Organizations looking to benefit from the artificial intelligence (AI) revolution should be cautious about putting all their eggs in one basket, a study from the University of Waterloo has found.
A group of researchers found that there is no precise method for deciding whether a given problem may be successfully solved by machine learning tools.
“We
have
to
proceed
with
caution,”
said
Shai
Ben-David,
lead
author
of
the
study
and
a
professor
in
Waterloo’s
School
of
Computer
Science.
“There
is
a
big
trend
of
tools
that
are
very
successful,
but
nobody
understands
why
they
are
successful,
and
nobody
can
provide
guarantees
that
they
will
continue
to
be
successful.
“In
situations
where
just
a
yes
or
no
answer
is
required,
we
know
exactly
what
can
or
cannot
be
done
by
machine
learning
algorithms.
However,
when
it
comes
to
more
general
setups,
we
can’t
distinguish
learnable
from
un-learnable
tasks.”
Ben-David and his colleagues considered a learning model called estimating the maximum (EMX), which is a system that captures many common machine learning tasks. Through this learning model, they found that for some tasks there would be no mathematical method that would ever be able to tell whether or not an AI-based tool could handle that specific tasks or not.
“This finding comes as a surprise to the research community since it has long been believed that once a precise description of a task is provided, it can then be determined whether machine learning algorithms will be able to learn and carry out that task,” said Ben-David.