Big$Data$&$Culturomics$ Peter Danielson Univ. of British Columbia COGS Mar $
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1 BigData&Culturomics Peter Danielson Univ. of British Columbia COGS Mar
2 WeekPlan BigDatabackground UnderstandingNaturalLanguage Newdata<setsformachinelearning Non<humandistributedcogni?on NewToolsforSocialScience Cogni?veArcheology Next:NewEthicsChallenges Privacy(Mario) Consent De<Iden?fica?on Needforanewsocialcontract
3 BigData Bigdataisanall<encompassingtermforany collec?onofdatasetssolargeandcomplex thatitbecomesdifficulttoprocessusing tradi?onaldataprocessingapplica?ons.
4
5 BigGovernment USCensusoriginaldriver(decennial) 1890censuspredictedtotake13yearstofinish! Mayer-Schonberger p. 52 Hollerith IBM punchcards&sorter hzp://ed<thelen.org/comp<hist/knuth<sort.html#hollerith US: NSA UK:VideoSurveillanceleader
6
7 Business Wal<Mart&UPS Supplychain&logis?cs Amazon&Ne`lix Recommenda?ons(preferencepredic?on) Target Targetedcouponing:pregnancyasbreakpoint Duhigg HowCompaniesLearnYourSecrets hzp:// habits.html Google&Facebook Targetedadver?sing
8 BigScience Astronomy: WhentheSloanDigitalSkySurvey(SDSS)begancollec?ngastronomicaldata in2000,itamassedmoreinitsfirstfewweeksthanalldatacollectedinthe historyofastronomy.con?nuingatarateofabout200gbpernight,sdsshas amassedmorethan140terabytesofinforma?on.whenthelargesynop?c SurveyTelescope,successortoSDSS,comesonlinein2016itisan?cipatedto acquirethatamountofdataeveryfivedays.[1] Genomics: Decodingthehumangenomeoriginallytook10years[ ]to process,nowitcanbeachievedinlessthanaday:thednasequencershave dividedthesequencingcostby10,000inthelasttenyears,whichis100?mes cheaperthanthereduc?onincostpredictedbymoore'slaw.[28] ClimateScience: TheNASACenterforClimateSimula?on(NCCS)stores32petabytesofclimate observa?onsandsimula?onsonthediscoversupercompu?ngcluster.[29]
9 E.g.Language: Sciencevs.Engineering Anderson TheEndof Theory:TheDataDeluge MakestheScien?fic MethodObsolete (Wired 2008) hzp:// technology/archive/ 2012/11/noam<chomsky< on<where<ar?ficial< intelligence<went<wrong/ / Norvig: OnChomskyand thetwoculturesof Sta?s?calLearning hzp://norvig.com/ chomsky.html
10 E.g.SceneComple?on(Hayes&Efros)
11 Q1 Accordingto'TheUnreasonableEffec?venessofData',whichofthefollowingcould bemosteasilysolvedbyasta?s?calmachinelearningapproach? A)Thedevelopmentofaprogramthattakesasentenceasitsinput,andprintsonthe screenthenumberofnounsfoundinthatsentence B)Aprogramthatloopsthroughalibrary scollec?onofe<books,automa?cally taggingeachbookundercategoriessuchas autobiography, history, horror C)DevelopingApple svirtualassistantfortheiphone,'siri',tounderstanditsowner s voicecommands D)Twooftheabove E)A,B,andC MichaelW.
12 RateQuizQues?on1 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
13 Q2 AccordingtoHalevy,Norvig,andPereira,whichofthe followingisachallengeinengineeringaccurateseman?cweb services? A.Formalizingandwri?ngontologicalrules B.Compe??onanddisagreementoverontologies C.Recognizinginaccuracies D.Implementa?ondifficul?es,especiallyforsmallersites E.AlloftheabovearechallengesinbuildingSeman?cWeb services Wesley
14 RateQuizQues?on2 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
15 Q3 "Manypeoplenowbelievethereareonlytwoapproachestonaturallanguageprocessing: Adeepapproachthatreliesonhandcodedgrammarsandontologies,representedascomplexnetworks ofrela?ons;and asta?s?calapproachthatreliesonlearningn<gramsta?s?csfromlargecorpora. Inreality,threeorthogonalproblemsarise:" WhichofthefollowingisNOToneofthethreeproblems. A.choosearepresenta?onlanguage. B.encodeamodelinthatlanguage C.performinginferenceonthatmodel. D.comparerepresenta?onlanguageandnaturallanguage. RichardW
16 RateQuizQues?on3 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
17 Culturomics ~ Genomics Experimentsforeveryone! FromGoogleFightstoScience&& In2weeks! SuperHumanCulturalHistory Thecorpuscannotbereadbyahuman.
18 Q4 Whichofthefollowingisa"1<gram"? a)a b)chocolate c)cogni?vesystems d)aandb e)a,b,andc Jason
19 RateQuizQues?on4 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
20 N<Grams Whyreducethescannedtextston<gram counts? Shouldcopyrightcovern<grams? Howlongshouldcopyrightrun?
21 Q5 Inthenearfuturehumanswilltrytocolonizemars.Thiseventiscoined"The giant<leap."imagineintheextendedfutureanothercoloniza?onoccurs whichiscoined"mars2."peoplebegintocall"thegiantleap""mars1"soon axer.whatwouldexpectfromthewordfrequencydata? A)"Thegiant<leap"frequencydecreasesand"Mars1"frequencyincreases andplateaus. B)"Thegiant<leap"frequencystaysthesameand"Mars1"frequency increases. C)"Thegiant<leap"frequencyincreasesfurtherand"Mars1"frequency decreases. D)"Thegiant<leap"frequencydecreasesand"Mars1"frequencydecreases. Imran
22 RateQuizQues?on5 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
23 LevelsofChange Othermeaningshixs Usingwildcard Replacements Misspelledwords Spellingshixs Scanningerrors Especiallybefore1800
24 Q6 AccordingtoJean<Bap?steMichel,greatsuccessin whichofthefollowingprofessionswillleadtothe most"fame"? A.Poli?cian B.Writer C.Actor D.Biologist Paige
25 RateQuizQues?on6 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
26 Famousvs.Notorious Adolf inwolframalfavsg<books Curatedvs.WildData:
27 Q7 Accordingtothedefini?onof"culturomics"givenintheMicheletalar?cle, whichofthefollowingisavalidapplica?onofculturomics? a)analyzingthealgorithmiccomplexityinherentinthewayaflockofbirdsmove throughtheair. b)atargeted,in<depthstudyofoneveryimportanthistoricfigure,throughthespecific perspec?veofhiswife'spersonaldiary. c)usingalargeartimagedatabasetotrackthetrendsofhowspecificconceptsare representedinpain?ngsover?me. d)usingame?culouslyhand<coded,manuallyannotated,butverysmallcorpusto generateelegant"laws"ofsocialscience. Leo
28 RateQuizQues?on7 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
29 DiscussionQues?on ThestudybyMichel,etal.hingesmanyofitscultural conclusionsonlanguageuseinbooks.isthisaneffec?ve methodforstudyingculture?istherea'publishingbias' thatmightskewsuchconclusions? Moreover,whatmighttheresultslooklikeifweranthis studyagainfixyyearsfromnow?wouldwearriveat differentculturonomicconclusionsifin2065weanalyzed languageuseindigitalcommunica?ons(ratherthan books)? Wesley
30 RateDiscussionQues?on1 A. Excellent B. VeryGood C. Good D. Acceptable E. Poor
31 Books.SearchTerms Googletrends: N<turkey porn
32 HardScience&SoxData AtmosphericCO2,globaltemperature Quan?fied Buts?ll CO2 & globaltemperature notco2 andglobaltemperature
33 SocialProblemsSolved PublishingData Genera?ngdata Organizingdata Accesstodata Bookngrams(andcopyright) Searchterms Genomes(human) HealthCareRecords
34 References Reynolds, D. (2014) The Long Shadow: The Great War and the Twentieth Century. Simon & Schuster Ltd, Michel, Jean-Baptiste, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, Google Books Team The, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden Quantitative Analysis of Culture Using Millions of Digitized Books. Science 331, no. 6014: Ayres, Ian Super Crunchers. Bantam Dell. Lazer,D.,Pentland,A.,Adamic,L.,Aral,S.,Barabsi,A.,Brewer,D.etal.(2009). Computa?onalSocialScience.Science,323(5915),721<723. Halevy,A.,Norvig,P.,&Pereira,F.(2009).Theunreasonableeffec?venessofdata. IEEE&Intelligent&Systems,24(2),8 12 Mayer-Schonberger, V. & Cukier, K. (2013) Big data: A revolution that will transform how we live, work, and think. NY:Houghton Mifflin Harcourt. Rudder,Chris?an.Dataclysm:&Who&We&Are&(when&we&think&no&one's&looking). Crown,2014.
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Unsupervised joke generation from big data
Unsupervised joke generation from big data Saša Petrović School of Informatics University of Edinburgh sasa.petrovic@ed.ac.uk David Matthews School of Informatics University of Edinburgh dave.matthews@ed.ac.uk
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