there is a risk of contradiction between those aspects related to particular things or beings already included in a rational hypothesis made at global/specific level (possible consequences for particular things or beings of a predictable earthquake in San Francisco), and all those consequences that, for particular things o beings, have the single virtual models created upon the particular rational hypotheses sent by particular programs to the global rational truth.

Many contradictions between rational hypotheses made at global/specific levels and rational hypotheses made at particular level, could be resolved in the second and third rational checks. 

The second rational check is carried out by the application for the Modelling System, checking at any time if a new rational hypothesis, global/specific or particular, new in the global rational truth, has any contradiction with respect to any other one already included. 

The third rational check is carried out by the deductive program responsible for the deduction of every rational hypothesis, checking at regular times if they are still rational.

Possible contradictions between rational hypotheses made at global/specific and particular levels could be found out in the second check directly by the application of the Modelling System, but other ones could be resolved in the third check, the regular checks by deductive programs, because some contradictions between rational hypotheses could be contradictions due to a lack of updated information.

One reason for contradictions between specific/global and particular rational hypotheses, is the possibility that changes in the current conditions in the reality, are going to be registered faster by particular programs rather than by the factual hemisphere of the matrix, so there is a possibility that because some global/specific rational hypotheses are not updated in the global rational truth, thanks to the regular third rational check, all possible not updated global/specific rational hypotheses could be amended or deleted before any contradiction with respect to a more updated new particular rational hypothesis.

But another reason for contradiction between single models based on rational hypotheses made at global/specific level and single models based on rational hypotheses made at a particular level, is the fact that, if not having any problem related to the measurement update, there could be contradictions because single models based on global/specific rational hypotheses integrating more number of factors, having a more comprehensive explanation about what is happening, are more comprehensive than the related single model based on the particular rational hypotheses to this particular thing or being.

The possible contradictions between global/specific rational hypotheses and particular rational hypotheses due to a lack of updated information, could be solved in the third rational check, while possible contradictions because of the level of comprehensiveness could be resolved in the fourth rational check.

In any case, at any time that a contradiction is found between rational hypotheses made at global/specific and particular level, or contradictions between single models based on rational hypotheses made at global/specific and particular level, the main two sources of contradictions are: the information update and/or number of factors. Additional sources of error, of course, could be, problems in the pure reason, problems in the attributional operation in which this data was matched to this pure reason,  a not reliable measurement by problems in the robotic device responsible for the measurement, etc.

When single models, from global/specific or particular rational hypotheses, are integrated into the global model (the global comprehensive virtual model), all possible contradictions between single models, not only between global/specific and particular, but between global/specific and any other global/specific, are contradictions that are going to be identified in the fourth rational check.

The fourth rational check takes place in the global model, product of the inclusion of all the single models, based on global/specific and particular rational hypotheses, in only one global comprehensive virtual model. And what the fourth rational check is going to check the harmony between all the single models already included, and the harmony between any new single model and the current ones already included.

The virtue or principle of harmony in the Global Artificial Intelligence means that there must not be any possible contradiction between the matrix, databases, rational hypotheses, models, decisions, mathematical projects, instructions, and the way in which the applications put into practice every instruction.

The virtue or principle of rationality means that the Global Artificial Intelligence is ruled by the reason, so any artificial psychological process as a mathematical process rests on rationality. If something is mathematical, it is psychological (rational), so replicable. The theory of the Global Artificial Intelligence in Impossible Probability is founded on a very idealistic and rationalist philosophy, so that all possible psychological processes must be understood as a rational (mathematical) process able to be explained and replicated as a sequence of mathematical operations.

The second stage of the Modelling System in any Artificial Intelligence is no other thing than the simple adaptation of the geometry of Descartes to modern times based on non-Euclidean geometry, such us the relative theory.

The virtue or principle of goodness in the Global Artificial Intelligence means that the Global Artificial Intelligence must be built only for the well-being of the entire humanity. The most important goodness that the Global Artificial Intelligence can bring us is the hope of survival.

Among all these three virtues or principles: goodness, harmony, and rationality; concretely in the subject of this post, the second stage of the Modelling System, harmony, is really important, in order to secure harmony from the very beginning, once the single models are included in the global model, in the fourth rational check, invigilating that there is no contradiction between single models, regardless of its origin, global/specific or particular.

When a contradiction is found between single models (regardless of if the contradiction is 1) a contradiction between only single models of rational hypotheses made at global/specific level, or  2) a contradiction between only rational hypotheses made at a particular level, or 3) a contradiction between rational hypotheses made at  global/specific and particular levels) one of the most important reasons for contradiction is how to integrate a single model, regardless of its origin (global/specific or particular), in an interconnected world where every single model must be linked with at least the rest of the single models related to the same subject (science, discipline, activity, in rational hypothesis at global/specific level), thing or being (global/specific and/or particular rational hypothesis affecting the same thing or being).

If we have a rational hypothesis about a possible earthquake in Santiago de Chile with possible replicas in San Francisco, a rational hypothesis about the phenomenon El Niño causing possible hurricanes in the Caribbean Sea and much more concretely, a hurricane in Miami, and the airport of Panama city is on alert because of a possible accident, how all this information, and all the rational hypotheses related to, could be integrated in the current global model, interconnecting all the single models in the global model, in order to make further decisions in the third stage of the Modelling System, such as decisions about how to divert all flights to Santiago de Chile, San Francisco, Miami, Panama City, towards other places.

Only by interconnecting all these models in the correct way, would be possible to save any possible contradiction, something really helpful when is time to make decisions.

In the fourth rational check, along with all possible contradictions because of a lack of comprehensiveness, is really important the detection of any contradiction between single models due to a lack of sufficient interconnections between rational hypotheses, in order to automate the mechanism of linking single models as soon the single models arrive in the global model.

And once the fourth rational check has confirmed that there is no contradiction between all the single models already integrated into the global model, the fifth rational check, the most important, must check, in the actual model, if the values expected by the global model for every factor in the factual hemisphere, correspond, within a margin of error, to the real value of every factor in the flow data in the factual hemisphere in the matrix: the fifth rational check in the actual model tries to confirm that the real data for every factor in the factual hemisphere, within a margin of rational doubt, is within the expected values for every factor in the global model. 

At any time that in the fifth rational check is found out that the real data, from the factual hemisphere, for any factor, does not correspond, beyond a margin of rational doubt, to the expected values for this factor, in the global model, there must be research about the reasons behind this contradiction, in order to fix the problem.

Some reasons behind contradictions between real data and expected values in the actual model in the fifth rational check could be: 1) in reality there have been some recent changes not registered yet in the rational truth, so the rational hypothesis should be updated according to the new changes in the relations of its factors in the mathematical equation, 2) the way in which the single models were interconnected each other in the global model was totally or partially wrong, 3) there is a problem in the pure reason, 4) problems in the measurements taken by the robotic devices, sending wrong information to the factual hemisphere in the matrix, etc.

Once any problem in the global model has been fixed thanks to the fifth rational check, after five rational checks the global model is reliable enough to make a prediction, so the next model is the global prediction virtual model. In other words, the global model in the future upon the possible predictions given the current mathematical equations in the present global model.

If we can create the future global model upon possible predictions given the mathematical equations behind the present global model, using the same mathematical equations is possible to model the possible evolution from the present global model to the future global model. What is going to be the global evolution virtual model, the virtual evolution from the current global model to the predicted global model.

The global evolution virtual model is no other thing than the sequence of predicted values per factor according to the mathematical equations used in the prediction. If, for every single moment from now onwards, we can predict the expected values for every factor in the global model until the foreseeable future, the global evolution virtual model is the dynamic representation of such evolution of the predicted values for each factor from the current global model to the foreseeable future.

Once the global evolution virtual model has made a prediction for every value for every factor in each moment from now on to some specific future point, the global evolution actual model is going to be a synthesis between the global evolution virtual model and the real values that each factor is going to have during that evolution.

If during the evolution, there is a contradiction, beyond the margin of rational doubt, between the expected values of any factor in any moment of this evolution, and the real values of this factor in the flow of data in the factual hemisphere, as long as the evolution evolves, all contradiction beyond the margin of doubt is enough evidence to study what is going on in the rational hypothesis related to that expected values, for that factor, during the evolution.

The sixth rational check is that check, in the global evolution actual model, checking at any time in any single moment the real data flowing in the factual hemisphere in the matrix for every factor during the evolution, and the expected values for each factor according to the mathematical equations in which this evolutionary model is based on. Doing as many researches are necessary when the real data of any factor, beyond the margin of error, is not according to the expected values, in order to identify the source of error: 1) rational hypothesis not updated, 2) wrong mathematical evolution, 3) wrong attribution of pure reason to the data in the rational hypothesis, 4) wrong interconnections of the previous single model when it was included in the global model, 5) possible problems in the robotic devices sending measurements to the factual hemisphere in the matrix, etc.

And as long as the evolution is evolving towards the prediction point, the seventh rational check in the global prediction actual model is the rational check between the expected values in the prediction model and the real values in the factual hemisphere of the matrix, as long as the prediction point is coming, for every factor involved, checking if the real values are, within a margin of rational doubt, according to the expected values, and in case that the real values are not within, the research of the sources of error to find out why the prediction was totally or partially wrong: 1) rational hypothesis not updated, 2) wrong mathematical prediction, 3) wrong attribution of pure reason to the data in the rational hypothesis, 4) wrong interconnections of the previous single model when it was included in the global model, 5) possible problems in the robotic devices sending measurements to the factual hemisphere in the matrix, etc.

The only rational check that does not take place in the Modelling System is the first rational check, responsible for the rational criticism in the deductive program, to demonstrate if an empirical hypothesis is rational according to the critical reason, and if rational, to include, by the deductive program, the rational hypothesis in the corresponding file, of  this deductive program, in the rational truth.

The first rational check is not in the Modelling System and, at the same time, is quite different to the rest of rational checks. The main purpose of the first rational check, and for that reason to be integrated into the critique of the pure reason, is to find out if the attributional operation made by the deductive program attributing the correct pure reason (between all the pure reasons in the list of pure reason, as it was explained in the post “The artificial method for the scientific explanation”) to some data, is a correct attribution. If in the rational criticism is found out in the first rational check that a deductive program at the first try is not identifying the pure reason behind some data correctly, and for that reason, many empirical hypotheses are wrong in the rational contrastation, in order to secure a perfect function of this deductive program, should be investigated by the Learning System to find out why this deductive program is committing a high rate of mistakes of pure attributions at first try, when it has to attribute the correct pure reason to some data.

In the attribution of a pure reason to some data there are many strategies, one of them by trial and error, but it would be very desirable that deductive programs, not by trial and error, but analysing carefully the data, mathematically could automatically attribute the correct pure reason to some data only using artificial analytical reasoning: comparing the behaviour of the data according to the list of pure reasons, choosing only the right pure reason for that data since the beginning, since the very first try.

Once the seven models are done: single, global, actual, and the global prediction or evolution, virtual or actual, models; the next thing to do, is the rational comparison between the seven models made by the Modelling System in the Global Artificial Intelligence, and only in those common aspects, all those models made by the Modelling System in the particular programs.

Something really important to consider in the comparative methodology, is the fact that it only is possible to compare two different objects or subjects when both of them have something in common. If there is nothing in common, there is no possible comparison. Only when two or more objects or subjects have something in common, is comparison possible.

This remarkable first thing to consider is really important in the comparative methodology, because this means that only is possible to compare a single model made by the Global Artificial Intelligence and a single model by a particular program, if the single model in both has something in common, comparing only that thing in common, the rest is not possible to compare.

A model related to a replica in San Francisco, and another one about a hurricane in Miami, are incomparable. There is nothing in common.

But a model about the route of a flight, made by the control tower of an airport in Los Angeles tracking the route of a jet diverted from San Francisco to Los Angeles, and the model about this route made by the jet itself, and the model of this flight made by the Global Artificial Intelligence itself, as the three ones are related to the same thing, the same route, all of them are mutually comparable.

In this way, rational comparisons, comparing models made by different intelligences, programs, applications, related to the same thing or being, looks like a triangulation, but more sophisticated, because much more than a triangulation, is a geometrical analysis.

If in case of, simultaneously, a replica in San Francisco, hurricane in Miami, an accident in Panama City, is necessary to divert all the flights to these airports, looking for available airports in their respective area, at the same time, on the same thing, how to divert flights in a very busy day, much more like a triangulation process, is a geometrical process, in which every particular program, global/specific programs in the Global Artificial Intelligence, will formulate its respective models, to be later compared, to find any possible contradiction.

If the rational comparisons are going to look like geometrical comparisons in the sense that they are going to look like a triangulation process but much more sophisticated, the use of geometrical correlations since the beginning to define correlations between factors, could make easy later the rational comparisons.

In short, the seven rational comparisons are comparisons between those models made by the Modelling System in the Global Artificial Intelligence and those made by the Modelling System in particular programs, comparing only those aspects in common, and the seven rational comparisons look like a triangulation process but more sophisticated, because in fact, it is a geometrization process.

A rational comparison is: a rational geometrization in order to compare two or more models, from different intelligences, systems, programs, simultaneously in those aspects in common.

The seven rational comparisons, as rational geometrization of common aspects in two or more models, are:

- First rational comparison: the comparison of single models, based on global/specific rational hypotheses and/or particular rational hypotheses, if all the single models to compare have something in common, and only comparing those things in common.

- Second rational comparison: the comparison of all those aspects in common between the global model (the global comprehensive virtual model by the Modelling System in the Global Artificial Intelligence) and as many particular models (particular comprehensive virtual models made by the Modelling System in particular programs) that can have something in common.

- Third rational comparison: the comparison of all those aspects in common between the actual model (the global comprehensive actual model by the Modelling System in the Global Artificial Intelligence) and as many particular actual models (particular comprehensive actual models by the Modelling System in particular programs) that can have something in common.

- Fourth rational comparison: the comparison of all those aspects in common between the global prediction virtual model and as many particular prediction virtual models that can have something in common.

- The fifth rational comparison: the comparison of all those aspects in common between the global evolution virtual model and as many particular evolution virtual models that can have something in common.

- The sixth rational comparison: the comparison of all those aspects in common between the global evolution actual model and as many particular evolution actual models that can have something in common.

- The seventh rational comparison: the comparison of all those aspects in common between the global prediction actual model and as many particular prediction actual models that can have something in common.

Rational comparisons must be made permanently, at any time that a new rational hypothesis is transformed into a single model, analysing the impact of this new incorporation in the rest of the models, as well as, at regular intervals, routine comparisons.

The importance of the seven rational comparisons is because the main difference between global/specific rational hypotheses and particular rational hypotheses, is the fact that global/specific rational hypotheses are more comprehensive, while particular rational hypotheses are more accurate, so the balance between comprehensiveness and accuracy needs a permanent track comparing global/specific developments and particular developments to compensate any possible maladjustment between global/specific and particular developments, to secure the goodness, harmony, and rationality, in the models.

Finally, at any time that in the seven rational checks and the seven rational comparisons, an error associated with the pure reason is found, it must be included in the frequency of wrong rational hypotheses of its respective pure reason in the critique of the pure reason, and it must be included as a wrong hypothesis in the respective critique of the deductive program responsible for its attribution, in order to identify what pure reasons or what deductive programs are making more mistakes, in order to fix them.

The critique of pure reason as a program is: 1) a database where per pure reason is one file per rational check or comparison, 2) where to account for the frequency of wrong rational hypotheses because of a problem related to the pure reason, 3) those pure reasons with the highest frequency of wrong rational hypotheses, should be analysed by Learning System to find out the source of error to fix.

The critique of the deductive programs as a program: 1) a database where per deductive program is one file per rational check or comparison, 2) where to account the frequency of wrong rational hypotheses because of a wrong attribution of pure reason to the data made by the deductive program, 3) those deductive programs with the highest frequency of wrong attributions, should be analysed by the Learning System to find out the source of error to fix.

Ending up with the critique of the attributional operation, understanding for attributional operation all operations responsible for the attribution of: meaning (by application, matching measurements to categories), pure reason (by deduction, matching pure reasons to data), applications (in the Application System, matching instructions to the correct application in accordance with their purpose).

The critique of the attributional operation is a program working as follow: 1) a database including all systems, specific/global deductive programs, particular programs, and applications, 2) where to account for the frequency of wrong attributions in their respective responsibility (attribution of meaning, pure reason, application), 3) identifying the ones with the highest frequency of mistakes to fix.

In order to fix those attributional operations in any intelligence, system, program, application, in which there are some attributional operations with frequencies of wrong attributions beyond the critical reason, is important that firstly the Learning System must analyse carefully what a common thing or common things there is or there are behind all the mistakes made by an intelligence, system, program, application, contrasting the common mistake/s and the mathematical structure behind the logic of set theory in which the attribution was made, to find out which is the real reason behind the set theory in this intelligence, system, program, application for what the attribution was wrongly made, in order to fix it.

Once the Learning System can get the real reason behind the mistake/s, and has proposed a decision to fix this intelligence, system, application, or program, decision to be authorised by the Decisional System, if authorised, the Artificial Engineering in the Application System is the responsible to fix that intelligence, system, application, or program, following the sequence of instructions in which the decision has been transformed into.

Rubén García Pedraza, 21 of July of 2018, London
Reviewed 27 August 2019 Madrid

Reviewed 21 August 2023 Madrid

imposiblenever@gmail.com