Accelera’ng Science: A Grand Challenge for AI€¦ · The Gospel of the Flying Spaghetti Monster....

PennsylvaniaStateUniversityCollegeofInforma5onSciencesandTechnology

Ar5ficialIntelligenceResearchLaboratoryCenterforBigDataAnaly5csandDiscoveryInforma5cs

Vasant Honavar, AAAI Fall Symposium on Accelerating Science: A Grand Challenge for AI

Accelera'ngScience:AGrandChallengeforAI

VasantGHonavarAr5ficialIntelligenceResearchLaboratory

CollegeofInforma5onSciencesandTechnologyComputerScienceGraduateProgram

CenterforBigDataAnaly5csandDiscoveryInforma5csPennsylvaniaStateUniversity

ConvergenceofDataandCompu5ngTaskForce,CCC

Ar5ficialIntelligenceTaskForce,CCC

[email protected]://faculty.ist.psu.edu/vhonavar



WelcomeandThanksØ  Welcome!Ø  Thanks!

§  Par5cipantsandspeakers§  Associa5onfortheAdvancementofAr5ficialIntelligence(AAAI),

especiallyCarolHamilton§  NortheastBigDataHub:anNSF-fundedacademic-industrypartnership§  CRAStaff:HelenWright,KhariDouglas§  Co-organizers:CarlaGomes,ChiKaBaral,GregHager,AnnDrobnis§  Compu5ngCommunityConsor5um(CCC)

§  AnNSF-fundedstandingcommiKeeoftheCompu5ngResearchAssocia5on(CRA)

§  Chargedwithdeveloping,ar5cula5ngandcommunica5ngabold,mul5-themedvisionforcompu5ngresearch

§  LedbyaCouncilrepresen5ngthecompu5ngresearchcommunity




Accelera'ngScience:AGrandChallengeforAI

•  Talkbasedinparton:Accelera5ngScience:ACompu5ngResearchAgenda.ACompu5ngCommunityConsor5umWhitePaper,VasantHonavar,MarkHill,andKatherineYelick,2016hKp://cra.org/crn/2016/03/3501/




Accelera'ngScience:AGrandChallengeforAIØ  Allscienceiseitherstampcollec5ngor“physics”Ø  Bigdata=spectacularstampcollec5ons!Ø  Bigdata≠Demiseofthescien5ficmethodØ  Accelera5ngsciencepresentsagrandchallengeforAI1:

§  Analysisandsynthesisofcomputa5onalabstrac5onsofboth§  universesofscien5ficdiscourse§  scien5ficar5factsandscien5ficprocess

§  Cogni5vetoolsthataugmentandextendhumanintellect§  Collabora5vehuman-machineinfrastructureforscience

Ø  Accelera5ngsciencecallsforfounda5onaladvanceswithinandacrossvirtuallyallsubfieldsofAr5ficialIntelligence!

Ø  Symposiumseekstodevelopacommunityresearchagenda


1Vasant Honavar, Mark Hill, and Katherine Yelick, Accelerating Science: A Computing Research Agenda. A Computing Community Consortium White Paper, 2016 http://cra.org/crn/2016/03/3501/



RecentHistory

•  NSFWorkshoponDiscoveryInforma5cs,February2012•  AAAIFallSymposiumonDiscoveryInforma5cs,November2012•  CMUSVSymposiumonCogni5veSystemsandDiscoveryInforma5cs,2013•  AAAIFallSymposiumonDiscoveryInforma5cs,November2013•  AAAIWorkshoponDiscoveryInforma5cs,July2014•  ACMSIGKDDWorkshoponDiscoveryInforma5cs,August2014•  PSBWorkshoponDiscoveryInforma5cs,January2015•  CCCWhitePaperonAccelera5ngScience:ACompu5ngResearchAgenda,

February2016•  AAAIFallSymposiumonAccelera5ngScience:AGrandChallengeforAI




Tradi'onalacceleratorsofscience•  Advancesin:

–  Dataacquisi5on(Newsensingmodali5es,instrumentsofobserva5on,improvementsinresolu5on,rate,capacity)

–  Mathema5calorcomputa5onalabstrac5onsorrepresenta5ons(e.g.,calculusinthecaseofphysics)

–  Sharingofscien5ficar5facts(data,experimentalprotocols,methods,models,conjectures…)withinandacrossdisciplines

–  Toolsfordataanalysis,modelingandsimula5on




Personal

Public

Social

HumanSensors HealthCare

DigitalMediaOmics

IM EMAIL

VOIP BLOGS

VIDEO

Source: Keith Marzullo

Advancesindataacquisi5onà“BigData”.

MOBILE


Sense

Iden5fy

Assess

Intervene

Evaluate



Prac'ceofScienceØ  Sciencecirca16thcentury

–  TychoBrahegathered20yearsofextremelyaccurateastronomicalmeasurements:posi5onsofthestarsandplanets:bigdata

–  JohannesKepler,workingforTychoBrahe,fitthedataineverywayimaginabletodiscoverlawsofplanetarymo5on:bigdataanaly5cs

–  IsaacNewton’sinven5onofcalculusprovidedthelanguagetoexpress,analyze,andcommunicatetheunifiedlawsofmo5on:knowledgerepresenta5onforphysics

Ø  Modernsciencereliesonscien5ficmethod,abodyoftechniquesforabodyoftechniquesforacquiringorupda5ngknowledge(throughsystema5cobserva5on,experimenta5on,hypothesisgenera5on,tes5ng,inanitera5vefashion)




Acartoonofthescien5ficmethod

Model

Data

Hypotheses

Predictions

Deduction

Observation Experimentation

Induction

Abduction

Review, Replication, Communication, Publication

Synthesis, Integration

Background Knowledge




Does“Bigdata”renderthescien'ficmethodobsolete?

•  “Petabytesallowustosay:“Correla5onisenough.”Wecanstoplookingformodels.

•  “Wecananalyzethedatawithouthypothesesaboutwhatitmightshow”.

•  “Wecanthrowthenumbersintothebiggestcompu5ngclusterstheworldhaseverseenandletsta5s5calalgorithmsfindpaKernswheresciencecannot.”

•  “Correla5onsupersedescausa5on,andsciencecanadvanceevenwithoutcoherentmodels,unifiedtheories,orreallyanymechanis5cexplana5onatall.”




Iscorrela'onallweneedinaworldawashwithdata?


1Henderson, 2008. The Gospel of the Flying Spaghetti Monster.

Isglobalwarmingcausedbyshrinkingnumbersofpirates1?•  Iscorrela5onallweneed?Clearlynot!•  Doesbigdatamakethescien5ficmethodobsolete?•  Absolutelynot!



Roleofscien'ficmethodinaworldawashwithdataØ  Allscienceiseitherstampcollec5ngorphysics1–RutherfordØ  Datarevolu5onimpliesanexponen5algrowthinthevolume,velocity,

varietyofstampcollec5on!Ø  Compu5ngrevolu5onenablesustosort,label,organize,catalogand

annotatetheresul5ngstampcollec5onmoreefficientlythaneverbefore!Ø  Connec5vityrevolu5onallowsustoshareandlinkdisparatestamp

collec5ons!Ø  Muchresearchhasfocusedononthecollec5on,management,analysisof

bigdataØ  Challenge:Makingtheleapfrom:

–  stampcollec5ngto“physics”–  descrip5vesciencetopredic5vescience–  correla5ontocausality


1“Physics” here is used as a metaphor for predictive science



Accelera'ngscience:Theprac'calimpera've

•  “Weareclosetohavinga$1,000genomesequence,butthismaybeaccompaniedbya$1,000,000interpreta5on1”

•  >1300NARgenedatabases•  1Mnewbiomedicaljournalar5cles

publishedperyear(2700/day)


SlidecourtesyLarryHunter

1Bruce Korf, Former President, American College of Medical Genetics



Theimpera'veforaccelera'ngscience•  Automa5onof“Bigdata”acquisi5on,managementandanaly5cs

accelerates–  Brahe’spartofthescien5ficendeavor(dataacquisi5on,management)

–  Andthankstoadvancesinmachinelearningincreasingly,Kepler’spart(dataanaly5csandmodelbuilding)

–  Butforthemostpart,leavesuntouched,theotheraspectsofscience,whichbecometheratelimi5ngstepsinscience

Ø  Accelera5ngsciencerequiresaccelera5ngtherate-limi5ngstepsof

thescien5ficmethod!




Acartoonofthescien5ficprocess

Model

Data

Hypotheses

Predictions

Deduction

Observation Experimentation

Induction

Abduction

Review, Replication, Communication, Publication

Synthesis, Integration

Background Knowledge




Accelera5ngsciencethroughautoma5on?

http://www.wired.com/wiredscience/2011/04/robot-scientist-language/




Science 3 April 2009: Vol. 324 no. 5923 pp. 85-89The Automation of Science

RossD.King,JemRowland,StephenG.Oliver,MichaelYoung,WayneAubrey,EmmaByrne,MariaLiakata,MagdalenaMarkham,PinarPir,LarisaN.Soldatova,AndrewSparkes,KennethE.WhelanandAmandaClare

ABSTRACTThe basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist

“Adam,” which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. …




Howclosearewetofullyautoma'ngscience?

Ø Notvery,exceptperhapsinverycarefullyconstrainedsetngsØ  Collabora5vehuman-machinesystemsmightofferamore

realis5capproachtoaccelera5ngscienceØ  Accelera5ngsciencepresentsagrandchallengeforAI:

§  Analysisandsynthesisofcomputa5onalabstrac5onsofboth§  Universesofscien5ficdiscourse§  Scien5ficar5factsandthescien5ficprocess





Computa'onalabstrac'onsofscien'ficdomainsØ  Computa5onalabstrac5onsincreasinglyservearoleinmany

sciences,arolesimilartothatplayedbycalculusinphysicsØ Evolu5on(e.g.,Holland,Valiant)Ø Generegula5on(e.g.,Dougherty)Ø Ra5onality(e.g.,Roughgarden)Ø Socialnetworks(e.g.,Kleinberg)Ø Learning(e.g.,Valiant,SuKon,Langley,Pearl)Ø Brain(McCulloch,Eliasmith,Gallant)

Ø  Accelera5ngsciencecallsforcomputa5onalabstrac5onsofuniversesofscien5ficdiscourse,alongwithmethodsforefficientinference




Example:generegula'on

•  Genesa,b,c,dcodeforproteinsA,B,C,D•  ProteinsAandBformahetero-dimerthatac5vatestheexpression

ofgenec•  ProteinCinhibitstheexpressionof(andco-regulates)genesbandd•  ProteinDisnecessaryforthetranscrip5onofproteinB




AComputa'onalabstrac'on:Directedlabeledgraphs

•  Nodescorrespondtogenes•  Edgescorrespondtoregulatoryinterac5ons•  Edgelabelscanbeusedtodenotethetypesofinterac5ons,orlistsof

regulatorsandtheirinfluenceonthespecificedge,e.g.,KEGGpathways•  Canhelpuncoversequencesofregulatoryevents,cycles(feedback

regula5on),redundancy…




Acomputa'onalabstrac'on:Booleannetworks

•  Genesaremodeledbybinaryvariables-on,off(1,0)•  Statesofgenesareupdatedindiscrete5mesteps•  Stateofageneat5met+1isaBooleanfunc5onofthestatesat5metofthe

genesthatinfluenceit•  AnNgeneBooleannetworkcaninprinciplebeinoneof2Nstates•  Canhelpdetermineifthenetworkcangetfromonestatetoanother,the

effectofgeneknockout,etc.Vasant Honavar, AAAI Fall Symposium on Accelerating Science: A Grand Challenge for AI



Computa'onalabstrac'onsofscien'ficscien'ficprocesses

Ø  Accelera5ngsciencecallsforcomputa5onalabstrac5onsofscien5ficar5factsandscien5ficprocesses§  Acquiring,organizingandmaintainingbackground“knowledge”§  Formula5ngandpriori5zingques5ons§  Formula5ng,priori5zing,andplanningstudies§  Designing,priori5zing,planning,execu5ng,monitoringexperiments

§  Drawinginferences,construc5ngexplana5onsandhypotheses§  Synthesizingfindingsfromdisparateobserva5onalandexperimentalstudies

§  Expressingandreasoningwithscien5ficarguments




Computa'onalabstrac'onsofscien'ficscien'ficprocessesØ  Accelera5ngsciencecallsforcomputa5onalabstrac5onsof

scien5ficar5factsandscien5ficprocesses§  Buildingaccurate,communicable,testablemodelsfromknowledge,observa5onsandexperiments

§  Linkingdata,models,scien5ficarguments,hypotheses,experiments

§  Linkingandreasoningwithmodelsatdifferentlevelsofabstrac5onoracrossdifferentfacets

§  Sharingdata,models,hypotheses,andotherscien5ficar5facts§  Documen5ngandreplica5ngstudies§  Integra5ngresultsintothelargerbodyofknowledge




Human-MachineCollabora5onØ  Accelera5ngsciencecallsfor

§  Cogni5vetoolsthataugmentandextendhumanintellect§  Sharableandcommunicablerepresenta5onsofscien5ficar5facts§ Data,experimentalprotocols,analysis,models,hypotheses,scien5ficarguments,workflows

§  Organiza5onalstructuresandprocessesforcollabora5vescience§ Assemblingteams§ Decomposingtasks,assigningsubtasks,combiningresults§  Incen5vizingpar5cipants§ Ci5zenscience




ACartoonofthescien5ficprocess

Model

Data

Hypotheses

Predic5ons

Deduc5on

Observa5onExperimenta5on

Induc5on

Abduc5on

Review,Replica5on,Communica5on,Publica5on

Synthesis,Integra5on

BackgroundKnowledge




Examplesofcogni5vetoolsAscien5st’sassociatethat:–  Givenbackgroundknowledgeandaccesstoliteratureintwoormoredomains,useliteratureinonedomaintogeneratehypothesesorexplana5onsinanotherdomain?

–  Givencausalinforma5onlearnedfromrestrictedexperimentsinmul5plesetngs(saymouse,pig),whenpossible,infercausalinforma5oninatargetsetng(sayhuman)?

–  Givenascien5ficques5on,andasocialnetworkofresearchersandtheirscien5ficoutpute.g.,publica5ons,iden5fyacollec5onofresearchersthatarebestequippedtoaddresstheques5on?




SummaryØ  Allscienceiseitherstampcollec5ngor“physics”Ø  Bigdata=spectacularstampcollec5ons!Ø  Bigdata≠Demiseofthescien5ficmethodØ  Accelera5ngsciencepresentsagrandchallengeforAI:

§  Analysisandsynthesisofcomputa5onalabstrac5onsofboth§  universesofscien5ficdiscourse§  scien5ficar5factsandscien5ficprocess


Ø  Accelera5ngsciencecallsforfounda5onaladvanceswithinandacrossvirtuallyallsubfieldsofAI!




Agenda:ThursdayMorning•  Session1:9:00am–10:30am

–  VasantHonavar,PennsylvaniaStateUniversity–  CarlaGomes,CornellUniversity–  VipinKumar,UniversityofMinnesota–  PatLangley,Ins5tutefortheStudyofLearningandExper5se(ISLE)

•  CoffeeBreak:10:30AM •  Session2:11:00amto12:30pm

–  SamanthaKleinberg,StevensIns5tuteofTechnology–  EliasBareinboim,PurdueUniversity–  DavidJensen,UniversityofMassachuseKs–  KunZhang,CarnegieMellonUniversity

•  Lunch:12:30pmto2:00pm




Agenda:ThursdayAUernoon

•  Session3:2:00pmto3:30pm–  AshokGoel,GeorgiaTech–  AndrewMcCallum,UniversityofMassachuseKsAmherst–  HoifungPoon,MicrosoxResearch–  JamesEvans,UniversityofChicago

•  CoffeeBreak:3:30pm •  Session4:4:00pmto5:30pm

–  ChandraBhagavatula,AllenIns5tueforAr5ficialIntelligence–  YolandaGil,UniversityofSouthernCalifornia/Informa5onSciences

Ins5tute–  GullyBurns,Informa5onSciencesIns5tute–  DashunWang,NorthwesternUniversity

•  Recep'on:6:00pm •  06:00PM Recep'on




Agenda:FridayMorning

•  Session5:9:00amto10:30am–  PeterKarp,SRIInterna5onal–  LarisaSoldatova,BrunelUniversity,London–  AndreiRzhetsky,UniversityofChicago,ConteCenter–  MichaelDumon5er,StanfordUniversity

•  CoffeeBreak:10:30am•  Session6:11:00amto12:30pm(BreakoutSessions)

–  ResearchChallengesinAI(KR,NLP,Planning,DecisionMaking)–  ResearchChallengesinAI(Machinelearning,causality).–  Infrastructure(includingcollabora5vescience,ci5zenscience)–  DrivingApplica5ons(LifeSciences,SocialSciences,BrainSciences,

Cogni5veSciences,PublicPolicy) •  Lunch:12:30pmto2:00pm




Agenda:FridayAUernoon

•  Session7:2:00pmto3:30pm–  ZoranPopovic,UniversityofWashington–  CharloKeLee,UniversityofWashington–  KevinCrowston,SyracuseUniversity–  KlaraNahrstedt,UniversityofIllinoisUrbana-Champaign

•  CoffeeBreak:3:30pm •  Session8(AgencyRoundtable):4:00pmto5:30pm

–  PhilBourne,NIH–  FenZhao,NSF–  LynneParker,NSF–  Mar5nApple,CouncilofScien5ficSocietyPresidents–  RenéBaston,NEBigDataHub

•  PlenarySession:(Briefhighlightsfromallsymposia):6:00pm




Agenda:SaturdayMorning

•  Reportwri'ng:10:00amto12:30pm–  VasantHonavar–  SamanthaKleinberg–  PatLangley–  KevinCrowston–  KlaraNahrstedt–  Addi5onalvolunteerswelcome!


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Accelera’ng Science: A Grand Challenge for AI€¦ · The Gospel of the Flying Spaghetti Monster....

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