Todai Finance Innovation Program Lecture 12
The 12th lecture of the Todai Finance Innovation Program was held at the University ofTokyo Hongo Campus Kojima Conference Hall on the 21st November 2017 on the topic of AI andwearable sensors. We had two guest lecturers for the session; Dr. Kazuo Yano, Corporate Chief
Scientist at the Research and Development Group of Hitachi, Ltd. / Adjunct Professor at Tokyo
Institute of Technology Department of Computational Intelligence and Systems Science, and Yohei
Sadoshima, Cork Inc. Co-Founder / CEO, who has in the past been in charge of editing hit works
such as Vagabond (Takehiko Inoue), Dragon Zakura (Norifusa Mita), and is currently involved in
editing the likes of Investor Z (Norifusa Mita) and The End of the Matinee (Keiichiro Hirano). The
first half of the lecture followed a lecture-styled presentation by Dr. Yano, while the second half
took the form of a dialogue between the two lecturers.

Adapting to Change
Our current society has been based around replicating a winning formula throughbreaking it down into its component parts in the form of a manual (exemplified by the roll out ofchain stores). Although such method is highly efficient for the repetition of a particular process, it is
helpless against change and variation. In other words, it is completely useless to look in the backmirror and continue something because it has brought success in the past, because the environment
is constantly changing around us. An example of this is the drastic reduction in the price of solar
energy over the past three years. So what should we do if we don’t know what the future holds? In
baseball terms we must simply step up to the batter box. We must swing, miss, adjust our techniqueand try again until we can hit the ball out of the park. However, the problem at the moment is that in
the business scene there is currently very little room for experimenting. This is exactly the area

where AI has huge potential for implementation.

When most people hear the word AI they imagine the computer that could only performa particular function within the limits of what it was programmed to do. However, the whole conceptof AI lies in the flexibility of its application, and indeed it is becoming more and more widely
applicable by the day. There are three necessary conditions for AI to be utilized in such manner: 1)
Data on outcome of action, 2) Data on action performed by AI, 3) Data on conditions. If these 3
conditions are met, it is possible for the AI to experiment, learn, and repeat towards achieving the setoutcome. An example of such an AI is “H” produced by Hitachi, which allows a robot to learn how
to ride a swing by bending its knees at the right time. It learns from the data regarding the timing(condition) of bending its knees (action) and the resulting amplitude of the swing (outcome).
Eventually it learns to bend its knees at both sides, becoming even better at riding the swing than anormal human being. Without changing any of the software it is also able to learn to swing on a steel
bar from its arms using the momentum from bending its knees. This flexibility in application isexactly what sets AI apart from previous software that is programmed to perform a specific task andthat task only.

Becoming Outcome Orientated
In the past we have failed repetitively in the implementation of AI by trying to use it toanalyze data to identify a pattern, so that we can utilize the finding for implementing an appropriate
strategy to achieve our final goal. This has led us to take the reverse approach and clearly define theoutcome we desire before all else. People are then tasked with coming up with possible strategies toachieve the set goal (usually limited by agreements with clients etc.), before passing the task ofrepeating the PDCA cycle to the AI system. Such movement is already being observed in thebusiness scene, for example at warehouses to determine the order in which items should be pickedfrom shelves to shorten the total work hours per day (has resulted in an 8% increase in productivity).

As has been established in the previous paragraphs, the implementation of AI requires us
to shift our focus to become outcome orientated. However, our society at present is far from this andis governed by rules that are not necessarily ideal in achieving their desired outcomes. For example,
in the finance industry most Japanese banks have a rule that prevents the lending of money to single
mothers. In other words, just because someone belongs to the category of “single mothers”, they areunable to borrow money from the bank even if they may actually be more than capable of using themoney to positively contribute to society and eventually repay the debt. Banks know this but
because of the difficulty in making this discrimination, have preferred the usage of rules to minimize
the risk of issuing loans that may default. However, now with the availability of data and AI, weshould be able to differentiate between those who are truly risky and those who are not with greateraccuracy.

Until now because of the lack of such technology we could only efficiently maintainsocietal order using such rules, but now with availability of data and AI there exist many
possibilities to better manage such decision-making. Although some people say that AI is too
dangerous, it should be pointed out that our current rule-based society is in fact also highly
dangerous. At the same time it does not mean that all rules and regulators will become unnecessary
in the future to maintain societal order. Some things are much more simply and efficiently governed
by rules (e.g. driving on the left side of the road), but the areas where AI should be implemented arethose where the current rules are not truly in the best interest of their intended outcomes. In otherwords, our current society is trying to control things that are complex and involve multiple
conditions using simple rules, so that is where AI can help to better achieve our intended goal.

Measure of Productivity – Happiness
After Hitachi pulled out of the semiconductors industry approximately 14 years ago, itbegan to focus on the possibilities of the data that could be collected by computers. In particular,
there was a lack of data concerning human activity, so in 2006 Hitachi developed a wearable sensor
that could track the movement of the wrist of the person wearing it. Although each individual pieceof data collected is close to being worthless, if you gather them all together and look for patterns
there is a completely new value produced. When trying to measure the productivity in a workplace,there was the issue that there was no available quantitative measure for it. However, just at the sametime as the search for an appropriate quantitative scale for productivity, scientific papers werestarting to be produced suggesting the connection between people’s productivity and happiness.

Having identified such papers as a starting point for Hitachi’s research, they then set off
to ask 468 people 20 questions regarding their subjective happiness over the past week on a scale of
0-3 (days you felt happy, days you felt lonely, days you felt sad etc.). Next participants were made towear accelerometer sensors by their chest, and very interestingly a very high correlation between
subject survey score and variability in movements of subjects was found (R value of 0.94). For

clarification, this does not mean that if you run around lots you will be classified as being happy,
rather the sensor detects unconscious movements made whilst sitting down (stationary). In the quest
to find how to maximize output at firms, Hitachi has found that the key lies in being able tomaximize happiness of the firm’s employees (measurable by sensor).
At a call center a 30% - 40% difference in performance was observed between good days
and bad days, and these differences were attributed to how happy the telemarketer was on the day. Itwas found that factors such as who the manager talked to first had a large impact on employee
happiness. An AI was given the task of maximizing happiness of the employees using the happinessdata obtained by the sensors, and provided advice on who the manager should talk to first on thatday. What was remarkable was that the performance of the call center was 27% higher with theimplementation of the AI than without it. Dr. Yano often receives comments along the lines of“ifeveryone starts to use this technology we won’t make profits anymore” but this is a complete
misunderstanding because this is exactly where AI differs from traditional software of the past.
Although traditional software could only process data in a single set way outlined by the program,AI will take into consideration the different strategies employed by different firms and respond to
the different data inputs to produce completely different outputs using a single system.

Dialogue: Yohei Sadoshima x Kazuo Yano
Sadoshima: The examples of AI being implemented mentioned in the slides were all of successstories but were there any cases where it didn’t work?
Yano: Sometimes we didn’t have the required data to begin with so we did not see any results, butthen after making the decision to collect the required data we began to see positive results. There
were other cases where we made errors in setting the final goal. As an example, for a cram school
we asked the AI what we should do to make students’ grades go up, but we came to the realizationthat even if the students’ scores go up, it does not necessarily mean that the cram school will makemoney.
Sadoshima: Why did you include Joseph Campbell’s poem “Heroes and the Monomyth” at the endof the presentation?
Yano: At the end of the day, the advancement of technology on its own is not enough. You need ahero like the one mentioned in the poem to actually be able to change the mindset of the current
rule-based society and initiate the movement of decision-making using AI. Interestingly enough, thepattern of hero stories observed all over the world can be represented using this single pattern, andCampbell’s reasoning for this is that this sort of battle is built into each person’s DNA. I know it ishighly philosophical but I believe that in the end the story we have concerning AI is philosophical atits center.

Sadoshima: Actually I am currently in the process of developing a software that analyzes novels andrecognizes the different patterns that are behind successful ones. I thought it would be very
interesting if AI could give advice on how changes should be made to make the story moreinteresting. At the same time I was wondering if it would be possible for the AI to propose acompletely new pattern of novel writing altogether?

Yano: I think that such thing is possible. For example, computers can now beat professional players
at the traditional Asian board game of Go and people have always said that this is because the AI isstrong towards the end as it can simulate all of the possibilities using past data. However, in reality
the AI was actually strongest at the start and towards the middle of games, because it had built up
knowledge of moves through trial and error that people are usually unwilling to try. If you think
about it most humans rely on past experiences for their decision-making, and if they have won agame using a particular method in the past not many people would not be willing to try a move they
have never tried before in a game. So in that sense AI could have the potential to suggest acompletely new pattern of story that has not been tested before.

Sadoshima: Final question, with the wristband can you see what chance someone has of say going
into depression? I am currently wearing a Fitbit and recently read an article on how it has
accumulated a significant amount of data, but I wanted that to be reflected in the advice that it gives
you ... perhaps advising you when you are at risk of becoming ill?

Yano: Professor Nakamura at the University of Tokyo has found clear differences in activity patterns
between healthy people and those suffering from depression using the accelerometer. Although it
has yet to be put into practical usage, there exists large potential for use for such purposes as we are
now accumulating lots of data. The question is how we are going to turn that into a viable business.
It could perhaps be connected to medical check ups or health insurance and those wearing themotion sensor could receive a discount.