# Recognition of All Categories of Entities by AI

Hiroshi Yamakawa 1,2, Yutaka Matsuo 1,

###### Abstract

Human-level AI will have significant impacts on human society. However,
estimates for the realization time are debatable. To arrive at human-level AI,
artificial general intelligence (AGI), as opposed to AI systems that are
specialized for a specific task, was set as a technically meaningful long-term
goal. But now, propelled by advances in deep learning, that achievement is
getting much closer. Considering the recent technological developments, it
would be meaningful to discuss the completion date of human-level AI through
the ”comprehensive technology map approach,” wherein we map human-level
capabilities at a reasonable granularity, identify the current range of
technology, and discuss the technical challenges in traversing unexplored
areas and predict when all of them will be overcome. This paper presents a new
argumentative option to view the ontological sextet, which encompasses
entities in a way that is consistent with our everyday intuition and
scientific practice, as a comprehensive technological map. Because most of the
modeling of the world, in terms of how to interpret it, by an intelligent
subject is the recognition of distal entities and the prediction of their
temporal evolution, being able to handle all distal entities is a reasonable
goal. Based on the findings of philosophy and engineering cognitive
technology, we predict that in the relatively near future, AI will be able to
recognize various entities to the same degree as humans.

## Introduction

For better or worse, human-level AI will have significant impact on human
society. Thus, the debate over when it will emerge has gained considerable
attention. However, estimates for the realization time range from a few
decades to the 22nd century and beyond. In the 2000s, with the technology at
hand, it was not easy to envision a complete human-level AI. Based on this
perspective, it was estimated that AI would be realized in the 2040s,
calculating the point at which the computational resources used for AI would
be at the human level(Kurzweil 2005) .

The technical problem with AI systems prevalent then was that they were
specialized for a specific task and could not solve a wide range of tasks in a
manner similar to humans. Therefore, artificial general intelligence (AGI) was
set as a technically meaningful long-term goal, where one AI system can solve
many tasks (Goertzel 2014). However, a comprehensive list of the tasks to be
solved is necessary to estimate when advanced AI will be realized in a
general-purpose manner. Therefore, as a first step toward the realization of
AGI, attempts were made to list the task-solving abilities that a human-level
AI should acquire during development (Adams et al. 2012). However, the
exhaustiveness of the list cannot be guaranteed because there are infinite
variations of possible tasks.

By contrast, the technological landscape of AI had changed dramatically since
2013, when deep learning began to develop. By the late 2010, machine learning
AIs (ML-AIs) could complete most individual tasks for which sufficiently
training data were available. In the 2020s, large-scale ML-AI has demonstrated
that a single AI can perform many tasks, and research is underway to use it
for knowledge transfer between tasks. Recent large-scale language models have
demonstrated human-level capabilities in a relatively broad range of language
processing tasks disconnected from real-world actions and perceptions.

Considering these technological developments, it would be meaningful to
discuss the completion date of human-level AI through the ”comprehensive
technology map approach” as follows: First, we develop a map covering human-
level capabilities at a reasonable granularity from a certain technological
perspective. Next, on the map, the current range should be identified, and its
success factors should be analyzed. Subsequently, we should discuss the
technical challenges in traversing the unexplored areas and predict when all
of them will be overcome.

A precedent for this is the discussion of dual-process theory as a
comprehensive technology map. In summary, this argument states that deep
learning has already realized System 1 level, which is that it can respond
fast, although System 2 level, a deliberation requiring sequential inference,
is to be developed in the future. There are two weak points with this
argument. One is that the technology map has only two nodes, System 1 and
System 2, which makes the map extremely coarse-grained. The other is that the
characterization of System 1 and System 2 differs significantly from
researcher to researcher.

Therefore, this paper proposes the option of using the ontological sextet
t(Smith 2005) (see below for details), an exhaustive map of existence, as a
comprehensive technical map. The primary capacity for a subject to be
intelligent is modeling the world, which primarily relies on recognizing
distal entities and predicting their temporal evolution (Suzuki 2015; Saigo
and Taguchi 2019). Hence, an AI with the ability to recognize all categories
on the ontological sextet is a reasonable goal by which one could judge the
attainment of human-level AI.

The subject’s ability to handle various entities means that it can process to
find various patterns that can be considered preserved in the world. This
includes physical reality, property (color, size, etc.), events, and all other
entities we perceive. In this paper, we call the process of perceiving
existence ”entification. And the benefits of the subject’s ability to
entification are great. First, imagining a real task makes it possible to
design drawings and use tools. Furthermore, it makes it possible to give
instructions and communicate using symbols.

### Ontological Sextet

Figure 1: Ontological Sextet: Categories are a combination of substantial /
quality / process and particular / universal.

.

Since Aristotle’s time, there have been attempts to create mutually disjoint
and exhaustive categorization of existents. We utilizes the ontological sextet
(Arp, Smith, and Spear 2015), which encompasses entities in a way that is
consistent with our everyday intuition and scientific practice.

The ontological sextet is composed of six categories, which have a two-row,
three-column structure described as follows (see fig.1): First, they are
classified as continuant, which remains identical as long as it exists, and
occurrent (process), which develops across a specific time interval.
Continuants are further divided into substantial, which can exist alone, and
quality, which exist in dependence on substantial. For each of these three
categories, there is the particular, which exists concretely without stating
what it is in a sentence, and the universal, which says what it is in a
sentence and is shared by multiple particulars. In other words, “to be is to
be the value of a bound variable” (Quine 1948) In addition, following formal
relationships exist between the categories: substantial particulars are
participants in process particular; quality particular is inherent in
substantial particular; and quality universal differentiates substantial
universal. Furthermore, all universals are formed by instances of
corresponding particulars. The aforementioned formal relationship shows that
substantial particular is the basis of other categories. Substantial
particular is a concrete object in front of you, such as this person or this
cat. In contrast, substantial universal is a kind, such as a person or a cat.
Quality particular is an entity that inheres in substantial particular, such
as this white, this tan, etc. Process particular is a process in which the
substantial particular participates, such as this walk or this collision.

Section 2 summarizes the reasons why entification is needed and the required
functions. In Section 3, the mechanism of entification is described. In
Section 4, current and future development of the entification on ontological
sextet is surveyed. Section 5 provides a summary.

## Basis of entification process

This section clarifies basis of entification for substantial particular, which
is the basic part of the ontological sextet.

### Request to compress the world

Definitions of intelligence vary widely (Legg and Hutter 2007); however, in
general, intelligence is ”the ability to solve complex problems” (Tegmark
2017). Most of it will rely on ”the ability to make predictions about the
world” (Conant and Ross Ashby 1970; Friston, Kilner, and Harrison 2006).

Here, we review the quantitative assumptions necessary to model the world and
make predictions. First, the information that a subject (part of the world)
can obtain from sensors and its computational power is finite. Second, some
order exists in the world owing to the uniformity principle. Third, the entire
world is much vaster than the subject’s capabilities can handle. Fourth,
compression and prediction can be performed based on informational redundancy.

Considering these assumptions, a subject compresses information of the world,
increasing its prediction ability. The compression process transforms a given
set of cases into a more minimalistic representation. In addition, the goal of
intelligence is to increase predictability, and compression is a tool for
that. Hence, the perspective of Epicurus’ multiple explanatory principles,
pursuing diversity, is also essential.

### Sub-functions to construct entification

Here, we investigate the functions necessary for an entification for the
substantial particular and the quality particular.

####  Ability to recognize diachronic identical entities

The condition for an existing object to be identical over time (”diachronic
identity”) has been examined in philosophy. This answers the question, ”What
does it mean for an object or substance to remain the same?” Entification must
recognize a set of observed patterns that satisfy diachronic identity as one
entity.

Even in contemporary philosophy, no established theory for diachronic identity
exists. We adopt a condition of diachronic identity that seems relatively
moderate and appeals to the following notion of continuity(Kurata 2017).

Condition of diachronic identity:
$x$ at point $t$ and $y$ at time $t^{\prime}$ are diachronically identical and
is a conjunction of the following three

  * •

Spatiotemporal localization: $x$ and $y$ are spatiotemporally continuous.

  * •

Qualitative continuity: $x$ and $y$ are qualitatively continuous.

  * •

Kind-specific sameness: $x$ and $y$ belong to the same kind.

In short, the condition of diachronic identity is established when some
regularity is observed in the physical world, including qualitative changes
localized in space–time.

Here, we will discuss the partial functions of entification corresponding to
each condition. The function of extracting a continuous region on the observed
data that corresponds to the spatiotemporal region occupied by an individual
object from the observed data is called the ”single-out function.”
Entification can satisfy the spatiotemporal localization condition by having
this function.

Next, we will consider the essential aspects of existence: the kind-specific
sameness and qualitative continuity. We assume that the single-out and
alignment processes have already been applied. Regarding kind-specific
identity, it should be noted that the essence of an object varies from kind to
kind. For example, the sameness of “dogs” as living things, that of ”boats” as
artificial objects, and that of ”rivers” as natural objects are different.
Therefore, objects belonging to different kinds have different conditions of
diachronic identity(Wiggins 1967; Yokoro 2021). To satisfy the condition of
diachronic identity, it is necessary to have a kind-classification function
that determines which kind the observed object belongs to and a criterion of
kind-specific sameness that determines whether the sameness criteria regarding
the same kinds are satisfied. The fulfilment of the condition of qualitative
continuity can then be confirmed by the fact that the kind-classification
function does not change its kind-classification even if the observed
information about an object changes.

#### Function to obtain observation data in table format

To compress information, the data must be in a table format that enables
inductive inference. The table format consists of columns(columns for each
attribute), cases(rows), and fields (elements within a case). A value is
stored in each field. A primary key exists as a column that uniquely
identifies each case. The cases and columns are sets, and the rows and columns
are in arbitrary order. This format corresponds to a table in the relational
database. Therefore, a case set sampling function is always required to obtain
tabular data.

Data obtained from remote sensors are often in table format as design
specifications. For example, in most video data, the video is 30 frames per
second, and the number of columns has the size of the number of pixels
$\times$ the number of channels. In other words, the set of cases is obtained
as designed by isochronous sampling.

However, in the case of counting objects by moving one’s gaze across an image,
as in ”Find Wally,” counting a single wally corresponds to a single instance.
In the case of spoken language recognition, units such as phonemes, words,
sentences, paragraphs, and sentences are considered a single instance. This
function of dynamically selecting cases based on some clues is called the case
set sampling function.

####  General properties of remote sensing

It is generally assumed that the observed data obtained by most remote sensors
satisfy the following conditions to capture some sort of invariant pattern to
distant things.

General properties of remote sensing
In general, the data acquired by a remote sensor is a table consisting of a
set of mutually positioned (specification relation) and comparable columns.

A typical example is a signal of intensity $I(x(t))$ received at the time $t$
at position $X$. In other words, in the set of values $I$ that the sensor
retrieves as a given case (time $t$), they are comparable to each other and
positioned at $x$.

####  Alignment function to correct distorted observations of distant things

The essence of entification, which recognizes the existence of distal objects,
is to identify the patterns preserved in distal things by processing
information obtained from remote sensor observations. The outline of this
process is explained as a three-step transformation in Fig. 2: sensing SPM,
alignment SPM, and encoding. Here, because the observed data obtained by
sensing has the general properties of remote sensing, a structure-preserving
map (SPM) related to it can be used.

Sensing SPM refers to mapping patterns obtained from distant things to
observed data. Even if the distant object is invariant, the pattern obtained
as observation data on the sampled table changes depending on the relative
positional relationship (distance, direction, etc.) with the object to be
observed. For example, in the case of a camera, the sensing SPM is obtained
through a mechanism in which reflected light from the object is refracted by a
lens and converged on an array sensor. Partial patterns of observed data in
different cases originate from the same distant object and thus form
equivalence classes that are matched via the appropriate SPM (Identity SPM in
Fig. 2)

Alignment SPM aligns some of the patterns in the observation data and
transforms them into an alignment table representing the entity description.
This is the alignment function, i.e., the function of extracting and aligning
partial patterns preserved in the object from the equivalence class as
diversified by the indefiniteness of the observation by the remote sensor.
This is precisely the function that reflects the philosopher Quine’s words,
”There is no entity without identity” (Quine 1969). From this positioning,
alignment SPM is, in principle, the inverse transformation of sensing SPM.

Encoding maps multiple equal entity descriptions on an aligned table to a
specific position in the encoded representation. This is concerned with the
compression requirement and diachronic identity mentioned earlier and is the
basis of the kind-classification and kind-specific sameness functions.

Figure 2: Principle of Entification: Entification occurs through a variety of
remote sensors, although it is best illustrated by the visual information
example

.

## Mechanism of Entification

Here, the entification mechanism for realizing functions discussed in the
previous section is proposed.

The entification mechanism presented in this section consists of an encoding
map and preprocesses to obtain an alignment table (Fig. 3). The preprocesses
include sequential processes corresponding to the functions of the case set
sampling, singling out, and alignment (the order of these can be changed). Any
of the preprocesses are adjusted to increase the compression ratio in the
following encoding.

Figure 3: Mechanism of Entification: Mainly consists of a pre-process and an
encoding map.

### Case set sampling process

The case set sampling function, which can flexibly generate table format data,
can be realized by the following mechanism proposed in (Yamakawa 2021). As
shown on the left side of fig.3, this is ”a mechanism consisting of a set of
reading procedures that read values from a specific value set according to
each value of the primary key.”

The sampled table obtained by this mechanism automatically satisfies the
following three inductive inference premises (Yamakawa 2011). Fist, Specifying
relation: A relationship in which fields on different columns in the same case
can be mutually specified. Second, Equivalence of specifying-relation
equivalence: A second-order relation in which the same specifying relation
holds even if the case is changed. Third, Comparability: A relation that
enables at least value identity or similarity to be determined between any
fields on the same column.

Because the sensor information in a particular modality satisfies the general
properties of remote sensing, all reading procedures are read from a single
value set, as shown in Fig. 3. However, it is acceptable to refer to a
different value set for each type of reading procedure in cases such as
sampling a case consisting of multiple modalities.

### Single-out process

Single-out function is realized by a process that removes the background of an
individual in object recognition from images. In general, the process extracts
spatiotemporally adjacent regions contained within the boundaries of an
individual. Single-out processing can be divided into two main types: one is
to segment the range of information from time and space for regions with
locally similar features, such as image textures. However, this approach
renders it challenging to separate multiple overlapping individuals. The other
approach combines methods to extract a range of information that is consistent
with the data generated top-down from the encoding map on a global basis.

### Alignment process

The alignment process realizes the alignment function, which corrects the
observation of distal entities to obtain the entity description on the aligned
table.

This process is a common alignment SPM-based transformation within the same
sensor modality.

In the figure, the patterns of the target F and G are transformed by alignment
SPM $T()$. Here, $\theta_{F}$ and $\theta_{G}$ are the transformation
parameters and correspond to the external attributes such as the position and
orientation of the target, as observed from the subject.

### Encoding map

The encoding map projects the data distribution range in the aligned table to
the encoded representation. The encoding map compresses information by
extracting invariant factors as a kind from the repeated pattern of entity
descriptions. The remaining variable elements in the encoding representation
are mapped to several qualities inheres in substantial particular. The kind-
specific sameness function is realized by the encoding map from a specific
entity description to a certain site of the encoded representation. The kind-
classification function determines whether the site to which a particular
entity description is mapped to the encoded representation is within the
coverage of a particular kind.

As discussed in section 2, intelligent subjects model the world by compressing
based on the redundancy of observed information. Therefore, recognition and
learning in all previous processes are optimized to increase the compression
ratio of the encoding map. A wide variety of methods are used for this
optimization, including error backpropagation, maximum likelihood estimation,
and Bayesian estimation. For example, in predictive coding, optimization is
performed to ensure that the predictions produced by the encoding map match
the observed values.

### Entification and each sub-process

Thus, each mechanism in pre-process is primarily responsible for each
function: the case set sampling process is responsible for the case set
sampling function for the flexible acquisition of tabular data, the alignment
process is responsible for the alignment function to correct observations of
distal entities, and the single-out process is responsible for the
spatiotemporal localization function, which is a condition for diachronic
identity. The encoding map is responsible for the kind-classification and
criterion of sameness, which are conditions for diachronic identity.

Tab. 1 shows the comparison of characteristics of the partial process,
including context (e.g., task) dependency, modality dependency, specialized
for remote sensors, hierarchy, spatiotemporal locality, usage for particular
entification, and usage for universal entification. As can be observed from
the table, the encoding map is a generic process; however, each preprocess has
different characteristics.

Considering this mechanism as a whole, preprocess and encoding map have
different properties as follows: preprocess can preserve patterns obtained
from the outside world, although they cannot provide an evaluation for
optimization. By contrast, an encoding map can provide an optimization index
by the compression ratio, although it loses patterns obtained from the outside
world. Therefore, preprocess, and encoding map are combined and complement
each other to realize entification.

This entification mechanism can be repeatedly stacked and corresponds to the
hierarchical structure in neocortical circuits, transformer-type deep neural
circuits, and others.

Name of process |  Context-dependency |  Uni-/Multi- modal |  Specific to sensor |  Hierarchy |  Spatio–temporal locality |  Particular |  Universal
---|---|---|---|---|---|---|---
Case set sampling |  Exist |  Uni/Multi |  No |  Category hierarchies (is-a relation) |  Single entity size |  isochronous/ synchronous sampling |  set of particulars in one kind
Single-out |  Somewhat |  Uni |  Yes |  - |  Local |  Execute |  -
Alignment |  None |  Uni |  Yes |  Compositionality (part-of relation) |  Single entity size |  Execute |  -
Encoding map |  Very little |  Uni/Multi |  Somewhat |  Various hierarchy |  Do not care |  Execute |  Execute
Table 1: Characteristics of each sub-process

##  Current Trends and Future of Entification

The degree of achievement, success factors, remaining areas, and technical
obstacles of entification are investigated on the ontological sextet as a
comprehensive technical map.

### Achievement of current ML-AI

In current ML-AI, recognizing the substantial particular is making progress.
For example, research on physical entities from visual information, has been
progressing 111https://iclr.cc/virtual/2022/workshop/4554. One of the results
of this technology is the ability to decompose a still image into meaningful
components and combine them based on linguistic instructions (e.g., ”an
astronaut riding hose in a photorealistic style”) to create an image that is
not experienced (Ramesh et al. 2022).

In the following, the technical elements that support the success of deep
neural networks are described.

#### Encoding map

As mentioned in the subsection ”Request to compress the world,” compressing
and modeling the world is a central theme in intelligence research, mainly
concerned with encoding maps. For example, autoencoder, which was recognized
as a neural network trained by error backpropagation in the 20th century, was
a network for compression. Currently, a generative model (Jebara 2012) is
being developed that infers the encoded representation and predicts the
observed information from it. The predictive encoding algorithm (Rao and
Ballard 1999) is well-established in more neuroscientific areas. Many related
research topics exists, including self-supervised learning, the free energy
principle (Friston, Kilner, and Harrison 2006), the good regulator (Conant and
Ross Ashby 1970), and emulation theory of representation (Grush 2004).

#### Pre-process in general

All three sub-processes of the pre-process can be realized as an attention
mechanism. The attention mechanism was developed from neocognitron and matured
as a pooling layer in the convolutional neural network. In another stream, a
sequential attention mechanism was proposed for multiple objects in a still
image (Eslami et al. 2016). Furthermore, since 2017, deep neural network have
been actively incorporating attention mechanisms (Vaswani et al. 2017). Today,
transformer architectures that make extensive use of attention mechanisms are
successful.

#### Alignment method

Contrastive learning (Chen et al. 2020), which was developed as a self-
supervised learning for images, learns to move representations derived from
individuals with augmented images closer together and images derived from
different individuals farther apart to acquire an Entity description. This
technique can learn alignment SPM for visual processing. Advances in research
on recognizing the position of each part of an artifact with moving parts
(Kawana, Mukuta, and Harada 2022) suggest that hierarchical alignment
processing of part-of-relationships (part-of-relationships) is also being
realized.

#### Single-out method

Semantic segmentation, which crops out localized regions with similar image
features, is progressing. Contrastive learning, which provides identity
information through data expansion, maps pixel-by-pixel cropped image regions
to text (Li et al. 2022). Semantic pixel embeddings can be generated to create
object masks (Van Gansbeke et al. 2021). Instance segmentation has been
studied as a technique for identifying single-out objects. For example, when
counting the number of chairs in a room, if the observed object is in the
range corresponding to a chair in the encoded representation, it can be added
to the count as a chair. Furthermore, panoptic segmentation can separate
objects and stuff (ex. ground, sky, sea, etc.) in a still image and assign IDs
only to objects (Kirillov et al. 2019).

#### Case set sampling method

In entification for a particular entity, the case set is often sampled
isochronously, depending on the characteristics of the sensor. In spoken
language processing, for example, synchronous sampling is performed on
phonemes or words. In any case, sampling in these cases is determined bottom-
up by the designed algorithm and thus does not pose the challenge of finding
new case set sampling.

Therefore, all the processes that support entification of substantial
particular are being realized by ML-AI. In actuality, the preprocess and
encoding map correspond to the attention and full connect layers of the
transformer.

### Future entification development along the sextet

Considering these developments, ML-AI will be able to handle other categories
of ontological sextets in the future.

#### Advance to Substantial universal

As mentioned previously, the kind, substantial universal is a specific range
on the encoded representation. Therefore, its foundation has already been
realized.

To date, however, we have not been able to properly handle hierarchies based
on inheritance relations (is-a relations) between kinds. For example, cats and
dogs belong to mammals, and mammals and fish belong to vertebrates. Here, the
kinds hierarchy essentially depends on the coverage of the attributes of the
substantial universal. In other words, concrete entities have a rich set of
attributes, whereas abstract entities cover a wide range of concepts. For
example, whereas the attribute ”oviparity” is common to all mammals, howling
is an attribute unique to dogs.

Studies are already underway to classify substantial universal by providing
teacher labels for kinds. For example, some studies distinguish between kinds
of vehicles and kinds of animals(Van Gansbeke et al. 2021). In addition, the
situation decomposition method can extract a hierarchy of kinds through
unsupervised learning (Yamakawa 1998). Based on these previous studies,
research will proceed on encoding maps that can acquire a hierarchy of kinds
using unsupervised learning.

#### Advances to Quality: Particular and Universal

Quality particular is an entity that depends on a substantial particular,
e.g., this headache, this tan, etc. Each quality value can be regarded as part
of the variables that appear in the encoded representation. In the late 2010,
ML-AI to extract quality was being studied as a research topic called
”disentanglement.” The typical task was moving MNIST as well as face
recognition and generation. However, at that time, the single-out method was
not mature; therefore, independent components were often extracted from the
entire video scene. This sometimes led to confusion between external
attributes, which are parameters in the alignment process (e.g., orientation
and speed in a figure image), and internal attributes (quality), which were
inherent in the substantial particular. In the future, the entification of
quality will progress on the scaffold of the entification of substantial
particular.

Next, quality universal is an entity that differentiates substantial
universal. For example, in the quality of ”blueness,” the blueness of the sky
and the blueness of cars are common. To build a mechanism for recognizing the
quality universal in the future, it is necessary to have a mechanism to
acknowledge quality beyond the kind of substantial entities.

#### Advances to Occurrent (Process): Particular and Universal

Occurrent(process) on ontology sextet is a time-evolving entity at some time
interval (temporal boundary), often associated with causality. Process
universal, for example, collision, walking, mating, transmission, movement,
influx, and support, etc., convey information about processes particular, such
as this collision or this walk.

Because occurrent has a time interval, its entification has the following two
difficulties compared to continuant entification. The first is the issue of
determining the time width of the cases to be retrieved in the case set
sampling. For example, to capture a scene of a ball hitting a wall, it is
necessary to determine the timing of the beginning and end points in each
case. Furthermore, it is necessary to define a primary key to identify every
case in the set. The second is the amount of information handled in each case.
Even if it is occurrent, once the time-varying information is deployed in
memory, various computational processes can be performed on it as a static
object, just as in the case of a continuum. However, by these deployments, the
memory capacity and processing to handle it will be enormous, so in reality,
it is necessary to perform appropriate abstraction. The commonsense policy
suggested by ontological sextet is to describe a process particular in a way
that multiple substantial particulars participate in it. In the example of a
ball colliding with a wall, the amount of processing can be reduced by
focusing only on the external attributes (position, speed, etc.) related to
the participating wall and ball. There are other issues to be addressed in
handling occurrent, although it is expected to develop rapidly as it has been
activated in recent years222https://crl-uai-2022.github.io/.

## Conclusions

This paper proposes a set of goals to enable AI to comprehensively recognize
all categories on the ontological sextet as an indicator to evaluate AI’s
reaching the human level (comprehensive technology map approach based on
ontology). Entification, the recognition of existence, is the process of
finding patterns (often in the distance) preserved in the world. Furthermore,
we identified the necessary functions for entification to recognize the
existence of a substantial particular, including the requirement to compress
the information of the world, the condition of diachronic identity, the
function to obtain observation data in a table format, and the function to
correct the observation of a distal existence. We then proposed a mechanism
for preprocessing, consisting of the case set sampling, single-out, alignment
sub-processing, and an entification mechanism, consisting of an encoding map,
to satisfy these necessary functions. Recent ML-AIs (e.g., deep learners with
transformer-type architectures) can recognize and further manipulate the
substantial particulars because such AIs cover all the aforementioned
subprocesses and integrate them. In the future, there are three main
directions to expand the coverage of entification on the sextet. Substantial
universal is already expressed as an invariant factor of the encoding map,
although expanding it to handle concept hierarchies based on inheritance
relations will be necessary. Quality universal has already been expressed as a
variable factor in the encoding map. However, it will be necessary to develop
a mechanism to extract universal attributes (e.g., color, shape, weight, etc.)
beyond kinds. In occurrent (process), it is necessary to handle the dynamic
behavior of the participating ”partial particulars.” Some issues are still to
be addressed. However, enabling the entification of occurrent is related to
understanding causal relationships worldwide and will be a significant
foundation for intelligence to solve various tasks creatively.

When each category of the ontological sextet is further subdivided, it may be
necessary to consider concepts that have not been handled in the previous
discussions. Some well-established upper-ontologies include BFO (Smith 2005),
DOLCE (Gangemi et al. 2002), YAMATO (Mizoguchi and Borgo 2022), and SOWA (Sowa
1995). A special issue was conducted recently (Borgo, Galton, and Kutz 2022).
Some difficulties exist in recognizing the existence of boundaries, domains,
functions, and roles in time and space contained in these upper ontologies.
However, because there are not many categories in the hierarchy of upper
ontologies, the goal of making all of them recognizable will remain valid.
This is because differences in the treatment of entification by ML-AIs occur
when the higher ontological categories are different, rather than by detailed
kinds, such as dogs and cats.

In the future, AI is expected to recognize every entity. The major remaining
obstacle is the entification of occurrent, although no critical difficulty can
be observed. Therefore, the time required to overcome it will be approximatley
five more years. The entification of advanced concepts such as space and
function will progress in parallel. Thus, human-level AI is expected to emerge
by 2030, capable of recognizing nearly any kind of existence.

Based on this study, the following are the several directions for the future
development of deep learning. First, compared to the focus on considering
variable factors obtained from the encoding map as attributes, less attention
is paid to invariant factors. However, in the case set sampling, the potential
value of invariant factors as kinds is relatively large. Humans have developed
the science by discovering new kinds, such as cells, elements, and black
holes. Second, the three sub-processes of the pre-process are mixed in the
transformer as the attention layer because they are all information selection
processes. However, they are very different in terms of context, modality,
hierarchy, and other properties. Therefore, conducting a study that
distinguishes between the subprocesses may be helpful. Third, case set
sampling for recognizing specific objects from visual information is pre-
designed; thus, there is no need to examine these functions in depth. However,
in a task such as counting the types of playing cards (there are four
different answers), it is necessary to control the sampling of each kind of
card. Therefore, a flexible case set sample still has enormous potential for
further research. Fourth, transformer’s successful visual object recognition
and spoken language recognition assume that objects and speakers are preserved
to some degree at the distal level during recognition. By contrast, in natural
language processing, which transformer first succeeded in, the nature of what
is preserved at the distal level is unknown. One possibility might be the
speaker’s personality. Therefore, it might be helpful to use character
consistency to perform contrastive learning. Fifth, setting different identity
criteria for a different kind may be helpful, e.g., in object recognition
techniques.

## References

  * Adams et al. (2012) Adams, S.; Arel, I.; Bach, J.; Coop, R.; Furlan, R.; Goertzel, B.; Hall, J. S.; Samsonovich, A.; Scheutz, M.; Schlesinger, M.; and Others. 2012.  Mapping the landscape of human-level artificial general intelligence.  _AI magazine_ , 33(1): 25–42.
  * Arp, Smith, and Spear (2015) Arp, R.; Smith, B.; and Spear, A. D. 2015.  _Building Ontologies with Basic Formal Ontology_.  MIT Press.  ISBN 9780262527811.
  * Borgo, Galton, and Kutz (2022) Borgo, S.; Galton, A.; and Kutz, O. 2022.  Foundational ontologies in action.  _Applied ontology_.
  * Chen et al. (2020) Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020.  A Simple Framework for Contrastive Learning of Visual Representations.  In Iii, H. D.; and Singh, A., eds., _Proceedings of the 37th International Conference on Machine Learning_ , volume 119 of _Proceedings of Machine Learning Research_ , 1597–1607. PMLR.
  * Conant and Ross Ashby (1970) Conant, R. C.; and Ross Ashby, W. 1970.  Every good regulator of a system must be a model of that system.  _International journal of systems science_ , 1(2): 89–97.
  * Eslami et al. (2016) Eslami, S. M. A.; Heess, N.; Weber, T.; Tassa, Y.; Szepesvari, D.; Kavukcuoglu, K.; and Hinton, G. E. 2016.  Attend, Infer, Repeat: Fast Scene Understanding with Generative Models.  In Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I.; and Garnett, R., eds., _Advances in Neural Information Processing Systems 29_ , 3225–3233. Curran Associates, Inc.
  * Friston, Kilner, and Harrison (2006) Friston, K.; Kilner, J.; and Harrison, L. 2006.  A free energy principle for the brain.  _Journal of physiology, Paris_ , 100(1-3): 70–87.
  * Gangemi et al. (2002) Gangemi, A.; Guarino, N.; Masolo, C.; Oltramari, A.; and Schneider, L. 2002.  Sweetening Ontologies with DOLCE.  In _Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web_ , 166–181. Springer Berlin Heidelberg.
  * Goertzel (2014) Goertzel, B. 2014.  Artificial General Intelligence: Concept, State of the Art, and Future Prospects.  _Journal of Artificial General Intelligence_ , 5(1): 1–48.
  * Grush (2004) Grush, R. 2004.  The emulation theory of representation: motor control, imagery, and perception.  _The Behavioral and brain sciences_ , 27(3): 377–96; discussion 396–442.
  * Jebara (2012) Jebara, T. 2012.  _Machine Learning: Discriminative and Generative_.  Springer Science & Business Media.  ISBN 9781441990112.
  * Kawana, Mukuta, and Harada (2022) Kawana, Y.; Mukuta, Y.; and Harada, T. 2022.  Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects.  In _The European Conference on Computer Vision (ECCV)_.
  * Kirillov et al. (2019) Kirillov, A.; He, K.; Girshick, R.; Rother, C.; and Dollar, P. 2019.  Panoptic Segmentation.  In _2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_. IEEE.  ISBN 9781728132938.
  * Kurata (2017) Kurata, T. 2017.  _Lecture on Contemporary Ontology II: Material Object, Kind, Function_.  shinyo sha.  ISBN 9784788515444.
  * Kurzweil (2005) Kurzweil, R. 2005.  _The Singularity Is Near: When Humans Transcend Biology_.  Penguin.  ISBN 9781101218884.
  * Legg and Hutter (2007) Legg, S.; and Hutter, M. 2007.  A Collection of Definitions of Intelligence.  In _Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006_ , 17–24. NLD: IOS Press.  ISBN 9781586037581.
  * Li et al. (2022) Li, B.; Weinberger, K. Q.; Belongie, S.; Koltun, V.; and Ranftl, R. 2022.  Language-driven Semantic Segmentation.
  * Mizoguchi and Borgo (2022) Mizoguchi, R.; and Borgo, S. 2022.  YAMATO: Yet-another more advanced top-level ontology.  _Applied ontology_.
  * Quine (1948) Quine, W. V. O. 1948.  On what there is.  _The Review of metaphysics_ , 2(1): 21–38.
  * Quine (1969) Quine, W. V. O. 1969.  _Ontological Relativity and Other Essays_.  Columbia University Press.  ISBN 9780231083577.
  * Ramesh et al. (2022) Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022.  Hierarchical Text-Conditional Image Generation with CLIP Latents.
  * Rao and Ballard (1999) Rao, R. P.; and Ballard, D. H. 1999.  Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.  _Nature neuroscience_ , 2(1): 79–87.
  * Saigo and Taguchi (2019) Saigo, S.; and Taguchi, H. 2019.  _What is ’Reality’? A transformation of the world view starting from mathematics and philosophy_.  Chikuma Shobo.  ISBN 9784480016904.
  * Smith (2005) Smith, B. 2005.  Against Fantology.  In Marek, J. C.; and Reicher, M. E., eds., _Experience and Analysis_ , 153–170. Vienna: HPT&ÖBV.
  * Sowa (1995) Sowa, J. F. 1995.  Top-level ontological categories.
  * Suzuki (2015) Suzuki, T. 2015.  _If we are a collection of atoms, why do we feel pain and sorrow? -Tackling the Hard Problem of Consciousness-_.  keisoshobo.  ISBN 9784326154340.
  * Tegmark (2017) Tegmark, M. 2017.  _Life 3.0: Being Human in the Age of Artificial Intelligence_.  Knopf Doubleday Publishing Group.  ISBN 9781101946602.
  * Van Gansbeke et al. (2021) Van Gansbeke, W.; Vandenhende, S.; Georgoulis, S.; and Van Gool, L. 2021.  Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals.  In _2021 IEEE/CVF International Conference on Computer Vision (ICCV)_ , 10032–10042.
  * Vaswani et al. (2017) Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017.  Attention is all you need.  In _Advances in Neural Information Processing Systems 30 (NIPS 2017)_.
  * Wiggins (1967) Wiggins, D. 1967.  _Identity and spatio-temporal continuity_.  Basil Blackwell.  ISBN 9780631103707.
  * Yamakawa (1998) Yamakawa, H. 1998.  Proposing Matchability Criterion for Situation Decomposition - Extracting Situations each of which Contains a Rule.  In Usui, S.; and Omori, T., eds., _The Fifth International Conference on Neural Information Processing, ICONIP R98, Kitakyushu, Japan, October 21-23, 1998, Proceedings_ , 514–517. IOA Press.
  * Yamakawa (2011) Yamakawa, H. 2011.  What is neural basis of flexible inductive inferences?  _The 21st Annual Conference of the Japanese Neural Network Society_ , P2–30.
  * Yamakawa (2021) Yamakawa, H. 2021.  Objectification Learning - To explode the scope of inference -.  In _Proceedings of the Annual Conference of JSAI_. The Japanese Society for Artificial Intelligence.
  * Yokoro (2021) Yokoro, Y. 2021.  _Identity and Individuals: Toward a Unified Theory Based on Sortal Concepts_.  Keiō gijuku daigaku shuppankai.  ISBN 9784766427608.