what happened is that the gaming market subsidized supercomputing for the next generation of artificial intelligence applications sometimesbig things begin as games todaythe nvidia titan xa gaming gpu that cost $ , at the end of can deliver peak of tflops in single precision trillion float operations per second that' about times more than what you can get out of modern laptop on titan xit takes only couple of days to train an imagenet model of the sort that would have won the ilsvrc competition few years ago meanwhilelarge companies train deep-learning models on clusters of hundreds of gpus of type developed specifically for the needs of deep learningsuch as the nvidia tesla the sheer computational power of such clusters is something that would never have been possible without modern gpus what' morethe deep-learning industry is starting to go beyond gpus and is investing in increasingly specializedefficient chips for deep learning in at its annual / conventiongoogle revealed its tensor processing unit (tpuprojecta new chip design developed from the ground up to run deep neural networkswhich is reportedly times faster and far more energy efficient than top-of-the-line gpus data ai is sometimes heralded as the new industrial revolution if deep learning is the steam engine of this revolutionthen data is its coalthe raw material that powers our intelligent machineswithout which nothing would be possible when it comes to datain addition to the exponential progress in storage hardware over the past years (following moore' law)the game changer has been the rise of the internetmaking it feasible to collect and distribute very large datasets for machine learning todaylarge companies work with image datasetsvideo datasetsand natural-language datasets that couldn' have been collected without the internet user-generated image tags on flickrfor instancehave been treasure trove of data for computer vision so are youtube videos and wikipedia is key dataset for natural-language processing if there' one dataset that has been catalyst for the rise of deep learningit' the imagenet datasetconsisting of million images that have been hand annotated with , image categories ( category per imagebut what makes imagenet special isn' just its large sizebut also the yearly competition associated with it as kaggle has been demonstrating since public competitions are an excellent way to motivate researchers and engineers to push the envelope having common benchmarks that researchers compete to beat has greatly helped the recent rise of deep learning algorithms in addition to hardware and datauntil the late swe were missing reliable way to train very deep neural networks as resultneural networks were still fairly shallow the imagenet large scale visual recognition challenge (ilsvrc)www image-net org/challenges/lsvrc licensed to