  This is a perspective paper inspired from the study of Turing Test proposed
by A.M. Turing (23 June 1912 - 7 June 1954) in 1950. Following one important
implication of Turing Test for enabling a machine with a human-like behavior or
performance, we define human-like robustness (HLR) for AI machines. The
objective of the new definition aims to enforce AI machines with HLR, including
to evaluate them in terms of HLR. A specific task is discussed only on object
identification, because it is the most common task for every person in daily
life. Similar to the perspective, or design, position by Turing, we provide a
solution of how to achieve HLR AI machines without constructing them and
conducting real experiments. The solution should consists of three important
features in the machines. The first feature of HLR machines is to utilize
common sense from humans for realizing a causal inference. The second feature
is to make a decision from a semantic space for having interpretations to the
decision. The third feature is to include a "human-in-the-loop" setting for
advancing HLR machines. We show an "identification game" using proposed design
of HLR machines. The present paper shows an attempt to learn and explore
further from Turing Test towards the design of human-like AI machines.
