Smart Industry 2/2018

Leaders of the Data Revolution
■ Tesla
public transport flows. Working with the UK
Government’s StreetWise program, it will
commence on-street testing in the complex
traffic of London in 2019.
In the middle, we will often find
specialized frameworks that focus
on specific tasks and processes
found in particular industries and
market verticals (some of which
are shown below). These come in
at different points of the process –
some come in at the time of data
capture, some do the heavy lifting
of the data collected, and others
focus on the data output, for example
in the way of virtual assistants.
Bringing dark to light
In industry, the first two will often
be the most significant ones because
they deploy relatively inexpensive
tools to capture, structure,
and analyze existing data and processes
to propose improvements
in processes along the entire value
chain without the need for spending
big on capital expenditure. The
raw data has existed for a long time
but it was largely unstructured and
thus “dark.” It is the combination of
data capture by deploying sensors,
the amalgamation of the various
data sources available in and to the
company, and, last but absolutely
not least, the analysis of ongoing
comparisons and the iterative improvement
AI affords, that is bound
to make the biggest difference to
the world we know. It lights up the
data we have to hand by providing
structured outputs that humans can
process and understand.
This is the true superpower that the
“I” in IoT holds for us.
Harnessing
the power of
the cloud
The various data
layers reside in the
company’s domain,
but the processing
takes place in
standardized AI
frameworks hosted
in the cloud.
Elon Musk, in his missionary Master Plan
Part Deux, explained in 2016 how Tesla uses
“fleet learning,” whereby it amalgamates
every mile driven in a Tesla to inform its AI
in order to improve autonomous driving
capabilities. Two years ago, he said Tesla
vehicles collected data for three million miles
daily and the number of cars has increased
annually. This would mean that Tesla now
has data from about five billion miles driven
to train its self-driving AI.
Although it is rumored that Tesla uses
components in its air-suspension system
from Bilstein, the same manufacturer as
Mercedes, and both have on-board GPS
systems, Mercedes has not yet made the
software connection that currently allows
Tesla vehicles to automatically adjust ride
height based on previously encountered
road conditions.
■ FiveAI
This Cambridge-based startup in the UK
not only builds out its own self-driving
system but combines it with data garnered
from Transport for London, the operator
of London’s traffic systems, as well as
from insurers and other stakeholders, to
ease traffic and optimize traffic flows and
the utilization of transport networks. Its
hardware gets “smart” by receiving the
traffic-related data from a number of sources,
including traffic lights, parking spaces, and
■ SenSat
This London-based startup creates
super-high-definition 3D point maps for
infrastructure and large-scale construction,
accurate to 6 mm (which is engineeringgrade).
Once construction is complete, any
sensor on the project can be assigned its
specific location and the map becomes a
real-time equivalent of environmental, traffic,
and other data in this location, which can be
used by planners, property managers, and
citizens. Data outputs can also be used to
shape future projects more effectively.
■ Big River Steel
At the other end of the spectrum, an
Arkansas-based steel mill is not the place
where you would expect high tech. It has
installed sensors and AI to optimize its
processes, improving its workflows and,
ultimately, its profits. Besides predicting
demand, sourcing, and scheduling based on
historical patterns which are continuously
updated as new data flows in, it also
deploys predictive maintenance to minimize
downtime of its machinery.
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