Bridging the Artificial Intelligence Skills Gap in the Machine Manufacturing Industry
flexibility
and
problem
solving
competencies as well as their willingness
to engage in lifelong learning; otherwise,
they will not be able to keep up with the
required changes in their workplaces and
work procedures. This challenge might also
explain why many companies are reluctant
to invest in Cyber Physical Systems (CPS),
which typically include AI. Competence
management on the organizational level,
as well as the reform of public education,
are important factors for introducing CPS.
C ASE S TUDY —M ACHINE L EARNING
A CADEMY (MLA)
Nowadays, it is relatively easy to find free
and general-level online training about
Artificial Intelligence and Machine
Learning from key technology providers
(such as IBM, Microsoft, Amazon and
Google) or from MOOCs organized by
prominent universities. One example is
”Elements of AI” 7 , a 6-module online
course created in co-operation by the
Finnish technology company Reaktor Ltd.
and the University of Helsinki. Typically,
the aim of this type of training is “to
demystify AI”, i.e. to encourage a broad
group of people to learn what AI is, what is
it good for and what are its limitations.
Machine Learning Academy 8 (MLA) is an
example of a more focused and industry-
tailored approach for closing or at least
narrowing the AI competence gap. It is
organized by DIMECC Ltd. in co-operation
with Futurice Ltd., a Finnish technology
consulting company with wide experience
in offering training on AI and ML to various
target groups from designers to board
members. The first MLA course, focusing
on the Finnish machine manufacturing
industry, was organized in Finland during
the autumn of 2018. The second course
closed at the end of April 2019. This
initiative was also highlighted in the final
report of Finland’s National AI Programme
as an innovative example of AI-related
education.
MLA’s primary target audience consists of
R&D supervisors and engineers as well as
business and product owners who are
managing and/or participating in AI/ML
development projects. In order to succeed
in these tasks, they need to understand
how to specify, plan, evaluate and manage
development or insourcing of sub-entities
that contain elements of AI and ML. For
example, for R&D engineers it is important
to understand how introduction of these
new technologies will change the
capabilities, boundaries, schedules and
interfaces of their product development
processes. After the course, participants
will have an understanding of the
fundamentals of AI and ML as well as an
ability to recognize and manage
development tasks that aim to benefit
from use of these new methodologies.
MLA consists of seven full-day training
modules with supporting pre-reading
materials, hands-on exercises and
homework. The training starts with high-
level topics, such as review of typical
business drivers and examples of ML
applications. In the next, more technical
modules, various ML methodologies are
7 https://www.elementsofai.com/
8 https://www.dimecc.com/dimecc-services/dimecc-machine-learning-academy/
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June 2019