Maybe it’s time to start thinking about
AI now often referred to as cognitive technologies
in new ways.
Recently two very useful new terms have been coined
to help differentiate efforts to build AI’s.
Artificial Narrow Intelligence (ANI) and Artificial
General Intelligence (AGI).
Artificial General Intelligence sometimes
to referred to as strong AI is a research
field attempting to build machines that are
generally cognitive.
These machines are not being built for any
specific purpose.
But, to replicate the full range of human-like cognitive function.
Some people argue that one of the aims of
AGI is to produce a conscious machine.
Much of this research currently focuses on
building systems that replicate neurological
processes and great strides have been made
in recent years.
AGI also raises the question is building an
Artificial General Intelligence simply a matter
of building a machine that models every connection
in the brain?
If we achieve that would we have a machine that behaves like a person?
However, there’s a significant gap in our
understanding that we must bridge
before this question can be answered.
On the other hand, Artificial Narrow Intelligence,
sometimes called applied AI, is a term used
to describe a system that performs some single
function
as competently or perhaps even better than a human.
ANI’s may be narrow, but they’re already
changing the world
and the impact to business has been huge.
More than half the equity shares traded in
most markets
are traded by algorithmic high-frequency traders, not people.
Autonomous vehicles are now a reality and
satellite navigation systems have long since
been able to plot an efficient route across
the city.
If we look back over the last century we start
to see the significant automation of physical
work, as machines started to take on heavy
lifting and repetitive tasks.
This automation started in agriculture, but
quickly spread
to manufacturing and other sectors.
In the same way machines have changed the
way that we approach physical work, a new generation
of AI powered technologies is increasingly
to be found in the workplace.
Supporting or even replacing the knowledge
worker; to form Knowledge Work Automation.
McKinsey predict that over the next decade
the impacts to the global economy of
automating knowledge work will be between                  $5 and 7 trillion per annum.
We are just at the beginning of this monumental
shift.
What’s more society is starting to accept
these narrow applications of AI.
It’s already becoming normal to devolve
more and more of our thinking to machines.
Few people like repetitive rote tasks and
adopting technology
frees us up to perform more meaningful work.
The rise of mobile apps that have marked the last
decade, are set to be replaced with consultative
bots that can give us robo-advice through
any of our favourite messaging platforms.
Many of the problems suffered by AI in the
1970s
can be solved by building narrow applications of AI.
We can overcome the shortfalls of the logical
approach to knowledge engineering
by adopting probabilistic programming techniques.
Many of those early techniques failed because
data sets were too tiny or because computing
power was some millions of times to slow by
comparison with today’s standards.
But it’s no longer necessary to employ a
large team software developers to build a
system that will radically change the way
you approach your business.
Tools like Rainbird are transforming contact
centres, improving operational efficiency,
increasing sales and managing governance and
risk.
Everything has changed.
