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Denmark 20190418 v5

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Denmark 20190418 v5

  1. 1. … about the Future of AI Jim from IBM (Jim Spohrer) Director, Measuring AI Progress Cognitive Opentech Group (MAP COG) See Center for Opensource Data and AI Technologies (CODAIT), http://codait.org Watson West, 505 Howard St, San Francisco, CA, USA, April 18, 2019 https://www.slideshare.net/spohrer/denmark-20190418-v5 4/18/2019 (c) IBM MAP COG .| 1
  2. 2. 4/18/2019 (c) IBM MAP COG .| 2
  3. 3. OpenTech AI Finland 2019 4/18/2019 (c) IBM MAP COG .| 3
  4. 4. 4/18/2019 (c) IBM MAP COG .| 4
  5. 5. Code and Response 4/18/2019 (c) IBM MAP COG .| 5
  6. 6. Wikipedia Natural Disasters 4/18/2019 (c) IBM MAP COG .| 6
  7. 7. GitHub Natural_Disasters 4/18/2019 (c) IBM MAP COG .| 7
  8. 8. Kaggle Natural_Disasters 4/18/2019 (c) IBM MAP COG .| 8
  9. 9. Papers With Code 4/18/2019 (c) IBM MAP COG .| 9
  10. 10. Wikipedia Poetry 4/18/2019 (c) IBM MAP COG .| 10
  11. 11. GitHub Poetry 4/18/2019 (c) IBM MAP COG .| 11
  12. 12. 4/18/2019 (c) IBM MAP COG .| 12
  13. 13. Center for Open Source Data and AI Technologies September 2018 / © 2018 IBM Corporation Watson West Building 505 Howard St. San Francisco, California CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise. Relaunch of the IBM Spark Technology Center (STC) to reflect expanded mission. 36 open source developers! Improving Enterprise AI lifecycle in Open Source Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-LearnPandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow CODAIT codait.org 13 The following slides from Fred’s ApacheCon keynote
  14. 14. IBMers using OSS IBMers contributing to OSS CODAIT Active IBM Users of Open Source (Certified to consume and/or contribute open source in 2018) 14September 2018 / © 2018 IBM Corporation >62,000 >1,000
  15. 15. CBI Insights AI Report 4/18/2019 (c) IBM MAP COG .| 15
  16. 16. Linux Foundation Deep Learning 4/18/2019 (c) IBM MAP COG .| 16
  17. 17. Products built with open source building blocks 4/18/2019 (c) IBM MAP COG .| 17 Collaborate and explore from data to models Scale and continuously improve Performance++: Watson AcceleratorTrust++: Watson OpenScale
  18. 18. Today’s talk • Introduction • AI at IBM: Past, Present, Future (Summary) • Types of Systems • AI at the peak of the hype cycle • What’s really going on? • Your data is becoming your AI… IA transformation • Part 1: Solving AI: Leaderboards • Roadmap and implications • Open technologies, innovation • Part 2: Solving IA: Better Building Blocks • Solving problems faster, creates new problems • Identity, social contracts, trust, resilience 4/18/2019 IBM Code #OpenTechAI 18
  19. 19. AI at IBM: Past (Nathan Rochester) 4/18/2019 (c) IBM MAP COG .| 19
  20. 20. 4/18/2019 © IBM UPWard 2016 20 AI (Artificial Intelligence) is popular again… you see it mentioned on billboards in SF However, pattern recognition does not equal AI Deep learning works if you have lots of data and compute power We finally have lots of data and compute power – hurray!!! So finally, deep learning for pattern recognition is working pretty well However, AI is more than deep learning for pattern recognition… AI requires commonsense reasoning – that will take another 5-10 years of research How do we know this? Look at the AI leaderboards – we will get to that…
  21. 21. 4/18/2019 (c) IBM MAP COG .| 21
  22. 22. Smartphones pass entrance exams? When? 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 22 … when will your smartphone be able to take and pass any online course? And then be your coach, so you can pass too?
  23. 23. IBM-MIT $240M over 10 year AI mission 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 23
  24. 24. IBM Quantum 4/18/2019 (c) IBM MAP COG .| 24
  25. 25. Quantum Risk Assessment 4/18/2019 (c) IBM MAP COG .| 25 URL: https://www.nature.com/articles/s41534-019-0130-6
  26. 26. 26September 2018 / © 2018 IBM Corporation
  27. 27. 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 27
  28. 28. 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) 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 28
  29. 29. Questions • What is the timeline for solving AI and IA? • 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? 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 29
  30. 30. Timeline: Short History 4/18/2019 © IBM Cognitive Opentech Group (COG) 30 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.
  31. 31. 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 314/18/2019 (c) IBM 2017, Cognitive Opentech Group 2080204020001960 $1K $1M $1B $1T 206020201980 +/- 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
  32. 32. Timeline: GDP/Employee 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 32 (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
  33. 33. Timeline: Leaderboards FrameworkAI 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 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 33 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
  34. 34. Who is winning 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 34 https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/
  35. 35. Robots by Country • Industrial robots per 10,000 people by country 4/18/2019 IBM #OpenTechAI 35 34
  36. 36. Sweden 4/18/2019 (c) IBM MAP COG .| 36
  37. 37. Economic Growth Rates 2035: AI Projected Impact 4/18/2019 (c) IBM MAP COG .| 37
  38. 38. 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 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 38
  39. 39. AI Risks • Job Loss • Shorter term bigger risk = de-skilling • Super-intelligence • Shorter term bigger risk = bad actors 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 39
  40. 40. 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.) 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 40
  41. 41. Stakeholders = service system entities • Individuals • Families • Businesses and other Organizations • Industry Groups and Professional Associations • Regional Governments: • Cities • States • Nations 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 41 “there is nothing as practical as a good abstraction” -> service science studies service system entities
  42. 42. “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
  43. 43. 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. 4/18/2019 IBM Code #OpenTechAI 43
  44. 44. 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. 4/18/2019 IBM Code #OpenTechAI 44
  45. 45. 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." 4/18/2019 IBM Code #OpenTechAI 45
  46. 46. Computing: Then, Now, Projected 4/18/2019 46 2035 2055
  47. 47. Be Prepared • Understand open AI code + data + models + stacks + communities • Leaderboards • Ethical conduct • Learn 3 R’s of IBM’s Cognitive Opentech Group (COG) • Read arXiv • Redo with Github • Report with Jupyter notebooks on DSX and/or leaderboards • Improve your team’s skills of rapidly rebuilding from scratch • Build your open code eminence • Understand open innovation • Communities + Leaderboards 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 47 1972 used Punch cards 2016 used IBM Watson Open APIs to win…
  48. 48. 4/18/2019 48 1955 1975 1995 2015 2035 2055 Better Building Blocks
  49. 49. 4/18/2019 © IBM UPWard 2016 49
  50. 50. Cupertino Teens • IBM Watson on Bluemix 4/18/2019 (c) IBM 2017, Cognitive Opentech Group 50 AI for NLP entity identification
  51. 51. 10 million minutes of experience 4/18/2019 Understanding Cognitive Systems 51
  52. 52. 2 million minutes of experience 4/18/2019 Understanding Cognitive Systems 52
  53. 53. Hardware < Software < Data < Experience < Transformation 4/18/2019 Understanding Cognitive Systems 53 Value migrates to transformation – becoming our future selves; people, businesses, nations = service system entities Pine & Gilmore (1999) Transformation Roy et al (2006) Data Osati (2014) Experience Life Log
  54. 54. Courses • 2015 • “How to build a cognitive system for Q&A task.” • 9 months to 40% question answering accuracy • 1-2 years for 90% accuracy, which questions to reject • 2025 • “How to use a cognitive system to be a better professional X.” • Tools to build a student level Q&A from textbook in 1 week • 2035 • “How to use your cognitive mediator to build a startup.” • Tools to build faculty level Q&A for textbook in one day • Cognitive mediator knows a person better than they know themselves • 2055 • “How to manage your workforce of digital workers.” • Most people have 100 digital workers. 4/18/2019 54 Take free online cognitive classes today at cognitiveclass.ai
  55. 55. 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 55 I have… Have you noticed how the building blocks just keep getting better?
  56. 56. Learning to program: My first program 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 56 Early Computer Science Class: Watson Center at Columbia 1945 Jim Spohrer’s First Program 1972
  57. 57. 4/18/2019 © IBM UPWard 2016 57 Fast Forward 2016: Consider this…
  58. 58. Microsoft CaptionBot June 19, 2016 4/18/2019 © IBM UPWard 2016 58
  59. 59. Microsoft CaptionBot June 20, 2016 4/18/2019 © IBM UPWard 2016 59
  60. 60. IBM Image Tagging 4/18/2019 © IBM UPWard 2016 60
  61. 61. Today: November 10, 2017 4/18/2019 © IBM DBG COG 2017 61 IBM
  62. 62. 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 62 Cognitive Mediators for all people in all roles
  63. 63. Occupations = Many Tasks 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 63
  64. 64. Watson Discovery Advisor 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 64 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/
  65. 65. 4/18/2019 (c) IBM MAP COG .| 65 Microsoft acquiring GitHub $7.5B 2018 John Marks on Open Source Models will run the world Why SW is eating the world
  66. 66. Types: Progression of models Models = instruction set of future 4/18/2019 Understanding Cognitive Systems 66 Task & World Model/ Planning & Decisions Self Model/ Capacity & Limits User Model/ Episodic Memory Institutions Model/ Trust & Social Acts Tool + - - - Assistant ++ + - - Collaborator +++ ++ + - Coach ++++ +++ ++ + Mediator +++++ ++++ +++ ++ Cognitive Tool Cognitive Assistant Cognitive Collaborator Cognitive Coach Cognitive Mediator
  67. 67. 4/18/2019 IBM Code #OpenTechAI 67
  68. 68. AI Fairness 360 4/18/2019 (c) IBM MAP COG .| 68
  69. 69. 4/18/2019 IBM Code #OpenTechAI 69
  70. 70. Step Comment GitHub Get an account and read the guide Learn 3 R's - Read, Redo, Report Read (Medium/arXiv), Redo (GitHub), Report (Jupyter Notebook) Kaggle Compete in a Kaggle competition Leaderboards Compete to advance AI progress Figure Eight Generate a set of labeled data (also Mechanical Turk) Design New Challenges build an AI system that can take and pass any online course, then switch to tutor-mode and help you pass Open Source Guide Establish open source culture in your organization 4/18/2019 IBM Code #OpenTechAI 70
  71. 71. Prepare for AI Future • Do you have a GitHub account? Get it. • Yes: proceed • No: sign up • Do you program? Either OK, partnering is best. • Yes: Learn and do 3 R’s (read, redo, report) • Github master: Code, Content (Data), Community (IBM Code can help) • No: Learn to read and execute code with partner (T2T) • Do you have favorite AI leaderboards? • Yes: Learn and do 3 R’s (read, redo, report advances) • Kaggle master: Combine top decorrelated solution, new solution • No: Find a mentor with favorites, do together • Are you AI prepared? Do you know/do data, models, solutions? • Yes: Find favorite leaderboards you can do 3 R’s for today • Figure-Eight master: Labeled data that matters most • No: Wait until one model: one model that can do them all • Then rapidly rebuild in least time, energy (“zorch”), data, code 4/18/2019 © IBM Cognitive Opentech Group 2018 71 1. Where do we get labeled data? We create it: Figure Eight, Mechanical Turk, etc. 2. External/internal challenge? 10M minutes from birth to adult 2M minutes from novice to expert Not just external states, but internal states are data as well… The challenge of data for AI models 3. AI models as ”data” instruction set Computer’s have instruction sets Arithmetic, Logic, etc. Models are becoming instructions Models are data/experience
  72. 72. Trust: Two Communities 4/18/2019 IBM Code #OpenTechAI 72 Service Science OpenTech AI Trust: Value Co-Creation, Transdisciplinary Trust: Ethical, Safe, Explainable, Open Communities Special Issue AI Magazine? Handbook of OpenTech AI?
  73. 73. Resilience: Rapidly Rebuilding From Scratch • Dartnell L (2012) The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm. Westminster London: Penguin Books. 4/18/2019 IBM Code #OpenTechAI 73
  74. 74. 4/18/2019 (c) IBM MAP COG .| 74
  75. 75. 4/18/2019 (c) IBM MAP COG .| 75
  76. 76. 4/18/2019 (c) IBM MAP COG .| 76 Join the for free and get monthly newsletter from the International Society of Service Innovation Professionals. Membership based non-profit professional association promoting people-centered smart service systems Fostering professional thought leadership of members through joint conferences, workshops, publications, members mentorship, and awards globally Catalyzing and elevating industry-academia- government collaboration in cutting edge research, best industry practices, innovative educational models, and policy influencing Join us: www.issip.org Members: 1200 +  ~200 universities  50 + companies  42 + countries Founders:
  77. 77. 4/18/2019 (c) IBM MAP COG .| 77
  78. 78. 4/18/2019 (c) IBM MAP COG .| 78
  79. 79. Our data is AI • What do companies that profit from AI owe us? • What do nations that profit from AI owe us? • What do service systems entities owe service system entities? • What value propositions and governance mechanisms connect us? • Henry Ford: “My employees are my future customers, I should therefore pay employees well today, so my customers pay me well tomorrow.” • Irene Ng: ”Your data is your future AI, we should therefore create a market for your data today (with the help of HATDEX/AI), so your AI will pay you well tomorrow.” 4/18/2019 (c) IBM MAP COG .| 79
  80. 80. Ruskin, Unto this last… five great service professions Gandhi’s transformation into Gandhi 4/18/2019 (c) IBM MAP COG .| 80 so that on him falls, in great part, the responsibility for the kind of life they lead; The lawyer, rather than countenance Injustice…
  81. 81. By 2035, T-Shaped Makers with great Building Blocks and Cognitive Mediators 4/18/2019 81 Empathy & Teamwork sector region/culture discipline Depth Breadth STEM Liberal Arts
  82. 82. Future-Ready T-Shapes 4/18/2019 © IBM UPWard 2016 82
  83. 83. 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 83 Physics Chemistry Biology Neuroscience Psychology Artificial Intelligence Engineering Management Public Policy Education Design Humanities Natural Systems Cognitive Systems Service Systems
  84. 84. What is a digital cognitive system (entity)? 4/18/2019 Understanding Cognitive Systems 84
  85. 85. 4/18/2019 (c) IBM MAP COG .| 85
  86. 86. Norway 4/18/2019 (c) IBM MAP COG .| 86
  87. 87. Jim from IBM – 20 years today! 4/18/2019 (c) IBM MAP COG .| 87
  88. 88. Complex Systems… 4/18/2019 (c) IBM MAP COG .| 88
  89. 89. What is a biological cognitive system (entity)? 4/18/2019 Understanding Cognitive Systems 89
  90. 90. What is a Service System Entity? 4/18/2019 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 90 “A service science perspective considers the evolving ecology of service system entities, their value co-creation and capability co-elevation interactions, and their capabilities, constraints, rights, and responsibilities.” Cognitive Systems Entities Service Systems Entities With Cognitive Mediators Add Rights & Responsibilities
  91. 91. Computer Science as Empirical Inquiry: Symbols and Search • "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… A physical symbol system is a machine that produces through time an evolving collection of symbol structures. Such a system exists in a world of objects wider than just these symbolic expressions themselves. ” • 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 4/18/2019 (c) IBM MAP COG .| 91
  92. 92. 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.4/18/2019 (c) IBM MAP COG .| 92
  93. 93. 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).4/18/2019 (c) IBM MAP COG .| 93
  94. 94. Service Science the study of service system entities 4/18/2019 (c) IBM MAP COG .| 94 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
  95. 95. Service Science the study of service system entities 4/18/2019 (c) IBM MAP COG .| 95 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.
  96. 96. Service Science the study of service system entities 4/18/2019 (c) IBM MAP COG .| 96 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.
  97. 97. Service Science the study of service system entities 4/18/2019 (c) IBM MAP COG .| 97 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.
  98. 98. Service Science: Conceptual Framework 4/18/2019 (c) IBM MAP COG .| 98
  99. 99. 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. 4/18/2019 IBM #OpenTechAI 99
  100. 100. Disciplines and some of the key entities they study 4/18/2019 (c) IBM MAP COG .| 100 Computer Science: Physical Symbol System Entities AI: Digital Cognitive System Entities Chemistry: Auto-Catalytic Molecular System Entities Biology: Biological Cognitive System Entities Service science: 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.
  101. 101. 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. 4/18/2019 (c) IBM MAP COG .| 101
  102. 102. David Cox (IBM-MIT AI Collaboration) • Think2019 presentation, Feb 2019, Moscone Center 4/18/2019 (c) IBM MAP COG .| 102
  103. 103. AI Successes 4/18/2019 (c) IBM MAP COG .| 103
  104. 104. AI Challenges 4/18/2019 (c) IBM MAP COG .| 104
  105. 105. 4/18/2019 IBM Code #OpenTechAI 105

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