the limitations of deep learning the space of applications that can be implemented with deep learning is nearly infinite and yetmany applications are completely out of reach for current deeplearning techniques--even given vast amounts of human-annotated data sayfor instancethat you could assemble dataset of hundreds of thousands--even millions--of english-language descriptions of the features of software productwritten by product manageras well as the corresponding source code developed by team of engineers to meet these requirements even with this datayou could not train deep-learning model to read product description and generate the appropriate codebase that' just one example among many in generalanything that requires reasoning--like programming or applying the scientific method--long-term planningand algorithmic data manipulation is out of reach for deep-learning modelsno matter how much data you throw at them even learning sorting algorithm with deep neural network is tremendously difficult this is because deep-learning model is just chain of simplecontinuous geometric transformations mapping one vector space into another all it can do is map one data manifold into another manifold yassuming the existence of learnable continuous transform from to deep-learning model can be interpreted as kind of programbutinverselymost programs can' be expressed as deep-learning models--for most taskseither there exists no corresponding deep-neural network that solves the task oreven if one existsit may not be learnablethe corresponding geometric transform may be far too complexor there may not be appropriate data available to learn it scaling up current deep-learning techniques by stacking more layers and using more training data can only superficially palliate some of these issues it won' solve the more fundamental problems that deep-learning models are limited in what they can represent and that most of the programs you may wish to learn can' be expressed as continuous geometric morphing of data manifold the risk of anthropomorphizing machine-learning models one real risk with contemporary ai is misinterpreting what deep-learning models do and overestimating their abilities fundamental feature of humans is our theory of mindour tendency to project intentionsbeliefsand knowledge on the things around us drawing smiley face on rock suddenly makes it "happy"--in our minds applied to deep learningthis means thatfor instancewhen we're able to somewhat successfully train model to generate captions to describe pictureswe're led to believe that the model "understandsthe contents of the pictures and the captions it generates then we're surprised when any slight departure from the sort of images present in the training data causes the model to generate completely absurd captions (see figure licensed to