Let's see what is the basic hardware element of a brain (human or animal).
There are some image-type models called M-models, which are associated with
the sense organs (eyes, ears and so on). M-models work in association with
some YM-models, which already exist in the brain. YM-models are concept
models. A concept-model is a simplified model which, in this way, fits a large
class of similar models.
Example of YM models: "dog", "table" and so on.
M-models have to discover as many as possible entities in the external reality
and to associate a YM model to any entity. Once an entity was firstly
associated with a YM, M-models will predict its evolution based also on that
YM.
Example: if an entity was associated with a YM-dog, the M-model is able to
predict how this YM performs in connection with all the other YMs of it.
Any prediction of M with that YM included is compared with the information
obtained by M from external reality. The information obtained by a M-model
from outside during the comparison process, is called "input reality" (IR).
We just introduced a new term as "input reality" or IR. IR is the information
obtained by an M-model from outside (from external reality or from other
models) to improve its predictions.
If the prediction meets IR, then M will try another prediction to improve its
quality. If one or more predictions do not meet IR, then M will replace that
YR with another, and the process will continue. This process will continue so
that all the entities which are discovered by M-models will be associated with
some YMs and all the predictions of M must confirm the M-model, unchanged.
Such a model is, thus, a stable model. When M is stable, all YMs are
integrated in M in a harmonic way.
The main function of M-models is to make a preliminary harmonic model (stable
model) associated with an external reality.
Conclusion: a M-model interacts with a section of the external reality. M will
be a model made in an informational way by analogy with that section of the
external reality. Because M is a model, all the elements are connected between
them in a harmonic way, so that the model is stable. This stability is
verified on and on in an automatic way, as long as a specific external reality
is in interaction with the specific M-model.
M-models interact with some other type models, called ZM-models. ZM-models
take some information from one or more M-models and continue the construction
of models associated with the corresponding external reality. To do this, ZM-
models interact with the other ZM-models of the brain to improve M-models.
M-models are just preliminary models based on YM-models. A ZM model will take
any information from any other M and ZM models of the brain, to improve it.
Example: an M-model is associated with a bus that transports people. A ZM-
model takes this information and tries to see if this bus transports tourists
or is a public transport vehicle. To do this, it will use information taken
from any other ZM-models and M-models. The aim is to make a ZM-model, which
reflects as well as possible a section of the external reality. Because ZM is
a model, it is stable and because this model is integrated in a structure of
other ZM-models, the structure of ZM-models is stable too. This problem will
be treated later in details.
ZM-models are long-range models. This term will be explained later. Here, the
"long-range model" is understood as a model, which already developed its
elements as self standing models.
ZM models are the main models, which reflect the external reality.
We define now two very important terms: knowledge and consciousness.
Knowledge is associated with the facility to predict the evolution of the
external reality based on a structure of harmonic/logic models. This structure
was made by a large number of interactions with many sections of the external
reality and so it already generated a large number of good predictions. This
means that the only guarantee of the correctness of the knowledge is the
confidence in that structure of models. This issue will be developed in
details later in the book.
The consciousness is the facility to make and operate a model, associated with
the external reality, where the person itself is an element of that model.
When such a model is activated, it will also find the position of the person
in the model and so it will predict the position of the person in the external
reality. This issue will also be developed in detail in another part of the
book.
We will now develop some issues associated with the term "knowledge". We
already defined knowledge as the capacity to predict in a correct way the
evolution of the external reality.
Here we use the term "correct". Let's see what it means. This term has two
definitions. One situation is when a model makes a prediction and the
prediction is compared with IR. If the prediction meets IR, then the
prediction is "correct". Unfortunately, there are very few situations when the
comparison between prediction and IR is possible.
For instance, building a bridge. A problem is, for instance, if the bridge
will be stable or not in case of an earthquake. Here we need a guarantee that
the bridge is properly built and there is no possibility to verify this based
on IR.
The second definition of the term "correct" is: the brain will consider as
"correct" any prediction based on a harmonic/logic structure of models. To be
harmonic, the structure was already verified, based on IR in many other
situations. So, the only guarantee of a "correct" prediction is the confidence
in that structure of models.
MDT is associated with the basic hardware functions of the brain. Once we
described the hardware structure, everything what the MDT predicts is based on
what the hardware is able to do. What MDT says about knowledge is not another
theory on knowledge but what the hardware is able to do.
Any experiment is based on a model. That model tells us what we are doing and
the same model tells us what we get and what we see. Any model that makes the
experiment just improves itself. An improved model will make better
predictions and that is all. There is no guarantee associated with the
knowledge except the confidence in our own structure of models.
Let's see another aspect. We saw that any experiment is based on a model. The
model tells us what we did and what we get and see. If there are many persons
who participate in an experiment, everyone will make his/her own model based
on his/her own structure of models. What everyone gets and sees depends on
one's own structure of models.
Example: up to around year 1500 everybody knew that the Earth was the center
of the Universe. This idea was supported by direct observation of the sky but
also by a powerful structure of models. So, in that period, the astronomers
were able to calculate Sun and Moon eclipses, understand and calculate many
parameters associated with the movement of the Moon, Sun and stars. Even the
Holy Book supported this idea, at least in an implicit way. In that period,
the idea that Earth is the center of the Universe was correct.
I want to emphasize again that the situation is generated by the work
principle of the brain. It does not matter if we like or not this situation!
The situation will be the same forever. For instance, Newton's Mechanics
considers that there is a fundamental field of forces called "gravity".
Everybody considers that the gravity exists. But Einstein says that there is
no such a field of forces; what we see is just an effect of the distortion of
the space due to mass. If Einstein is right, the idea that there is gravity is
not correct anymore. See also the applications.
So, in every moment, the brain will consider as correct everything which is
generated by its structure of stable models.
Some scientists could consider these assertions as unacceptable, but
regardless of the fact that we like or not such a situation, the brain is able
to do only what the hardware structure is able to do.
There is another term that has some associated problems. This term is "wrong".
If a model makes wrong predictions, this usually does not mean that the model
is wrong. It means just that the model is not suitable to the given external
reality.
Faced with a new external reality, the brain will activate the model which
makes the best predictions associated with that external reality. If a model
makes wrong predictions, we have to change the model with another one or to
modify the model.
Example: Newton's symbolic model of Mechanics makes wrong predictions
associated with the objects moving at a speed comparable to the speed of
light, but its predictions are good (correct) at lower speed.
In any situation, the terms "correct" and "wrong" must be associated with a
model or with a structure of models.
We already described the first basic hardware facility associated with the
brain (human or animal). It generates truth, reality, knowledge and
consciousness. Now we will describe the second basic hardware facility of the
brain. This is the action on the external reality.
We already saw that faced with a section of the external reality, the brain
makes at least one ZM model. A ZM model works in association with any
available (or several) M-model and with any other ZMs of that brain. The main
ZM is able to predict in a correct way the evolution of a section of the
external reality. Such a ZM is able to make a new class of long-range models
called ZAMs.
ZAMs are artificial and invariant. An artificial model is made without any
direct interaction with the external reality. An invariant model is a model,
which cannot be changed by direct interaction with the external reality.
A ZM model will make a ZAM model in order to modify the external reality. Once
a ZAM is made, it becomes a reference model in changing the external reality.
To do this, the ZAM-model works in connection with a number of AZM models. An
AZM is a model which is already connected to the execution organs of a being
(for human beings these are legs, hands and so on).
Once a ZAM is activated, it will simulate the requested action using any
information from all models of the brain. Based on simulations, ZAM will
determine if it is able or not to meet the goal. If the simulation shows that
the action is possible, then the ZAM will activate AZM models for action on
the external reality. The ZAM will control the AZMs to act on the external
reality exactly as in the successful simulation, with good chances of success.
If by any simulation the objective is impossible to reach, the brain will be
blocked to do that activity.
Example: if a person has to jump over an obstacle, that person will know very
fast if the jump is possible or not. The person knows this, because a ZM makes
a ZAM-model, which is associated to the external reality (the person itself,
the supporting surface and the obstacle, as main elements). The ZAM then
simulates the jump on the model. If the simulated jump fails, the brain is
blocked to do the action. If the jump is done with success in the simulation,
the ZAM will control the body during the jump exactly as it was in the
simulation, with good chance of success.
No action on the external reality is possible without a successful simulation
of that action. The action will be as in the successful simulation. Both in an
immediate action and in an activity that has to be done in the future, any
brain follows this procedure.
We shall add some considerations about the speed of action on external
reality. So, when we walk on a plane surface, for each step there is at least
one simulation before the step is done. Due to a large number of internal and
external factors, any step is unique. Thus, if we walk on a raw surface (a
stony trail in the mountains, for instance) not only every step in based on a
simulation but even during the execution of a step, it is possible to make a
new simulation based on new data and so a step in execution can be modified at
all time to meet the goal as ZAM requires. Thus, a very complicated activity
as walking on a mountain trail, can be done very easily and even elegantly,
based on continuous predictions and simulations associated with every step.
As it was already emphasized before, this procedure to simulate in advance any
activity on external reality is followed in all situations, regardless if the
activity is immediate or it has to be done in the future.
We have already described the two main hardware facilities of the brain (human
or animal). Here is a preliminary abstract of the main hardware models of the
brain:
M-models: these models are associated to sense organs. The brain tries to make
a preliminary model of the external reality. To do this, it uses a number of
YM concept models. The main activity is to find the entities of the external
reality and to associate to any entity a YM model. Then, by simulation on the
model, M-models try to integrate any YM model in the structure in a harmonic
way. That is, any simulation of interaction between a YM and any other YM-
model must confirm the M-model, unaltered.
If, for instance, some predictions of an YM1 model in relation with an YM2
model are not compatible with the prediction of the YM2 model in relation with
the YM1 model, then M has to change YM1 or YM2, or some relations, or some
other YMs, so that the M-model is stable. M-models work in an automatic way,
trying to be stable in interaction with the associated section of the external
reality.
YM-models: they are concept models associated with all the entities, which
have already been discovered by the brain by M-model activity. When a new
being is born, there are practically no YMs. They are made by direct
interaction with the external reality.
ZM-models: they are the main long-range models of the brain. They generate
knowledge and consciousness. Also they make YMs, ZAMs and AZMs. They are able
to take any information from any other model of the brain. ZMs can replace a
YM-model with another if something is not OK after an advance prediction and
simulation based on any available data. They also control ZAM-models during
their activity.
ZAM-models: they are artificial and invariant models. An artificial model is
not generated by direct interaction with the external reality. An invariant
model is a model, which cannot be changed by direct interaction with the
external reality. ZAMs are models, which act on the external reality. Once a
ZAM was made and activated by a ZM, it will simulate the activity, using any
information from any model of the brain. By one or more simulations, the ZAM
will find the right solution. If it fails to find a solution, then the ZM will
make another ZAM and the process continues.
AZM-models: they are associated in a direct way to the organs which can act on
external reality. They are ready-made when a being is born, but, to be used,
they have to be dynamically calibrated by the activity of the ZAMs. That is, a
ZAM has to know everything is association with the external organs of a body
(e.g. hands, legs for a human). When a ZAM has to make a simulation, it has to
know all the parameters of the muscles, for instance. An AZM has to know and
transmit such parameters. To do this, AZMs keep a model of any external organ
of that being.
All these models are associated with the hardware implementation of the brain.
We will see later some others types of models which are associated with the
software implementation of the brain.