>>> I doubt that neural networks can "reason" in an abstract way Unless there is of course some underlying "structure" to our phenomenological reality that af

>>> I doubt that neural networks can "reason" in an abstract way Unless there is of course some underlying "structure" to our phenomenological reality that affords us these insights ;) I'd take a look at the recent CV paper on "Taskonomy" from the Stanford Graphics Lab. Instead of semantic labeling of training data by human operators. We can begin to apply learning to high resolution sensor data. Such as RGBA, motion and depth images. And uncover hidden emergent relationships inherent in their geometric and physical properties. Since these "laws" are universal. They can essentially be transferred to myriad other tasks in vision. http://taskonomy.stanford.edu/ http://taskonomy.stanford.edu/ It is precisely this type of learning that DeepMind seeks to quantify with its tool. No one can say precisely why we as humans can look at a thing for the first time and comprehend its "thingness": its utility, its purpose, its meaning and social context. But I believe we are far closer than we imagine to robust meta-learning for intelligent machines on a grand scale!