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Talent Pipelines for Smart Cities, Hong Kong Productivity Council, 21 sept2017
- 1. 1 TALENT PIPELINES FOR SMART FUTURES h1ps://www.martechadvisor.com/arBcles/account-‐based-‐markeBng-‐2/the-‐marriage-‐of-‐ai-‐and-‐abm-‐a-‐perfect-‐match-‐for-‐b2b-‐organizaBons/ • September 21, 2017 • Martha G Russell • mediaX at Stanford University
- 2. Deep Knowledge with Wide Applicability IN THE HEART OF SILICON VALLEY IN A CULTURE OF RAPID ITERATION, WHERE DISRUPTION IS CELEBRATED, WHERE TALENT, INFORMATION & CAPITAL RESOURCES FLOURISH THE ISSUE IS NOT THE RATE OF TECHNOLOGY TRANSFER THE ISSUE IS THE EFFECTIVENESS OF INNOVATION AND KNOWLEDGE TRANSFER WE CALL THIS “COLLABORATIVE DISCOVERY” The mediaX approach WORK ON BOLD IDEAS WITH BUSINESS, TEST SUCCESS/FAILURE CONDITIONS, ITERATE RESULTS QUICKLY, TRANSFER INSIGHTS AT EVERY STAGE H-STAR HUMAN SCIENCES AND TECHNOLOGIES ADVANCED RESEARCH INSTITUTE
- 3. Memberships for Discovery CollaboraBons
- 4. CSLI Eng EE Psy Ed SSP SCIL SCAnS PBLL GSB Ling CHIMe Art Discovery Collabora/ons Span Stanford Labs School of Education; Education and Learning Sciences Digital Art Center Graduate School Of Business Communication Between Humans and Interactive Media Stanford Center for Innovations in Learning Project Based Learning Laboratory Symbolic Systems Program Engineering & Product Design Center for the Study Of Language & Information Stanford Center for Anatomical Imaging Computer Science Psychology Linguistics Phil Philosophy Law Center for Legal Informatics LIFE Learning in Informal and Formal Environments CS Electrical Engineering SHL Stanford Humanities Lab VHIL Virtual Human Interaction Lab PBLL Work Technology & Organization CIBR Interdiscipinary Brain Research Center Des Stanford Joint Program in Design d.school
- 5. Key Points • 1. A perfect Storm for Change and TransformaBon • Data is the new gold – for digital ciBes, digital living, digital working • Technologies, talent, markets, and resources are local & global • 2. The Human Dimension of “Smart Futures” is Crucial • Partnerships of people and technology • Smart people to make smart decisions • Smart Futures (I o T) are IdenBty Systems • Skilling and reskilling are urgent • System leadership is crucial • 3. Shared Vision and Trust Accelerate InnovaBon • InnovaBon ecosystems are built on relaBonal capital • Stories ma1er -‐ we are “Homo NarraBve”
- 7. #1 A PERFECT STORM FOR CHANGE AND TRANSFORMATION
- 9. We Have Survived Previous Transformations! 2 – 4 – 8 – 16 -‐ ? Zero
- 10. #1-A Data is the New Gold – for Living, Working, Mobility
- 11. Expert Crowd-‐sourcing with Flash Teams h1p://flashteams.stanford.edu/ h1p://www.youtube.com/watch?v=teOBdtZR5uQ&list=PL2rro4X-‐RbDFWjQV_-‐XlHklj2bD3aNAr5#t=37 #1-B Know-How, Talent, Markets are Local and Global
- 12. #1-B Pop-up Employers: Build Team, Do Work, Say Good-bye h1ps://www.nyBmes.com/2017/07/12/business/economy/flash-‐organizaBons-‐labor.html?smprod=nytcore-‐iphone&smid=nytcore-‐iphone-‐share New York Times By NOAM SCHEIBERJULY 12, 2017
- 13. 13 Partnerships of Human and Automated Intelligence Iden/ty Systems Smart Decisions Smart Re-‐skilling Smart System Leadership # 2 THE HUMAN DIMENSION OF “SMART” IS CRUCIAL
- 14. “SMART” is an Ecosystem of Data–Tech–People CompuBng Infrastructure & Energy h1p://www.vaqueronet.com/colocaBng.php h1p://www.alpheuscommunicaBons.com Algorithms & Service Infrastructure h1p://mashable.com/2009/04/30/facebook-‐friends-‐page/ USERS & Social Networks Technology Infrastructure and Support # 2-‐B PARTNERSHIPS OF HUMAN AND AUTOMATED INTELLIGENCE
- 15. 15 Smart Buildings, Smart CiBes, Smart Environments
- 16. 16 Data Flows Through ApplicaBon Programming Interfaces Agile Ontologies
- 17. 17 Smart Futures Involve Smart Decisions Using API Ecosystems
- 18. Smart Future = Technology To Enhance the Human Experience
- 19. #2-‐A Smart CiBes Are IdenBty Systems for People and for Things
- 20. #2-‐C Smart Futures Require People Making Smart Decisions In automaBon and AI, we see the intertwining of humans as meaning-‐makers and as sense-‐makers
- 21. Smart Futures Require People Making Smart Decisions Non-‐verbal & Verbal Dialogues with Automated Agents
- 22. Smart Futures Require People Making Smart Decisions PersonaliBes and Back-‐Stories of Automated Agents
- 23. Smart Futures Require People Making Smart Decisions SMART = Sense-‐Making + Making Sense
- 24. #3-‐C Smart Futures Enable ConBnuous Re-‐skilling for Smart People Workforce and learning pathways are shiring
- 25. #3-‐C Smart Futures Enable ConBnuous Re-‐skilling for Smart People Roy Pea David Jacks Professor of EducaBon and Learning Sciences and Faculty Director, H-‐STAR InsBtute “Technology is not simply an amplifier, it re-‐organizes the acBvity systems in which learning, conversaBon, work, and representaBonal pracBces are achieved.” Mar/n Carnoy Vida Jacks Professor of EducaBon and Economy and Faculty Co-‐Director Lemann Center “There is an increasingly close relaBonship between physical and human capital. A major complementary investment is required in the human capital required for modernizing 21st Century systems. ”
- 26. Skilling and Re-‐skilling for Smart Futures The Increasingly Close RelaBon Between Physical and Human Capital • “As urban systems developed and built to support 20th century economic development and demographics rapidly become anachronisBc, they will be replaced by new soluBons using much more sophisBcated technologies that are more appropriate to 21st century development. • The choice of soluBons, the type of physical capital and the technologies inherent in those soluBons, and the implementaBon, installaBon, operaBon, and maintenance of the new technologies require a major complementary investment in the human capital required for these different stages of modernizing the system. – MarBn Carnoy, Professor and EducaBonal Economist, Graduate School of EducaBon, Stanford University
- 27. Skilling and Re-‐skilling for Smart Futures “1: The more general the knowledge, the less likely private firms will pay for transferring the knowledge to their employees. Vice-‐versa for knowledge highly specific to a parBcular technology (Becker, 1964). 2: The more abstract and general the knowledge to be transferred, the more the training requires face to face interacBon and powerful teaching; ie, business decision-‐making training for execuBves. 3: The more specific the knowledge to be transferred, the more the experBse can be taught in direct contact with the technology, or virtually; ie, flight simulators for pilot training.” Axioms for Developing Training Programs MarBn Carnoy, Professor and EducaBonal Economist, Graduate School of EducaBon, Stanford University
- 28. Skilling and Re-‐skilling for Smart Futures General Axioms for VocaBonal EducaBon • “In the context of most market economies, vocaBonal educaBon that is preparing workers in secondary schools for specific jobs has not been very successful unless the vocaBon is one that is in especially high demand at the Bme the students graduate.” • Specific job preparaBon is most successful when: – It takes place in firms training already employed workers on equipment that the workers will actually use arer training, OR – In training insBtuBons such as HKPC Training InsBtute, that are linked to groups of firms and train workers employed in those firms for tasks specified by the firms. • “Training for tasks using new technology requires access of trainers and trainees to the new technology, usually requiring the training to take place at the firms already possessing the technology by trainers familiar with it.” – MarBn Carnoy, Professor and EducaBonal Economist, Graduate School of EducaBon, Stanford University
- 29. Skilling and Re-‐skilling for Smart Futures Preparing Experts to Design the 21st Century HK
- 30. Skilling and Re-‐skilling for Smart Futures Training the Technicians Who Will Install, Operate, and Maintain the New Systems
- 31. Skilling and Re-‐skilling for Smart Futures Preparing ExecuBves to Move into the Next GeneraBon of Technologies and Beyond Henley Business School
- 32. Skilling and Re-‐skilling for Smart Futures Opportunity 1: Personalized learning at scale Always-‐on networked world of educaBonal opportuniBes that opBmize learning at many levels of difficulty and assistance. – Mapping dependency relaBonships – Metadata tagging – RecommendaBon engines – Digital assessment and feedback systems – IdenBty and relaBonships in the technology-‐enhanced learning process Roy Pea, Professor Learning Sciences and Technology Design, Stanford University, Na=onal Research Council’s Grand Challenges for Educa=on for Life and Work.
- 33. Skilling and Re-‐skilling for Smart Futures Opportunity 2: MulB-‐modal learning MediaRoom -‐ UNI-‐Konstanz Virtual InteracBon Lab -‐ Stanford InteracBon science and technology; Sensory modaliBes and mulB-‐sensory experiences of embodied semioBc interacBons, meaning in context; Technologies and methodologies for integraBng diverse types of data (from sensor streams, workbench streams and human-‐ coded data for pa1ern detecBon –sense making); In situ experiences for rich interacBon with data stream interrelaBonships – Roy Pea, Professor Learning Sciences and Technology Design, Stanford University
- 34. #2-‐E System Leadership is Crucial • The ‘Commons’ refers to shared resources in which all stakeholders have interests • It concerns a Common Set of Problems/SoluBons • Elinor Ostrom, Nobel Laureate, Governing the Commons: – “All efforts to organize collecBve acBon . . . Must address a common set of problems.” – Commitment, governance, resources – This includes informaBon, data and issues about public knowledge, the public domain, open science, and the free exchange of ideas. • System leadership is the criBcal element
- 35. Metrics Must Include Quality of Life, Sustainability & Inclusion
- 36. • The listener’s brain determines how a story is heard • Influence by – framing – parBcipaBon Put ME in the Story #3 Shared Vision and Trust Accelerate InnovaBon
- 37. The Way We Think About Organizations New OrganizaBonal Chart Based on RelaBonships Relationship-Focused Co-Creation Infrastructure Change and transforma=on are interlocked through key people – informa=on flow, norms, mental models.(Davis,1996) #3-A Shared Vision Is Built on Relational Capital
- 38. Agents Events Impact Coalitions Shared Vision TransformaBon Iterative Alignment Interact & Feedback Co-Create Value Through Context, Culture, Capital Russell, M.G., Still, K., Huhtamäki, J., and Rubens, N., “Relational Capital for Shared Vision in Innovation Ecosystems,” in Gebhardt, C., and Meig, H.A. (Eds.), Special Issue: The Spatial Dimension of Innovation: Triple Helix and the City, Triple Helix Journal, Forthcoming. #3-A Shared Vision Guides the Transformation
- 39. #3-‐B Stories Ma1er -‐ We Are “Homo NarraBve”
- 40. • The listener’s brain determines how a story is heard • Influence by – framing – parBcipaBon Put ME in the Story h1p://uphillwriBng.org/wp-‐content/uploads/2011/11/Reality-‐Mirror.gif Inclusion Means Being Part of the Story
- 41. Key Points • 1. A Perfect Storm for Change and TransformaBon • Data-‐driven • Local & Global • 2. The Human Dimension of “Smart CiBes” is Crucial • Partnerships of Human and AI • Smart Decisions • IdenBty Systems • Re-‐skilling • System Leadership • 3. Shared Vision and Trust Accelerate InnovaBon • RelaBonal capital • Stories • Martha G Russell • Martha.russell@stanford.edu • mediax.stanford.edu Talent Pipelines for Smart Futures
- 42. What is the Story for Smart Hong Kong?