Instrumentation & Measurement Magazine 23-2 - 71

futureI&M
trends in

Wai Lok Woo

Human-Machine Co-Creation in the Rise of AI
A note from Melanie Ooi: In this issue, Wai Lok Woo shares with us
some insights into how we will evolve with the rise of artificial intelligence. It is an extremely engaging column that applies to almost all
of our work in instrumentation and measurement today. I hope you
will enjoy reading this as much as I did.

T

he emergence of Artificial Intelligence (AI) is creating new
dimensions and redefining the concept and meaning of work in industrial
settings. Documented success has been
reported where AI is transforming industrial scenes such as scaling large operation
processes, speed of execution, flexibility of
processes where rigid manufacturing by
dumb robots is replaced with smart individualized production following real-time
customer choices, decision-making in which a huge amount of
data can be quickly available at the fingertips of workers on
the factory floor or even prevent problems before they happen,
and personalization where AI uses data to deliver personalized
user experience. According to the market research firm Tractica, the global AI software market is expected to experience
massive growth in the coming years, with revenues increasing
from around US $9.5 billion in 2018 to an expected US $118.6
billion by 2025.
Despite the tangible benefits, AI has various societal shortfalls. Job loss has been a subject of numerous business cases.
Low income and low skilled workers will be the worst hit
by this change. As AI becomes smarter by the day, even the
high paid, high skill workers become more vulnerable to job
losses as, given the high cost of skilled workers, the companies get better margins by automating their work. There are
concerns that AI will cause harm to humankind. For example, autonomous weapons which can be programmed to kill
other humans. As AI becomes more powerful, it also brings
several trust-related issues on its ability to make decisions that
are fair and for the betterment of humankind. With AI slowly
reaching human-like cognitive abilities, these trust issues become all the more significant. Despite defeating humans in
many traditional and modern video games, AI algorithms
are quite vulnerable to adversarial attacks. Attackers could
April 2020	

target autonomous vehicles by using stickers or paint to create an adversarial traffic sign to mislead the vehicle to interpret
differently.
An essential part of human interaction is empathy and contextual awareness, and humans are born with an intuition for
both. Perhaps it is intuition that we are trying to introduce to
AI. Instead of brute-forcing its way through the dataset, successful AI learns to do things by stacking multiple algorithms
together. However, machines still fail to generalize much beyond already known data, such as a new pronunciation of a
word or an unconventional image, and have trouble dealing
with limited amounts of data. At this point, the most important difference that comes into the spotlight is that humans
have consciousness. Lacking consciousness, computers remain task-driven, meaning that they do nothing unless they
have a set goal. Humans are those who give the goal and meaning to what AI does for us and with us. If we stop thinking of
machines as continuation of humanity, we can benefit from
their cooperation without feeling threatened by robots-they
become cobots. The root idea of co-creation between humans
and AI is to enhance each other's strengths: the leadership,
teamwork, creativity, social skills of the former, and the speed,
scalability, and quantitative capabilities of the latter. This cocreation envisages that every participant has their role, be it a
domain specialist getting meaning out of scattered raw data or
the selected AI algorithms.
There are two types of human-machine co-creation, namely,
humans-assisting-machines and machines-assisting-humans.
The humans-assisting-machines (or human/machine co-operation) system is designed to make sure that a sensitive task
always depends on a person, even in situations where an automated system has done all the preparatory work and would be
quite capable of completing the task itself. Here, humans perform three crucial roles. They must train machines to perform
certain tasks; explain the outcome of those tasks, especially
when the results are counterintuitive or controversial; and
sustain the responsible use of machines. For example, a facial
recognition system where humans train the AI to identify suspect travelers, interpret the results, and continually update the
system to ensure its operation is ethically correct.
The machines-assisting-humans or human-in-the-loop system involves the use of AI that is not capable of handling a task

IEEE Instrumentation & Measurement Magazine	71
1094-6969/20/$25.00©2020IEEE



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