generative deep learning grantedthe artistic productions we've seen from ai so far have been fairly low quality ai isn' anywhere close to rivaling human screenwriterspaintersand composers but replacing humans was always beside the pointartificial intelligence isn' about replacing our own intelligence with something elseit' about bringing into our lives and work more intelligence--intelligence of different kind in many fieldsbut especially in creative onesai will be used by humans as tool to augment their own capabilitiesmore augmented intelligence than artificial intelligence large part of artistic creation consists of simple pattern recognition and technical skill and that' precisely the part of the process that many find less attractive or even dispensable that' where ai comes in our perceptual modalitiesour languageand our artwork all have statistical structure learning this structure is what deep-learning algorithms excel at machine-learning models can learn the statistical latent space of imagesmusicand storiesand they can then sample from this spacecreating new artworks with characteristics similar to those the model has seen in its training data naturallysuch sampling is hardly an act of artistic creation in itself it' mere mathematical operationthe algorithm has no grounding in human lifehuman emotionsor our experience of the worldinsteadit learns from an experience that has little in common with ours it' only our interpretationas human spectatorsthat will give meaning to what the model generates but in the hands of skilled artistalgorithmic generation can be steered to become meaningful--and beautiful latent space sampling can become brush that empowers the artistaugments our creative affordancesand expands the space of what we can imagine what' moreit can make artistic creation more accessible by eliminating the need for technical skill and practice--setting up new medium of pure expressionfactoring art apart from craft iannis xenakisa visionary pioneer of electronic and algorithmic musicbeautifully expressed this same idea in the sin the context of the application of automation technology to music composition: freed from tedious calculationsthe composer is able to devote himself to the general problems that the new musical form poses and to explore the nooks and crannies of this form while modifying the values of the input data for examplehe may test all instrumental combinations from soloists to chamber orchestrasto large orchestras with the aid of electronic computers the composer becomes sort of pilothe presses the buttonsintroduces coordinatesand supervises the controls of cosmic vessel sailing in the space of soundacross sonic constellations and galaxies that he could formerly glimpse only as distant dream in this we'll explore from various angles the potential of deep learning to augment artistic creation we'll review sequence data generation (which can be used to generate text or music)deepdreamand image generation using both variational autoencoders and generative adversarial networks we'll get your computer to dream up content never seen beforeand maybe we'll get you to dreamtooabout the fantastic possibilities that lie at the intersection of technology and art let' get started iannis xenakis"musiques formellesnouveaux principes formels de composition musicale,special issue of la revue musicalenos - ( licensed to