The
third stage in the standardised Decisional System is responsible for the
transformation of all rational decisions without contradiction on the mathematical
projects, into a range of instructions, to be sent to the Application System
for their implementation.
Every decision that is processed by the Decisional System, and its respective
instructions processed by the Application System, is a decision on the
mathematical projects. Once the range of instructions has been completed by
the Application System till the end, till the last instruction, or once a
possible modified decision has been completed till the last instruction
according to the adjustments made, is when a decision is off the mathematical
project.
In
the third stage of the Application System, once a range of instructions, or a
range of instructions depending on adjustments have been completed, the third stage of the Application
System in addition to the report sent, by all the robotic devices responsible
for the implementation of every instruction, to the Learning System, the
Application System reports the total completion of all the instructions related
to one decision to the Decisional System in order that the Decisional System
can turn the decision off the mathematical projects.
All
decision, indicating if it is on or off, is stored in the historical records of
the Decisional System, and the Decisional System must permanently revise the
historical records each time that new decisions are filed in the database of
decisions in the first stage, in order to decide if that decision could be
considered by the Decisional System as an automatic decision, according to its
records in the historical records of the Decisional System.
One
decision can become automatic when the relation of decision and combination of
measurements in a combination of factors, within a margin of error, has a
regular pattern, at least a regular
pattern so as to transform that decision into automatic to be turned on at any
time that the Decisional System through the actual projects on the actual
models, having already access to the global matrix and the global model,
realizing that the conditions related to that automatic decision: specific
combination of measurements in a specific combination of factors; is a
combination on the global matrix and/or the global model so as to turn on
automatically on the global project the corresponding automatic decision
related to that combination of measurements and factors.
In
order to transform any decision into an automatic decision, at any time that new
decisions are on the database of decisions, the Decisional System revises the
historical records of decisions identifying If in the past this decision or
similar, within a margin of error, not having contradiction have been implemented,
having that decision a regular relation respect to some kind of combination of
measurements and factors in the global matrix and model.
For
the consideration of any decision as an automatic decision, it is not so important
the question of whether originally that decision was a routine decision, an extreme
decision, or a normal decision.
In
fact, there are many routine decisions that are not specifically related to a
specific combination of measurements of factors so as to become automatic. The decision
to withdraw money from an ATM is a routine decision, but not always related to
the same causes. In general, if I use an ATM to withdraw cash is because I do not
have cash, but not always. I can have cash, but not enough for a transaction
which demands more quantity of cash.
Additionally,
nowadays more and more and more people are getting used to working without cash, only
using credit or debit cards as a method of payment even in routine
transactions.
There
are many reasons why I need cash, and it is not something that can be automatic. If a
particular program for a particular person automatically withdraws cash at any
time when this particular person is out of cash, maybe be useful, but not always recommended.
Routine
decisions in general are more likely to become automatic decisions rather than
normal decisions, but not for that reason. All routine decisions must be
automatic. There are many routine decisions that we practically make every day
about our financial status, due to their personal connotation, how we
manage our own resources, and what is no other thing but how we are, our most
human inner personality, is something that should not be automatic.
Instead,
there are many extreme decisions, that not being very frequent so as to be
routine decisions, except in some countries, earthquakes in Chile or Japan, or
hurricanes in the Caribbean Sea, are extreme decisions susceptible to becoming
automatic because, although not being very frequent but as they are related to
some extreme combination of measurements
and factors, the automation of these decisions at any time that this
combination of measurements and factors happen, the automation of this decisions
can save lives and damages.
For
the transformation of any decision into an automatic decision, if frequency is an
important criterion, frequency must not always be taken as the most important
criterion. There are situations in which in spite of the frequency,
psychological aspects of a decision can make this decision not very suitable
for its transformation into an automatic decision.
In
order to resolve this situation, as soon the third phase of standardization is
achieved, and because is something that is going to make faster the decisional
process, the creation of particular programs for particular applications for
particular things or beings, including particular programs for particular
applications for every particular human being, evolving into a superior level
in the cyborg psychology, is absolutely necessary.
Routine
decisions, automatic decisions, and cyborg psychology are going to relieve
the pressure over the Global Artificial Intelligence, a machine that must be
able to process millions and millions of decisions per minute, second or less.
The
decisions to be transformed into instructions in the third stage of the
Decisional System in general can be described as: quick decisions (routine and
extreme), normal decisions, automatic decisions, and any possible adjustment in
any decision as if it were in fact another decision more.
As
a methodological proposal for the transformation of all decisions into
instructions, I suggest the following procedure, as I did in the post “The third stage in the specific Decisional System”, but now for the third stage in
the standardised Decisional System:
- Identification
of all factors in the mathematical expression of any decision.
-
Identification of what actions are required for every identified factor in the
mathematical expression of any decision.
-
Transformation of every action, for every factor in the mathematical expression
of any decision, into robotic operations.
In
fact, the possible mathematical expressions in which a decision could be expressed
can be catalogued as: equations, trigonometrical correlations, artificial
learning based on empirical probabilities associated with subjects or options, decisions
based on solving mathematical problems.
As
all possible decision is based on a mathematical expression, the transformation
of any mathematical expression (equation, trigonometry, probability,
arithmetical operation) into instructions requires basically the identification of: factors
involved, actions required, translation of these actions into robotic
operations; in essence is the transformation of mathematical sentences into
robotic acts, the translation of
mathematical language into robotic acts. Basically, dialectic language and
function.
At
some point, the transformation of any decision based on a geometric, algebraic, or arithmetic sentence into robotic operations is the translation of
mathematical language into robotic functions.
The
transformation of decisions into instructions as translation of mathematical
expressions into robotic functions, is going to be very important for those
normal decisions which for first time are made, not having any previous record,
not relative frequency at all, so at any time that a completely new decision,
not having neither records nor relative frequency, has to be transformed into
practice, the translation of the mathematical expression (geometric, algebraic,
arithmetic) into robotic functions, must be done with high accuracy, because
based on the records of this very first time in which this decision has been
made for first time, at any time in the future that this decision is made
again, the robotic functions in which this decision was transformed for first
time, are going to be the robotic functions that are going to be taken as a base
for further adaptations in the future to any new position, activity, priority
level, in which this decision could be made again in the future.
As
any mathematical expression of any decision is transformed into robotic
functions, the robotic functions associated with this decision, are stored in
the historical records of the Decisional System, as well as any possible
further adjustments, as a base for further decisions in which this very same
decision can be made.
Routine
decisions as they have been implemented before with some relative frequency,
the range of instructions to be sent to the Application System, much more than
a new analysis of the instructions in which these decisions must be transformed
at any time that they are on the mathematical project, what the Decisional
System should do, to save time and resources, is to take the structure of past
range of instructions related to that routine decision in the past, adapting
the previous ones to the actual conditions in the actual projects.
If
a routine decision was made in the past, but in different position or even
different sub-section related to a different specific activity, but the very
nature of that decision is the same, for instance when you are driving there
are many decisions that, regardless of where you are, are mostly routine
decisions, such as waiting when the traffic light is red, a decision that you
can make in different activities, when driving in your free time, or in a
professional activity, the range of instructions of this routine decisions
driving, is a range of instructions to be applied at any time that you drive,
the only thing that your personal program for your personal application has to
do when you drive, is the adaptation of this routine decisions: in every new
position (sub-factor) according to the activity that you are doing (sub-level);
adapting also this routine decision to the priority level: in general you must
stop when the traffic light is red, but if in the backseat you are carrying
someone with a very grave haemorrhage , or a woman about to giving birth, giving
an extreme priority level the particular program could authorise not to wait
for the traffic light.
In general, the transformation of a routine
decision into a range of instructions, is the adaptation of the range of
instructions associated with this routine decision in the past, adapting this
range of instructions to the current sub-factor (position), subject (sub-section),
priority level, in which now this routine decision is on the mathematical
project.
Likewise,
the transformation of an automatic decision into a range of instructions, is
the adaptation of the automatic range of instructions associated with the
automatic decision, to every sub-factor (position), sub-section (subject),
priority level, in which the automatic decision is automatically on.
Automatically, my AI friend Yolanda can decide to open the umbrella at any time it rains; this automatic
decision, in order to be applied, must be adapted to every new position and
activity in which Yolanda is making this operation automatically.
If
working, helping the passengers to get on or off the aeroplane, Yolanda would not only use the umbrella to cover herself but also the passengers whom she is
assisting. But doing the shopping in her free time, she would only need to cover
herself, and at any time that she gets in or out of any shop, she should close or
open the umbrella.
In
general speaking, routine and automatic decisions to be transformed into range
of instructions, the only thing that the third stage of the Decisional System
must do, is the adaptation of the current range of instructions related to
these decisions in its historical records to the actual position (sub-sector),
activity (sub-section), priority level, in which this routine or automatic
decision has to be implemented.
Normal
decisions, although not having a high relative frequency in the past, but not being
applied for first time, being already transformed into a range of instructions
in the past, so not being this time the first time to be made, the historical
records about the range of instructions in which these decisions were made
before, are the base as well for the future implementation of these decisions,
at any time that these decisions are made again, although not very often,
adapting as well again the stored range of instructions in the historical
records of this decision to every new position, subject, priority, each time.
Normal
decisions without absolutely any record or relative frequency, being implemented
for the first time, are the most typical case in which the third stage of the
Decisional System must analyse the factors in the mathematical expression of
these decisions, identifying what actions require every factor in the
mathematical expression, and transforming this actions into robotic operations:
robotic functions.
Every
robotic operation, function, is an instruction; the total number of robotic
operations, functions, to be made in all factors involved in the mathematical
expression of a decision, are in total the whole range of instructions to be
sent by the Decisional System to the Application System.
All
decisions to be implemented: quick (routine or extreme), normal (for the first time
or not), automatic; having the third stage of the Decisional System translated
the mathematical expression into robotic function in those ones to be made for
first time, or having the Decisional System adapted the previous range of
instructions, stored in the historical records, to the new position, subject,
priority, in all those decisions which have some historical record, all
instruction is sent to the Application System.
When
sending the instructions to the Application System, what the Decisional System
has to do, is: once every new instruction for every new decision without
records or relative frequency, or those instructions from decisions with some
records in which the stored instructions on the records of this decision have
been now adapted to the position, subject, priority, having already translated/adapted
every decision, every instruction must be sent to the correct file in the
database of instructions in the Application System.
The
database of instructions in the Application System is the first stage of the
Application System, but the responsible for the delivery of the correct
instruction to the correct file in the database of instructions must be the
Decisional System, because according to: the sub-factor, sub-section, priority;
of every instruction, the Decisional System must file every instruction in the
correct file in the database of instructions in the Application System,
according to: sub-factor, sub-section, priority.
In
fact, when translating the mathematical sentence of a completely new decision
into robotic functions, or adapting a decision to the current situation, the
third stage of the Decisional System, in addition to: sub-factor, sub-section,
priority; must add two new categories: time and order; the category of time for
every instruction indicates when the instruction must be implemented. The
category of the order of an instruction indicates the order in which every
instruction, within the range of instructions in which the instructions were
translated, have been ordered, among the total number of instructions in ordinal
number. Every instruction within a range of instructions, should be ordered in
terms of: first, second, third, …nth; what ordinal number corresponds to every
instruction in the range of instructions, indicating the specific order of that
instruction in that specific range of instructions, in addition to when, time,
the instruction should be implemented.
The
importance of adding time and the ordinal order for every robotic function is not
only because is going to help the Application System in order to know when
every instruction has to be implemented, but because having labelled every
single robotic function in which every single instruction is expressed saying
time and order of application, at any time that there is a change in the
mathematical projects due to new adjustments, according to the time in which
the adjustment is made, and according to what instruction is about to be
applied by the Application System in
accordance with the order in which the instructions were set up chronologically
over time, as soon the adjustment is made, the adjustment must imply that all
the former instructions not applied yet, must be adjusted following the new
instructions according to the new adjustment. This is only possible if every
instruction has been previously labelled with the time and order to be applied.
For
that reason, the prediction and evolution of virtual and actual projects are so
important, because on real-time are going to provide information about the
global matrix and as of the second instant the global model, as long as the range of instructions of every
decision is put into practice in chronological order over time, so at any time
that a rational adjustment is necessary for any decision, the adjustments made
over the prediction and evolution virtual and actual projects are going to
affect all those instructions not applied yet. These instructions not applied
yet are going to be the real object of these adjustments.
In
more detail, at the end of this post, I will develop the rational
adjustments due to the implications that Probability and Deduction have for
this process.
Once
the Decisional System has translated/adapted every decision into robotic
functions for the current position, subject, priority, having ordered the decisions,
the Decisional System files every instruction in the correct file in the
database of instructions in the Application System, according to: sub-factor
(position), sub-section (subject), priority, time (when it must be
implemented), order (after what other instruction it must be implemented, and
what other instruction goes after).
The
way in which the Application System is going to work with the instructions
already stored in the database of instructions as the first stage for the
Application System is as follows:
-
Once the Decisional System has filed every instruction in the correct file in
the database of instructions according to: position, subject,
priority, time, order; the Application System is going to make the first
rational supervision, supervising that there is no contradiction between this
instruction and those ones already stored across all the database of
instructions.
-
Having supervised that the instruction has no contradiction, according to the
sub-factoring level and sub-section, the Application System has access to
the conceptual: schemes, sets, maps, models; related to all systems, programs,
applications, devices, working for the Global Artificial Intelligence (provided
by the Unified Application in the fourth phase, in the fifth phase the
conceptual hemisphere of the matrix), the attribution operation that the
Application System does is to match the instruction to that intelligence, system,
program, application, device, working for that sub-section in that
sub-factoring level.
Having
the Application System match the instruction to that intelligence, system,
program, application, device, working for the sub-section in the sub-factoring
level in which the instruction has been designed, then that intelligence,
system, program, application, device, put into practice the robotic functions
according to the time and order in which the instruction has been filed in the
database of instructions.
Once
the intelligence, system, program, application, or device, has been put into practice
in the sub-section of its sub-factoring level the instruction assigned at that time and the order in which it was instructed, the intelligence, system, program,
application, send a report with the results: if the instruction required was
completed successfully or not (as for instance, when we send a fax and the fax
gives a report saying if the message was ok or there was an error, or when
doing our computer an operation, if there is an error, tells us the code of
error); to the database of the Learning System.
The
database of the Learning System is the first stage in Artificial Learning,
and the second stage of Artificial
Learning analysing the records of
efficiency, efficacy, productivity of every intelligence, system, program,
application, device, working for the Global Artificial Intelligence, if any of
them have a relative frequency of errors equal to or greater than a critical
reason, then the Artificial Learning in the third stage studies the origin of
this error in order to send the Decisional System any possible decision
about how to fix this intelligence, system, program, application Devise, analysis
that the Learning System does in the third stage.
Once
in the second stage, the Learning System has identified an intelligence, system,
program, application, device, whose relative frequency of errors (according to
the reports sent to the database) is equal to or greater than a critical
reason, in the third stage the Learning System compares this intelligence,
system, program, application, device, with that other similar intelligence,
system, program, application, device, doing the same activity in the same
subject (sub-section) in other position (other sub-factoring level), with best
results having the least number of errors, so the least empirical probability
of error, or at least an empirical probability of error equal to or less than a critical
reason.
If
comparing two different intelligences, systems, programs, applications,
devices, doing the same activity in the same subject (sub-section), although in
different positions, one of them have the best results, and the results of the
other one are below the critical reason, in order to fix the second one with
worse results, the Artificial Learning compares the robotic and artificial
psychological structure of both intelligences, systems, programs, applications,
devices, and if there is any different between them (and this difference is not
explainable due to different weather, geological conditions, or something like
that related to their positions), the different structures in the intelligence,
system, program, application, device with worse results must be modified in
order to be the exact replica of the structure of that other intelligence,
system, application, program, device, with much better results (if the
different results are not related to climatic or geological conditions, or any
other variable depending on the position).
Once
the Artificial Learning has identified what robotic or artificial psychological
structures to fix in the intelligence, system, program, application, device,
with worse results, in order to be an exact replica of that other one with
better results (providing that the differences are not related to specific
conditions in the respective positions), the decisions about what structures to
fix in the worse one to be an exact replica of the best one, are decisions that
the Artificial Learning sends to the Decisional System, and if the Decisional
System, after passing the quick check or rational adjustments, authorises these
decisions, the Artificial Engineering is the responsible for fixing that
intelligence, system, program, application, device, according to the
instructions, sent by the Learning System and approved by the Decisional System,
in order to transform this intelligence, system, program, application, device,
as exact replica of that other intelligence, system, program, application,
device with much better results.
The
global Decisional System must make mathematical projects about absolutely
everything, especially in the sixth phase, even mathematical projects about the
Global Artificial Intelligence itself, in order to auto-replicate the Global
Artificial Intelligence at any time, making as many robotic or artificial
psychological subjective auto-replications, in addition of how many objective
auto-replications does at any time that makes a decision over any real object in
the synthetic world, the reality as another synthetic product is in fact the
real object to be really auto-replicated by the Global Artificial Intelligence.
Any
decision on any real object in the synthetic world is, in fact, a real objective
auto-replication, the auto-replication of the reality itself.
Finally, I will end up making some comments about “Probability and Deduction”, whose
ideas I will develop in an independent book in the future.
In
essence, the main purpose of Probability and Deduction is to prepare the way
for the seventh phase, making possible the union of the three stages of the
final Global Artificial Intelligence in only one stage, the reason itself. In
this only one stage the three stages of: matrix, deduction,
modelling/projection; can be reduced to only one stage with two expression:
geometrical/algebraic; geometrically the matrix, the models, and the projects,
can be reduced not to a single cloud of points where to include absolutely all
factors, because in fact this would not be a simple cloud, this must be a
universe of points. The reduction of the matrix, models, projects, into a
universe of points would be the geometrical expression where to make
deductions, and directly over the rational equations over the universe of points
directly to model and project at the same time. But the algebraic expression of
this universe of points will be the reduction of the original matrix of data to
a matrix of equations, so every single column and every single file in the
original matrix of data in the sixth phase could be reduced to equations,
reducing every single column into an equation, and reducing every single file
into an equation, substituting the matrix of data for a matrix of equations.
In
the seventh phase, instead of three stages, there will be only two
different expressions of the same thing, the reality, a reality in a permanent
process of auto-replication. The permanent auto-replication of the reason
itself will be the permanent auto-replication of the reality itself and vice
versa, the permanent auto-replication of
the reality itself will be the permanent auto-replication of the reason itself.
In
the seventh phase, instead of working with a matrix of data, the matrix of data
will have become a matrix of equations, and the solution of the matrix of equations
will be the closest we can get to the pure truth itself. The solution itself resides in the transformation of the universe of data into a matrix of equations.
In
relation to the third stage of the Decisional System, what is really important
is to consider every single adjustment over any decision on the mathematical
projects, as a decision itself.
There
are two kinds of adjustments depending on how deep the contradiction is: the
elimination of the decision if the contradiction is full, and the amendment of the decision if it is a partial contradiction.
The
criterion to distinguish if an adjustment is for the elimination or the amendment
of a decision depends if the contradiction between two or more decisions can
be solved or not.
If
the contradiction cannot be solved, it is a full contradiction, the decision with
the lower level of priority is eliminated, leaving the decision with higher
priority.
If
the contradiction can be solved, the decision to be amended is the decision
with the lower priority level.
There
are at least four methods for the amendments, depending on how the decision to be amended was made.
- Solving
maths problems, if the decision with the
lower level of priority is only a solution for a mathematical problem, the
method for the adjustment of this decision includes the new contradiction
found in the adjustment between the factors to consider for the mathematical resolution
of the problem, so having included this new information, the adjusted decision
is a result of resolving the mathematical problem: re-identifying the factors
in the problem including the new contradiction, identifying the arithmetical
relations between the factors being aware of that contradiction, resolving the
operations giving a new result, a new decision, to be translated into robotic
functions as new instructions to be sent to the Application System.
-
Artificial learning, if a decision was made depending on the empirical probability
of some option or subject, but this option or subject causes contradictions, the possibility to
resolve the contradiction by choosing another suitable different option or subject, having an
empirical probability within the rational doubt, to substitute the other one.
-
Trigonometrical correlations, depending on what contradiction is found, in the
tangent or any other trigonometrical value, rearrange the trigonometrical
values according to the new data.
-
Probability and deduction, if the contradiction is due to the fact that there
is a change in real data producing a change in one equation, if this change in
this equation produces contradictions with respect to other equation, to make as
many algebraic transformations in that other equation in order to fix the
equation with that one with changes in its real data. If a big hurricane hits Florida, having been expected a big impact, and there is expected an
increment in the financial resources that Banks in the United States will have
to divert to insurance and credit (increment of bank loans, credit cards, new
mortgages, etc…) in Florida, the transformation of all the equations ruling
insurances and credits in Florida, in accordance with the expectations on these products, in accordance with the impact of the hurricane, adjusting the rest of
financial resources in other financial sectors and other States to keep the
banking balance respect to the increment of resources to divert to save Florida.
Having
found a contradiction any rational adjustment, if the contradiction can be
solved by: solving mathematical problems, artificial learning, trigonometry,
Probability and Deduction; the solution of the adjustment is to fix the
inferior decision, that one with the lower priority, to be adjusted to the superior
decision, that one with the higher priority. But not having found any solution
by any method, the decision with the lower level of priority is eliminated in
order to save the decision with the higher level of priority.
For
that reason even decisions obtained by Probability and Decision, having been
found the rational equation in the deduction process in the second stage in the
Global Artificial Intelligence, once the rational equations gets the Decisional
System, must be labelled as well with the corresponding priority level, through
the application of the global Impact of the Defect and the global Effective
Distribution, whose explanation was given more precisely in the post “Third stage in the Modelling System in the integration process”.
In
order to decide, in case of contradiction in the Decisional System, what
decision must be eliminated if full contradiction, or what contradiction must
be amended if partial contradiction, is necessary that all possible decisions, regardless of their origin or
method in which it was made (Probability and Deduction, trigonometry, artificial
learning, solving mathematical problems) must be labelled with the
corresponding level of priority, assigned by the Impact of the Defect and the
Effective Distribution, at global level assigned by the global Impact of the Defect
and the global Effective Distribution.
Once
a rational equation (rational hypothesis) is deduced by global/specific
deductive programs, the model of the rational hypothesis (equation) in the Modelling
System is practically the same model used in the deduction, the only
contribution that the third stage of the Modelling System is going to do on the
rational equation (hypothesis) is to attribute the correct level of priority according
to the Global Impact of the Defect and global Effective Distribution. And the
only changes that the rational equation (hypothesis) could have in the Decisional
System, in case of contradictions are, once the rational equation (hypothesis)
has been labelled with a priority level (in the third stage in the Modelling
System), the transformation of the original rational equation (hypothesis) only
in case that its priority level is inferior compared to the priority level of
that other decision, if not, if the other decision has a lower priority level,
is the other one, the one to be adjusted.
At
any time that a rational equation (hypothesis) made by deduction, is
transformed, the transformed rational hypothesis (equation) must be
communicated to the database of rational hypothesis, in order to include the
single model of that transformed rational hypothesis (equation), what is going
to be practically the double consideration, once the third instant is
achieved, of this rational hypothesis as rational equation simultaneously,
working: as rational hypothesis for the Modelling System, and as rational
equation for the Decisional System.
In the case of having been transformed, the rational hypothesis (equation) as a factor as an option in the global matrix, then the transformation of this factor as an option in the
global matrix in accordance with the transformation made in the rational
equation (hypothesis).
Rubén García Pedraza, 16th of September of 2018, London
Reviewed 20 October 2019, Madrid
Reviewed 20 October 2019, Madrid
Reviewed 20 September 20123, London
Reviewed 16 May 2025, London, Leytostone
imposiblenever@gmail.com
