Intelligence in Artificial Intelligence
  The elusive quest for intelligence in artificial intelligence prompts us to
consider that instituting human-level intelligence in systems may be (still) in
the realm of utopia. In about a quarter century, we have witnessed the winter
of AI (1990) being transformed and transported to the zenith of tabloid fodder
about AI (2015). The discussion at hand is about the elements that constitute
the canonical idea of intelligence. The delivery of intelligence as a
pay-per-use-service, popping out of an app or from a shrink-wrapped software
defined point solution, is in contrast to the bio-inspired view of intelligence
as an outcome, perhaps formed from a tapestry of events, cross-pollinated by
instances, each with its own microcosm of experiences and learning, which may
not be discrete all-or-none functions but continuous, over space and time. The
enterprise world may not require, aspire or desire such an engaged solution to
improve its services for enabling digital transformation through the deployment
of digital twins, for example. One might ask whether the "work-flow on
steroids" version of decision support may suffice for intelligence? Are we
harking back to the era of rule based expert systems? The image conjured by the
publicity machines offers deep solutions with human-level AI and preposterous
claims about capturing the "brain in a box" by 2020. Even emulating insects may
be difficult in terms of real progress. Perhaps we can try to focus on worms
(Caenorhabditis elegans) which may be better suited for what business needs to
quench its thirst for so-called intelligence in AI.
