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Judgment integration: A machine may be uncertain about
something or lack the necessary business or ethical context to
make decisions. Humans must be prepared to sense where,
how, and when to step in and provide input. Not just IQ and
EQ, a new dimension of AI-Quotient will emerge.
Intelligent interrogation: Humans simply cannot probe massively complex systems or predict interactions between
complex layers of data on their own. Humans need the ability to ask machines the right smart questions across multiple
levels.
Bot-based empowerment: Cobots to assist humans become
better at their jobs. Leveraging the power of AI agents can extend human's capabilities, reinvent processes, and even boost
a human's professional career.
Holistic melding: The re-imagination of processes becomes
possible when humans create working mental models of how
machines work and learn, and when machines capture userbehavior data to update their interactions.
Reciprocal apprenticing: Humans have learned how to use machines, but with AI, machines are learning from humans, and
humans, in turn, learn again from machines.
Relentless reimagining: The skill to reimagine how things currently are and to keep reimagining how AI can transform and
improve work, organizational processes, business models,
and even entire industries.
There will be a continuous circle of learning, an exchange
of knowledge between humans and machines. Humans can
work better and more efficiently with the help of AI. In the long
term, companies will start rethinking their business processes,
and as they do, they will cover the needs for new humans in
the new ways of doing business. Before we rewrite the business processes, job descriptions, and business models, we

need to answer these questions: "What tasks do humans do
best? And, what do machines do best?" The transfer of jobs is
not simply one way. In many cases, AI is freeing up to creativity and human capital, letting people work more like humans
and less like robots. It is expected that company roles will
be redesigned around the desired outcomes of reimagined
processes, and corporations will increasingly be organized
around different types of skills rather than around rigid job titles. Not everyone will be able to do it. A new class of people
may arise-people who are not just unemployed, but unemployable, because they choose not to adapt to life-training. For
others, the deployment of human-machine co-creation when
misused or when there is perhaps an over-reliance on it could
lead to "mental obesity," a condition akin to physical obesity
where digital data assumes the role of food.
The rise of AI has affected all of us. The compelling question we need to address is: "How can we seamlessly integrate
human and machine intelligence and allow the integrated
intelligence to dynamically evolve for a sustainable development?" If we are able to treat AI just the same way we extend
diversity and inclusivity to our fellow humans, then we are not
too far away from answering the question.
Wai Lok Woo (M'11-SM'12) (wailok.woo@northumbria.
ac.uk) is currently a Professor of Machine Learning with Northumbria University, UK. Previously, he was the Director of
Research for the Newcastle Research and Innovation Institute,
and the Director of Operations for Newcastle University, UK,
where he received the B.Eng. degree in electrical and electronics engineering and the M.Sc. and Ph.D. degrees in 1993, 1995,
and 1998, respectively. He has published more than 400 papers
on mathematical theory and algorithms of machine learning
on various journals and international conference proceedings. He aims to answer the global question of how artificial
intelligence and machine learning advances humanity, fuels
the economy and sustains the ecosystem in the current digital
transformation era.

For Further Reading
P. R. Daugherty and H. J. Wilson, Human + Machine: Reimagining Work in the Age of AI. Boston, MA, USA: Harvard Business Review Press, 2018.
M. Tegmark, Life 3.0: Being Human in the Age of Artificial Intelligence. New York, NY, USA: Penguin Random House, 2018.
F.M. Zanzotto, "Human-in-the-loop Artificial Intelligence," J. Artificial Intelligence Research, vol. 64, pp. 243-252, 2019.

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