Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 OF AI-DRIVEN LIVECODING GENERATIVE ART:STUDY CASE A ON THEHUMANROLEIN compositional, conceptual,andphenomenological implications. live codingmusicalperformance, withspecialfocusgiventoaesthetic, musical andcomputer science theorytoapracticalcaseofgenerating a Here wetracethepath fromalgorithmstointelligence,applyingboth a casestudyrootedin Anastatica’s developmentandperformance. .Finally, wediscussthevarious implicationsof AI inartthrough performance, partinstallationbuiltonthebasis ofdata-drivengenerative foundation forourwork. Then, weintroduceAnastatica(2020),apart research ingenerativeartwithaspecialinterest inmusicthatservedas for artisticpurposes.First,wepresentexamples ofcurrentpracticeand implications ofblack-boxgenerativealgorithms andtheirapplications aims tounderstandtheroleofhumaningenerative artbydemystifying in therealmthathasbeenreservedforhumans –creativity. This paper for makingsenseofmoderntechnologiesthatincreasinglyseemtostep vehement emergenceofgenerativealgorithmsthereisanimmediateneed trajectories ofacceleratingtechnologicaldevelopment.Inthecurrent The constantevolutionofphilosophicalviewsonartisinterwovenwith ABSTRACT Keywords: ; Generative art;Livecoding; ;. [email protected] [email protected] VisageTechnologies GORDAN KREKOVIĆ [email protected] Independent Scholar ANTONIO POŠĆIĆ

46 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 Anastatica (2020). an interactive, AI-driven live-codinginstallation-performancenamed role ingenerativeart.Ourfindingsareboth exhibited byandbasedon in art,specificallymusic,andtoprovideapossible viewonthehuman 1994). augmentation ofhumancognitiveandperformative capabilities(Roszak, creativity andhumanartistry, whileotherssee immensepossibilityinthe believe AI istobringon“thedeathofart”through replacementoftrue perhaps moresothananyotherdigitaltoolhasbeeninthepast.Some wider sociologicalnarratives,theusageof AI inartisacontroversialtopic, modifications andsubversionsof AImechanisms.Similartotheimpactin of technologiesismultifaceted,fromsimpleblackboxusagestocomplex 2019). The wayinwhichartistsandcomputerscientistsapproachthisset through osmosis,bothinacademiccontextsandmainstreamart(Parker, 2016). tentatively becharacterizedasbecomingDaseinthemselves(Herrera, recontextualizing thediscussionaround AI technologieswhichcould advanced contemporarytechnologiesfurtherblursthelinesofenframing, determinism orsocialconstructivism–arestillvalid,theessenceof Whilst thesecritiques–eitherfromperspectivesoftechnological that predatethetechnologies(Heidegger, 2009[1954];Latour, 1999). pitfalls. This isalsotrueoftheirapplicationsinart. societies strugglewithunderstandingtheirnatureandpotentialethical techniques andprocessesisfaststeep,individuals,communities, (Mlynár etal.,2018).Whilethetechnologicalprogressofassociated forms of AI havemadetheirwaysintoalllayersofsocietyanddailylife made availablethroughsmartphonesandotherpersonaldevices,various poses. algorithmic mechanismsitcomprisesorthesociologicalimplications effects of AI withoutprovidingaframeworkforunderstandingeitherthe philosophically correct,thisdefinitionbrieflyfocusesonlyonthedirect (Poole etal.,1998;Russel&Norvig2003).Whiletechnologicallyand action inaneffort tomaximizetheirsetgoalsandexpectedresults agents capableofperceivingtheirsurroundingcontextsandundertaking deals withartificialintelligenceiscommonlyconcernedcomputational exhibited bymachines(Nilsson1998). The fieldofcomputersciencethat In broadterms,artificialintelligence(AI)isanysortofintelligence 1. INTRODUCTION This paperthusaimstostarttheprocessofdemystification of AI Artificial intelligenceandmachinelearninginfiltratedartisticpractices These questionshavebeensubjectsofphilosophicaldebate With theriseofcomputationalpowerandubiquitouscomputing 47 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 technology itself. substantial interventionstothemakeupofalgorithms, questioningthe boxes, whilesomeartistsandresearchers(e.g., Roh,2018)make of thetechnology’s originalparameters, asparameterizedtoolsorblack (Wannarumon etal.,2008). Here, AI isoftenusedwithintheboundaries be describedasmathematicanddetachedfrom humanpreference pieces (Lugrinetal.,2006),andabstractworks whoseaestheticcould to generatedynamic3Dartworks(Romero,2008), virtualreality generated works. as acuratorwhoselectsthemostcompellingpiecesinvastspaceof the system,toadjustparametersofandfinallyact Here, theartisthasthreeroles:toselectdatasetsusedfortraining not machinic,whilealsorevealingofthealgorithm’s intrinsicproperties. the usedtechnologies. The resultingaestheticisstillverymuchhuman, the originaldatasets,butwhichoftensurfacespecificartifactsinherentto modify theirparameters,andgeneratenewpiecesinstylesthatmimic and tools,trainthemondatasetsofexistingartlikemasters’ portraits, arts (Schmitt2018). These artiststakepubliclyavailablealgorithms (GANs) havebecomethemostwidelyacceptedformof AI invisual Barrat, GeneKogan,andMike Tyke, generativeadversarial networks Championed byartistslikeMarioKlingemann,Memo Akten, Robbie VISUAL ARTS 2.1 research presentedinthispaper. the field,itexplainsinfluencesthatledtoAnastaticaandartistic either buildsuponorquestions.Whilenotacomprehensivereviewof artistic practicesthatwedeemedmostrepresentativeandourwork through mathematicallydescribedsignals. which followsimilartracksandonconceptscanbeapplied progress isfastestandmosteasilyobservableinvisualartsmusic, advances inalgorithms,andopen-sourceavailabilityofthesetools. The power available to individuals (both in mainstream and academic settings), AI useforartisticpurposeslargelyowingtotheincreaseincomputational unexplored field. Thelatterpartofthe2010ssawasignificantincreasein 2003), theirwiderandconsistentlyapplieduseisstillafreshrelatively in arthavebeenaroundforseveraldecades(Kugel,1981;Galanter, While ideasofusinggenerativeandproto-artificialintelligencetechniques has notyet found acommonmode ofoperation,instead adoptingdiverse Unlike visual arts,whereGANsare prevalent,inthecontext ofmusic AI 2.2 MUSIC 2. ARTIFICIAL INTELLIGENCE AND ART INTELLIGENCE AND 2. ARTIFICIAL Apart fromGANs,artistsemployvariousevolutionary algorithms In thefollowingtwosubsections,wepresentanumberofsuch 48 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 departure towardsanewaestheticframework (Smart,2019,p.1): explain theirartisticprocessbehindtherecord Blossoms(2019)and unexpected possibilitiesoutofdatasetsthey sourcedthemselves. They Purgas insteademployedmachinelearningtechniques toexplorenew, harness the AI purelyasasample-generator. JamesGinzburg andPaul them, optingneitherforafullygenerativeprocessnorchoosingto a continuum,experimentalanddronemusicduoEmptysetfallsbetween Herndon mentionsnaming,anthropomorphizing,andraisingthe AI entity. of performativityandcuriousexplorationthesystemitselfatplay, as conventional electronicmusicaesthetics,butwithanadditionaldimension in Herndon’s hands. The resulting recordfitswithinexpectationsof in awaythathumancannot,whilethefinalactofcompositionremains compositions. Here,the AI isusedasatool forgeneratingsoundsamples samples. These soundsandphraseswerethen arrangedintothefinal to Spawn,andthenusedthesystemtransformgeneratenew the AI wasiterative.Herndoncreatedandrecordedthemusic,fedit album withaspecificroleinmind. Theprocess oftrainingandusing Dryhurst createdandtrainedtheSpawn AI for Herndon’s PROTO (2019) their process,researchersandmusiciansHollyHerndonMathew vast output. cherished astheresearchers/musicianscuratesegmentsfromDadabots’ of AI inartarecontrastedto AI forgeneralpurposes.Here,errorsare neural synthesis”,emphasizinghowdifferent theroleandexpectations authors state,“wearedelightedbytheuniquecharacteristicartifactsof experiences outofisolatedandsubvertedblackmetalelements. As the by Dadabotsbringforwardatentativenewaesthetic,birthingacousmatic (Lee etal.,2009).Becauseofthesecharacteristics,thepiecesproduced techniques duetotheiruseoftimbreandspaceascompositionaldrivers rock canbeconsideredchaoticanddifficulttoanalyseusingconventional recognizable. Additionally, thestylisticelementsofblackmetalandmath material thattheyuseisbothcontemporaryandhighlyaesthetically similarities withtheuseofGANsinvisualarts,distinctionisthat rock similes(Zukowski&Carr, 2018).Whilethisapproachhas many the Dadabotssystemandemployedittogenerateblackmetalmath creation purposes. focus onfourdistinctpieceswhichillustratehow AI canbeusedformusic approaches. Ratherthantryingtosummarizecurrentmeta-narratives,we outcomes as the system sought out coherence from within this outcomes as thesystemsoughtout coherencefromwithin this This collection ofelectronicandacoustic soundsformed unexpected hours ofimprovisedrecordings usingwood,metalanddrumskins. was suppliedfromacollection oftheirexistingmaterialaswell10 a sonicknowledgebase ofmaterialtolearnandpredictfrom. This extensive audiotraining, aprocessofseedingsoftwaremodelwith The machinelearningsystemforBlossomswas developedthrough If HerndonresidesononeandZukowski/Carrtheothersideof While ZukowskiandCarrwelcometheunexpectedoutcomesof Adapting theSampleRNNarchitecture,ZukowskiandCarrcreated 49 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 piano. Bychoosing theseorganic and realinstruments,we clashthe preparation process:audio recordingsofaviolinandelectro-mechanic of theperformanceare determinedbythesamplesemployedin the performancetoits original stateofinfinitealgorithmicpossibility. each individual.Meanwhile,theMusician’s rolegraduallyfades,returning generated code,withthechoicebetweenaugmentation anderosionleftto and anarchy. The audienceisgivenachancetomanipulate thecomputer- an interactiveinstallationthatextendstheoriginal duopolyintodemocracy audience viaaweb-enabledinterface. Then, Anastatica (2020)becomes as iscommonforlivecodingperformances. interactions betweenMusicianandalgorithm are projectedonascreen, in therelationshipbetweenharmonyanddisharmony. All thewhile, changing fluxofmachineacousmaticsandexploringvariousmodalities improvise andinterveneinthealgorithmicresults,adaptingtoever- influence fromabackgroundtoforegroundpresenceandback. They Musician createsanaccompanimentforthealgorithm,switchinghis generated music. Musician, whoimprovisesandalternatelybuildsupondestroysthe the audienceisintroducedtoit,joinedbyabeingoffleshandblood, This seeminglyendlessgenerativeprocess,whichmightstartevenbefore generates linesofcodethatmanipulateaudiosamplesandcreatemusic. – inherentlyincessantanduntiringinitsrolllikearoseofJericho relationship betweenhumansandagenerativealgorithm. The algorithm of aninstallation.Itcomesintobeinginrealtimebydrawingfromthe experience whichcombinesperformativeelementswithcharacteristics own aesthetic and philosophical framework, Building ontopoftheworkspresentedinprevioussectionandour 3. ANASTATICA sense, itisthemostself-awareofpresentedapplications AI inmusic. characteristics ofWalshe’s identity, namelyhervoiceandface.Inthis and an AI whichactsasamimeticpartnerandabsorbsthemain ULTRACHUNK (2018)isanimprovisedpiece,aduetbetweenWalshe the workexploresemergingworldofcomputationalintelligence. Realized incollaborationwithvisualartistandresearcherMemo Akten, an exampleofapiecethatfocusesonthephenomenology AI itself. Finally, composerJenniferWalshe’s workULTRACHUNK (2018)is sounds andmanifestinganemergentnon-humanmusicality. finding correlationsandconnectionsbetweenseeminglyunrelated obscure mechanismsofrelationalreasoningandpatternrecognition, the contradictionsofsonicdataset. The systemdemonstrates vastly diversesourcematerial,attemptingtoformalogicfromwithin Aside fromthesource code makeupofthealgorithm,aesthetics At arandompoint,theperformanceopensitself forinputfromthe Using livecodingandothercomputermusictechniques,the Anastatica (2020) is a musical 50 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 possible. cognitive andperformative capabilitiesbeyondwhatwouldotherwise be new instrument,instilledwithourownsenseof aesthetics,andextendour from scratchandinterveneinitonalowlevel. Bydoingso,wecreatea instruments asapartofthefuturemusic,we buildthe AI-algorithm coding intermsofimprovisation. challenge thecreativityofMusicianandfurther pushthefieldoflive electronic musictropes.Byemployingan(antagonistic) AI, weseekto aesthetic andartisticoutputs,frequentlytightly tiedtoidentifiable music arevast,withinthefieldoflivecodingtheyoftenresultinsimilar and thepossibilityofintervention. black boxbygivingtheaudienceadirectviewofperformancecode potential mysticismwovenaroundthegenerativeprocess.Itbreaks and the(in)existenceofchoice,whilesimultaneouslyunravellingany becomes acommentontheroleofcomputersincontemporarysociety inorganic worldsintherawestsense.Here,Anastatica(2020)inherently code. Inthisspacewecanobservetherelationshipsbetweenorganicand level, understandabletobothcomputersandhumansthroughprogram Firstly, itexposeshuman-machineinteractiononasemanticandsyntactic The ideabehindAnastatica(2020)joinstogetherthreelinesofthought. 3.2 INFLUENCES AND MOTIVATION segment. Curtainfalls. Everything isintheircontrolorperhapsnothingis.It’s ashortbutendless The audienceisnowalonewiththealgorithmicghostinmachine. aforementioned webinterface.Ultimately, theMusicianleavesstage. audience intervenesviamodificationsofgeneratedcodethroughthe of thelaptopaslivecodinginterface. At specifictimesandintervals,the to themachine,whichincludesinfluencingalgorithmitself,bymeans Musician appearsandstartsplayinginconjunctionwithcontrast to beinglateanacousmaticconcert.Oncetheaudienceisseated, installation withnobeginningorend.Perhapsitconjuresafeelingakin the musicianoraudiencesentervenue,givingasenseofan A laptopcomputerissetonstage.Musicgeneratedevenbefore either 3.1 STRUCTUREOFTHEPERFORMANCE instruments, creatingtextures,harmonies,andrhythms. rigidity andpredictabilityofthealgorithmwithimperfectnature Thirdly, basedon Thor Magnusson’s (2019)ideasofself-builtdigital Secondly, whilethemodesofinteractionandstylesprogramming 51 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 synthesis. meanderings ofthecollectivemind,edgingtowardsdissolutionor indeterminate andcontext-dependent.Itwillvarybasedonthe extemporaneous installation. The outcomeof theperformanceis algorithm, allthewhileparticipatingincreationofaninteractive, can derailtheflowandactsubversivelyagainstMusicianand the performancecore.Dependingonatmosphereandmood,audience ubiquitous smartphones,viaatwo-waywebinterfacepipeddirectlyinto role ofLuigiRussolo’s intonarumori(Serafin,2012). Theydosobyusing the algorithmanditsnon-deterministicvariantofapianolaindistilled decide inwhichwaytoimpacttheperformance,actingagainstoralong interface. The audiencebecomesacrucialpart ofAnastaticaastheycan chance toinfluencetheperformancedirectlyviaaweb-basedlivecoding members withelementsfromboththeseapproachesandgivesthema al. 2019). examples suchasHollyHerndon’s previouslymentionedSpawn(Sturmet human capabilitiesonacognitive-compositionallevel,wherewefind is theemploymentofartificialintelligencetoexpandspectrum (McLean etal.,2017). The secondtrendinhuman-computerinteractions feedback cello(Eldridge&Kiefer, 2016)andvarioustextile-based systems & Pošćić,2019).Examplesofsuchinterfacesaretheself-resonating encourages innovativemodesofimprovisationinlivecoding(Kreković states whichareunnaturalandfreeoflearnedbehaviours. This, inturn, interfaces, performersandtheirbodiesaremadetomoveinhabit is thefirstofthem.Byemployingtechniquesdictatedbyinnovative Experimentation withvariouscomputer-humancommunicationchannels identified inhowhumansinteractwithmachinesthedomainofmusic. research tendencies,twometaphysicallycontrastingapproachescanbe By observingcombinationsofelectronicclubmusicandbleeding-edge 3.3 PARTICIPATION In itscondensedform,Anastatica(2020)presentsaudience

Figure 1: A 2020performanceof Anastatica. ©N.Klarić 52 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 techniques containedinthem. such artisticacts,warpingandchallengingthebasicimprovisational dimensions oflivecoding,itprovidesapeekintotheinnerworkings choice ofthenextsegmentbytouchingsnippetswiththeirfingers. code hasbeenexecutedonachannelandinfluencethealgorithmin interface ontheirmobiledevices,theaudiencecanseewhichpieceof might nothaveanyrealrepercussions. Additionally, usingtheweb change inthemusic,dispellingaudiencesuspicionsthattheiractions are goingtobeplayed.Eachmodificationiscoupledwithanobservable snippets ofgeneratedcode,whilethealgorithmdecidesifandwhenthey generated codeissimpleandstraightforward.Itenablestheselectionof with TidalCycles, thewebinterfacethatexposesinnerworkingsof complementary interactions. that iskeyhere,setinshapeshiftingdynamicsofantagonisticor of computersinmusic–becomesacorollary. It’s thegenerativepart The aspectoftranslatingcodeintomusic–usuallythemainfunctionality specificity ofAnastatica(2020)istheclosenessparticipants’ roles. a naturalpositionofsharedmediumbetweenhumanandmachine,butthe the samesemioticlevelashumanparticipants.Now, computercodeisin object. Instead,itisperceivedasasubjectthatcreatesmusic,workingon understandable tocomputers. in clearandtraceableways,whilealsobeingalanguagethat’s easily performances, TidalCycles enablesahumantoexpressmusicalintentions compact syntax. Thanks toitsarchitectureandorientationtowardslive (McLean, 2014)becomesobvious,duetoitsrealtimecharacteristicsand generates music.Underthesepremises,thechoiceof TidalCycles in theperformanceuseandcommunicatethroughcomputercodethat level playingfield. Thismeansthatbothorganicandinorganicparticipants A majorideabehindAnastaticaistojoinhumansandalgorithmsona dilemmas from theaspectofalgorithmic composition. creation process,opens selectedinsightsintechnicalandcompositional creation. This section,aimedat demystifyingthehumantouchin AI generative system,they alsoaroseaschallengesduringAnastatica ’s upon intheperformance andthemanifestationaspectsofunderlying autonomy andhumanwill.Whileallofthesedualisms havebeentouched and humans,betweenthealgorithmcreativity, andbetweenmachinic Anastatica (2020)representsanexplorationoftherelation between AI 4. FROM ALGORITHMICITY TO INTELLIGENCE CONSIDERATIONS 3.4 SYNTACTIC, SEMANTIC, AND TECHNOLOGICAL While theperformancedoesnotquestionbasicextra-musical Since itisexpectedthatmostaudiencememberswillnotbefamiliar By “writing” TidalCycles code,thecomputerisnolongerjustan 53 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 scales, pioneeredbyIannis Xenaxis(Serra,2009;Loque,2009), broader include tendenciestounify compositionaltechniqueondifferent time processes inwaysthat werepreviouslynotpossible.Numerousexamples calculating soundobject’s parameterstodesigningandimproving formal human interventiondid notdisappear. Instead, itshiftedfrommanually thereby reducing“humanintervention”.However, thenecessityof approaches, butrelievingcomposersoftheburden ofmanualwork, seamlessly tookoveralongthesamepathof exploring novelformalized electronics. Fromthecurrentpointofview, theappearanceofcomputers and laterunprecedentedexplosionofexpressive possibilitiesdrivenby in variousformsofserialism,aleatoricmusic, thesoundmasstechnique, approaches andresultedwithradicalideasin musicthatwereentwined in naturalandtechnicalsciences,significantly influencedcompositional of intellectualthoughtandacceleratedscientificbreakthroughs,especially morphed. where thescopesof“formalprocesses”and“humanintervention”are approaches, butitalsoblendswellwithourpost-algorithmicmodernity a distinction,yetnaturalevolutionfromtraditionalcompositional The beautyofthisdefinitionliesinthefactthatitpreciselycaptures composition: composition isthefirstparagraphof Adam Alpern’s paperonalgorithmic One ofthemostinfluentialandwidelyciteddefinitionsalgorithmic COMPOSITION 4.1 HISTORY AND COMPOSITIONAL DILEMMASOF ALGORITHMIC systems, andvariousotheralgorithms.(Alpern,1995,p.1) various formalisms,suchasrandomnumbergeneration,rule-based intervention. This isoftendoneonacomputer, withtheaidof using someformalprocesstomakemusicwithminimalhuman The areaofautomatedcompositionreferstotheprocess The 20thcenturywasahistoricalgreenhouseofformalisms. The rise Figure 2:Anastatica’s Web Interface.© A. Pošćić&G.Kreković. 54 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 relations on highertimescales. chain cancapture onlylocalstatistical similarities,whilefailing atinducing from acorpusofpre-existing compositions.Itsmainweaknessisthat the Markov chainisamachine learningalgorithmwithparametersinduced to modelmelodiesand musicalphrases(Ames,1989;Pachet,2011). The 1957; Pinkerton,1956), butremainedaviable,althoughlimited,approach formalized musicbackinthe1950s(Hiller&Isaacson, 1958;Brooksetal., generative systems.Markovchainsenabledthe earliestexperimentson algorithmic compositionmakesitasomewhat outdatedchoiceinmodern process isaMarkovchain,well-provenmodel whoselonghistoryin when thealgorithmstarts,andmusicbegins. showcase ofhumaninfluencesonvariouslevels thatstopatthemoment significantly emergefromthematerial.Such an approachisillustrative However, themajorityofparametersandoverallresultingaesthetic to linesofcodethatserveasbuildingblocksforthegenerativeprocess. selection ofthematerial.IncontextAnastatica,materialrefers interventions onalllevels–fromthealgorithmdesigntopreparationand and amachinelearningsystem.Itisdata-drivenalgorithmthatrequires The generativeenginebehindAnastaticaresidesbetweenahardwired 4.2 A GENERATIVE ENGINE object’s parameters. formal processandlittlehumaninterventionisneededinchoosingsound holds becausethedeductivestepofmachinelearningalgorithmsisa composers turnedtheirfocusbacktomusicmaterial.Still,thedefinition the machinelearningerainwhichalgorithmlostitsappealwhen compositional approachesstayedqualifiedasalgorithmicevenin and whentheyfocusedongenerativealgorithmsinstead.However, their heavymanualworkofchoosingparticularsoundobject’s parameters definition. Compositionbecamealgorithmicwhencomposersceased algorithms automatehumanwork,butalsoopennewpossibilities. would bepracticallyimpossible. As aresult,notonlydomachinelearning Moreover, settingparametersmanuallyforalmostanynon-trivial model but itsparametersautomaticallylearnedwithouthumanintervention. suitable fortheirtasksisnotthealgorithmicprocedure(whichgeneric), their performancefromdata. The ingredientthatmakessuchalgorithms obtained bymachinelearningisthatthelatterhaveanabilitytoimprove the keydifference betweenmanuallyengineeredprocessesandthose step ofmachinelearningalgorithmsisalsoaformal,proceduralprocess, outperform procedurallyassembledalgorithms.Eventhoughthedeductive is happeningtodaywhenmachinelearningalgorithmsreplaceand A different, yetsimilartransformationofthenotion“humanintervention” et al.,2011), andtherestofwideworldsoundsynthesis. explorations ofmicrosound(Roads,2004),sonificationtechniques(Thomas The underlyingmechanismandfoundationof Anastatica’s generative This historicaldevelopmentconfirmsthefarreachof Alpren’s 55 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 existing live codingperformances, theyareconstructedalgorithmically. code, butinsteadofinducing transitionsbetweenthestatesfrompre- The statesoftheMarkov chainsarepredefinedblocksof TidalCycles to adjustthemethod thematerialandachieveadistinctiveaesthetic. The useoftheMarkov chaininAnastaticaissignificantlyaltered inorder column: Parameters),orbyinterveninginthematerial(column:Corpus). adjusting somegeneralparameters(e.g.,thedistributionofrandom pausesbetweengeneratedblocks, were addressedbyadaptingthepre-processingorgenerative algorithm (column: Algorithm), by Figure 3: Aesthetic considerationswhiledesigning interventions. for minimalhuman sonic resultsand Possible unexpected overall piece of movementsandthe Inadequate duration musical piece movements ofthesame A lackofvarietyamong the overallpiece musical architectureof A lackofcontrolover same sequences Falling intolongloopsof Risk rithm Algo- ● ● Param- Anastatica’s generative engine. These considerations eters ● ● ● Corpus ● unprocessed states. transition probabilitiesto them arelowerthan states sothatallof already processed of transitioningto to reduceprobabilities algorithm wasdesigned The preprocessing interventions. and forminimalhuman musical expression created foradiverse and thecorpuswere generative process as arisk,sincethe This isnottreated is setasaparameter. generated codeblocks The numberof corpus canbedefined. more variety, aseparate configured. Foreven to resetthestateas algorithm wasdesigned The generative musical form. to buildahigher-level parameters inorder restarted withdifferent algorithm canbe The generative Solution 56 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 examples ofmajoraesthetic issuesthatwefaced. on variouslevels.While thischallengewascrucial, Table 1listsadditional process, adeliberateartistic approachentailscreativehumanintervention shaped bythecompositionalprocess. of sounds(e.g.,overwhelmingtonalcontents), butthosepossibilitiesare did notchange,therewerestillpossibilitiesof combiningundesiredtypes distribution ofrhythmic,tonal,andtexturalcontents. Sincethealgorithm into different channels. Therefore, eachchannelhasitsapproximate distribution ofcodeblocksthatproducecertain typesofsonicresults preparation throughiterativetrialsandeventually reflectedthedesired material forAnastatica. The guidelineemergedtogetherwiththecorpus process, sothesolutionwastocreateasimple guidelineforpreparingthe approach –tointerveneinthematerial. the channels.Insteadofinterveninginalgorithm,weoptedforanother the pre-processing)is overridden by the lack of such organizationbetween the effect ofalgorithmiccoherencewithinindividualchannels(achievedby this isadesiredbehaviour, buttheaestheticresultsarelessdesired,as generic systemcapableofproducingawiderangemusicalexpressions, code sequencesfordifferent blocksatthesame time.Inthecontextofa to music. This madeAnastatica’s enginegeneratemutuallyunrelated which simultaneouslyinterpretassignedcodeblocksandtranslatethem One specificchallengewasthat TidalCycles supportsmultiplechannels, 4.3 FIGHTING AESTHETIC INDETERMINACY that shouldbegenerated,etc. generated blocks,thedistributionofrandomtempo,numberblocks generative algorithm,suchasthedistributionofrandompausesbetween the corpusofcodeblocksandtotuningsomegeneralparameters concerns shiftedfromindividualtransitionstoorganizingand“composing” intervention. The scopeof approach shapedthenatureofcomposer’s the generativeprocess. the sameconceptthatsyntacticlevelof TidalCycles codeinfluences predefined corpusesofdifferent contentsandsizes,buttoalwaysreflect algorithm wasthereforedesignedtobewidelyanduniformlyapplicable than writesnewones(Kreković&Pošćić,2019). The pre-processing during liveperformancesusuallymodifiesexistingcodeblocksrather from oneblocktoanotherinthesamemannerasahumanmusician code blockswritteninthosestates. This solutionfavourssmallerchanges states isnegativelycorrelatedwiththeLevenshteindistancebetween deterministic way. The resultingtransitioning probabilitybetweentwo predefined corpusofcodeblocksandcalculatestheprobabilitiesina This pre-processingstepofsettingtransitionprobabilitiesrunsona This exampleshowsthat evenwithsuchagenericgenerative However, thematerialisflexibleanddependentonacompositional Such ascalable,generic,andinherentlydata-drivenpre-processing 57 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 obvious parameters? That isavalid usecasethatcan lead tointeresting algorithms withoutsignificant interventionsandonlybymanipulating come intothehandsof someonewhointendstousethesegenerative possible hyperproduction ofart.WhatifatrainedLSTMorAnastatica clear artistictendencies. intervention. Moreover, aninterventionthatrequiresspecializedskillsand distributions ofexamples.Suchaneffort definitelyqualifiesasahuman than thecurrentcorpusofcircathousandcode blocks–withrelevant prepared alargecorpus–perhapsatleastan orderofmagnitudelarger has selectedtheappropriatehyperparameters oftheneuralnetworkand Anastatica’s currentalgorithm?Probablyyes, assumingthatsomeone learn howtoproducebettermusicwithlesshuman interventionthan intelligence. inner workingsled AlphaZero tovictorythat provedhighercomputational reinforcement learning. And ittookonlyfourhours.Itsradicallydifferent chess fromhumans,butplayingitwithitselfusingatechniqueof 2016. The importanceliesinthefactthat AlphaZero didnotlearntoplay powerful Stockfish8program,theworld’s computerchesschampionfor that in2017defeatedthealmostthousandtimesmorecomputationally to generativeart,anexcellentexampleisGoogle’s AlphaZero program less proceduraldetails,butwithbetterabilitytolearn. Although notrelated encapsulate morecomplexityandrequirelessdatathananalgorithmwith distribution, onethatreliesmoreonproceduralandstructuralaspectswill intervention. Iftwoalgorithmscreateoutputswithsimilarstatistical produce awiderdistributionofoutputswithdifferent levelofhuman or feedthealgorithmaretightlyrelatedtoabilitygeneralizeand inner workingsofgenerativealgorithmsanddatanecessarytotrain creativity isstillinevitable? memory (LSTM)neuralnetwork,wouldwebeabletoarguethathuman employed anevenmoregenericapproach,suchasalongshort-term adjustments, andthecustompre-processingalgorithmAnastatica in traditionalapproaches.IfinsteadofrelyingonaMarkovchain,its human interventionandcreativity, thoughwithadifferent focusthan seamlessly generalizedtosaythatgenerativealgorithmsstillrequire A validquestioniswhetherobservationsdrawnfromAnastatica canbe 4.4 THEENDOFHUMANINTERVENTIONS? context ofhighlyautonomousgenerativesystemsbasedon AI. of humaninfluenceinthegenerativemusicmayseemchangedthe formalization ofhumancreativityandintuition(Nierhaus,2015),thenature music-making tool(Guedes,2017),andevenaplatformforunderstanding formalization ofcompositionaltechniques(Nierhaus,2009)servingasa While algorithmiccompositionsaroseasadirectautomatizationand The finalconcernmight berelatedtotheblack-boxapproachand So, wouldanLSTMtrainedonthousandsof TidalCycles codeblocks Before answeringthequestion,itisimportanttoacknowledgethat 58 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 stone towardwiderexaminations. approaches. As such,whileacompletedartwork,Anastaticaisstepping that shouldfurtherchallengecurrentlyprevalent notionsofblack-box in music,whilesimultaneouslyopeningavenues offutureresearch In doingso,wehavepartiallydemystifiedthe useofthesetechnologies art, andthestillimportantroleofhumancreativityinthiswholeprocess. insight intotheworkingsofgenerativemusic,challengesusing AI in historical, procedural,andtechnologicalperspective,wethenprovided prevalent aestheticdeterminismoflivecoding. by allowingintrospectionanddirectinteraction,tochallengethe between humansandalgorithms,tobring AI closertowideraudiences performance andinstallationartworkthataimstoquestiontherelationship technological research,weintroducedAnastatica,acombinationof for ourowncontributions.Basedontheseworksandphilosophical in artwithafocusonmusicthatservedasfoundationandinspiration In thispaperwehavefirstoverviewedexamplesofartificialintelligence 5. CONCLUSION process completely. enframing, whilesimultaneouslyunabletocontrolandunderstandthe as revealersoftruth(Heidegger, 2009[1954]),abletodispel threatsof ideaofartists that simultaneouslystandsagainstandwithHeidegger’s will delegatetheircreativitytogenerativealgorithms.Itisanapproach artistic results.Fromthatpointofview, thereisnofearthathumans underlying technologyandwhichmayentailradicalunprecedented requires masteryproportionallydemandingtothecomplexityof an (atleastpartially)unboxedandcommittedapproachwhichusually become expectedandcommon.However, therewillalways be spacefor traditional artisticvalues,itleadstoimmediateresultsthateventually are traditionallyneededtoproduceart,therebyjeopardizingsome possibilities. boundaries ofartisticexpressions,andtoimmerseintoendless should notstopthecivilizationalurgetounboxmachine,broaden popularization ofart,andfun.However, suchablack-boxapproach results, newexperiencesandknowledgeonthepersonallevel, Simultaneously, the AI shouldbemade antagonisticallyinclined towards replacing Markov chainswithmore advancedtechniques likeLSTM. of researchistoimprove theartificialintelligencealgorithm,possiblyby this paperasbranching off intothreedistinctdirections. The firstavenue We seetheworkstartedwithAnastatica andouranalysispresented in 5.1 FUTUREWORK By dissectingandanalysingAnastaticabothfromaperformative, While black-boxusagereducesthenecessityforfineskillsthat 59 Journal of Science and Technology of the Arts, Vol. 12, n. 3 (2020): pp. 45-62 https://doi.org/10.34632/jsta.2020.9488 and tutorial.Leonardo,22(2),175-187. Ames, C.(1989). The Markovprocessasacompositionalmodel: A survey http://alum.hampshire.edu/~adaF92/algocomp/algocomp95.html Hampshire College. (1995). Alpern, A. 6. 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