that' the magic of deep learningturning meaning into vectorsinto geometric spacesand then incrementally learning complex geometric transformations that map one space to another all you need are spaces of sufficiently high dimensionality in order to capture the full scope of the relationships found in the original data the whole thing hinges on single core ideathat meaning is derived from the pairwise relationship between things (between words in languagebetween pixels in an imageand so onand that these relationships can be captured by distance function but note that whether the brain implements meaning via geometric spaces is an entirely separate question vector spaces are efficient to work with from computational standpointbut different data structures for intelligence can easily be envisioned--in particulargraphs neural networks initially emerged from the idea of using graphs as way to encode meaningwhich is why they're named neural networks the surrounding field of research used to be called connectionism nowadays the name neural network exists purely for historical reasons--it' an extremely misleading name because they're neither neural nor networks in particularneural networks have hardly anything to do with the brain more appropriate name would have been layered representations learning or hierarchical representations learningor maybe even deep differentiable models or chained geometric transformsto emphasize the fact that continuous geometric space manipulation is at their core key enabling technologies the technological revolution that' currently unfolding didn' start with any single breakthrough invention ratherlike any other revolutionit' the product of vast accumulation of enabling factors--slowly at firstand then suddenly in the case of deep learningwe can point out the following key factorsincremental algorithmic innovationsfirst spread over two decades (starting with backpropagationand then happening increasingly faster as more research effort was poured into deep learning after the availability of large amounts of perceptual datawhich is requirement in order to realize that sufficiently large models trained on sufficiently large data are all we need this is in turn byproduct of the rise of the consumer internet and moore' law applied to storage media the availability of fasthighly parallel computation hardware at low priceespecially the gpus produced by nvidia--first gaming gpus and then chips designed from the ground up for deep learning early onnvidia ceo jensen huang took note of the deep-learning boom and decided to bet the company' future on it complex stack of software layers that makes this computational power available to humansthe cuda languageframeworks like tensorflow that do automatic differentiationand keraswhich makes deep learning accessible to most people licensed to