  AI has been dealing with uncertainty to have highly accurate results. This
becomes even worse with reasonably small data sets or a variation in the data
sets. This has far-reaching effects on decision-making, forecasting and
learning mechanisms. This study seeks to unpack the nature of uncertainty that
exists within AI by drawing ideas from established works, the latest
developments and practical applications and provide a novel total uncertainty
definition in AI.
  From inception theories up to current methodologies, this paper provides an
integrated view of dealing with better total uncertainty as well as
complexities of uncertainty in AI that help us understand its meaning and value
across different domains.
