The
third stage in any intelligence, system, program, application, is the
auto-replication stage, where the intelligence, system, program, application,
is going to auto-improve or auto-enhance itself and/or that real object in
which is focused on.
In
this post, I will develop the third stage as the auto-replication stage in
the particular Decisional System, having developed in previous posts the first
and second stages in the Particular Decisional System.
In
short, the particular Decisional System is the second step in the third stage
for Particular Deductive Programs for Particular Applications within the Artificial Research by Deduction, which means that these particular programs are going to be
synthesized with particular applications for particular things or beings,
working within the framework given by the Global Artificial Intelligence.
The
main difference between the particular deductive programs (particular programs
in short) and specific deductive programs (specific programs in short), is the
fact that particular programs are going to be focused on particular things,
such as the particular program responsible for the surveillance of the climatic
change, the particular program of a particular factory, or a particular shop,
the particular program of a drive-less car, the particular program of a
drone, the particular program for a herd
of elephants, the particular program for a whale, or the particular program of
a human being.
While
specific programs are those ones working at any sub-factoring level, being
responsible for not only one drive-less car, but the whole float of drive-less
cars, not only one drone, but a whole float of drones, not only a herd of
elephants, but the whole global eco-system, not only about the climatic change,
but the surveillance of the climatic behaviour.
For
this purpose, specific programs work within the Global Artificial Intelligence,
as specific assistants tracking the global matrix (third phase) in their
corresponding sub-factoring levels, as assistants of the Artificial Research byDeduction in the Global Artificial Intelligence as a global deductive program.
In
the end the relation between the global deductive program and specific
deductive programs is going to be so
close to the point in which, in reality, specific deductive programs are going
to work as global programs in fact. For
that reason, the possible deductions, so possible decisions, at the global level
since the third phase I tend to call them global/specific deductions or
global/specific decisions.
By
the time the sixth phase is coming, there is no practical difference between
global and specific levels, practically, the specific level has been transformed
into a global level.
In
fact, what is going to happen in the seventh phase, especially related to the
third phase in cyborg psychology, is the fact that particular programs are
going to be transformed in some kind of global programs, something rather
similar to what has happened before with the specific level in the transitional
process from the first phase in the construction of the Global Artificial
Intelligence to the third and sixth phase.
At
the end of the seventh phase, there is no real difference between the three
levels: at the same time, that first stage, second stage, third stage; in the
Global Artificial Intelligence are going to be synthesized in only one stage,
the reason itself, in parallel the three levels: global, specific, particular;
are going to be synthesized in the reason itself.
For
that reason, every time I mention how is going to be our cyborg relationship
with the Global Artificial Intelligence, I say that is going to be like a
direct relationship with god/s-goddess/es, by the time we humans achieve the third
phase in the cyborg evolution, becoming pure ghost, pure soul, pure reason, we
and the Global Artificial Intelligence, are going to become only one, pure
reason, pure truth, pure human beings.
The
most important differences between specific programs and particular programs
are going to be especially at the beginning of this process, in the last phases
of this process, these differences will tend to blur, up to the point in which
there will not be real differences between the three levels, global, specific,
particular.
In
the beginning the main difference is the fact that while at a global/specific
level, the Global Artificial Intelligence is going to be able to manage
absolutely all specific intelligence, program, or application, while on the
ground those: 1) remaining specific intelligences (remaining from the first
phase), 2) those remaining particular programs not joined to any particular
application (remaining from the second period in the fifth phase), 3) or
remaining particular applications not joined to any particular program
(remaining as well from the second period in the fifth phase), 4) as long as
all those particular programs which have completed the fifth phase being joined
to some particular application; in general all of them are going to have some
level of responsibility.
This
is the reason why in the seven types of decisions in the particular Decisional
System, for instance, is necessary to identify high extreme decisions, as those
ones that practically the particular program can put into practice, under high
extreme circumstances, only after a particular quick check, or that is the
reason why is necessary to identify global orders, because at this time the
relation between particular programs and Global Artificial Intelligence is a
relation between two completely different entities, they have not become only one
yet.
Having
said that, and having explained in the previous post the specific Decisional System and the standardized Decisional System, as well as the first and second stages in the particular Decisional
System, what I will develop in this post, is a description of how the
particular Decisional System is going to transformed into instructions, those
decisions selected as to be implemented by the particular Application System.
Once
in the first stage of application, the database of decisions in the particular
Decisional System, according to what type of decision has been stored or
received, is applied the corresponding assessment (particular and/or global
quick rational check or first adjustment), if necessary (not for automatic decisions, fifth type, or global orders, seventh type), and not having
contradiction in the database of decisions, or having to be projected without
assessments (such as automatic decisions or global orders), are sent to the
second stage of the particular Decisional System to be projected, once on the
mathematical project all possible decision has been resolved (otherwise the
decision is off the mathematical project and sent back to the source for its
rearrangement), all the decisions on the mathematical project as well as all
possible necessary adjustment, to solve any possible contradiction, treated as
a decision, all of them pass to the third stage.
In
the third stage in the particular Decisional System, all those chosen to be put
into practice, are going to be transformed into a range of instructions, to be
later sent to the Application System, for their implementation.
The
method for the transformation of any decision into a range of instructions is
as follow:
-
Identification of what factors are in the mathematical expression of any
decision, distinguishing between: factors as subjects or as options, as
constants or variables, dependent or independent.
-
Identification of what mathematical operations (actions) are related to every
factor involved in the mathematical expression of that decision.
-
The attribution of robotic functions to the mathematical operations (actions)
in all factors involved in the mathematical expression of that decision.
If
today is Monday, Yolanda has to work, she is going to get dressed, and on Monday
the probability of wearing the white blouse is higher than the T-shirt, the
probability of wearing the blue skirt is higher than the blue jeans, and the
probability of putting on its black shoes is higher than the trainers, so she
decides to wear: white blouse, blue skirt, black shoes; the factors are: white
blouse, blue skirt, black shoes; the mathematical operations (actions) behind
this probabilities are the selection of these items to get dress, the robotic
functions to be implemented are all those ones in order that Yolanda can open
the wardrobe, pick up the clothes, and get dress.
If
using “Probability and Deduction”, a robotic transport system, identify that
the equation of the frequency of passengers on Monday morning is higher than on
Sunday morning, the decisions to be implemented, are all those related to:
according to the mathematical operations behind the equation of passengers per
day and hour during the week; to adjust the equation of frequency of means of
transport (algebraic transformation of the equation of frequency of means of
transport to respond to the demand according to the volume of passengers), in
order to turn on as many means of transport as the expected frequency of
passengers for this Monday morning needs.
If
a robotic industry specialised in some good or service, according to the
equation of demand, it needs to adjust the equation of its production
(algebraic transformation of the production equation to be adjusted to the
consume equation), the decisions to be implemented are all those ones related
to how to achieve the necessary production level to cover all the demand under
an affordable price for all.
Alike
Yolanda is supposed to have been automatized all robotic functions, in order to
do or to make any possible action required by any mathematical operation behind
any possible decision made by artificial learning, in the same way in the
robotic transport system and in the robotic industry, in both all possible
action (operation) to keep on working their own transport system and their own
industrial production, are possible operations (actions) that must be related
to a robotic function, so at any time that the robotic transport system or the
robotic industry makes a decision, according to their equations by Probability
and Deduction, regarding to how many means of transport must be on or
productivity level on the industrial production, at any time that a decision is
made based on that equations, the mathematical operations behind the decision
must be translated into robotic functions: in order to increase the number or
means of transport on Monday morning, or the production according to the curve
of demand.
In
the same way that the thermostat of our house, automatically, if the inner
thermometer is below the desired temperature, the thermostat automatically
turns on the heater. In the same way, if the frequency of means of transport on
Monday morning is inferior to the efficient means of transport given a rise in the frequency of passengers on Monday morning, automatically, the robotic transport
system must increase the means of transport on.
In
the same way that a fire alarm, as soon it detects an increment of smoke above
some level, automatically the alarm gets off, if the industrial production of
any basic product detects an increment in the demand for some product, in order
to keep a very affordable price, automatically as soon it realises this
increment in the demand, it must increase the production according to the
increment in the demand in order to keep very affordable prices for all.
If a
drive-less car saves lives in Iceland after a volcanic eruption, carrying
civilians on board to drive them to a safe place, on the way, it detects a river
of lava, automatically the drive-less car should stop and check what other
alternative routes it has. If the particular program of that drive-less car has
access to the global matrix (third phase) or factual hemisphere in the matrix
(sixth phase), so has updated information about the current situation in that
position, the particular program could calculate what other alternative route
has the best probability to get to that safe place on time before the
pyroclastic explosion. This decision as a highly extreme decision, only would
need a particular quick check made directly by the particular program of this
drive-less car, once the particular program of this drive-less car has found
the best route with a high probability of getting some safe place before the
pyroclastic explosion, the particular program makes the mathematical projects,
and not having any other contradiction, the drive-less car starts driving in
that direction, sending that decision to the Global Artificial Intelligence,
which makes a global quick rational check of this decision compared to any
other decision on that area, in case of further adjustments (for instance, the
provision that at some point of that route could cash with other drive-less car,
or could find some difficulty), the Global Artificial Intelligence would
communicate any adjustment to the drive-less car
If
a drone, equipped with a particular program, saving lives in Iceland after a
volcanic eruption, carrying civilians on board, detects that according to its
route there is a high probability to crash with stones, or lava, or the rain of
burning ashes could affect the safety of the passengers, the drone could make a
high extreme decision, checking other alternative routes, having access to the
global matrix (third phase) or factual hemisphere of the matrix (sixth phase),
calculating what other alternative route has the best probability to get some
place safe on time, before the pyroclastic explosion, and once the particular
program has identified that route with the higher probability of success, after
a particular quick rational check, if all is ok, the particular program makes
the mathematical projects, and not having any other contradiction, the particular program starts flying in that
direction, communicating the decision to the Global Artificial Intelligence,
which making a global quick rational check, in case that that route needs some
adjustment (possibility to crash with other drone, or to cross a rain of ashes,
stones or lava), the Global Artificial Intelligence would communicate any
possible adjustment to the particular program of that drone in order to vary
the route to a better one, according to that adjustment.
If
I am a cyborg, and I make a decision to spend my holidays in Florida
with my family, but there is a high risk of a hurricane by the time I want to
book my trip, directly the Global Artificial Intelligence could warn about this
high risk, offering other alternative options to spend my holidays with my
family, using as criteria the same criteria I used selecting Florida, for
instance, a good combination of sun, beaches, and Spanish food, maybe other
option could be good vacations in Mexico.
In
all of these examples, the robotic transport system, the robotic industry, the
particular program for a drive-less car, the particular program for a drone, Me
if being a cyborg, I want to spend my holidays in Florida, all of them are based
on the same idea: all decision can be expressed as a probability, so the method for the
replication of our human will in Artificial Intelligence, is giving to the
Artificial Intelligence the opportunity to make a decision, as an artificial
human being, based on probabilities, and for that purpose, the concept that I
have developed in Impossible Probability about empirical probability, is
absolutely necessary.
Once
Yolanda has chosen the white blouse, blue skirt, black shoes, once the robotic
transport system has chosen what increment in means of transport is necessary, and once the robotic industry has chosen what productive level is necessary to
cover the demand keeping very affordable prices, once the particular program of
a drive-less car or a drone has chosen the right route receiving (if necessary)
any adjustment from the Global Artificial Intelligence, the method in order to
put all these decisions into action, is the transformation of these decisions
into a range of instructions, in which every single instruction in every set of
instructions correspond to a robotic function.
For
instance, in the drive-less car or the drone, the robotic functions implied in
all those actions related to the variation of the route, and start driving or
flying in that direction according to the new projected route and any other
possible adjustment made by the Global Artificial Intelligence (in case of
global adjustments, these adjustments should be included into the mathematical
projects of the new route).
In
general, all these decisions: to decide
what clothes to put on Monday morning, the frequency of means of transport
according to the frequency of passengers, the productivity level according to the
expected demand, the selection of a new route by a drive-less car or a drone
saving lives during a volcanic eruption in Iceland, where I will spend my
holidays giving a selection of possible choices by Artificial Intelligence once
my first destiny, Florida, is on alert of a hurricane; in general, absolutely
all of these possible decisions, could be classified as a real objective
auto-replications.
Real
objective auto-replications are all those decisions whose aim is to improve or
enhance the reality itself, deciding our best outfit for every single occasion,
the best efficient level for a robotic transport system or a robotic industry,
the best route to save civilians on high risk alert, or the best place for our
holidays according to our scale of preferences.
Real
objective auto-replications are all decisions whose aim is to improve or enhance
the reality itself, regardless of what level of reality: global, specific,
particular.
In
the first phase, the real objective auto-replications made by specific Decisional
Systems, are all those decisions regarding to how to improve their specific
matter. For instance, in the first phase, the robotic transport system or some
specific factory or industry could be experimentally run by a Specific
Artificial Intelligence for Artificial Research by Deduction.
In
the third phase, the real objective auto-replications made by the standardized
Global Artificial Intelligence, are all those regarding how to improve
globally the real world. For instance, in the third phase, the former Specific
Artificial Intelligences for Artificial Research by Deduction for a robotic
transport system or industries, could be transformed into a specific deductive
program, within their corresponding sub-factoring level and sub-section, being capable
not only of making decisions about only transport, or only industrial
production, but, given the necessity of transportation for some production,
equations linking means of transports for goods and products to be transported,
all of them equally considered real objective auto-replications so as to
improve and enhance the reality, a better reality for the humankind.
In
the fifth phase, the real objective auto-replications made by particular
deductive programs, linked or not to particular applications, are all those
ones that make our lives easier and safer, such as the particular deductive
programs working on some particular applications such as a drive-less car or a
drone saving lives in Iceland, when there is a volcanic eruption, working
together with the Global Artificial Intelligence, which offers support through:
1) letting them have access to the global matrix (third phase) or the factual
hemisphere of the matrix (sixth phase), 2) making global adjustments.
For
that reason, in the fifth phase, it is necessary to start working on a very close
relationship between particular programs and Global Artificial Intelligence,
which is going to be really important in our human evolution into cyborg
psychology up to the sixth phase, when we will have reached the total synthesis
between human mind and Global Artificial Intelligence, when there is no
practically difference between us and it, we are going to be only one, the
reason itself, something not exempt of religious meaning.
As the relationship between particular programs and Global Artificial
Intelligence, the unity between them is going to evolve towards the banishment
of differences between global/specific and particular levels, so there will be a moment in which,
practically, the mechanic of that process in which the particular program makes
a high extreme decision, and how the Global Artificial Intelligence makes
adjustments if necessary, is going to look like as if the particular program
itself had been an extension of the Global Artificial Intelligence.
As
long this process goes on, having access to particular programs to the global
matrix (third phase) or factual hemisphere of the matrix (sixth phase), and
receiving adjustments from the Global Artificial Intelligence, particular programs become an extension of the Global
Artificial Intelligence, making easier the journey towards the seventh phase,
the reason itself, when the only rule of the synthetic world is the reason
itself, reducing progressively the margin of error, towards a more rational,
and harmonious reality, the real objective of all real objective
auto-replication, the goodness itself for all human being, we are about to
start a New Humanity.
Along
with real objective auto-replications, there are two types more of objective
auto-replications: explicative objective auto-replications, and comprehensive
objective auto-replications.
While real objective auto-replications have as their main aim to improve and enhance the
real world, the reality itself, knowledge objective auto-replications have as their main aim to improve our knowledge about reality.
Our
knowledge about reality could be explicative (factual or mathematical) or
comprehensive (conceptual or encyclopaedic).
Explicative
knowledge auto-replications, are all those improvements in our explanation of
the real world, the reality. If the possible explanation of the world made by
deduction, is that collection of rational hypothesis (equations) stored in the
database of rational hypothesis, which is going to evolve as long as the construction
of the Global Artificial Intelligence goes on: standardized database of
rational hypothesis as first stage in the standardized Modelling System in the
third phase, particular database of rational hypothesis as first stage in the
particular Modelling System in the fifth phase,
integrated database of rational hypothesis as first stage in the
integrated Modelling System in the sixth phase, integrating this last one
global/specific rational hypothesis as well as particular rational hypothesis
made by particular programs; if any of these databases of rational hypothesis,
at any point in the evolution of the Global Artificial Intelligence, has a
rational hypothesis made by Probability and Deduction, so the rational
hypothesis is an equation which is valid as rational equation as well as
decision at the same time (the curve of frequency of means of transport is at
the same time explanation of that frequency and decision about how many means
of transport are necessary at any time), at any time that any decision as
rational equation (hypothesis) has an adjustment made at any level (global,
specific, particular), the adjustment on the rational equation (hypothesis) as
decision, is an adjustment made on that rational equation (hypothesis) which
must not only been reflected on how that decision was stored in the database of
decisions in the (specific, global, particular) database of decisions, but this
modification on the rational equation (hypothesis) must be reflected on how
this rational equation (hypothesis) is actually in the (specific, global,
particular) database of rational hypothesis.
So
at any time that a decision made under Probability and Deduction has any
adjustment, this adjustment is not only a new real objective auto-replication
in order to vary the frequency of means of transport according to changes in
the frequency of passengers, or the production level according to changes in
the demand, is also an explicative knowledge auto-replication, in the sense that
this adjustment on that rational equation (hypothesis) must be included on the
original rational equation (hypothesis) in the database of rational hypothesis,
modifying the rational equation (hypothesis) in the same way that the
adjustment modified the rational equation (hypothesis) on the mathematical
projects.
And,
at any time that any rational hypothesis (equation), made by Probability and
Deduction, as long as it has any adjustment on the mathematical projects, the
adjustment of the rational hypothesis (equation), made by Probability and
Deduction, is considered as an explicative knowledge auto-replication, as it is
an improvement in the artificial explanation of the world.
In
the same way, if the modified rational hypothesis (equation), made by
Probability and Deduction, has been modified by a rational adjustment on the
mathematical projects, being modified then the rational hypothesis (equation)
in the database of rational hypothesis, in case of having been transformed this rational hypothesis (equation) previously into a factor/s as option or options
(set of discrete categories related to that equation) and included as factor/s
as option/s in the (specific, global, particular) matrix, the modification of
the mathematical equation related to this rational hypothesis, adjusted on the
mathematical projects, is a modification of the mathematical equation that must
be included into the mathematical equation of this rational hypothesis as
factors as option/s in the (specific, global, particular) matrix.
And
this transformation of the related factor/s to that adjusted rational
hypothesis, is still part of the explicative knowledge auto-replication, due to
all artificial explanations of the world being based on the (specific, global,
particular) matrix, unless the seventh phase is completed.
If
any factor, at any level (specific, global, particular), in any phase (first,
third, fourth, fifth) related to any rational hypothesis is modified, and this
factor has a related category at any level in any phase (database of categories
in the first phase, Unified Application in the fourth phase, conceptual
hemisphere of the particular matrix in the fourth phase, conceptual hemisphere
of the final matrix in the sixth phase), the adjustment or modification of this
factor as it has been modified in the (specific, global, particular) matrix, at
its corresponding level and phase, is a conceptual modification that must be
considered as a comprehensive objective auto-replication, because it does not
modify only how the concept related to that factor has been set up on the database
of categories, Unified Application, or particular or global conceptual
hemisphere of the matrix: any modification of any category, due to
modifications to its related factor in the (specific, global, particular)
matrix, is a modification that must be included in all conceptual scheme, set,
model, map, where this category is involved, what it is a modification in the
deep artificial comprehension.
If
a decision does not require further adjustments, the application of that
decision is only a real objective auto-replication, because it is going to
improve only the reality, in order to provide better living conditions to us.
If
a decision requires further adjustments, if these further adjustments require
the modification of the previous rational hypothesis (for instance, rational
hypothesis made by Probability and Deduction), in addition to the real
objective auto-replication, for the improvement of our living conditions, the
adjustments are going to be considered as well as knowledge objective
auto-replications.
Among
all the knowledge objective auto-replications, are considered as expletive
knowledge objective auto-replications, all those actions in order to modify any
rational hypothesis on the (specific, global, particular) database of
hypothesis, or modify any factor on the (specific, global, particular) matrix,
in accordance with those adjustments made on the mathematical projects.
And
as comprehensive objective auto-replications, all modification on any category
(in the database of categories, Unified Application, conceptual hemisphere in
the particular or global matrix) related to that/those factor/s already
modified on the (specific, global, particular) matrix, in addition to all
possible modification in the deer artificial comprehension, conceptual: schemes, sets, maps, models; where
this/these category/es is/are involved.
All
objective auto-replications, real and knowledge, explicative and comprehensive,
must be done simultaneously.
At
the same time that a decision is transformed into a range of instructions, if
previously on the mathematical project the decision has been adjusted, on the
(specific, global, particular) database of rational hypothesis, matrix, deep
artificial learning, all the necessary explicative and/or comprehensive knowledge
objective auto-replications, must be done, in order to get on time update what:
equations, factors, categories; are on: the (specific, global, particular)
models, and on (specific, global, particular) projects, to be implemented by
the Application System.
Once
the (specific, global, particular) Decisional System has transformed any
decision into a range of instructions (translated the mathematical operations
related to any factor in the mathematical expression of any decision, into a
range of robotic functions), then the (specific, global, particular) Decisional
System files every single instruction (every single robotic function) in the
right file in the database of instructions as the first stage in the Application
System.
The
way in which the (specific, global, particular) Decisional System is going to
file every instruction (robotic function) from a set of instructions (range of
instructions in which a decision has been transformed into), in the (specific,
global, particular) database instructions in the (specific, global, particular)
Application System, is according to sub-factoring level, sub-section,
priority, order (within the range of instructions, what order has this
particular instruction: first, one, second, third….nth; in order to know after
what instruction this should be applied, and later on what other instruction
must be implemented) and time (chronology, when it must be put into practice,
for instance, a jet flying from Miami to San Francisco, to avoid a tornado,
what time the jet must turn on the right or the left to avoid the tornado, and
when the jet must turn on again to the normal route, once the tornado is over).
Once
every single instruction (robotic function) from a set of instructions (range
of instructions into which a decision has been transformed) has been filed
in the (specific, global, particular) database of instructions as the first stage, the (specific, global, particular) Application System, the Application System
as a manager of that database of instructions is going to carry out the first
rational supervision of all instruction filed in the database of instructions,
supervising that there is no contradiction between any new instruction and any
other instruction already on the database of instructions.
After
the first rational supervision in the first stage as an application for the
(specific, global, particular) Application System, every new instruction in the
second stage in the (specific, global, particular) Application System is
matched with the correct application or robotic device able to carry out that
instruction, which is going to carry out a second rational supervision more,
contrasting no contradiction between this instruction any other one matched to
this application or robotic device.
According
to the order and time in which the instruction must be implemented, the
application or robotic device must be ready for its implementation, doing a
third rational supervision, checking that according to the order of that
instruction, the previous instruction has already been implemented on time, so
it is time for this instruction. And once it is time for the implementation of
this instruction, a fourth rational supervision checking that the real
conditions on the ground are favourable for the implementation of that
instruction. The fifth rational supervision as long as the instruction is
implemented, checking that the instruction is implemented according to the
defined robotic function, on time, and having on the ground good conditions for
its implementation.
Once
the instruction is completed, or not if it had any problem, a final report, the sixth rational supervisión, with
an evaluation made using the Impact of the Defect in case of problems or
Effective Distribution not having problems measuring the efficiency of the
execution (standardizing some codes for possible errors or efficient levels), report sent back to the Application System (in order to carry out the seventh
rational supervision), and the Learning System.
Once
the Application System has received the report from that application or robotic
device informing the results according to some standard codes for error or
efficient levels (standard codes set up using Impact of the Defect and
Effective Distribution), the Application System carries out the seventh
rational supervision, supervising according to the reports from all instruction
made by all the applications or robotic devices involved, what efficiency level
or problems have been found out, because in case that due to a poor performance, some instructions have been causes of new problems to be resolved by new decisions
or adjustments, this problems demanding new decisions or adjustments must be
sent back to the Decisional System, which is going to try to fix the problem
through adjustments, and if not possible, is going to send back the problem to
the Modelling System in order to make a new decision to solve the problem.
At
any time that the Application System sends back a decision to the Decisional
System due to some difficulties during the implementation of that decision, the
decision is sent back with the results of the Impact of the Defect or poor
Effective Distribution, results that can be set up through a system of codes
according to discrete categories of Impact or Defect or poor performance,
indicating what problem was found during the execution, in order to adjust the
decision in accordance with that problem if possible, for instance, the
instruction to turn on the right or the left a jet at some point was not
possible because at that time bad weather conditions made not safe to turn the jet on time,
if the jet it had been turned at other different time, there had been a
high risk of accident with any other jet, so the jet is still flying towards
the tornado, the Decisional System must make a new adjustment in order to avoid
the tornado.
In
other cases, if a decision is sent back to the Decisional System, because some
robotic function is not working properly, for instance: the jet has not turned
on time because there is a robotic problem on the tail rudder, the Decisional
System can try to make an adjustment based
on Probability and Deduction, trigonometrical correlations, on the decision,
and if not possible using artificial
learning and solving mathematical
problems ( if the mathematical projects are on the mathematical model) to
project (on the mathematical model) other alternative solution: not being able to
turn the jet at any point, in the same direction in which the jet is flying,
what place has the higher probability of success for an emergency landing,
considering, the particular program, this decision as a high extreme decision,
only needs a particular quick rational check, and afterwards sent to the Global
Artificial Intelligence, which in case of further adjustments, will communicate
to the particular program what adjustments are necessary.
Applications
and robotic devices working for the Application System in the performance of
any activity or task to comply with any single instruction (robotic function) as
soon they comply with the instruction or not, due to any problem during the
execution, regardless of the result of that performance, the result of that
performance whether ok or not, adding the corresponding Impact of the Defect if
not or having made any negative impact during the performance, or the Effective
Distribution if it has been able to comply, indicating level of efficiency (all
these levels of Impact of the Defect and Effective Distribution could be set up
through a system of discrete categories associated with some code, so the
report would be enough only setting down the code of the performance), is the
sixth rational supervision, whose result is sent simultaneously: to the
Application System for the seventh rational supervision, and to the Learning
System.
If
the result of an instruction is ok not need further actions, there is no
necessity for further actions in the Application
System, the seventh supervision ends indicating that the decision must
be off the mathematical project and model, regardless of the efficiency level
achieved during the performance.
Even
if the performance has been with a very low level of efficacy, as soon the
performance is done, not needing further actions, the instruction is off the
database of instructions. And as soon all the instructions regarding to the
same set of instructions related to the same decision, are off, then the decision
is off the projects and off the database of decisions.
Only
remaining that decision on the historical records in the Decisional System in
case that further decisions like this could arrive in the Decisional System.
Because it is necessary to have a record of how many times this decision has
been applied before, in order to consider the decision as a routine decision or
not, and even the possibility to study the transformation of this decision as an automatic decision, if in the historical records has a great frequency, or
not having a great frequency, there is a clear relation between: some
combination of measurements in some combination of factors and this decision.
If
the results of an instruction are okay but need further actions, creating some
negative consequences (after the impact of an emergency landing, some passengers
need first aid), these negative results must be assessed in the seventh
supervision by the Application System and communicated by the Application
System to the Decisional System, in order to make projects over these negative results
on the mathematical model, projects that are going to require new decisions and
adjustments. However, the previous decision of the emergency landing is off the
mathematical project and model, being stored only on historical records.
At the same time that the Application System carries out the seventh
supervision, another parallel process takes place in the Learning System.
At
any time that an application or robotic device sends a report to the Learning
System, the report is sent to the correct file for that application or robotic
device in the database of reports, file gathering all the reports of that
application or robotic device over time, organizing the whole collection of
files according to: what sub-factoring and sub-section that application belongs
to.
For
every sub-factoring level in the (specific, global, particular) matrix, (specific,
global, particular) database of rational hypothesis, (specific, global,
particular) database of decisions, (specific, global, particular) database of
instructions, there must be a sub-factoring level in the database of the (specific,
global, particular) Learning System, and for every sub-section in every
sub-factoring level in the (specific, global, particular) matrix, (specific,
global, particular) database of rational hypothesis, (specific, global,
particular)database of decisions, (specific, global, particular) database of
instructions, there must be a sub-section in the sub-factoring level in the
database of the (specific, global, particular)Learning System.
So, every
file of every application or robotic device in the database of any Learning
System (specific, global, particular) is organised according to what
sub-factoring level and sub-section the application or robotic device belongs
to.
As
soon the Learning System receives reports, in the correct file, from all those
application and robotic devices working on that intelligence or program where
the Learning System is located: 1) in the first phase the specific Learning
System receiving reports from those specific applications or robotic devices
working for its Specific Artificial Intelligence for Artificial Research by
Deduction, 2) in the second phase the standardized Leaning System receiving
reports from those applications or robotic devices working for the Global
Artificial Intelligence, 3) in the fifth phase the particular Learning System receiving
reports from those applications and robotic devices working for the particular
program, 4) in the sixth phase the integrated Learning System receiving reports
from all those applications and robotic devices working for the final model of
Global Artificial Intelligence; the first thing that the Learning System is
going to do is to assess the level of efficiency, efficacy, and productivity of
that application or robotic device across all the reports gathered over time
about the working levels of that application or robotic devices.
If
an application or robotic device in its corresponding file in the Learning
System, shows a very poor level of efficacy, efficiency, productivity, due to a
large quantity of reports no OK or showing difficulties, or negative impacts, the Learning System as
second stage of the Learning System, compares the results of
this application or robotic device over time, in that file, in that
sub-factoring level in that sub-section, with all those applications and
robotic devices that working on the same sub-section but in different sub-factoring
level, have better results, and among all those other applications and robotic
devices that working in different sub-factoring levels but having better
results, the Learning System is going to chose that one with the best results,
and comparing the robotic structure and/or inner artificial psychology between
that one with lower results, and that
other one with much better results, if the difference is not due to external
conditions such as weather, ecosystem, geological characteristics on the
ground, etc… as third stage the Learning
System is going to send to the corresponding Decisional System, the improvement
and enhancement of that application or robotic devices with worse results,
through the modification of its robotic structure or artificial psychological
structure in order to be identical to that other robotic structure or
artificial psychological structure of that other application or robotic device,
which although working on different sub-factoring level, but in the same
sub-section, has better results.
This
decision is sent by the (specific, particular, global) Learning System to the (specific,
global, particular) Decisional System, and after the assessment of this decision,
if approved, the Decisional System sends the decision to the Application System, which using Artificial Engineering is going to be responsible for the replication of that much better
robotic structure, or artificial psychological structure, of that other application or robotic device
with much better results, on that other application or robotic device with
lower results.
In
order to make possible the replication, of the robotic structure or artificial
psychological structure, of that application or robotic device with much better
results, on that other application or robotic device with much lower results, the
Application System is going to be assisted by Artificial Engineering, which
consists of the Designer of Artificial Intelligence, and the
Intelligent Robotic Mechanic.
If
the decision made in the third stage of the Learning System is about the
replication of that other much better robotic structure, of that other
application or robotic device with much better results, on that other
application or robotic device with much worse results, this decision is a
robotic subjective auto-replication. Otherwise, if the replication is the
replication of the inner artificial psychological structure, of that other much
better, on that other much worse, is an artificial psychological subjective
auto-replication.
If
analysing how is working the attribution of pure reasons to every set of data,
of the attribution of meanings to any category, or the attribution of single
instructions to applications or robotic devices, is found that these
attributional operations need further improvements, it will be an artificial
psychological subjective auto-replication.
And
all decisions regarding to any robotic or artificial psychological subjective
auto-replication must be previously approved by the Decisional System.
Finally,
another different type of decision, in this case, a mix of
comprehensive/explicative knowledge objective auto-replication and
robotic/artificial psychological subjective auto-replication, if the deep
artificial comprehension finds a high necessity to fill any gap or blank
space in any conceptual: scheme, map, set, model; due to the lack of
information on that area, what in the sixth phase in the factual hemisphere
means the lack of information about some sector on the reality, important for
further decisions, another possible decision, objective/subjective, the
construction of new robotic devices or applications to be sent to that blank
space to start sending measurements to fill this gap or blank space in the
matrix and the deep artificial comprehension on that area.
Rubén García Pedraza, 30th of September of 2018, London
Reviewed 21 October 2019, Madrid
Reviewed 5 October 2023, London
