Visual Turing Test millions of dollars into computer vision research, and are trying to build systems that closely resemble the human visual system. Recently Facebook announced its new platform M, which looks at an image and provides a description of it to help the visually impaired. Such systems might be able to perform well on the VTT. Visual Turing Test Computer vision research is driven by standard evaluation practices. The current systems are tested by their accuracy for tasks like object detection, segmentation and localization. Methods like the convolutional neural networks seem to be doing pretty well in these tasks, but the current
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,