Modeling of human intelligence applied to general educationof informatics in AI era
Yoshiyuki Mizuno
Kyoto Women’s Universitymizuno@kyoto-wu.ac.jp「人間知能」のモデル化の提案とAI・データ科学の情報リテラシー教育
水野 義之
京都女子大学

Abstract
A novel modeling of human intelligence is proposed. The current status of AI is found positioned
in the model. The modeling is a decomposition of human intelligence into the four components,
namely intellect, reasoning, sensitivity, and understanding. The decomposition is actually the links
which connect the five states of information, namely data, information, knowledge, wisdom andunification. Thereupon the position of science is found inside the proposed mapping of informationand intelligence. We note that this modeling of human intelligence is compatible with the historical
Descartes’ “reason" and Kant’s “idealism”. Since the relationships of these, namely of informationand intelligence, are thus clarified objectively, it is found possible to position the AI of the presentera. A proposal for an improvement of general education for informatics in AI era is also suggested,which is based on the modeling of human intelligence herein proposed.

1 Introduction
Ever since the naming of AI, the Artificial
Intelligence, has been proposed in 1956 atDartmouth by Jim McCarthy, probably because
the naming AI was so catchy, that we the humanhas had a difficult time to express what is
intelligence. In fact, the definition of AI is known
to vary from scholar to scholar. This fact would
have hampered any development of AI inotherwise possible fashion.
The lack of understanding on AI by scholar is
not welcome since 1) social understanding
becomes inevitably insufficient , 2) thus any
application of AI would be hard to nourish, 3)then in turn the AI researchers would get annoyed
without notice via the lack of social
understanding.
In a sense AI loses opportunity for thedevelopment due simply to the catchy wording of“intelligence”. Thus another definition of AI,
without using “intelligence”, has been actually

proposed in literature, such as the one:“continuing advance of automation” by Jerry
Kaplan [1]. This definition of AI would be a good
one in its functionality. But our profound question“what is intelligence?” still remains unanswered.
This would make the intelligence moremysterious if we follow this alternative definition
by Kaplan on the profound question of“intelligence”.
In this paper we try to answer the question onhuman intelligence in a clear and straightforwardmanner. This clarity is achieved in our present
approach by starting from the definition of
information, in order finally to reach at the thedefinition of intelligence. Anybody knows theword “information". But it may not be trivial in
the differentiation of data against information orthat of information against knowledge. This
questioning on the term “information” would help
us to disclose a top secret of human intelligence.
In Section 2, we recapitulate the definition ofdata, information, and knowledge”. In Section 3

and 4, we put up “wisdom” beyond knowledge,
and further we position “unification” beyond
wisdom. We then propose to position “science” inthis context of the sequence of information inSection 5 and 6. In Section 7 and 8, we propose adecomposition of human intelligence into a set offour components, namely intellect, reason,sensitivity, and understanding. In Section 9, wethereupon demonstrate how we could apply thepresent proposed modeling of intelligence in thegeneral education of informatics in the AI era.
Summary and conclusion is in Section 10.

2 What is Data, Information andKnowledge ?
To begin with, we redefine the usual “data”,“information”, and “knowledge” in the following
way.
Data is defined as a defined sequence ofnumbers or characters. Picture, for example, isalso a sort of data, and it is data in the sense that it
is expressed with a defined set of binary numbers,
for example. There is a definition therein, butthere is no meaning at this stage.
Information is defined as an attached meaning
onto the data. In fact, the word of information isdecomposed grammatically as in+form+ation, and“in” is a prefix to mean the forced direction of thefollowing word “form”, meaning in total to create
a form. This suggests in fact the present definitionof information as an attached meaning, a sort ofform.
Knowledge is defined as a structured set of anaggregate with plural pieces of information.
Knowledge is at a higher level of information,
since a piece of information is nothing but anexisting piece of information, but once theknowledge structure is reduced into a randomized
sequence of the same pieces of information, it isnever again the knowledge. Knowledge is adefined structure.
These definitions are depicted in Fig.1 as isclear form these into an ordered set of the boxes.
We should note that without knowledge, no data
has its meaning to become information, andwithout knowledge no information could bestructured in any knowledge. In this sense,

3 What is the next one to “Knowledge” ?
What is the next one to knowledge? It must be
wisdom, since wisdom requires knowledge but it
is not a part of the knowledge. In this sense,
wisdom is defined as an emergent “someknowledge” based on the sub-level data,information, and knowledge.
The position of “wisdom” becomes clearer
when we note an analogy between this ordering
and what we do in science research.
Science research would start from data. We put
those data into a graph to find the tendency. This
is nothing but to find the meaning or information
inside the data. We make use of the existing or
known knowledge in order to interpret the dataand information. If we get the interpretation
successful, then we are happy. But if it isunsuccessful by any trial, then we are also happy.
This is because it means that we must havediscovered a new law of nature, which we may
call wisdom, an emergent some knowledge.
The whole structure of these four, namely asequence of data, information, knowledge, andwisdom, is depicted in Fig.2.

Fig.2: Analogous structure between 1) a method ofdata analysis in natural science, and 2) a sequence of
data, information, knowledge and wisdom.

4 What is the next one to “Wisdom” ?
What is the next one to wisdom? It must be aunification. This view is again suggested byFig.1: Definition of data, information, and knowledge.

science research, in particular by physics
research.
In physics research, we may discoverindividual laws, one by one, inspired byindividual experimental data. However, ouraspiration would be to demand a unified view ofthe Nature. It is just like a unified theory ofparticle physics as it is at the present stage.
Just like this, we would never be satisfied byindividual wisdom. We would also aspire a
unified view with some unified object, orunification.
In Fig.3 we depicted this situation.

This is just like a person facing at a beautiful
and artistic painting, who would certainly wonder
why it looks nice and beautiful. This is the
beginning of the latter stage of science in its
analysis phase.
This situation is depicted in Fig.4.

6 Model for “information development”
Now that we have reached at a stage of aninterim summary, we now name the model:“Model of information Development”. This idea
has been published in [2-4]. The interestingoutcome of this model is that science produces
the data, which themselves are the starting pointof the whole process.

Fig.3: Analogous view of the flow data in Naturalscience and that of informatics, namely data,information, knowledge, wisdom, and unification.

5 What is the next one to “Unification” ?
What is the next one to unification? It must be“science" in the sense that 1) science is a process
to quest for unification, and that 2) science is aprocess to analyze the unification for better
comprehension. In fact what is unified would behighly abstract and complex, which would
hamper for normal layperson to understand. But
anyone must aspire for understanding theunification.

Fig.4: Dual process of science. One is the processfrom data to a unified view, the other (backward) is
the one in order to analyze the unification.

Fig.5: Model of “information development”

That is to say, it is a helix model to predict thatthe humankind would live the process forever(until no one survives our world) to aim at abetter world. This suggestion seems highly
pedagogical for pupil and students at schools anduniversities in the sense that they can embrace
this model with the so-called “sustainabledevelopment; our common future” or SDGs.
No one would object against “Our CommonFuture” nor deny the SDGs. In this sense, SDGs
must be consistent with our future of “science andtechnology” or current era of IT and AI (ArtificialIntelligence). The above proposed “model ofinformation development” satisfies this condition,
since it is a dynamical model for the future with aconvergent characteristics (not divergentlyuncontrolled).

The trouble is that it is not so evident where toput the AI in this model, although AI is just akinto IT or information itself.

7 Decomposition of Human Intelligence
It is interesting to note that by way of themodel of “information development”, we notice a
possibility of a decomposition of human
intelligence. Human intelligence could be
decomposed into at least four components, suchas intellect, reason, sensibility, and understanding.
The decomposition is not unique evidently. But
it is clearly possible since we found just fivestages in “information development”, andtherefore we have to utilize four = (five-1)
different kind of intelligence. It is thus depicted asfollows in Fig.6.

inspired by the helix feature of “informationdevelopment” as in Fig.5.
The result is depicted in Fig.7. Here we add
two important note. One is that in the backward
flow in the righthand part of Fig.8, the ordering ofintelligence as Sensibility and Understanding isjust inverted. This is because, in this way, theconcept of epistemology with intelligence
discussed by Immanuel Kant becomes identicalwith our model. Second note is that we find again
an interesting correspondence of Rene Descartes“Reason” described in “Discourse of Method”
just with our model of intelligence.
This beautiful correspondence may be animplicit justification of our model of human
intelligence, at least in a historical context ofphilosophy. In fact, since human intelligence mustbe generated by “material” in our brain or genetic
information, only a span of 400 years since
Descartes must be too short to alter ourintelligence. We must be treating the same humanintelligence.

Fig.6: Decomposition of human intelligencecorresponding to “information development”
in Fig.5

8 Induction and Deduction in Intelligence
We are now able to compare our modeling ofintelligence with the other models of intelligenceby historical philosophers, such as Rene
Descartes or Immanuel Kant. In doing so, ourmodel of intelligenceis found related toempiricism and rationalism, or induction anddeduction. This generalization is performed byinverting the model of intelligence (like Fig.6)
into the opposite ordering. This operation isFig.7: Correspondence of our model of intelligencewith the historical epistemology of I.Kant and theReason theory of methodology by R.Descartes.

9 Where is “AI” in Human Intelligence?
Finally we try to place our friend AI (ArtificialIntelligence) into the present mapping (modeling)of human intelligence. It is clear that AI’s
intelligence is only at the left bottom corner in themapping of human intelligence. It is depicted in
Fig.8. This is because 1) AI requires data input, sothat it must include “Intellect”, 2) human started
to understand our own “Reason” only during thelast 400 years since Descartes, so that we areinsufficiently competent to perform programmingour “Reason” into computer.
Fig.8 shows where AI sits in data, informationand human intelligence. AI resides only at a leftbottom corner, presently. This picture may dictate
a proper positioning of present AI, free from over/under estimation if any.

10 Human Intelligence Model in Education
In general education of informatics or data scienceat university level, it would be standard to start fromthe definition of data or digitization of information.
Already at this stage, data and information have atendency to get confused. If we do so, then, students
are also confused between data and information.
Therefore, it is far better to teach the keen distinction
between data and information at the very start ofteaching/learning.
Only in this way, it is possible to recognize the“information development”, as has been emphasized
repeatedly in this paper. Only in this way, it is possible
to gain the important insight into our human
intelligence. Only in this routing, it is possible toproperly position the AI at our present era.
Without any cognitive image, eduction would fail.
This is because if we have no map for navigation,
then, any navigation would result in somewhere which
may not be our purpose land.

Fig.9: Region of AI inside a summary of “informationdevelopment” (middle blob), science (left/rightpinky blob), and “human intelligence” (red arrows).

11 Summary and Conclusion
In this paper, we have shown three things. One isthat information is reinterpreted as a part of the model

On top of Fig.9, three names of Kant, Descartes,of “information development”. Second is thatand Mizuno (author) are added just for a guide,“science” can be reinterpreted inside this development

because the picture is fully complicated.

model. In this context, science has a dual meaning inthat it is a process of integration of information ascontents, and also it is a process of analysis as amethod as well. Thirdly, human intelligence is shown
to be decomposed into four components. These arenamely, intellect, reason, sensibility, and understanding
with a reference to historical philosophers, I.Kant andR.Descartes.
Also proposed explicitly in this paper is that we areable to position the AI (weak AI of the present era) in
the above-mentioned model of “informationdevelopment”, science, and “human intelligence”.
It is thereupon proposed that it would be possible toFig.10: Names of Kant, Descartes and Mizuno (author),
added on Fig.9.

improve the general education for informatics or data
science by suggesting the possibility of modeling on
the information, science and intelligence.
Further studies are required in order to improve thepresent proposed model with reference to other relatedworks and/or historical thoughts.

References
1.

2.

3.

4.

Jerry Kaplan, “Artificial Intelligence: what
everyone needs to know”, Oxford UniversityPress, 2016.
Y.Mizuno, “Development model of ‘information’in information society” (in Japanese),
Proceedings of 24th Annual meeting of Japan
Society of Social Informatics, pp.184-187, 2009.
Y.Mizuno, “Active learning of AI ethics based on‘informed consent’” (in Japanese), Proceedings ofAnnual meeting of JUCE (Japan Universities
Association for Computer Education), 2019.
Y.Mizuno, “Revitalize ICT general education on
informatics for humanities students—from Active
Learning (AL) to Artificial Intelligence (AI)” (inJapanese), in “Thoughts on ICT education —present status and issues of ICT education inhumanity faculties”, pp.33-85, noa Publishing,2020.