Artificial Intelligence Applications, Research, and Economics

IkhlaqSidhu,contentauthor

Ikhlaq SidhuFounding Faculty Director Sutardja Center for Entrepreneurship & TechnologyDepartment of Industrial Engineering & Operations Research IEOR Emerging Area Professor Award

ArtificialIntelligenceApplications,ResearchandEconomics


MyTopicsforToday:

1. TechnicalevolutionofAI/ML

2. Berkeleyresearchperspective

3. JobLoss,Economics,andSingularityvsMultiplicity


MyPerspective:Sutardja CenterforEntrepreneurship&Technology

CollegeofEngineering,UCBerkeley

ApproachBerkeleyMethod:Ø EntrepreneurshipØ InnovationLeadership


Lots of Students Our Model Brings Bay Area Executives and Entrepreneurs into the Classroom

1600 Undergraduates100 Ph.D / Graduate Students100 Executives10 Global Partners

Michael Marks, KKR, former CEO, FlextronicsShomt Ghose, Venture Partner, Onset VenturesUdi Manber, VP Engineering, GoogleMarc Andreesen, Founder, NetscapeLarry Baer, COO, San Francisco GiantsAmine Haoui, CEO, Sensys NetworksStacey Lawson, Founder In Part, Executive SeibelJim Davidson, Managing Director, Silverlake PartnersDonna Dubinsky, Former CEO, PalmMatt Caspari, co-founder, Aurora BiofuelsRichard Gorman, SVP, Siebel SystemsMike Olson, founder and CEO, ClouderaBrodie Keast, EVP, TiVoDavid Ladd, Managing Director, MayfieldJeff Miller, CEO, DocumentumEva Miranda, SVP, Sony CorporationRavi Mohan, Managing Director, Shasta VenturesTed Hoff, Inventor,of the MicroprocessorNat Goldhaber, Managing Director, Claremont Creek VenturesPeter Thiel, co-founder and CEO, PayPalVictoria Hale, founder and CEO, Medicines 360Steve Newcomb, founder , Powerset (part of Microsoft’s BING)Pehong Cheng, CEO, Broadvision

We focus on the Mindsets & Behaviors needed for Innovation and Entrepreneurship(in context of technology change)


• Detectionoffakenews

• Predictionoflong-termenergypricesto

solveWallStreetproblem

• Predictionapplicationsstockmarket,

sportsbetting,andmore

• AIforcrimedetection,trafficguidance,

medicaldiagnostics,etc.

• AversionofZillowthatisrecalculated

withtheeffectsofAirBnBincome

andmanymore…

My newest course: IEOR 135 Applied Data Science with Venture ApplicationsSample Data-X Projects


Data and AI Technology:Where is it going?


Traditionally2Tasks:Classification&PredictiveScoring

Themostfamousapplicationhasbeenrecommendation:“whichotheruserismostlikeyou”

ExtractedDataofteninTableFormat

Classification:CatsandDogs,SpeechRecognitionMovieRecommendation

Scoring:CreditScore,MovieRatingHeathScore, AnyIsoquant…


X Y

X Y

X YMLAlgorithmsGuessthisfunctionF(x)

WehavenowswitchedtoNeuralNetworksasFunctionApproximators


Neuralnetresultsareclosethumanresults


PeterAbbeel – DeepReinforcementLearning

PeterAbbeelProfessoratUCBerkeley


MostRecentAINews


Does this mean AI Can Do Everything Better than Humans


Perfect Information vs. Real World

fully observed uncertaindiscrete multi-agentsingle agent

infinite time horizoncontinuous

finite

KenGoldbergUCBerkeley

Eventhen,AICannotSolveSolveRealLifeProblemsBetterThanHumansAndinfact,AICannotevenWorkwithoutHumans

KenGoldbergLeadingAIResearcheratBerkeley

ProfessorandDepartmentChair,IEOR

WilliamS.FloydJr.DistinguishedChair


AcknowledgementtoKenGoldbergUCBerkeley

AISystemsOnlyWorkbecauseofHumanarePartoftheSystem

GoogleOperations

People WriteWebPages People atGoogleTunetheResults

People ClickonWhatTheyWant

Result

FeedbackByclicks

MassiveData

Thereisno“intelligence”,“Desire”,or“Existence”inAIwithoutPeopleThereareonlypeoplewho“investin,designandoperatethemachines”


37 faculty

AtBerkeley,wehavealotofresearchon“HowMachinesWillWorkasPartofLargerSystems

thatWorkwithPeople”


MyDriveHomeFrom

Berkeley


Autonomous Driving and Driver-Assist

•Communicating intent •Driver-in-the-loop modeling •Two-way learning: knowledge transfer between vehicle and driver •Safety in autonomous and assisted driving

Principal investigators:

Ken Goldberg UC Berkeley

Anca Dragan UC Berkeley

Trevor Darrell UC Berkeley

Francesco Borrelli UC Berkeley

Ruzena Bajcsy UC Berkeley

Source:KenGoldberg,CPAR,PeopleandRoboticsInitiative

IkhlaqSidhu,contentauthorSource:KenGoldberg,CPAR,PeopleandRoboticsInitiative

Safety in Human-Robot Interaction: Guarantees and Verification

Safety-constrained motion planning for efficiency in factory human-robot interaction

Learning and prediction for safety in HRI

Provably safe human-centric autonomy

Masayoshi Tomizuka UC Berkeley

Principal investigators:

Claire Tomlin UC Berkeley

Francesco Borrelli UC Berkeley


LongerTermFutureNarratives:Multiplicy vsSingularityApocalypse

Multiplicity:diversecombinationsofpeopleandmachinesworktogethertosolveproblemsandinnovate.Multiplicityisnotsciencefiction.Extendingonthecombinationofmachinelearning,thewisdomofcrowds,andcloudcomputingalreadyunderliesmanycomplextasksperformedeveryday

KenGoldberg,UCBerkeley

MultiplicitySingularity

Singularity:Google’sdirectorofEngineering,RayKurzweil,haspredicted singularitytotakeplacearound2045Singularityisatippingpointwhentherobotspoweredbyartificialintelligence willbecomemoreintelligentthanhumanbeings.Singularityisthetime whenartificialintelligencewon’trequire humaninterventiontobecomemoreintelligent.Computerswillbeself-sufficient,theywillmaketheirowndecisions

http://techbakbak.com/singularity-will-happen/

https://www.wsj.com/articles/the-robot-human-alliance-1497213576


AI and Job Loss


AUTOMATION HAS BEEN CHANGING THE JOB LANDSCAPE FOR MANY YEARS

Over many decades:

– Routinejobs(manualorcognitive)havedeclined.

– Onlynon-routinejobshavecontinuedtogrow.(Source:Economist)

Now: The most famous study on Job Lossand AI, by Carl Frey and Michael Osbourne,predicts that 47% of the workforce is indanger.

9/6/2017 Automation and anxiety

https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety 5/11

Economist

IkhlaqSidhu,contentauthorIkhlaqSidhu,UniversityofCalifornia,Berkeley

TextilevsHandweaving:Duringthe19thcentury,amountofclothasingleweaverinAmericacouldproduce=50Xgain.Laborrequiredfellby98%.Result:clothbecamecheaper,demandgreater,4Xmorejobswerecreatedinthesamesector.

Economists and historians claim that job disruption actually helped the economies that participated.

AutovsHorse-basedtransportation:Thisledtoadeclineinhorse-relatedjobs.However,theautomobileindustryitselfgrewfast.Jobswerealsocreatedindifferentsectors,e.g.motelandfast-foodindustriesthatarosetoservemotoristsandtruckdrivers.

ATMMachinesatBanks:Automatedtellermachines(ATMs)reducethenumberofbankclerks(20/bankin1988to13/bankin2004)bytakingoversomeoftheirroutinetasks.However,bankbranchesgrewinnumbersby43%andtotalemployeesgrew.

Reference:Doweunderstandtheimpactofartificialintelligenceonemployment?|Bruegel

OneCaveat:TheMcKinseyGlobalInstituteestimatesthat,comparedwiththeIndustrialRevolutionofthelate18thandearly19thcenturies,AI’sdisruptionofsocietyishappeningtentimesfasterandat300timesthescale.


PWC:DefiningAI

- EconomytoGrow$15.7TUSDby2030

- earlyvaluefromproductivity(2017-2024)– $6.6T

- latervaluefromincreasedconsumption (2024+)$9.1T

We therefore consider four elements of artificial intelligence:

• Automated intelligence: Automation of manual, routine tasks

• Assisted intelligence: Helping to perform tasks faster and better

• Augmented intelligence: Helping people to make better decisions

• Autonomous intelligence: Automating decision-making processes without human intervention

Figure 1: The scope of artificial intelligence

Hardwired/specific systems

Adaptive systems

PwC Data & Analytics

Human-in-the-loop No Human-in-the-loop

Assisted intelligence

AI systems that assist humans in making decisions or taking actions. Hard-wired systems that do not learn from their interactions.

Automation

Automation of manual and cognitive tasks that are routine. This does not involve new ways of doing things – automates existing tasks.

Augmented intelligence

AI systems that augment human decision making continuously learn from their interactions with humans and the environment.

Automation intelligence

AI systems that can adapt to different situations and can act autonomously without human assistance.

6 The economic impact of artificial intelligence on the UK economy

Figure 3: Where will the value gains come from with AI?

During the first phase of the impact (2017-2024), productivity growth could account for a relatively larger share of the gains than the period that follows, when the consumption-side impacts are likely to dominate. This is due to the fact that it takes time for firms to enter the marketplace and supply new varieties of AI-enhanced products to consumers following the stimulation in consumer spending from higher real wages and initial product improvements. As this takes place, competition within AI goods-producing markets increases dramatically, leading to further increases in the value of goods to consumers and therefore greater expenditure on these products as their affordability and attractiveness rises. Overall, the impact on UK GDP will likely be more heavily-weighted towards the consumption-side impacts than other countries4. This is partially because employment in the UK is not highly concentrated in sectors that are automatable, but is also related to the particularly large impact of new goods varieties on the affordability of products in the UK due to the competitive market structure.

Notably, although the labour productivity channel is a ‘production side’ effect, the GDP gain is not only restricted to increased efficiency of production. As real wages rise with productivity, consumer spending increases since goods become more affordable, regardless of the downwards price pressure from increased production efficiency.

The potential for artificial intelligence to impact the UK economy is slightly higher compared to the potential in Northern Europe more generally. Our recent report5 assesses the global potential for AI and the likely impact for regional economies. The analysis concludes that GDP in Northern Europe could be up to 9.9% higher in 2030. The UK could see larger gains as a result of having stronger foundations in technology already – many technology companies have their EMEA headquarters located in the UK – and greater access to the talent and skills required to develop AI technologies.

On the topic of jobs, the adoption of ‘no-human-in-loop’ technologies will mean that some posts will inevitably become redundant, but others will be created by the shifts in productivity and consumer demand emanating from AI, and through the value chain of AI itself. Along with jobs in the development and application of AI, the technologies will need to be built, maintained, operated and regulated. For example, we will need the equivalent of air traffic controllers to control the autonomous vehicles on the road. Same day delivery and robotic packaging and warehousing are also resulting in more jobs for robots and for humans. Furthermore, the extra demand generated in the economy, as a result of artificial intelligence increasing output and incomes, will lead to the creation of jobs not directly related to AI in non-AI intensive sectors. All of this will facilitate the creation of jobs that would not have existed in a world without AI.

4 PwC (2017), “Sizing the prize: What’s the real value of AI for your business and how can you capitalise?”5 Ibid.

Phase 1: Productivity-driven impact Phase 2: Consumption-side impacts dominate

£ bi

llion

0

50

100

150

200

250

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Labour productivity Personalisation Time saved Utility

12 The economic impact of artificial intelligence on the UK economy

Thetransitiontimingisstillabigfactor

Thistime,itwillbe10Xfasterandscaleof300Xthanthelastindustrialrevolution.Wehavenotseenthislevelofdisplacementbefore.SourceMcKinsey


ArgumentNarrative

• Couldyouhavestoppedthefirstindustrialrevolution?No,ifyouchosetonotparticipate,youwouldbecomeirrelevant:– Whobenefited:i)people/governmentswhocreatedthetools.ii)peoplewholearnedto“operatethemachine”,iii)thosethatinventordesigntheuse-caseorprocess.

– Whosuffered:displacedworkersanddisplacedeconomies.

• Massdisplacementalsomeanstherewillbealackofspending,butsofarnoeconomistshavevalidatedthisconcern.

• Innovativepeoplewithentrepreneurialbehaviorsarebestsuitedtosurvivetransition.Retraining willbeessential.Psychologyisthebiggestfactor.Weshouldconsiderhowtobestre-trainforthischange.


Contact:

IkhlaqSidhuFoundingFacultyDirector,CenterforEntrepreneurship&TechnologyIEOREmergingAreaProfessor,UCBerkeleysidhu @berkeley.edu,scet.berkeley.edu

Artificial Intelligence Applications, Research, and Economics

Technology

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