24-5 Sakuragaoka-cho, Shibuya-ku, Tokyo 150-0031
Tel: 03-5456-8082 Fax: 03-5456-8388
Mail: info@japancivilization.org
http://www.japancivilization.org/

Newsletter Vol. 12 Summer 2018

On 23rd May 2018, a special lecture under the theme, “Will Artificial
Intelligence (AI) surpass human intelligence?” took place for the 12th
Symposium of the Japanese Civilization Institute. Mr. Yutaka Matsuo,
associate professor at the University of Tokyo gave the lecture, while
Naoki Inose, director of the Japanese Civilization Institute moderated
the lecture. The lecture featured what we should keep in mind in aworld that is drastically changing with the fast development of deeplearning, and the current situation of research in artificial intelligencein Japan and in what way the Japanese industry can survive provided
the circumstances. (Reprinted from “Shukan Dokushojin,” 10th
August, 2018, No. 3251 & Web Dokushojin)

Greetings

Masashi Takai

Board of Director of Japanese Civilization Institute, Chairman and President of Kinokuniya Company Ltd.

I have served as Chairman of this Japanese Civilization Institute ever since it was founded. The Chairman should be here today giving the
speech, but I will take her place. Today, we have Mr. Matsuo, leading AI researcher, giving a lecture to us all, which I am looking forward tovery much. Mr. Matsuo has written numerous books, ranging from academic books to manga, which are all selling extremely well at ourbookstore. Our theme today is “Will Artificial Intelligence (AI) Surpass Human Intelligence?” I personally am looking forward to discovering
whether business management will definitely be better if artificial intelligence took place of CEOs. I think this is a good opportunity for us
all, so if you have any questions, don’t hesitate to ask. I hope you enjoy the lecture to the very end. Thank you.

“Spirituality” required in the era of Artificial Intelligence
Toshio Goto

President of Japanese Civilization Institute
When we think about artificial intelligence, which we are going tolearn about today, and relate it to Japanese Civilization, what comesto my mind is Daisetz Suzuki who spread zen to the world in English. Artificial intelligence is said to surpass people’s intellectual,
intelligence and whatever part our left-brain dominates. It’s nearing
the realm of creativity too, including knowledge, logic and reason,and research is pursued every day in order to go beyond that andreach the area of sensitivity. A couple years ago, a book written by
artificial intelligence was chosen as a candidate for the “Nikkei
Hoshi Shinichi Award” (sponsored by the Nihon Keizai Shimbun)
and brought controversy.
Other words, there are possibilities for artificial intelligence toreach the standards of humans or even surpass humans in literature.
Similarly, many music created by robots can be heard on the Internet today. Artificial intelligence may surpass both our left-brain,
which dominates logic, and our right-brain, which dominates creativity. And in the end, will there be an area in which artificial intelligence cannot cover? While thinking about this, I came across theword “spirituality,” which Daisetz Suzuki mentioned.
So, what exactly is spirituality? Daisetz Suzuki calls it somethingthat lies in the middle of our spirit that cannot be completely embraced, and something that is related deeply to our religious con-

About the groupJapanese Civilization InstituteEstablished in August 2015

sciousness. He did not refer to a particular religion, but instead hecited it to be an absolute religious existence. Concerning this, hestated;“When the spirit confronts material substances, and thereby suffers
in shaking off bonds, if an opportunity to touch their own
spirituality is provided, the suffering of conflict will naturally fadeaway.”
He said, spirituality will not come into existence unless humans go
through suffering like any other humans do. Other words, babies will
not know spirituality at all. In addition, Japan as a nation recognizedspirituality for the first time, during the Kamakura Era (1185-1333)
and amongst it was zen.
People think of Daisetz Suzuki as a researcher in zen, but when
you look at his life carefully, you can see that he spent his whole life
researching spirituality, particularly “Japanese spirituality.” While
artificial intelligence is nearing the realm of logic and creativity
today, I think we should focus more on understanding humans. The
more artificial intelligence becomes the heart of our lives, the more
important civilization becomes. In addition, the soul, which exists
in the realm of what Daisetz Suzuki calls spirituality—that lies
somewhere deeper than knowledge or creativity—is put into
question. I think if we listen to Professor Matsuo’s lecture while
keeping this notion of “Japanese spirituality” in mind, it might give
us a clue as to how we should face the future to come.

Address: 24-5 Sakuragaoka-cho, Shibuya-ku, Tokyo 150-0031
Tel :03-5456-8082 Fax:03-5456-8388
Website: http://www.japancivilization.org/

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A Report on Our 12th SymposiumSpecial Lecture“Will Artificial Intelligence(AI) surpass human
intelligence?”
Yutaka Matsuo

Big innovation in
deep learning
Matsuo: 2 years ago, computer AI
AlphaGo, developed by Google
DeepMind, beat Le Se-dol, a South
Korean Go master with 9 dan. The
following year it won over Chinese
Go master Ke Joe, who also has 9dan, so there is practically no one
who can beat the machine. AlphaGolearns the go players’ playing recordand continues to compete itself
while becoming stronger. Then finally, in December last year, AlphaZero was developed and thisdidn’t even need go data for learning. It became stronger by competing itself in areas not only in go, butalso in chess and shogi. Meanwhile,elmo beat Ponanza at the World
Computer Shogi Championships,
but AlphaZero won over elmo in
merely 2 hours, learning fromscratch. These artificial intelligencedevices only need 2 hours toaccomplish what these masters build
all their lives. The technology at thecore of these artificial intelligence

2

MC

vs.

Naoki Inosemachines is called deep learning.
Today, artificial intelligence has
become extremely popular, but infact, artificial intelligence is in its
third boom. Research on artificial
intelligence started in 1956, so 62
years have passed since it was firstrealized. Since then, people havebeen reciting the fantasy that “someday, artificial intelligence will surpass
human intelligence.” However, technology never caught up and that
fantasy turned into disappointment
and dark years kept coming again
and again. Amid the boom we have
today, I think we need to know exactly what artificial intelligence cando and cannot do.
By the way, did you know that
research on automotive driving goesback well over 10 years ago? Roughly
speaking, the technology we have
today is all the result of small improvements of what we accomplishedyears ago. Moreover, most of thethings that we couldn’t do back then,we still can’t do today. Still, amongst
all these technologies, the technology
of deep learning has seen a great
innovation.

So what exactly can be done withdeep learning? It comes in 3 steps.
The first thing it can do is to “recognize.” Next, it can “learn movements,” and lastly it can “understandlanguage.”
To begin with, let me go into
detail about “recognition.” People
can distinguish a cat, a dog, and awolf instantly when shown a photograph, right? But this is difficult for
a computer to do.
Therefore, we provide it with adefinition for classification like, “ifits eyes are round, it’s a cat.” Next, wegive it a criterion for judgement like
“if its eyes are long and thin and itsears are loopy it’s a dog, but if it’s got
pricked ears, it’s a wolf.” Seemingly,
one would think that they can
acknowledge under these rules, butthere are in fact multiple kinds ofdogs in the world. For instance,
Siberian Husky have pricked ears but
they are not wolves. It’s in fact
extremely difficult to define a dog
and a wolf.
This notion of “looks like〜” isknown as “feature amount.” So longas humans are defining these featureYutaka Matsuo

amounts, the accuracy of image recognition will not increase. Humans
are unconsciously learning thisnotion of “looks like〜.” Computers
too, need a system where they canlearn “feature amount.”
This became possible with deeplearning. The “Google Cat” is famous. The system they developed
goes like this: you simply show thecomputer a huge number of picturesthat you find on YouTube and it willlearn what a cat looks like.
The year 2012 was an epochmaking year for image recognition.
All over the world, computers tried
recognizing images using the same
set of data and they competed in thepercentage of accurate answers theygained. Surprisingly, it turned outthat the winning team of 2012,suddenly produced an error ratio of
16%, which was 10 % lower thanany previous record. This was
possible due to the development of asystem in which deep learning
automatically learned “feature
amounts” that were initially definedby humans. After this, the error
ration of image recognition starts todrop fast. In 2013 it was 11 %, in2014 it was 6.7 % and now it’s 2.3%. If a human goes through thesame task, the error ration is said tobe 5.1 %. This means that in thefield of image recognition, computers
have already surpassed humans. Thiskind of revolution has happened inmerely 5 years.
Image recognition is done throughvarious tasks, including detection of
objects in the image and segmentation in which the domain of the object is cut out. For example, an object
detection program called YOLOv2
can read individual objects in an image as fast as 47 flames in 1 seconds.
This program is shared around theworld, and it can easily be found on
the Internet.

From recognition tolearning movement
and understanding
the meaning ofwords
Furthermore, if you combine thisimage recognition with robot technology, “learning movement” becomes possible. When humans continue doing the same movement,
they reinforce the movement they aregood at, which makes them better at
it even more. This is called “reinforcement.” People are trying to develop a system so that computers cando the same thing. For example,
various tasks such as combining
bumps and dents of blocks, opening,and closing of lids, placing hangers

onto poles of different heights are acknowledged by the eye and adjusted.
Then a dozen computers photographthe movement sequences with acamera, share that experience andself-learn at high speed. Robot andmachine technology up until now
couldn’t make adjustments to holdthings of different shapes and sizes,
because the accuracy of image recognition was bad and it couldn’t see.
Humans, on the other hand, learn
to hold things at different spots in notime, after repeating the movementof holding and dropping things eversince they are a baby. Robots are now
learning to do the same.
The fact that computers are
finding it difficult to do things thatchildren can do is known as Moravec’s paradox. Research on computers
making a medical diagnosis, playing
chess and proving mathematicaltheorem has shown improvement tosome extent, but things like image
recognition that 3-year-olds can do
are lagging behind. This is because
humans can’t explain how they’re
doing it themselves.
However, this situation changed
with the third artificial intelligenceboom. What’s so brilliant about
artificial intelligence of recent years is
not how advanced it has become, but
how it has come to be able to do easy
things that 3-years-olds can do. We
need to think about how the world
will change with this. First comesrecognition, then movement andnext comes “words.” This process issimilar to what a child goes through;first, it becomes good at moving its
body, then it starts touching various
things and dropping them and thenit starts to understand the concept. It
is after this that they start to understand the meaning of language.
By the fall of 2014, it was predicted that artificial intelligence technology will develop through the following 6 steps; 1) increase in accuracy
on image recognition 2) predictionof movement 3) learning of movement that isn’t affected by environ-3

mental changes 4)technology of
movement while integrating smoothly with the surrounding environment5) language understanding 6)consuming a wide range of knowledge.
It will probably develop throughthese 6 steps and complete by roughly 2030. When artificial intelligenceachieves recognition of steps 1) and2), diagnosis of medical images,
crime prevention, and surveillance
will become possible. When learningof movement of stages 3) and 4) isachieved, automotive driving, watching over distribution and construction, automotive farming, nursing,cooking, and cleaning will become
possible. Lastly, when AI reaches
stages 5) and 6) and starts understanding the meaning of language,
maybe translation and assistance inoverall white-collar jobs will become
possible. It’s actually proceeding likethis. But what we couldn’t predict
was that it’s proceeding 3 to 5 times

4faster than expected.
At the current stage, computers
are starting to understand the meaning of language. One is called image
captioning, a technology to create asentence after looking at an image.
For example, when it takes in a picture of a man playing the guitar, it
will display the sentence, “man in theblack shirt is playing guitar.” It isstarting to learn to do vice versa too.
When you put the sentence, “A verylarge commercial plane flying in blue
skies,” you get an image of a large
plane flying against a blue sky.
Here, the computer is not searching for an image. It is instead creating an image based on the sentence it
receives. We unconsciously create images in our head when we hearsomething. Artificial intelligence isdoing exactly the same. From 2 years
ago, Google translation has switched
to deep learning and has increased its
accuracy. For example, translationfrom English to Spanish and French
to English stands pretty comparable
to what humans translate. But translation from English to Chinese or
Japanese is difficult for even humans
to do, due to its difference in language structure, so there is still a difference in the quality of translation
done by humans and computers. But
even so, it’s nearing that of humans.
Having said that, no matter how
much computer increases its accuracy, it will never take place of the subtitle translator Natsuko Toda. Humans translate while taking into
consideration people’s feelings and its
delicate changes as well as thecultural background.
Similarly, in the future, it willprobably be difficult for automatic
machines to create novels and haiku
that moves our emotions. Novels andhaiku are living expressions that
express our emotions and instincts.
Perhaps we can compare the relation-

ship of humans and artificial intelligence to that of birds and flying. Humans can understand the principle
engineering of flying and create
planes, but planes do not chirp in themorning. Similarly, artificial intelligence does not have vivid emotions
and conflicts. Artificial intelligence isthe result of humans trying to solve
human intelligence with engineering.

Evolutionof artificial
intelligence, withthe “discovering ofvision”
There is a book by Andrew Parker
titled “In the Blink of an Eye” that
explains the Cambrian Explosion.
The Cambrian Period is said to be aperiod in which all existing species
came to present during the merely 10
million years in the long 4.6 billion
years history of the earth. There aremany theories as to why the Cambrian Period happened, but Parker
stresses that it was because vision
evolved, calling it the “Light Switch
Theory.” Until then, living things
didn’t have eyes, so they moved slowly using their sense of smell andtouch, and ate when they bumped
into something and ran away whensomething bumped into them. But
then a trilobite with acute eyes
appears for the first time. It obviously
had advantages so it increased its
number enormously. Soon, thosethat ran away started to grow eyes
and began swimming fast or acquire
mimicking abilities so it wouldn’t getcaught. By having eyes, strategies forsurviving became diverse and with it,
species became diverse too.
I think the same thing will happen
in the world of robots. By discovering vision, tasks that can be accomplished and varieties in machines will
increase enormously. Camera’s image
sensors are equivalent to the crea-

Naoki Inoseture’s retina. We see by processing
signals, which we received from theretina, at the brain’s visual area. Thisvisual area is equivalent to deeplearning. Our civilization up until
now has been a history of how weshould use machines that lack eyes.
Products produced at factories
needed to be the same in shape andmaterial. Factory’s pipeline too is systematized by machines with no eyes.
Today, the area that isn’t automatedare the areas that machines with noeyes find it hard to handle. The
prominent example of this would befarming, construction and various
food processing industries.
Humans are the ones controlling
tractors and combine harvesters.
Robots can be used for say rice andpotato harvest, which you canharvest in chunks, but for harvesting
vegetables and fruits like tangerines,
apples, tomatoes and cucumbers, thehuman eye is essential. In fact, thehuman eye is necessary for almost every process of farming. That’s why
there is a shortage of hands in farming. Similarly, construction sites need
the human eye too for labor like setting up reinforcing steel, welding andcasting concrete. Food processing too
has to have eyes or they wouldn’t beable to cut the ingredients, placethem on the frying pan and turnthem over when they are cooked.
But with the technology of vision,

there are possibilities that farming,
construction sites, and food industries to become automatic. This willstir a huge impact. If it were farming,
tomato harvest would be relatively
suitable for becoming automotive
with reasonable cost. If it wereconstruction sites, welding is meniallabor that lacks a working hand, so I
think it might be feasible. Cooking
requires extremely complex routines
so it might be a difficult area for automation, but I think we can start by
putting the dishes in the dishwasher.
Like these examples, we can start automating things where we can, andgradually develop it from there.
In fact, Japan is strong when it
comes to machines for farming,
construction and food processing,and boasts a huge share in the world.
I think Japanese companies can excel
in developing automotive machines
by combining hardware and deeplearning. Japan has been economically suffering these 20 years, being unable to start a big business in the Internet field. However, I think it can
do well against the world, indeveloping automatic machines withvisual capabilities. During the firstindustrial revolution, the steam
engine was invented, replacing what
used to be done by human muscles
or farm animals. Similarly, if eyes areadded to machines, “recognition”
will be taken away from humans and5be relocated where society needs it.
In reality, the medical field is one
step ahead in diagnosis using X-ray,
C T, M R I , d e r m a t o l o g y, a n dendoscope. Automotive machinesthat surpass overall doctors, havealready been developed. Just lastmonth, The Food and Drug Administration (FDA) of the U.S. government authorized fundus testing
equipment that uses deep learning.
Recently, artificial intelligence not
only supports doctor’s jobs, but also
are close to replacing doctors altogether.
The field that’s progressing next isfacial recognition. We all know that
iPhone X has incorporated it, as well
as China’s Tencent Inc. for supervising their employees’ entry and exit.
There are over 100 million cameras
across China, so deep learning is
spreading fast in that country. There
will no longer be the need for people
to monitor rooms for surveillance.

6

Moreover, it will only take 10 seconds to check which camera the person you want to find showed his orherself last. China wants to watch
over its people so there is a great
match here. There is a great concern
as to how this kind of trend will
evolve in the future. Also, the visionof artificial intelligence can be used
for looking over patients 24 hours
straight, at nursing homes and hospitals.
Moreover, presently, there arecleaning robots but there are no tidying robots. This is because tidying isan act of recognizing an object andplacing it back to where it used to be.
But with the technology we have
today, there is a good chance that
this tidying robot will become aproduct in the near future. If tidying
robots become common, you canleave your home in the morning andcome back at night with everythingback in place, like a hotel. Our living

environment will no doubt improveenormously.

Deep learning
equals to the leastsquares method
So let me explain to you in detailabout the structure of deep learning.
In one word, deep leaning is “leastsquares method that uses the deepfunction.” Least-squares method is amethod used for statistics. For example, if you draw a scatter diagramwith Excel, and add an approximation straight line, you have a line.
The algorithm used to draw this line
is a least-squares method.
For instance, say you drew a scatter diagram of a store, stating the relationship between the temperatureand how many drinks you sell onthat day. You draw a line by adding

an approximation straight line andyou get the equation, <y’=a+by>.
This y’ expresses estimated value andthe error is calculated by measuring
how much gap there is compared tothe actual value and how much eachpoint is apart from this line. Thereare errors going in the plus direction
and minus direction, so you square
the error and add it up. All points ofthe square sum are more or less outof line, so when you decide on thevalue of a and b so it becomes minimum, you are able to find a line thatrides on top of the points.
We just created a formula using
the amount raised on beverages
against one variable stating temperature, but let’s make it two variables oftemperature and humidity. Then, it
becomes three dimensional and theline becomes a surface. But even so,
the process is the same: you find out
how much y’ is apart from the actual
value with the equation <y′=a+bX1+cX2> that uses two variables, X1
and X2. You then square the error
and add it up and figure out thevalue of a, b and c so that the squaresum becomes minimum. I justconverted one variable to twovariables, but the method is the sameeven if it’s 10,000 variables.
One wouldn’t regularly comeacross a case that requires 10,000
variables but it’s needed for people
who want to find out if a cat is in animage of 100×100. Other words, indeep learning, you need to find afunction that returns 1 for a cat and
0 if it wasn’t a cat against 10,000
number of variables. Again, what you
do is the same: you create theequation <y ′ =K0+K1X1+K2X2+.........+K10000X10000> using
1 0 , 0 0 0 va r i a b l e s f ro m X 1 t o
X10000, take the square sum of theerror compared to the actual value
and decide on the value from K0 to
K10000 that makes it minimum.
Image recognition of a cat is acquired
like this with minimum square sum.
Deep leaning is close to multiple
regression analysis and multivariate

analysis of economics and marketing.
However, about 30 to 40 variables
are used for multiple regression
analysis and multivariate analysis at
the most, but deep learning requires
tens of thousands of variables tohundreds of millions of variables.
You need tons of data and in order toestimate the parameters, you need ahuge amount of power from calculators. Other words, deep learning is
like a monster with the minimum
square sum. Moreover, I have somewhat simplified the explanation up
until now, so there are parts where
it’s a little different from reality.
Equations used for deep learning is a
deep equation, not a linear equation.
Rather than defying functions directly, you defy a different, intermediatefunction of f1 or f 2......f100 out ofvariables X1 or X10000, and use thisfunction to create a cat function. The
equation would be <f cat(X) =K0+
K1X1+K2X2+ ......K10000X10000>
and the more you increase the number of functions, the more 2 layers, 3
layers, 4 layers, 5 layers—layers will
increase. But what you do, again is
the same: you will be able to find theparameter that deeply defies the catfunction through minimum squaresum. No matter how much you defy
intermediate function to a linear
equation, it remains linear, but if you
add a non-linear element to this, it
tries to imitate the function of neuron—a nerve cell that ignites whenstimulation exceeds to a certain degree. It creates a sigmoid function(σ) in which 1 is selected when thevalue of X is a plus and 0 is selected
when the value of X is a minus. By
placing (σ) before the head of eachfunction, you get <f1 (X) = σ (K01
+ K11X1 + K21X2 + .......... +K100000, 1X100000)>. Nonlinearity of each intermediatefunction becomes strong and theoverall cat function turns into a nonlinear function. But at the bottom of
this parameter too, we’re again using
minimum square sum.The reason we
deepen function is that the world welive in is hierarchical, so we needfunctions that are hierarchical too.
Also, when we think about space thatis structured by different kinds of images, the area occupied by a “cat” is a
subspace, but that space is twisted
and bent, forming a complex shape.
In order to cut off such a shape, weneed to build a function that is highin the power of expression. Normally, to increase the power of expression, we need a huge number of parameters, but in this case, we need a
lot of data. When we think of a way
to keep the number of parameters
low, while heightening the power of
expression, the most effective way is
to build layers of simple functions.
Building functions with deep
layers and predicting data out ofparameters, is something researchers
have been trying to do from decadesago, but with little success. The
reason they couldn’t do it was because they lacked power and data.
Today, that problem has been answered and minimum square sums
with massive parameters—like amonster, can be solved.

From attractive
Sigmoid function torealistic ReLU
Today, people have started shiftingfrom a Sigmoid function to an activation function called ReLU (Rectified Linear Unit). The equation of
ReLU is expressed as <max (0, x)>
and it changes to 0 when in the areaof a minus and to X when in the areaof a plus. The reason why ReLU isbetter than a Sigmoid function isthat with Sigmoid function, themore input value X becomes bigger,
the more output value Y nears 1, andsticks to it and stops moving. It tries
to differentiate and search for an optimal value but most differentiation
end up to be 0, so it can’t get close toan optimal solution. However, thevalue of ReLU is X, so a constant7

term always remains when it is differentiated, so it ends up in an optimal
solution.
They started using ReLU around2012. Ever since researchers started
studying artificial intelligence neuralnetworks decades ago, they wereusing the mostly Sigmoid function,
because its equation <σ(X) = 1/(1 +e)> was attractive. Compared to this,the equation of ReLU; <max (0, x) >was not. At the time, deep learning
was unrealistic, so if they can’t makeanything out of it, they thought theymight as well use the one that wastheoretically beautiful.
Lastly, let me just refer to you another method called Batch Normalization. When you optimize deepfunction with a neural network,
there is a method called error backpropagation, in which you returnerrors due to how much you mademistakes. Up until now, the ratio ofchange was converged to 0 or released endlessly, and thereby deepfunction was said to be hard to optimize. But with Batch Normalization,
they can adjust each value’s output toan average of 0 and dispersal of 1.
Other words, they decided to createa deviation rate out of the value output of each neuron. It’s very simple,
but this made it work. Differentiation was no longer converged to 0 or
released endlessly, and each neuron
released some kind of deviation
rate—a fixed value. As a result, thequality of optimization improved
immensely.
This was suggested in 2015. They
were doing research on this for
decades, but nobody attempted tonormalize it. People involved instatistics go through these kinds ofprocedures quite naturally. Everyone
thought it wasn’t possible, so it
lagged behind for decades, but once
that barrier was taken away, itevolved at a high speed, resulting in
the rise of accuracy we have today.

8

Is deep learning, atechnology suitable
for the Japanesepeople?
In short, deep learning is a “leastshares method” in which you canpredict the value of Y if you enter thevalue of X. By using 10,000 to 100
million variables, and creating a
deep, non-linear equation by pilingup simple intermediate value, it
imitates the process of neuron andmakes deep learning possible.
At any rate, it’s least-shares method, so it’s all about thinking what toput in X and Y. If you put an image
in X and a class, a cat or a dog in Y,
image recognition becomes possible.
Similarly, if you set it up so that it
outputs an image in X and a classand bounding box (a rectangular
grid-line that surrounds the image)in Y, you get an algorithm of object
detection and if you put English sentences in X and Japanese sentences inY, you are provided with a translation. If you think of ways to connectthat with neural network, it can basically learn anything. The basic
principle is simple.
I think the fact that the basic
principle of deep learning is simple
and that it requires numerous small
tuning, makes designing deeplearning a suitable job for Japanesepeople to tackle. The more they
revise small circuits, the more tuningof parameters is done in detail andthe more data is collected, the moreaccuracy is increased. Other words,
the more effort you put in, the more
results you get. Moreover, you cangain huge merit in the way you
combine the design of deep learningand hardware. That’s also something
the Japanese people are good atdoing.
However, Japan is way behindwhen it comes to technology in deeplearning. When you look at 1500

academic essays related to deeplearning and see how many times
they have been quoted, the mostquoted essay has been quoted 17000
times. It’s a legendary essay of 2012
about image recognition written byprofessor Geoffrey Hinton andothers.
Moreover, if you rank theseresearchers on the number of times
they have been quoted, the top few
are occupied by leaders of the deeplearning field such as Hinton, Yoshua
Bengio, and Yan LeCun. These areall exceptional people who continued
to research during the dark years andsucceeded in breaking the barriers.
But apart from these people, thesedeep learning researchers are all in
the late twenties or early thirties.
Also, most of these essays were
written after 2012. When you lookat academic essays of other areas andline them up in order of how manytimes they have been quoted, yourealize there are very few young
people in the higher ranks. This justshows you how much deep learningis new in the field, something completely different from conventional
technology. Unfortunately, there are
no Japanese people in these ranks.
The highest would be the 400th
place. Regrettably, in Japan, countries, and companies invest in artificial intelligence, which comes before
deep learning. And this competition
is won by how much you can gain
good personnel. Excellent personnel
who are brilliant at deep learning aremostly young people. People ranking
in the top 300 of the list I mentioned
earlier, receive a starting salary ofover 50 million yen a year. One more
0 is added to those who rank in the
top 30. There are people who receive
a couple billion yen a year, similar tothat of a top soccer athlete. There areover 100 brilliant brains such as thesein Deep Mind. When it comes toGoogle and Facebook, there are acouple hundred. Similarly, companies like China’s Alibaba and Tencent
are gathering talent fast. Meanwhile,Japan is not joining the competitionat all.
To begin with, the problem withJapan is that it’s old. It can’t keep up
with the fast pace of deep learning,which keeps evolving. Of course,
there are young Japanese people whoare enthusiastically tackling deeplearning, just like when the Internet
appeared. But the older generation,
who have the authority to manage
personnel and operate the company,
needs to understand its value, or elsethey will keep investing in fields ofthe past.
The second problem is that it’sslow. There are big chances for
manufacturing industries, but mostJapanese corporations are bad at
decision making. They cannot makedecisions so they spread their targets
out to everything sparingly. I call thisphenomenon; “passive sparing inevery area.” Even though there are
departments of big corporations thatdeal in artificial intelligence, they are
just there. They don’t operate at all.
The third problem, which is themost critical, is that they don’t invest
in people. Even though the government creates a budget for this area,
what they choose to do in the end isbuy a supercomputer with it. This isbecause, with the current Japanesesystem of personnel, they can’t afford
to pay tens of millions of yen to ayoung employee.
From 2015, I have a researchgroup at University of Tokyo where I
teach, that specializes in deeplearning. 70 people gathered in thefirst year, but the number is growing.
190 students joined in the second
year, while 360 students joined in thethird. The year 2015 is the same yearthat top universities of the world
started teaching deep learning, so I
have confidence that we’re no way
behind. If you acquire the skills,
chances will emerge and several people can launch startup companies. 2
companies have been listed from
Matsuo Research Group so far.

Building structureof the industry andcultivating humanresources
What we’re dealing in is extremely
innovative technology, so I figured
we need to build an industr y
structure. That’s why I launched theJapan Deep Learning Association inJune last year. One of its aims is toincrease engineers who can handle
deep learning. We can only produce
a few from Matsuo Research Group,
so I wanted to provide an intensive
learning opportunity to people fromother universities too, together withpeople who are in the business ofvarious fields, people who are working and students.
I also prepared a certificate exam
because artificial intelligence is a field
in which it’s hard to tell a fake fromreal. One is an engineering certificate(E Certificate), given out to those
who have a certain level of skills fordeep learning. The other is G Certificate, which examines how much theperson understands the basics ofdeep learning, and how that person
can use it in business.
One of the reasons why theJapanese IT industry failed to keep
up in world competition, is because
deep learning literacy of the person
who orders and the users, including
the country, the local government,
medical and financial institutions,were very low. Therefore, the price isstill paid according to workload. If
this were a restaurant, it’s the same assaying; “This dish is 10,000 yen, so
it’s delicious.” Normally, customers
try and find delicious restaurants thatare also cheap, and this ultimately
increases competition and raises thequality of the industry.
If we want to change the industry
with deep learning, we need toincrease the level of those who order.
This is why I created G Certificate. I

hope it will be used by presidents ofcompanies who are thinking ofbusiness using artificial intelligence,people who are starting a new
business, people in human resources
and people who are engaged inmarketing. It’s a certificate, which
you can acquire in 1 to 2 weeks ofstudying. If you gain it, you will
understand what you should value
when you talk to IT vendors. The
first G Certificate took place in December last year, and half of approximately 1500 people who took theexam passed. We intend to hold it 3
times a year.
I think ‘monozukuri’ (making
things) using deep learning is a big
chance for Japan. There are highpossibilities that the technology of
the “eye” will solve the issue of lackof labor brought upon by thedecreasing birth rate and aging
population. But if we continue atthis speed, the U.S. and China will
win the competition, and there will
be no place for Japan. If we want tocatch up from here, we need toseriously think about how we’regoing to invest in deep learning andhow we’re going to do everythingwith more speed.

Creating aComprehensive
Deep Learning
Research
Institution in Japan
Inose: So, you’re saying heightening
the technology of deep learning issuitable for the Japanese people. But
the Japanese system of promotion byseniority, lifetime employment
results in people in their 50s and 60s
making decisions. Almost every
engineer in the world who areleading the field of deep learning arein their 20s and 30s, but Japanesecompanies can’t pay young people
tens of millions of yen for their start-9

ing salary, because if they do so, theiremployment system will collapse.
The only way they can survive is tocreate their own company, or maybe
companies can form a separate company.
Matsuo: Yes, there are many companies that are forming separate companies. But I think it’s best if theychange the existing human resource
system altogether, if they can. Companies are all in need of people who
can handle deep learning. But theirsalaries are kept low. There are nochanges there. If they don’t change
the basis of how they think, nothing
will evolve. I’m sure that once they
set an appropriate salary, people will
come flooding in.
Inose: You’re saying it’s an area thatis suitable for the Japanese people,
but the system in which it is developed, and the old way Japanese

10

companies are managed is resulting
in Japan to get left behind from Silicon Valley and China’s Shenzhen.
Although, I think Matsuo ResearchGroup is standing up like a Silicon
Valley on its own (laugh). In thatkind of situation, it’s meaningfulthat G Certificate was establishedand acknowledged by society. By theway, is the Ministry of Economy,
Trade, and Industry of any help?
Matsuo: They do offer support...
The truth is, I think qualification
systems such as these should be established by the government. I wish
they could quickly start expanding
education too. There are no chances
for Japan to win in the academic research world of deep learning, butthat doesn’t matter. Why? Because
Japanese automotive and electronic
companies never really invented theengine or the transistor. Instead,they added originality to what wasinvented overseas and opened a newmarket. We should do the same withdeep learning too.
Inose: How many researchers are
there in Matsuo Research Group?
Matsuo: Officially there are lessthan 30 people. But there are 60 to
70 people involved in the project.
We need to spread our knowledge
not only inside our group, but also
to other universities and people
living in local areas. So, we havetaken multiple approaches like
opening online courses. We hope we
can have people from technicalcollege tackle deep learning too.
Inose: The faster you acquire theskills the better, right?
Matsuo: Yes, that’s right. There are
graduates of a technical college in
Matsuo Research Group too, butthey move their hands than complain. In addition, there are many
good hardware companies in local
areas that are in need of people withdeep learning skills. I think we could
raise personnel in local technicalcolleges and have them contribute tothese local companies.
Also, times are changing drastically, so I recommend that people intheir 40s and 50s start studying fast.
I’m quite sure they’ll profit if theydo.

something like a Comprehensive
Deep Learning Research Institution,
where we can objectively analyze thesituation of artificial intelligence inJapan and examine where we’re losing and where we have a chance towin.

Inose: I wrote the book “Defeatedin War in the Summer of 1941,” butJapan seems to be not at all different
from those times, which is sad.

(Reprinted from “Shukan Dokushojin,”
10th August 2018 Issue, No. 3251
& WEB Dokushojin)

Inose: Yes, we surely need a Comprehensive Deep Learning ResearchInstitution in order to excel. The nation as a whole, should strategicallytackle this issue.

Matsuo: I think we need a to create

The panelists
Yutaka Matsuo
Associate professor of the University of Tokyo School of Engineering, Institute of Engineering Innovation,
Center for Knowledge Structuring and Department of Technology Management for Innovation. Matsuo isa leading researcher in artificial intelligence in Japan.
Born in Kagawa Prefecture in 1975. He graduated University of Tokyo, School of Engineering in 1997. In
2002, he received a Ph.D. degree from the University of Tokyo in 2002 and is a doctor of engineering.
From 2002, he served as a researcher at the National Institute of Advanced Industrial Science andTechnology and from 2005 he was a visiting scholar at Stanford University. He has been serving as an
associate professor of University of Tokyo since 2007. He received the Japanese Society for Artificial
Intelligence Best Paper Award in 2002, and the Information Processing Society of Japan Nagao Special
Researcher Award in 2007. His books include “Is Artificial Intelligence Exceeding man? Deep learningand beyond,” and “Why Artificial Intelligence will change the future.”

Naoki Inose
Author. Born in 1946. In 1986, he received the Souichi Ooya Nonfiction Award for his book “Mikado
no Shozo” (Portrait of the Emperor).” In 1996, he received the Bungeishunju Readers’ Award for his book
“Nipponkoku no Kenkyu” (A Report on Japan).” In June 2002, Prime Minister Junichiro Koizumi appointed
him to the Promotion Committee for the Privatization of the Four Highway-Related Public Corporations.
He served as Tokyo Governor from December 2012 to December 2013. In December 2015, he became
Special Advisor to Osaka City. His books include “Show 16-nen no Haisen” (The Defeat in Showa 16),
“Persona—Mishima Yukio Den” (Persona: The Story of Mishima Yukio) and “Picaresque—Dazai Osamu
Den.” (Picaresque: The Story of Dazai Osamu). Upcoming books include “Kyushutsu” (Rescue), “Senso,
Tenno, Kokka” (War, Emperor, State), “Seigi ni tsuite Kangaeyo” (Let’s Think about Justice), “Minkei”(Posse Man), “Tokyo no Teki” (Tokyo’s Enemy) and in collaboration with Lully Miura, “Kokumin Kokka no
Riarizumu” (Realism and the Nation-state).

Translation: Ayako Karino
11

Japanese Civilization Institute 2018 13th Symposium
As human beings, the last stronghold against artificial intelligence would probably be our 5 senses. However, in the modern world,
we have come to lose our 5 senses. Our life used to be filled with scent and sound.
Kodo (traditional incense-smelling ceremony) which Shino Soshin first established in the Muromachi Era (1336-1573) in Kyoto
has passed down its tradition, without breaking off once. On this occasion, we will have Hachiya Souhitsu, young Soshu whocarries on the tradition of Shino School of Kodo, lecture us on Kodo with actual demonstrations.
They say, incense is something that you listen to. How did the Gion Shoja bells of “The Tale of the Heiko” sound like? Crystal ballplayer Ms. Asami Ishizuka will give a performance, while we immerse ourselves in ancient sounds and monko (listening to the
incense).
▶ Panel discussion:

Making full use of the five senses—What is traditional Japanese scent and sound?
Hachiya Ittshiken Souhitsu

Asami Ishizuka, Crystalist Asami

Naoki Inose

21st successor andhead of Shino School of Kodo

Representative director of the Crystal Bowl Academy
Japan Foundation and
CEO of Crystal Bowl Academy Japan.

Author, director of the Japanese
Civilization InstituteDate: 28th August (Tuesday), 2018, 7 p.m.-9 p.m. (doors
scheduled to open at 6:30 p.m.)
Venue: Japan University of Economics, Tokyo Shibuya Campus
Hall (the hall seats approximately 100 people)
Address: 25-17, Sakuragaoka-cho, Shibuya-ku, Tokyo, 151-0031

Traditional Crafts
Online Site

Admission: 2,000 yen (please pay at the door on the day)How to attend: apply through the site below:
http://www.japancivilization.org/
Contact: Japanese Civilization Institute03-5456-8082

Worldwide：https://shops.japancivilization.org
Japanese：http://japancivil.shop9.makeshop.jp

Messages from Director
Japanese Civilization Institute has been introducing and selling traditional crafts of Japan.
Although it is said that there is approximately 1,200 types of traditional crafts in Japan, its
production is declining. As part of our activities, we hope to discover valuable traditional crafts
spread across the nation, enjoy Japanese craftsmanship and its beauty inherited over the
centuries, and develop it with you.

Tsumami Kanzashi(Crepe)Folding Screen(half size)

Folding Screen(full size)

KYO-YAKI
Earthenware

Japanese Bamboo
Basket Tokyotrad

Odoshi Samurai Armor

Contact：Japanese Civilization Institute 24-5 Sakuragaoka-cho, Shibuya-ku, Tokyo 150-0031 Phone : 03-5456-8082 Mail : info@japancivilization.org