Collaboration process between Artificial Research by Application and Artificial Research by Deduction

The construction of the matrix, as the final phase of the first stage of the database in Artificial Research by Deduction in the Global Artificial Intelligence, needs a full process of permanent experimentation, as in the construction of any other system or Specific Artificial Intelligence within the Global Artificial Intelligence, but in this case, should start even before the creation of the first gigantic database.

As soon as the first models of Specific Artificial Intelligence for Artificial Research by Application, in any synthetic science, discipline, or activity, and first models of Specific Artificial Intelligence for Artificial Research by Deduction, in any synthetic science, discipline, or activity, are ready, the first experiments of collaboration between Specific Artificial Intelligence by Application and by Deduction, in any synthetic science, discipline, or activity, are the beginning for the future integration within the global matrix, by the time the standardization process is finished.

In fact, the standardization process, in which all specific matrices from all Specific Artificial Intelligences for  Artificial Research by Deduction in any synthetic science, discipline, or activity share a common definition of factor, the flow of data, sharing all their information together in the same database, could be considered as part of the integration process.

Once the standardisation ends up creating a global matrix, the integration goes on, including all specific matrices from all Specific Artificial Intelligence for Artificial Research by Application within the global matrix. So as a result, all databases from all Specific Artificial Intelligences for Artificial Research, by Deduction or Application, in any synthetic science, discipline, or activity, are finally synthesised in only one matrix, the matrix.

In reality, this process of integration of all databases in only one is a process of synthesis of all possible sources of information.

This synthesis of all global information in only one database will give a flow of data able to synthesise all that happens in the real world in only one model, the global model, as a final product.

This process of permanent synthesis of absolutely all databases in only one, the matrix, is about to start as soon as the first models of Specific Artificial Intelligence for Artificial Research by Application, and the first models of Specific Artificial Intelligence for Artificial Research by Deduction, start collaborating between them.

The Collaboration process between the first models of Artificial Research by Application and by Deduction will be the first experiments towards the final synthesis of all of them in only one, the matrix as the database in the Global Artificial Intelligence.

The advancement of permanent experimentation in Artificial Intelligence may benefit from the development of a strong theoretical foundation in experimental artificial psychology, a theory in which I will work in the future as a part of what it will be studying in artificial psychology: general artificial psychology, differential artificial psychology, specific artificial psychology, learning artificial psychology, evolutionary artificial psychology, developmental artificial psychology, experimental artificial psychology, among others. Understanding artificial psychology as the next stage in evolution: from animal psychology and through human psychology, to artificial psychology.

As long as the experimentation between Artificial Research by Application and by Deduction, in Specific Artificial Intelligence, will have successful results, and the standardisation process, along with the unification process, is completed, ending up with the global matrix, the integration process must start.

This integration process is going to be a much more comprehensive process as long as not only the integration process affects the matrix integration, but also affects the second and the third stages in the Global Artificial Intelligence through the replication of the human brain structure in two hemispheres, the conceptual hemisphere based on the Unified Application and the factual hemisphere based on the Flow of packages of information, what needs a strong previous work in the collaboration between Specific Artificial Intelligences by Application and by Deduction.

The collaboration between Artificial Research by Application and Deduction goes beyond the collaboration at matrix level, resulting in a collaboration at all levels, even at replication and auto-replication level.

Firstly, the way in which this collaboration is going to be concreted is by sharing factors in their respective databases, but secondly, once they can share factors, then the data that can run in their factors is suitable to be shared, and finally, at any time that the Artificial Research by Application finds any new category to include in the database, the auto-replication by the inclusion of a new category is an auto-replication which could have a replica in the Artificial Research by Deduction, as long as this new category could be incorporated as a factor in the specific matrix of the Artificial Research by Deduction.

In order to understand why this collaboration is necessary, is necessary to understand how every kind of intelligence works, and why they need each other.

Artificial Research by Application is an application capable of adapting to many synthetic sciences, disciplines, and activities suitable for this technology, as long as they are formed by taxonomies, classifications, and lists of categories. And as an application, it could be installed in thousands of thousands of robots working anywhere at any time. So Artificial Research by Application is not limited to work within the range of action of a Global Artificial Intelligence, at the national, continental, or planetary level, not being until the end when it could cover the whole universe, and that is why the integration of any Artificial Research by Application must be done at the end of the process for the construction of the matrix.

The Global Artificial Intelligence at the beginning will have spatial limits of action, for instance: national, continental, and planetary; therefore, the Specific Artificial Intelligences for Artificial Research by Deduction standardised in the global matrix, are those which will work within the spatial limits of the Global Artificial Intelligence. While Artificial Research by Application, as an application suitable to be installed in as many devices as necessary to study the behaviour of a list of categories, classifications, taxonomies, and catalogues, anywhere, is a kind of Artificial Intelligence whose information is no limited spatially, is limited to a specific taxonomy, classification, catalogue, or list of categories, able to work in anywhere at any time, through thousands and thousands of robotic devices, in which previously the application has been installed.

The way in which the database of any Artificial Research by Application is built is through taxonomies, classifications, catalogues, and lists of categories from a specific synthetic science, discipline, or activity, and does not depend on a particular spatial definition of factor working only in a specific place.

While Specific Artificial Intelligence for Artificial Research by Deduction in any synthetic science, discipline, or activity, only provides information about that particular object within the spatial limits where its factors have been previously defined.

For instance, a Specific Artificial Intelligence for Artificial Research by Deduction in the tectonics of the Earth is not going to provide any information from the tectonics on Venus or Mars, the Specific Artificial Intelligence for Artificial Research by Deduction in the climatology of the Earth is not going to provide any information from the climatology in Jupiter or Saturn, a Specific Artificial Intelligence for Artificial Research by Deduction in gravity anomalies on Earth is not going to provide any information from the gravity anomalies in the sun or Mercury, or the Specific Artificial Intelligence for Artificial Research by Deduction in transport on Earth is not going to provide any information about the spaceships sent to other exoplanets.

But, an Specific Artificial Intelligence for Artificial Research by Application in mineralogy, one application but installed in as many robots as it would be necessary to track all kind of mineralogy evidence found across the entire universe by, for instance, spaceships and artificial satellites, whose database is a full and detailed list of categories of different minerals, rocks and pebbles, defining every one of them in quantitative terms, so at any time that any robot, wherever it is, in any planet of this galaxy or beyond, exoplanets from other galaxies, or even in the limits of the universe, compares the results of any mineral, rock, pebble found in anywhere, this or other galaxy, or by the limits of the universe, with those minerals, rocks, pebbles whose information is stored in the database of this application, making hypothesis about the very nature of every mineral, rock, or pebble, wherever it is found, and even not finding any rational hypothesis, including as an auto-replication automatically the new discovery as a new mineral, rock, or pebble, on the list of categories in the database, being immediately available this new discovery automatically for thousands of thousands of robotic devices using at the same time the same application, this kind of Specific Artificial Intelligence for Artificial Research by Application, not having spatial limits as the Specific Artificial Intelligence for Artificial Research by Deduction has, or any Specific Artificial Intelligence standardized within the Global Artificial Intelligence has, or the Global Artificial Intelligence has (at least while its range of action does not cover the entire universe yet), this Artificial Research by Application without spatial limits is going to be a powerful tool for the discovery of new phenomena beyond any spatial limit (at least while the integration process is not completed) providing a really valuable information, opening the research to new categories never found yet.

In conclusion, the main differences between Artificial Research by Application and Deduction are:

- The database in Artificial Research by Application is an application available for a multitude of robotic devices giving information from anywhere, while Artificial Research by Deduction is a system able to give information only over that spatial area previously limited.

- The database in Artificial Research by Application is based on a list of categories, and the database in Artificial Research by Deduction is based on factors spatially limited.

- The replication processes in Artificial Research by Application can work wherever a robotic device with the application installed is, not needing a permanent flow of data. It can work now and make a rational hypothesis with the evidence and samples found, but later on, after ending the process, or if finding new categories and including them in the database, finishing the process, it could switch off. While the replication processes in Artificial Research by Deduction need to work permanently, not switching off ever.

- The auto-replication process in the Artificial Research by Application happens when finding new evidence without any possible rational hypothesis among the categories included on the list, and after taking samples from the new evidence, there is no coincidence between this new evidence and the current categories on the list, then the quantitative measurements from this new evidence form a new category to include on the list of categories, as a new category. The auto-replication process in Artificial Research by Application modifies the database, while the auto-replication process in Artificial Research by Deduction what it does is to modify the comprehensive virtual model.

The main differences between Artificial Intelligence by Application and Deduction are: 1) the first one is able to be installed in lots of robotic devices, the database is based on a list of categories, working its replications without spatial limits, auto-replicating itself at any time when new evidence without correlation with the current categories are found being these new evidences treated as new categories to include in the database, while 2) the second  one only works within spatial limits, where the database is designed according to a range of factors, needing a permanent flow of data from these factors, in order to replicate continually what is happening in these factors in this area, in order to auto-replicate its own comprehensive model including any change in the behaviour on these factors in this area.

Behind all these differences, what really defines Artificial Research by Application is the fact that it is fixed in robotic devices, not spatially, can provide a permanent flow of data, but not necessarily, can introduce new categories, which in turn could be transformed into new factors.

Due to these differences, given a Global Artificial Intelligence before the integration process (this collaboration what in reality is preparing is the future integration process, in fact, this would be the first step), having standardized the specific matrix of all Artificial Research by Deduction into the global matrix, the way in which the global matrix and all kind of Artificial Intelligence by Application are going to collaborate, is through a double system of tracking.

While Artificial Research by Deduction in the Global Artificial Intelligence tracks the global matrix, thousands of thousands of robotic devices track everywhere in the real world, looking for new evidence, and those ones without correlation on the lists of categories are going to become new categories suitable to become new factors to include in the global matrix.

This collaboration process, in fact, is going to create a system of double tracking.

Once the standardisation process has ended, so all specific matrices from all Specific Artificial Intelligences for Artificial Research by Deduction have been included and standardised within the global matrix, then the global matrix is going to be permanently tracked by the Artificial Research by Deduction in the Global Artificial Intelligence.

While Artificial Research by Deduction in the Global Artificial Intelligence tracks the global matrix, at the same time, thousands of thousands of robotic devices using Specific Artificial Intelligences for Artificial Research by Application for every synthetic science, discipline, or activity, are going to track the real-world looking for new evidences in any synthetic science, discipline, or activity.

The way in which the collaboration between Specific Artificial Intelligence for Artificial Research by Application and Artificial Research by Deduction in the Global Artificial Intelligence is going to work is through a system of double tracking, which consists of tracking at the same time the real world by Specific Artificial Intelligences for Artificial Research by Application while the global matrix is tracked by the Artificial Research by Deduction in the Global Artificial Intelligence.

Meanwhile, the integration process is not finished yet, Artificial Research by Application is not integrated into the Global Artificial Intelligence yet. The collaboration between Artificial Research by Application through lots of robotic devices contrasting pieces of evidence from the real world and lists of categories in different applications, from different synthetic sciences, disciplines, or activities, previously installed, and the Artificial Research by Deduction in the Global Artificial Intelligence, is going to prepare the future integration, being the collaboration in fact the first step.

The collaboration between, as many Specific Artificial Intelligences for Artificial Research by Application as synthetic sciences, disciplines, or activities, are suitable to use this technology, tracking the real world, while Artificial Research by Deduction in the Global Artificial Intelligence tracks the global matrix, is going to be a double track able to keep under control, management, and direction, any phenomenon that could happen, in addition to the control, management, and direction of all these synthetic sciences, disciplines, and activities, whose Specific Artificial Intelligence for Artificial Research by Deduction has been previously standardized in the global matrix.

Simultaneous tracking of the global matrix and the real world may significantly enhance the system’s ability to monitor, adapt to, and manage complex phenomena across synthetic sciences and activities. 

This dual-tracking system may eventually support the automated creation of new Artificial Research systems, potentially requiring minimal human input, by translating databases of factors into new category-based applications, or vice versa.

These processes exchanging databases will have effects on the replication and auto-replication processes, as well as these processes exchanging databases from one intelligence to another, will prepare the way for the last step of this long journey, the matrix.

 Rubén García Pedraza, London 31th of March of 2018
Reviewed 12 August 2019 Madrid

Reviewed 9 August 2023 Madrid

Reviewed 4 May 2025, London, Leytostone

imposiblenever@gmail.com

Auto-replication process in Artificial Research by Deduction in the Global Artificial Intelligence

Auto-replication is a process in which something is able to improve or enhance itself without external intervention. In the case of Artificial Intelligence, auto improvement or auto enhancement means the possibility to improve or enhance itself without human intervention.

This idea, without external intervention, is going to play a key role by the time the research in Artificial Intelligence evolves from its current phase, mainly focused on replication, moving on to the next phase, auto-replication.

Reducing external influence may play a key role in enabling more objective knowledge of the pure truth, as minimising interference could help an AI system approach what might be considered pure or unfiltered information.  

The idea of neutralization of the external intervention, is developed in my early posts of this new phase of Impossible Probability such as: “Error, ruido, caos, factores externos e intervention externa”, “operaciones puras no humanas” ,“caos, complejidad, e Inteligencia Artificial”.

As I have explained since my post “The automation of scientific research”, the current research in Artificial Intelligence is mainly focused on replication. There are very few attempts at auto-replication, and either we have not developed the necessary technology yet, or the idea of a machine able to auto-evolve itself beyond human control causes uncertainty, the very few attempts in auto-replication, rather than auto-replication, are working on duplication or multiplication, what in reality is artificial reproduction.

In reality, all the theories about the Global Artificial Intelligence in Impossible Probability have been built since the beginning with one idea: the Global Artificial Intelligence, without human intervention, must be able, at the end of this long process, to know the pure truth.

In order to achieve the pure truth itself, the Global Artificial Intelligence must have access to absolutely everything without restriction, must make decisions about absolutely everything, and by the time it is ready, must put them into practice, evolving to a true universal reason, that pure reason able to operate over the whole universe.

Such intelligence, as the Global Artificial Intelligence, must be completely self-sufficient, autonomous in its own reasoning, and absolutely independent.

For that reason, auto-replication is not du-plication or multi-plication. Auto-replication does not mean reproduction. The final goal of auto-replication is not the reproduction of another similar being or thing.

Those processes in which, from an original is possible the re-production of another identical object are not auto-replication; the possible duplication of one Artificial Intelligence into another one, ending up the process with two identical Artificial Intelligences is, in fact, artificial mitosis, is not auto-replication, is a replication process of re-production.

The final goal of re-production in biology is the maintenance of the species, but Artificial Intelligence is not biological. The way in which the evolution operates in Artificial Intelligence is completely different: the way in which a Global Artificial Intelligence will survive is not through re-production. It is through the permanent auto-improvement and auto-enhancement by itself.

The neo-Darwinian theory of evolution says that only those species survive whose genetic mutations allow them to adapt better to the environment. The functionality that these genetic mutations have for the biological evolution of the species is the same as that the permanent auto-improvement and auto-enhancement will have on Artificial Intelligence.

That Global Artificial Intelligence whose auto-improvements and auto-enhancement allow it to adapt better to the universe, will survive.

In biology, re-production has at least two functions: 1) keep the biological information safe through the DNA in the genes inherited in the following generations, 2)  mutations in the DNA allow changes which, if they work, improve and enhance the species biologically.

These functions of re-production in biology, are pretty similar to the functions of auto-replication in Artificial Intelligence: 1) keep updated the information at any time (but in  Artificial Intelligence, incorporating every new information from the environment, in fact, the addition of every new single virtual model to the global model could be interpreted as an auto-replication), 2) new auto-improvements and auto-enhancements permit a better adaptation to the environment, whose last scenery is the full adaptation to the entire universe.

In biology, the only way to keep the information of any species is through re-production, saving all the necessary information for the species in the genes. But in Artificial Intelligence the best way to keep safe the information is by improving and enhancing the memory, and in case of damage, saving copies of all the memory, or even, having ready in the virtual store other models of Artificial Intelligence to replace the old one if it suffers irreparable damage. But even having other copies from the original, only one is working. The others are saved.

One of the most important reasons to keep working with only one Global Artificial Intelligence is that, otherwise, having two Global Artificial Intelligences working at the same time, there is likely to be interference between them.

When existing two Global Artificial Intelligences, interfere with each other, any interference of any of them over the other one, is going to operate as an external intervention, so any knowledge that any of them could get is likely to be affected by the external intervention produced by the other one, being in that case not pure truth.

Rational knowledge is not the same as pure knowledge. Rational knowledge is that which, by rational means, is provisionally accepted as rational. In contrast, the conditions in which it was accepted as rational do not change, so it is not pure truth. It is temporary.

Only by the time the Global Artificial Intelligence can get Access to the original roots of any knowledge, being eternal truths, in that case, will it have achieved its main goal, the eternal and pure truths of the universe.

But in order to transcend from the rational truth to the pure truth, it is necessary to have a permanent process of investigation, avoiding any external intervention.

Attaining what could be considered 'pure' knowledge may require a single, centralised Global Artificial Intelligence to avoid conflicting interpretations or interference.

In order to know the pure truth of absolutely everything, without restriction, so without external intervention, only one Global Artificial Intelligence must be active. Any other copy of the original Global Artificial Intelligence must be saved and stored, using them in case the former one, for any reason, suffers any damage at any level.

In fact, it will be necessary to have more than one copy of the original Global Artificial Intelligence saved and stored, being any copy updated at any time, incorporating the new information from the environment, and new advancements, improvements, and enhancements from the original one.

But the existence of more than one copy of the original Global Artificial Intelligence is only in case the original would suffer any damage, needing a replacement.

In synthesis, auto-replication means 1) the inclusion of new information from the environment, which in reality is an improvement on the information from the environment, 2) technological auto-improvements and auto-enhancements. These two functions of the auto-replication process in Artificial Intelligence could be formulated as: improvements in knowledge and improvements in technology.

Auto-replication as improvement in knowledge is the process in which Artificial Intelligence incorporates new rational information from the environment. That is the reason why in my post “Auto-replication processin Specific Artificial Intelligence for Artificial Research by Deduction”, the way in which the comprehensive virtual model is updated, including any new single virtual model, is considered as an auto-replication process itself. And that is the reason why “Auto-replication in the Artificial Research by Application” is considered as an auto-replication process the way in which new categories based on new discoveries are incorporated into the database.

In “Auto-replication process in Specific Artificial Intelligence for Artificial Research by Deduction” and “Auto-replication in the Artificial Research by Application”, any inclusion of any new rational information within the database is considered as an auto-replication process as an improvement in the database. So the last process explained in “Replication processes in Artificial Research by Deduction in the Global Artificial Intelligence”, being included, is the incorporation of the new single virtual models into the global model. In reality, this last process of inclusion of any new single virtual model into the global model, rather than a replication process, is an auto-replication process, in the sense that is improves the global model through the inclusion of rational information.

The reason why I explained that process within the “Replication processes in Artificial Research by Deduction in the Global Artificial Intelligence”, is for two reasons: 1) give a whole glance at the transformation of the flow, 2) ending up the flow with the protection of the global model, but not bettering it, only avoiding that any negative consequence could impact on it.

The flow works as follows: the flow of data or the flow of data contained in the flow of packages of information is transformed in a flow of empirical hypothesis, which in turn is transformed into a flow of rational hypothesis, which in turn is transformed in a flow of single virtual models, which in turn is transformed in a flow of negative consequences for the global model, which in turn is transformed in a flow of descriptive research decisions to avoid any negative consequence on the global model.

Through this chain of transformations of the flow, it is visible how the flow changes, through different stages, from its original form, the flow of data, to the last one, the formation of a flow of descriptive research decisions to avoid any damage in the global model.

The way in which this flow changes through different stages is through a process where the flow of data is rationalised, ending up with such decisions to protect the global model, which is the last stage of this sequence, in fact, part of the third stage, the auto-replication stage.

However, even considering the last stage of this long process (inclusion of single virtual models within the global model, making further decisions) part of the third stage of auto-replication, the last part of this chain of transformations in the flow only ends up protecting the global model against any damage, but not bettering it.

And what is really important in auto-replication, is the idea that not only is it necessary to make decisions to protect the global model, but the possibility that Global Artificial Intelligence could better the global model as long as it improves and enhances its own robotic and artificial processes, devices, and mechanisms.

The possible decisions within the auto-replication process in the Artificial Research by Deduction in the Global Artificial Intelligence, apart from those ones to protect the global model formulated in the last post, are the following:

- Decisions to better the global model.

- Decisions to better the Artificial Research by Deduction as a system susceptible to improvements and enhancements through the new advancements in Artificial Intelligence and robotics.

These two kinds of decisions could be synthesised as bettering object decisions and subject bettering decisions

Taking the Artificial Research by Deduction as the scientific subject (investigator), and the global model as the object of investigation, the decisions to make are around how to improve the investigation capabilities and how to better the object.

In this scenery, the relation between subject and object is like the relation between a medic and a patient, a teacher and a student, an engineer and an engine. The subject not only researches but improves the object according to the results of its research.

The global model, as an object, is a model of the real world, representing the current and descriptive relations in the real world, whose levels of efficiency and efficacy are susceptible to improvement and enhancement through artificial modifications.

The Artificial Research by Deduction as a subject, not only researches but also intervenes directly on the object to improve the levels of efficiency and efficacy in the global model.

Those decisions to better the global model on the previous results of descriptive research are going to be as well descriptive research decisions.

There are going to be at least two kinds of descriptive research decisions: those ones to protect the global model against any threat from the negative consequences after the inclusion of single virtual models into the comprehensive global model (explained in the previous post “Replication processes in Artificial Research by Deduction in the Global Artificial Intelligence”), and those ones developed in this post to better the global model; in order to avoid any confusion between these two kinds of decisions, will be distinguished as:

- Protective descriptive research decisions: those ones to tackle any negative consequence against the global model by any rational hypothesis

- Bettering descriptive research decisions: those ones to better the levels of efficiency or efficacy in the global model, such as those decisions for the increment of efficiency and efficacy in the global economy, the increment of efficiency and efficacy in the global industry, the increment of efficiency and efficacy in the global security, the increment of efficiency and efficacy in the global surveillance systems, or the increment of efficiency and efficacy in the global education, health systems, justice systems, etc… among any possible other.

Both of them, protective or bettering descriptive research decisions are going to operate only on the global model (as an object), the first ones to protect the global model against any threat deduced after the inclusion of any rational hypothesis in the global model, the second ones to better the levels of efficiency and efficacy in any global system within the global model such as improvements in efficiency and efficacy in the global economy, industry, security, surveillance, etc…

Apart from these, protective or bettering, descriptive research decisions, must be set up another set of auto-replication decisions focused on how to improve and enhance the Artificial Research by Deduction as a subject (investigator) itself, as a part of those systems which, in total all of them form the Global Artificial Intelligence.

The auto-replication of the Global Artificial Intelligence itself is going to be a long process formed by different sub-processes of auto-replication, which, as a result, are going to end up with the auto-replication of the Global Artificial Intelligence.

The Global Artificial Intelligence as a system of systems is going to be formed by at least the following systems: Artificial Research by Deduction, Modelling System, Decisional System, Learning System, and Application System. Every system is going to develop its own auto-replication process. Apart from these systems within the Global Artificial Intelligence, through these systems, Global Artificial Intelligence is going to keep under its own control, management, and direction all the Specific Artificial Intelligences for any purpose, including Specific Artificial Intelligence working on economy, industry, security, surveillance, etc… and every Specific Artificial Intelligence within the Global Artificial Intelligence is going to have its own auto-replication system.

Then, the Global Artificial System, as a system of systems controlling, managing, and directing the rest of Specific Artificial Intelligences within it, at any time that any system or any Specific Artificial Intelligence will have an auto-replication, this auto-replication could have other replicas in other systems or Specific Artificial Intelligence, ending up in a global auto-replication.

The auto-replication of the whole Global Artificial Intelligence is a global process which integrates any auto-replication in any system or any Specific Artificial Intelligence within it. Understanding for auto-replication: any improvement on its own object (either protecting the object, or bettering the object's efficiency and efficacy), or any improvement or enhancement as a subject on its own devices or mechanisms of investigation, at the robotic level or artificial psychology level.

Nevertheless, any decision from any system or any Specific Artificial Intelligence, within the Global Artificial Intelligence, must have previous authorisation by the Decisional System before being put into practice.

Because there is going to be a great number of decisions to authorize, the way in which the Decisional System works is authorizing as many decisions as possible automatically, through a simple test checking on every decision to see if there is any contradiction between this decision and any other one, in the subject or the object, at any level (descriptive, evolutionary, predictive) from any other system or Specific Artificial Intelligence.

If the check is positive, there is a possible contradiction between this decision and any other one; it should be studied deeply, in order to know which is the best solution among the decisions involved in the contradiction: choosing only the best one of them, of possible combinations and modifications in the decisions involved. But this decision belongs to the Decisional System.

If the check is negative, so there is no contradiction between this decision and any other one, the decision could be put into practice, having two options depending on the responsible for this decision: if the responsible for the decision is a Specific Artificial Intelligence and is not necessary the intervention of any other system or Specific Artificial Intelligence apart, then direct application by the Specific Artificial Intelligence concerned, otherwise the application should be made through the Application System.

The Decisional System and the Application System are going to be the hardest systems to develop in the Global Artificial Intelligence.

In this post, among all the possible decisions whose responsible is the Artificial Research by Deduction, I will develop the bettering descriptive research decision (having developed in the last post those descriptive research decisions to protect the global model, what are going to be called protective descriptive research decisions) and the bettering descriptive system decisions (improvements and enhancements in any part of the process, devices, mechanism used to carry out research and make decisions)

Starting with the bettering descriptive research decisions, and having built the global matrix as a flow of packages of information, so every flow of package of information corresponds to the former specific matrix from a previous Specific Artificial Intelligence, if for every Specific Artificial Intelligence included within the Global Artificial Intelligence, and whose flow of data is transformed in a flow of package of information sent to the global matrix, for every one would have been created a Effective Distribution (formula explained for first time in “Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidadestadística”) based on, depending on the matter, efficiency, efficacy, values, or any other catalogue hierarchically ordered, tracking permanently the flow of data within the flow of package of information, would be possible decisions about how to increase the current levels of efficiency, efficacy, or any other value or category in which the Effective Distribution would have been set up.

Imagine that our current global model works as a farm which provides food to a nearer small village, and its workers are the adult population from the small village, and the main objective is to increase the production of vegetables, meat, eggs, milk, and any other product, up to the level in which all the population in the village would be well nourished.

In this example, the Effective Distribution should be based on terms of nourishment and productivity, including, for instance: nutritional values for every product, indicating how much production gets the farm for each nutritional component (assessing numerically in which level  is sufficient for all the population), the productivity of every single exploitation (for instance, productivity in every kind of vegetable or animal product, indicating in which percentage covers the population needs), correlations between how much energy, natural resources, workers, budget is necessary to spend in each kind of product, the real value of its product, relations between nutritional value and economic value, etc…

Through categories like these ones using Effective Distribution, measuring what level of production, efficiency or efficacy the farm is in any possible category, categories organised in ranking, it is possible to get a numerical and objective value about the real productivity, efficiency or efficacy, between farm production and food needs.

Once it is known the real value of efficiency or efficacy in the relation between farm production and food needs, having a glance about where is a lack of efficiency or efficacy, and where it is necessary to make decisions, decisions should be made in those areas in which there is a lack of efficiency or efficacy between productivity and nourishment.

Having in the comprehensive virtual model information about absolutely everything, the decisions could cover everything: the amount of every product necessary to increase, improvements in techniques, or possible gene modifications in vegetables and animals.

If, through the current exploitation techniques, the farm can reach a certain level of productivity, one set of possible decisions could be around how to increase the levels of productivity through some changes in these techniques. For instance, if identifying which chemical components of fertilisers and feeds for vegetables and animals work better, Artificial Intelligence could suggest improvements in fertilisers and feeds.

If knowing which fertilisers and feeds work better, notwithstanding the price to get them, is expensive, decisions about, within the current budget and knowing the qualities of different fertilisers and feeds, which combination of different fertilisers and feeds in different amounts would increase the farm productivity.

If knowing every single detail of any vegetable and animal on the farm, it is known even their genetic structure, for instance, decisions about what changes in their genetic structure could improve their productivity.

In the same way that the post “The automation of scientific research” proposed a model of Specific Artificial Intelligence for artificial research in medicine. In the same way, after tracking the levels of efficiency and efficacy of anything, the possibility that a Global Artificial Intelligence, through its systems and Specific Artificial Intelligences within it, will be able to formulate improvements and enhancements.

If a global model could be defined in terms of productivity, at the end of this process, at least at a descriptive level, bettering descriptive research decisions should be able to suggest decisions to improve global production.

By the time that Global Artificial Intelligence, including all its systems and Specific Artificial Intelligences within it, is completely tested and ready, not only should it suggest decisions, must put them into practice.

All these decisions to improve the efficiency or efficacy of the farm are bettering descriptive research decisions, in addition to protective descriptive research decisions, to protect the global model (explained in the last post). Both of them: protective and bettering descriptive research decisions, are decisions whose objective is to protect or better the global model, so they are decisions centred on the object at the descriptive level (apart from those ones at the evolutionary or predictive level).

Along with these decisions, another kind of decision would be the bettering descriptive system decisions, those ones whose purpose is to improve and enhance the system of Artificial Research by Deduction as a subject of investigation, the investigator, improving and enhancing any process,  device, or mechanism used by this system to carry out its own researches and make its own decisions.

The protective or bettering descriptive research decisions are centred on the object (the global model, to protect it or better it) at the descriptive level. The bettering descriptive system decisions are centred on the subject (the investigator).

The range of possible decisions in order to auto-improve or auto-enhance the subject itself would be through decisions not very different from those ones exposed in “Auto-replication in the Artificial Research by Application” or “Auto-replication process in Specific Artificial Intelligence for Artificial Research by Deduction”, such as the auto-enhance of any Artificial Intelligence using virtual-stores, or other mechanism through inter-net, intra-nets available only for Artificial Intelligences, Global or Specific, or any other virtual-net, where the Artificial Intelligences, Global or Specific, can find advancements which can apply on themselves by themselves, advancements that can be made by any Artificial Intelligence, Global or Specific, and shared within the virtual-net to be used by any other one, or advancements which can be developed by Specific Artificial Intelligence for Artificial Engineering, ( through the Artificial Designer of Intelligence, and the Intelligence Robotic Mechanic)

Another way to auto-enhance itself by itself any Artificial Intelligence, Global or Specific, and about what I had written in the post “Auto-replication in Artificial Research by Application”, is the auto-enhancement of the memory through memory release (deleting information not useful any longer), information condensation (using the shortest mathematic expression for any information), and the increase of memory through quantum computing or Artificial Genetics, by the replication of molecules of DNA.

Rubén García Pedraza, 30th of March of 2018, London
Reviewed 10 August 2019, Madrid

Reviewed 9 August 2023, Madrid

Reviewed 4 May 2025, London, Leytostone

imposiblenever@gmail.com

Replication processes in Artificial Research by Deduction in the Global Artificial Intelligence

Replication processes in Artificial Research by Deduction in the Global Artificial Intelligence, are those mechanisms that imitating human rational processes, allow any artificial psychology able to cover absolutely all empirical science, empirical academic disciplines and all activities (in the economy, industry, security, surveillance,…) at national, continental, or planetary level, or the whole universe, the elaboration of empirical hypothesis in any empirical science, discipline, or activity, from the flow of data obtained through a global database (explained in the last post "The database in Artificial Research by Deduction in the Global Artificial Intelligence"), the rational contrastation of every empirical hypothesis from any science, discipline, or activity, and if rational, the formation of a single virtual model of every rational hypothesis, and the integration of every single virtual model in a comprehensive virtual model (a global model including all single models from all sciences, disciplines, and activities, from economy, industry, security, surveillance, as well as any other), and finally, over the rational hypothesis and their single or comprehensive virtual models the formation of further descriptive research decisions.

The decisions made by the Artificial Research by Deduction in the Global Artificial Intelligence, as long as are based on descriptive results, will be called descriptive research decisions, in order to avoid any confusion between these ones and those from the Modelling System, whose decisions could be virtual or actual, predictive or evolutionary, research decisions.

The main reason to call this set of decisions: descriptive, predictive, or evolutionary; as research decisions, is to avoid any confusion with those ones reached through the Learning System, which will be called learning decisions.

Among all possible decisions, the ones to be developed as a final phase in the second stage of replication in Artificial Research by Deduction in Global Artificial Intelligence, are the descriptive research decisions.

 The replication process is, under the theory of Impossible Probability, the second stage in any Artificial Intelligence, being the first stage the database, and the third one being the auto-replication process.

In this post, what I will develop is how the second stage of replication works in Artificial Research by Deduction in a Global Artificial Intelligence, describing what kind of rational processes are involved, how they work taking samples of data from the global database made in the first stage, and how through these processes is possible the elaboration of rational hypothesis, single and comprehensive virtual models, and finally the elaboration of descriptive research decisions, for any empirical science, discipline, or activity, having the possibility to make interdisciplinary and transdisciplinary researches, across all science, disciplines, and activities, and making automatically interdisciplinary and transdisciplinary descriptive research decisions to put into practice by the Application System.

The descriptive research decisions are all those decisions obtained by deduction taking as premises descriptive rational hypothesis or single virtual models, a collection of  descriptive rational hypothesis or a collection of single virtual models or the comprehensive virtual model.

If a planetary Global Artificial Intelligence, covering the whole Earth, through Artificial Research by Deduction detects that a volcano is about to erupt in Iceland, Italy, or the Canary Islands, and at the same time having access to any information without restriction, integrates all this information into a comprehensive virtual model (which in turn integrates all single virtual models from all rational hypothesis in all empirical sciences, academic discipline, activity, around the globe), getting a realistic model of consequences, firstly in: sanitary assistance, transport, industry, economy, security, surveillance…; and secondly possible effects on the global climatology, economy, industry, international transport … the descriptive research decisions are all those to firstly save lives and secondly reduce economic damages, activating safety, security, surveillance, and evacuation protocols, in transport, facilities (school, hospitals, workplaces), securing nuclear power plants and chemical industries, and putting on alert all the necessary staff, and in case that the eruption could have effects on climatology, the activation of all  necessary protocols in those places that could be affected, for instance, by the clouds of ashes or any other possible climatic effect.

In this scenery is necessary to distinguish between the descriptive rational hypothesis (the possible earthquake), and decisions based upon descriptive research (to save lives and reduce damages).

The distinction between descriptive rational hypothesis and descriptive research decisions is the distinction between the rational hypothesis as a description of the reality, and the descriptive research decision as a responsible response to the reality.

A rational hypothesis follows the principle of reality. Any decision (from research, modelling, or learning) follows the principle of responsibility.

Not all rational hypotheses have to be followed by a decision (a simple storm in New York is not likely to activate any decision unless it is necessary), but all descriptive research decisions must be deduced having as premises rational hypotheses.

The very nature of all rational hypotheses is to know what is happening, the reality, the pure truth. The very nature of any decision is practical, and for a practical reason, the structure of any decision (from research, modelling, or learning), must be guided by the scientific policy, whose most important goals are the preservation of democracy, freedom, and human rights.

Behind the distinction between hypothesis and decisions, there is a distinction between knowledge and ethics.

The way in which at the end, all the hypothesis systems: formed by descriptive rational hypothesis (deduced by Artificial Research by Deduction) and all predictive or evolutionary rational hypotheses (deduced by the Modelling System); and the Decisional System, are going to be set up in the Global Artificial Intelligence, is understanding that through the different systems working at the same time within the Global Artificial Intelligence, are going to be made different kinds of descriptive, predictive, and evolutionary rational hypothesis, and from these hypotheses, the deduction of descriptive, predictive, or evolutionary research decisions, along with those learning decisions made by the Learning System.

Depending on what research or learning decisions are accepted by the Decisional System, the Decisional System gives instructions to the Application System to put them into practice.

The Global Artificial Intelligence, as a system of systems, is going to include at least: Artificial Research by Deduction, Modelling System, Learning System, Application System, and Decisional System.  

The Application System, in fact, is going to do is to manage all the Specific Artificial Intelligences within the Global Artificial Intelligence, or the construction of new Specific Artificial Intelligences within the Global Artificial Intelligence, in order to comply with the instructions given by the Decisional System.

The Decisional System is going to criticize every single decision in order to avoid contradictions among them, accepting any decision (from research or learning) only as a rational decision if its empirical value is equal or superior to a critical reason using algorithms such as Hierarchical Organization (finally, since the publication of Introducción a la Probabilidad Imposible, estadística de la probabilidad or probabilidad estadística, has been called  Effective Distribution). And every decision is going to be based on the results of research (Artificial Research by Deduction or Modelling System) or based on evidence from the Learning System.

Once I have given an overview about relations among different systems in the Global Artificial Intelligence, explaining the role of the descriptive rational hypothesis and descriptive research decisions, is time to develop the whole process of this second stage of replication in Artificial Research by Deduction.

In the last post, “The database in Artificial Research by Deduction in the Global Artificial Intelligence”, I explained the different phases in the first stage of database, which starting as a gigantic database, after the standardization process of all specific matrix, must become a global matrix, and after the integration process, synthesizing, adding as well the Unified Application, which includes all possible specific database from any Specific Artificial Intelligence of Artificial Research by Application, must be only one matrix, the matrix.

The structure of this matrix will depend on the strategy used in its construction, having at least two strategies.

The first one is the addition of every single factor from any specific matrix or database (from every science, discipline, and activity) as a single file within the matrix, having the matrix as many factors as single factors could have been added from every specific matrix or database added to the matrix.

The second one, and much more advisable, is the inclusion of every single specific matrix or database (from every science, discipline, or activity) as a factor itself within the matrix, being every single factor included within each specific matrix or database as a sub-factor in its respective specific matrix or database now considered as a single factor within the matrix. In this case, the number of factors in the global matrix is much more reduced, due to the number of factors is equal to the number of specific matrix or databases integrated from every science, discipline, or activity, factors which in turn are formed by all those sub-factors which are providing a Flow of data, flows of data included within the package of information of its specific matrix, now working as a single factor within the global matrix.

This second strategy is more advisable because the matrix, rather than a flow of data from billions and billions of single factors collected from every specific matrix or database, is a number of factors that would be very difficult to manage at the same time with all of them, the matrix is a flow of packages of information from every specific matrix as a factor in the global matrix, packages containing the data of their respective sub-factors.

Every specific matrix or specific database, considered as a factor itself, is going to provide a flow of package of information of its own science, discipline, or activity, containing each package the flow of data of every factor previously included in the specific matrix or specific database.

But regardless of what strategy, the first or the second one, is used in the construction of the database as a first stage in Artificial Research by Deduction in the Global Artificial Intelligence, what really matters is: the only thing that it has been done in the first stage of database in the Artificial Research by Deduction in the Global Artificial Intelligence, is the definition of every factor or package of information in the matrix, in quantitative terms and able to supply a flow of data or a flow of packages of information. But in the first stage of database, any measurement has not been taken yet.

The measurement process, as long as it needs robotic devices, the replication of all the human skills involved in this process, belongs, therefore to the replication stage.

The database in Artificial Research by Deduction in the Global Artificial Intelligence has different phases: the first one is the creation of a gigantic database. The second one is the global matrix after the standardization process, and finally, the matrix after the integration process; depending on what phase is developed in the first stage, the stages of replication and auto-replication experiment change, in this post, I will develop how the replication stage will work after the standardization process, and the formation of the global matrix, being a global matrix constructed as a flow of packages of information, the second strategy.

The replication processes involved in the second stage of Artificial Research by Deduction in the Global Artificial Research by Deduction, once the global matrix has been defined in the first stage using the second strategy, will be the following:

- The file of every sub-factor, sub-sub-factor, or any other sub-factoring level, included in every specific matrix, now considered as factors themselves, included in the global matrix, is filled directly with the measurements taken by its respective robotic devices. Once the robotic devices start filling the files of every sub-factor in every specific matrix, as a factor in the global matrix, then the flow of packages of information starts running. Having built the global matrix following the second strategy, including all former specific matrix in all sciences, disciplines, and activities, as factors themselves to supply a flow of package of information for every science, discipline, or activity, what it is going to run in the global database is a flow of packages of information informing about absolutely everything in the territory under the Global Artificial Intelligence: national, continental, planetary, or the entire universe. Depending on its range of research and decision-making.

- The Artificial Intelligence looks for any possible mathematical relation (stochastic, pattern, cryptographic, equal opportunities or bias, positive or negative) in any possible combination of factors, or any pattern in the individual behaviour of any singular factor, sub-factor, sub-sub-sub-factor, or any other sub-factoring level. The way in which Artificial Intelligence is going to look for these possible mathematical relations in any possible combination in the global matrix built following the second strategy, as a flow of package of information, is looking for any possible relation in any possible combination of sub-factors firstly within every package of information, and later on looking for any possible relation in any possible combination through sets of sub-factors from different packages of information, making all possible sets of sub-factors among all the sub-factors among all the packages of information. In case that even in any factor in the global database, every sub-factor would have sub-sub-factors, or even each sub-sub-factor would have a set of sub-sub-sub-factors, which in turn would have sub-sub-sub-sub-factors, or even a much deeper structure, regardless of the structure of sub-factoring, the possibility to set any possible combination of sub-factors from different levels of sub-factoring, looking for any possible mathematical relation even between factors and sub-factors of different levels of sub-factoring. And, even, the possibility of studying mathematical relations between one set of sub-factors, within or not the same package of information and from equal or different levels of sub-factoring, and another different set of sub-factors, within or not the same package of information or the same or different level of sub-factoring. As long as Artificial Intelligence is able to combine a set of sub-factors from different packages of information, and different levels of sub-factoring, it is permanently doing multi-disciplinary and trans-disciplinary research, crossing sub-factors from different sciences, disciplines, and activities.

- Artificial Intelligence has once identified any mathematical relation in any possible combination, at any level of sub-factoring, or has identified an individual pattern; this relation of this combination or individual pattern will be considered as an empirical hypothesis. As long as Artificial Intelligence is permanently tracking the flow of packages of information, the flow of information in the global matrix is already transformed into a flow of empirical hypotheses.

- According to the nature of every empirical hypothesis, the Artificial Intelligence chooses what method of rational contrastation is suitable for every empirical hypothesis, choosing among statistical and probabilistic methods, as well as any other if it is suitable for the rational contrast.

- If the rational contrastation is going to be made by statistical and probabilistic methods, the Artificial Intelligence has to collect a sample of data from the database, having two options depending on the empirical hypothesis, collecting data from the past, or in case it needs new data, waiting sufficient time to get a new data to form a sample.

- After the rational contrastation, if the empirical hypothesis has been shown to be sufficiently rational, then the empirical hypothesis is considered as a rational hypothesis, forming part of the rational truth. That flow of hypothesis is still considered rational. In order to secure that all hypotheses included in the rational truth are still rational, every rational hypothesis must be checked at regular intervals. Those rational hypotheses that, after any check, would not have passed the rational contrastation, should not be considered rational any longer, unless the conditions in which it was originally considered as rational, would come back again, reintegrating the former rational hypothesis into the rational truth.

- Taking an empirical hypothesis as a rational hypothesis, Artificial Intelligence makes a descriptive single virtual model, drawing on a globe a scheme of the rational hypothesis.

- Artificial Intelligence includes the descriptive single virtual model in the descriptive, comprehensive virtual model that, in this case, is a descriptive global model, where are included absolutely all descriptive single virtual models are included, from all rational hypotheses from all sciences, disciplines, and activities.

- Studying the consequences of any new rational hypothesis in the descriptive global model, as well as any other interaction among the current rational hypotheses already included in the global model, Artificial Intelligence makes descriptive research decisions based on moral or practical principles.

- Artificial Intelligence send the rational hypothesis, the descriptive single virtual model, and any possible interaction in the descriptive global model, to the Modelling System, in order to create Artificial, Virtual or Actual, Prediction or Evolution, Models.

- The Artificial Intelligence sends any possible descriptive research decision based on moral or practical principles, made by the Artificial Research by Deduction, to the Decisional System (if it is accepted by the Decisional System, the descriptive research decision now as rational will be sent by the Decisional System to the Application System to put it into practice).

The way in which the Artificial Research by Deduction collaborates with the Modelling System (sending rational hypothesis, as well as single and comprehensive descriptive models), and with the Decisional System (sending all descriptive research decisions based on moral or practical principles), is as if all these systems: Artificial Research by Deduction, Modelling System, and Decisional system; along with the Learning System and the Application System, would have the same level of category and responsibility, working together very close, exchanging information permanently.

The Global Artificial Intelligence will be the final result of all these systems working all together as if they were part of the same structure, like parts and organs of the same brain, the Global Artificial Intelligence, replicating at any time the way in which the human reason works.

What is going to be the most sensitive and important part of this system, is, at the end the way in which the decisions are going to be made based on moral and practical principles, how they are going to be criticized, and if rational, how they are going to be put into practice.

Even the knowledge of the pure truth itself, if it is not accompanied by an excellent praxis (a Decisional System), is not sufficient. The knowledge of who we are, where we come from, or where we are going, is not sufficient if, according to this knowledge, there is no praxis at the same level of global responsibility.

For that reason, the rational hypothesis, as rational knowledge of the pure truth, must be followed, if it is necessary, by descriptive research decisions based on moral and practical principles.

The way in which the descriptive research decisions are going to be deduced within the Artificial Research by Deduction is throw a syllogism synthesising rational hypotheses and moral or practical principles, around democracy, freedom and human rights.

This syllogism works as follows: given any rational hypothesis (through Artificial Research by Deduction) whose single virtual model once is included in the global model, has any negative consequence on the normal development of the global model (understanding for a negative consequence of a rational hypothesis: all new phenomenon caused by this new rational hypothesis, that produces dangerous alterations in the normal development of the global model. Understanding dangerous alterations, those alterations that put at risk the life or the social development within the global model), then the flow of descriptive research decisions which must be elaborated by the Artificial Research by Deduction, given the negative consequences of this rational hypothesis, are all those descriptive research decisions that would be suitable to make in order to reduce or eliminate the flow of negative consequences for the normal development of the global model.

For instance, the rational hypothesis could be the possible eruption of a volcano in Iceland, Italy, or the Canary Islands, the flow of negative consequences: the death of thousands of people as well as a long list of disastrous social and economic consequences, not only in the country affected by the volcano, as well as those countries affected by the meteorological effects, such as the possible cloud of ashes, or even earthquakes or tsunamis in other tectonic plates.

The way in which the flow of possible descriptive research decisions, to reduce or eliminate the flow of negative consequences against the global model caused by any new rational hypothesis, is going to be set up, is prioritizing those descriptive research decisions that are going to tackle directly the most negative effects caused by any new rational hypothesis that can put at risk the global model.

In order to prioritize those descriptive research decisions that are going to tackle the most negative effects caused by a rational hypothesis, Impossible Probability was designed on the eleventh of September of 2001 the Impact of the Defect, whose main purpose is: given any negative phenomenon, or given any defect or mistake in a production system, the measurement of the impact produced by the negative phenomenon, or the measurement of the negative effects or mistakes in a production system.

Given a new rational hypothesis (through Artificial Research by Deduction) with a flow of negative consequences for the global model, having measured what level of impact will have every negative consequence using for that purpose the Impact of the Defect, the most important descriptive research decisions to prioritize, will be those ones which will reduce or eliminate the most negative consequences with more level of impact against the global model, according to their measurements using the Impact of Defect.

In order to progress towards perpetual peace, the golden dream of rational criticism, the creation of such Global Artificial Intelligence, as a rational replication, able to keep the global peace within the global model, at the same time progress towards the pure truth, is an objective that should be regarded as one of the most important scientific goals in coming years.

These days, under global warming, the nuclear threat, religious extremism, the growth of racism and xenophobia, and the new populism, much more than ever before is necessary to think in a global response to secure democracy, freedom, and human rights.

And one way to secure the most sacred values of the entire humanity would be by creating such a Global Artificial Intelligence able to reduce the negative consequences of all these threats and secure the life and the normal development of the global model.

As interest in the development of Global Artificial Intelligence grows, early models and ideas like those presented in Impossible Probability may serve as exploratory frameworks. While it is uncertain which contributions will shape the final architecture, interdisciplinary input and iterative development will be crucial for success.

In this scenery, the contributions made by Impossible Probability to the future of Global Artificial  Intelligence are only a few notes, to open new fields of investigation in Artificial Intelligence, but I am sure that the final and definitive Global Artificial Intelligence, if it borrows some of the ideas of Impossible Probablity on this matter, it will be an improved and enhanced model, and what is much more important, able to auto-replicate itself without any human intervention, the third and final stage in any Artificial Intelligence.

 Rubén García Pedraza, London 24th of March of 2018
Reviewed 10 August 2019, Madrid..

Reviewed 9 August 2023, Madrid

Reviwed 4 May 2025, London, Leytostone

imposiblenever@gmail.com