Bulls n Bears Entrepreneurship Zone :: Artificial intelligence is already here. Soon it will be everywhere.

[Bulls n Bears]

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Over the last five years, artificial intelligence (AI) has exploded out of
the research laboratory and is thriving in the outside world. AI
technologies have become so successful, so rapidly, that their presence is
now ubiquitous in many sectors. AI algorithms drive the systems that choose
the content displayed on social media feeds, rank product recommendations on
e-commerce sites, and allow smart speakers and digital personal assistants
to parse and respond to spoken questions. So far, the most significant
impact of AI has been felt in the consumer space, but according to a new
report from DHL, AI technologies are poised to transform the B2B economy
too.

To understand the potential of AI, say the report’s authors, it’s important
to recognise that the term refers broadly to human intelligence exhibited by
machines. These systems make use of data and learning frameworks to solve
the kinds of problems humans solve, interact with humans and the world as a
human does, and create ideas like humans. It’s vital to understand the
capabilities and limitations of today’s AI techniques. Computing systems of
the past were, by and large, deterministic in nature. They were designed to
achieve specific objectives and operated using explicit rules created by
their developers, where a set of inputs generated a fixed set of results.
While they excelled in speed and accuracy, conventional computer systems
were unable to learn from experience or find new, creative solutions to
previously unseen challenges. AI shifts this paradigm by demonstrating the
ability to perceive, translate and understand the content of vast amounts of
data generated by individuals, systems and businesses every day.


Narrow but powerful


While AI practitioners debate the feasibility of building a machine with
the flexibility, reasoning and imagination necessary to rival the human
mind, this “general artificial intelligence” remains a purely theoretical
concept today. Real-world applications are commonly referred to as “narrow
AI”; these systems can cope with noise and uncertainty, produce novel
solutions and improve their own performance over time. Unlike a person,
however, narrow AI systems can’t switch easily between tasks; AI programs
that beat world champion board game players like Lee Sedol in Go, or Garry
Kasparov in chess, could not perform lifestyle services conversationally the
way Amazon’s Alexa could.

AI technologies have some characteristics that have hampered their adoption
in business and industrial applications, however. AI systems are stochastic
in nature: like a person, they get things right most of the time, but not
every time. In some contexts, that doesn’t matter too much. A website that
serves up irrelevant recommendations is only irritating. A system that makes
business- or safety-critical decisions needs to consistently perform better
than the approach it replaces. That’s happening today. Virtual assistants
now achieve word recognition rates of more than 94%, exceeding average human
comprehension in normal conversation. Similarly, while autonomous vehicles
have been involved in a handful of high-profile collisions, human drivers
cause comparatively more traffic accidents, without the advantage of
computational memory that never forgets a driving manoeuvre or scenario.

The performance of AI technologies isn’t the only factor reaching a tipping
point. They are also becoming much more accessible. AI practitioners are
placing many of their technologies into the public domain via open source
tools. Established companies and startups are packaging those technologies
into robust products or cloud-based services. Finally, as more industries
embrace digital products and services, they can use their own historical
data to train various AI technologies for specific use cases.


A new industrial revolution


AI technologies are already being applied to do everything from sorting and
grading vegetable produce to medical diagnoses. “AI will eventually be
contained within every software product on the market. This will bring us a
new generation of tools that fundamentally augment and enhance the quality,
reach and velocity of human expertise,” says Ben Gesing, one of the report’s
authors. In logistics, for example, DHL and its partners are already
exploring the use of AI techniques to provide better responses to customer
queries, predict service delays and even determine delivery requirements
based on customer profiles.

Ultimately, says Gesing, one of the biggest risks for companies lies in
long-term non-competitiveness from not investing in AI technologies soon
enough. “Artificial intelligence platforms are fundamentally different from
other investments,” he says. “Because these systems can learn and improve
their own performance, the business case for their use is very different
from conventional assets. Rather than diminishing over time, the benefits
tend to grow over time.”—Howwemadeitinafrica 



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