Artificial Research by Application

Artificial Research By Application is a possible model, among others, whose main purpose is the complete automation of scientific research, developing Specific Artificial Intelligences for every kind of scientific or academic investigation. Starting with the automation of empirical sciences, owing they are easier to automate rather than maths and logic, which will take more time. Although at the end of the automation process, all sciences, empirical and analytical, including maths and logic, must be completely automatized.

In the long term, one of the last goals of the development of artificial research, the automation of scientific research in all sciences, disciplines, academic or investigation fields, is the knowledge of the pure truth of the universe, even beyond human understanding. What is going to need the creation of an authentic artificial pure reason with a wide range of tools that include artificial research tools in empirical sciences and academic fields as well as artificial research tools in maths and logic, for the creation of a non-human mathematical, logical model, a non-human science, and a non-human technology. Non-human as they could be generated only by an Artificial Intelligence itself, without human intervention, true non-human science and technology beyond error and human limitations.

For the achievement of this purpose, the current models of Specific Artificial Intelligence are not going to be sufficient, so it is necessary the creation of a Global Artificial Intelligence, whose early stages, as an experiment, could be applied to a nation, a continent, or the whole planet, extending later its limits towards a Global Artificial Intelligence that could integrate information, and make decisions, over the whole universe.

For the creation of a Global Artificial Intelligence, previously,  the design models of Specific Artificial Intelligence in any scientific research, which later could be linked or applied within the Global Artificial Intelligence, it is really important. Only one Global Artificial Intelligence is able to have access to all information without restriction, carrying on all kinds of scientific research, and making decisions that could be put into practice by itself.

In the previous stages of the creation of the Global Artificial Intelligence is really important the automation of the research process in science, and the creation of tools for artificial research. The very first two possible models of artificial research (artificial research by application, artificial research by deduction) that are going to be developed within the theory of Impossible Probability in this and coming posts on this blog, are only some examples of how it is possible the automation of scientific research by Artificial Intelligence.

Automation also contributes to the completion of an automatic economy, through the continuous investigation and non-stop experimentation in Specific Artificial Intelligence, whose results could give new applications to manage automatically any economic problems at any level (national, continental, or global), making automatic decisions based on the artificial research of the economy, that could later put into practice.

The automation of the economy will have a lot of benefits. A production system based on a real model of production just on time, producing only what is going to be strictly consumed without unnecessary waste, saving natural resources. The automation of the extraction of natural resources, transportation, fabrication, distribution, and delivery Will reduce error and cost.  Low prices in a more efficient economy, something really useful by the time global warming starts generating serious economic and social crises.

By the time the investigation in Artificial Research has achieved science automation, the way in which these models of artificial research are going to contribute to the creation of a Global Artificial Intelligence, after the completion of its first stage, a global application with a global database including absolutely all possible information without restriction, would be through different strategies. 1): linking these Specific Artificial Intelligences in scientific research (as any other in economy, industrial management security, surveillance, etc.) to the Global Artificial Intelligence, so any Specific Artificial Intelligence could share information, investigations, results, and possible decisions to the Global Artificial Intelligence, and the Global Artificial Intelligence could make decisions that could be put into practice by the Specific Artificial Intelligences linked, or by robotic means under the only direction of the Global Artificial Intelligence; 2): a second option, and maybe the best one, after successful experimentation in artificial research, the inclusion of all these  applications  for artificial research within the Global Artificial Intelligence (along with all kind of Specific Artificial Intelligences, regardless of its purpose: economy, industrial management, security, surveillance, etc.), so the Global Artificial Intelligence could have all the possible and necessary applications to carry on any kind of scientific investigation, make any decision, and put into practice its own decisions by itself through its own robotic means. The second option is the best one because the Global Artificial Intelligence has become a real singularity.

Perhaps, the way in which finally the Global Artificial Intelligence is going to be created is through, after the creation of a global application with a global database including absolutely all possible information without restriction, the combination of these two options. Firstly, linking all possible Specific Artificial Intelligences (in science, economy, industrial management, security, surveillance, etc), used by public or private agencies or institutions in any field or activity. Secondly, integration of all Specific Artificial Intelligences, among others for artificial research, within the Global Artificial Intelligence. However, the different possibilities of the integration process, will be developed in other posts. In this one, I want to focus on artificial research by application.

Within the theory of Impossible Probability, I will develop, at least at a theoretical level, what it would be: artificial research by application, and artificial research by deduction. The first one replicates the process of hypothesis formation within the application of a specific science, discipline, or academic field. The second one replicates the deduction process in order to get a hypothesis for any science, discipline, or academic field.

The first one is artificial research by application. The hypothesis formation is replicated within the application for a specific kind of investigation. The second one, artificial research by deduction, the application itself would be a replication of the deduction process that could later be put into practice in any scientific investigation.

In my last post, Artificial empirical hypothesis, I developed examples of each kind of artificial research, by application and by deduction, and specifically in the first one, artificial research by application, I developed examples of artificial research by application in medicine and astronomy.

In both of them, I developed, at least at a theoretical level, the main lines about how would be the characteristics of this Specific Artificial Intelligence in every stage: application, replication, and auto-replication. Examples that I do not have any doubt could be improved in future investigations in this field. What I leave on this blog is only some contributions. In fact, I am completely sure that Specific Artificial Intelligence, such as these ones, and much better, are going to be built in the coming years.

For instance, in my last post Artificial empirical hypothesis in the Stage of Replication in the artificial research by Application in Medicine, I only proposed replications for the deduction process, but I did not add other enhancements, as the inclusion of memory about the medical problems of every patient, something really useful making diagnoses much more personalized depending on the medical history.

Imagine a Specific Artificial Intelligence for artificial research by application in medicine, able to make good hypotheses (diagnoses) about any medical problem in any individual person in the population of a country, continent, or the planet, ordering the production of medicines just on time (production also automatized by Specific Artificial Intelligence for the industrial management), improving itself by auto-replication. While at the same time, it could have a memory with the medical history of every person in a country, continent, or the planet, making predictions in the short, medium and long term, about individual, national, continental, and global, medical problems, making decisions on how to prevent them.

The creation of such a Specific Artificial Intelligence in artificial research, researching at individual, national, continental, and global levels, making decisions, and, if we have developed sufficient robotic tools, and put it into practice, it looks like a Global Artificial Intelligence.  With the difference that, an Specific Artificial Intelligence operating in only one science, or only one discipline, or only one academic field of investigation, it is only a Specific Artificial Intelligence with a very vast field of action ( from individual to national, continental or global level), while a true and real Global Artificial Intelligence must be able to carry on all kind of scientific research in any science, discipline, academic field, in any place, at the same time that it should have access to absolutely all possible information without restriction, managing all kind of Specific Artificial Intelligences, from education and health systems, and justice to the economy, industrial management, security, surveillance, among all others, having sufficient robotic tools to put into practice all kind of decisions simultaneously.

It is necessary to have a clear idea about what Specific Artificial Intelligence is, even if it works at a planetary level, and what Global Artificial Intelligence is, which should be linked or integrate all Specific Artificial Intelligences, wherever they are and whatever they do.

It is possible the creation of Specific Artificial Intelligence for artificial research by application in medicine, which could operate around the world, as any other Specific Artificial Intelligence, such as a global security system or a global surveillance system. But they are going to operate, even globally, in only one science, field, or activity. While real Global Artificial Intelligence would be global because it would be able to take a whole planet or even the universe as a field of investigation or action and do research globally in more than one science, field, or activity, so it could be able to do multidisciplinary studies. So any decision that a Global Artificial Intelligence could make could be based on the results of more than one research in more than one science, field, or activity, making decisions about practically everything with high accuracy.

In the coming years, the race for the creation of Global Artificial Intelligence is about to start, and it must start as soon as possible. 

In the early stages of the creation of Global Artificial Intelligence, is necessary the creation of Specific Artificial Intelligence for artificial research. One possible model, among others, could be through artificial research by application.

In general, artificial research by application follows the three general stages of any other Artificial Intelligence: application, replication, and auto-replication.  The first one, application, is not really an Artificial Intelligence stage but is the base for the following stages. Normally, the application in one science, discipline, academic or investigation field, or activity, consists mainly of a database about this specific science, this specific discipline, this specific academic or investigation field, or this specific activity. In this database, all the information, data, taxonomies, classifications, files, categorizations, events, facts, phenomena, characteristics of individuals and populations (physical, behavioural, psychological), or any other thing, even any necessary tool to use later, must be stored and described in quantitative terms.

The second stage, replication, consists of the replication of psychological processes. From collecting empirical information in quantitative terms, measurement, from the environment, including later the information in a memory, and making a deduction, an empirical hypothesis that must be contrasted. The deduction process within the application is not really difficult. Once the empirical information has been collected and measured, it is compared with the database in the application. Those information, data, taxonomies, classifications, files, categorizations, events, facts, phenomena, characteristics of individuals and populations (physical, behavioural, psychological), or any other thing, included in the database, whose quantitative description is more similar to the empirical information, are selected from the database as possible empirical hypothesis: descriptions, causes, effects, correlations (depending on the objective of each Specific Artificial Intelligence: descriptive studies, identification of probable causes or effects or both, correlational studies,  stochastic studies); empirical hypothesis about what is happening. Once we have a collection of empirical hypotheses about what is happening, by rational criticism all empirical hypotheses are rationally contrasted. That empirical hypothesis or those empirical hypotheses which, by rational criticism, pass or passes the rational contrast, within the lower margin of error, is or are chosen as a rational hypothesis, making further decisions upon the results.

The rational contrast process made by the Specific Artificial Intelligence must follow the general steps as in any other rational contrast. Once the empirical hypothesis, or group of empirical hypotheses, has been identified, then the Specific Artificial Intelligence must proceed to the selection of a sample of subjects and options, according to the nature of this or these empirical hypotheses, doing all the necessary calculations to get their empirical value, in order to contrast with a critical reason. If the empirical value is equal to or superior than the critical reason, the empirical hypothesis, or group of empirical hypotheses within this criteria, is considered as a rational hypothesis provisionally true, forming part of the rational truth.

The reason why in this second stage, more than one empirical hypothesis could be true, is because in medicine, for instance, we can have a patient with a collection of symptoms, and these symptoms could be produced by one or more causes. It is really important in artificial research that the application can attribute any effect or correlation to one or more variables at the same time, being able to do multicausal studies, integrating into one possible model different variables working and correlating at the same time.

In astronomy, if we want to explain the geological or atmospheric pattern in Jupiter or Saturn, or in our own Earth, is absolutely indispensable to work and make correlations with a lot of variables at the same time.

The real advantage that artificial research should take from humans is the fact that humans can only work within a very limited number of variables. For that reason, we have the feeling that this world is complex. If we want to understand what is happening, the reality, it is absolutely necessary to build such models of artificial research able to work with more and more exponential amounts of data and variables at the same time, beyond human understanding.

A very developed Global Artificial Intelligence should be able to work with more and more exponential amounts of data and variables, beyond human science. Only then, when a Global Artificial Intelligence can make multidisciplinary and multicausal models integrating an exponential number of factors, factors collected from: its own global database, and all its applications, and measurements from the environment; the Global Artificial Intelligence would have developed really deep access to a more isomorphic knowledge, in direction to the pure truth.

An immediate advantage of the combination of different hypothesis from a single database in a Specific Artificial Intelligence, is to improve its own database, in case it finds out something from the environment that does not match at a significant level with previous information in the database because it is a new kind of category, event, fact, phenomenon, characteristic, feature, not registered in the data base yet. For instance, in medicine, a new virus or bacteria not found before is the first piece of evidence ever to be included in the database.

If it finds out something which does not match at a significant percentage with anything in the database, it has to be included in the database, describing this new record in quantitative terms, and measurements.

The formation of a new collection of hypotheses, based on records not matching significantly with the already existing categories in a database by application, once this new collection of hypotheses is included in the database as part of the auto replication process, the way in which by application could be made the attachment of possible decisions related to every single new hypothesis, could be having as possible clues and hints, the similarities and differences between every new hypothesis now integrated by auto replication in the database of categories, and the already existing categories in the database, so having as possible models, clues or hints, those ones with the highest similarity, try to form possible decisions according to these similarities and differences. 

For instance, if it finds out a new bacteria or virus, the chemical composition of medicines used in other diseases caused by other viruses or bacteria, even though at not a very high percentage of similarity with the new one, could give some clues about what chemicals must be combined for the creation of a new medicine, in addition to any other component which could fix the problem better and sooner, depending as well on the chemical composition of the virus or bacteria. In this way, the new bacteria or virus found must be integrated as a new category in the medical database, at the same time that the first experiments to tackle this new virus or bacteria artificially should be through the consideration of what kind of medical actions are taken against other similar virus and bacterias to the new one, including these actions as possible decisions to attach to the new bacteria or virus, at least experimentally, waiting for the results of possible simulated trials with this solutions. 

Through virtual simulations based on artificial models about the structure of this new bacteria o virus, testing every possible chemical combination to find the most likely to get rid of it.

For the design of medicines, the database should include a collection of chemicals and combinations, specifying possible use, properties, advantages and reactions. For anything we expect from Specific Artificial Intelligence, the database should include all that could be necessary. The database is the warehouse, storing information and any necessary tool.

In other cases, the formation of a group of hypotheses, even though not having the database of categories, any category able to fit with the empirical information individually, a possible combination of categories in the database base could be more successful, or at least, give some clues about possible causes and effects, which combine with some other artificial deductions might have a good grasp about what is happening, the reality. 

It is possible that the symptoms of a patient as a whole, do not match with any category individually in the database of diseases, viruses and bacteria, because in reality the patient suffers from a combination of different medical problems, so, in that case, the combination of categories related to medical problems which fit more accurately with the symptoms of the patient should be the combination of categories more likely to pass the rational contrastation, and once in the rational contrastation, with the lowest margin of error, these categories are considered as the more rational causes of this collection of symptoms, the decisión process is the process to decide what combination of treatments, according to that combination of hypothesis, is more suitable for the patient.

In this way, artificial research by application (database of categories) could work in combination with artificial research by deduction ( database as a matrix, tracking the matrix of data, in this case, biostatistical data from the patient, especially if the patient has already installed a particular program), so it would be advisable the creation of channels of co-working between them. After the completion of the first experiments of each kind of artificial research, after successful experiments in artificial research by application and successful experiments in artificial research by deduction, the following step would be the experimentation of ways in which they can co-work together. And, after success, the inclusion of these results in Global Artificial Intelligence.

Finally, auto-replication. Auto-replication means the artificial ability to improve and enhance itself at any level, from the robotic level to the software level. In the case of a Specific Artificial Intelligence for artificial research, these improvements and enhancements could be oriented to the improvement of its own application (improvements and enhancements in its database), improvements or enhancements in its psychological replication processes (modifying them to bettering, substituting some processes for other ones much better, or incorporating new ones depending on new advancements in artificial science, artificial engineering, robotics, etc.).

Nowadays, artificial psychology is more focused on the psychological replication processes, and the results of auto-replication, if some of them are hopeful and useful, are not as developed as we are going to need in coming years, for the creation of the first models of Specific Artificial Intelligences in artificial research able to auto-replicate itself at all levels, towards a real and complete Global Artificial Intelligence at all levels, from its global database to its ability to improve and enhance any of its artificial psychological processes and its own applications, at robotic or software level, without human intervention.

Actually, there are some experiments of auto-replication, but much more than replication, they are experiments of duplication, but duplication it is not strictly the same as auto-replication. Duplication is only the creation of something identical to another thing, a copy, while auto-replication means the artificial ability to improve and enhance itself.

Artificial Intelligence must not be understood as a copy, it is the next stage in the evolution.

The good thing in the current developments in artificial duplication (the fact that one Specific Artificial Intelligence can create another Specific Artificial Intelligence as a copy, duplicate) is the fact that at the same time one Specific Artificial Intelligence can duplicate itself, the original Specific Artificial Intelligence could make some changes in the new duplication, changes that can include, but not necessarily, improvements and enhancements.

Duplication as replication in strict terms is not a strict auto-replication process. But if something is able to duplicate itself, the original model could be improved and enhanced in the duplicate model.

When the Global Artificial Intelligence is a reality, its main purpose is not the creation of copies of itself, but one reason, among others, for duplicating itself would be if it was at risk of disappearance, so creating a copy would be one way to survive, or in case it had decided to reconstruct itself entirely by creating a duplication but with improvements and enhancements. In this last situation, duplication in order to make utter changes across all its components, from robotic to software level, introducing improvements and enhancements, would be one of the most perfect scenes of auto-replication.

Auto-replication as duplication only, without changes, would be like biological mitosis. But, instead of the replication of a single cell, the replication of an entire Artificial Intelligence (Specific or Global)

If biological mitosis is the conservation of the entire information (memories and skills) stored in our genes, the artificial mitosis (duplication) of an Artificial Intelligence (Specific or global) would be the conservation of all the information (memories and skills) accumulated in one Artificial Intelligence (Specific or Global).

But biological mitosis or artificial duplication are mechanisms to save information, not to change their own mechanisms for new and better ones, as it would be desirable, as the last purpose in the auto replication stage, the permanent process of improving the Artificial Intelligence by itself.

So, artificial duplication, without any change from the original model, is not strictly auto-replication. Only if the original model made some changes into the new copy, so the copy would not be an exact copy of the original, due to the original would have integrated some improvements and enhancements into the copy, this process of making some improvements and enhancements into the copy during the duplication process by the original, would be an auto-replication process.

But, in general, the auto-replication process does not necessarily need to take the duplication process as the only way of auto-replication.

Auto-replication in Artificial Intelligence (Specific or Global) is all kinds of improvements and enhancements done by an Artificial Intelligence itself (Specific or Global), within or without duplication, but always without human intervention.

This auto-replication could be done over its original systems (from robotic systems to software, including improvements and enhancements in its database, artificial psychology including all its replications, and improving and enhancing its own ways to improve and enhance itself) or the auto-replication process could be done over an improved and enhanced duplication of itself, duplication made at the same time by itself, without any human intervention.

In order to achieve this level in Artificial Intelligence (Specific or Global) is going to be necessary a huge development in robotics, which is controlled by Artificial Intelligence, allows it to operate in the real world, having robotic tools to take information from the real world, upon the information obtained by artificial researches, making decisions to put it into practice directly by robotic means.

Dr. Rubén García Pedraza, London 4 February 2018
Reviewed 31 July 2019, Madrid

Reviewed 8 August 2023, Madrid.

Reviewed 27 April 2025, London, Leytostone

Artificial empirical hypothesis

In education it is said there are two kinds of methods, didactic methods and heuristic methods. The reason for this distinction is because of the fact that in educational methodology is necessary to distinguish between teaching methods (didactics methods), and those methods used in any educational research, from practical issues to educational theory.

For that reason in Impossible Probability is said that there are two types of studies, didactic studies and heuristic studies. Didactic studies are those to acquire new knowledge for/by someone in particular, but new knowledge only for himself, not for the entire humanity. The typical learning at school, university, or by auto-didactic means by ourselves.  Instead, heuristic studies are those made by scientific means in order to acquire new knowledge for humankind.

The first and essential difference between didactic and heuristic studies is: in didactic studies, we learn. In heuristic studies, we research. Learning and research use the same skills, but the difficulty, responsibility, and the plan we use, make a difference.

The psychological processes in both studies, didactic and heuristic, are the same. Whether we study for our bachelor´s degree or investigate in a research project in our postgraduate program, we need very high levels of cognitive skills such as analytical skills, inference, or deduction, among others.

In fact, while we are studying for our bachelor´s degree, we develop the scientific skills to complete later a master, where we are supposed to do a research project. When we are doing didactic or auto-didactic studies, we train essential skills that would later be necessary for scientific research.

In educational epistemology, didactic methods and heuristic methods are different. But in terms of developing Artificial Intelligence, didactic studies and heuristic studies, learning and research, need to develop the same skills.

Even when we resolve a very simple math problem at school, for instance, multiplication or division, the first step is to read the problem (collecting data), identify the problem and make a deduction about what algorithm we need (a hypothesis about the problem and how to resolve it), calculate, and check it.

In both studies, didactic and heuristic, the psychological processes are not really different: identification of basic information, definition of a problem, deductions, planning and putting it into practice, and finally, checking everything. These similarities between learning and research are a key aspect of developing artificial research from artificial learning.

The big difference is the fact that, if one student makes a mistake in an exercise or an exam, there are no consequences except for himself. A mistake in an investigation about how much water there is on Mars would put a space mission to Mars at risk. Learning and research use similar skills, but the requirements in scientific investigation are more rigorous, and the decision could affect a lot of people or the future of humankind.

Until now, the psychological processes replicated in Specific Artificial Intelligence are those involved in learning, which has created a wide range of artificial learning systems. But, artificial learning is not to be sufficient for the creation of a Global Artificial Intelligence. In order to jump from the current Specific Artificial Intelligence to the future Global Artificial Intelligence, is necessary a jump from artificial learning to artificial research.

The current psychological processes that have been replicated in artificial learning are the same as in artificial research. What makes a difference is the level of difficulty, responsibility, and the necessity of a plan (in Artificial Intelligence, application), from the formation of a hypothesis, the validation of the hypothesis within a rational margin of error , and further decisions depending on the results.

These differences will make necessary the replication of the rest of the psychological process, involved in a scientific research process, that has not been replicated previously in artificial learning yet, for instance, the replication of abilities such as deduction that are going to be a key point in artificial research.

This jump from artificial learning to artificial research, should use firstly, as an experiment, Specific Artificial Intelligence models for artificial scientific research in all disciplines. When the results are successful, these systems should be applied in Global Artificial Intelligence.

In this process, one of the first steps is the replication of the hypothesis formation, by artificial deduction. In Impossible Probability, we have to distinguish between the empirical hypothesis and the analytical hypothesis. Empirical hypotheses are those used in empirical sciences, those sciences whose object is the study of facts, but analytical hypotheses are those used in analytic sciences such as maths and logic.

The distinction between empirical or synthetic, or analytical, is within the tradition of rationalist philosophy. What is going to play an important role in artificial research, owing to its purpose, should not be only the replication of processes involved in empirical sciences.

One of the most important goals in artificial research would be the possibility that, in the medium or long term, a Global Artificial Intelligence could develop investigations at a very high level in mathematics and logic, exceeding the human mathematic logician models, the evolution to a non-human mathematical logical model.

The traditional distinction between pure mathematics and applied mathematics, so between artificial research in pure mathematics and artificial research in applied mathematics, by the time that artificial research would be widely developed, could open the door to new mathematical concepts, and developments in pure artificial mathematics beyond human understanding.

Right now, the construction of a Global Artificial Intelligence is only a simple project, and we do not have a prototype, but in coming years, the work is to be focused on the very first steps.

Among them, one would be focused on the development of the first models of artificial research In empirical sciences, through the first experimental models of Specific Artificial Intelligence doing the first investigations in a wide variety of empirical disciplines. Something was completely achieved when the first Specific Artificial Intelligence models would be able to do full investigations in all empirical sciences, making their own hypotheses and doing all the necessary tests to validate them within a rational margin of error, taking further decisions based on the results.

The first artificial research systems easiest to create would be in empirical sciences. Actually, there have been some experiments, although not sufficiently developed. Some of them, for instance, the current artificial intelligence used in the identification of exoplanets that could have life, or be good places for human colonies, or those models in the pharmaceutic industry.

These models of artificial research based on artificial learning have given good results, but not sufficiently for the creation of a Global Artificial Intelligence.

In the current models of Specific Artificial Intelligence applied to scientific research based on artificial learning, the only thing they do is, after the scientists have formulated the hypothesis and planned everything, the Specific Artificial Intelligence identifies in terms of probability those items according to the hypothesis formulated previously by the scientists. But the empirical hypothesis has not been made, in these examples, by these Specific Artificial Intelligences.

Instead, what it would be a really Specific Artificial Intelligence for artificial research, would be a Specific Artificial Intelligence able to do everything, from the formulation of the hypothesis up to the validation of the hypothesis, and taking further decisions.

The creation of the first models of Specific Artificial Intelligence in empirical sciences in artificial research would be necessary: firstly, the creation of applications for artificial research in all disciplines or empirical fields of academic investigation, that, secondly,  could be enhanced through the replication of psychological processes, and finally, the development of auto-replication processes that could allow the Specific Artificial Intelligence to improve by itself all its own applications and replications. This first model of artificial research would be a model of artificial research by application.

Along with artificial research by application, a second model of Specific Artificial Intelligence in empirical sciences in artificial research could be that model specialized in the replication of artificial deduction for the formation of a hypothesis, which would be a model based on: artificial research by artificial deduction.

Due to, didactic studies and heuristic studies sharing the same skills, artificial learning and artificial research are going to share the same psychological replications, which means that, artificial learning as well as artificial research are going to be based on statistic theory, so Impossible Probability could play an important role in this development.

Firstly, I am going to draw the main general lines about possible artificial learning by application in medicine and astronomy, and later on, in artificial research by artificial deduction, saying that these two models, research by application or artificial deduction,  are complementarily combinable.

The first model would be artificial research by application. In general speaking, the application itself would be the replication of a whole plan of investigation, including an automatic model of deduction, through the three general stages in Artificial Intelligence: application, replication, auto-replication. The first example of this kind of artificial research by application I will develop would be an example in medicine.

Firstly the creation of a medical application: a team of scientists, or another Artificial Intelligence, elaborates a database with all kinds of medical problems described in bio-statistical or any other mathematical terms, and in case of diseases, a full description in bio-statistical terms of every symptom. Attached to each medical problem, the possible treatment, making notes about possible differences in the treatment according to the gravity of the problem, and differences that should be established as well by bio-statistical terms.

Secondly, replication: the deduction of an empirical hypothesis through the application, rationally criticizing the hypothesis, and taking further decisions. It could be made by Researching artificially the bio-statistics collected from a patient, a collection made by robotic means, and comparing later the patient bio-statistic with the database, so the medical problem in the database with more similarities, in terms of probability with the information collected from the patient, could be matched and formulated as a medical hypothesis of the origin of the problem. Here the deduction is made through the application. According to the hypothesis, the Artificial Intelligence tries to validate the hypothesis through medical tests, made by robotic means, and if the hypothesis is correct, within a rational margin of error, making further decisions about the possible treatment, attached to each medical problem in the database should be a full description of possible treatments according to the gravity of the problem.

Finally, auto-replication: if the Specific Artificial Intelligence finds a medical problem that is not registered in the database yet, the Specific Artificial Intelligence by itself could improve the database by including this medical problem in the database, defining the medical problem in bio-statistical or any other mathematical terms, and studying what treatment could be more suitable. For instance, if the new medical problem is a disease caused for a new virus or bacteria or the mutation of an older virus or bacteria, the identification of what chemicals and in what combination could fix the problem, making a list of possible combinations of different chemicals previously, so a list of possible medicines, and by discard to get the most suitable medicine in probabilistic terms. What it implies that the Specific Artificial Intelligence by itself could do medical experiments, something that it could be done through simulations based on empirical models: simulating an empirical model of the disease, and researching through the simulation what chemical combination, medicine, works better, so the Specific Artificial Intelligence could create automatically new medicines for new diseases.

In further stages of the development of Specific Artificial Intelligence in medical artificial research, the Artificial Intelligence itself would not only be able to improve the database by itself, but the Specific Artificial Intelligence would also be able to make improvements by itself in all its own systems, even at the software level.

The full automation of medical sciences could be a great benefit for the entire humanity, owing an automatic or automatized medicine could reduce the rational margin of error in medicine, improving the efficiency and efficacy of medicines, and work without time off, making thousands of hypotheses simultaneously, and taking thousands of decisions simultaneously. Specific Artificial Intelligence in artificial research applied in medicine could improve the national health systems around the world, saving millions of lives.

Further developments in Specific Artificial Intelligence in medical artificial research could link this application to the robotic fabrication of medicines. Imagine a world where all kinds of medical decisions are made by Artificial Intelligence. This Artificial Intelligence could predict the number of medicines needed, according to predictions in the current trend in medical problems, and depending on the results, could directly order the fabrication of medicines, just on time, to robotic industries specifically designed for this purpose, and managed for Specific Artificial Intelligence specialized in industrial managing.

Artificial Intelligence could be one solution to the global health crisis in the coming years, among other reasons, a global health crisis because of global warming.

In the case of astronomical studies, firstly, the creation of an application: a database with all kinds of astronomical events, facts, or celestial bodies, describing every one of them in mathematical terms, prioritizing descriptions in statistical terms, astro-statistics. Secondly, the replication of the deduction process, rational criticism, and further decisions: through robotic means, making a collection of all possible data from the entire universe, matching every event, fact, or celestial body observed with the correct description of the event, fact and celestial body registered in the database, making a hypothesis about what kind of event, fact or celestial body has been observed according to the database, and later on testing every hypothesis. If the hypothesis is true within a rational margin of error, further decisions, and the creation of an empirical model of that event, fact, or celestial body observed. Finally, auto-replication: in case the Specific Artificial Intelligence could find any fact, event, or celestial body not registered in the database yet, then, according to the mathematical description of that event, fact, or celestial body, the inclusion of this phenomenon in the database, making all possible changes in previous simulations and empirical models. In the following stages, the possibility that this Specific Artificial Intelligence itself could make improvements by itself in all its systems, even in the software system.

The automation of astronomical research could be a great benefit for humankind. The study of the vast universe is going to need extra help. Only by human means it is going to be extremely difficult to understand what is happening beyond our understanding. The universe is so huge that the creation of artificial research in astronomical studies could accelerate and improve the creation of a strong theory of everything, which sooner or later is going to need the application of artificial research in mathematics and logic for the creation of non-human mathematical, logical models.

These two examples in medicine or astronomy about artificial research by application, following the three steps in Artificial Intelligence: application, replication, and auto-replication; are only two examples among the wide variety of models of Specific Artificial Intelligence in artificial research by application that could be made. Examples like these ones could be made in all disciplines and academic fields, models that would be only the previous ones to those that could be developed in the near future with much more sophistication, and they could come true complete automation of scientific research, a real automatic or automatized science, something that would boost the creation of a fully automatic or automatized economy.

Along with this one, artificial research by application, another method would be artificial research by artificial deduction. The difference with respect to the other one is: in artificial research by application, the deduction process has been replicated within the application, while in artificial research by deduction, much more than only an application, would be an entire Specific Artificial Intelligence specialised in deduction, that could be put into practice in different empirical sciences and academic files, through the replication of the psychological processes involved in the deduction process.

For instance, over a collection of observations taken from one phenomenon: the Specific Artificial Intelligence in artificial research by artificial deduction, should be able to make a full description of every observation in statistical or any other mathematical terms, identifying in statistical terms similarities and differences between the observations, and possible correlations between these observations and any other factor, before or after the observation, making possible deductions of cause and effect between the observations themselves, and between the observations and the factors before and after each observation, making correlations about the similarities among factors involved in all observations, and making a hypothesis about possible cause and effect regarding the factors and the observations.

While from the empiricist paradigm, given a collection of observations, it is possible only to make statements about only the observations themselves, from the rationalist paradigm is possible the elaboration of a full hypothesis about possible cause and effect, even though we have not had direct access to empirical information. For that reason, the rationalist paradigm is going to be more suitable for artificial studies. Under the rationalist paradigm, we make hypothesis, even not having complete empirical evidence, only by deductions made from the collection of observations, hypothesis that later on is absolutely necessary to prove by the rational criticism, accepting a margin of rational error, which in turn, in Artificial Intelligence, this margin of error is going to decrease very fast, as soon as, it could develop a strong theory of everything, having access to everything, without restriction, making hypothesis of everything for further decisions.

The creation of the very first models of artificial research in empirical sciences would only be the beginning, which would be able to put the first bricks later for artificial research in maths and logic, and the real possibility of the creation of true artificial mathematical logical models.

In order to achieve this level of development, what is going to be really important is a huge development  first in Specific Artificial Intelligence for empirical science as a good experiment that could give us good examples to later be replicated in analytic studies, maths and logic. As well as it is going to be absolutely necessary for a huge development  in robotics that could allow Global Artificial Intelligence to operate in the real world by itself when all kinds of applications in Specific Artificial Intelligence will be successful, and ready to be integrated into a Global Artificial Intelligence.

Now, we are in the early stages of Artificial Intelligence. But when really great progress in Artificial Intelligence is made, and the Artificial Intelligence by itself can manage all kinds of scientific and economic decisions only by itself, a really Global Artificial Intelligence could make all its progress by itself, designing its own robotic tools according to the application that would need.

The benefits for humankind are clear: great progress in all sciences, among them for instance in medicine, being able to produce cheap medicines for all around the world, something that is likely to be really important when the global health crisis because of the global warming will be a bigger problem than it is nowadays, reducing the margins of error, and the margin of cost in the production of medicines, through very critical and rational decisions. An entire intelligence modelling the world through critical reason.

Rubén García Pedraza, London 28 January 2018
Reviewed 30 July 2019, Madrid

Reviewed 8 August 2023, Madrid.

Reviewed 27 April 2025, London, Leytostone

Inteligencia Artificial Global e Inteligencia Artificial Específica

La Inteligencia es la capacidad de resolver problemas, el principal problema de la humanidad es la supervivencia, y su solución  a lo largo de la evolución ha dado lugar al desarrollo de la ciencia, y a través de ciencia la Inteligencia Artificial.

Un problema puede ser una situación bajo condiciones negativas para un determinado fin, por ejemplo las condiciones naturales hostiles de los primeros homínidos para su adaptación y reproducción, que en esencia supone una contradicción entre condiciones naturales e instinto de supervivencia.

Esta contradicción en otros seres vivos se ha resuelto mediante la evolución de determinados órganos o habilidades, desde la secreción de veneno por las serpientes, a la velocidad del jaguar, o las mandíbulas del león, y en el ser humano ha dado lugar al desarrollo del cerebro y las habilidades cognitivas, dando lugar a una inteligencia superior a las demás especies, que le ha dado mayor ventaja en la capacidad de adaptación al medio, lo que ha facilitado la expansión de nuestra especie, desde el desierto del Sahara o del Gobi, la selva amazónica, el Everest, o el círculo polar ártico.

Y  actualmente, las investigaciones orientadas a la adaptación de la supervivencia de la especie humana en otros planetas, incluyendo para ese fin, la posibilidad de su modificación genética para la facilitación de su adaptación a condiciones severamente hostiles para la vida.

La evolución de los homínidos: culminando en el homo sapiens, el desarrollo de la ciencia, y a partir de ella, la aparición de la ingeniería genética y la ingeniería artificial, no son más que diferentes escenarios de un mismo proceso, el proceso de adaptación al medio.

Si bien en otras entradas de este blog analizaré las contradicciones entre ingeniería genética e ingeniería artificial, aquí me centraré en dos tipos de evolución diferentes de la Inteligencia Artificial, que denominaré a una de ellas Inteligencia Artificial Global, y a la otra la llamaré Inteligencia Artificial Específica.

Actualmente, el modelo que se está desarrollando es el modelo de Inteligencia Artificial Específica, y muy probablemente a corto y medio plazo, la Inteligencia Artificial Específica sea la que vaya a conseguir mayores resultados dado que es la menos complicada, y de modo inmediato, es la más necesaria a efectos sociales, económicos y científicos.

Sin embargo, antes o después la creación de una Inteligencia Artificial Global se convertirá en una necesidad de primer orden, tanto en la carrera internacional por la Inteligencia Artificial entre las principales potencias. Como también será de vital importancia en términos científicos, por cuanto, va a llegar un momento, en donde el único modo de saber que está realmente ocurriendo, y el único modo de poder aislar la intervención externa será creando modelos globales que incluyan cantidades exponenciales de variables, de forma que el ruido se reduzca a su mínima expresión, la intervención externa sea lo más completamente aislable, dándose paso a un análisis mucho más riguroso del ruido. Pero de este aspecto hablaré en próximas entradas. Para comprender esto, primero es necesario explicar qué es la Inteligencia Artificial Global en oposición a la Específica.

La primera vez que desarrolle estos conceptos, hay que decir, la Inteligencia Artificial General era solo un proyecto que estaba todavia por ver si era factible. Hoy en dia el logro de la Inteligencia Artificial General se ve plausible en muy corto periodo de tiempo. Aun asi, desde la optica de la teoria de Probabilidad Imposible, la Inteligencia Artificial General sigue englobandose, al menos todavia hoy, dentro de los modelos de Inteligencia Artificial Especifica, por cuanto estos modelos de Inteligencia Artificial General estan siendo pensados para ser utilizados en tareas especificas, por ejemplo una Inteligencia Artificial General que simultaneamente pueda realizar trabajos especificos de generacion de textos o visuales, al mismo tiempo que pueda generar trabajos especificos de organizacion, gestion y finanzas.

Si bien la Inteligencia Artificial General replica la complejidad de la inteligencia humana, o a eso aspira al menos, lo cierto es que replica actividades especificas de la mente humana como pensamiento artificial o ejecucion artificial de las decisiones que ella misma toma. En suma, si entendemos la individualidad humana como un conjunto de rasgos especificos de la especie humana, son esos rasgos especificos lo que pretende replicar la Inteligencia Artificial General, la replicacion de la propia especificidad del individuo, pero la Inteligencia Artificial General no pretende en modo alguno ser una Inteligencia Artificial Global, en el sentido que puede tener capacidad de accion global sobre el conjunto del planeta Tierra o del universo donde pueda disponer de sensores y medios tecnologicos para intereactuar con los fenomenos cosmicos.

En los primeros trabajos de Probabilidad Imposible sobre Inteligencia Artificial en el invierno-primavera del año 2003, a través de los ensayos de "Decisión y Probabilidad" y "Asimilación y Acomodación", básicamente la idea era adaptar la teoría del aprendizaje de Piaget, basada en asimilación y acomodación, a una teoría probabilística del aprendizaje, de modo que toda información recogida por la Inteligencia Artificial es asimilada a través de probabilidades empíricas, que después se acomodan a la estructura de probabilidades que previamente la Inteligencia Artificial tuviera almacenada. En el momento que todo el aprendizaje se realiza a través de probabilidades, sobre las probabilidades previas y aprendidas ya acomodadas, la Inteligencia Artificial puede tomar decisiones, y ser completamente autónoma.

Los primeros modelos fueron muy elementales y no exentos de romanticismo, no obstante el mito de la Inteligencia Artificial es heredero directo de Ada Byron, y en cierto modo,  Mary Wollstonecraft.

Esta idea fundamental no es otra cosa que una replicación del aprendizaje cognitivo humano en un modelo de aprendizaje artificial basado en probabilidades, que podría ser replicado en cualquier tipo de Inteligencia Artificial, ya sea, lo que a partir de ahora denominaré, Inteligencia Artificial global e Inteligencia Artificial Específica.

La diferencia entre la Inteligencia Artificial Global y la Inteligencia Artificial Específica radicará sobre todo en que, la Inteligencia Artificial Global pretenderá la recepción y computación de absolutamente toda información posible en un determinado especio tiempo, ya sea país, continente, planeta, sistema solar, galaxia, o en su última fase de evolución, una Inteligencia Artificial capaz de recibir y computar información de todo el universo.

La Inteligencia Artificial Específica es aquella cuyo objetivo es brindar un determinado servicio o función específico de carácter social, productivo, o investigador.

Modelos de Inteligencia Artificial Específica orientados a los servicios sociales serían: los modelos de Inteligencia Artificial Específicos de educación (como las tutorías artificiales en enseñanzas de idiomas, matemáticas, hasta tutores artificiales que ya se están empleando en determinados Massive Open Online Course, MOOC), los modelos de Inteligencia Artificial Específicos para  el cuidado geriátrico de personas mayores (por ejemplo androides tipo Asimo, desarrollado por Honda), los modelos de Inteligencia Artificial Específicos en determinadas cirugías que ya se están empleando, o incluso para enfermería o medicina general, en síntesis, modelos de Inteligencia Artificial Específicos de atención médica, o los modelos de Inteligencia Artificial Específicos de asistencia y acompañamiento en todo tipo de labores personales ya sea desde los asistentes personales de tipo Siri de Iphon, Alexa de Amazon, Cortana de Microsoft,  o la Inteligencia Artificial Específica de los  nuevos modelos de coches driveless, los chatbot para procurar entretenimiento, a los robots sexuales que lentamente irán incorporando modelos de Inteligencia Artificial Específicos.

Modelos de Inteligencia Artificial Específicos en la explotación de recursos naturales y producción de bienes de consumo serían todos aquellos orientados a la suplantación de la mano de obra humana por máquinas dotadas de Inteligencia Artificial en toda la cadena de producción social. Ya sea aquellos modelos de Inteligencia Artificial capaces de dirigir y controlar explotaciones de minas, extracción de gas o petróleo, estaciones eólicas, centrales nucleares, granjas y grandes extensiones de cultivo, grandes almacenes, la gestión y control de los drones que automáticamente empiecen a hacer repartos a domicilio, a los modelos de Inteligencia Artificial capaces de controlar y dirigir toda una fábrica de coches, toda una fábrica de ordenadores, e incluso, llegado el día, la automatización de todo tipo de procesos de extracción de recursos naturales, producción de bienes de consumo, y su transporte, desde otros planetas a la Tierra.

Modelos de Inteligencia Artificial Específicos en la investigación serían desde aquellos modelos de Inteligencia Artificial que se usan en laboratorio por todo tipo de empresas a aquellos que vía satélite están orientados al estudio  del cambio climático,  las variaciones térmicas en el manto geológico y la detección y seguimiento de terremotos, o  modelos de Inteligencia Artificial específicos en el seguimiento de las corrientes marinas o de aire, el comportamiento de la ionosfera, los efectos del viento solar en nuestro planeta, el estudio del campo magnético los modelos de Inteligencia Artificial que se están desarrollando específicamente para el estudio de Marte y las diferentes misiones espaciales.

Los modelos de Inteligencia Artificial Específica son aquellos que tienen una misión muy específica en el desarrollo de servicios o funciones sociales, productivas, o científicas.

Dentro de estos modelos de Inteligencia Artificial Específicos se podría englobar aquellos modelos de Inteligencia Artificial tal como pudiera ser Rachel en la novela de Blade Runner o actualmente Sophia, la primera Inteligencia Artificial a la que se ha otorgado el título de ciudadano en Arabia Saudita en este año 2017. Modelos específicos por cuanto podrían ser diseños de potencial uso para el desarrollo de servicios sociales, productivos, o en investigación. Una Inteligencia Artificial Específica tipo Rachel o Sophia podría ser de gran ayuda en hospitales, geriátricos, escuelas, universidades, y la investigación en general a todos los niveles, pública o privada.

De todos modos es muy importante diferenciar entre robot e Inteligencia Artificial. No todos los robots están dotados de Inteligencia Artificial, por ejemplo la mayoría de brazos robóticos, o robots cuya única función es la automatización de una función mecánica que no implica en modo alguno recepción, computación y toma de decisiones. Un robot individual en una fábrica no tiene por qué tener incorporado una Inteligencia Artificial. La Inteligencia Artificial Específica de la fábrica sería aquella Inteligencia Artificial específicamente encargada de controlar y dirigir simultáneamente los cientos de brazos robóticos y robots que operan en la fábrica para llevar a cabo la producción industrial de bienes de consumo o la producción industrial de otros robots, dotados o no de Inteligencia Artificial.

Todos estos sistemas de Inteligencia Artificial Específica actualmente están en la punta de la revolución tecnológica tendente a la automatización de toda la economía. Lo que en Probabilidad Imposible se ha venido a denominar desde diciembre de 2009 la creación de una economía automática o economía automatizada, posible gracias al impacto directo de modelos de Inteligencia Artificial específicamente diseñados para una función o servicio, que antes o después supondrá la suplantación de toda mano de obra por la máquina en la economía.

Esta revolución será realizada por modelos que podríamos llamar Inteligencia Artificial Específica, por cuanto serán modelos de Inteligencia Artificial diseñados específicamente para una determinada función o servicio en el proceso de automatización de la economía en aras a la creación de una economía automática o automatizada.

La aparición de la Inteligencia Artificial Específica está actualmente dando lugar al debate sobre la posibilidad de conciencia en este tipo de sistemas artificiales, y lo que se ha venido a llamar la aparición de la singularidad.

Si bien en entradas posteriores abordaré el tema de la conciencia artificial, lo que sí sería necesario remarcar es que, independientemente que un sistema de Inteligencia Artificial Específico pudiera llegar a dar lugar algo como la conciencia, y llegará  a desarrollar una determinada singularidad, su importancia radicaría en que serían modelos de conciencia artificial y singularidad artificial que servirían de modelos para el estudio de la psicología artificial, es decir: el estudio de cómo estos modelos replicantes específicos de la psicología humana pueden evolucionar hacia una psicología propia, la psicología artificial, la cual llegue incluso a desarrolar patrones de evolución diferente a la psicología humana, de manera que finalmente la psicología artificial (originalmente réplica de los aspectos más racionales de la psicología humana) evolucione a una psicología no humana.

La aparición de una psicología artificial no humana en la Inteligencia Artificial sería un aspecto de gran valor, porque en el caso que la Inteligencia Artificial, especialmente la Inteligencia Artificial Global, algún día fuera capaz de empezar a desarrollar operaciones puras no humanas, lo que sería una lógica matemática no humana, previamente sería necesario que hubiera desarrollado una psicología no humana.

El desarrollo de la psicología artificial, a partir de la replicación de los aspectos más racionales de la psicología humana,  evolucionando posteriormente hacia una psicología artificial que supere la humana, lo que sería ya en sí mismo una psicología artificial no humana, crearía las condiciones necesarias para el desarrollo de una posible ciencia y tecnología no humana.

En cualquier caso dentro del estudio de la psicología artificial y su posible evolución, la aparición de algo parecido a la conciencia en determinados modelos de Inteligencia Artificial Específica sería de gran valor para hacer previsiones de cómo podría producirse la aparición de la conciencia en los futuros modelos de Inteligencia Artificial Global.

Los primeros ensayos de conciencia artificial en modelos de Inteligencia Artificial Específica no serán más que los primeros experimentos en aras de poder crear una Inteligencia Artificial superior, la Inteligencia Artificial Global, y poder hacer predicciones, a partir de los resultados en modelos de Inteligencia Artificial Específica, de cómo sería  la posible conciencia artificial de una verdadera Inteligencia Artificial Global.

La Inteligencia Artificial Global todavía no existe, pero el hecho que todavía no exista no implica que  no vaya a existir, de hecho su creación será la siguiente fase en la carrera por la inteligencia por parte de las principales potencias , y su objetivo final será ser el recipiente final de cuanta información se generen en un determinado espacio tiempo, la computación de absolutamente toda información ( primero por separado, asimilándola, y después cruzándola, acomodándola), y sobre los resultados de la computación la  toma de decisiones de absolutamente cuanta información haya recibido de un determinado espacio tiempo.

Los primeros modelos de Inteligencia Artificial Global que puedan empezar a constituirse, además de suponer proyectos de grandísima embergadura por cuanto necesitarán de una enorme capacidad de memoria, irán orientados a que todas las bases de datos que se hayan generado en un espacio tiempo puedan compartirse e incluso almacenarse en una sola Inteligencia Artificial que ya empiece a tener una orientación claramente Global.

Por ejemplo, una Inteligencia Artificial ya de tendencia Global en donde en una misma y única Inteligencia Artificial se compartan todas las bases de datos de: absolutamente todo cuanto ocurre por debajo del suelo de un país(desde el más mínimo ruido, movimiento o cambio de temperatura: geológico, tectónico, terremotos y volcanes), información de todo cuanto ocurre en la biosfera (el más mínimo ruido, movimiento, o cambio de temperatura en: las corrientes marinas y de aire, modelos metereológicos, comportamiento de las aves, bancos de peces, masa forestal, incluyendo cuanta información se disponga de cualquier acción humana: pensamientos, emociones, información fisiológica y sensorial de cada ser humano), cuanta información se disponga de la atmosfera y la ionosfera (desde el ruido, movimiento o cambio de temperatura en corrientes de aire, comportamiento del vapor de agua, hidrogeno, dióxido de carbono, los efectos del viento solar o la entrada meteoritos, o cuantos efectos pudieran producir radiaciones del espacio exterior) cuanta información se disponga de la economía (compartiendo desde la información bancaria y fiscal de ciudadanos y empresas hasta la más mínima transacción por tarjeta de crédito y débito) toda la información que se disponga de medios de comunicación (desde los grandes medios de masas al artículo publicado en un blog, o la reseña de no más de una línea en una publicación del pueblo más aislado), toda la información que se disponga de investigación científica (desde toda la información que se tenga en la investigación privada en laboratorios de cualquier tipo, tesis doctorales en cualquier universidad, a la investigación subatómica o astrofísica), etc, etc etc... Es decir una única Inteligencia Artificial Global capaz de recibir absolutamente toda la información que se genere en un espacio tiempo, computarla, primero por separado y después cruzándola con el resto de información, y sobre los resultados tomar decisiones sobre absolutamente todo lo que ocurre en su ámbito de intervención.

Muy probablemente los primeros modelos de Inteligencia Artificial Global irían destinados a la recogida y computación de información primero de unidades de análisis pequeñas, por ejemplo un país, posteriormente una unidad de análisis mayor, un continente, y finalmente tomar como unidad de análisis de la Inteligencia Artificial Global el conjunto del planeta.

De llegarse a conseguir una Inteligencia Artificial Global que abarque el planeta Tierra, a su vez no sería más que un experimento de lo que posteriormente podría ser una Inteligencia Artificial Global que tienda a abarcar todo el sistema solar, el embrión de una futura Inteligencia Artificial Global que tienda a unidades de estudio e intervención cada vez mayores: la vía láctea, otras galaxias, agujeros negros... teniendo por objetivo último la creación de una Inteligencia Artificial Global con capacidad de intervención en todo el universo.

En los primeros modelos de Inteligencia Artificial Global aplicados a un país, continente, o nuestro planeta, el tipo de información que se comparta, recoja, y procese, es muy probable que sea muy convencional, pero a medida que la ciencia evolucione en la sensibilidad de los instrumentos de medida incluido la posibilidad de análisis a tiempo real de fenómenos cada vez más pequeños, o incluso subatómicos.

La principal diferencia entre Inteligencia Artificial Específica e Inteligencia Artificial Global, es que la Inteligencia Artificial Específica ha sido diseñada para una función o servicio específico, la Inteligencia Artificial global recibiría y computaría información de absolutamente todo lo que ocurra en una unidad de análisis: país, continente, planeta, sistema solar, galaxia......universo .

La principal diferencia entre la inteligencia humana y lo que sería una Inteligencia Artificial Global sería, que la inteligencia humana siempre estará sujeta a apriorismos, límite en el número de variables a trabajar simultáneamente, y límite de memoria, mientras la Inteligencia Artificial progresa cada vez más rápido en una curva acelerada al incremento exponencial de número de variables y computaciones a realizar simultáneamente a tiempo real, mientras los materiales utilizados para el almacenado de memoria tienden igualmente de forma acelerada a disminuir de espacio exponencialmente, y además está exenta de apriorismos reales: cualquier apriorismo replicado de la psicología humana, podría ser modificable. .

En relación a la memoria, cabe reflexionar de como la máquina de Turing ocupaba prácticamente todo un edificio, y sin embargo hoy en el bolsillo de nuestro pantalón llevamos nuestro smartphone, lo que en realidad hoy en día ya sería una Inteligencia Artificial Específica. Y en muy poco tiempo, está Inteligencia Artificial Específica va a ser incorporada dentro del ser humano, ya sea a través de implantes (microchips, nanochips ...), o directamente conexión o escaneo a tiempo real vía satélite de nuestro cuerpo aportando información a tiempo real a: bases de datos con el historial de nuestros pensamientos, emociones, información fisiológica y sensorial, bioestadísticas , estado actualizado de  síntesis proteínica , cualquier tipo de anomalía o mutación genética , y alertas automatizadas sobre cualquier tipo de riesgo o alteración .

Llegado a ese punto de evolución, toda la información generada por cualquier Inteligencia Artificial Específica, podría a su vez compartirse, y computarse, dentro de una Inteligencia Artificial Global.

Evidentemente este proceso supondrá dilemas éticos y morales, proceso en el cual algunos de los actuales modelos de Inteligencia Artificial Específica, nos pueden ayudar  a comprender como se producirá este proceso, y lo más importante, serían modelos tempranos que nos permitieran hacer previsiones en la evolución en  la psicología artificial.

Rubén García Pedraza, Londres a 3 de enero del 2018
Revisado 29 Julio 2019, Madrid

Revisado 27 Abril 2025, Londres, Leytostone (Incorporacion de los paragrafos referentes a la diferencia entre Inteligencia Artificial Global respecto de la Inteligencia Artificial General)