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Introduction to AEIOU Framework - Abstract Entity Interaction Universals - what questions to answer to improve conceptual foundations of service science, and what formal methods can answer those ...

Introduction to AEIOU Framework - Abstract Entity Interaction Universals - what questions to answer to improve conceptual foundations of service science, and what formal methods can answer those questions

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Frontiers 20140628 v3 Presentation Transcript

  • 1. AEIOU Framework: Abstract-Entity-Interaction-Outcome-Universals Towards “Laws of Service” Across Time-Space-Scale Jim Spohrer, IBM Haluk Demirkan, University of Washington Frontiers in Service, Miami, FL June 28, 2014 6/28/2014 (c) 2014 IBM UP (University Programs) 1 This presentation with speaker notes is available for download at: http://www.slideshare.net/spohrer/frontiers-20140628-v3
  • 2. S-D Logic 6/28/2014 (c) 2014 IBM UP (University Programs) 2
  • 3. Entities (Actors) as Resource Integrators 6/28/2014 (c) 2014 IBM UP (University Programs) 3
  • 4. Entities (Actors) as Institutions Ostrom Framework Ontology Logic Language Theory Epistemology “Lawful” Learning Model Axiology Likeness Levels 6/28/2014 (c) 2014 IBM UP (University Programs) 4 • A set of designed constraints imposed on human interactions for a purpose (desired outcomes) • “Lawful” – Entities – Interaction – Outcomes
  • 5. Evolving Ecology of Entities-Interactions-Outcomes 6/28/2014 (c) 2014 IBM UP (University Programs) 5
  • 6. Types of Structure 6/28/2014 (c) 2014 IBM UP (University Programs) 6
  • 7. Entities (Actors) as Service Systems 7 Economics Marketing Computer Science Operations Supply Chain Management Management Science Design “service science is the transdisciplinary study of service systems & value co-creation” “a service system is a human-made system to improve provider-customer interactions and value co-creation outcomes, studied by many disciplines, one piece at a time.” Systems Engineering Systems Sciences Psychology Political Sciences Management Finance Law Information Systems Information Systems Cognitive Science and many others…
  • 8. Types of Service System Entities 6/28/2014 © IBM 2013 IBM University Programs worldwide accelerating regional development (IBM UPward) 8 http://www.service-science.info/archives/1056 Nation State/Province City/Region University College K-12 Cultural & Conference Hotels Hospital Medical Research Worker (professional) Family (household) For-profits: Business Entrepreneurship Non-profits Social Entrepreneurship U-BEE Job Creator/Sustainer U-BEEs = University-Based Entrepreneurial Ecosystems “The future is already here (at universities), it is just not evenly distributed.” “The best way to predict the future is to (inspire the next generation of students to) build it better.” “Multilevel nested, networked holistic service systems (HSS) that provision whole service (WS) to the people inside them. WS includes flows (transportation, water, food, energy, com development (buildings, retail ,finance, health, education), and governance (city, state, nation). ” University Four Missions 1. Learning 2. Discovery 3. Engagement 4. Convergence
  • 9. Scale: Nested, Networked Structure 6/28/2014 (c) 2014 IBM UP (University Programs) 9
  • 10. ISPAR: Interactions & Outcomes 6/28/2014 (c) 2014 IBM UP (University Programs) 10 Maglio, P. P., Vargo, S. L., Caswell, N., & Spohrer, J. (2009). The service system is the basic abstraction of service science. Information Systems and e-business Management, 7(4), 395-406.
  • 11. Why now? 6/28/2014 (c) 2014 IBM UP (University Programs) 11
  • 12. Drivers shaping service phenomena • Global economic change • ICT-enablement or technology change • Outsourcing • Business model change (value migration) • Where people live (demographic change) • How long people live • The nature of family life • A rising education level • A rising dependence on universities • A rising dependence on non-profit organizations 12
  • 13. New Era of Computing: Cognitive Technologies & Componentry 13  Natural Language – Reasoning, Logic & Planning – Symbolic Processing – Natural Language Processing – Ranking of Hypotheses – Knowledge Representations – Domain-Specific Ontologies – Information Storage/Retrieval – Machine Learning, Reasoning – Von Neumann Componentry – OpenPOWER Systems  Pattern Recognition – Recognition, Sensing & Acting – Pattern Processing – Image & Speech Processing – Ranking of Hypotheses – Pattern Representations – Domain-Specific Neural Nets – Information Storage/Retrieval – Machine Learning, Perception – Neuromorphic Componentry – SyNAPSE Systems AI for IA: Intelligence Augmentation Cognitive Systems that boost learning, discovery, engagement, transformation, and long-range planning. Cognition as a Service
  • 14. Questions This view leads to a new set of questions for service scientists to answer, about the nature of entities, interactions, outcomes, and their dynamics over time. • What types of entities are capable of service interactions? • What types of interactions do service system entities engage in? • What types of outcomes can result when service system entities interact? • How do the types of entities and interactions change over time? • How do the spatial distributions of types of entities change over time? • How do the hierarchical structure and network relationships of entities change over time? • How do the knowledge, competencies, resources owned and accessed by the entities change over time? 14
  • 15. A tableau of primitive economic activities 15 {P,D,C,R} P|{D,C,R} {P,D}|{C,R} {P,D,C}|R {P,D}|C|R {P,C}|{D,R} {P,C}|D|R {P,R}|{D,C} {P,R}|D|C D|{P,C,R} C|{P,D,R} R|{P,D,C} P|D|C|R {P,D,C,R} P|{D,C,R} {P,D}|{C,R} {P,D,C}|R {P,D}|C|R {P,C}|{D,R} {P,C}|D|R {P,R}|{D,C} {P,R}|D|C D|{P,C,R} C|{P,D,R} R|{P,D,C} P|D|C|R P Production D Distribution C Consumption R Recycling Jointness {} Separation | TIME S P A C E 169 possible patterns of service system interactions, time, space, and scale. For example, energy generation at home, city, state, national levels. For example, local to global to local again (e.g., circular economy). Based on: Betancourt, R., & Gautschi, D. (2001). Product innovation in services: A framework for analysis. Advances in Applied Microeconomics, 10, 155-183.
  • 16. Value Co-Creation Process 6/28/2014 (c) 2014 IBM UP (University Programs) 16 service systems (market and business strategy) resources (people, technology, information, organizations) dynamically configure access rights (own, leased, shared, privileged) stakeholders (customers, providers, authorities, competitors) have value propositions have Interactions (person-to-person, system-to-system, person-to-system, system-to-person) outcomes (lose-win, win-win, lose-lose, win-lose) with develop coordinate/ motivate measures (quality, productivity, compliance, sustainable innovation, others) establish impacted impact risks impact generate enable win-win value- cocreation services (business processes, architecture and infrastructure services) execute have Demirkan, H & Spohrer, J (2014) “Understanding Service Systems & Innovations in Time-Space Complexity: The Abstract-Entity-Interaction-Outcome-Universals Theory,” Working Paper.
  • 17. Intuitive Examples: Entities-Interaction-Outcomes Domain Entities Pattern Physics Atoms fission, fusion, reactions Physics Celestial Bodies orbit, collide, sling shot Chemistry Molecules equilibrium, reactions Biology Organisms mutualism, predator-prey Business Firms exchange, divest, merge Government Nations trade, dissolution, annex 6/28/2014 (c) 2014 IBM UP (University Programs) 17
  • 18. Six questions 18 Question Description Does the entity still exist after the interaction? Some interactions do or do not preserve (conserve) entities. Does the interaction giver rise to new entities? Some interactions do or do not give rise to new entities. Does the interaction change the state of the entities? Some interactions do or do not change an entity’s state. Does the state change include a record of the interaction? Some entities can and some cannot record interaction histories. Does the state change include a process-of-valuing the outcome? Some entities can and some cannot estimate value of outcomes. Does the state change include the result of simulating other entities? Some entities can and some cannot simulate other entities valuing.
  • 19. What is meant by “lawful” • Physical interaction laws do not change* – However, innovations change the costs – Intel, IBM, OpenPOWER (computing costs) – AT&T, Corning, Cisco (communications costs) • Social interaction laws do change – And innovations change the costs – Google (Internet search) and copyright – Uber (ride sharing) and taxi regulations – Airbnb (home sharing) and rental regulations 6/28/2014 (c) 2014 IBM UP (University Programs) 19 * = of course, our understanding of physical laws does change, other caveats apply.
  • 20. Do any social interaction laws not change? Yes, mathematical truths! • Ricardo – Law of Comparative Advantage – Do a little more of what you do best (low cost) – Do a little less of what you do least well (high cost) – Learning curve effects in people, businesses, countries (interaction can be mutually beneficial) • Assumptions (On when to specialize…) – Entities can do multiple things at variable costs – Learning interaction is not zero cost transfer 6/28/2014 (c) 2014 IBM UP (University Programs) 20
  • 21. Dynamics: Changing Rules 6/28/2014 (c) 2014 IBM UP (University Programs) 21
  • 22. Dynamics: Changing Roles 6/28/2014 (c) 2014 IBM UP (University Programs) 22
  • 23. Dynamics: Changing Values 6/28/2014 (c) 2014 IBM UP (University Programs) 23 Service science offers fresh perspective to reorient the debate on what is ‘progress’ and whether or not it is slowing down, and if so, what might be done to reframe progress ‘at the speed limit of what is possible’ with universities.
  • 24. In Sum, The Quest • Service science to better understand the laws of service that can inform systematic service innovation – Get the conceptual foundations right – Not unlike “Factory Physics” quest • Articulate a “Moore’s Law” of service system scaling – Investments that lead to sustainable and resilient value co-creation and capability co-elevation 6/28/2014 (c) 2014 IBM UP (University Programs) 24
  • 25. Abstract Entity-Interaction-Outcomes Universals • When entities interact, what are the logically possible outcomes? • For example: – Game Theory: Win-Win, Lose-Lose, Win-Lose, Lose-Win – Pi-Calculus: Set of rules for agents-processes- channels to model and reason about complex systems (cell to city) – Service Science: ISPAR, Ecology, etc. 6/28/2014 (c) 2014 IBM UP (University Programs) 25
  • 26. From Biological to Organizational Ecology: Populations of Entities-Interactions-Outcomes 6/28/2014 (c) 2014 IBM UP (University Programs) 26
  • 27. Populations of Entities Evolving 6/28/2014 (c) 2014 IBM UP (University Programs) 27
  • 28. Come visit! 6/28/2014 (c) 2014 IBM UP (University Programs) 28 IBM Research – Almaden, San Jose, CA spohrer@us.ibm.com http://www.service-science.info/archives/2233
  • 29. Test 6/28/2014 (c) 2014 IBM UP (University Programs) 29
  • 30. Service science transdisciplinary framework 30 SYSTEMS DISCIPLINES transportation & supply chain water & waste food & products energy & electricity ICT & cloud building & construction retail & hospitality banking & finance healthcare & family education & work city secure state scale nation laws behavioral sciences e.g., marketing management science e.g., operations political sciences e.g., public policy learning sciences e.g., game theory & strategy cognitive sciences e.g., psychology system sciences e.g., industrial engineering information sciences e.g., computer science organization sciences e.g., knowledge management social sciences e.g., econ & law decision sciences e.g., stats & design run professions e.g., knowledge worker transform professions e.g., consultant innovate professions e.g., entrepreneurs changevalue technology information organizations transform (copy) systems that govern stakeholdersresources customer provider authority competitors people Innovate (invent) history (data analytics) run future (roadmap) systems that focus on flows of things systems that support people's activities Observing the stakeholders (As-Is) Change Potential: Thinking (Has-Been & Might-Become) Observing their Resources & Access (As-Is) Value Realization: Doing (To-Be)
  • 31. A New Era of Smart Systems • Cognitive systems allow us to do more and dream bigger, boosting both productivity and creativity
  • 32. How many cognitive systems? • Are you using cognitive systems yet?
  • 33. Non-Zero is a deep principle • Service Definition – Win-win (Non-Zero Sum) – Informal: Knowledge application for mutual benefits – Formal: Value co-creation and capability co-elevation – Context: Abstract-Entity-Interaction-Outcome- Universals (AEIOU) [Evolving Ecology of Nested- Networked Service System Entities] • Service Science in Brief (How to integrate…) – An emerging transdiscipline that borrows from all disciplines without replacing any of them – Short for Service Science, Management, Engineering, plus Design, Arts, Public Policy (SSME+DAPP) 6/28/2014 (c) 2014 IBM UP (University Programs) 33
  • 34. Early Motivations (Aspiration 1) 6/28/2014 (c) 2014 IBM UP (University Programs) 34
  • 35. 6/28/2014 (c) 2014 IBM UP (University Programs) 35
  • 36. Ten Reasons • Universities are complex service systems of fundamental importance. • Disciplines are infusing service innovation concepts into curriculum. • Service science can help universities overcome discipline silos. • University-based startups are often new types of online service. • Professional associations are adding service science SIGs. • Cities, home to most universities, are complex service systems. • Service failures can be costly and can derail the careers of students. • Service science can help universities move up in rankings. • Service science can contribute to good industry-university relations and interactions. • Service science can help all universities improve their service excellence "game.” 6/28/2014 (c) 2014 IBM UP (University Programs) 36
  • 37. 6/28/2014 (c) 2014 IBM UP (University Programs) 37
  • 38. Higher Education: Five Trends • Revenue from key sources is continuing to fall, putting many institutions at severe financial risk. • Demands are rising for a greater return on investment in higher education. • Greater transparency about student outcomes is becoming the norm. • New business and delivery models are gaining traction. • The globalization of education is accelerating. 6/28/2014 (c) 2014 IBM UP (University Programs) 38
  • 39. 6/28/2014 (c) 2014 IBM UP (University Programs) 39
  • 40. Higher Education Business Model • Who do we serve, and what are they trying to do? • How do we help those we intend to serve do what they are trying to do? • How do we deliver our services to those we are trying to serve? • What is the nature of the relationship we have with those we serve? • How do these prior components translate into revenue for our institution? • What are the key activities that create the services we provide? • What are the key resources we need to create the services we provide? • Who are the key partners that help us create the services we provide to those we serve? • How do the key partners, resources, and activities translate into our institution's cost model? Denna, E. (2014) The Business Model of Higher Education. ViewPoint. EDUCAUSE Review. March 24, 2014. URL: http://www.educause.edu/ero/article/business-model-higher-education 6/28/2014 (c) 2014 IBM UP (University Programs) 40
  • 41. 6/28/2014 (c) 2014 IBM UP (University Programs) 41
  • 42. IT Solutions • Administrative solutions for education • Asset management for education • Campus solutions for higher education • Classroom solutions for education • Data and analytics for Smarter Education • Enterprise risk management for higher education • Framework for smarter education • Academic performance and insights • Business analytics software for education • VCL solutions for cloud • Innovation in research • School solutions 6/28/2014 (c) 2014 IBM UP (University Programs) 42
  • 43. What is Service Science? • Early motivations & aspirations • Six principles, concepts, scope • Growing literature • Service-Dominant Logic • In sum, service science 6/28/2014 (c) 2014 IBM UP (University Programs) 43
  • 44. Early Motivations (Growth 1) 6/28/2014 (c) 2014 IBM UP (University Programs) 44
  • 45. Early Motivations (Convergence) 6/28/2014 (c) 2014 IBM UP (University Programs) 45
  • 46. The Elephant in the Room 6/28/2014 (c) 2014 IBM UP (University Programs) 46
  • 47. Growing literature 6/28/2014 (c) 2014 IBM UP (University Programs) 47
  • 48. Scope 6/28/2014 (c) 2014 IBM UP (University Programs) 48
  • 49. HAT: Hub-of-All-Things • The HAT project’s impact on policy lies in informing current policies on personal data privacy and legal issues. By creating a platform for ‘digital labour’, we aim to demonstrate how markets could be created from incentivising more digital visibility in return for offerings to serve lived lives. 6/28/2014 (c) 2014 IBM UP (University Programs) 49
  • 50. Innovation (Rick Miller, Olin) 6/28/2014 (c) 2014 IBM UP (University Programs) 50
  • 51. 6/28/2014 (c) 2014 IBM UP (University Programs) 51
  • 52. Gartner Maverick Report • Control – Institutions – Individuals • Autonomy – Low – High 6/28/2014 (c) 2014 IBM UP (University Programs) 52 From: Surviving the Rise of 'Smart Machines,' the Loss of 'Dream Jobs' and '90% Unemployment.
  • 53. 6/28/2014 (c) 2014 IBM UP (University Programs) 53
  • 54. AI Will Disrupt Higher Education • Our next move: My [Dr. Dyens deputy provost McGill University] proposal is to think of chess as an analogy for education. • Gary Kasparov, in the New York Review of Books… wrote: • The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process. 6/28/2014 (c) 2014 IBM UP (University Programs) 54
  • 55. Universities Matter #1 6/28/2014 © IBM 2013 IBM University Programs worldwide accelerating regional development (IBM UPward) 55 Japan China Germany France United KingdomItaly Russia SpainBrazil Canada India Mexico AustraliaSouth Korea NetherlandsTurkey Sweden y = 0,7489x+ 0,3534 R² = 0,719 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 %globalGDP % top 500 universities Nation’s % WW GDP and % Top 500 Universities (2009 Data)
  • 56. Universities Matter #2 6/28/2014 © IBM 2013 IBM University Programs worldwide accelerating regional development (IBM UPward) 56 …But it can be costly, American student loan debt is over $900M
  • 57. Universities Matter #3 6/28/2014 © IBM 2013 IBM University Programs worldwide accelerating regional development (IBM UPward) 57 “When we combined the impact of Harvard’s direct spending on payroll, purchasing and construction – the indirect impact of University spending – and the direct and indirect impact of off-campus spending by Harvard students – we can estimate that Harvard directly and indirectly accounted for nearly $4.8 billion in economic activity in the Boston area in fiscal year 2008, and more than 44,000 jobs.”
  • 58. Universities Matter #4 6/28/2014 © IBM 2013 IBM University Programs worldwide accelerating regional development (IBM UPward) 58 http://www.service-science.info/archives/1056 Nation State/Province City/Region University College K-12 Cultural & Conference Hotels Hospital Medical Research Worker (professional) Family (household) For-profits: Business Entrepreneurship Non-profits Social Entrepreneurship U-BEE Job Creator/Sustainer U-BEEs = University-Based Entrepreneurial Ecosystems “The future is already here (at universities), it is just not evenly distributed.” “The best way to predict the future is to (inspire the next generation of students to) build it better.” “Multilevel nested, networked holistic service systems (HSS) that provision whole service (WS) to the people inside them. WS includes flows (transportation, water, food, energy, com development (buildings, retail ,finance, health, education), and governance (city, state, nation). ” University Four Missions 1. Learning 2. Discovery 3. Engagement 4. Convergence
  • 59. 6/28/2014 (c) 2014 IBM UP (University Programs) 59
  • 60. Reimagining Higher Education • “Universities weren’t designed to change curricula and introduce new classes at the pace required by changing industry requirements.” – Dennis Yang, president and chief operating officer of Udemy 6/28/2014 (c) 2014 IBM UP (University Programs) 60
  • 61. Example 6/28/2014 (c) 2014 IBM UP (University Programs) 61
  • 62. 6/28/2014 (c) 2014 IBM UP (University Programs) 62
  • 63. Trading Zone: Economist, Policymakers & Service Scientists
  • 64. Future of higher education (one possible path & assumptions) Years Change – Possible Progress Path Service Science Aspect 0-5 Revenue Continuous Improvements Data Science & Cloud 5-10 Learning Continuous Improvement Organization Science 10-15 Engagement Continuous Improvement Economic Science 15-20 Discovery Continuous Improvements Cognitive Science 6/28/2014 (c) 2014 IBM UP (University Programs) 64 Four Missions Four Types of Costs Service Science Aspect Learning/Teaching & Lectures Knowledge Transfer Specialization Discovery/Research Knowledge Creation Specialization Engagement/Entrepreneurship & Employment Knowledge Application Integration Convergence/Consilience Knowledge Integration Integration
  • 65. Backup: Readings (some details) • Spohrer, J., Fodell, D., & Murphy, W. (2012). Ten Reasons Service Science Matters to Universities. Educause Review, 47(6), 52-64. • Lusch, R., & Wu, C. (2012). A service science perspective on higher education—Linking service productivity theory and higher education reform. Center for American Progress, August. • Denna, E. (2014) The Business Model of Higher Education. Educause ViewPoint. March 24, 2014. • Henry, T, Pagano, E, Puckett, J, Wilson, J (2014) Five Trends to Watch in Higher Education. BCG Perspectives. • Meeker, M (2014) Internet Trends 2014 – Code Conference • Sledge, L & Dovey-Fishman, T (2014) Reimagining higher education: How colleges, universities, businesses, and governments can prepare for a new age of lifelong learning. Deloitte University Press. • IBM (2014) Education for a Smarter Planet • Goldbloom, A (2011) Making data science a sport. O’Reilly Media Strata Conference. • Johnson, RC (2013) IBM Unveils Cognitive Systems Institute. EETimes. October 3, 2013. • MSU & IBM (2014) T-Summit 2014: Cultivating Tomorrow’s Talent Today. Website & Conferences. • Spohrer J (2014) 21C Talent and 21C Citizens. Service Science Community Website Blog Post Entry. • Dyens, O (2014) How artificial intelligence is about to disrupt higher education. UA/AU University Affairs Affaires universitaires. April 30, 2014. • Kenneth F. Brant , KF, Gupta, A, Sommer, D (2013) Maverick* Research: Surviving the Rise of 'Smart Machines,' the Loss of 'Dream Jobs' and '90% Unemployment.’ • Spohrer, J., Giuiusa, A., & Demirkan, H. (2013). Service science: reframing progress with universities. Systems Research and Behavioral Science, 30(5), 561-569. • Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin. • Moore, GA (2012) Escape Velocity: Free Your Company’s Future From The Pull Of The Past. Harper Business. • Florida, R (2009) Who’s Your City? Basic Books. • Ng, Irene (2013) Hat: Hub-of-All-Things website. Research Councils UK (RCUK) Digital Economy. • Carmichael, A (2011) Announcing: The Complete QS Guide to Self Tracking. Quantified Self website. January 12, 2011. • Board of Life Science (2014) Convergence: Facilitating Transdisciplinary Integration of Life Science, Physical Sciences, Engineering, and Beyond. 6/28/2014 (c) 2014 IBM UP (University Programs) 65
  • 66. 6/28/2014 (c) 2014 IBM UP (University Programs) 66
  • 67. 6/28/2014 (c) 2014 IBM UP (University Programs) 67
  • 68. Internet Trends 2014 6/28/2014 (c) 2014 IBM UP (University Programs) 68
  • 69. Industry Transformation Donald Clark, TEDxGlasgow
  • 70. 6/28/2014 (c) 2014 IBM UP (University Programs) 70
  • 71. Kaggle: Making Data Science a Sport (146 competitions) 6/28/2014 (c) 2014 IBM UP (University Programs) 71
  • 72. Cognitive Systems 6/28/2014 (c) 2014 IBM UP (University Programs) 72
  • 73. 6/28/2014 (c) 2014 IBM UP (University Programs) 73
  • 74. T-Shaped Talent • Academia Optimizes – I for individual work – Individual IQ – Disciplines • Business Optimizes – T for team work – Team IQ – Systems • Both Important – Depth & Breadth – Disciplines & Systems 6/28/2014 (c) 2014 IBM UP (University Programs) 74
  • 75. 6/28/2014 (c) 2014 IBM UP (University Programs) 75
  • 76. University & Industry Score Card • do your annual performance evaluations for your employees include coaching student teams? • do the coached team projects have multidisciplinary participants? • do the coached team projects include industry participants from diverse sectors • do the coached team projects have multicultural participants? • do the coached team projects focus on real world challenges to improve local systems? • what percentage of your customer offerings change every year? • do new offerings highlight new research finding from journals that highlight new knowledge? • do new offerings highlight new entrepreneurial ecosystem partners, applying new knowledge to create value? • do new offerings and team projects build the social networks of your employees? • do your courses include team projects for your students? • do the team projects have multidisciplinary teams? • do the team projects include industry participants from diverse sectors? • do the team projects have multicultural teams? • do the team projects focus on real-world challenges to improve local systems? • what percentage of your course lectures change every year? • do new lectures highlight new research finding from journals that highlight new knowledge? • do new lectures highlight new entrepreneurs, applying new knowledge to create value? • do new lectures and team projects build the social networks of your students? 6/28/2014 (c) 2014 IBM UP (University Programs) 76
  • 77. Escape Velocity • What if there is some hidden force that is working against your best efforts? That force, I submit, is the pull of the past... • The larger and more successful the enterprise, the greater the inertial mass, the harder it is to alter course and speed. 6/28/2014 (c) 2014 IBM UP (University Programs) 77
  • 78. 6/28/2014 (c) 2014 IBM UP (University Programs) 78
  • 79. 6/28/2014 (c) 2014 IBM UP (University Programs) 79
  • 80. What are Cognitive Systems? • 3 L’s = Language, Learning, Levels • How many cognitive systems? • How much investment? • Technology underlying new era… • In sum, a picture… 6/28/2014 (c) 2014 IBM UP (University Programs) 80
  • 81. Questions & Framing • In 5-10-15-20 years, what will be different? – How will higher education have changed? – How will skills & jobs have changed? – How will business & society have changed? • Service science, a lens for looking at change – Capabilities & constraints – technology systems – Rights & responsibilities – rule systems – What is “lawful” (physical, social) change? 6/28/2014 (c) 2014 IBM UP (University Programs) 81
  • 82. Watson Business Unit • $1B Investment: Far beyond Jeopardy! Watson Foundations Big Data and Analytics Cognitive Systems82 Ecosystem Program Business Partners Developers Researchers Solutions Customer Engagement Healthcare Finance Accelerated Research Services Watson Discovery Advisor Watson Explorer Watson Analytics
  • 83. 83 Land-population-energy-carbon Carlo Ratti: Senseable Cities
  • 84. IBM Platforms for Entrepreneurs • Smarter Cities Intelligent Operations Center Platform • IBM Watson & Cognitive Computing Platform • IBM UP helping university startups to scale-up (growth) 6/28/2014 © IBM 2013 IBM University Programs worldwide accelerating regional development (IBM UPward) 84
  • 85. Universities Matter 6/28/2014 (c) 2014 IBM UP (University Programs) 85
  • 86. Concepts 6/28/2014 (c) 2014 IBM UP (University Programs) 86
  • 87. In sum, service science • Service System Entities – Types: Businesses, Universities, Governments, etc. – Nested & Networked Globally – S-D Logic (A2A; Resource Integrators) • Value Co-Creation Interactions – Types: Value-Proposition & Governance Mech-based – Collaboration & Competition Blended – SD Logic (Operant & Operant Resources) • Builds On… – Decades of Service Research (Marketing, Operations, etc.) – SSME+DAPP; From I to T to Pi-shapes… and beyond! – T Summit 2014 & 2015.. • Measures – Productivity, Quality, Compliance, Sustainable Innovation – Holistic Service Systems • Quality of Life, Balance Challenge & Routine • Innovativeness, Equity, Sustainability, Resilience
  • 88. Today’s talk • Preamble • What is service science? Service systems? • What are cognitive systems? • What are the trends? – Why makes universities/cities such special service systems/cognitive systems? • Backup: Readings (some details) 6/28/2014 (c) 2014 IBM UP (University Programs) 88
  • 89. Preamble • Abstract & Readings Summary • Future of higher education – One possible path & assumptions – Best way to predict future is to design it • Questions & framing • What is meant by “lawful” • Do any social interaction laws not change? • Service science preliminaries • Who I am & my biases • Universities and our future/history 6/28/2014 (c) 2014 IBM UP (University Programs) 89
  • 90. Abstract • Patterns of Change: Transformation of Higher Education From Service Science and Cognitive Systems Perspectives This talk will discuss the forces reshaping higher education from service science and cognitive systems perspectives, and presents an optimistic view of the likely outcome. These same forces are reshaping business and society globally. Higher education is just one of many interconnected service systems that make up our world. However, higher education is special in many ways. For most, higher education is the bridge to cross from youthful family life to meaningful service to society. Also, all great cities have a major university that includes the broad spectrum of human knowledge, concentrated in experts and an army of energetic students within typically a square mile region. Universities are increasingly startup engines for regional economic development and growth. Within two decades most people on the planet will have a smart phone (disrupted and reconfigured), including a personal cognitive system, which is both an expert professional coach and an executive assistant. Cloud, Big Data Analytics, Mobile, Social, Cognitive, Internet of Things and Humans provide the integrated platform for reframing the meaning of progress with universities, leading to an era of T- shaped professionals engaged in meaningful, creative cognitive sport. 6/28/2014 (c) 2014 IBM UP (University Programs) 90
  • 91. Readings Summary • Spohrer, J., Fodell, D., & Murphy, W. (2012). Ten Reasons Service Science Matters to Universities. Educause Review, 47(6), 52-64. • Lusch, R., & Wu, C. (2012). A service science perspective on higher education—Linking service productivity theory and higher education reform. Center for American Progress, August. • Denna, E. (2014) The Business Model of Higher Education. Educause ViewPoint. March 24, 2014. • Henry, T, Pagano, E, Puckett, J, Wilson, J (2014) Five Trends to Watch in Higher Education. BCG Perspectives. • Meeker, M (2014) Internet Trends 2014 – Code Conference • Sledge, L & Dovey-Fishman, T (2014) Reimagining higher education: How colleges, universities, businesses, and governments can prepare for a new age of lifelong learning. Deloitte University Press. • IBM (2014) Education for a Smarter Planet • Goldbloom, A (2011) Making data science a sport. O’Reilly Media Strata Conference. • Johnson, RC (2013) IBM Unveils Cognitive Systems Institute. EETimes. October 3, 2013. • MSU & IBM (2014) T-Summit 2014: Cultivating Tomorrow’s Talent Today. Website & Conferences. • Spohrer J (2014) 21C Talent and 21C Citizens. Service Science Community Website Blog Post Entry. • Dyens, O (2014) How artificial intelligence is about to disrupt higher education. UA/AU University Affairs Affaires universitaires. April 30, 2014. • Kenneth F. Brant , KF, Gupta, A, Sommer, D (2013) Maverick* Research: Surviving the Rise of 'Smart Machines,' the Loss of 'Dream Jobs' and '90% Unemployment.’ • Spohrer, J., Giuiusa, A., & Demirkan, H. (2013). Service science: reframing progress with universities. Systems Research and Behavioral Science, 30(5), 561-569. • Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin. • Moore, GA (2012) Escape Velocity: Free Your Company’s Future From The Pull Of The Past. Harper Business. • Florida, R (2009) Who’s Your City? Basic Books. • Ng, Irene (2013) Hat: Hub-of-All-Things website. Research Councils UK (RCUK) Digital Economy. • Carmichael, A (2011) Announcing: The Complete QS Guide to Self Tracking. Quantified Self website. January 12, 2011. • Board of Life Science (2014) Convergence: Facilitating Transdisciplinary Integration of Life Science, Physical Sciences, Engineering, and Beyond. 6/28/2014 (c) 2014 IBM UP (University Programs) 91
  • 92. What is meant by “lawful” • Physical interaction laws do not change* – However, innovations change the costs – Intel, IBM, OpenPOWER (computing costs) – AT&T, Corning, Cisco (communications costs) • Social interaction laws do change – And innovations change the costs – Google (Internet search) and copyright – Uber (ride sharing) and taxi regulations – Airbnb (home sharing) and rental regulations 6/28/2014 (c) 2014 IBM UP (University Programs) 92 * = of course, our understanding of physical laws does change, other caveats apply.
  • 93. Do any social interaction laws not change? Yes, mathematical truths! • Ricardo – Law of Comparative Advantage – Do a little more of what you do best (low cost) – Do a little less of what you do least well (high cost) – Learning curve effects in people, businesses, countries (interaction can be mutually beneficial) • Assumptions (On when to specialize…) – Entities can do multiple things at variable costs – Learning interaction is not zero cost transfer 6/28/2014 (c) 2014 IBM UP (University Programs) 93
  • 94. Who I am & my biases • Change is hard to make happen (“predict”) – My professional experiences • No shortage of useful things to do – I am very optimistic about the future • Better mechanisms needed – “Cognitive sport” & “improve weakest link” 6/28/2014 (c) 2014 IBM UP (University Programs) 94
  • 95. Universities and our future • The future is already here at universities, it is just not yet well distributed. – With apologies to Gibson/King • The best way to predict the future is to inspire the next generation of students to build it better. – With apologies to Kay/Engelbart
  • 96. Early Motivations (Growth 2) 6/28/2014 (c) 2014 IBM UP (University Programs) 96 Gerstner decides to grow service
  • 97. Early Motivations (Aspiration 2) 6/28/2014 (c) 2014 IBM UP (University Programs) 97
  • 98. Principle 1: Resources 6/28/2014 (c) 2014 IBM UP (University Programs) 98
  • 99. Principle 2: Value Propositions 6/28/2014 (c) 2014 IBM UP (University Programs) 99
  • 100. Principle 3: Access Rights 6/28/2014 (c) 2014 IBM UP (University Programs) 100
  • 101. Principle 4: Outcomes 6/28/2014 (c) 2014 IBM UP (University Programs) 101
  • 102. Principle 5: Dynamics 6/28/2014 (c) 2014 IBM UP (University Programs) 102
  • 103. Principle 5: Dynamics (Revisited) 6/28/2014 (c) 2014 IBM UP (University Programs) 103
  • 104. Principle 6: Entities 6/28/2014 (c) 2014 IBM UP (University Programs) 104
  • 105. In sum, a picture… Courtesy Jean Paul Jacob, IBM Research Emeritus Professional Cognitive Assistants - Language - Learning - Levels
  • 106. 106 What are the (system) trends? Digital Immigrant Born: 1988 Graduated College with PhD: 2014 Digital Native Born: 2014 Enters College: 2032
  • 107. 107 Transportation: Self-driving cars
  • 108. 108 Water: Circular Economy
  • 109. 109 Manufacturing: Circular EconomyBaxter: Building the Future Maker-Bot: Replicator 2
  • 110. 110 Energy: Artificial Leaf
  • 111. 111 Technology: Cognitive Computing
  • 112. 112 112 Example: Leading Through Connections with… Universities Collaborate with IBM Research to Design Watson for the Grand Challenge of Jeopardy ! Assisted in the development of the Open Advancement of Question-Answering Initiative (OAQA) architecture and methodology Pioneered an online natural language question answering system called START, which provided the ability to answer questions with high precision using information from semi-structured and structured information repositories Worked to extend the capabilities of Watson, with a focus on extensive common sense knowledge Focused on large-scale information extraction, parsing, and knowledge inference technologies Worked on a visualization component to visually explain to external audiences the massively parallel analytics skills it takes for the Watson computing system to break down a question and formulate a rapid and accurate response to rival a human brain  Provided technological advancement enabling a computing system to remember the full interaction, rather than treating every question like the first one - simulating a real dialogue Explored advanced machine learning techniques along with rich text representations based on syntactic and semantic structures for the Watson’s optimization Worked on information retrieval and text search technologies http://w3.ibm.com/news/w3news/top_stories/2011/02/chq_watson_wrapup.html
  • 113. 113 Buildings: Circular Economy China Broad Group: 30 Stories in 15 Days
  • 114. 114 Retail & Hospitality: Social Media
  • 115. 115 Finance: Crowd Funding
  • 116. 116 Health: Robotics & 3D Printing
  • 117. 117 Education: Challenge-Based Sport
  • 118. 118 Government: Parameterized Meta-Rules • Innovativeness • Equity – Improve weakest link • Sustainability • Resiliency
  • 119. 119 Competitive Parity – Achieved. • The NFL touts parity—the idea that any team can win on any given Sunday. But this year, parity has truly run wild. • Through six weeks, 11 of the NFL's 32 teams are 3-3. • The Journal asked the statistical gurus of Massey-Peabody Analytics to run a coin-flip simulation…
  • 120. 120 2030 and Beyond…. Government, Health, Education, Finance, etc.
  • 121. Next Generation: T-Shaped Adaptive Innovators Many disciplines Many sectors Many regions/cultures (understanding & communications) Deepinonesector Deepinoneregion/culture Deepinonediscipline
  • 122. Welcome to the new age of platform technologies and smarter service systems for every sector of business and society nested, networks systems