the limitations of deep learning in shortdeep-learning models don' have any understanding of their input--at leastnot in human sense our own understanding of imagessoundsand language is grounded in our sensorimotor experience as humans machine-learning models have no access to such experiences and thus can' understand their inputs in humanrelatable way by annotating large numbers of training examples to feed into our modelswe get them to learn geometric transform that maps data to human concepts on specific set of examplesbut this mapping is simplistic sketch of the original model in our minds--the one developed from our experience as embodied agents it' like dim image in mirror (see figure embodied human experience real world abstract concepts in human mind labeled data exemplifying these concepts machine-learning model (xmay not always transfer well to the real world figure doesn' match the human mental model it came from matches the training data current machine-learning modelslike dim image in mirror as machine-learning practitioneralways be mindful of thisand never fall into the trap of believing that neural networks understand the task they perform--they don'tat least not in way that would make sense to us they were trained on differentfar narrower task than the one we wanted to teach themthat of mapping training inputs to training targetspoint by point show them anything that deviates from their training dataand they will break in absurd ways local generalization vs extreme generalization there are fundamental differences between the straightforward geometric morphing from input to output that deep-learning models doand the way humans think and learn it isn' only the fact that humans learn by themselves from embodied experience instead of being presented with explicit training examples in addition to the different learning processesthere' basic difference in the nature of the underlying representations humans are capable of far more than mapping immediate stimuli to immediate responsesas deep networkor maybe an insectwould we maintain complexabstract models of our current situationof ourselvesand of other peopleand can use these models to anticipate different possible futures and perform long-term planning we can merge together known concepts to represent something we've never experienced licensed to