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This is a perspective paper inspired from the study of Turing Test proposedby A.M. Turing (23 June 1912 - 7 June 1954) in 1950. Following one importantimplication of Turing Test for enabling a machine with a human-like behavior orperformance, we define human-like robustness (HLR) for AI machines. Theobjective of the new definition aims to enforce AI machines with HLR, includingto evaluate them in terms of HLR. A specific task is discussed only on objectidentification, because it is the most common task for every person in dailylife. Similar to the perspective, or design, position by Turing, we provide asolution of how to achieve HLR AI machines without constructing them andconducting real experiments. The solution should consists of three importantfeatures in the machines. The first feature of HLR machines is to utilizecommon sense from humans for realizing a causal inference. The second featureis to make a decision from a semantic space for having interpretations to thedecision. The third feature is to include a "human-in-the-loop" setting foradvancing HLR machines. We show an "identification game" using proposed designof HLR machines. The present paper shows an attempt to learn and explorefurther from Turing Test towards the design of human-like AI machines.

A design of human-like robust AI machines in object identification