Visual Turing Test a very recent work published on March 9, 2015, in the journal "Proceedings" of the National Academy of Sciences, by researchers from Brown University and Johns Hopkins University. It evaluates how the computer vision systems understand the Images as compared to humans. Currently the test is written and the interrogator is a machine because having an oral evaluation by a human interrogator gives the humans an undue advantage of being subjective, and also expects real time answers. The Visual Turing Test is expected to give a new direction to the computer vision research. Companies like Google and Facebook are investing
Turing completeness one instruction set computer) and the ability to change an arbitrary amount of memory ("e.g.", the ability to maintain an arbitrary number of data items). Of course, no physical system can have infinite memory; but if the limitation of finite memory is ignored, most programming languages are otherwise Turing complete. In colloquial usage, the terms "Turing complete" or "Turing equivalent" are used to mean that any real-world general-purpose computer or computer language can approximately simulate the computational aspects of any other real-world general-purpose computer or computer language. Real computers constructed so far can be functionally analyzed like a single-tape Turing