exceptional cases, and it reduces the agent autonomy since the scientist is
the one prepares him the data. Instead it should be able independently explore
the environment and to cope with the data it perceives along with the ability
to choose/control the data it attends or filters. This factor should be also
part of the AGI goal to organize its own gathered data in a consistent,
comprehensive and usable way.

Also, it is for effective learning, e.g. knowing when to disregard information
taught to you. An information that is way over your understanding. For
example, learning a 12th grade material while you are in a 2nd grade level.

###  7.2 Learning the modeling

Here, model learning mechanism is proposed, where two contrary but completing
learning approaches in AI are combined [Day, 2022]: empirical, i.e. from
examples (induction), and expertise (rule-based). This is a learnable symbolic
manipulation, or can also be referred to as a hybrid approach or Neuro-
Symbolic, see [Marcus, 2003]. Empirical is bottom-up (from examples to rules),
e.g. via observation or passive interaction. Rule-based is straight from/in
the top, via rules in abstract language, e.g. via conversation or observation.
It can then descend to examples of these rules (deduction). Examples applying
these approaches can be seen in Appendix A.8.1.

These approaches can explain the development process of an infant. We can
assume that at infancy, the infant starts with observation (visual), then
fuses visual objects with audio simultaneous stimuli, also via observations.
This is the bottom-up learning process. Physical interaction could be
exercised too, but we will assume it is insignificant. After infancy, he can
use language to update and refine his preliminary and raw models, as it is
done in childhood, and at school. This is the top(-down) learning process. So
we could say that just like gradual involvement of senses, so is the gradual
involvement of interactions. The idea of how models are developed is
illustrated in Figure 33.

(a) Bottom-Up

(b) Top-Down

Figure 33: Bottom-Up and Top-down methods to learn models

This explains the first step of infant’s learning, i.e. observing or
imitating, since he is yet to know the proper response to his perception. The
first bottom-up step enables him the learning of models, and also the learning
of implicit and explicit intentions embedded in the observed behaviors. Which
can be the basis for his own intentions in the future, especially in
conversation and other interactions. I.e. it might be very possible that
different types of will or intention are first learned.

For example, in Kim [2022]they talk about ”the ultimate goal of this language
is to align AI and our values”, and ”the essence of the alignment problem is
the disconnect between intention (the purpose that we desire) and results (the
purpose that we put into the machine)”. I.e. we have no interpretability in
current DL, since it produces results without any intention behind it. Hence
there is the need to supply or teach the AI with intentions, and not just any
intentions, but those understandable to humans (hence the ”alignment”).

Interactions we have can be passive (observing/mimicking) or active (e.g. RL),
see Fig. 34. But it is not only for modeling external processes, but more
generally it could model inner ones (meta-knowledge), such as introspection,
or how to update models or create new ones, etc. For example, there are
general problem-solving approaches, such as forward/backward chaining,
however, this is also consider to be model, and perhaps there should be
different models (such as there are different heuristics) for different
subjects. These models may be external as any other learned model, which can
be recognized or associated when necessary.

Figure 34: Human create models from interaction, taken from Komarovsky
[2022c].

These interactions, inner or outer, represent different models in the world.
Next, we can wonder, why will is needed to be part of these models. Because
most of classic AI studies assume that cognition is mainly about logic, which
result with models represented merely by logic. But in our opinion, all these
interactions presented above, are not represented merely by logic, but they
also include very fundamental and crucial component - will. We assume that
will is in every interaction an agent has with the world/environment. Either
it poses its own will (animals and humans) or it has some function/role in
someone else’s will, e.g. an inanimate object has will embedded in its meaning
or interpretation to the human (or AI agent). To its perspective or perception
of the world.

Figure 35: Will is significant in modeling.

For example, as seen in Fig. 35, when we encounter an object, we try to
evaluate its function or its potential use for some purpose. While in
encountering an animal, we try to figure out the animal inner state so that we
would know how to anticipate and understand its behavior. Similarly, while
encountering humans (single or a group), we try to extract their will from
their decisions, actions or behavior, since we obviously assume that these
outputs are derived from their inner will. Finally we externalize will to
other realms, such as philosophical queries, where we wonder about the purpose
of the world, our purpose in it, and so on.

From these examples and the discussion above, we can conclude that will is
rooted deeply within us, perhaps it’s our basic prior knowledge. This brings
us to the belief, that a full model of any entity is consists both of
knowledge about it and about some will associated to it. Or in a formula:

Full model of anything = Knowledge + will. (3)

This assumption can also demonstrate what a partial model is. One good example
of a partial model can be seen in teacher-student interaction. For example in
math classes, where the student do not comprehend the material he learns
deeply. Though he can certainly absorb only the shallow view of it. Either it
is a simple memorization, or a technical understanding of the material, such
as not understanding the meaning of stuff but only their utilization, i.e.
technical understanding. In other words, the student can end up with just
knowing how a method or some formula can be used, without deep and full
understanding of the concepts, such as determinant in algebra or Green’s
theorem in calculus.232323These examples of misunderstandings in learning were
the drive for the SciSoft idea: to make one detailed system of all theories
and models, with all the necessary information presented to the student. More
about it is discussed in A.3.3. These are examples of partial model.

Back to the bottom-up, top-down learning approaches. These top-down and
bottom-up approaches might contain models of concepts that do not belong to
them both, but only to one of them. For example, concepts that are hard to
define, like love, God, beauty, and tacit/unconscious knowledge like walking
and breathing - all can be modeled simply by examples. Similarly are the sub-
symbolic features, like audio and visual inputs - they do not have
logic/linguistic/symbolic meaning, hence should be modeled by examples, as it
is done in DL nowadays. 242424Also, as said by John Von Neumann: ”In
mathematics you don’t understand things. You just get used to them”, it may
imply that children and adults perceive mathematics also by examples, without
real comprehension of the concepts, i.e. perceive only their utilization and
understanding their consistency. Hence, these non-symbolic concepts can be
learned via usual non-interpretable DL. On the other hand, abstract concepts,
like those in math and sciences, that appear less in the physical reality, can
be learned solely in the top levels of LTM.

Moreover, in this hybrid approach, DL is used twice: the extension of DL and
the use of DL as the base level on top of which symbolic processing develops.
On the one hand, DL is extended from its too constraint program-search to be
much more flexible, if more operations are added as building blocks, see 5.1.
Hence, symbolism is learned and adaptive just like in DL, differently from
most expert/rule-based AI. On the other hand, different input sensors are
fused (e.g. visual stimuli with textual one) to represent specific
symbols/concepts, i.e., the uninterpreted features in DL become symbolic
tokens (Fig. 36).

This utilization above of DL is hybrid because on the one hand classic AI is
about enforcing our exact view of how the AGI system should be (symbolic
knowledge representation), while in contrary, DL is about letting the AGI
system to find its own way with minimum bias from us. It is also the tension
between heritage verse environmental influence. Same is here. What we do not
know for now to put implicitly as inductive bias we put explicitly in a form
of direct program (”heritage”).

More about the reasons for advocating a hybrid approach can be found in
Appendix A.1.

Figure 36: Proposed cognitive model basic diagram

Additionally, the topmost level in Fig. 36 is actually temporal and used for
creativity and problem-solving. In this level, temporal new abstractions are
created, by stripping off attributes/actions/relations, thus connecting
distant or different abstractions to perform for example analogy or transfer
learning between different domains [Gentner, 1983]. For example, the
abstraction is via the number of edges in the polygon classes (Fig. 36). Or by
abstracting the branching property of trees we could recognize ”treeness” in a
picture of a human lung. More generally, differently to structural similarity,
i.e. in the relations, analogy could be based upon pragmatic similarity, such
as in the goals/purposes. Note that abstraction can be applied to any element
of knowledge, beyond objects, e.g. actions, and even complex objects/actions,
i.e. any group of classes.

An example for using analogy, to transfer one problem to another problem, from
a different domain, and abstract its solution, then specialize it for the
target problem and eventually apply it, can be seen in Bassok [2003]. As can
be seen, it is about ascending models and descending them. It is actually
about searching solution in the immediate neighborhood, by the available
association and admissible actions. Then, if we get stuck and do not find a
solution, we can rise to a more abstract level, to create some temporal (or
existing) class(es), from which we can search for similar problems/solutions
much farther then our current location.

Finally, learning models are not done in isolation. These learned models
should be coordinated or consistent with each other, to avoid failures and
confusions. Consistency-check is about learning model(s) in consistency with
other existing models. It is beyond any type of transfer learning [Schunk,
2012]:

  * •

Zero, positive and negative transfer measure how learning new task influences
previous acquired tasks. Though positive is always preferred, negative is
sometimes a consequence of previous incomplete or erroneous knowledge that
should be revised. Still, if negative learning is not being regulated it may
even result with forgetfulness. So the discussed consistency should be
guaranteed within some range.

  * •

Forward and backward transfer [Wang et al., 2019], is about transfer from old
tasks to new ones (forward) or from new ones to old ones (backward).

  * •

Near and far transfer, is about the distance of new task to previous ones.

All these different transfer processes require active online cognitive effort
(also referred to as ”High road transfer”) or offline reorganization of
knowledge (also referred to as ”Low road transfer”), to reveal new connections
and analogies.

Finally, the operational modeling idea, the outcome of the learning approaches
described so far, is not connectionistic like DL, but made as classic AI, or
logic-based. It is our goal intelligence. But in order to reach it, we propose
DL alongside with consolidation that described earlier. In neuroscience we can
see evidence that higher cognitive animals use this kind of system, e.g. in
Piza et al. [2023], where marmosets (type of monkeys) use a “gaze-based”
spatial navigation, contrary to the “place-based” navigation observed in rats.
That is, to navigate they use more cognitive skills like understanding the
functioning and the model of the objects they recognize, instead of merely
physical orientation in the surroundings.

Similarly, while we recognize objects by similar techniques to DL, i.e. CNNs,
our models are still logic-based, e.g. in recognizing digits (by
V1$\rightarrow$V2$\rightarrow$V3$\rightarrow$V4 brain perception) verse
understanding their generation rules, e.g. 9 = counterclockwise small circle +
clockwise half an arc.

##  8 Implementation directions

###  8.1 Model separation

Another issue that precedes learning, is how to obtain separate models at all.
One way, is like the DENN (Dynamic and evolving NN) idea [Komarovsky, 2022a],
i.e. always learning the ”model of everything” while refining it more and more
with every new experience, such that new sub-models are produced. The idea
here, is like Jeff’s hierarchy [Hawkins and Blakeslee, 2007], where the top
model is always reached, at perception from senses, and it decides which lower
model will handle the situation. E.g. model for problem-solving, for learning,
and for story message (where it connects separate events sequentially). And it
can go on further. For example, problem-solving model chooses the most
appropriate model for solving a particular problem. Or, a learning model
selects the most appropriate model for assimilating new knowledge. Or,
conversational model being about taking turns, waiting till me/other side is
finished, recognizing self models252525We can adapt here the philosophical
perspective of identifying myself as an observer, i.e. an observer of
thoughts, actions, etc. Meaning, treating my self model as equivalent to
others self model. This way we also remove the ego (and perhaps any selfish
will) from the agent. Also this way embodiment is a way to recognize its body
as a tool to fulfill its will., deciding whether the sender’s message imply
the updating of some models due to conflicts for example, or its simply was
not interpreted correctly or something wrong with the message hence should be
clarified or repeated262626Hence, the need for prior knowledge inserted via
some inner information embedded in the models, such as consolidation measure,
or in this case uncertainty parameter and/or the rate of recent model
updates., etc. Or in perceiving: perceiving fictional information is treated
differently than factual information, and so on. Or in ”act like” missions:
act like some of the known models of other agents/people, or act more
creatively (creative model).

The idea from Komarovsky [2022d] can present similar duality here also. The
above refinement can be regarded as top-down model evolving, while there could
be also bottom-up one, where existing models can be grouped into a new or
existing model, similar to the opposite operations in Komarovsky [2022d]:
merging verse splitting. Hence, the modeling itself can be refined to
convergence as the famous AI’s generalization and specialization operations,
using positive and negative examples.

Furthermore, there is this common idea that an infant holds already about 100
billion neurons, and what change along the development is only the connections
between neurons. This enforce our view of separation, since we could have a
static NN, and the learning will only change the separation into models via
different connections.

In conclusion, this option was produced since problem-solving and alike are
very complex models, which is why the proposal to separate them from the
knowledge models. But it extends further - perhaps there is separation of
model representation. May be some models can be represented as operational
classes, but others cannot272727See similar idea in Godel’s ideas, where
truths are more generally are not algorithmic. Or in where it is stated that
there is separation of mathematical truth verse sensing, and that we have more
access to mathematical objects than to perception, of a rose for example..
These other models could be not interpretable nor can be explained by the
agent, e.g. models that are placed in the background of thinking itself, thus
they are “hidden” or implicit. See Fig. 37.

Figure 37: Separating cognitive and knowledge models

One way to implement model separation is proposed in https://matt-
rickard.com/mixture-of-experts-is-gpt-4-just-eight-smaller-models, where they
propose switch transformers, to generate sparse distribution among experts.

Another way to implement model separation is by nested NNs (or NN of NNs)
[Beniaguev et al., 2021, Gidon et al., 2020], see Fig. 38(a), where
consolidation occurs in multiple scales. It can be encountered through many
phenomena in nature, e.g. in the universe (consolidation into stars/solar-
systems and galaxies), in fractals (such as snow-flakes), and in other
recursive structures. It can be also seen in the transition from some initial
network of unlearned models Fig. 38(b) to Fig. 38(c), as a consolidation in
multiple levels, both in micro (within models), and in macro (between models).
The modeling or the reorganization of inner elements, is occurring at many
levels of models, i.e. from the basic models to the most complex ones.

Recently, a neuroscience article [Gidon et al., 2020] discovered evidence in
the brain, that supports our system twice:

  1. 1.

First, it extends the normal binary operation of all-or-none message passage
between neurons, i.e. if the signal overcome some threshold. Now they found
additional logical functions, that are quite basic and necessary in our
system: AND, OR, and even XOR.

  2. 2.

Second, it admits that these complex action functions considered so far to
require multi-layer NN to be implemented: ”These action potentials allow
single neurons to solve two long-standing computational problems in
neuroscience that were considered to require multi-layer neural networks.”.

Moreover, as it is well known [Minsky, 1961], DNNs can implement logic gates
(AND, OR) with 1 layer and more complex gates like XOR with several layers.
Hence, combining with the article above [Gidon et al., 2020], it strengthens
our hypothesis that brain neurons are DNNs themselves.

(a) Nested DNN structure

(b) Initial Nested DNN

(c) Learned Nested DNN

Figure 38: Nested DNNs for model learning

###  8.2 Attention

After assuming a set of unlearned models, Fig. 38(b), it can be assumed
further, that will or more probably consciousness, acting like a flashlight or
a beacon, produces consistent attention (over time) to learn/attend each model
(or several of them) individually.

This concept explains why an infant is usually very focused over his toys
(e.g. a ball), and tracking them is essential for this process. This effect
sticks till adulthood, also in the process of using the cognitive model (i.e.
after the learning stage). It is the need to be attentive only to a limited
set of models ($7\pm 2$ items in WM) [Plass et al., 2010]. See examples in
Fig. 39.

(a) in general 1

(b) in general 2

(c) in problem-solving

Figure 39: Attention in models

In other words, will is coming from the top (or from the side of an hierarchy,
if it does not interested in top-down focusing but on specific models at any
level), shining like a projector, focusing on one/few models, while tracking
it/them in the real world.

Therefore, the utilization of attention can be illustrated in an example of an
infant learning a ”ball” model. During waking hours, an infant is gathering
instances of its current models, e.g. a ball, and at sleeping, he uses these
instances to train his models, for the purpose of making sense. First, he
tries to figure out different models, then he tries to model them also in
time, thus he is able eventually to track them, which is a validation of the
correctness of his ”ball” model [Ganguli, ]. Because the final test of his
model is prediction, hence temporal modeling is what enables prediction, or
more specifically forecasting (prediction in time) [Ganguli, 2018].

Also, attention is needed to construct a coherent picture of visual
perception, see Roelfsema [2023], which explains it by attending each object
in a given scene separately.

Note, that attention to a few models also implies that just as humans, AGI
agent need not to understand and model everything, but only what it is focused
on or interested with. Also, there is the idea of bidirectional attention,
which is bottom-up (external) verse top-down (its own will), and describes the
competition between having a (strong) will and being highly influenced by the
outside. In AGI’s case, it should be mostly navigated by external guidance, if
will is not engineered into it.

In addition, attention can have different ”focal length”, like the theory of
vision, having small pinhole perception at lower levels (e.g V1), and a bigger
one at higher ones (e.g. V4). Meaning, the ability to sometimes see small
details and sometimes see the big picture. In model attention it is the same:
we can both have low-level more detailed attention on smaller models, upto a
high-level attention for more general or composite models. In comparison to
classical object detection in computer vision, high-level classes/concepts use
only the higher-level features for the classification task, but more generally
there is no reason not to be attentive to low-level features whenever is
needed (hence the use of residual connections as in ResNet and Transformer).

Moreover, in regarding to focus (or continuous attention), see
https://medium.com/publishous/lack-of-focus-heres-what-you-need-to-know-from-
a-neuroscience-point-of-view-90b6f33942d7, there is the problem of associative
behavior against focusing on purposeful thought, e.g. at problem solving. It
means, there is on the one hand the process of recalling relative past
knowledge, verse on the other hand the process of current situation handling.
It is also a duality (between attending the past and the present/future).

In addition, it is well known that sub-conscious processes are like seeds,
working in the background, where suddenly an idea can pop-up, what’s called
”aha” moments [Sadler-Smith, 2015]. This phenomena can encourage our idea of
splitting attention between the main consciousness and the hidden one, which
works like a detective, trying to fill up missing information, deduct new
conclusions, and sometimes even solve larger problems. See more about
attention splittings in Appendix A.8.2.

Additionally, there is this battle between senses influencing models (bottom-
up attention), verse models influence how senses are interpreted (top-down
attention) or whether they are being distorted to fit the models better. See
more in Auguste et al. [2022] and in Franke et al. [2022] about how our inner
state influences sensory attention, i.e. to specific mode of perception.
Similarly, in optical illusion Laeng et al. [2022], they showed that
anticipation to darkness can affect the eye dilation.

Finally, attention in our perspective is very similar to the attention in DL,
only without regulation. Meaning, without considering the ideal state of
consolidating into symbolic reasoning of models and operations and most
importantly allowing for dynamic abstraction. However, DL’s attention is
similar by also allowing for multiple implicit functions in a given learning
NN, since it reacts differently depending on the input. In other words, the
DNN can be regarded as a group of undeclared models/functions, generated by
attention units, thus implicitly implement compositionality and reusability.

Moreover, DL has an issue with unspecified prior knowledge, which can be
perhaps resolved by attention. One of the difficulties in early DL studies, is
the decision upon the inner connections in NN or its structure. It could be
for example the decision about sharing or grouping features Schlichtkrull et
al. [2018] or instead separating features. E.g., when traffic data is set
apart from weather data Koesdwiady et al. [2016], or roads from stations Huang
et al. [2014] and are being fused only later (how later is also a prior
knowledge to be decided upon). Or when tasks are separated to groups Huang et
al. [2014]. Or when NN structure is sparsifyed/pruned Ioannou [2018]. All this
prior knowledge is difficult to recognize apriori to different tasks, hence we
could use NAS or attention which deal with this dynamically.

Also, there is the paradigm that the brain works on causality of chemical and
electrical processes. On the other hand, in contrary to this deterministic
view, there is a non-materialistic paradigm [Bentovish, 2022], which allow the
free-will, where the mind is not causal, but instead governed by a soul.

If we dig further, in https://bigthink.com/the-well/eastern-philosophy-
neuroscience-no-self/, eastern philosophy and neuroscience research advocate
the idea that there is no ”self”, and that it is only illusion in our mind. It
is actually similar to relativity theory, where there is no space without
time. In analogy, it means that the brain is materialistic and includes ”self”
as part of its models. Therefore, it may mean that the soul, separated from
this physical brain, is merely a passive, non-participating observer, while
thoughts are appearing externally by some unknown source. That is, the
observer soul, simply observes external stream of thoughts, and falsely
attribute some of them to a ”self”. In the article they argue this, since the
thoughts are not controlled by some ”self”, although being attributed to some
fictitious ”self”, one of the many models we have of different objects and
humans in the world. Hence, our idea of self-model is similar. It is treating
ourselves as another ”thing” to model. This observing soul is one possible
source for attention. Similarly, the sources can be awareness or
consciousness.

Lastly, attention mechanism also suggest that memories not under attention are
still there, accessible, ready to be addressed. Moreover, examples like
attending a movie/story while simultaneously trying to comprehend it and fill
up missing information imply subconscious processing in the background (see
attention beams in the inference in simple examples 6.2.2).

###  8.3 Multi-version model

Side note: this section is still under construction, hence should be skipped.

It is very like the Pathways Language Model (PaLM) idea [Chowdhery et al.,
2022], encouraging sparsity, multi-modality and continual learning of tasks
(unlike multi-tasking where tasks are pre-defined). Also it resembles the
multi-head attention in Transformers [Vaswani et al., 2017], where several
versions of attention are learned.

One suggestion to implement multi-version is by using 3d NN instead of the
usual 2D one, where the paths are not limited to the same 2D network, but also
have the freedom to move to other direction in 3D space. One simple option of
this idea is a stack of 2D NNs, let say $N$ of such NNs, where the paths can
go from any NN to another. This way we can implement cyclic learning, where at
each interaction with the environment, the current version of the NN is

See Fig. 40.

Figure 40: Single-version verse Multi-version implementation suggestion.

####  8.3.1 Functional programming search

It is well known that the DL representing DNNs is functional, i.e. in order to
allow for back-propagation (BP) or differentiation from target to source, it
must be a cascade of functions282828This idea, or how assembly programming
language works, or any simple machine language, or Turing machine - all might
suggest that any algorithm, even if it includes (condition/loop) flows, is a
sequence of actions, hence MOM propose that any such algorithm representing a
method in OOP is an aggregation of other actions in a sequence., i.e.:

$\begin{split}Y&=g\circ f_{K}\circ f_{K-1}\circ...f_{2}\circ f_{1}(X)=\\\
&=g(a(...a(W_{2}\cdot a(W_{1}\cdot X+B_{1})+B_{2})...+B_{K}))\end{split}$ (4)

Where $X,Y$ are input and output respectively; $W_{i}$ and $B_{i}$ are weight
and bias matrices of layer $i$ in the DNN, respectively; $a$ is some
activation function (e.g. sigmoid or ReLU); and $g$ is special function for
the specific task in the DNN’s last layer; and finally $f_{i}(X)=a(W_{i}\cdot
X+B_{i}),\quad\forall i=1..K$.

Similarly to the DNN described above, the learnable symbolic manipulation
should be a cascade of functions. Hence, here we transform common programming
language tools, the basic building block of any function, into a form of
operators or functions.

First we introduce some common symbolic/logical tools that are used in
programming:

  * •

Arithmetic operators (+,-,*,/,modulus, power/square-root),

  * •

Mathematical operators (min/max, absolute value, rounding, norm, factorial,
sinusoidal/exponential/logarithmic/polynomial functions) and their constants
($e,\pi,\tau$, …),

  * •

Assignment operators,

  * •

Comparison operators (equality and non-equality),

  * •

Logical operators (and, or, not, all, each, (in)equalities, exists, count),

  * •

Flow operations (while, for, if, if-else, if-elif, …)

  * •

Identity operators,

  * •

Membership operators (if a sequence is presented in an object),

  * •

and Bitwise operators (shifting, AND, OR, NOT, XOR, etc).

Then we present some examples of transferring them into functions, in the
form: $\text{tool}\Longrightarrow\text{function}$:

$\begin{split}\text{if x then y}&\Longrightarrow if(x,y)\\\ \text{if x then y
else z}&\Longrightarrow ife(x,y,z)\end{split}$ (5)

So for example a composition could be:

$\begin{split}&\text{if x then y elif x2 then y2 elif x3 then y3 else y4}\\\
&\Longrightarrow ife(x,y,ife(x2,y2,ife(x3,y3,y4)))\end{split}$ (6)

$\begin{split}\text{for x do y}&\Longrightarrow for(x,y)\\\ \text{while x do
y}&\Longrightarrow while(x,y)\end{split}$ (7)

Also expressions:

$\begin{split}\text{x and y}&\Longrightarrow and(x,y)\\\ \text{x and y and
z}&\Longrightarrow and(x,y,z)\\\ \text{x or y}&\Longrightarrow or(x,y)\\\
\text{not x}&\Longrightarrow not(x)\\\ \text{x + y}&\Longrightarrow
plus(x,y)\\\ \text{min(x,y,z)}&\Longrightarrow min(x,y,z)\\\
\text{x}\leftarrow\text{y}&\Longrightarrow assign(x,y)\end{split}$ (8)

As seen, functions can have specific arguments, or unknown list/dictionary of
them, as in Python for example. Some of them can be set with default value at
function entrance. Also, they allow recursion. Also, Lambda function, taken
from HOL, makes it easy to produce functions on-the-fly. It is an anonymous
function, hence it is not needed to be pre-defined. This idea can be utilized
similarly to temporal abstractions, i.e., the addition of temporal actions
when it is required.

All these temporal items can be stored in some storage unit, for cases when
some of them are reoccurred. In such cases, they are transferred to LTM. See
more about temporal abstractions in Section 7.2 and consolidation in Section
7.1.

Finally, how different NN structures are equivalent to a rule-based algorithms
(or usual programming)is explained in Appendix A.6.

After the proposal of the basic building blocks, the next issue is what
learning approach is should be utilized to consolidate the learning process
into the correct program. Two approaches proposed:

  1. 1.

Generative: The program-search is the opposite of DL. In DL we start from
large number of hypotheses and as more examples are used (either more epochs
or more examples), the number of possible hypotheses is reduced. But this is
due to fixed/static architecture to apply BP in it. Here, on the other hand,
is the opposite, we start from no hypotheses and generate multiple hypotheses
dynamically. Meaning, we propose each time a new composition of basic
functions. Since we do not have fixed structure we cannot use BP. However,
this method is computationally heavy, since there is never guarantee that a
good hypotheses will be found, especially with consistency with other models.

  2. 2.

Eliminating: similar to DL we transfer the set of functions into a DNN. It
will be described further in the following.

  3. 3.

Additionally, perhaps there is some combination of these methods. For example,
DL is applied first to narrow the number of hypotheses, then the generative
approach is applied somehow.

Related Work

In literature, there are many code representation methods, for tasks like
classification (e.g. naming the code or its purpose, decide if it is a
malware, etc.) or producing a sequence similar to translation, e.g. code
completion or code captioning [Alon et al., 2018]. Here presented some code
representations and their model that is used for accomplishing some task:

  * •

syntax-based methods [Bielik et al., 2016] represent program as a bag of
words/tokens. It can be processed for some task via RNN/LSTM for example or a
Transformer, e.g. via BERT [Feng et al., 2020].

  * •

code2vec method [Alon et al., 2019], represents a program as a bag of all
possible paths between any pair of nodes in a tree representing the program.
It uses a Transformer model in order to discriminate different paths with
different importance. Thus they use attention mechanism.

  * •

Another more natural method to represent a program tree is via graph neural
network (GNN) [Fernandes et al., 2018].

The methods above concentrate on code representation, to accomplish some task
given the code. This is discriminating AI. While our problem is generative,
i.e. we need to generate code for the specific task of model predictiveness,
for example as any LM: via forecasting next sensory inputs.

One approach handling this is Genetic Programming (GP) [Koza et al., 1994,
Lehman et al., 2022] based an evolutionary approach. It works similarly to GA.
For example the population is a vector of numbers, where each number is
interpreted as a code to select from a list of production rules to
constructing a function. These production rules represent correct syntax or
feasible expansion of a code tree. Here is an example:

  1. 1.

$\text{S}\rightarrow\text{bool}$

  2. 2.

$\text{S}\rightarrow\text{exp}$

  3. 3.

$\text{bool}\rightarrow\text{and bool bool}$

  4. 4.

$\text{bool}\rightarrow\text{not bool}$

  5. 5.

$\text{exp}\rightarrow\text{ife bool exp exp}$

  6. 6.

$\text{arg}\rightarrow\text{while bool exp}$

  7. 7.

$\text{exp}\rightarrow\text{mul exp exp}$

  8. 8.

$\text{arg}\rightarrow\text{input}$

where arg represents any expression or argument, starting at the bottom as the
input to the program. Then the right side of each rule (the terminal) is the
function and the number of its arguments. We could produce initial population
of vectors step-by-step, till the program is complete. For example, assuming
maximal depth of a tree to be 3, and a Depth-First-Search method:

  1. 1.

1: $and(,)$, depth=1

  2. 2.

4: $and(while(,),)$, depth=2

  3. 3.

2: $and(while(not(input),),)$, depth=3

  4. 4.

3: $and(while(not(input),),)$, depth=3

  5. 5.

$\text{arg}\rightarrow\text{input 0}$

##  9 Related/Prior Work

###  9.1 Comparison to other cognitive architectures

NOTE: This sub-section is also under construction, hence can be skipped.

The first important difference between MOM and other cognitive architectures,
is that MOM does not have a strict block diagram separating different modules
of different functionalities. Instead we embrace holistic approach, setting
all different functionalities in one place. For example, the knowledge models
include different memories and cognitive operations such as abstraction. The
only separation for now is that of knowledge models, supervising models and
I/O models. It can be regarded as layered structure, where the I/O layer is
the most external, then the knowledge layer, and finally the most internal
layer is the supervising one. It can be viewed in Fig. 41. We can see that
potentially, any model can be either of symbolic type (interpretable) or of
sub-symbolic type (uninterpretable). However, each layer has some dominant
type.

(a) Abstraction capabilities

(b) Main components in MOM

Figure 41: Overview of MOM in different aspects

Also, mention about logic general explainability, verse narrow specific type
(structured) of explainability tools in current DL: SHAP values, LIME,
gradient heat-maps, etc. Most of which simply represent feature relative
significance. This is more of an analyzing tool than a real explainability.
Some say similarly about attention mechanism in Transformers [Jain and
Wallace, 2019].

Real explainability is logical, where like a step-by-step reasoning, there is
a model-based narrative guiding the listener with the relevant elements, that
eventually construct a full-proof model to explain the action/decision of an
AI agent, as it is in humans. Hence, MOM strives to deal with this issue
generally.

Will is expressed both internally, to handle a given situation, such as how to
react? E.g. how to solve some given situation/problem. Either immediately,
e.g. via instinct, or in a delay, as a plan. This reactive mind state is both
for passive will and for active one, i.e. in either way it’s either react or
simply act. And it is expressed externally, to understand a given situation,
such as comprehending a message/story/phenomena.. In both cases (my/others
will), which is about control, what precedes it, is making-sense/understanding
will. I.e., internally organizing models from past experiences, and externally
– understanding a given state using these models.

Traditional behaviorists speak of controlling variables (i.e., variables that
control the animals behavior), meaning
input$\rightarrow$behavior$\rightarrow$output, or
stimulus$\rightarrow$response. whereas control theorists speak of controlled
variables (i.e., variables that the animal attempts to control). That is,
animal behavioral output functions to reduce the discrepancy between the
perceived actual state and the reference desired state. Thus, the function of
the behavior is the control of perception, rather than the stimulus causing
the behavior. In Traditional humans are mechanistic, like billiard balls. In
the control, they are purposeful, agentic creatures. I partly disagree:
Traditional=open-loop, Control=closed-loop or feedback. Still in both we learn
models. And in both we have will. It’s simply a matter of direct or indirect
interaction with the environment. In the diagram you see opposite causality of
behavior. Including will can implement both cases, since we deal with all
sorts of interaction, hence also various learning regimes, as we showed in RL
case. The left diagram is more general, while the right one is only in
problem-solving case. As we’ve seen – there’s also designing, etc. There’s
also emotion as indicator of alignment with will. E.g. in learning: as RL –
after a good/bad result/reward which comes after an action, and at perception:
when the state is recognized as good/bad – the optimal/appropriate action is
selected. Example: “when a child sees a lollipop and has a positive emotional
response based on past experiences eating a lollipop, the anticipated pleasure
is serving an incentive function. When the child is struggling to open the
wrapper and finally rips it with his teeth, shifting his experience from
frustration to pleasure, then the emotional response has reinforced the
behavior and he is more likely to rip it with his teeth in the future.”

Fig. 42.

(a) Powers model of animal and human behavior

Figure 42:

In summary, …

###  9.2 Comparison to previous versions of MOM

Here all models developed so far are compared, in Fig. 43.

Figure 43: Comparison table of models discussed so far

In summary, DENN stores each new combination of events, while AKREM stores
dynamically in episodic memory any newly encountered combination of basic
events. Basic events are stored separately in two types of memory: concepts
and actions. MOM on the other hand, unites those separate memories into one
dynamic operational memory, consisting of concepts, actions, relations and any
instance of those.

##  10 Conclusion

The following is the summary of the paper including some key takeaways.

First, a new cognitive model is introduced to join the existing cognitive
architectures, in the form of dynamic operational memory of models and
instances. In it, a holistic approach is embraced, assuming that intelligence
should be highly versatile and diverse. As opposed to ”picking one side”,
which makes it not fully representative and unbalanced, since it is inclined
to one or few directions only.

Next, our model includes will as an essential part of the modeling process.
Thus, in case of its absence, it turns most of the learned models to be very
partial. In the OOP formulation, the will is an additional variable/attribute,
and it is mostly significant in the top level while it is least significant in
the lowest one. Moreover, our assumption is that will is derived from states
or the representation of the world, via different channels (emotions, rule
systems), and it affects the actions of our accumulated knowledge, by learning
to align with them.

Next, operationability turns static knowledge representation into a dynamic
one, thus enabling cognitive processes. The actions are learned via regular
program search, via either DL or other tools, in a self-supervised manner.

Next, one way to ensure that the continual learning is consistent, is by
implementing local learning, i.e., concentrating on updating only one/few
model(s), while confirming compatibility with other models. A model can be
learned either from examples or directly by using existing operations
(logically). Another way to ensure that learning is consistent, is by a slow
process of consolidation. It is ensured by maintaining a high level of
flexibility over a long period, while pursuing more and more consistency
within and between models.

Next, reusability is utilized to enhance connectivity between models, instead
of learning them as separate entities.

Finally, the cognitive model is designed via inverse engineering. Meaning,
starting from our highly aware and mature cognitive state of mind, and then
tracking back in time to study its evolution.

###  10.1 Future Work

The main problem is how to implement a cognitive system, that produces the
appropriate models, i.e. how grouping/clustering occur, to generate the right
models. Also, how these models produce new ones by the correct compositions.

In addition, there is the issue of how sub-symbolic become symbolic. Perhaps
the models produce objects and actions directly upon sub-symbolic data.
Furthermore, if continuing this line of thought, then models may be removed at
all, which converts this problem to pure DL-based approach.

Additionally, relevant to the last issue, is model evolving. Is it model
refinement of some main model to sub-models, which controls how models of
knowledge are used? Or is it that all the models are separate? And if it is by
refinement, is it one large DL-based model, and all the rest are knowledge
models? Or if we continue this line of thought - again, end up with pure DL-
based one huge model, containing implicitly all the different models, their
actions and attributes. But then how elements and abstraction are implemented
in such a DL model? More about the suggestion above see Section 8.1.

These two examples ending up with DL pure approach (without logic
representation), may imply that this theory can be utilized as a prior
knowledge in DL models, without the inclusion of explicit logic in the final
representation. This is certainly viable/legitimate idea to pursue.

Also, though hierarchy by abstraction can be implemented, but then how can it
be implemented by will, i.e. how to decide when and what to group in such a
hierarchy?

Other MOM’s challenges include:

  1. 1.

How to learn concepts and their features, or more generally classes.

  2. 2.

How to learn different wills, to be utilized for action alignment.

These are all open questions to deal with.

P.S.: Neuro-Symbolic AI for us is merely taking inspiration of class-based
structure, to act as the final stage of learning, while DL is the main tool to
reach it. So it is all about flexibility of DNNs, only that we use
consolidation to finally reach symbols. Another implication of this, is
memory. First, since it is vague and mostly reconstructed during replay
(recalling). It means it is not recorded accurately. And second, since it is
probably used in sleeping periods similarly in a vague/foggy form.

##  Appendix A Appendix

###  A.1 Hybrid approach

It is our opinion, that DNN represents a very basic computational model,
comprised either from a sequence of (non-linear) operations or from a sequence
of ”if” operations. This limitation might be the reason for the counter-
intuitive relations found when analyzing DNNs’ interpretability. I.e., the
effect of DNN finding wrong rules, is probably due to:

  * •

the set of restricted primitives (ifs only), though DNNs are general enough to
implement if-else structure, since to produce specific output - some of the
neurons must be not triggered (inhibited).

  * •

no relation to other relevant models, to assist the DNN in choosing most
appropriate rules consistent with the other models.292929Actually, it is not
entirely correct, since DNNs use shared parameters which can be considered as
”shared functions” that are utilized by higher layers. However, on the other
hand these are local functions, temporal ones relevant only to the trained
task(s).

One advantage of DNN’s is their layered structure, which is equivalent to
chain-of-thought (CoT) [Wei et al., 2022] or dynamic programming (DP) [Bellman
and Dreyfus, 2015]. I.e. it is a step-by-step process, which may imply perhaps
why the more layers (deeper) the DNN has, the better it is, since it allows
for more steps of computation.

But this is also its disadvantage: if it is not dynamic in structure, it
always has the same number of cognitive steps, while in reality, each model
should have different steps, and more precisely - each model can have
different number of steps depending on the input (just like in an algorithm).

Subsequently, we advocate what researchers like Gary Marcus [Marcus, 2003]
have claimed for many years - the adoption of neuro-symbolic hybrid approach.
The modeling we propose is exactly this - learnable symbolic manipulation. As
the caricature in Fig. 45 shows, we must have symbolic explicit (and not
implicit via DL) representation of knowledge. Especially due to communicative
aspects, as discussed in Komarovsky [2022d].

Another shortcoming of DNNs is that the features comprising the model are
unrelated to any concept, hence cannot be communicated as actions over
concepts. This problem was tried to be dealt with by numerous studies, such as
converting features to interpretable concepts [Blazek and Lin, 2021], the
training on objects instead of pixels paper.., and dynamic evolving NN
[Komarovsky, 2022d] which construct the NN neurons step-by-step by relevancy.

Another argument that DL solely is not enough, is since we need abstraction
for multiple reasons, such as analogy and other functions. One way to realize
this is via symbolic manipulation as in OOP. It is mentioned in many places,
such is in Dickson [2022] article: ”Despite the impressive achievements of
deep learning, some of the field’s problems remain unsolved. Among them are
causality, compositionality, common sense, reasoning, planning, intuitive
physics, and abstraction and analogy-making.”. Though, there are studies in DL
that try to implement modularity, e.g Modular Neural Network Azam [2000], and
see modular NNs in Neuro-Symbolic section/presentation…

We can view it also from a prior knowledge point-of-view: it is the trade-off
between how much external prior/bias we should induce, verse how much should
be learned from the data, i.e. how much of our proposed model is a prior and
how much of it is flexibility and freedom to adapt. Hence, whatever AGI
architecture we will choose, we must be prepared to insert artificially, e.g.
externally by learning, some of the prior embedded in human brain, perhaps
inherited right from birth. For example, physical world priors303030It may
just be that physical conservation principle (object do not appear/disappear)
is learned, as stated by Piaget, since it is not merely programming, but it is
also descriptive (behavioral). [Khatib et al., 2022, Piloto et al., 2022], or
the self and others models, and more. Additionally, some things will have to
be guided explicitly, since AGI do not posses a will of its own, e.g. telling
it what to transfer from WM to LTM (what is important). Moreover, some basic
functionalities perhaps needed to be rule-based, i.e. written algorithmically,
in the AGI. These functionalities are probably coming from the human genome,
mainly regulating the main systems or memory. Similarly to Marcus [2003].

Nowadays, Machine Learning (ML) theory assumes best performance is in DL, see
Fig. 44(a). Especially for large data set [Chalapathy and Chawla, 2019], see
Fig. 44(b), where the main effect is for big data, since then there is a
difference between the models. But in small data the different sizes of NNs
are all behave approximately the same.

However, in our humble opinion, unlike the place where AGI supposed to be: in
the most right part of the scale, where we almost totally dependent on data
(minimal prior knowledge), it is actually the opposite - we need full
knowledge for designing AGI (lots of assumptions), which do not depend on data
at all. Then the AGI is flexible and adaptive, and is able to tune gradually
and gently according to data. Whose more important here? It is not the data,
but rather the assumptions and lots of planning of AI. This is like planning
an adaptive or any control system, which is deployed only after knowing
apriori all possible data/cases/scenarios there could be, encounter by the
agent.

Same idea is in Ioannou [2018]: _”learning ability does not come for free, and
is far from automatic. It relies on very specific assumptions that are mostly
encoded in the design of the NN architecture itself - here denoted as
structural priors.”_.

(a) No free Lunch for highly-designed AI.

(b) Performance verse the data scale/dimension, taken from Chalapathy and
Chawla [2019].

Figure 44: Comparison between AI methods to comprehend the environment.

Moreover, DNNs are not that far from symbolic manipulation, since the input is
perhaps sub-symbolic, but the actions on it are pure logic: if, sum,
activation function, etc.

Figure 45: Caricature of interpretation in communication

We see in the figure that DNN has very strict and rigid structure, which of
course allows it to efficiently learn, but on the other hand it denies from it
the freedom to model the actual relations and interactions in the data to
represent it in its correct form/model. Handing the learning module more
freedom (higher variance), might have larger search space for the best model,
but eventually, after enough time, it will settle on the most appropriate one.

This is the exact argument against the DL immediate-results perspective [Ball,
, De Bono, 2016]. First, because there is no gradual learning in the
complexity of data, as a school student experiences throughout his school
years. And second, it takes years for an infant to acquire even simple skills,
while in DL it is expected to do the same and more (the performance is
compared to adults), in a scale of days.

Note, that Fig. 45 is not about the issue of explainability of DNNs, but
rather it is about the inappropriate structure to represent the correct model,
to enable correct reasoning and communication. Although, when learning the
correct model, it is also explainable in its inner structure. But more
importantly, it is explainable by communicating with.

Also, due to the gap between the set of hypotheses or models that a DNN can
produce and the ideal model that represent some trained data, you can notice
the shortcuts that the DNN produces to learn the data, such as predicting a
cow in an image, if it spots a sky. I.e. in this case it has nothing to do
with a cow or its features. Similar effect, of not being able to generalize in
LLMs performing multi-step composite tasks. Doing this also by shortcuts, or
more specifically by pattern matching and subgraph matching, instead of
comprehending the tasks [https://arxiv.org/pdf/2305.18654.pdf] This effect is
reasonable, since there are no constraints or bias over how the DNN should
arrive to truly representing approximated function. It is because there is a
huge amount of suitable functions to map a limited amount of inputs to their
corresponding outputs, see also in Guest and Martin [2023]. This huge amount
of potential functions explains over-fitting in DL, since many possible
functions can represent the training data without any respect to the general
data and knowledge in the world.

It can be solved in a system, where different models are not learned in
isolation, and it learns a model with alignment and consistency-check with
other models learned so far, which MOM supplies (continual learning). That is,
the learning performed in interaction and feedback from other models, in order
for each of them to be maximally correct. I.e., every model should be learned
without conflicts with other models, but rather in cooperation with them,
which enforces each model to be aligned to its true functions.

Moreover, any ML system has to be provided with a well-defined objective,
which means that the designer needs to be very punctilious about his objective
and the constraints of the problem. All this rigorous process is redundant if
we use natural language, since we assume a minimalistic form of communication,
where very little is said, and most of it is completed by previous knowledge
and context.

####  A.1.1 Neuro-Symbolic AI in literature

Neuro-Symbolic AI is widely studied in ML community. For example, the Neuro-
Symbolic Concept Learner for Visual-Question-Answering tasks [Mao et al.,
2019], which is an object-based representation for a scene, and run a semantic
parsing module to translate a question into executable and symbolic program,
given a domain specific language (DSL), and executes it on object features to
get the answer. They use (image, question, answer) tuples for supervised
training. The DSL contains a set of operations over objects’ features or
concepts in the question. These concepts are neural operations, learned in
training. Hence, the program executor is quasi-symbolic, and the program is a
nested sequence of operations, e.g. _Query(Shape, Filter(Red, Relate(Left,
Filter(Sphere))))_. The training is end-to-end excluding the visual component
that produces some embedding, to be operated upon.

Differently, Yi et al. [2018] presents purely symbolic executor, since instead
of using sub-symbolic features, it uses a scene parser that de-renders the
image to obtain a structural scene representation, i.e. generate symbolic
features of the objects, e.g. their size, shape, material and color
categories. And they are used as parameters in program execution. Here the
training is partitioned to object detection with the categories above, and
then the symbolic part is trained via supervised learning on question-program
tuples following with RL REINFORCE algorithm for question-answer tuples, where
the answer is the reward.

It raises the question, whether we should convert the sub-symbolic to symbolic
[Yi et al., 2018], or leave it as sub-symbolic and operate symbolically on it,
as in [Mao et al., 2019].

In Verma et al. [2018], they use deep RL (DRL) to learn a neural policy
network first and then do local search on programmatic policies to minimize
the distance between policy generated by DRL and programmatic policy. Thus
generating an interpretable policy. They claim that they perform this
conversion, since program cannot be learned directly, since it is not
differentiable. But we propose to find differentiable type of program
learning, see Section 8.3.1.

###  A.2 How will is learned at the beginning

Inverse reinforcement learning (IRL) Zhifei and Joo [2012] or imitation
learning is an RL variation focusing on learning a reward function from human
feedback. We argue that just as mirror neurons discovered by
neurosciencetists, mimicking other peoples’ behavior, similarly IRL and
perceiving a story or a message from a sender (as described in AKREM) is all
about constructing the actual intention from the observed behavior. Hence
infants, due to lack of developed memory must use visual (besides audio) input
as a ”story-telling” channel, to learn intentions of objects, either physical
or living (e.g. animals and humans).

Mimicking is quite central in early life, such as pretending games Wolf
[2022], where other perspectives are learned for social interaction. Though in
early childhood it may also be unrealistic perspective, or more imaginary.

However, the more fundamental question is whether will can be externally
learned or it is embedded internally, inherently, or may be both.

###  A.3 Knowledge representations

Here we survey some common knowledge representations, and inspect them in
comparison to MOM.

####  A.3.1 Scene Graphs and Knowledge graphs

In AI, knowledge is expressed usually by objects, relations and attributes,
via _Knowledge graphs_ (KG), and in DL it is often realized via _Scene Graphs_
(SG) [Agarwal et al., 2020, Zhu et al., 2022].

SGs are uniquely describe a given image, while KGs are expressing general
knowledge.

One of the SGs limitations, is that it produces very large network of too much
details. It cannot distinguish between the important and the minor, between
the essence and the details.

Sometimes, KGs and SGs combined together, in VQA tasks for example.

Anyway, we assume that memory data should not be in the form of merely KG or
SG, since they are fixed and rigid, while we need more flexibility. In
thinking we simulate different and new scenarios by applying new actions on
the data.

So unlike static representations of knowledge, i.e. static maps, operational
representation enables the dynamic structure of this knowledge, by applying
admissible actions in the knowledge structure.

####  A.3.2 OPM

Another approach, known as Object-Process-Methodology (OPM) [Dori, 2011], is a
conceptual modeling of systems. It contains two types of elements: objects,
processes and links between them. Processes represent activity, action, and
procedure.

One of OPM’s limitations: it is locally designed, with arbitrary names for
objects/processes, while it should be universal (like John Ball’s Super KG
https://medium.com/pat-inc/how-semantics-enables-super-knowledge-graphs-
part-1-bda3c4a4386c). Also: Many conceptual discriminations/categorizations
should be learned and not pre-defined. E.g. physical verse logical (temporal
verse non-temporal), environmental verse systematic, which actually should be
in a continuum, because there are several degrees of relevance to the
discussed object. Also types of relations: structural verse procedural (should
be learned). Or the separation of processes and relations. We consider all as
actions, though we should have primitive actions to start with. We, unlike
OPM, do not separate into different categories, since we cannot predict if
there will not be others also along the way. Only the basic atoms of knowledge
should remain: objects, attributes, and actions, all else should be learned.

Additionally, OPM based on physical reality, while AGI is more general - it
allows also abstract concepts. Hence objects could be non-spatial, and
”processes” can be a private case of actions, which generally are not
necessary temporal, but could be sometimes.

Moreover, OPM is largely based on previously presented representations (e.g.
semantic networks).

Furthermore, the state changing is simple assigning operation. They also
distinct main object/process/function from secondary ones (like systemic verse
environmental). We have it as additional attribute, of an action/object, about
how relevant is one to the other, i.e. action admissibility. It is very
important as prioritization among different options in the process of
association. It can be the result of consolidation, where some are
consolidated strongly, while others weakly.

OPM can also model spatial data, e.g. an architecture of an apartment or a
room. Conversely, we use semantic-like operators (above/below, left/right,
up/down, next to, in-front-of/behind) with relative strength to model spatial
data. Although DL treats semantics as being represented in continuous space,
via embedding.

Finally, OPM’s zooming-in diagrams support limited WM hypothesis (or limited
cognitive load) Dori [2011], as in AKREM and MOM.

####  A.3.3 SciSoft model

This is operational representation of knowledge. It is about representing
scientific theories and models, such as classic mechanics or electro-magnetism
in physics. It was designed specifically to present equation development in
these theories. The main motivation is to make the data highly connected, as
in Wikipedia and other web-based platforms, to enable maximal understanding of
any topic. It is done specifically by connecting a topic to all its relevant
concepts so that nothing would be missing from the user to understand the
topic to its fullest.

The final product of this idea is a knowledge base, represented by a graph,
containing various types of objects: animations, images, text, mathematical
expressions and visualizations (as graphs and shapes). All objects are
connected by relevancy, but also have attributes, such that no piece of data,
no matter how small it is, will be missing. For example, each equation should
have access to all its components (e.g. variables, parameters and operations),
with each component as an object have its attributes and connections.

The necessity in such detailed system comes from failures in teaching
scientific subjects (from the teacher) or difficulties from the student side.
The root of all these problems is our language. It is highly context-based and
minimalistic, i.e. like an efficient code, we use minimal set of words to
describe something - we say much less then we should, and expect everything
else to be completed in the recipient’s head. But unfortunately, most of the
time students lack some ”obvious” details, which the teacher considers they
have in their model. This fact generates misunderstanding, which generates the
need for Q&A.

All this can be avoided if we have all the details in one system, for the
student to observe and bounce from one concept to another, filling any gap he
has.

Note that SciSoft is obviously following entropy of information principle,
i.e. when I perform operations on equations, the result cannot restore its
origins, because some operations are irreversible. Hence, SciSoft is a
directed graph, representing the development of equations from some
fundamental assumptions or axioms. Meaning, like any operation, as in MOM, if
it is irreversible, then information is lost in the process. For example, the
operation in Fig. 30(a) is irreversible, since $2(y-3)+4=10$ become $2y-2=10$
which cannot reproduce its origins: $2x+4=10$ and $x=y-3$.

Since any specific topic or subject can have multiple theories/models,
sometimes in conflict, then SciSoft enable multiple parallel versions of the
same thing. Similarly, it is enabled in OPM and in MOM.

Finally, it is noticeable that most rule-based systems, like OPM and SciSoft,
construct the knowledge manually. However, AGI, or in our case MOM, should
learn it by itself.

###  A.4 Bias

Bias is required in our reality. We need it for survival and fast response. It
is also due to consolidation, or similarly described as the function collapse
in quantum theory. See more in Section 7.1. We always pick patterns in an
unpatterned reality at its core. Hence, it is related to the right and left
brain theory, where the left hemisphere might act as unbiasing or resetting
function, to take us back to all the possible options, before we selected one
specific, in every topic and model.

Left-brain creative part, unlike right-brain pattern-recognition system, is
where we set free from patterns. It is unlimited space for imagination. Hence
activating it allows using non-learned actions while thinking. For example the
imagining of an elephant flying with his ears, is not learned from experience.

Similar idea should be applied in art, e.g. painting. It should not be a
random and a weird mash of things, as it is so often occurs in current DL
generating tools as DALL-E [Reddy et al., 2021]. Classical art should be
created via structured imagination, i.e. having some sense/logic, though it
may be not physical or it can be infeasible.

Back to Bias. It is related to our subjectivity. We born unbiased (or slightly
biased due to our genes), and as we learn models, we gain our character and
mold our identity, or our uniqueness. The sum of all our models is what define
us, and differentiate us from others, and generate our unique perspective and
our subjective interpretation. You can see it as if every model, of something,
is a superposition of all possible models it could be, while we simply
collapsed into one of them.

Hence unbiasing is the act of collective thinking. Where I behave less as a
unique subject, or individual, and more as a part of some collective.

We cannot unbias ourselves, but rather we can either deepen our models by
incorporating more details, options, and conditions, that reduce simple
prejudice view over some topic (e.g. over groups of people, i.e. racism); or,
we can add more versions of the same model (which is like including the models
of other people in me).

But eventually, these two options are equivalent. It is simply the question of
how we update our models.

Simply put, we like to have hierarchical models to abstract details away and
simplify as much as possible, since we dislike to remember lots of details. We
hence strive to construct hierarchical associative models. This idea can be
seen here for example, Friesen [2021], claiming: ”It feels good when many
items come together in a simple manner. It feels bad when there is an
exception to some general rule.”

###  A.5 Linguistic aspect

Here an advantage of AKREM in language is proposed. Figure 46 describes some
issues in Natural Language Processing (NLP). Figure 46(a) shows that syntax is
not enough to contain also meaning (semantics), while Figure 46(b) shows that
local semantics or solving Coreference Resolution (finding what refers to
what) is not enough to ensure logic and coherence within and between
sentences.

(a) Syntax issue

(b) Semantics issue

Figure 46: Language processing issues

So Figure 46(b) shows that semantics is also not enough. We argue that both
John Ball [Van Valin Jr, 2015, Ball, 2017] and Chomsky [Chomsky, 1956] tried
to do the right thing - to construct a model from a sequential input. Syntax
parsing [Abney, 1997] only evaluate the entities themselves, while John Ball
includes the relations between the entities or their roles - thus introduce
the meaning of a full sentence. Though meaning reduce the number of illogical
phrases and sentences, it is only local meaning. We propose to continue to
check relations within and among sentences, and continue in the same manner to
higher aggregating levels. All levels in this hierarchy have purpose, just
like in a military: imagine a general that wants to occupy some country to get
its natural resources. He then command his officer to invade some city that is
strategically blocking the access to these resources. He gives the officer the
reason for the command: most of the enemy is in this city. Consequently the
officer gives order to his soldiers, to attack in specific formation, while
giving them the reason: given the acquired intel it is the most effective way
to occupy with least casualties.

We see that every level acts with some purpose, hence the associative
hierarchy is a hierarchy of will313131Though in MOM we claim it is a hierarchy
by abstraction, only that it is generated by will’s field, see Section 3.4.3.

It is a top-down modeling that constructed from the bottom, therefore while a
person is hearing a story/message, he always checks for its consistency (even
though the highest intention is usually revealed at the end, such as in
thrillers/mysteries).

John Ball claims that syntax is combinatorial, i.e. that it is not restricted
enough, so his meaning-based approach argue that it is a more restrictive
model. But as it seems, this approach is not restrictive enough too. So we
filter along the way, to end up with a more refined model to represent correct
human language.

Ball’s main statement is that semantics/meaning is not based on syntax. We
claim similarly, however our meaning is beyond simple objects and relations -
it also represents will in its different forms.

Here [van Tiel et al., 2021] their argument is also against the
compositionality of semantics, i.e. that the meaning of a complex phrase is
not derived from the meaning of its constituents, but rather from context. We
also hold for compositionality, though of a different kind.

Beyond the deficits presented above, these language models are categorizing
words and relations on pre-defined classes, e.g. Verb or Actor, etc. This is
another limitation, since the constructed model should be specific and
appropriate to the sentence(s). Part-Of-Speech (POS) parsing deals with it
simply by adding more production rules or in Ball’s case enlarging the Super
KG https://medium.com/pat-inc/how-semantics-enables-super-knowledge-graphs-
part-1-bda3c4a4386c, both are symbolic AI approaches, which result with the
famous rule-based explosion problem.

Also, John claims that language is about pattern matching or labeling, words
and compositions of them (phrases). Similarly, we propose that it is about
recognizing the right model. However, it is not about recognizing simple
static pattern, but an operational one.

Finally, linguistics is concerned mainly about language processing/generating,
but as we mentioned earlier, it cannot be separated from the full AGI model
(holisticity), e.g. language model has to consider also refining/updating
knowledge models and the cognitive functions (e.g. inference).

###  A.6 DNNs’ equivalence to Programming Languages

Here we will show the equivalence of different NN structures to a rule-based
algorithms (or usual programming). For example, we can see that NNs (multiple
if’s), decision trees (nested if-else), RNN (for loop), recursive NN
(recursion) and other ML methods are all actually program-like.

Decision trees as parametric methods, are highly unstable (hence ensemble
models or random forest are usually preferred), since they produce multiple
solutions. In our opinion, it may be due to insufficient set of tools to
express any function. They use only ”if-else” blocks, as oppose to universal
approximating DNNs. There is also the difference of structure: decision trees
have dynamic structure (non-parametric model), while DNNs usually have fixed
structure (parametric model323232Parametric models have a fixed number of
adaptable parameters, independent of the amount of data, while non-parametric
models have a variable number of parameters, i.e. the parameters change to
adapt to the amount of data.), which constrain the solution space, for better
or for worse.

It is very hard to obtain the equivalence of DNN to programming syntax.
General DNN encapsulates in it many of the programming operations mentioned
above, in a declarative manner: ”if-else” rules by using activation function
(see in Aytekin [2022]), ”or” rules by using sum of weighted neurons, and
layers enable multiple steps of a program. RNN adds also iterative operators.

Attention is equivalent to element-wise operation, see Weiss et al. [2021],
i.e. matrix/vector type of manipulations, e.g. the element-wise matching of
matrix/vector to matrix/vector, to perform some specific operation.

Also attention can be compared to other NN forms: unlike fully-connected
layers, it changes the attended parts in the input instead of accounting for
the whole input, all the time. This mechanism converts the fixed architecture
of NN into a dynamic one, depending on the input, similar to hypernetwork
[Galanti and Wolf, 2020], where the weights determined by the input. It is
also about finding relations. Whereas convolution layers [Li et al., 2021]
take into account only nearby relations, hence need many layers to locate also
distant relations, attention and fully-connected layers capture these distance
relations more straightforward.

Moreover, transformers might be so successful in vision and language nowadays,
due to these layer-after-layer self-attention, which may be equivalent to
regular algorithm, that finds only specific variables from the full input set
of variables, to be manipulated at each phase of calculation.

###  A.7 Evolution in AKREM

In this section, the general learning method in AKREM via episodic memory is
presented. It starts with defining episodic memory. Then episodic memory’s
interaction with LTM through waking and sleeping periods is discussed. Then, a
more broader view of human development is discussed. Finally, model learning
is appended to this development process.

####  A.7.1 Episodic memory in AKREM

Here some trained DNN is assumed, containing basic concepts and actions, to be
treated as a base memory, upon which an episodic memory can be constructed,
see Fig. 47(a) and similar discussion in ”Issues with the proposed DLM”
section in Komarovsky [2022c].

This episodic memory can be based on AKREM, storing all the generated
hierarchies. Episodic memory’s function is not only to store data. In our
opinion, it can also be used for rearranging the concept memory to deal with
multi-tasking. In other words, any new task requires the rearrangement of the
whole concept memory, to cope with all previous tasks and the new one
(consistency checking).

This episodic memory cannot create unlimited amount of memories. Therefore, a
forgetting mechanism is essential. It might act like LIFO (last-in-first-out):
new memories enter daily, and old mostly un-accessed ones are decay or
deleted, to keep the memory fresh and with relevant information, and to
respect the limited memory capacity. See Fig. 47(b).

(a) Different memory representations in some DNN.

(b) Episodic memory, for storing and shaping base memories

Figure 47: Evolution of cognition

Forgetting is also necessary, since in humans it helps reasoning and
understanding by forcing them to generalize and abstract Kuhl et al. [2007].
It improves associations, relevancy, and extracting the right patterns in a
given situation, since it differentiate data to be scaled from being most
common to least common.

Moreover, forgetting is necessary due to the no-free-lunch principle [Adam et
al., 2019], since any model cannot do best in all tasks, i.e. there is always
some trade-off due to finite constraints, where it is better in some tasks
while worse in others. Hence, adaptivity through forgetfulness allows for
learning new tasks on-the-fly, thus putting all the weight of our current
focus on the new learned task. This explains why the material after learning a
course is so fresh and highly effective in preparing for exams. Meanwhile,
after some period, due to the lack of using it, this material is partially
forgotten, thus its utilization performance is reduced.

Finally, the base memory can consist not only from concept memory, but also
from a procedural one. Because, they both are like tool memories, containing
the basic elements from which not only events can be constructed, but also
skills, recipies or any operational memory of how things are done.

####  A.7.2 Wake-Sleep Periods

In such a system, the learning occurs via two modes: waking and sleeping
periods. During the waking period, the episodic daily memory acts like a
capacitor, i.e., it is charged with events. It can also perform online
learning, where a quick response is required, see Fig. 48(a). At sleep, these
events are discharged into LTM and perform a reorganization of the perceiving
system or what is referred to as the concept and procedural memories, which
are also function as the LTM. This complex and heavy learning can be regarded
as offline learning, see Fig. 48(b).

This idea is supported by DL [Liu et al., 2019] and by neuroscience, e.g.
[Golden et al., 2022], where it is stated that “Sleep helps reorganize
memories and presents them in the most efficient way”.

In our opinion, at infancy, most of the cognitive effort is directed to the
reorganization of the base memories, and less or not at all to store memories
into LTM. It is because it is well known that infancy years are not remembered
at adulthood. Our hypothesis is that it is because the concepts or the
language by which we store these memories are not yet formalized or converged
to be stable enough in order to be the same concepts as those used at
adulthood.

(a) Online Learning.

(b) Offline Learning

Figure 48: Wake-Sleep Cycles

Another aspect can be deduced from the wake-sleep cycles: different regimes of
learning. In daytime we cannot process data too long, it must be fast real-
time such as online-learning, for survival. Whereas, in a relaxed or sleep
mode, the brain is free to process memorized data. The processing is of a
sequence of data, which can infer cause-and-effect relationships for example.

Therefore, in survival mode we probably have access to only few state(s),
representing perceived information in a sequence: few previous states (n) and
few for prediction (m), due to limited resources. While in relaxed mode we can
use more memory and processing power, hence assume large $k>n$ and large
$l>m$, which can produce more complex learning and abstractions.

In other words, in relaxed mode we can learn from memory, based farther to the
past (e.g. using many associations of past episodic events). And we can
predict much farther to the future. See Fig. 49.

Figure 49: Different modes

In summary, learning can be divided into online and offline modes. In waking
periods, i.e. when sensors are active, the learning is online and minimal
since most resources are dedicated to fast response (e.g. fast optimization
into local optimums). However, during sleeping periods, sensors are inactive,
and previous memories can be used for improving models to make overall sense,
e.g. larger time scale is used to generate causal relations in models. In such
a case, it is a slow processing, to allow fast optimization or inference
during waking periods. It is implemented e.g. as a Neural Architecture Search
(NAS) or a Genetic Algorithm (GA) [Binitha et al., 2012], to get out of local
minima and search for a global one.

####  A.7.3 Human Development

As we have seen in Section A.7.1, the episodic and the base memories are
interconnected, therefore the way I remember events is a function of the
current concepts I have, or a function of the words that constructing event’s
description. That is why I do not remember my infancy years. Because my
concept world, or the language to describe events has changed drastically
compared to the language I use as an adult. So both base memories and the
episodic memory are changing frequently at infancy.

The overall evolution is described as follows. The infant has no explicit
guidance, but only some limited feedback. Hence, most of his learning occurs
via unsupervised or even self-supervised mode. At childhood he has some basic
concepts that are last until adulthood therefore he can only vaguely recall
childhood events. But still the concept world or the language is evolving in
school from the various subjects that extend his concept world and change it
significantly, such as Math, Sciences, and also social interactions. Only at
adulthood he can recall exact memories, with all the details, if it is not a
function of natural forgetfulness.

From the assumption above we can propose that sleep functions as testing the
best model considering continual learning, by testing it on all or recent
memories in the episodic memory, to make sure it is consistent with all/most
of them, see https://medium.com/liecatcher/a-noise-model-for-why-we-sleep-and-
lie-too-69bf723b3ba9. It can be accomplished for example by sampling some of
the recent memories, and performing self-supervised learning.

In parallel to the evolution of memories, there is also evolution of logic.
Logic is about how consistent and comprehensible the world we experience. It
is where we understand accurately how everything is operating and functioning
in the world. More precisely, it is a symbolic reasoning. This logic is under-
developed at infancy, and actually starts to evolve in school years, until we
reach adulthood, and then it is stabilizes.

It may even be, that at infancy the episodic memory function is to organize
data (e.g. at sleep), i.e. as a prediction tester, while at adulthood it is
just a memory (aggregating specific events).

See this development process in Fig. 50.

Figure 50: Development plot

Finally, we assume that people perform by default unsupervised learning, while
a supervised one is experienced only in guidance or tutoring. Also, external
supervised learning is performed initially via communication, and later it can
be done via observation also (such as learning from videos, books, …).

####  A.7.4 Consolidation in model learning

In model learning via DL style, we start with random, uninterpretable
features, and with time, these features become more symbolic, and perhaps
represent concepts and actions. So with time the NN become more symbolic. This
idea can be presented in Figure 51:

(a) Human development

(b) Consolidation

Figure 51: Evolution of cognition

The transition from an uninterpretable representation of knowledge to a
symbolic one is depicted in Figure 51(a). In it we see the combination of the
gradual interaction from infancy both from the sensory and the actuatory
modules, lead to a more consolidated and less flexible knowledge.

So, we complete the developmental process of memories and logic, from Fig. 50,
with the inclusion of modeling development, from Figure 33. Resulting with a
gradual evolution of interaction, starting from infancy, involving the sensory
and the actuatory modules. We can see gradual extension of the infant tools,
to allow more types of interaction, which mold and upgrade his models.

Interestingly, we can see in Figure 51(a), that qualitatively, most of the
changing in modeling occurs at infancy, then a bit less at childhood, and the
least is at adulthood. While quantitatively (in the amount of knowledge), it
is the opposite.

In Figure 51(b) on the right, there is a DNN with consolidation through
sparsity, either over paths (in red) or/and over classes/objects in a layer
(in yellow). On the left, it is a consolidation of an algorithm or a
hypothesis out of many possible ones. The sparsification in model searching is
actually the manifestation of Occam’s razor principal or the minimal
description length, i.e., we look for the simplest algorithm as possible. This
constraint could be AGI main’s objective. It should, as we proposed, be
searching for the fastest/smallest models, in order to have fast response for
different situations, such as for survival, or for fewer steps of thought.
Consequently, in the problem of combining DL with rule-based approach, we can
distinguish between soft-combination: where DL consolidate into rule-based
form, and a hard-combination: learnable model-structure right from the start.

Interestingly, similar consolidation mechanism were recently discovered in
neuroscience [Fujimoto et al., 2023], in what referred to as pruning. They
discovered a protection/punishment machinery that would strengthen certain
connections and kickoff the pruning of others, thus resulting with branches
that get pruned to leave a single strong connection.

###  A.8 Other implications of MOM

####  A.8.1 How Logic operations such as in/de-duction are possible in this
framework?

As stated previously, in Section 7.2, induction and deduction may be part of
the learning mechanism itself. Hence, either we treat it is prior knowledge
applied in the AGI system, or less preferred option: treating them as another
operators, thus they can belong to a ”logic” class, containing all relevant
admissible operations of logic classes. Hence any logic class can naturally
inherit these operations as part of being associated with the ”logic” class.
However, for now, we assume these are not part of the learnable system, and
considered as cognitive operators, separately from the learned knowledge
operators.

To implement these operations we use HOL, or $\lambda$-calculus, used in
semantics in NLP/NLU. It is simply the implementation of compositionality in
language, i.e. where you can assign any object where there is slot for it.

For example, ”I love you” is simple $\lambda x,y.love(x,y)(I,you)$. But we
could make it composed: ”I love to create tools”=$\lambda
x,y.love(x,y)(I,\lambda x.create(x)(tools)$. In our case, it is simple OOP,
where we assign the ”create” class to the y variable of ”love” class, which is
actually an action class.

Similarly, we could have compositional sentence: ”I stand on a chair, that was
built in a factory near my home”=stand(I, chair(origin(factory(near(home))))).

Another example of logic is via classes and instances, e.g. ”All men are
mortal. Peter Parker is a man. Therefore Peter Parker is mortal”, is an
example of updating temporarily (in WM) the ”men” class to have mortal
property, then creating instance of ”men” called Peter Parker. Then following
his object, finding that he is mortal due to inheritance. This is deductive
reasoning: from general (class) to specifics (instance).

Nevertheless, even these examples demonstrate an obvious inference processes,
they still require a guiding will, see 3.6.

There is also the opposite reasoning: inductive, from specifics to general,
e.g. Data: ”All firetrucks I’ve seen are red”. Hence, generalization: ”Most
firetrucks are red”. This is induction directly from logic, and not from roar
examples.

Another example, is in analogy tests, e.g. ”Smile : mouth :: wink : eye”,
which as described earlier is accomplished by active thinking: recognizing the
problem - analogy between two objects, then recognizing the source object
(mouth), smile as its representing action, then searching for the same
relation in the target object (eye), thus resulting with winking. More
broadly, this is an example of case-based reasoning [Fuchs et al., 2020],
where source problem is used to solve a target one, by mapping and adapting
source functionalities or actions to the target problem.333333Based on
category theory.

The logic inference rules are discussed [Marquis et al., 2020]:

  * •

Contraposition: if A → B then NOT B → NOT A;

  * •

Transitivity: if A → B and B → C then A → C;

  * •

Simplification of disjunctive antecedents: if (A OR A’) → B then A → B and A’
→ B.

Simplification is simply the function of ”OR” action, while Transitivity is
expressed via state-space as defined previously, where actions as vectors can
be cascaded. That is, Transitivity is simply one of the features of vector
state space (wills/actions).

Contraposition is expressed when problem-solving is applied backwards. It is
actually how designing works: finding the problem of a goal or the cause to
symptoms in health inspection. It is also referred to as abduction reasoning,
which is about searching for explanations (cause) from incomplete observations
(effect) and some background knowledge (such as rules).

In summary, we consider any-order logic as a stiff formalism and poor
imitation of an actual flexible thinking. In our opinion it is a special
framework, an abstract and mathematical one, that is learned in peoples’ late
years. It is not natural and fundamental as it might have being considered so
far. For example, De Morgan’s laws and the converting from first-order logic
to conjunctive normal form (CNF) [Russell, 2010] are not intuitive at all.
Similar is the Resolution process and other things in logics. In our opinion,
it should be simple mechanism, such as propagating in a network of states.

####  A.8.2 Creativity

MOM enables creativity in two dimensions: either via non-admissible actions
applied or via temporal abstractions.

The well known System 1 & 2 [Daniel, 2017], combined with our attention
mechanism as the projector of will, can explain how creativity sets into
action.

It can be viewed by at least two processes. On the one hand, when we are
contemplating about stuff while performing simple chore like walking or
driving for example. In this case our attention is on the thinking process,
hence it is less creative, since we use our consolidated regular logic. The
less attended chore is move to the background.

On the other hand, in the opposite case, when we diverge our usual thinking
and concentrate on a simple or usual chore - creativity pops in, when sudden
ideas comes to mind, like during a shower. In our framework, it is explained
again with the attention. Since the simple chore is the one being focused,
then the higher models are freed, this time to be handled by subconscious.
This realm is not under our logical consolidated control, but it is free to
”play” with the models as it wishes, without our constrained reality-based or
physically-grounded thinking. In neuroscience, there is default mode network
(DMN) which is activated when we do not do anything, or mind-wandering, and it
shows that some brain regions instead of being less active, they become more
active, which may indicate self organization or creativity pops in, to produce
new connections and more.

This could be explained in other words also. As it is well known, creativity
resides in the right hemisphere, where the thinking is more divergent. The
creativity comes to play mostly when we are less looking for patterns and
categorization [Beaty et al., 2015]. I.e., we can be biased or stuck in past
patterns, when we recognize them, to find a solution to a problem. It can
happen both in System 1 & 2, where we look for the best model to resolve the
current situation, as in a recognition process.But sometimes we should dismiss
the familiar, in order to find novel ways to solve problems
https://www.thinkwrongbook.com/. In our case, it might be about bypassing the
automatic recognition mode, and search for a more appropriate solution, via a
more thorough search in different models, ending up with generating new model
for the given problem.

Overall, we can unite will with attention, by stating that will projecting via
attention ”beacons”. Hence, similarly as we have main will and sub wills, or
wills with different intensity - similarly our attention is splitted into
parallel beams, where there is the main one and smaller ones. For example, in
hearing a message/story, we are mostly attentive to the incoming message in
sequence, but a smaller attention is applied in stitching the different parts
of the story together, as if we constructing a puzzle. Similarly in the
examples above, there is main attention (e.g. thinking while walking,
showering, etc) and the secondary attention (e.g. walking). This splitting of
attention to parallel processing is important for survival. Because sometimes
we have no choice but to handle several stuff simultaneously. For example, to
handle distractions, or as a selective attention between different sensors
(such as hearing and seeing an object), or also within each sensor: as in
vision we first grasp multiple objects, then concentrate only on few of them.
Or as an alternating attention by switching from one thing to another, see
[Abdalaziz et al., 2023] about parallel multi-tasking, or as a dividing
attention by focusing on several things at once, such as driving and talking
at the same time. However, splitting attention has more preference on parallel
non-interfering splittings instead of ones in conflict, such as splitting
within the same modality.

More generally, attention splitting can be used in any problem solving, as
described above in the example of message/story comprehension, by
concentrating of ”processing units” over the incoming objects to perform
inference for filling missing information, but also for making overall sense
(connect between different objects). This split of attention supported also by
neuroscience, e.g. in [Abdalaziz et al., 2023], experienced memorizing of
several elements simultaneously, and in [Howarth et al., 2012] showing an
example of mental load attention splitting, e.g. between visual and audio
sensory perception.

Finally, we can argue philosophically, that the attention as a top-down
process revealing our will, is actually a separated entity from the physical
realm that interacts with the physical brain constantly, as long as we are
alive. This idea can even have evidence from neuroscience, such as here:
https://iai.tv/articles/brain-scans-tell-us-nothing-about-conciousness-
auid-2514: describing a battle between physicalism (consciousness is in the
physical brain) and idealism (the experience is separated from its partial
expression in the physical mind), similar to person looking at me, sees only
the front side of me, but not the back. Another paper [Pinotsis et al., 2023],
can only unintentionally hint this idea. It describes ”Cytoelectric Coupling”:
arguing that the brain’s electrical fields, created by neural network
activity, can influence the physical configuration of neurons’ sub-cellular
components to optimize network stability and efficiency. In another
perspective we can treat electrical brain waves as the closest side of the
soul, and thus view the process described above as the soul influence the
brain physical activity.

####  A.8.3 Information

Our MOM has some basic axioms, regarding how information is interpreted.

###  A.9 AGI additional characteristics

Here are a few small comments about the AGI system to be constructed.

First, our opinion, is that the research of explainable AI (or XAI) is not
totally correct in its core. We believe that DNN in its nature should not be
explainable at all. It is merely the vessel of intelligence, while logic and
explainability are the products of intelligence. There is no rule to require
these human outcomes to reside in the cognitive system in the first place. We
advocate that these two concepts to be accurately defined and separated.

Moreover, as mentioned in Section 3.4, many generative models that are used
for explanation, e.g. Principal Component Analysis (PCA) [Abdi and Williams,
2010] or sparse/denoising auto-encoders [Bank et al., 2020], are themselves
black-boxes. Also, most of DL explainability methods are very narrow type of
explainability, e.g. merely feature relative significance.

Next, we do not see the necessity for AGI or any AI to be conscious. We do not
comprehend why there is so much talk around this (in our opinion)
insignificant topic. AGI should not emulate humans at all nor to be
autonomous. It should only be a data processing machine, as regular
PC343434Personal Computer, to comprehend natural language, learn, and
accomplish tasks. The actual meaning of things, in human perspective, should
be left to humans.

Next, AGI should not be about strong computational power as it is nowadays. We
see evidence for this in our own intelligence: we use papers to store and
elaborate our ideas and thoughts, presentations, drawings, 2D/3D (interactive)
visualizations, simulations, etc. Similarly it is in current generative AI:
the various action GPT tools like AutoGPT, AgentGPT, BabyAGI, MetaGPT, LETI,
ToolLLM, WebGLM and more - which use external applications and sources. All
these are tools extending our limited capabilities, without the need to extend
ourselves. Just as we use calculators to perform hard calculations, or planes
to fly, the AGI should function similarly. It should not be fast at arithmetic
calculations, but rather it should be good at thinking and solving problems,
while provided various tools for its disposal, as it is for humans. And
similar to humans, it can create the necessary tools/technology for solving
problems. See for example in
https://www.lesswrong.com/posts/K4urTDkBbtNuLivJx/why-i-think-strong-general-
ai-is-coming-soon. Also see https://clivethompson.medium.com/the-weird-power-
of-transactive-memory-7c7e324c9425 about how we extend our memory by socially
distribute skills and knowledge among members in a close community.

Next, as we embrace the holistic perspective for designing AGI, similarly we
encourage implementing both self-supervised learning and RL in the same
system. We argue here, that model learning should encapsulate these both
regimes, since part of the modeling is to learn within interactive setup.
Hence, there is no need for a reward to be gathered from the environment,
since internal ”reward” or intrinsic motivation is sufficient and represents
the current will. Meanwhile the feedback is gathered simply from the sensors,
i.e. the system is always stays open-loop, but during interactions it becomes
(or it is considered as) closed-loop, similarly as RL. We can see these two
regimes in Fig. 29.

Next, we think that current meta-learning in DL/RL is not fit to serve as a
tool for AGI, if we consider having shared parameters learned between tasks.
Because most of the skills we learn are not related to previous tasks at all.
Hence it should be progressive in learning.

Next, our purpose in AI is to reach the most natural language model (LM) to
perform tasks for us. However, reaching this goal requires very slow and
gradual process. Thus, the evolution of AI research so far can be described in
the following. It started with low-level machine programming code (assembly),
then moved on to high-level programming languages (e.g. C#, Python, Java),
then moved to NLP LLMs such as GPT3 and DALL-E2 and others that use prompts,
which are less ”programmitive”, but still require practice and expertise in
writing the most appropriate prompts to get the desired response. Hence, our
model suggest that AGI agent must be learned via human interaction, to learn
the alignment of our wills from the way we express it linguistically, e.g.
like a teacher demonstrates or behave in-front of his student. In other words,
unlike the batch learning usual policy costumed in DL, where the AI agent is
”shoved”/bombarded with a huge amount of data, we encourage a more humane
treatment - a gradual and progressive learning (via online and offline modes,
but with incremental learning).

Additionally, for constructing AGI framework we practice both the bottom-up
approach (neuroscience, mainly based on Jeff Hawkins) and top-down approach
(psychology, mainly based on Daniel Kahneman). In general, this should be the
duality principle we encourage in AGI research. That is, embracing the
combination of different disciplines together, to capture intelligence’s many
facets and features.

Finally, as computational models (automata) [Chater and Christiansen, 2008]
have shown, in order to achieve a Turing machine model [De Mol, 2018], we must
have unlimited memory and unlimited access to it. Hence the AGI model should
also have these capabilities. Our proposed cognitive model express/reveals
these capabilities via the use of LTM (past knowledge) and WM (current
memory).

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