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20210322 jim spohrer eaae deans summit v13

This document summarizes a presentation on the future of artificial intelligence given by Jim Spohrer. Some key points: - AI and digital technologies are accelerating the transformation of society, including how people work, learn, and interact. - Service science predicts that as business and society transform, responsible entities will increasingly compete for collaborators through win-win interactions that improve capabilities. - The future of AI involves "Responsible Entities Learning" - both people and machines learning and collaborating. - Measuring socio-technical capabilities and determining what tasks can be safely delegated to machines will be important questions going forward.

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The Future of AI: Responsible Entities Learning to Nurture Sustainability & Resilience Wisely Jim Spohrer Director, IBM Cognitive OpenTech Questions: spohrer@gmail.com Twitter: @JimSpohrer LinkedIn: https://www.linkedin.com/in/spohrer/ Slack: https://slack.lfai.foundation Presentations on line at: https://slideshare.net/spohrer Thanks to Dean Ole Gustavsen for invitation to present at EAAE Deans Summit April 22, 2021!
4/22/2021 (c) IBM 2020, Cognitive Opentech Group 2
Jim Spohrer, IBM Director, Cognitive OpenTech Jim Spohrer directs IBM’s open-source Artificial Intelligence developer ecosystem effort. 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. He was CTO IBM Venture Capital Group, co-founded IBM Almaden Service Research, and led IBM Global University Programs. With over ninety publications and nine patents, he received the 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 as LF AI Technical Advisory Board Chairperson and ONNX Steering Committee Member (2020-2021). 4/22/2021 (c) IBM 2020, Cognitive Opentech Group 3
4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 4 Physics Chemistry Biology Neuroscience Psychology Artificial Intelligence Engineering Management Public Policy Education Design Humanities Natural Systems (MIT) Cognitive Systems (Yale) Service Systems (IBM)
IBM’s Service Journey: A Summary Sketch 4/22/2021 (c) IBM MAP COG .| 5 Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.
4/22/2021 (c) IBM MAP COG .| 6
Trust: Two Communities 4/22/2021 IBM Code #OpenTechAI 7 Service Science Artificial Intelligence Trust: Value Co-Creation Responsible Entity Collaborators Transdisciplinary Community Trust: Secure, Fair, Explainable Machine Collaborators Open Source Communities Special Issue AI Magazine? Handbook of OpenTech AI?
Today’s Talk: • Title: Future of AI: Responsible Entities Learning to Nurture Sustainability and Resilience Wisely Abstract: The 2020 pandemic is accelerating the digital (information technologies) transformation of society, including online working, learning, playing and belonging. The future of Artificial Intelligence (AI) will bring even greater acceleration and transformations, including Responsible Entities Learning. Service science predicts that in this transformation of business and society that competing for collaborators will increasingly shape value co- creation interactions and capability co-elevation outcomes between responsible entities learning. 4/22/2021 (c) IBM MAP COG .| 8 Consumers and society at large are expecting more from business. Embracing those responsibilities can be good for shareholders, too.
Outline 1. Pandemic perspective - accelerating digital transformation – platform society and T-shaped (l)earners. 2. Service science perspective – transformation (collaborate with people/responsible entities) 3. Artificial intelligence perspective – automation (collaborate with machines) 4. Intelligence Augmentation perspective - transformation and automation (collaboration with people and machines, and the organizations, AKA responsible entities, that produce the machines) 5. Measurement question: How do we measure socio-technical extension factors? Augmented physical, perceptual, cognitive, and social capabilities. 6. Philosophical question: What do we do ourselves and what can we safely delegate in win-win games? Build vs buy.
Accelerating digital transformation and shift to robotics… How will COVID-19 effect the need for and use of robots in a service world with less physical contact? Will robots improve or harm livelihoods/jobs? Robots Rule Retail? Taking away jobs Telepresence Robot World? Adding more jobs Robots at Home? Reducing need to have a job T-shaped (L)earners You will be assigned to a small team to discuss. Please have a team member to take notes of the most important insights and/or questions that emerge from your discussion. Your notes will be crucial for us to create a conference report, send to contact@creatingvalueconf.com What is most probable to happen? What is desirable? Spohrer
Accelerating shift - from employees to earners in platform society Farrrel D, Grieg F (2014) Online Platform Economy.
Upskilling… T-shapes (l)earners… on multiple platforms Rodgers S (2016) Jeremiah Owyang on the Collaborative Economy. Kenny M, Zysman J (2016) The Rise of the Platform Economy.
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.
4/22/2021 (c) IBM MAP COG .| 14 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
References – Post-pandemic world • Autor D, Mindell D, Reynolds E (2020). The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. MIT Work of the Future Task Force. URL: https://workofthefuture.mit.edu/wp-content/uploads/2020/11/2020-Final-Report.pdf • Farrel D, Grieg F (2014) Online Platform Economy. JP Morgan Chase. URL: https://www.jpmorganchase.com/institute/research/labor-markets/jpmc-institute- online-platform-econ-brief • 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 • Hunt V, Simpson B, Yamada Y (2020) The case for stakeholder capitalism. McKinsey Report. URL: https://www.mckinsey.com/business-functions/strategy-and- corporate-finance/our-insights/the-case-for-stakeholder-capitalism • ILO (2017) Helping the gig economy work better for gig workers. URL: https://www.ilo.org/washington/WCMS_642303/lang--en/index.htm • Kenny M, Zysman J (2016) The Rise of the Platform Economy. Issues in Science and Technology. Vol. XXXII, No. 3, Spring 2016. URL: https://issues.org/the-rise-of- the-platform-economy • Moghaddam Y, Demirkan H, Spohrer J (2018) T-Shaped Professionals: Adaptive Innovators. Business Expert Press. URL: https://www.amazon.com/T-Shaped- Professionals-Innovators-Yassi-Moghaddam/dp/194784315X • Rodgers S (2016) Jeremiah Owyang on the Collaborative Economy. Dassault Systemes – Navigate the Future. URL: https://blogs.3ds.com/northamerica/jeremiah- owyang-on-the-collaborative-economy/ • Sapjic DJ (2019) The Future of Employment –30 Telling Gig Economy Statistics. Small Business by the Numbers. URL: https://www.smallbizgenius.net/by-the- numbers/gig-economy-statistics/#gref • Spohrer JC (2011) On looking into Vargo and Lusch's concept of generic actors in markets, or “It's all B2B… and beyond!”. Industrial Marketing Management. 2011;2(40):199-201. • Spohrer J (2017) IBM's service journey: A summary sketch. Industrial Marketing Management. 2017 Jan 1;60:167-72. • Spohrer J, Kwan SK, Fisk RP. (2014) ”Marketing: A Service Science and Arts Perspective”. In Roland T. Rust and Ming-Hui Huang Handbook of Service Marketing Research (489-526). [Competing for collaborators is the constant across time] • Torpey E, Hogan A (2016) Working in a gig economy. USA Bureau of Labor Statistics. URL: https://www.bls.gov/careeroutlook/2016/article/mobile/what-is-the- gig-economy.htm • Van Dijck J, Poell T, De Waal M (2018) The platform society: Public values in a connective world. Oxford University Press. [book review] • WEF (2017) Towards a reskilling revolution - a future of jobs for all. URL: http://www3.weforum.org/docs/WEF_FOW_Reskilling_Revolution.pdf
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 16 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)
Future of Service Science Smarter and Wiser Service Systems: Entities transform to better future versions of themselves by inventing win-win games and competing for collaborators Past Present Future Organizational Units Family Local Clan Family Business/Nation Family Platform Society Change Individual Generalist (Breadth) Individual Specialist (Depth) Individual T-shaped (L)earners Constant Competing for collaborators: win-win games Competing for collaborators: win-win games Competing for collaborators: win-win games 4/22/2021 (c) IBM MAP COG .| 17
Service Science: Conceptual Framework 4/22/2021 (c) IBM MAP COG .| 18 Service Science
(c) IBM MAP COG .| 19 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)
Future of AI • What is the timeline for solving AI and IA? • TBD: When can a CEO/anyone buy AI capability <X> for price <Y>? • Who are the leaders driving AI progress? • What will the biggest benefits from AI be? • What are the biggest risks associated with AI, and are they real? • What other technologies may have a bigger impact than AI? • What are the implications for stakeholders? • How should we prepare to get the benefits and avoid the risks? 4/22/2021 (c) IBM 2020, Cognitive Opentech Group 20
Timeline: 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 21 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 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
Timeline: GDP/Employee 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 22 (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 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 23 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
Who is winning 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 24 https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/
Robots by Country • Industrial robots per 10,000 people by country 4/22/2021 IBM #OpenTechAI 25 34
Sweden 4/22/2021 (c) IBM MAP COG .| 26
Economic Growth Rates 2035: AI Projected Impact 4/22/2021 (c) IBM MAP COG .| 27
AI Benefits • Access to expertise • “Insanely great” labor productivity for trusted service providers • Digital workers for healthcare, education, finance, etc. • Better choices • ”Insanely great” collaborations with others on what matters most • AI for IA = Augmented Intelligence and higher value co-creation interactions 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 28
AI Risks • Job Loss • Shorter term bigger risk = de-skilling • Super-intelligence • Shorter term bigger risk = bad actors 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 29
Other Technologies: Bigger impact? Yes. • Augmented Reality (AR)/ Virtual Reality (VR) • Game worlds grow-up • Blockchain/ Security Systems • Trust and security immutable • Advanced Materials/ Energy Systems • Manufacturing as cheap, local recycling service (utility fog, artificial leaf, etc.) 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 30
“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
Artificial Leaf • Daniel Nocera, a professor of energy science at Harvard who pioneered the use of artificial photosynthesis, says that he and his colleague Pamela Silver have devised a system that completes the process of making liquid fuel from sunlight, carbon dioxide, and water. And they’ve done it at an efficiency of 10 percent, using pure carbon dioxide—in other words, one-tenth of the energy in sunlight is captured and turned into fuel. That is much higher than natural photosynthesis, which converts about 1 percent of solar energy into the carbohydrates used by plants, and it could be a milestone in the shift away from fossil fuels. The new system is described in a new paper in Science. 4/22/2021 IBM Code #OpenTechAI 32
Food from Air • Although the technology is in its infancy, researchers hope the "protein reactor" could become a household item. • Juha-Pekka Pitkänen, a scientist at VTT, said: "In practice, all the raw materials are available from the air. In the future, the technology can be transported to, for instance, deserts and other areas facing famine. • "One possible alternative is a home reactor, a type of domestic appliance that the consumer can use to produce the needed protein." • According to the researchers, the process of creating food from electricity can be nearly 10 times as energy efficient as photosynthesis, the process used by plants. 4/22/2021 IBM Code #OpenTechAI 33
Exoskeletons for Elderly • A walker is a “very cost-effective” solution for people with limited mobility, but “it completely disempowers, removes dignity, removes freedom, and causes a whole host of other psychological problems,” SRI Ventures president Manish Kothari says. “Superflex’s goal is to remove all of those areas that cause psychological-type encumbrances and, ultimately, redignify the individual." 4/22/2021 IBM Code #OpenTechAI 34
4/22/2021 35 1955 1975 1995 2015 2035 2055 Better Building Blocks
Intelligence Augmentation (IA) = Socio-Technical Extension Factor on Capabilities • Engelbart (1962) • Spohrer & Engelbart (2002) 4/22/2021 (c) IBM MAP COG .| 36 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 4/22/2021 (c) IBM MAP COG .| 37 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 4/22/2021 (c) IBM MAP COG .| 38 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 4/22/2021 (c) IBM MAP COG .| 39 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. 4/22/2021 (c) IBM MAP COG .| 40
4/22/2021 (c) IBM MAP COG .| 41
Smartphones pass entrance exams? When? 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 42 … when will your smartphone be able to take and pass any online course? And then be your coach, so you can pass too?
Resilience: Rapidly Rebuilding From Scratch • Dartnell L (2012) The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm. Westminster London: Penguin Books. 4/22/2021 IBM Code #OpenTechAI 43
Artificial Intelligence/ Computer Science • "Computer science is the study of the phenomena surrounding computers. ... We build computers and programs for many reasons. We build them to serve society .... One of the fundamental contributions to knowledge of computer science has been to explain, at a rather basic level, what symbols are. ... Symbols lie at the root of intelligent action, which is, of course, the primary topic of artificial intelligence. For that matter, it is a primary question for all of computer science. For all information is processed by computers in the service of ends, and we measure the intelligence of a system by its ability to achieve stated ends in the face of variations, difficulties and complexities posed by the task environment.” • Tenth Turing Awards Lecture: Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols and Search,”Communications of the ACM. vol. 19, No. 3, pp. 113-126, March,1976. Available online at: • https://www.cs.utexas.edu/~kuipers/readings/Newell+Simon-cacm-76.pdf 4/22/2021 (c) IBM MAP COG .| 44
IBM-MIT $240M over 10 year AI mission 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 45
4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 46
IBM Quantum 4/22/2021 (c) IBM MAP COG .| 47
Karpathy and Li, 2015
“Teddy Bear” Meret Oppenheim, Le Déjeuner en fourrure
Narrow AI Emerging Broad AI Disruptive and Pervasive General AI Revolutionary ▼ We are here 2050 and beyond 50 IBM Research AI © 2018 IBM Corporation The evolution of AI Borrowed from David Cox, IBM-MIT Lead
Timeline: Short History 4/22/2021 © IBM Cognitive Opentech Group (COG) 51 Dota 2 “Deep Learning” for “AI Pattern Recognition” depends on massive amounts of “labeled data” and computing power available since ~2012; Labeled data is simply input and output pairs, such as a sound and word, or image and word, or English sentence and French sentence, or road scene and car control settings – labeled data means having both input and output data in massive quantities. For example, 100K images of skin, half with skin cancer and half without to learn to recognize presence of skin cancer.
52 September 2018 / © 2018 IBM Corporation Petaflops = 1,000,000,000,000,000 or a million billion = 10 ** 15 Megaflops = 1,000,000 = million = 10 ** 6 Gigaflops = 1,000,000,000 = billion = 10 ** 9 Larges Super Computer in the World, = 13 MegaWatts of Power (HOT!)
53 September 2018 / © 2018 IBM Corporation Exascale = 1,000,000,000,000,000,000 or a billion billion = 10 ** 18 Megaflops = 1,000,000 = million = 10 ** 6 Gigaflops = 1,000,000,000 = billion = 10 ** 9 Human Brain = 20 Watts (COOL!)
4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 54 I have… Have you noticed how the building blocks just keep getting better?
Learning to program: My first program 4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 55 Early Computer Science Class: Watson Center at Columbia 1945 Jim Spohrer’s First Program 1972
4/22/2021 © IBM UPWard 2016 56 Fast Forward 2016: Consider this…
Microsoft CaptionBot June 19, 2016 4/22/2021 © IBM UPWard 2016 57
Microsoft CaptionBot June 20, 2016 4/22/2021 © IBM UPWard 2016 58
IBM Image Tagging 4/22/2021 © IBM UPWard 2016 59
Today: November 10, 2017 4/22/2021 © IBM DBG COG 2017 60 IBM
10 million minutes of experience 4/22/2021 Understanding Cognitive Systems 61
2 million minutes of experience 4/22/2021 Understanding Cognitive Systems 62
4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 63 Cognitive Mediators for all people in all roles
Occupations = Many Tasks 4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 64
Watson Discovery Advisor 4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 65 Simonite, T. 2014. Software Mines Science Papers to Make New Discoveries. MIT. November 25, 2014. URL: http://m.technologyreview.com/news/520461/software-mines-science-papers-to-make-new-discoveries/
Outline • Context • Disciplines (and the entities they study) • Computer Science, AI, SD logic, Service Science • Part 1: AI • Seven Questions • Better Building Blocks • Your data is becoming your AI… transformation • Part 2: Service Science • Covid accelerating AI, Robotics adoption • Open Technologies: From Smarter to Wiser • Access Rights: Trust and Responsibility 4/22/2021 IBM Code #OpenTechAI 66 “there is nothing as practical as a good abstraction.”
Icons of AI Progress • 1956: Dartmouth Conference organized by: • John McCarthy (Dartmouth, later Stanford) • Marvin Minsky (MIT) • and two senior scientists: • Claude Shannon (Bell Labs) • Nathan Rochester (IBM) • 1997: Deep Blue (IBM) - Chess • 2011: Watson Jeopardy! (IBM) • 2016: AlphaGo (Google DeepMinds) 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 67
AI at IBM: Past (Nathan Rochester) 4/22/2021 (c) IBM MAP COG .| 68
Disciplines and some of the key entities they study 4/22/2021 (c) IBM MAP COG .| 69 Computer Science: Hardware, Software, Algorithms Physical Symbol System Entities AI: Intelligence, “NN Models” Digital Cognitive System Entities Chemistry: Atoms, Molecules, States of Matter, Auto-Catalytic Molecular System Entities Biology: Cells, DNA, Biological Cognitive System Entities Service science: Service, Value Co-Creation, Service system entities Service science studies the evolving ecology of service system entities, their capabilities, constraints, rights, and responsibilities their value co-creation and capability co-elevation interactions, as well as their outcome identities and reputations.
Brian Arthur - Economist • The term “technological unemployment” is from John Maynard Keynes’s 1930 lecture, “Economic possibilities for our grandchildren,” where he predicted that in the future, around 2030, the production problem would be solved and there would be enough for everyone, but machines (robots, he thought) would cause “technological unemployment.” There would be plenty to go around, but the means of getting a share in it, jobs, might be scarce. We are not quite at 2030, but I believe we have reached the “Keynes point,” where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. Before that, farm labor, small craft workshops, voluntary piecework, or inherited wealth provided access. Now access needs to change again. However this happens, we have entered a different phase for the economy, a new era where production matters less and what matters more is access to that production: distribution, in other words—who gets what and how they get it. We have entered the distributive era. 4/22/2021 IBM #OpenTechAI 70 Arthur WB (2017) Where is technology taking the economy. McKinsey Quarterly. October.
4/22/2021 (c) IBM MAP COG .| 71 https://www.youtube.com/watch?v=WGK8MY8iZHA
Service-Dominant logic worldview and mindset Year Publication Service Resource Integrators 2004 Vargo SL, Lusch RF (2004) Evolving to a new dominant logic for marketing. Journal of marketing. 68(1):1-7. The application of specialized skills and knowledge is the fundamental unit of exchange. Operant resources are resources that produce effects 2011 Vargo SL, Lusch RF (2011) It's all B2B… and beyond: Toward a systems perspective of the market. Industrial marketing management. 40(2):181-7. The central concept in S-D logic is that service — the application of resources for the benefit of another party — is exchanged for service That is, all parties (e.g. businesses, individual customers, households, etc.) engaged in economic exchange are similarly, resource-integrating, service- providing enterprises that have the common purpose of value (co)creation — what we mean by “it is all B2B.” 2016 Vargo SL, Lusch RF. Institutions and axioms: an extension and update of service-dominant logic. Journal of the Academy of Marketing Science. 2016 Jan 1;44(1):5-23. value creation can only be fully understood in terms of integrated resources applied for another actor’s benefit (service) within a context, including the institutions and institutional arrangements that enable and constrain value creation. To alleviate this limitation and facilitate a better understanding of cooperation (and coordination), an eleventh foundational premise (fifth axiom) is introduced, focusing on the role of institutions and institutional arrangements in systems of value cocreation: service ecosystems. 4/22/2021 (c) IBM MAP COG .| 72
Service Science the study of service systems entities Year Publication Service Science Service System 2007 Spohrer J, Maglio, PP, Bailey J, Gruhl, D (2007) Steps toward a science of service systems, IEEE Computer, (40)1:71-77. Services science is an emerging field that seeks to tap into these and other relevant bodies of knowledge, integrate them, and advance three goals—aiming ultimately to understand service systems, how they improve, and how they scale. The components of a service system are people, technology, internal and external service systems connected by value propositions, and shared information (such as language, laws, and measures. 2008 Spohrer, J, Vargo S, Caswell N, Maglio PP (2008) The service system is the basic abstraction of service science, HICSS-41, NY: IEEE Press, Pp. 1-10. Service science is the study of the application of the resources of one or more systems for the benefit of another system in economic exchange. Informally, service systems are collections of resources that can create value with other service systems through shared information. 2008 Maglio PP, Spohrer J (2008) Fundamentals of service science. Journal of the academy of marketing science. 36(1):18-20. Service science is the study of service systems, aiming to create a basis for systematic service innovation. Service systems are value-co-creation configurations of people, technology, value propositions connecting internal and external service systems, and shared information (e.g., language, laws, measures, and methods). 4/22/2021 (c) IBM MAP COG .| 73
Service Science the study of service system entities 4/22/2021 (c) IBM MAP COG .| 74 Year Publication Service Science Service System 2009 Spohrer J, Maglio PP (2009) Service science: Toward a smarter planet. In Introduction to service engineering, Eds. Karwowski and Salvendy. Pp. 3-10 Service science is a specialization of systems science. So service science seeks to create a body of knowledge that accounts for value-cocreation between entities as they interact… Service system entities are dynamic configurations of resources. As described below, resources include people, organizations, shared information, and technology. 2012 Spohrer J, Piciocchi P, Bassano C (2012) Three frameworks for service research: exploring multilevel governance in nested, networked systems. Service Science. 4(2):147-160. SSME+D is built on top of the Service-Dominant logic (SD Logic) worldview A service system entity is a dynamic configuration of resources (at least one of which, the focal resource, is a person with rights). 2013 Spohrer J, Giuiusa A, Demirkan H, Ing D (2013) Service science: reframing progress with universities. Systems Research and Behavioral Science. 30(5):561- 569 Service science is an emerging branch of systems sciences with a focus on service systems (entities) and value cocreation (complex non- zero-sum interactions). … complex adaptive entities - service systems - within an ecology of nested, networked entities… From a service science perspective, progress can be thought of in terms of the rights and responsibilities of entities
Service Science the study of service system entities 4/22/2021 (c) IBM MAP COG .| 75 Year Publication Service Science Service System 2014 Spohrer J, Kwan SK, Fisk RP (2014)Marketing: a service sci ence and arts perspective, Handbook of Service Market ing Research, Eds. Rust RT, Huang MH, NY:Edward Elgar, pp. 489-526. Service science (short for Service Science, Management, Engineering, Design, Arts, and Public Policy) is an emerging transdiscipline for the (1) study of evolving service system entities and value co-creation phenomena, as well as (2) pedagogy for the education of 21st century T- shaped service innovators from all disciplines, sectors, and cultures. So like all early stage scientific communities, the language for talking about service systems and value co-creation phenomena continues to evolve. … Service system entities are economic and social actors, which configure (or integrate) resources. … A formal service system entity (SS-FSC3) is a legal, economic entity with rights and responsibilities codified in written laws. 2015 Spohrer J, Demirkan H, Lyons (2015) Social Value: A Service Science Perspective. In: Kijima K. (eds) Service Systems Science. Translational Systems Sciences, vol 2. Tokyo: Springer. Pp. 3-35. Service science is an emerging transdiscipline for the (1) study of evolving service system entities and value co-creation phenomena and (2) pedagogy for the education of twenty-first-century T-shaped service innovators from all disciplines, sectors, and cultures Formal service system entities (as opposed to informal service system entities) can be ranked by the degree to which they are governed by written (symbolic) laws and evolve to increase the percentage of their processes that are explicit and symbolic.
Service Science the study of service system entities 4/22/2021 (c) IBM MAP COG .| 76 Year Publication Service Science Service System 2016 Spohrer J (2016) Services Science and Societal Convergence. In W.S. Bainbridge, M.C. Roco (eds.),Handbook of Science and Technology Convergence, pp. 323-335 Service science is an emerging transdiscipline for the (1) study of evolving ecology of nested, networked service system entities and value co-creation phenomena, as well as (2) pedagogy for the education of the twenty-first- century T-shaped (depth and breadth) service innovators from all disciplines, sectors, and cultures. As service science emerges, we can begin by “seeing” and counting service system entities in an evolving ecology, working to “understand” and make explicit their implicit processes of valuing … 2016 Spohrer J (2016) Innovation for jobs with cognitive assistants: A service science perspective, In Disrupting Unemployment , Eds. Nordfors, Cerf, Seng, Missouri: Ewing Marion Kauffman Foundation, Pp. 157-174. Service science is the emerging transdiscipline that studies the evolving ecology of nested, networked service system entities, their capabilities, constraints, rights, and responsibilities. There are perhaps twenty billion formal service system entities in the world today, each governed in part by formal written laws. Every person, household, university, business, and government is a formal service system entity, but my dog, my smartphone, and my ideas are not.
Service Science the study of service system entities 4/22/2021 (c) IBM MAP COG .| 77 Year Publication Service Science Service System 2017 Spohrer J, Siddike MAK, Kohda Y (2017) Rebuilding evolution: a service science perspective. HICSS 50. Service science is the study of the evolving ecology of service system entities, complex socio-technical systems with rights and responsibilities – such as people, businesses, and nations. Service systems are dynamic configurations of people, technology, organization and information that interact through value proposition and co- create mutual value. 2019 Pakalla D, Spohrer J (2019, forthcoming) Digital Service: Technological Agency in Service Systems. HICSS 52. For the purposes of this paper, service science can be summarized as the study of the evolving ecology of service system entities, their capabilities, constraints, rights, and responsibilities, including their value co-creation and capability co- elevation mechanisms . Service systems are a type of socio- technical system, such as people, businesses, and nations, all with unique identities, histories, and reputations based on the outcomes of their interactions with other entities.
Service Research • Artificial Intelligence in Service • "The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks.” • Huang MH and Rust RT (2018) Artificial Intelligence in Service. Journal of Service Research. 21(2):155–172. • Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences • "expand the relevant set of antecedents beyond the established constructs and theories to include variables that are particularly relevant for AI applications such as privacy concerns, trust, and perceptions of “creepiness.” • Ostrom AL, Foheringham D, Bitner MJ (2018, forthcoming) Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences. In Handbook of Service Science, Volume 2, Eds, Maglio, Kieliszewski,Spohrer,Lyons,Patricio,Sawatani. New York: Springer. Pp. x-y. 4/22/2021 (c) IBM MAP COG .| 78
4/22/2021 (c) IBM MAP COG .| 79 Microsoft acquiring GitHub $7.5B 2018 John Marks on Open Source Models will run the world Why SW is eating the world
Step Comment GitHub Get an account and read the guide MAX CODAIT’s Model Asset Exchange Learn 3 R's - Read, Redo, Report Read (Medium/arXiv), Redo (GitHub), Report (Jupyter Notebook) PapersWithCode Stay on top of recent advances; Do 3 R’s. Kaggle Compete in a Kaggle competition Leaderboards Compete to advance AI progress Linux Foundation AI Help end-to-end open source industry AI & Data infrastructure Mozilla Common Voice Donate your speech; Label and verify data; Recruit others. Figure Eight Generate a set of labeled data (also Mechanical Turk) Design New Challenges Build for Call for Code/Code and Response; Build your AI Helper; Build test-taker, that can switch to tutor-mode; Etc. Open Source Guide Establish open source culture in your organization 4/22/2021 IBM Code #OpenTechAI 80
Is it fair? Is it easy to understand? Is it accountable? So what does it take to trust a decision made by a machine? (Other than that it is 99% accurate)? Did anyone tamper with it? #21, #32, #93 #21, #32, #93
IBM AI Explain a transaction Deployment: Claim Approval Model name: Claim Model AI Fairness 360 toolkit Trust and transparency integral to AI on the IBM Cloud Explainability, fairness, lineage are critical principles of trusted AI Open source toolkit to check for unwanted bias in datasets and machine learning models © 2019 IBM Corporation 82 DENIED APPROVED CONFIDENCE 90% 10% POLICY HOLDER AGE: 18 RESPONSIBLE PARTY: Self CAR BRAND: Oldsmobile Cutlass POLICE REPORT: Yes CAR VALUE: $20,000 POLICY AGE: 5 Years 65% 17% 23% 13% 13% 5% Factors contributing to a DENIED confidence level Factors contributing to an APPROVED confidence level
Is it fair? Is it easy to understand? Is it accountable? Did anyone tamper with it? FAIRNESS EXPLAINABILITY ROBUSTNESS LINEAGE Our vision for Trusted AI Pillars of trust, woven into the lifecycle of an AI application
84 4/22/2021
85 4/22/2021
86 4/22/2021
Join: https://callforcode.org/ 87 This multi-year global initiative rallies developers to create practical, effective, and high-quality applications based on cloud, data, and artificial intelligence that can have an immediate and lasting impact on humanitarian issues. Call for Code brings startup, academic, and enterprise developers together and inspires them to solve the most pressing societal issues of our time - for example, faster and more resilient recovery from natural disasters.
In conclusion… Situation Competence 3 R’s On Ramps 1. Platform & ecosystem competition for data and AI workloads 2. However, AI is hard; many capabilities 2-4 decades away 3. Industry in open source collaboration-competition mode 1. Read: Learn state-of-art 2. Redo: Apply and infuse in use cases/workloads 3. Report: Share back, others may improve 1. LF AI Landscape: Community projects 2. IBM CODAIT: Cloud Pak for Data (CPD), etc. – Enterprise workloads with Trusted AI 3. Red Hat ODH: OpenShift – Hybrid cloud platform and ecosystem
4/22/2021 (c) IBM MAP COG .| 89
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
94 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
4/22/2021 (c) IBM MAP COG .| 95 Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3

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20210322 jim spohrer eaae deans summit v13

  • 1.
    The Future ofAI: Responsible Entities Learning to Nurture Sustainability & Resilience Wisely Jim Spohrer Director, IBM Cognitive OpenTech Questions: spohrer@gmail.com Twitter: @JimSpohrer LinkedIn: https://www.linkedin.com/in/spohrer/ Slack: https://slack.lfai.foundation Presentations on line at: https://slideshare.net/spohrer Thanks to Dean Ole Gustavsen for invitation to present at EAAE Deans Summit April 22, 2021!
  • 2.
    4/22/2021 (c) IBM2020, Cognitive Opentech Group 2
  • 3.
    Jim Spohrer, IBMDirector, Cognitive OpenTech Jim Spohrer directs IBM’s open-source Artificial Intelligence developer ecosystem effort. 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. He was CTO IBM Venture Capital Group, co-founded IBM Almaden Service Research, and led IBM Global University Programs. With over ninety publications and nine patents, he received the 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 as LF AI Technical Advisory Board Chairperson and ONNX Steering Committee Member (2020-2021). 4/22/2021 (c) IBM 2020, Cognitive Opentech Group 3
  • 4.
    4/22/2021 © IBM 2015,IBM Upward University Programs Worldwide accelerating regional development 4 Physics Chemistry Biology Neuroscience Psychology Artificial Intelligence Engineering Management Public Policy Education Design Humanities Natural Systems (MIT) Cognitive Systems (Yale) Service Systems (IBM)
  • 5.
    IBM’s Service Journey:A Summary Sketch 4/22/2021 (c) IBM MAP COG .| 5 Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.
  • 6.
    4/22/2021 (c) IBMMAP COG .| 6
  • 7.
    Trust: Two Communities 4/22/2021IBM Code #OpenTechAI 7 Service Science Artificial Intelligence Trust: Value Co-Creation Responsible Entity Collaborators Transdisciplinary Community Trust: Secure, Fair, Explainable Machine Collaborators Open Source Communities Special Issue AI Magazine? Handbook of OpenTech AI?
  • 8.
    Today’s Talk: • Title:Future of AI: Responsible Entities Learning to Nurture Sustainability and Resilience Wisely Abstract: The 2020 pandemic is accelerating the digital (information technologies) transformation of society, including online working, learning, playing and belonging. The future of Artificial Intelligence (AI) will bring even greater acceleration and transformations, including Responsible Entities Learning. Service science predicts that in this transformation of business and society that competing for collaborators will increasingly shape value co- creation interactions and capability co-elevation outcomes between responsible entities learning. 4/22/2021 (c) IBM MAP COG .| 8 Consumers and society at large are expecting more from business. Embracing those responsibilities can be good for shareholders, too.
  • 9.
    Outline 1. Pandemic perspective- accelerating digital transformation – platform society and T-shaped (l)earners. 2. Service science perspective – transformation (collaborate with people/responsible entities) 3. Artificial intelligence perspective – automation (collaborate with machines) 4. Intelligence Augmentation perspective - transformation and automation (collaboration with people and machines, and the organizations, AKA responsible entities, that produce the machines) 5. Measurement question: How do we measure socio-technical extension factors? Augmented physical, perceptual, cognitive, and social capabilities. 6. Philosophical question: What do we do ourselves and what can we safely delegate in win-win games? Build vs buy.
  • 10.
    Accelerating digital transformationand shift to robotics… How will COVID-19 effect the need for and use of robots in a service world with less physical contact? Will robots improve or harm livelihoods/jobs? Robots Rule Retail? Taking away jobs Telepresence Robot World? Adding more jobs Robots at Home? Reducing need to have a job T-shaped (L)earners You will be assigned to a small team to discuss. Please have a team member to take notes of the most important insights and/or questions that emerge from your discussion. Your notes will be crucial for us to create a conference report, send to contact@creatingvalueconf.com What is most probable to happen? What is desirable? Spohrer
  • 11.
    Accelerating shift -from employees to earners in platform society Farrrel D, Grieg F (2014) Online Platform Economy.
  • 12.
    Upskilling… T-shapes (l)earners… on multipleplatforms Rodgers S (2016) Jeremiah Owyang on the Collaborative Economy. Kenny M, Zysman J (2016) The Rise of the Platform Economy.
  • 13.
    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.
  • 14.
    4/22/2021 (c) IBMMAP COG .| 14 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
  • 15.
    References – Post-pandemicworld • Autor D, Mindell D, Reynolds E (2020). The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. MIT Work of the Future Task Force. URL: https://workofthefuture.mit.edu/wp-content/uploads/2020/11/2020-Final-Report.pdf • Farrel D, Grieg F (2014) Online Platform Economy. JP Morgan Chase. URL: https://www.jpmorganchase.com/institute/research/labor-markets/jpmc-institute- online-platform-econ-brief • 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 • Hunt V, Simpson B, Yamada Y (2020) The case for stakeholder capitalism. McKinsey Report. URL: https://www.mckinsey.com/business-functions/strategy-and- corporate-finance/our-insights/the-case-for-stakeholder-capitalism • ILO (2017) Helping the gig economy work better for gig workers. URL: https://www.ilo.org/washington/WCMS_642303/lang--en/index.htm • Kenny M, Zysman J (2016) The Rise of the Platform Economy. Issues in Science and Technology. Vol. XXXII, No. 3, Spring 2016. URL: https://issues.org/the-rise-of- the-platform-economy • Moghaddam Y, Demirkan H, Spohrer J (2018) T-Shaped Professionals: Adaptive Innovators. Business Expert Press. URL: https://www.amazon.com/T-Shaped- Professionals-Innovators-Yassi-Moghaddam/dp/194784315X • Rodgers S (2016) Jeremiah Owyang on the Collaborative Economy. Dassault Systemes – Navigate the Future. URL: https://blogs.3ds.com/northamerica/jeremiah- owyang-on-the-collaborative-economy/ • Sapjic DJ (2019) The Future of Employment –30 Telling Gig Economy Statistics. Small Business by the Numbers. URL: https://www.smallbizgenius.net/by-the- numbers/gig-economy-statistics/#gref • Spohrer JC (2011) On looking into Vargo and Lusch's concept of generic actors in markets, or “It's all B2B… and beyond!”. Industrial Marketing Management. 2011;2(40):199-201. • Spohrer J (2017) IBM's service journey: A summary sketch. Industrial Marketing Management. 2017 Jan 1;60:167-72. • Spohrer J, Kwan SK, Fisk RP. (2014) ”Marketing: A Service Science and Arts Perspective”. In Roland T. Rust and Ming-Hui Huang Handbook of Service Marketing Research (489-526). [Competing for collaborators is the constant across time] • Torpey E, Hogan A (2016) Working in a gig economy. USA Bureau of Labor Statistics. URL: https://www.bls.gov/careeroutlook/2016/article/mobile/what-is-the- gig-economy.htm • Van Dijck J, Poell T, De Waal M (2018) The platform society: Public values in a connective world. Oxford University Press. [book review] • WEF (2017) Towards a reskilling revolution - a future of jobs for all. URL: http://www3.weforum.org/docs/WEF_FOW_Reskilling_Revolution.pdf
  • 16.
    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 16 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)
  • 17.
    Future of ServiceScience Smarter and Wiser Service Systems: Entities transform to better future versions of themselves by inventing win-win games and competing for collaborators Past Present Future Organizational Units Family Local Clan Family Business/Nation Family Platform Society Change Individual Generalist (Breadth) Individual Specialist (Depth) Individual T-shaped (L)earners Constant Competing for collaborators: win-win games Competing for collaborators: win-win games Competing for collaborators: win-win games 4/22/2021 (c) IBM MAP COG .| 17
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    Service Science: ConceptualFramework 4/22/2021 (c) IBM MAP COG .| 18 Service Science
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    (c) IBM MAPCOG .| 19 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)
  • 20.
    Future of AI •What is the timeline for solving AI and IA? • TBD: When can a CEO/anyone buy AI capability <X> for price <Y>? • Who are the leaders driving AI progress? • What will the biggest benefits from AI be? • What are the biggest risks associated with AI, and are they real? • What other technologies may have a bigger impact than AI? • What are the implications for stakeholders? • How should we prepare to get the benefits and avoid the risks? 4/22/2021 (c) IBM 2020, Cognitive Opentech Group 20
  • 21.
    Timeline: Every 20years, 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 21 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 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
  • 22.
    Timeline: GDP/Employee 4/22/2021 (c)IBM 2017, Cognitive Opentech Group 22 (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
  • 23.
    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 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 23 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
  • 24.
    Who is winning 4/22/2021(c) IBM 2017, Cognitive Opentech Group 24 https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/
  • 25.
    Robots by Country •Industrial robots per 10,000 people by country 4/22/2021 IBM #OpenTechAI 25 34
  • 26.
  • 27.
    Economic Growth Rates2035: AI Projected Impact 4/22/2021 (c) IBM MAP COG .| 27
  • 28.
    AI Benefits • Accessto expertise • “Insanely great” labor productivity for trusted service providers • Digital workers for healthcare, education, finance, etc. • Better choices • ”Insanely great” collaborations with others on what matters most • AI for IA = Augmented Intelligence and higher value co-creation interactions 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 28
  • 29.
    AI Risks • JobLoss • Shorter term bigger risk = de-skilling • Super-intelligence • Shorter term bigger risk = bad actors 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 29
  • 30.
    Other Technologies: Biggerimpact? Yes. • Augmented Reality (AR)/ Virtual Reality (VR) • Game worlds grow-up • Blockchain/ Security Systems • Trust and security immutable • Advanced Materials/ Energy Systems • Manufacturing as cheap, local recycling service (utility fog, artificial leaf, etc.) 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 30
  • 31.
    “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
  • 32.
    Artificial Leaf • DanielNocera, a professor of energy science at Harvard who pioneered the use of artificial photosynthesis, says that he and his colleague Pamela Silver have devised a system that completes the process of making liquid fuel from sunlight, carbon dioxide, and water. And they’ve done it at an efficiency of 10 percent, using pure carbon dioxide—in other words, one-tenth of the energy in sunlight is captured and turned into fuel. That is much higher than natural photosynthesis, which converts about 1 percent of solar energy into the carbohydrates used by plants, and it could be a milestone in the shift away from fossil fuels. The new system is described in a new paper in Science. 4/22/2021 IBM Code #OpenTechAI 32
  • 33.
    Food from Air •Although the technology is in its infancy, researchers hope the "protein reactor" could become a household item. • Juha-Pekka Pitkänen, a scientist at VTT, said: "In practice, all the raw materials are available from the air. In the future, the technology can be transported to, for instance, deserts and other areas facing famine. • "One possible alternative is a home reactor, a type of domestic appliance that the consumer can use to produce the needed protein." • According to the researchers, the process of creating food from electricity can be nearly 10 times as energy efficient as photosynthesis, the process used by plants. 4/22/2021 IBM Code #OpenTechAI 33
  • 34.
    Exoskeletons for Elderly •A walker is a “very cost-effective” solution for people with limited mobility, but “it completely disempowers, removes dignity, removes freedom, and causes a whole host of other psychological problems,” SRI Ventures president Manish Kothari says. “Superflex’s goal is to remove all of those areas that cause psychological-type encumbrances and, ultimately, redignify the individual." 4/22/2021 IBM Code #OpenTechAI 34
  • 35.
    4/22/2021 35 1955 19751995 2015 2035 2055 Better Building Blocks
  • 36.
    Intelligence Augmentation (IA)= Socio-Technical Extension Factor on Capabilities • Engelbart (1962) • Spohrer & Engelbart (2002) 4/22/2021 (c) IBM MAP COG .| 36 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.
  • 37.
    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 4/22/2021 (c) IBM MAP COG .| 37 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)
  • 38.
    IA Progression –Tool, Assistant, Collaborator, Coach, Mediator 4/22/2021 (c) IBM MAP COG .| 38 Rouse & Spohrer (2018) Siddike, Spohrer, Demirkan, Kodha (2018) Araya (2018) Spohrer& Siddike (2018)
  • 39.
    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 4/22/2021 (c) IBM MAP COG .| 39 Spohrer & Maglio (2006) SSME, Slide #42 Spohrer (2020) Platform Economy and Shift in Work
  • 40.
    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. 4/22/2021 (c) IBM MAP COG .| 40
  • 41.
    4/22/2021 (c) IBMMAP COG .| 41
  • 42.
    Smartphones pass entranceexams? When? 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 42 … when will your smartphone be able to take and pass any online course? And then be your coach, so you can pass too?
  • 43.
    Resilience: Rapidly Rebuilding FromScratch • Dartnell L (2012) The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm. Westminster London: Penguin Books. 4/22/2021 IBM Code #OpenTechAI 43
  • 44.
    Artificial Intelligence/ Computer Science •"Computer science is the study of the phenomena surrounding computers. ... We build computers and programs for many reasons. We build them to serve society .... One of the fundamental contributions to knowledge of computer science has been to explain, at a rather basic level, what symbols are. ... Symbols lie at the root of intelligent action, which is, of course, the primary topic of artificial intelligence. For that matter, it is a primary question for all of computer science. For all information is processed by computers in the service of ends, and we measure the intelligence of a system by its ability to achieve stated ends in the face of variations, difficulties and complexities posed by the task environment.” • Tenth Turing Awards Lecture: Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols and Search,”Communications of the ACM. vol. 19, No. 3, pp. 113-126, March,1976. Available online at: • https://www.cs.utexas.edu/~kuipers/readings/Newell+Simon-cacm-76.pdf 4/22/2021 (c) IBM MAP COG .| 44
  • 45.
    IBM-MIT $240M over 10year AI mission 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 45
  • 46.
    4/22/2021 © IBM 2015,IBM Upward University Programs Worldwide accelerating regional development 46
  • 47.
    IBM Quantum 4/22/2021 (c)IBM MAP COG .| 47
  • 48.
  • 49.
    “Teddy Bear” Meret Oppenheim,Le Déjeuner en fourrure
  • 50.
    Narrow AI Emerging Broad AI Disruptiveand Pervasive General AI Revolutionary ▼ We are here 2050 and beyond 50 IBM Research AI © 2018 IBM Corporation The evolution of AI Borrowed from David Cox, IBM-MIT Lead
  • 51.
    Timeline: Short History 4/22/2021 ©IBM Cognitive Opentech Group (COG) 51 Dota 2 “Deep Learning” for “AI Pattern Recognition” depends on massive amounts of “labeled data” and computing power available since ~2012; Labeled data is simply input and output pairs, such as a sound and word, or image and word, or English sentence and French sentence, or road scene and car control settings – labeled data means having both input and output data in massive quantities. For example, 100K images of skin, half with skin cancer and half without to learn to recognize presence of skin cancer.
  • 52.
    52 September 2018 /© 2018 IBM Corporation Petaflops = 1,000,000,000,000,000 or a million billion = 10 ** 15 Megaflops = 1,000,000 = million = 10 ** 6 Gigaflops = 1,000,000,000 = billion = 10 ** 9 Larges Super Computer in the World, = 13 MegaWatts of Power (HOT!)
  • 53.
    53 September 2018 /© 2018 IBM Corporation Exascale = 1,000,000,000,000,000,000 or a billion billion = 10 ** 18 Megaflops = 1,000,000 = million = 10 ** 6 Gigaflops = 1,000,000,000 = billion = 10 ** 9 Human Brain = 20 Watts (COOL!)
  • 54.
    4/22/2021 © IBM 2015,IBM Upward University Programs Worldwide accelerating regional development 54 I have… Have you noticed how the building blocks just keep getting better?
  • 55.
    Learning to program: Myfirst program 4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 55 Early Computer Science Class: Watson Center at Columbia 1945 Jim Spohrer’s First Program 1972
  • 56.
    4/22/2021 © IBM UPWard2016 56 Fast Forward 2016: Consider this…
  • 57.
    Microsoft CaptionBot June19, 2016 4/22/2021 © IBM UPWard 2016 57
  • 58.
    Microsoft CaptionBot June20, 2016 4/22/2021 © IBM UPWard 2016 58
  • 59.
  • 60.
    Today: November 10,2017 4/22/2021 © IBM DBG COG 2017 60 IBM
  • 61.
    10 million minutesof experience 4/22/2021 Understanding Cognitive Systems 61
  • 62.
    2 million minutesof experience 4/22/2021 Understanding Cognitive Systems 62
  • 63.
    4/22/2021 © IBM 2015,IBM Upward University Programs Worldwide accelerating regional development 63 Cognitive Mediators for all people in all roles
  • 64.
    Occupations = ManyTasks 4/22/2021 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 64
  • 65.
    Watson Discovery Advisor 4/22/2021 ©IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 65 Simonite, T. 2014. Software Mines Science Papers to Make New Discoveries. MIT. November 25, 2014. URL: http://m.technologyreview.com/news/520461/software-mines-science-papers-to-make-new-discoveries/
  • 66.
    Outline • Context • Disciplines(and the entities they study) • Computer Science, AI, SD logic, Service Science • Part 1: AI • Seven Questions • Better Building Blocks • Your data is becoming your AI… transformation • Part 2: Service Science • Covid accelerating AI, Robotics adoption • Open Technologies: From Smarter to Wiser • Access Rights: Trust and Responsibility 4/22/2021 IBM Code #OpenTechAI 66 “there is nothing as practical as a good abstraction.”
  • 67.
    Icons of AIProgress • 1956: Dartmouth Conference organized by: • John McCarthy (Dartmouth, later Stanford) • Marvin Minsky (MIT) • and two senior scientists: • Claude Shannon (Bell Labs) • Nathan Rochester (IBM) • 1997: Deep Blue (IBM) - Chess • 2011: Watson Jeopardy! (IBM) • 2016: AlphaGo (Google DeepMinds) 4/22/2021 (c) IBM 2017, Cognitive Opentech Group 67
  • 68.
    AI at IBM:Past (Nathan Rochester) 4/22/2021 (c) IBM MAP COG .| 68
  • 69.
    Disciplines and someof the key entities they study 4/22/2021 (c) IBM MAP COG .| 69 Computer Science: Hardware, Software, Algorithms Physical Symbol System Entities AI: Intelligence, “NN Models” Digital Cognitive System Entities Chemistry: Atoms, Molecules, States of Matter, Auto-Catalytic Molecular System Entities Biology: Cells, DNA, Biological Cognitive System Entities Service science: Service, Value Co-Creation, Service system entities Service science studies the evolving ecology of service system entities, their capabilities, constraints, rights, and responsibilities their value co-creation and capability co-elevation interactions, as well as their outcome identities and reputations.
  • 70.
    Brian Arthur -Economist • The term “technological unemployment” is from John Maynard Keynes’s 1930 lecture, “Economic possibilities for our grandchildren,” where he predicted that in the future, around 2030, the production problem would be solved and there would be enough for everyone, but machines (robots, he thought) would cause “technological unemployment.” There would be plenty to go around, but the means of getting a share in it, jobs, might be scarce. We are not quite at 2030, but I believe we have reached the “Keynes point,” where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. Before that, farm labor, small craft workshops, voluntary piecework, or inherited wealth provided access. Now access needs to change again. However this happens, we have entered a different phase for the economy, a new era where production matters less and what matters more is access to that production: distribution, in other words—who gets what and how they get it. We have entered the distributive era. 4/22/2021 IBM #OpenTechAI 70 Arthur WB (2017) Where is technology taking the economy. McKinsey Quarterly. October.
  • 71.
    4/22/2021 (c) IBMMAP COG .| 71 https://www.youtube.com/watch?v=WGK8MY8iZHA
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    Service-Dominant logic worldviewand mindset Year Publication Service Resource Integrators 2004 Vargo SL, Lusch RF (2004) Evolving to a new dominant logic for marketing. Journal of marketing. 68(1):1-7. The application of specialized skills and knowledge is the fundamental unit of exchange. Operant resources are resources that produce effects 2011 Vargo SL, Lusch RF (2011) It's all B2B… and beyond: Toward a systems perspective of the market. Industrial marketing management. 40(2):181-7. The central concept in S-D logic is that service — the application of resources for the benefit of another party — is exchanged for service That is, all parties (e.g. businesses, individual customers, households, etc.) engaged in economic exchange are similarly, resource-integrating, service- providing enterprises that have the common purpose of value (co)creation — what we mean by “it is all B2B.” 2016 Vargo SL, Lusch RF. Institutions and axioms: an extension and update of service-dominant logic. Journal of the Academy of Marketing Science. 2016 Jan 1;44(1):5-23. value creation can only be fully understood in terms of integrated resources applied for another actor’s benefit (service) within a context, including the institutions and institutional arrangements that enable and constrain value creation. To alleviate this limitation and facilitate a better understanding of cooperation (and coordination), an eleventh foundational premise (fifth axiom) is introduced, focusing on the role of institutions and institutional arrangements in systems of value cocreation: service ecosystems. 4/22/2021 (c) IBM MAP COG .| 72
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    Service Science thestudy of service systems entities Year Publication Service Science Service System 2007 Spohrer J, Maglio, PP, Bailey J, Gruhl, D (2007) Steps toward a science of service systems, IEEE Computer, (40)1:71-77. Services science is an emerging field that seeks to tap into these and other relevant bodies of knowledge, integrate them, and advance three goals—aiming ultimately to understand service systems, how they improve, and how they scale. The components of a service system are people, technology, internal and external service systems connected by value propositions, and shared information (such as language, laws, and measures. 2008 Spohrer, J, Vargo S, Caswell N, Maglio PP (2008) The service system is the basic abstraction of service science, HICSS-41, NY: IEEE Press, Pp. 1-10. Service science is the study of the application of the resources of one or more systems for the benefit of another system in economic exchange. Informally, service systems are collections of resources that can create value with other service systems through shared information. 2008 Maglio PP, Spohrer J (2008) Fundamentals of service science. Journal of the academy of marketing science. 36(1):18-20. Service science is the study of service systems, aiming to create a basis for systematic service innovation. Service systems are value-co-creation configurations of people, technology, value propositions connecting internal and external service systems, and shared information (e.g., language, laws, measures, and methods). 4/22/2021 (c) IBM MAP COG .| 73
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    Service Science thestudy of service system entities 4/22/2021 (c) IBM MAP COG .| 74 Year Publication Service Science Service System 2009 Spohrer J, Maglio PP (2009) Service science: Toward a smarter planet. In Introduction to service engineering, Eds. Karwowski and Salvendy. Pp. 3-10 Service science is a specialization of systems science. So service science seeks to create a body of knowledge that accounts for value-cocreation between entities as they interact… Service system entities are dynamic configurations of resources. As described below, resources include people, organizations, shared information, and technology. 2012 Spohrer J, Piciocchi P, Bassano C (2012) Three frameworks for service research: exploring multilevel governance in nested, networked systems. Service Science. 4(2):147-160. SSME+D is built on top of the Service-Dominant logic (SD Logic) worldview A service system entity is a dynamic configuration of resources (at least one of which, the focal resource, is a person with rights). 2013 Spohrer J, Giuiusa A, Demirkan H, Ing D (2013) Service science: reframing progress with universities. Systems Research and Behavioral Science. 30(5):561- 569 Service science is an emerging branch of systems sciences with a focus on service systems (entities) and value cocreation (complex non- zero-sum interactions). … complex adaptive entities - service systems - within an ecology of nested, networked entities… From a service science perspective, progress can be thought of in terms of the rights and responsibilities of entities
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    Service Science thestudy of service system entities 4/22/2021 (c) IBM MAP COG .| 75 Year Publication Service Science Service System 2014 Spohrer J, Kwan SK, Fisk RP (2014)Marketing: a service sci ence and arts perspective, Handbook of Service Market ing Research, Eds. Rust RT, Huang MH, NY:Edward Elgar, pp. 489-526. Service science (short for Service Science, Management, Engineering, Design, Arts, and Public Policy) is an emerging transdiscipline for the (1) study of evolving service system entities and value co-creation phenomena, as well as (2) pedagogy for the education of 21st century T- shaped service innovators from all disciplines, sectors, and cultures. So like all early stage scientific communities, the language for talking about service systems and value co-creation phenomena continues to evolve. … Service system entities are economic and social actors, which configure (or integrate) resources. … A formal service system entity (SS-FSC3) is a legal, economic entity with rights and responsibilities codified in written laws. 2015 Spohrer J, Demirkan H, Lyons (2015) Social Value: A Service Science Perspective. In: Kijima K. (eds) Service Systems Science. Translational Systems Sciences, vol 2. Tokyo: Springer. Pp. 3-35. Service science is an emerging transdiscipline for the (1) study of evolving service system entities and value co-creation phenomena and (2) pedagogy for the education of twenty-first-century T-shaped service innovators from all disciplines, sectors, and cultures Formal service system entities (as opposed to informal service system entities) can be ranked by the degree to which they are governed by written (symbolic) laws and evolve to increase the percentage of their processes that are explicit and symbolic.
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    Service Science thestudy of service system entities 4/22/2021 (c) IBM MAP COG .| 76 Year Publication Service Science Service System 2016 Spohrer J (2016) Services Science and Societal Convergence. In W.S. Bainbridge, M.C. Roco (eds.),Handbook of Science and Technology Convergence, pp. 323-335 Service science is an emerging transdiscipline for the (1) study of evolving ecology of nested, networked service system entities and value co-creation phenomena, as well as (2) pedagogy for the education of the twenty-first- century T-shaped (depth and breadth) service innovators from all disciplines, sectors, and cultures. As service science emerges, we can begin by “seeing” and counting service system entities in an evolving ecology, working to “understand” and make explicit their implicit processes of valuing … 2016 Spohrer J (2016) Innovation for jobs with cognitive assistants: A service science perspective, In Disrupting Unemployment , Eds. Nordfors, Cerf, Seng, Missouri: Ewing Marion Kauffman Foundation, Pp. 157-174. Service science is the emerging transdiscipline that studies the evolving ecology of nested, networked service system entities, their capabilities, constraints, rights, and responsibilities. There are perhaps twenty billion formal service system entities in the world today, each governed in part by formal written laws. Every person, household, university, business, and government is a formal service system entity, but my dog, my smartphone, and my ideas are not.
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    Service Science thestudy of service system entities 4/22/2021 (c) IBM MAP COG .| 77 Year Publication Service Science Service System 2017 Spohrer J, Siddike MAK, Kohda Y (2017) Rebuilding evolution: a service science perspective. HICSS 50. Service science is the study of the evolving ecology of service system entities, complex socio-technical systems with rights and responsibilities – such as people, businesses, and nations. Service systems are dynamic configurations of people, technology, organization and information that interact through value proposition and co- create mutual value. 2019 Pakalla D, Spohrer J (2019, forthcoming) Digital Service: Technological Agency in Service Systems. HICSS 52. For the purposes of this paper, service science can be summarized as the study of the evolving ecology of service system entities, their capabilities, constraints, rights, and responsibilities, including their value co-creation and capability co- elevation mechanisms . Service systems are a type of socio- technical system, such as people, businesses, and nations, all with unique identities, histories, and reputations based on the outcomes of their interactions with other entities.
  • 78.
    Service Research • ArtificialIntelligence in Service • "The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks.” • Huang MH and Rust RT (2018) Artificial Intelligence in Service. Journal of Service Research. 21(2):155–172. • Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences • "expand the relevant set of antecedents beyond the established constructs and theories to include variables that are particularly relevant for AI applications such as privacy concerns, trust, and perceptions of “creepiness.” • Ostrom AL, Foheringham D, Bitner MJ (2018, forthcoming) Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences. In Handbook of Service Science, Volume 2, Eds, Maglio, Kieliszewski,Spohrer,Lyons,Patricio,Sawatani. New York: Springer. Pp. x-y. 4/22/2021 (c) IBM MAP COG .| 78
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    4/22/2021 (c) IBMMAP COG .| 79 Microsoft acquiring GitHub $7.5B 2018 John Marks on Open Source Models will run the world Why SW is eating the world
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    Step Comment GitHub Getan account and read the guide MAX CODAIT’s Model Asset Exchange Learn 3 R's - Read, Redo, Report Read (Medium/arXiv), Redo (GitHub), Report (Jupyter Notebook) PapersWithCode Stay on top of recent advances; Do 3 R’s. Kaggle Compete in a Kaggle competition Leaderboards Compete to advance AI progress Linux Foundation AI Help end-to-end open source industry AI & Data infrastructure Mozilla Common Voice Donate your speech; Label and verify data; Recruit others. Figure Eight Generate a set of labeled data (also Mechanical Turk) Design New Challenges Build for Call for Code/Code and Response; Build your AI Helper; Build test-taker, that can switch to tutor-mode; Etc. Open Source Guide Establish open source culture in your organization 4/22/2021 IBM Code #OpenTechAI 80
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    Is it fair? Isit easy to understand? Is it accountable? So what does it take to trust a decision made by a machine? (Other than that it is 99% accurate)? Did anyone tamper with it? #21, #32, #93 #21, #32, #93
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    IBM AI Explain atransaction Deployment: Claim Approval Model name: Claim Model AI Fairness 360 toolkit Trust and transparency integral to AI on the IBM Cloud Explainability, fairness, lineage are critical principles of trusted AI Open source toolkit to check for unwanted bias in datasets and machine learning models © 2019 IBM Corporation 82 DENIED APPROVED CONFIDENCE 90% 10% POLICY HOLDER AGE: 18 RESPONSIBLE PARTY: Self CAR BRAND: Oldsmobile Cutlass POLICE REPORT: Yes CAR VALUE: $20,000 POLICY AGE: 5 Years 65% 17% 23% 13% 13% 5% Factors contributing to a DENIED confidence level Factors contributing to an APPROVED confidence level
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    Is it fair? Isit easy to understand? Is it accountable? Did anyone tamper with it? FAIRNESS EXPLAINABILITY ROBUSTNESS LINEAGE Our vision for Trusted AI Pillars of trust, woven into the lifecycle of an AI application
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    Join: https://callforcode.org/ 87 This multi-yearglobal initiative rallies developers to create practical, effective, and high-quality applications based on cloud, data, and artificial intelligence that can have an immediate and lasting impact on humanitarian issues. Call for Code brings startup, academic, and enterprise developers together and inspires them to solve the most pressing societal issues of our time - for example, faster and more resilient recovery from natural disasters.
  • 88.
    In conclusion… Situation Competence 3 R’s OnRamps 1. Platform & ecosystem competition for data and AI workloads 2. However, AI is hard; many capabilities 2-4 decades away 3. Industry in open source collaboration-competition mode 1. Read: Learn state-of-art 2. Redo: Apply and infuse in use cases/workloads 3. Report: Share back, others may improve 1. LF AI Landscape: Community projects 2. IBM CODAIT: Cloud Pak for Data (CPD), etc. – Enterprise workloads with Trusted AI 3. Red Hat ODH: OpenShift – Hybrid cloud platform and ecosystem
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    4/22/2021 (c) IBMMAP COG .| 89
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    IfM, IBM (2010) Succeedingthrough service innovation: a service perspective for education, research, business and government. University of Cambridge Institute for Manufacturing, Cambridge UK 2010
  • 91.
    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.
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    94 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
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    4/22/2021 (c) IBMMAP COG .| 95 Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3