Collaboration in the second stage between Artificial Research by Application and Artificial Research by Deduction

In the first stage, database as application, the collaboration between by Application and by Deduction is mainly focused on how they can share elements of their databases, in the sense that any factor as an option can be understood as a category and vice versa, giving the opportunity to exchange factors as options to form databases of categories in by Application. And vice versa, databases of categories could be included as factors as options in by Deduction. At the end of this process, given a global database, there is a possibility to form a unified database of categories taken from the global matrix, all those factors which work as options. Otherwise, another method to construct a unified database of categories is, in only one database of categories, the addition of all the categories from all databases of categories from all the existing Specific Artificial Intelligences for Artificial Research by Application.

In the second stage, replication, the collaboration from by Deduction to by Application is, given the possibility to transform in factors as options all those rational hypothesis which admit this possibility, including them as options within the, specific or global, matrix, then the transformation of these factors as options as categories as well within the corresponding specific database of categories, or the Unified Application if it is ready. 

The collaboration from by Application to by Deduction is, given a robotic device in which has been installed an Application or the Unified Application, permanently working in the same location within the spatial limits of a, specific or global, matrix, the possibility to include the flow of measurements that the robotic device could take for every category taken now as a factor as an option within the Specific Artificial Intelligence by Deduction or the Global Artificial Intelligence itself.

In the same way that a robotic device could have installed an Application or the Unified Application, adding new categories at any time that the robotic device finds out any new one not included yet in the Application or the Unified Application, if the robotic device is located permanently in the same position, not only can provide information to the Application or the Unified Application, having a permanent position the robotic device could have installed instruments to measure factors from the specific matrix or the global matrix, so at any time that a robotic device finds out a new category, not only the new category could be included in the Application or Unified Application, or added as a factor as an option in a specific or global matrix, the robotic device could provide as well a flow of data from this new factor as an option added to the specific or global matrix measuring this new factor permanently as an option for the specific or global matrix.

The third stage, auto-replication, is going to be mainly focused on how any new category found by Application could be integrated into by Deduction, as well as any new single virtual model by Deduction whose rational hypothesis is susceptible to becoming a factor as an option to work as a category by Application, is a single virtual model to integrate within those virtual models in the Application.

In this post, I will develop the collaboration in the second stage using different examples for that purpose to clarify how this collaboration works.

The first model of collaboration is the possibility that given a rational hypothesis found by any Specific Artificial Intelligence for Artificial Research by Deduction, or Artificial Research by Deduction in the Global Artificial Intelligence, this rational hypothesis could be transformed into a factor as an option capable of being integrated in a specific matrix, or the global matrix, and/or the corresponding database of categories in a Specific Artificial Intelligence for Artificial Research by Application, or the unified database of categories in the  Unified Application.

The way in which by Deduction tracks any matrix, specific or global, is by looking for any possible mathematical relation in any possible combination of factors, or taking an individual factor to track the possibility of any individual pattern.

The possible mathematical relations that by Deduction, Artificial Intelligence looks for are possible stochastic relations (cause and effect, positive or negative directly proportional relations, inversely proportional relations), patterns, cryptographic relations, relations based on the Second Method of Impossible Probability such as equal opportunities or bias, positive or negative.

At any time that any possible relation in any combination of factors is found, the relation is considered an empirical hypothesis contrasting the relation using samples taken directly from the flow of data, of those factors involved, in the matrix. If the empirical hypothesis is right, then the empirical hypothesis is considered a rational hypothesis, creating a single virtual model to include in the comprehensive virtual model.

Along with this process, if the single virtual model produces any negative consequence in the comprehensive virtual model, then it is possible to measure the impact through the Impact of the Defect, and considering what effects are more negative on the comprehensive model, the possibility to make decisions to reduce or eliminate this negative consequences. These decisions for the protection of the comprehensive model are going to be called protective descriptive research decisions.

In addition to this set of decisions, another set, evaluating, in general, the productivity, efficiency, and efficacy of the comprehensive virtual model, through Hierarchical Organisation (finally in Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadística, the Hierarchical Organisation appeared under the name of Effective Distribution), is the possibility to make decisions in order to improve the comprehensive virtual model.

But in all this process, what is going to draw the virtual relations and possible decisions in the third stage of replication, are going to be the possible rational hypotheses found previously in the second stage of replication.

Rational hypotheses, in addition to the rest of the functions developed in Artificial Research by Deduction, in a Specific or the Global Artificial Intelligence, able to work as factors as options.

An example of how a pattern could work as a factor as an option is, for instance, the way in which some patterns are considered as factors in which, in addition to any other measurement, it is possible to measure their frequency. One pattern, for instance, the lunar cycle, and another one, for instance, the solar cycle. In transport, for instance, the pattern of what we consider a rush hour or the pattern of traffic jams in a city. Another one is the cellular pattern, starting and ending with cellular mitosis, or as a pattern, all the biological cycles of any living being from its birth to its death, the planetary cycles, one of the most important for us is the planetary rotation movement and how we measure the time, the geological patterns, the climatology patterns, the ionosphere patterns, or the solar storm patterns.

The way in which it is possible to measure the frequency of any pattern is rather similar to the way in which cryptography is measured, how many times any character is repeated to find the code. That is why I suggest the use of cryptographic methods to track any matrix.

Any pattern or any cryptographic combination of factors in any matrix, after its acceptance as a rational hypothesis by Deduction, could be incorporated as a factor as an option in the matrix, specific or global,  to measure the frequency in which it happens, and as a category in a, specific or unified, database of categories, by Application.

Another possibility is found in a rational hypothesis  of cause and effect, the possibility to transform this rational relation into a factor as an option, as well as a category. There are many situations in which, given a cause and effect, we study the frequency in which this relation happens.

For instance, given a rational hypothesis about which is the antecedent of any earthquake and understanding that this antecedent can cause an earthquake, or is a symptom of an upcoming earthquake, not only to track the specific matrix in tectonics, or to track within the global matrix those factors corresponding to tectonics in order to find when this antecedent is going to happen in order to prevent negative consequence making the corresponding decisions, but the possibility to study the frequency in which this antecedent happens, and the frequency in which after the antecedent is there an earthquake.

In the possible mathematical relation between antecedent and earthquake, what is possible to transform in factors as options, to study the frequency, is then: the antecedent itself as a factor as an option to study the frequency in which this antecedent happens, and the frequency in which after the antecedent is there an earthquake taking the possible relation itself as a factor as option itself.

In climatology, if before any hurricane, it is observed that previously any antecedent has happened, and this possible hypothetic relation between antecedent and hurricane is rational, then the antecedent itself could be considered as a factor as an option, measuring its frequency, and the frequency in which after the antecedent is observed a hurricane.

A rational relation of cause and effect could be considered as a factor, as an option, to study the frequency in which it happens, as well as the cause itself could be considered as an option as well, studying the frequency in which it happens.

The cause as an option, and the relation between cause and effect, after contrasting if it is rational, could be considered as factors to be integrated into the, specific or global, matrix, by Deduction, to study their frequency, as well as they could be integrated as categories in a specific or unified, database of categories by Application.

In studies within the Second Method focused on equal opportunities or bias, positive or negative, observing that the distribution of any set of phenomena, the distribution is, within the margin of error, of equal opportunities or the distribution is always biased, normally having within the set one subject or option with a more positive bias and others with a more negative bias, then the transformation of these results, if rational, in factors as options or categories, is rather possible.

If finding a new mineral on a planet, and taking samples of rocks, stones, and pebbles, in which this mineral is concentrated, in absolutely all rocks, stones, and pebbles, the chemical composition keeps an identical statistical distribution, so all chemical components of this mineral in every single sample keep the same proportion, then the chemical proportion in this mineral along the sample is a proportion of equal opportunities.

Having accepted as a rational hypothesis that the chemical composition of this mineral keeps an equal opportunity relation, then this rational relation could be incorporated as a factor as an option in a specific or global, matrix, and as a category in a specific or unified, database.

At any time that an Artificial Research, by Deduction or by Application, in a Specific or Global, Artificial Intelligence or Unified Application, finds this new mineral in any place, even in the farthest corner of the universe, it could study the frequency in which this mineral keeps its chemical composition within a relation of equal opportunities.

In the same way, studies focused on the bias, positive or negative, having observed a biased rational relation between a sample of subjects or objects, for instance, the way in which grows up different plants, in which some of them can develop a more positive bias than others in specific aspects such as height, weight, depth of their roots, level of chlorophyll produced, resistance to bad weather, number of seeds produced, or the development of positive or negative bias in the presence of some chemical component in the distribution of their chemical composition, among others…; studying in what frequency this bias is repeated in different environments.

Once an empirical hypothesis has been accepted as rational, the rational hypothesis is transformed into a single virtual model, to be introduced in the comprehensive virtual model, and in case that supposes any negative consequence, it could bring some decisions. But, at the same time, at any time that a rational hypothesis is accepted, the rational hypothesis could be transformed into a factor as an option to integrate in the specific or global, matrix to study its frequency.

And at the same time that the rational hypothesis is transformed into a factor to study its frequency in the matrix, it could be integrated, as well as category, in the respective Specific Artificial Intelligence for Artificial Research by Application in that synthetic science, discipline, or activity, responsible for the research in the area in which this category was found, or even the possibility to integrate this new category in the unified database of categories if the  Unified Application is ready.

Finally, the collaboration from by Application to by Deduction, what is going to be possible between those robotic devices in which the Specific Artificial Intelligence for Artificial Research, by Application or by the Unified Application, has been installed, and works within the spatial limits of an Artificial Research by Deduction in a, Specific or Global, Artificial Intelligence.

Coming back to the example in mineralogy, if a Specific Artificial Intelligence for Artificial Research by Application in mineralogy, or the Unified Application (in which, obviously, it has previously been integrated as well the categories in mineralogy), works in mines within the spatial limits of a Specific Artificial Intelligence for Artificial Research in Mineralogy by Deduction, or the Artificial Research by Deduction in the Global Artificial Intelligence, in general, how a specific or global level the collaboration from by Application to by Deduction is possible, as long as by Application has been installed in a robotic device working permanently within the spatial limits where by Deduction is working, that robotic device can provide a permanent flow of data to the matrix if that robotic device has a permanent position.

For example, imagine that an application in mineralogy has been installed in robotic devices working permanently in different mines in the United Kingdom, and at the national level, there is a Global Artificial Intelligence, whose national matrix in the Artificial Research by Deduction has included as factors the flow of data in mineralogy provided by this robotic devices.

For each robotic device is possible to set up in the national matrix one factor (defined in terms of latitude, longitude, and depth) so every robotic device in which the application is set up provides a flow of data about the chemical composition of the earth that is being extracted in the mine where is working.

In the end, including in a possible national matrix in the UK (along with all possible factors within its range of action, UK, from economy, industry, security, surveillance, etc..), information about the minerals detected in all the mines in the country, the inclusion of this set of factors for each robotic device, is going to generate, when this information in the matrix is used in the Modelling System, a virtual and actual map about the current mineral extraction that is going on, being possible even the possibility to make predictions upon the current information.

If these robotic devices are already integrated into the national matrix through a mineralogy application, it would be practical to explore expanding their functionality by installing additional applications, such as for temperature or tectonics, using the same spatially defined factors.  

And the same time, due to this, robotic devices are working at an underground level. The possibility that having installed an Application in tectonics, every robotic device in every mine could send, in a different set of factors to the national matrix, a flow of data regarding the tectonic activity in every mine.

Given the expected growth of Artificial Intelligence in the coming decades, it is reasonable to anticipate a shift in focus, from designing isolated applications to developing unified systems capable of managing multiple data streams simultaneously.   

If in the robotic devices in this example, in order to generate such a flow of information is necessary to install three different applications: for tectonics, mineralogy, and temperature; in the end, what is going to be more useful is the possibility to unify all the possible applications in only one, a Unified Application, that could send direct information to the global matrix directly, sending the Unified Application directly to the global matrix the exact location where the Unified Application is working, setting up directly the Unified Application for every location in the global matrix as many factors as flows of data can send the Unified Application to the global matrix at the same time.

Rubén García Pedraza, 2th of April of 2018, London
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

La metodología

 La metodología es el estudio del método científico, ya se entienda la existencia de un método general para toda la ciencia, desarrollado en los métodos particulares de cada ciencia en concreto, o de rechazarse la idea de un método general y otros particulares, y se entiende que depende de las particularidades de cada investigación, o paradigma o modelo de referencia, sería el estudio de  los métodos científicos según grado de adaptación a las diferentes investigaciones, o estudio de los métodos de los diferentes paradigmas y modelos.

Si se entiende la existencia de un método científico general, la metodología es el estudio de dicho método, el cual se desarrollará posteriormente en los métodos específicos de cada ciencia. Por ejemplo, ya se entienda que el método científico es deductivo, según el idealismo o el racionalismo, o sea inductivo, para el empirismo, positivismo, y materialismo, en función de la filosofía de partida la metodología es el estudio de dicho método, deductivo o inductivo, y la forma en que se aplica a los métodos particulares de las diferentes ciencias, por ejemplo, además del estudio del método deductivo o inductivo, la metodología estudiaría su modo de aplicación al método experimental, el método comparado, el método histórico, y así sucesivamente en todos los métodos más específicos.

En caso de rechazarse la idea de un método general desarrollado en métodos particulares, se entiende que en ausencia de método general depende de las características de cada investigación, a la que el método se adapta, luego la metodología sería el estudio de los diferentes métodos, y los criterios de selección del método según tipología de la investigación, o bien, en caso que se entienda que dada la variedad de paradigmas y modelos científicos la elección del método depende del paradigma o modelo científico de referencia, la  metodología sería el estudio de los diferentes métodos asociados a la diversidad de paradigmas y modelos de referencia.

Ya se entienda la metodología como el estudio del método científico y su desarrollo en métodos particulares, o el estudio de los métodos científicos según características concretas de cada investigación , o el estudio de los métodos de los diferentes paradigmas y modelos científicos, el origen de la metodología se encuentra en la filosofía, dependiendo en gran medida de la teoría del conocimiento, gnoseología de partida, y de la cual depende la epistemología de referencia, disciplinas, gnoseología y epistemología, que tienen su origen y han sido desarrolladas  en la filosofía de la ciencia.

La metodología depende de la gnoseología, teoría del conocimiento, por cuanto según la teoría del conocimiento la metodología tendrá rasgos propios. Dentro de la teoría del conocimiento ya en la filosofía clásica Platón y Aristóteles sientan las bases de los paradigmas idealistas y empiristas, el primero se basa en su profundo escepticismo sobre los sentidos, la información sensorial es falsa, mientras el segundo defiende los sentidos como la única vía de conocimiento válido, la única información que disponemos del mundo es la información sensorial. Dichas teorías en la modernidad evolucionan a nuevos modelos, por un lado las nuevas versiones modernas del idealismo, que se concretan en el racionalismo, el autor más importante Descartes, claramente deductivista. En el campo del inductivismo en la modernidad el empirismo evoluciona al escepticismo empiricista de Hume. La síntesis entre ambos, Descartes y Hume, la elabora Kant en el racionalismo crítico, aunque desde una visión claramente deductiva. En el siglo XIX se termina de formalizar el positivismo de Comte, y en el siglo XX el neopositivismo, el Círculo de Viena, desde unos parámetros claramente inductivistas, especialmente en el Círculo de Viena .

Además durante el siglo XIX de la fusión de idealismo hegeliano y materialismo surgirá el materialismo moderno, Marx y Engels, teoría en la cual el conocimiento no tiene por origen ni las ideas ni los sentidos, el  conocimiento surge de la actividad práctica, praxis. Si para Kant la razón se dividía en razón pura, cognoscitiva, y razón práctica, moral, esta división se supera en la praxis, donde se produce una síntesis entre la dimensión ético moral y cognoscitiva.

La revolución en la teoría del conocimiento que se opera en la modernidad, donde se pasa del sujeto pasivo al sujeto activo, tiene mucho que ver con la incorporación del método experimental por Roger Bacon, S. XIII. El experimentalismo moderno será una síntesis entre empirismo clásico aristotélico y una nueva noción en el concepto de investigación,  ahora la investigación moderna, filosófica o científica, ya no es la investigación de la antigüedad clásica, cuando la investigación se limitaba a la contemplación pasiva de las ideas o la naturaleza reservado a aquellas personas que por su status social se permiten dedicar su tiempo de ocio al estudio del mundo. En la época moderna la investigación, filosófica o científica, es una actividad, introduciendo un cambio significativo en la definición de experiencia. Mientras para Aristóteles y los empiristas clásicos el conocimiento es dado por la experiencia sensorial pasiva, a partir de Roger Bacon se pasa a un nuevo modelo de experiencia activa, ahora experiencia significa experimentación.

Aunque en la antigüedad clásica ya hay filósofos y científicos que utilizan el método experimental para poner a prueba algunas de sus teorías, sin embargo no existe una sistematización sobre el método experimental, que sólo se hará a partir de la tradición experimental moderna que inaugura Roger Bacon, y será desarrollada por todos los nuevos científicos de la nueva ciencia, siendo especialmente importantes Galileo y Newton.

Este cambio que se opera en la modernidad, el reconocimiento de que el origen del conocimiento es la propia actividad, llevará al nepositivismo, especialmente Wittgenstein, al reconocimiento de que la filosofía es una actividad, superando la etapa donde la filosofía es sólo contemplación de las ideas, Platón, o contemplación de la naturaleza, Aristóteles. En  la época moderna hay una clara distinción entre los que siguen defendiendo que la filosofía es contemplación, ya sea desde el idealismo o el empirismo, defendiendo la necesidad de la metafísica contemporánea en tanto que procedimiento o resultado de la contemplación del mundo de las ideas o la naturaleza, según sean metafísicos idealistas o empiristas, frente quienes rechazan la contemplación y defienden que la filosofía y la ciencia son una actividad.   Dentro de los defensores de que la filosofía y la ciencia son una actividad hay diferentes corrientes, desde los defensores que la filosofía y la ciencia son una actividad e implica acción social, las teorías del cambio social, entre ellas el materialismo moderno, o aquellas otras corrientes que defienden que la filosofía es sólo un tipo determinado de actividad científica, entre ellas el neopositivismo.

Una de las características de la epistemología a partir del siglo XX es que ahora la investigación científica será una actividad profesional, desarrollada por Estados y grandes empresas privadas, las universidades, todo tipo de instituciones públicas o privadas de investigación científica, y agencias de investigación internacionales. Universidades, agencias e instituciones, públicas o privadas, que elaboran planes de investigación liderados por científicos de renombre que dirigirán importantes instituciones científicas. Este tipo de fenómenos que empezarán a ser frecuentes en el siglo XX eran hasta entonces prácticamente desconocidos. Entre las agencias de investigación más emblemáticas cabe destacar aquellas que durante la Primera Guerra Mundial y la Segunda Guerra Mundial fueron creadas para la investigación y desarrollo de nuevas armas, las agencias de investigación espacial o de investigación en energía nuclear que fueron creadas después de la Segunda Guerra Mundial, por ejemplo el CERN ubicado en Suiza, y muchos de los organismos internacionales que desarrollan verdaderos planes de investigación independientes, desde la UNESCO a la Organización Mundial de la Salud, o los equipos de investigación internacionales que crean las agencias líderes de la gobernanza global, desde el informe PISA de la OCDE, a los departamentos de investigación del Fondo Monetario Internacional o el Banco Mundial, o los departamentos de investigación de importantes Organizaciones No Gubernamentales. Además de la profesionalización que ha experimentado la labor científica en todo tipo de laboratorios de investigación y departamentos de investigación en la empresa privada, buen ejemplo de ello la popularización de los departamentos de Investigación Más Desarrollo en la empresa privada desde finales del siglo XX, especialmente en campo de la biotecnología, robótica, nuevas energías, y las tecnologías de la información, la comunicación, y el transporte, que han experimentado importantes revoluciones en las últimas décadas.

La metodología por tanto es una disciplina que depende en gran medida de la teoría del conocimiento, gnoseología, si bien la teoría del conocimiento abarca un campo mucho más amplio que la metodología, por cuanto la metodología sólo se limita al método por el cual se logra conocimiento científico, mientras la teoría del conocimiento abarca cualquier conocimiento en general, sobre la propia vida y nosotros mismos, y que en definitiva se orientan a la construcción de toda una filosofía, incluyendo el sentido de la vida, si la tiene, y de la propia naturaleza, incluida humana, siendo la teoría del conocimiento, gnoseología, una parte importante de la cosmología y la antropología, además de parte importante de la metodología.

De igual manera la metodología formaría parte de la epistemología pero sólo una parte, la epistemología abarcaría mucho más, en la medida que la epistemología desarrolla de forma global y holística el discurso y al construcción de la ciencia.

En relación al debate sobre la existencia de un método científico general, que después pueda desarrollarse en los demás métodos particulares de las ciencias, analíticas o sintéticas, Probabilidad Imposible defiende la tradición cartesiana de la existencia del método científico, el cual, tal como se explica a lo largo de Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadística, especialmente en el último apartado, continuando la filosofía cartesiana es el método deductivo, tanto para ciencias analíticas y ciencias sintéticas, sólo que en las ciencias sintéticas adopta la forma de hipotético deductivo.

Es a partir de la deducción el modo por el cual se producen las ideas objeto de análisis, y sólo serán aceptadas verdaderas provisionalmente una vez que hayan sido criticadas de forma racional, admitiendo en todo caso siempre un margen de duda proporcional al margen de error aceptado en la razón crítica.

La forma en que el método hipotético deductivo se pone en práctica en los métodos particulares de las ciencias concretas es a través de diferentes modelos de crítica racional adaptados a las propias características de los métodos de las diferentes ciencias, ya sea el método experimental, el método comparado, o el método histórico, en tanto que crítica racional si los datos manifiestan cambios significativos como para suponer tendencias racionales, en las variables experimentadas, los elementos que se comparan, y las tendencias históricas, o cualquier otro método particular.

En relación a si la metodología es el estudio del grado de compatibilidad entre método y tipo de investigación, sólo señalar que este debate es más propio de las ciencias sociales que en las ciencias naturales. Mientras durante la modernidad las ciencias naturales lograron el consenso unánime de que sólo se podrían llegar a descubrimientos sólidos utilizando métodos cuantitativos, para lo cual fueron importantes las contribuciones de la nueva ciencia desde Copérnico, y siendo esencial la introducción del método experimental por Roger Bacon, en ciencias sociales ha habido durante el siglo XX un debate profundo todavía no resuelto a principios del siglo XXI sobre si la metodología más adecuada para estas ciencias son metodologías cualitativas o cuantitativas.

Se podría decir que existen al menos dos razones de por qué las ciencias sociales no han logrado los mismos progresos que las ciencias naturales en el siglo XX, llevando a las ciencias sociales a principios del siglo XXI al cuestionamiento de casi los mismos interrogantes que durante el siglo XIX: la ausencia de consenso sobre la utilización de métodos cuantitativos, y la ausencia de consenso sobre el modo en que intervienen las variables ideológicas en la construcción de la ciencia.

Precisamente si una de las grandes luchas en ciencias naturales fue la liberación de las ciencias del predominio de la metafísica y la superstición, simbolizada por el triunfo del heliocentrismo sobre el geocentrismo, o el darwinismo sobre el creacionismo, las ciencias naturales han logrado progresar porque los científicos naturales fueron capaces de sintetizar métodos cuantitativos y la lucha contra el misticismo y el espiritualismo. Hoy en día ya nadie pone en duda los principales descubrimientos de las ciencias naturales modernas, si bien en un principio contaron con el rechazo de la Iglesia, los imperios y los Estados. Sin embargo, quizás porque las ciencias sociales surgieron muy a posterior de las ciencias naturales y todavía le queda mucho que madurar, este tipo de consenso que sí alcanzaron las ciencias naturales, la unión de métodos cuantitativos y la lucha contra los dogmas, todavía no se ha logrado en ciencias sociales.

De esta manera, si el debate en la metodología contemporánea es sobre cual es el método más conveniente según la tipología de la investigación, desde Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadística, se ha defendido siempre la necesidad de superar la dicotomía entre ciencias naturales y ciencias sociales, tratar a todas las ciencias sintéticas de misma forma, aplicando el método hipotético deductivo a todas las ciencias sintéticas, naturales o sociales, utilizando para tal fin métodos cuantitativos en el desarrollo del método hipotético deductivo en sus métodos particulares correspondientes, experimentales, comparados, históricos, u otros cuales sean, sintetizando en el método científico el uso de métodos cuantitativos y la comprensión de variables ideológicas y políticas, síntesis que se produce en la razón crítica, en función de la cual la política científica establece los criterios de aceptación de una hipótesis.

Sobre si el debate en la metodología es si se dedica al estudio de los métodos de los diferentes paradigmas o modelos científicos de referencia, en Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadística lo que se defiende es una síntesis de los principales paradigmas de la historia de la filosofía y la ciencia motivo por el cual, en coherencia con el paradigma positivista y neopositivista, Probabilidad Imposible defiende la necesidad de la equiparación del método de investigación a todas las ciencias sintéticas, la aplicación a las ciencias sociales de los métodos que han permitido importantes progresos a las ciencias naturales. El estudio por tanto de todas las ciencias sintéticas, naturales o sociales, debe partir de hechos positivos, sobre los cuales realizar una crítica racional, desde una razón crítica que integre variables ideológicas y políticas, la política científica.

En la medida que parte de la necesidad de la crítica racional a los datos positivos, la teoría de Probabilidad Imposible ya asume la síntesis de positivismo y racionalismo crítico, y en la medida que en la razón crítica se integran aspectos éticos y morales, la ideología política de la política científica, se entiende que la crítica es un tipo de praxis, sintetizando de esta forma el materialismo moderno al positivismo y al racionalismo crítico. En síntesis, la teoría de Probabilidad Imposible es una teoría ecléctica que recoge los aspectos más positivos del positivismo, el racionalismo crítico y el materialismo moderno.

 

La forma en que dicha síntesis se opera es a través del reconocimiento universal del método deductivo para todas las ciencias, analíticas o sintéticas, y dentro de las sintéticas, naturales o sociales, en las cuales adopta la forma de método hipotético deductivo, para el cual el principal método de estudio sintético será la estadística de la probabilidad o probabilidad estadística, la cual a su vez, en tanto que entidad matemática es en sí misma analítica.

 

La estadística y la probabilidad en tanto que disciplinas matemáticas son analíticas, y en tanto que métodos de estudio aplicado son métodos sintéticos.

 

La investigación pura en el campo de la estadística y la probabilidad tiene por objeto el desarrollo de nuevas teorías y modelos matemáticos de probabilidad y estadística, luego cualquier progreso en el desarrollo de nuevas teorías y modelos de estadística y la probabilidad sólo puede hacerse deduciendo dichas teorías y modelos a partir del análisis matemático de la estadística y probabilidad.

 

La investigación aplicada de la estadística y la probabilidad son todas aquellas investigaciones en el campo de las ciencias sintéticas, sean ciencias naturales o sociales, que para el desarrollo de nuevas teorías y modelos sintéticos aplican el método matemático de la estadística y la probabilidad para la formación de nuevas proposiciones sintéticas. Dichas proposiciones sintéticas a partir del uso de la estadística y la probabilidad pueden ser proposiciones sintéticas de carácter descriptivo o inferencial.

 

Las proposiciones sintéticas descriptivas únicamente describen una realidad empírica a partir de los datos cuantitativos de esa realidad obtenidos por la aplicación de métodos cuantitativos, en primer lugar la medición y en segundo lugar un método matemático, entre los cuales destacan la estadística y la probabilidad, y dentro de ellas la estadística descriptiva que nos permite una descripción estadística de lo que ocurre.

 

Las proposiciones sintéticas inferenciales permiten la inferencia de una serie de casos o situaciones particulares a todo el posible universo de casos o situaciones que guarden semejanza o similitud frente los casos o situaciones estudiadas. La inferencia no es otra cosa que la extrapolación o generalización de una serie de casos o situaciones particulares a todo el posible universo de casos o situaciones.

Si esa inferencia es inductiva, lo único que hace es sobre la descripción de una serie de casos o situaciones, siempre que en todas ellas halla una constante, generalizar a todos los casos o situaciones posibles del universo dicha constante.

Si la inferencia es deductiva, se parte de una hipótesis previa, síntesis de ideas y teorías previas y las nuevas informaciones obtenidas de los casos o situaciones observados, y la inferencia lo que pretende es validar en la serie de casos o situaciones que la hipótesis es provisionalmente verdadera, para lo cual desarrolla modelos de contraste de hipótesis, un modelo de prueba estadística en que en Probabilidad Imposible se denomina crítica racional, y que puede hacerse utilizando la estadística de la probabilidad o probabilidad estadística.

De esta manera la estadística y la probabilidad son en sí mismas disciplinas analíticas dependientes de las matemáticas cuyo desarrollo dependen del análisis deductivo o deducción analítica. Y la estadística y la probabilidad en relación a las ciencias sintéticas son métodos científicos particulares desde los que desarrollarse el método científico general de las ciencias sintéticas, el método hipotético deductivo.

El método analítico para el desarrollo de la estadística de la probabilidad o probabilidad estadística, en tanto que campo de conocimiento que fusiona estadística y probabilidad, campo de conocimiento desarrollado por la teoría de Probabilidad Imposible, es el método analítico denominado el silogismo de la tendencia, que no es otra cosa que el análisis lógico de la tendencia. El motivo por el que se denominó el silogismo de la tendencia es porque formula deducciones analíticas en forma de silogismos observando los diferentes modelos de tendencia en estadística y probabilidad.

Sobre las deducciones lógicas en el análisis de la tendencia, Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadística, desarrolla un nuevo modelo matemático de estadística y probabilidad, que para su aplicación a las ciencias sintéticas genera una serie de métodos cuantitativos, el Segundo Método, el Impacto del Defecto, la Distribución Efectiva, y los estudios de ranking.

Los diferentes métodos aplicados que genera el estudio analítico de la estadística de la probabilidad o probabilidad estadística, utilizan de modo universal el método deductivo, que al aplicarse a las ciencias empíricas adoptan la forma de hipotético-deductivo, desde parámetros cuantitativos, adaptándose a los diferentes métodos particulares de las diferentes ciencias, ya sean las ciencias estocásticas, las ciencias experimentales, las ciencias comparadas, las ciencias históricas, o cualquier otro tipo de ciencia, natural o social.

Rubén García Pedraza, 23 agosto del 2014

 

 

 

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