  As AI algorithms increasingly participate in daily activities that used to be
the sole province of humans, we are inevitably called upon to consider how much
machines are really like us. To address this question, we turn to the Turing
test and systematically benchmark current AIs in their abilities to imitate
humans. We establish a methodology to evaluate humans versus machines in
Turing-like tests and systematically evaluate a representative set of selected
domains, parameters, and variables. The experiments involved testing 769 human
agents, 24 state-of-the-art AI agents, 896 human judges, and 8 AI judges, in
21,570 Turing tests across 6 tasks encompassing vision and language modalities.
Surprisingly, the results reveal that current AIs are not far from being able
to impersonate human judges across different ages, genders, and educational
levels in complex visual and language challenges. In contrast, simple AI judges
outperform human judges in distinguishing human answers versus machine answers.
The curated large-scale Turing test datasets introduced here and their
evaluation metrics provide valuable insights to assess whether an agent is
human or not. The proposed formulation to benchmark human imitation ability in
current AIs paves a way for the research community to expand Turing tests to
other research areas and conditions. All of source code and data are publicly
available at https://tinyurl.com/8x8nha7p
