CUHK Business School Researcher Shares his View on AI and its Limitation

HONG KONG,
CHINA�- Media OutReach - March 6, 2019 - Artificial intelligence (AI) is
inevitable. Coined
in 1955 by American scientists to describe a new computer science division, AI
has successfully penetrated into our daily lives: When you pick up your
smartphone and talk to Siri — that is AI at work. When you text with a customer
service chatbot on Amazon, you are also being served by AI technology.

 

Whether we are enjoying the convenience brought by
AI technology or worrying about when machines will eventually steal all our
jobs, there is no way for us to stop seeing its development.

 

As ubiquitous as it is, AI is not able to solve
everything, at least not yet.

 

Prof. Michael Zhang, Associate Dean (Innovation and
Impact) and Professor of Department of Decision Sciences and Managerial
Economics at The Chinese University of Hong Kong (CUHK) Business School points
out that the applications of AI in computer sciences are different from those
in economics.

 

“There is a big difference between how
computer scientists and economists look at technological innovations.
Currently, artificial intelligence is used more for prediction and
classification. But, the focus of economists’ research is to find causal
relationships and explain the underlying mechanisms of things,” says Prof.
Zhang, adding that interesting results can be produced if artificial
intelligence and some of the data analysis models built by economists can be
combined.

 

Unpredictable Events in Finance

In the
financial market, it is often difficult to identify the relationship between
cause and effect, as unpredictable events still happen despite the usage of
complex financial models. That’s why he thinks researchers should not assume AI
can solve everything.

 

“In
finance, there are multiple levels of risks. Once the volatility is written
out, we have already assumed a certain probability distribution. But in many
cases, we cannot possibly know about the probability distribution of financial
events, such as the stock market,” he says.

 

He cites
the example of the collapse
of Long-Term Capital Management L.P. (LTCM) and its fund in the late
1990s, leading to an agreement among 16 financial institutions for a US$3.6
billion bailout under the supervision of the US Federal Reserve.

This and
other unexpected events in the financial market including the 2008
financial crisis are termed as “black swan”
events, which are random and unpredictable.

 

Traditional
risk analysis approach assumes probability as independently and identically
distributed. However, Prof. Zhang believes that this traditional approach is
inefficient in explaining the past black swan events. Hence, without changing
the approach, the use of AI will yield very little success in predicting
events.

 

“There
are no physical laws governing social actions, and in many real-life situations
the probabilities of events are not available. In the field of social sciences,
therefore, in addition to outcome uncertainty (risks), decision-making tasks
are often subject to distribution uncertainty (ambiguity),” he says.

 

From his
perspective, black swan events seem surprising only because past researchers
neglected the influence of ambiguity in the financial market. This concept is
what he was explaining in his working paper titled “Statistical Inference
with Ambiguity” in which he suggested a model that incorporates ambiguity
in statistical inference to study uncertain events.

 

“When
ambiguity is considered, the derived uncertainty is demonstrably larger than
the case only when risk is present. The derived uncertainty grows at a faster
speed in ambiguity than in risk. So when ambiguity dominates, previously
rejected hypotheses can no longer be rejected,” he explains.

 

In his
study, the team used mathematical formulae to show that the confidence level
plunged from 95 percent to 50 percent even with a moderate increase in
ambiguity. This explains why black swan events happen even when the probability
of such events is very small. Based on this theory, the next step is to find
out the measurement for ambiguity — and that is where AI comes in.

 

“We can use
AI technology to identify if the underlying distribution will change. If we can
identify the change, then we know our underlying model needs to be changed. As
such, we are trying to create a measurement based on AI algorithms,” Prof.
Zhang explains.

 

Will China Be
the Future Leader of AI?

The U.S. and China are considered to be leading the race in artificial
intelligence of the world. In 2017, China
has announced its ambition to be the world leader in AI and the
country’s AI industry is worth almost US$150 billion. In addition, China
provided 48 percent of the total equity funding for AI startups globally.

 

How will AI technology grow in China and compete with the U.S.?

 

“In the near future, I think the AI development in China will be
very fast. But there is a huge difference between the development in China and
that in the U.S. as well as in Europe,” he says.

 

“In the U.S., algorithms come first; in China, applications come
first.”

 

According to him, engineers in tech giants like Google and Amazon
continue to push the boundaries of science to come up with original algorithms.
However, due to severe competitions in China, not many firms can afford to
invest a huge amount of time in such scientific research.

 

As for who will win the AI race, he thinks it is too early to say.
However, despite each country’s huge effort in development of the technology,
the capability of AI in daily applications is still far from being fully
realised. He reckons it will take at least another 5 to 10 years for AI to
completely infiltrate into our daily lives.

 

“We tend to overestimate what we can achieve in the short run and
underestimate what we can achieve in the long run,” he says. “In the
long run, I believe AI will deliver a lot of the fancy things that we couldn’t
even imagine today. Just like when the internet first came about, no one was
able to foresee how much it would affect our lives as it is now,” he adds.

 

The Irreplaceable Role of Humans

Like it or
not, AI is already part of our lives and will continue to further impact us in
future. The question, therefore, is not whether or not we should work with AI
but rather “how to work with machines so that we, humans, remain in charge”,
as Prof. Zhang points out.

 

In his opinion,
AI is a tool which helps to free up
our time spent in ordinary routine work so that we can focus our time and
energy in achieving bigger tasks.

 

“For
example, if machines can answer my long list of emails every day, that would
spare me a lot of time for more meaningful, creative tasks,” he says.

 

In the same
way, one can imagine how much time the technology can save other professions
for the benefit of society. “If doctors can spare their time from treating
ordinary cases to concentrating on finding solutions for complicated diseases
through the help of AI, that would be a good thing for our community.”

In the
business world, there are also things that can’t be replaced by machines such
as leadership.

 

“Many
leaders nowadays are making data-driven decisions, but I don’t think machines
can help them formulate strategies,” says Prof. Zhang.

 

Reference:

Yu Liu,
Lihong Zhang and Xiaoquan (Michael) Zhang. 2019. “Statistical Inference
with Ambiguity.” (Working paper)

 

This
article was first published in the China Business Knowledge (CBK) website by
CUHK Business School: https://bit.ly/2EAQkWY.

 

About CUHK Business School

CUHK
Business School comprises two schools — Accountancy and Hotel and Tourism Management — and four
departments — Decision Sciences and
Managerial Economics, Finance,
Management and Marketing. Established in Hong Kong in 1963, it is the first
business school to offer BBA, MBA and Executive MBA programmes in the region.
Today, the School offers 8 undergraduate programmes and 20 graduate programmes including MBA, EMBA,
Master, MSc, MPhil and Ph.D.

 

In the Financial
Times Global MBA Ranking 2019, CUHK MBA is ranked 57^th. In FT‘s 2018 EMBA ranking, CUHK EMBA is ranked 29^th in the world. CUHK Business School has the largest number
of business alumni (35,000+)
among universities/business schools in Hong Kong
— many of whom are key business leaders. The School currently has about 4,400
undergraduate and postgraduate students and Professor Kalok Chan is the Dean of
CUHK Business School.

 

More information is available at www.bschool.cuhk.edu.hk or by
connecting with CUHK Business School on
Facebook: www.facebook.com/cuhkbschool
and LinkedIn: www.linkedin.com/school/3923680/.