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20220307 utah state dixon_class v15

The document provides an itinerary for a visit by Huntsman Scholars from Utah State University to the UC Santa Cruz Silicon Valley Campus. The schedule includes welcome remarks, introductions of visitors and speakers, presentations on topics like human-computer interaction and the future of AI from an industry perspective, and a possible walking tour of the building and site before departing for lunch. The event aims to provide the scholars with insights into Silicon Valley's innovation ecosystem from faculty members at the Santa Cruz campus.

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2022 Welcome to Silicon Valley – Huntsman Scholars of USU Silicon Valley as an Innovation Ecosystem + Future of AI: An Industry Perspective Jim Spohrer Retired IBM Executive UDIP Senior Fellow & Member ISSIP.org Questions: spohrer@gmail.com Twitter: @JimSpohrer LinkedIn: https://www.linkedin.com/in/spohrer/ Slack: https://slack.lfai.foundation Presentations online at: https://slideshare.net/spohrer Thanks to Professor Mike Dixon, Polly Conrad, and Yasi Moghaddam for the opportunity March 7, 2022 – Huntsman Scholars of Utah State University visit UC Santa Cruz – Silicon Valley Campus. Highly recommend: Humankind: A Hopeful History By Dutch Historian, Rutger Bregman <- Thanks To Ray Fisk For suggesting this book, see My summary here. https://huntsman.usu.edu/scholars/index https://siliconvalley.ucsc.edu/
Schedule 8:45 - Visitors arrive at parking lot and proceed to entrance: UCSC Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA 95054, https://siliconvalley.ucsc.edu/ Note: Visitors to have mask and proof of vaccination 9:00 - Welcome and Schedule Review, Host: Yassi Moghaddam, Executive Director, Human-Computer Interaction Program, USCS 9:15 – Visitors’ Goals & Scholars’ Introductions , Prof. Mike Dixon (USU, Business School) and Polly Conrad, Faculty Visitors (List of visitors, see next page) 9:30 – Speaker 1 - Human-Computer Interaction (UCSC faculty), Associate Prof. Norman Su (UCSC, Computational Media) 10:00 – Speaker 2: Silicon Valley as an Innovation Ecosystem + Future of AI: An Industry Perspective, Jim Spohrer, ISSIP & Retired IBM Executive 11:00 – Speaker 3 - Game and Play/Serious Games (UCSC faculty), Prof. Michael John (UCSC, Computational Media) 11:45 – Wrap and possible building/site walking tour 12:15 - Depart for Il Fornaio restaurant; host and speakers invited, if they can join
Silicon Valley: Past, Present, and Future Steve Blank www.steveblank.com Twitter: sgblank
The Popular View of Silicon Valley History 1910 1960 1970 1980 2000 1990 Innovation Networks Fruit Orchards Personal Computers Integrated Circuits Internet 1930 1940 1950 1920 Fruit Orchards Fruit Orchards Steve Jobs Moore/Noyce Marc Andreessen Hewlett & Packard
The Real Story of Silicon Valley History 1910 1960 1970 1980 2000 1990 Innovation Networks Microwaves/ Defense Personal Computers Integrated Circuits Internet 1930 1940 1950 1920 Test Equipment Vacuum Tubes
Terman and the Cold War Silicon Valley’s 1st Engine of Entrepreneurship Entrepreneurs Military Finance Crisis Profit Motivation Cooperative Culture Entrepreneurial Outward-Facing Tech Universities Risk Capital 24/7 Utilities Predictable Economic System Infrastructure Stable Legal System Technical Labs/Universities Steve Blank 23 Sept 2008 Free flow of People/Information
Venture Capital Silicon Valley’s 2nd Engine of Entrepreneurship Crisis Profit Motivation Cooperative Culture Entrepreneurial Outward-Facing Tech Universities Risk Capital 24/7 Utilities Predictable Economic System Infrastructure Stable Legal System Technical Labs/Universities Steve Blank 23 Sept 2008 Free flow of People/Information Entrepreneurs Venture Finance
Capacity Building for the Knowledge Economy Carl Dahlman World Bank Institute November 26, 2002
What Do We Mean by the Knowledge Economy? There are many definitions of the “Knowledge Economy”, many emphasizing just information technology and high technology We take a broader definition: “An economy that creates, acquires, adapts, and uses knowledge effectively for its economic and social development.” ©© Knowledge for Development, WBI
What is the Conceptual Framework? Four Pillars of the Knowledge Economy: Economic and institutional regime that provides incentives for the efficient use of existing and new knowledge and the flourishing of entrepreneurship Educated, creative and skilled people Dynamic information infrastructure Effective national innovation system ©© Knowledge for Development, WBI
Strong Correlation: GDP/Capita & KEI Knowledge Economy Saudi Arabia Argentina Russia Mongolia Japan Tunisia Algeria Kenya Lithuania Latvia Estonia Germany France Kuw ait Italy Australia Denmark Canada Ireland Norw ay USA y = 269.02x2.1863 R2 = 0.8789 0 5000 10000 15000 20000 25000 30000 35000 40000 0.00 2.00 4.00 6.00 8.00 10.00 KEI 1999 GDP per capita ©© Knowledge for Development, WBI
Global R&D Effort in Comparative Perspective © Knowledge for Development, WBI Japan Korea Singapore India Thailand Philip. Malaysia China USA 0 1000 2000 3000 4000 5000 6000 0 0.5 1 1.5 2 2.5 3 3.5 R&D expenditure as % of GNI (PPP, 1996) Scientists & Engineers per million people Total R&D Expenditure (PPP, 1996)
Nations
Businesses
People
Gandhi P, Khanna S, Ramaswamy S (2016) Which Industries Are the Most Digital (and Why)? Harvard Business Review URL https://hbr.org/2016/04/a-chart-that-shows-which-industries-are-the-most-digital-and-why
• Summary. Free and open source software (FOSS) is essential to much of the tech we use every day — from cars to phones to planes to the cloud. While traditionally, it was developed by an army of volunteer developers and given away for free, companies are increasingly taking a more active role in its development. But as companies buy up open source companies, bring development in house, and spin off their own for-profit versions of FOSS products, they could be endangering the future of this essential software. To maintain the viability and security of FOSS, companies should: 1) have a clear policy towards open source — preferably one that encourages employees to contribute to FOSS if feasible, 2) raise their level of awareness about the FOSS that they use and stay apprised of its vulnerabilities, and 3) keep the stability of the software they use in mind and incentivize their employee contributions to focus on both features useful to the company as well as general security and maintenance. Lifshitz-Assaf H, Nagle F (2021) The Digital Economy Runs on Open Source. Here’s How to Protect It. Harvard Business Review. September 2, 2021 URL: https://hbr.org/2021/09/the-digital-economy-runs-on-open-source-heres-how-to-protect-it
Two disciplines: Two approaches to the future Artificial Intelligence is almost seventy-years-old discipline in computer science that studies automation and builds more capable technological systems. AI tries to understand the intelligent things that people can do and then does those things with technology. (https://deepmind.com/about “... we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to expand our knowledge and find new answers. By solving this, we believe we could help people solve thousands of problems.”) Service science is an emerging transdiscipline not yet twenty-years- old that studies transformation and builds smarter and wiser socoi- technical systems – families, businesses, nations, platforms and other special types of responsible entities and their win-win interactions that transform value co-creation and capability co-elevation mechanisms that build more resilient future versions of themselves – what we call service systems entities. Service science tries to understand the evolving ecology of service system entities, their capabilities, constraints, rights, and responsibilities, and then then seeks to improve the quality of life of people (present/smarter and future/wiser) in those service systems. 26-30 July 2015 3rd International Conference on The Human Side of Service Engineering 24 Artificial Intelligence Automation Generations of machines Service Science Transformation Generations of people (responsible entities) Service systems are dynamic configurations of people, technology, organizations, and information, connected internally and externally by value propositions, to other service system entities. (Maglio et al 2009)
Timeline Future of AI: Every 20 years, compute costs are down by 1000x • Cost of Digital Workers • Moore’s Law can be thought of as lowering costs by a factor of a… • Thousand times lower in 20 years • Million times lower in 40 years • Billion times lower in 60 years • Smarter Tools (Terascale) • Terascale (2017) = $3K • Terascale (2020) = ~$1K • Narrow Worker (Petascale) • Recognition (Fast) • Petascale (2040) = ~$1K • Broad Worker (Exascale) • Reasoning (Slow) • Exascale (2060) = ~$1K 25 2080 2040 2000 1960 $1K $1M $1B $1T 2060 2020 1980 +/- 10 years $1 Person Average Annual Salary (Living Income) Super Computer Cost Mainframe Cost Smartphone Cost T P E T P E AI Progress on Open Leaderboards Benchmark Roadmap to solve AI/IA OECD_Alistair Nolan to Everyone: “It has been stated that the number of engineers proclaiming the end of Moore's Law doubles every two years.” Rouse WB, Spohrer JC. (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Apr 3;8(1-2):1-21.
Timeline: GDP/Employee 3/7/2022 (c) IBM 2017, Cognitive Opentech Group 26 (Source) Lower compute costs translate into increasing productivity and GDP/employees for nations Increasing productivity and GDP/employees should translate into wealthier citizens AI Progress on Open Leaderboards Benchmark Roadmap to solve AI/IA
Timeline: Leaderboards Framework AI Progress on Open Leaderboards - Benchmark Roadmap Perceive World Develop Cognition Build Relationships Fill Roles Pattern recognition Video understanding Memory Reasoning Social interactions Fluent conversation Assistant & Collaborator Coach & Mediator Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions Chime Thumos SQuAD SAT ROC Story ConvAI Images Context Episodic Induction Plans Intentions Summarization Values ImageNet VQA DSTC RALI General-AI Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation WMT DeepVideo Alexa Prize ICCMA AT Learning from Labeled Training Data and Searching (Optimization) Learning by Watching and Reading (Education) Learning by Doing and being Responsible (Exploration) 2018 2021 2024 2027 2030 2033 2036 2039 3/7/2022 (c) IBM 2017, Cognitive Opentech Group 27 Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer? Approx. Year Human Level -> +3 See: https://paperswithcode.com/sota
Service Science: Conceptual Framework 3/7/2022 (c) IBM MAP COG .| 28 Service Science
(c) IBM MAP COG .| 29 Service Science: Transdisciplinary Framework to Study Service Systems Systems that focus on flows of things Systems that govern Systems that support people’s activities transportation & supply chain water & waste food & products energy & electricity building & construction healthcare & family retail & hospitality banking & finance ICT & cloud education &work city secure state scale nation laws social sciences behavioral sciences management sciences political sciences learning sciences cognitive sciences system sciences information sciences organization sciences decision sciences run professions transform professions innovate professions e.g., econ & law e.g., marketing e.g., operations e.g., public policy e.g., game theory and strategy e.g., psychology e.g., industrial eng. e.g., computer sci e.g., knowledge mgmt e.g., statistics e.g., knowledge worker e.g., consultant e.g., entrepreneur stakeholders Customer Provider Authority Competitors resources People Technology Information Organizations change History (Data Analytics) Future (Roadmap) value Run Transform (Copy) Innovate (Invent) Stackholders (As-Is) Resources (As-Is) Change (Might-Become) Value (To-Be)
“The best way to predict the future is to inspire the next generation of students to build it better” Digital Natives Transportation Water Manufacturing Energy Construction ICT Retail Finance Healthcare Education Government
3/7/2022 31 1955 1975 1995 2015 2035 2055 Better Building Blocks
Service in the AI era Science science Service dominant (S-D) logic Service Dominant Architecture (SDA) Service in the AI era revisited Core message? Better automation and augmentation improve service Better science improves understanding Better logics improve interactions Better architectures improve adaption (change) X+AI requires investing wisely to improve service Where are the better models? Technology Disciplines Minds Enterprise Minds + AI Enterprise + AI What type of model? Technology System (T) Quantitative & Qualitative (I) Mental Model in Person (P) Distributed Organizational (O) P+O+I+T Service in the AI Era: Science, Logic, and Architecture Perspectives (Spohrer, Maglio, Vargo, Warg – in progress)
Service Innovation Questions • What real-world service system studied? • What interaction process improved? How measured? • What change process improved? How measured? • Which stakeholders (responsible entities) benefitted? How measured? If you have concise and clear answers to these service innovation questions for your paper, Then please consider applying for the ISSIP Excellence in Service Innovation Award Recognition includes an ISSIP Digital Badge and opportunity for great visibility for the work. Go to ISSIP.org website, recognition menu, second menu item - to learn more, and apply by filling out form
Jim Spohrer, Board of Directors, ISSIP.org Jim Spohrer serves on the Board of Directors of the International Society of Service Innovation Professionals, and as a contributor to the Linux Foundation AI and Data Foundation. He is a retired IBM Executive since July 2021, and previously directed IBM’s open-source Artificial Intelligence developer ecosystem effort, was CTO IBM Venture Capital Group, co-founded IBM Almaden Service Research, and led IBM Global University Programs. After his MIT BS in Physics, he developed speech recognition systems at Verbex (Exxon) before receiving his Yale PhD in Computer Science/AI. In the 1990’s, he attained Apple Computers’ Distinguished Engineer Scientist and Technologist role for next generation learning platforms. With over ninety publications and nine patents, he received the Christopher Loverlock Career Contributions to the Service Discipline award, Gummesson Service Research award, Vargo and Lusch Service-Dominant Logic award, Daniel Berg Service Systems award, and a PICMET Fellow for advancing service science. Jim was elected and previously served as LF AI & Data Technical Advisory Board Chairperson and ONNX Steering Committee Member (2020-2021), UIDP Senior Fellow for contributions to industry-university collaborations. 35 From 2002 - 2009, Jim co-founded (with Paul Maglio) and directed IBM Almaden Service Research helping to establish service science, applying science, technology, and T-shaped upskilling of people to business and societal transformation. Who I am 2021 A big year: (1) hit 65, (2) career award, (3) retired from IBM
Who I am: Take 2 The Three Ages of Man (Giorgione) Thanks to Alan Hartman for kind inspiration (slides) (recording) Service, when responsible entities apply their knowledge for mutual benefits win-win/non-zero-sum games/value co-creation/capability co-elevation Service is a central, fundamental concept of the value of systems interacting (entities-interactions-outcomes)
What I study Service Science and Open Source AI – Trust is key to both Service Science Artificial Intelligence Trust: Value Co-Creation/Collaboration Responsible Entities Learning to Invest Transdisciplinary Community Trust: Secure, Fair, Explainable Machine Collaborators Open Source Communities
3/7/2022 (c) IBM MAP COG .| 38 T-shaped Adaptive Innovator: Deep Problem-Solving and Broad Communication/Collaboration Advanced Tech: AI to IoT to Quantum, GreenTech, RegTech, etc. Work Practices: Agile, Service Design, Open Source Mindset: Growth Mindset, Positive Mindset, Entrepreneurial Many disciplines Many sectors Many regions/cultures (understanding & communications) Deep in one sector Deep in one region/culture Deep in one discipline
Upskilling… T-shaped (l)earners Gardner P, Maietta HN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Moghaddam Y, Demirkan H, Spohrer J (2018) T- Shaped Professionals: Adaptive Innovators.
“AI won’t replace entrepreneurs, but entrepreneurs who use AI will replace those who don’t.” What does it mean to become a digital entrepreneur?
IBM’s Service Journey: A Summary Sketch 3/7/2022 (c) IBM MAP COG .| 42 Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.
IfM, IBM (2010) Succeeding through service innovation: a service perspective for education, research, business and government. University of Cambridge Institute for Manufacturing, Cambridge UK 2010
Intelligence Augmentation (IA) = Socio-Technical Extension Factor on Capabilities • Engelbart (1962) • Spohrer & Engelbart (2002) 3/7/2022 (c) IBM MAP COG .| 54 Dedicated to Douglas E. Engelbart, Inventor The Mouse (Pointing Device) The Mother of All Demos Bootstrapping Practice/Augmentation Theory Note: Bush (1945) and Licklider (1960) created funding programs that benefitted Engelbart in building working systems.
IA as Socio-Technical Extension Factor on Capabilities & Values IA (human values) is not AI (technology capability) Difference 1: IA leads to more capable people even when scaffold removed Difference 2: IA leads to more responsible people to use wisely the capabilities 3/7/2022 (c) IBM MAP COG .| 55 Superminds Malone (2018) Things that Make Us Smart Norman (1994) Worldboard Augmented Perception Spohrer (1999) Bicycles for the Mind Kay & Jobs (1984) Techno-Extension Factor Measurement & Accelerating Socio-Technical Design Loop Kline (1996)
IA Progression – Tool, Assistant, Collaborator, Coach, Mediator 3/7/2022 (c) IBM MAP COG .| 56 Rouse & Spohrer (2018) Siddike, Spohrer, Demirkan, Kodha (2018) Araya (2018) Spohrer& Siddike (2018)
Bigger IA Trend in Human Time Usage & Skills As smartphone apps grow up and people have 100 digital workers “earning” for them (owners) on platforms • Hunter Gathers – local sourcing, generalist • Agriculture – local sourcing, generalist – cities specialists • Manufacturing – outsourcing to production business, specialists • Clothing to Shopping • Service (pre-AI) – outsourcing to service businesses, specialist • Cooking to Restaurants • Service (post-AI & miniaturization) – insourcing, T- shapes • T-shaped (l)earners in platform society, home again 3/7/2022 (c) IBM MAP COG .| 57 Spohrer & Maglio (2006) SSME, Slide #42 Spohrer (2020) Platform Economy and Shift in Work
References • Araya D (2018) Augmented Intelligence: Smart Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28. • Bush V (1945) As we may think. The Atlantic Monthly. 1945 Jul 1;176(1):101-8. • Engelbart D (1962) Augmenting human intellect. Summary report AFOSR-3223 under Contract AF. 1962 Oct;49(638):1024. • Gardner P, Maietta HN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Business Expert Press. URL: https://www.amazon.com/Advancing-Talent-Development-Undergraduate-Education/dp/1951527062 • Kay A, Jobs S (1984) Wheels for the Mind. Apple Computer. • Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press; 1995. • Licklider JC (1960) . Man-computer symbiosis. IRE transactions on human factors in electronics. 1960 Mar(1):4-11. • Malone TW (2018) Superminds: The surprising power of people and computers thinking together. Little, Brown Spark; 2018 May 15. • Norman D (1994) Things that make us smart: Defending human attributes in the age of the machine. Diversion Books; 2014 Dec 2. • Rouse WB, Spohrer JC (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Feb 7:1-21. • Siddike MA, Spohrer J, Demirkan H, Kohda Y (2018) A Framework of Enhanced Performance: People's Interactions With Cognitive Assistants. International Journal of Systems and Service-Oriented Engineering (IJSSOE). 2018 Jul 1;8(3):1-7. • Spohrer JC (1998) Information in places. IBM Systems Journal. 1999;38(4):602-28. • Spohrer JC, Engelbart DC (2004) Converging technologies for enhancing human performance: Science and business perspectives. Annals of the New York Academy of Sciences. 2004 May;1013(1):50-82. • Spohrer J, Siddike (2018) The Future of Digital Cognitive Systems: Tool, Assistant, Collaborator, Coach, Mediator. In Ed. Araya D. Augmented Intelligence: Smart Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28. • Spohrer J (2020) Online Platform Economy and Gig Workers: A USA Perspective. Presentation. • Spohrer J & Maglio PP (2006) Service Science Management and Engineering (SSME): An Emerging Discipline. IBM Presentation. 3/7/2022 (c) IBM MAP COG .| 58
Upskilling 2030: Service Innovation Roadmaps and Responsible Entities Learning Jim Spohrer (IBM & ISSIP) International Exploration of Service Science (IESS 2.1) March 25, 2021
IfM, IBM (2010) Succeeding through service innovation: a service perspective for education, research, business and government. University of Cambridge Institute for Manufacturing, Cambridge UK 2010
What is a SIR? • Service Innovation Roadmap (SIR) is a kind of Business Model Canvas (BMC) that responsible entities create for themselves to describe three types of investments in learning/upskilling activities: • Run: BMC for optimize activities (e.g., agile improvement method) • Transform: BMC for copy activities (e.g., find role models) • Innovate: BMC for invent activities (e.g., research, pilot, prove, monetize) • Based on March (1991) • March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87.
Business Model Canvas
64 How responsible entities (service systems) learn and change over time History and future of Run-Transform-Innovate investment choices • Diverse Types • Persons (Individuals) • Families • Regional Entities • Universities • Hospitals • Cities • States/Provinces • Nations • Other Enterprises • Businesses • Non-profits • Learning & Change • Run = use existing knowledge or standard practices (use) • Transform = adopt a new best practice (copy) • Innovate = create a new best practice (invent) Innovate Invest in each type of change Spohrer J, Golinelli GM, Piciocchi P, Bassano C (2010) An integrated SS-VSA analysis of changing job roles. Service Science. 2010 Jun;2(1-2):1-20. March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87. URL: exploit explore
3/7/2022 (c) IBM MAP COG .| 65 Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3

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20220307 utah state dixon_class v15

  • 1.
    2022 Welcome toSilicon Valley – Huntsman Scholars of USU Silicon Valley as an Innovation Ecosystem + Future of AI: An Industry Perspective Jim Spohrer Retired IBM Executive UDIP Senior Fellow & Member ISSIP.org Questions: spohrer@gmail.com Twitter: @JimSpohrer LinkedIn: https://www.linkedin.com/in/spohrer/ Slack: https://slack.lfai.foundation Presentations online at: https://slideshare.net/spohrer Thanks to Professor Mike Dixon, Polly Conrad, and Yasi Moghaddam for the opportunity March 7, 2022 – Huntsman Scholars of Utah State University visit UC Santa Cruz – Silicon Valley Campus. Highly recommend: Humankind: A Hopeful History By Dutch Historian, Rutger Bregman <- Thanks To Ray Fisk For suggesting this book, see My summary here. https://huntsman.usu.edu/scholars/index https://siliconvalley.ucsc.edu/
  • 2.
    Schedule 8:45 - Visitorsarrive at parking lot and proceed to entrance: UCSC Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA 95054, https://siliconvalley.ucsc.edu/ Note: Visitors to have mask and proof of vaccination 9:00 - Welcome and Schedule Review, Host: Yassi Moghaddam, Executive Director, Human-Computer Interaction Program, USCS 9:15 – Visitors’ Goals & Scholars’ Introductions , Prof. Mike Dixon (USU, Business School) and Polly Conrad, Faculty Visitors (List of visitors, see next page) 9:30 – Speaker 1 - Human-Computer Interaction (UCSC faculty), Associate Prof. Norman Su (UCSC, Computational Media) 10:00 – Speaker 2: Silicon Valley as an Innovation Ecosystem + Future of AI: An Industry Perspective, Jim Spohrer, ISSIP & Retired IBM Executive 11:00 – Speaker 3 - Game and Play/Serious Games (UCSC faculty), Prof. Michael John (UCSC, Computational Media) 11:45 – Wrap and possible building/site walking tour 12:15 - Depart for Il Fornaio restaurant; host and speakers invited, if they can join
  • 6.
    Silicon Valley: Past, Present,and Future Steve Blank www.steveblank.com Twitter: sgblank
  • 7.
    The Popular Viewof Silicon Valley History 1910 1960 1970 1980 2000 1990 Innovation Networks Fruit Orchards Personal Computers Integrated Circuits Internet 1930 1940 1950 1920 Fruit Orchards Fruit Orchards Steve Jobs Moore/Noyce Marc Andreessen Hewlett & Packard
  • 8.
    The Real Storyof Silicon Valley History 1910 1960 1970 1980 2000 1990 Innovation Networks Microwaves/ Defense Personal Computers Integrated Circuits Internet 1930 1940 1950 1920 Test Equipment Vacuum Tubes
  • 9.
    Terman and theCold War Silicon Valley’s 1st Engine of Entrepreneurship Entrepreneurs Military Finance Crisis Profit Motivation Cooperative Culture Entrepreneurial Outward-Facing Tech Universities Risk Capital 24/7 Utilities Predictable Economic System Infrastructure Stable Legal System Technical Labs/Universities Steve Blank 23 Sept 2008 Free flow of People/Information
  • 10.
    Venture Capital Silicon Valley’s2nd Engine of Entrepreneurship Crisis Profit Motivation Cooperative Culture Entrepreneurial Outward-Facing Tech Universities Risk Capital 24/7 Utilities Predictable Economic System Infrastructure Stable Legal System Technical Labs/Universities Steve Blank 23 Sept 2008 Free flow of People/Information Entrepreneurs Venture Finance
  • 11.
    Capacity Building forthe Knowledge Economy Carl Dahlman World Bank Institute November 26, 2002
  • 12.
    What Do WeMean by the Knowledge Economy? There are many definitions of the “Knowledge Economy”, many emphasizing just information technology and high technology We take a broader definition: “An economy that creates, acquires, adapts, and uses knowledge effectively for its economic and social development.” ©© Knowledge for Development, WBI
  • 13.
    What is theConceptual Framework? Four Pillars of the Knowledge Economy: Economic and institutional regime that provides incentives for the efficient use of existing and new knowledge and the flourishing of entrepreneurship Educated, creative and skilled people Dynamic information infrastructure Effective national innovation system ©© Knowledge for Development, WBI
  • 14.
    Strong Correlation: GDP/Capita& KEI Knowledge Economy Saudi Arabia Argentina Russia Mongolia Japan Tunisia Algeria Kenya Lithuania Latvia Estonia Germany France Kuw ait Italy Australia Denmark Canada Ireland Norw ay USA y = 269.02x2.1863 R2 = 0.8789 0 5000 10000 15000 20000 25000 30000 35000 40000 0.00 2.00 4.00 6.00 8.00 10.00 KEI 1999 GDP per capita ©© Knowledge for Development, WBI
  • 15.
    Global R&D Effortin Comparative Perspective © Knowledge for Development, WBI Japan Korea Singapore India Thailand Philip. Malaysia China USA 0 1000 2000 3000 4000 5000 6000 0 0.5 1 1.5 2 2.5 3 3.5 R&D expenditure as % of GNI (PPP, 1996) Scientists & Engineers per million people Total R&D Expenditure (PPP, 1996)
  • 16.
  • 17.
  • 18.
  • 22.
    Gandhi P, KhannaS, Ramaswamy S (2016) Which Industries Are the Most Digital (and Why)? Harvard Business Review URL https://hbr.org/2016/04/a-chart-that-shows-which-industries-are-the-most-digital-and-why
  • 23.
    • Summary. Freeand open source software (FOSS) is essential to much of the tech we use every day — from cars to phones to planes to the cloud. While traditionally, it was developed by an army of volunteer developers and given away for free, companies are increasingly taking a more active role in its development. But as companies buy up open source companies, bring development in house, and spin off their own for-profit versions of FOSS products, they could be endangering the future of this essential software. To maintain the viability and security of FOSS, companies should: 1) have a clear policy towards open source — preferably one that encourages employees to contribute to FOSS if feasible, 2) raise their level of awareness about the FOSS that they use and stay apprised of its vulnerabilities, and 3) keep the stability of the software they use in mind and incentivize their employee contributions to focus on both features useful to the company as well as general security and maintenance. Lifshitz-Assaf H, Nagle F (2021) The Digital Economy Runs on Open Source. Here’s How to Protect It. Harvard Business Review. September 2, 2021 URL: https://hbr.org/2021/09/the-digital-economy-runs-on-open-source-heres-how-to-protect-it
  • 24.
    Two disciplines: Twoapproaches to the future Artificial Intelligence is almost seventy-years-old discipline in computer science that studies automation and builds more capable technological systems. AI tries to understand the intelligent things that people can do and then does those things with technology. (https://deepmind.com/about “... we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to expand our knowledge and find new answers. By solving this, we believe we could help people solve thousands of problems.”) Service science is an emerging transdiscipline not yet twenty-years- old that studies transformation and builds smarter and wiser socoi- technical systems – families, businesses, nations, platforms and other special types of responsible entities and their win-win interactions that transform value co-creation and capability co-elevation mechanisms that build more resilient future versions of themselves – what we call service systems entities. Service science tries to understand the evolving ecology of service system entities, their capabilities, constraints, rights, and responsibilities, and then then seeks to improve the quality of life of people (present/smarter and future/wiser) in those service systems. 26-30 July 2015 3rd International Conference on The Human Side of Service Engineering 24 Artificial Intelligence Automation Generations of machines Service Science Transformation Generations of people (responsible entities) Service systems are dynamic configurations of people, technology, organizations, and information, connected internally and externally by value propositions, to other service system entities. (Maglio et al 2009)
  • 25.
    Timeline Future ofAI: Every 20 years, compute costs are down by 1000x • Cost of Digital Workers • Moore’s Law can be thought of as lowering costs by a factor of a… • Thousand times lower in 20 years • Million times lower in 40 years • Billion times lower in 60 years • Smarter Tools (Terascale) • Terascale (2017) = $3K • Terascale (2020) = ~$1K • Narrow Worker (Petascale) • Recognition (Fast) • Petascale (2040) = ~$1K • Broad Worker (Exascale) • Reasoning (Slow) • Exascale (2060) = ~$1K 25 2080 2040 2000 1960 $1K $1M $1B $1T 2060 2020 1980 +/- 10 years $1 Person Average Annual Salary (Living Income) Super Computer Cost Mainframe Cost Smartphone Cost T P E T P E AI Progress on Open Leaderboards Benchmark Roadmap to solve AI/IA OECD_Alistair Nolan to Everyone: “It has been stated that the number of engineers proclaiming the end of Moore's Law doubles every two years.” Rouse WB, Spohrer JC. (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Apr 3;8(1-2):1-21.
  • 26.
    Timeline: GDP/Employee 3/7/2022 (c)IBM 2017, Cognitive Opentech Group 26 (Source) Lower compute costs translate into increasing productivity and GDP/employees for nations Increasing productivity and GDP/employees should translate into wealthier citizens AI Progress on Open Leaderboards Benchmark Roadmap to solve AI/IA
  • 27.
    Timeline: Leaderboards Framework AIProgress on Open Leaderboards - Benchmark Roadmap Perceive World Develop Cognition Build Relationships Fill Roles Pattern recognition Video understanding Memory Reasoning Social interactions Fluent conversation Assistant & Collaborator Coach & Mediator Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions Chime Thumos SQuAD SAT ROC Story ConvAI Images Context Episodic Induction Plans Intentions Summarization Values ImageNet VQA DSTC RALI General-AI Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation WMT DeepVideo Alexa Prize ICCMA AT Learning from Labeled Training Data and Searching (Optimization) Learning by Watching and Reading (Education) Learning by Doing and being Responsible (Exploration) 2018 2021 2024 2027 2030 2033 2036 2039 3/7/2022 (c) IBM 2017, Cognitive Opentech Group 27 Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer? Approx. Year Human Level -> +3 See: https://paperswithcode.com/sota
  • 28.
    Service Science: ConceptualFramework 3/7/2022 (c) IBM MAP COG .| 28 Service Science
  • 29.
    (c) IBM MAPCOG .| 29 Service Science: Transdisciplinary Framework to Study Service Systems Systems that focus on flows of things Systems that govern Systems that support people’s activities transportation & supply chain water & waste food & products energy & electricity building & construction healthcare & family retail & hospitality banking & finance ICT & cloud education &work city secure state scale nation laws social sciences behavioral sciences management sciences political sciences learning sciences cognitive sciences system sciences information sciences organization sciences decision sciences run professions transform professions innovate professions e.g., econ & law e.g., marketing e.g., operations e.g., public policy e.g., game theory and strategy e.g., psychology e.g., industrial eng. e.g., computer sci e.g., knowledge mgmt e.g., statistics e.g., knowledge worker e.g., consultant e.g., entrepreneur stakeholders Customer Provider Authority Competitors resources People Technology Information Organizations change History (Data Analytics) Future (Roadmap) value Run Transform (Copy) Innovate (Invent) Stackholders (As-Is) Resources (As-Is) Change (Might-Become) Value (To-Be)
  • 30.
    “The best wayto predict the future is to inspire the next generation of students to build it better” Digital Natives Transportation Water Manufacturing Energy Construction ICT Retail Finance Healthcare Education Government
  • 31.
    3/7/2022 31 1955 19751995 2015 2035 2055 Better Building Blocks
  • 32.
    Service in the AIera Science science Service dominant (S-D) logic Service Dominant Architecture (SDA) Service in the AI era revisited Core message? Better automation and augmentation improve service Better science improves understanding Better logics improve interactions Better architectures improve adaption (change) X+AI requires investing wisely to improve service Where are the better models? Technology Disciplines Minds Enterprise Minds + AI Enterprise + AI What type of model? Technology System (T) Quantitative & Qualitative (I) Mental Model in Person (P) Distributed Organizational (O) P+O+I+T Service in the AI Era: Science, Logic, and Architecture Perspectives (Spohrer, Maglio, Vargo, Warg – in progress)
  • 33.
    Service Innovation Questions •What real-world service system studied? • What interaction process improved? How measured? • What change process improved? How measured? • Which stakeholders (responsible entities) benefitted? How measured? If you have concise and clear answers to these service innovation questions for your paper, Then please consider applying for the ISSIP Excellence in Service Innovation Award Recognition includes an ISSIP Digital Badge and opportunity for great visibility for the work. Go to ISSIP.org website, recognition menu, second menu item - to learn more, and apply by filling out form
  • 35.
    Jim Spohrer, Boardof Directors, ISSIP.org Jim Spohrer serves on the Board of Directors of the International Society of Service Innovation Professionals, and as a contributor to the Linux Foundation AI and Data Foundation. He is a retired IBM Executive since July 2021, and previously directed IBM’s open-source Artificial Intelligence developer ecosystem effort, was CTO IBM Venture Capital Group, co-founded IBM Almaden Service Research, and led IBM Global University Programs. After his MIT BS in Physics, he developed speech recognition systems at Verbex (Exxon) before receiving his Yale PhD in Computer Science/AI. In the 1990’s, he attained Apple Computers’ Distinguished Engineer Scientist and Technologist role for next generation learning platforms. With over ninety publications and nine patents, he received the Christopher Loverlock Career Contributions to the Service Discipline award, Gummesson Service Research award, Vargo and Lusch Service-Dominant Logic award, Daniel Berg Service Systems award, and a PICMET Fellow for advancing service science. Jim was elected and previously served as LF AI & Data Technical Advisory Board Chairperson and ONNX Steering Committee Member (2020-2021), UIDP Senior Fellow for contributions to industry-university collaborations. 35 From 2002 - 2009, Jim co-founded (with Paul Maglio) and directed IBM Almaden Service Research helping to establish service science, applying science, technology, and T-shaped upskilling of people to business and societal transformation. Who I am 2021 A big year: (1) hit 65, (2) career award, (3) retired from IBM
  • 36.
    Who I am:Take 2 The Three Ages of Man (Giorgione) Thanks to Alan Hartman for kind inspiration (slides) (recording) Service, when responsible entities apply their knowledge for mutual benefits win-win/non-zero-sum games/value co-creation/capability co-elevation Service is a central, fundamental concept of the value of systems interacting (entities-interactions-outcomes)
  • 37.
    What I study ServiceScience and Open Source AI – Trust is key to both Service Science Artificial Intelligence Trust: Value Co-Creation/Collaboration Responsible Entities Learning to Invest Transdisciplinary Community Trust: Secure, Fair, Explainable Machine Collaborators Open Source Communities
  • 38.
    3/7/2022 (c) IBMMAP COG .| 38 T-shaped Adaptive Innovator: Deep Problem-Solving and Broad Communication/Collaboration Advanced Tech: AI to IoT to Quantum, GreenTech, RegTech, etc. Work Practices: Agile, Service Design, Open Source Mindset: Growth Mindset, Positive Mindset, Entrepreneurial Many disciplines Many sectors Many regions/cultures (understanding & communications) Deep in one sector Deep in one region/culture Deep in one discipline
  • 39.
    Upskilling… T-shaped (l)earners Gardner P, MaiettaHN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Moghaddam Y, Demirkan H, Spohrer J (2018) T- Shaped Professionals: Adaptive Innovators.
  • 40.
    “AI won’t replaceentrepreneurs, but entrepreneurs who use AI will replace those who don’t.” What does it mean to become a digital entrepreneur?
  • 42.
    IBM’s Service Journey:A Summary Sketch 3/7/2022 (c) IBM MAP COG .| 42 Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.
  • 45.
    IfM, IBM (2010) Succeedingthrough service innovation: a service perspective for education, research, business and government. University of Cambridge Institute for Manufacturing, Cambridge UK 2010
  • 54.
    Intelligence Augmentation (IA)= Socio-Technical Extension Factor on Capabilities • Engelbart (1962) • Spohrer & Engelbart (2002) 3/7/2022 (c) IBM MAP COG .| 54 Dedicated to Douglas E. Engelbart, Inventor The Mouse (Pointing Device) The Mother of All Demos Bootstrapping Practice/Augmentation Theory Note: Bush (1945) and Licklider (1960) created funding programs that benefitted Engelbart in building working systems.
  • 55.
    IA as Socio-TechnicalExtension Factor on Capabilities & Values IA (human values) is not AI (technology capability) Difference 1: IA leads to more capable people even when scaffold removed Difference 2: IA leads to more responsible people to use wisely the capabilities 3/7/2022 (c) IBM MAP COG .| 55 Superminds Malone (2018) Things that Make Us Smart Norman (1994) Worldboard Augmented Perception Spohrer (1999) Bicycles for the Mind Kay & Jobs (1984) Techno-Extension Factor Measurement & Accelerating Socio-Technical Design Loop Kline (1996)
  • 56.
    IA Progression –Tool, Assistant, Collaborator, Coach, Mediator 3/7/2022 (c) IBM MAP COG .| 56 Rouse & Spohrer (2018) Siddike, Spohrer, Demirkan, Kodha (2018) Araya (2018) Spohrer& Siddike (2018)
  • 57.
    Bigger IA Trendin Human Time Usage & Skills As smartphone apps grow up and people have 100 digital workers “earning” for them (owners) on platforms • Hunter Gathers – local sourcing, generalist • Agriculture – local sourcing, generalist – cities specialists • Manufacturing – outsourcing to production business, specialists • Clothing to Shopping • Service (pre-AI) – outsourcing to service businesses, specialist • Cooking to Restaurants • Service (post-AI & miniaturization) – insourcing, T- shapes • T-shaped (l)earners in platform society, home again 3/7/2022 (c) IBM MAP COG .| 57 Spohrer & Maglio (2006) SSME, Slide #42 Spohrer (2020) Platform Economy and Shift in Work
  • 58.
    References • Araya D(2018) Augmented Intelligence: Smart Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28. • Bush V (1945) As we may think. The Atlantic Monthly. 1945 Jul 1;176(1):101-8. • Engelbart D (1962) Augmenting human intellect. Summary report AFOSR-3223 under Contract AF. 1962 Oct;49(638):1024. • Gardner P, Maietta HN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Business Expert Press. URL: https://www.amazon.com/Advancing-Talent-Development-Undergraduate-Education/dp/1951527062 • Kay A, Jobs S (1984) Wheels for the Mind. Apple Computer. • Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press; 1995. • Licklider JC (1960) . Man-computer symbiosis. IRE transactions on human factors in electronics. 1960 Mar(1):4-11. • Malone TW (2018) Superminds: The surprising power of people and computers thinking together. Little, Brown Spark; 2018 May 15. • Norman D (1994) Things that make us smart: Defending human attributes in the age of the machine. Diversion Books; 2014 Dec 2. • Rouse WB, Spohrer JC (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Feb 7:1-21. • Siddike MA, Spohrer J, Demirkan H, Kohda Y (2018) A Framework of Enhanced Performance: People's Interactions With Cognitive Assistants. International Journal of Systems and Service-Oriented Engineering (IJSSOE). 2018 Jul 1;8(3):1-7. • Spohrer JC (1998) Information in places. IBM Systems Journal. 1999;38(4):602-28. • Spohrer JC, Engelbart DC (2004) Converging technologies for enhancing human performance: Science and business perspectives. Annals of the New York Academy of Sciences. 2004 May;1013(1):50-82. • Spohrer J, Siddike (2018) The Future of Digital Cognitive Systems: Tool, Assistant, Collaborator, Coach, Mediator. In Ed. Araya D. Augmented Intelligence: Smart Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28. • Spohrer J (2020) Online Platform Economy and Gig Workers: A USA Perspective. Presentation. • Spohrer J & Maglio PP (2006) Service Science Management and Engineering (SSME): An Emerging Discipline. IBM Presentation. 3/7/2022 (c) IBM MAP COG .| 58
  • 59.
    Upskilling 2030: Service InnovationRoadmaps and Responsible Entities Learning Jim Spohrer (IBM & ISSIP) International Exploration of Service Science (IESS 2.1) March 25, 2021
  • 60.
    IfM, IBM (2010) Succeedingthrough service innovation: a service perspective for education, research, business and government. University of Cambridge Institute for Manufacturing, Cambridge UK 2010
  • 61.
    What is aSIR? • Service Innovation Roadmap (SIR) is a kind of Business Model Canvas (BMC) that responsible entities create for themselves to describe three types of investments in learning/upskilling activities: • Run: BMC for optimize activities (e.g., agile improvement method) • Transform: BMC for copy activities (e.g., find role models) • Innovate: BMC for invent activities (e.g., research, pilot, prove, monetize) • Based on March (1991) • March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87.
  • 62.
  • 64.
    64 How responsible entities(service systems) learn and change over time History and future of Run-Transform-Innovate investment choices • Diverse Types • Persons (Individuals) • Families • Regional Entities • Universities • Hospitals • Cities • States/Provinces • Nations • Other Enterprises • Businesses • Non-profits • Learning & Change • Run = use existing knowledge or standard practices (use) • Transform = adopt a new best practice (copy) • Innovate = create a new best practice (invent) Innovate Invest in each type of change Spohrer J, Golinelli GM, Piciocchi P, Bassano C (2010) An integrated SS-VSA analysis of changing job roles. Service Science. 2010 Jun;2(1-2):1-20. March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87. URL: exploit explore
  • 65.
    3/7/2022 (c) IBMMAP COG .| 65 Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3