Smart Industry 1/2020

Kropp says current estimates are
that around 80 percent of software
developers and programmers are
male. “The data with which these
people train artificial intelligence
represent their world,” she says.
For example, if a programmer has
mainly white friends, he will show
the AI photos of white people for
facial recognition. As a result, the AI
will only be able to fall back on less
diverse image material and will distinguish
faces of non-white people
less successfully.
The problems do not stop with racial
proclivities within the AI, notes
Kropp, by mirroring the world in
which the dominant white male
programmers live, many other
problems can arise.
AI Needs Better Data
“The solution to this problem is obvious:
AI needs better data, more
data, and, above all, more diverse
data,” says Kropp. This will only
happen when people from different
social and cultural contexts
program such machines. Unfortunately,
too few people with these
backgrounds have so far decided
on a career in software development.
“The EqualAI initiative is
working to ensure that more women
and people from minorities are
trained in the technology,” says
Kropp.
On a similar note, Stephane Rion,
senior deep-learning scientist for
Teradata in France, says that a key
aspect of AI implementations within
financial organizations is transparency:
“More and more banks and
financial institutions are focusing
their efforts not only on developing
the most performant predictive
models to catch fraud or agree on a
loan but also in understanding why
a model made a specific decision.”
In the area of deep learning, neural
networks can have a large number
of neurons or parameters that will
affect the final decision; being able
to understand this is vitally important
for a bank when it comes to
meeting regulations or even running
an audit, he explains.
AI allows
us to
improve
the safety
and reliability
of our
infrastructure
and
production
process.
Meghan Sharp
Managing Director,
BP Ventures
Pumping Data
Instead of Oil
Through its partnership
with Beyond
Limits, gas and oil
giant BP hopes to
improve the safety
and reliability of
its infrastructure
and production, as
well as changing
the way it locates
and refines crude.
By harnessing collective
knowledge,
BP believes it can
improve the speed
and quality of its
decision-making.
ting a boost through Sero’s crop
monitoring which uses AI to analyze
photographs and identify likely
diseases or infestations. The system
will have the ability to diagnose and
recommend treatments to farmers.
BP, the British multinational oil and
gas company, has invested $20m
in Beyond Limits, an AI company
with roots in NASA’s Jet Propulsion
Laboratory, to help it accelerate the
delivery of AI software that shows
promise of offering the energy sector
new levels of operational insight,
source ©: Beyond Limits Inc.
business optimization, and process
automation.
A hope of the partnership, according
to BP, is that it could enable a
change in the way it locates and
develops reservoirs, produces and
refines crude oil, and markets and
supplies refined products. Beyond
Limits’ software will help support
improvements in the speed and
quality of decision-making and
manage operational risks by harnessing
the collective knowledge
and experience of BP’s experts.
Beyond Oil and Gas
The company says it hopes the software
will also allow the oil company
to improve the safety and reliability
of its infrastructure and production
processes. Meghan Sharp, managing
director of BP Ventures, believes
the investment is an example of BP’s
ongoing support of entrepreneurs
and innovators that goes beyond
the traditional world of oil and gas.
Watson, IBM’s famous cognitive
supercomputer, has been applied
extensively in health-care management
both within the US and
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