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ture a maximum amount of wind
energy and also ‘disturb’ the flow of
the wind to any neighboring wind
turbines as little as possible.
Finally, smart systems evolve
towards ‘Autonomy’. When a product
is capable of monitoring itself or
carrying out an action – and making
that action as optimal as possible
– it can work autonomously. For
instance, there is the iRobot vacuum
cleaner robot, which is capable
of cleaning all sorts of surfaces in
the home, as well as detecting dirt,
finding its way round furniture and
avoiding tumbling down stairs. It
also ‘stores’ details of the layout of
a room in its memory for the next
time and makes its own way back
to its recharging station, where it
announces its safe arrival with a
triumphant sound signal!
Smart systems can also be
connected with each other so
that they can carry out actions in
tandem, learn from each other –
and so on. An ex-ample of this is the
idea of driverless cars and the road
infrastructure working together
so that if there is an accident
somewhere, cars further away from
the incident can be notified and the
appropriate action tak-en.
As we can see from these examples,
we will gradually evolve towards
systems that are capable of learning
and taking decisions by them-
selves. And equally gradually, we –
humans – will hand over the moni-
toring, control and optimization,
partly or in full, to machines. This
will happen faster in some sectors
than in others – and there are
various reasons for that. In the
mining industry, for instance, Joy
Global’s Longwall Mining System is
used to dig underground virtually
automati-cally, without any human
input. Staff sitting in the control
room above the ground keep a close
eye on everything going on and
only send en-gineers below ground
if it becomes necessary. So, for the
sake of peo-ple’s safety, mining has
evolved to the most advanced stage
of auton-omy – although people are
still very much a crucial factor of
operations.
People and AI systems
will become workmates
Human-like AI, human-centric AI,
human-in-the-loop AI – these are
all terms to indicate that human
beings are still very much central
to the story. Robots and machines
need to be made in such a way that
people can understand them, are
able to communicate with them and
can work efficiently with them. That
way, machines can carry out tasks
on behalf of and for the benefit of
humans.
A good example of this is the ‘cobot’,
or collaborative robot, developed to
assist Audi production line workers
in assembling cars. Whereas pre-
viously these types of machines
used to be placed in safety cages,
the cobot is able to carry out certain
actions safely close to and with the
help of its human workmates. This
means that tasks such as applying
adhesive can be carried out much
more precisely, consistently and al-
ways in the same way. Meanwhile,
the cobot’s human workmate is able
to control and direct it using hand
gestures.
There are still many challenges to
overcome in the area of communica-
tion between robots and humans.
For example, will a robot ever
be ca-pable of identifying our
intentions? Can a robot detect if we
say some-thing in a fearful or more
self-assured way? In which case,
this can be important in certain
situations. Or when we carry out an
action, what does this say about our
actual intentions? For instance, it is
no easy task to get a driverless car
to recognize whether a pedestrian
intends to cross the road, or is
simply standing at the side of the
road. Typical-ly, as a pedestrian,
we will try to make eye contact
with the driver to indicate that we
Fig 1:
In mining, for example, fully autonomous robots are used to work under
the ground, while everything is monitored closely from a control room above
the ground so that humans can inter-vene when necessary, for example if
repairs are needed. (Video from Joy Global / Komatsu Mining via https://hbr.
org/video/3819456791001/smart-connected-mining)
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