Visual Turing Test 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 systems are still not close to solving the ultimate problem of understanding images the way humans do. So motivated by the ability of humans to understand an image and even tell a story about it, Geman "et al." have introduced the Visual Turing Test for computer vision systems. As described in, it is “an
Church–Turing thesis three formally-defined classes of computable functions coincide with the "informal" notion of an effectively calculable function. Since, as an informal notion, the concept of effective calculability does not have a formal definition, the thesis, although it has near-universal acceptance, cannot be formally proven. Since its inception, variations on the original thesis have arisen, including statements about what can physically be realized by a computer in our universe (physical Church-Turing thesis) and what can be efficiently computed (Church–Turing thesis (complexity theory)). These variations are not due to Church or Turing, but arise from later work in complexity theory and digital physics.