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Expert Opinion | Information Technology
TOP AI TRENDS
FOR 2020
by Dr. Jassim Haji
Artificial Intelligence has been
the technology story in Bahrain
and round the world during 2019
and it doesn’t look like the excitement is
going away and it appears that it become
a reality outside of science fiction.
Here are some of the main AI trends
predicted by experts in the field for 2020:
Predictive text should get better and
better
Predictive text has been around for
some time now, but by combining it with
AI we may reach a point where the AI
knows what you want to write before
you do. "Smart" email predictive text is
already being tested on programs like
Gmail, for example.
If used correctly, this could
help users speed up their writing
significantly, and could be especially
useful for those with physical conditions
that make typing difficult. Of course,
many people will find themselves typing
out the full sentence anyway, even if the
AI correctly predicted their intentions.
Human and AI cooperation increases
More and more of us will get used
to the idea of working alongside AIpowered
tools and bots in our day-to-day
working lives. Increasingly, tools will be
built that allow us to make the most of
our human skills – those which AI can't
quite manage yet such as imaginative,
design, strategy, and communication
skills. While augmenting them with
super-fast analytics abilities fed by vast
datasets that are updated in real-time.
For many of us, this will mean
learning new skills, or at least new
ways to use our skills alongside these
new robotic and software-based tools.
The IDC predicts that by 2025, 75%
of organizations will be investing in
employee retraining in order to fill skill
gaps caused by the need to adopt AI.
This trend will become increasingly
apparent throughout 2020, to the point
where if your employer isn’t investing in
AI tools and training, it might be worth
considering how well placed they are to
grow over the coming years.
Quantum computing will supercharge AI
Another trend to watch in 2020 will
be advancements in quantum computing
and AI. Quantum computing promises
to revolutionize many aspects of
computer science and could be used to
supercharge AI in the future.
Quantum computing holds out the
hope of dramatically improving the
speed and efficiency of how we generate,
store, and analyze enormous amounts
of data. This could have enormous
potential for big data, machine learning,
AI, and privacy.
Facial recognition will appear in more
places
Facial recognition appears to be
en vogue at the moment. It is popping
up in many aspects of our lives, and is
being adopted by both private and public
organizations for various purposes,
including surveillance.
Artificial Intelligence is increasingly
being employed to help recognize
individuals and track their locations
and movements. Some programs in
development can even help detect
individual people by analyzing their gait
and heartbeat.
Machines will get better at
understanding-and generating their
own-speech and writing
A high-profile research org called
OpenAI grabbed headlines in early 2019
when it proclaimed its latest news-copy
generating machine learning software,
GPT-2, was too dangerous to publicly
release in full. Researchers worried
the passably realistic-sounding text
generated by GPT-2 would be used for
the mass-generation of fake news.
Synthetically produced data could make
AI cheaper
Ask any data scientist or company
toiling over a nascent AI strategy what
their biggest headache is and the
answer will likely involve data. Machine
learning systems perform only as well
as the data on which they’re trained,
and the scale at which they require it is
massive.
One reprieve from this insatiable
need may come from an unexpected
place: an emergent new machine
learning model currently best known for
its role in deepfakes and AI-generated
art. Patent applications indicate that
brands explored all kinds of uses for this
tech, known as a generative adversarial
network (GAN), in 2019. But one of its
unsung, yet potentially most impactful,
talents is its ability to pad out a dataset
18 January-February 2020