Visual Turing Test datasets and evaluation metrics so the performances can be compared. This led to the emergence of challenges like the Pascal VOC challenge and the ImageNet challenge. The availability of standard evaluation metrics and the open challenges gave directions to the research. Better algorithms were introduced for specific tasks like object detection and classification. Visual Turing Test aims to give a new direction to the computer vision research which would lead to the introduction of systems that will be one step closer to understanding images the way humans do. A large number of datasets have been annotated and generalised to benchmark
Turing completeness is that there exist problems for which it is impossible to predict what a (Turing-complete) system will do over an arbitrarily long time. The classic example is the halting problem: create an algorithm which takes as input (a) a program in some Turing-complete language, and (b) some data to be fed to "that" program; and which determines whether the program, operating on the input, will eventually stop or will continue forever. It is trivial to create an algorithm that can do this for "some" inputs, but impossible to do this in general. For any characteristic of the program's eventual output,