- Service science is the study of service systems and value co-creation between entities as they interact and integrate resources.
- A service system is a dynamic configuration of resources including people, technology, organizations, shared information and value propositions connecting internal and external service systems.
- Service science aims to understand and improve service systems and how they scale to create value.
1) The document discusses Patricia Diaz's research interests, which focus on developing a framework called CHRRIS to conceptualize innovative digital services through recombining digital artifacts in a digital sandbox.
2) Her dissertation research developed the CHRRIS framework and Diaz-CIDOMA analytical tool. Her current research aims to validate and extend CHRRIS, and investigate using it as the central mechanism in a service innovation system.
3) Long term, she is interested in how digital technology can help solve complex societal problems like healthcare, hunger, and oppression.
Social Innovation in Smart Tourism Ecosystems: How Technology and Institution...David Vicent
A fantastic article by authors from Salerno University ( Italy) . It proposes a very nice integrated model between technology and sustainability in natural areas, a good base for designing smart tourism models in rural areas. Very good Bibliography for 2030 Agendas in Tourism.
Figures of the Many - Quantitative Concepts for Qualitative ThinkingBernhard Rieder
This document discusses quantitative concepts and styles of reasoning used in data analysis. It begins by noting the proliferation of data and actors online, making generalization difficult. It then outlines two main styles of mathematics used in data analysis: statistics, which focuses on objects and their properties, and graph theory, which focuses on objects and their relations. The document analyzes comment data from a Facebook page using various statistical and visualization techniques common in exploratory data analysis, like histograms, scatterplots, and timelines. It aims to understand the different analytical gestures involved in working with large-scale digital data.
Engines of Order. Social Media and the Rise of Algorithmic Knowing.Bernhard Rieder
Talk given at the Social Media and the Transformation of Public Space Conference on June 19 at the University of Amsterdam. References and comments are in the notes section.
The document discusses big data sources and methods for social and economic analysis. It proposes a big data architecture to integrate non-traditional data sources and analysis methods for forecasting social and economic behaviors. Specifically, the architecture aims to manage the full data lifecycle, including data ingestion, analysis, storage and more, in order to extract valuable insights from large, heterogeneous data related to people, companies and organizations.
On the Diversity of the Accountability Problem. Machine Learning and Knowing ...Bernhard Rieder
This document discusses the accountability problem with machine learning algorithms. It notes that there are two types of algorithms - those explicitly coded and those that learn statistical patterns in data. These latter types can be difficult to assess and shift the focus from normative values to empirical patterns in data. It also discusses how algorithms can learn sensitive personal attributes from innocuous Facebook likes and how risk algorithms associate many data points with loan default risk. The document argues that accountability is not enough and that regulation will need to be domain-specific while exploring approaches like consumer protections and data restrictions. It concludes that algorithms reflect societal structures and turning this into profit always raises normative issues requiring attention to commercial influences and consideration of more egalitarian alternatives.
1) The document discusses Patricia Diaz's research interests, which focus on developing a framework called CHRRIS to conceptualize innovative digital services through recombining digital artifacts in a digital sandbox.
2) Her dissertation research developed the CHRRIS framework and Diaz-CIDOMA analytical tool. Her current research aims to validate and extend CHRRIS, and investigate using it as the central mechanism in a service innovation system.
3) Long term, she is interested in how digital technology can help solve complex societal problems like healthcare, hunger, and oppression.
Social Innovation in Smart Tourism Ecosystems: How Technology and Institution...David Vicent
A fantastic article by authors from Salerno University ( Italy) . It proposes a very nice integrated model between technology and sustainability in natural areas, a good base for designing smart tourism models in rural areas. Very good Bibliography for 2030 Agendas in Tourism.
Figures of the Many - Quantitative Concepts for Qualitative ThinkingBernhard Rieder
This document discusses quantitative concepts and styles of reasoning used in data analysis. It begins by noting the proliferation of data and actors online, making generalization difficult. It then outlines two main styles of mathematics used in data analysis: statistics, which focuses on objects and their properties, and graph theory, which focuses on objects and their relations. The document analyzes comment data from a Facebook page using various statistical and visualization techniques common in exploratory data analysis, like histograms, scatterplots, and timelines. It aims to understand the different analytical gestures involved in working with large-scale digital data.
Engines of Order. Social Media and the Rise of Algorithmic Knowing.Bernhard Rieder
Talk given at the Social Media and the Transformation of Public Space Conference on June 19 at the University of Amsterdam. References and comments are in the notes section.
The document discusses big data sources and methods for social and economic analysis. It proposes a big data architecture to integrate non-traditional data sources and analysis methods for forecasting social and economic behaviors. Specifically, the architecture aims to manage the full data lifecycle, including data ingestion, analysis, storage and more, in order to extract valuable insights from large, heterogeneous data related to people, companies and organizations.
On the Diversity of the Accountability Problem. Machine Learning and Knowing ...Bernhard Rieder
This document discusses the accountability problem with machine learning algorithms. It notes that there are two types of algorithms - those explicitly coded and those that learn statistical patterns in data. These latter types can be difficult to assess and shift the focus from normative values to empirical patterns in data. It also discusses how algorithms can learn sensitive personal attributes from innocuous Facebook likes and how risk algorithms associate many data points with loan default risk. The document argues that accountability is not enough and that regulation will need to be domain-specific while exploring approaches like consumer protections and data restrictions. It concludes that algorithms reflect societal structures and turning this into profit always raises normative issues requiring attention to commercial influences and consideration of more egalitarian alternatives.
Top 10 Read articles in Web & semantic technologydannyijwest
This document discusses the future of cloud computing in government IT. It begins by defining cloud computing and examining its growing adoption across both private and public sectors globally. The document then outlines challenges for governments transitioning to cloud computing, including workforce and computing resource issues. The author presents a six-step strategy for government agencies to migrate to the cloud. Finally, it explores implications of the continued cloud computing revolution for public sector organizations and the IT community.
From Algorithms to Diagrams: How to Study Platforms?Bernhard Rieder
YouTube is a platform that connects content creators, advertisers, and end-users. It does so through constructed infrastructures like search and recommendation algorithms, as well as interfaces and terms of service. These elements can be studied empirically to understand how they shape practices and outcomes. While algorithms are often blamed, platforms are actually complex systems influenced by technical and non-technical factors. Understanding requires examining how platform grammars intersect with subject-specific contexts.
Distributed renewable and interactive energy systemsmsibilla
European Policies consider a multitude of Low Carbon Technologies to transform cities to Low Carbon Cities. Some of these technologies can form distributed systems. These are new forms of Energy Networks which can contribute to reducing the vulnerability and homogenization of urban patterns as they evolve to become part of the urban infrastructure. This evolution process also involves computerizing elements of the infrastructure, and thus relates to the Smart City concept. In this sense, a Distributed and Renewable energy system becomes interactive promoting a set of novel system properties. Following a qualitative approach, this paper presents an innovative conceptual framework in order to establish, communicate and disseminate these new system properties
Service may be regarded as the application of competences for the benefit of others. Service science focuses on service as a system of interacting parts that include people, technology, and business. It is the study of services, service systems and value propositions. It integrates many service research areas and service disciplines. This paper is a brief introduction to the new field of service science. Matthew N. O. Sadiku | Adedamola Omotoso | Sarhan M. Musa "Service Science: An Introduction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28052.pdf Paper URL: https://www.ijtsrd.com/engineering/other/28052/service-science-an-introduction/matthew-n-o-sadiku
Determining relevance of “best practice” based on interoperability in Europea...ePractice.eu
Authors: Robert Deller, Guilloux Véronique.
eGovernment is one of Europe’s big challenges, and interoperability is a necessary condition encouraged by the European Commission. Interoperability is believed to ensure effective service to citizens and to perform governmental functions effectively as well as efficiently.
Frontiers scope of service science 2011072 v1ISSIP
The document proposes a framework for visualizing the scope of service science. It discusses service systems through the lens of three foundational premises: 1) Service systems dynamically configure resources to provide value, 2) Service systems calculate value from multiple stakeholder perspectives, and 3) Service systems reconfigure access to resources through value propositions that change over time. The framework is used to analyze interactions between service system entities and outcomes in various domains like transportation, healthcare, education, and more.
This document discusses analytical gestures used in social media data analysis. It begins by defining social media platforms as large databases that formalize different types of entities and connections. It then discusses that analytical gestures involve understanding the platform, selected analytical tools/methods, and researcher imagination. Examples of analytical gestures provided include visualizing friendship networks and co-liking networks from Facebook data. The document emphasizes that data analysis requires understanding the technical, social, and analytical aspects of the data.
Social network analysis is a method of big data analysis which reveals the nature
of connections between objects, including implicit connections. This is a tool of interest
since it can be applied to large data sets, manual processing of which is very laborintensive,
while automated processing through self-learning linguistic engines requires
a lot of resources. In this regard a study was carried out: it was aimed at development
and testing of social network analysis tools and creating a research algorithm which is
applicable to solve a wide range of analytical and search tasks. The current image of
Russia and its activities in the Arctic was chosen as a case.
The research algorithm helps to discover implicit patterns and trends, relate
information flows and events with relevant newsworthy events and news stories to form
a “clear” view of the study object and key actors which this object is associated with.
The work contributes to filling the gap in scientific literature, caused by insufficient
development of applied issues of using social network analysis to solve managerial
tasks, while theoretical papers, which describe the theory and methodology of such an
analysis, are abundant.
Top 5 most viewed articles from academia in 2020IJCSEA Journal
Data enter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital and operational costs required for running datacenters. These tools are helpful for business owners to improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can provide more opportunities to business owners to control costs .In addition, they also introduce an analytical structure in which anecdotal information can be cross-checked for consistency with other well-known parameters driving data center costs. This work focuses on comparing between number of proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included and not included in such tools and whether the tools are documented or not. Such an approach presents a solid ground for designing more and better tools and spreadsheets in the future.
The document discusses the concept of data-driven cities and provides examples from several major cities. It finds that while there is no single definition of a data-driven city, key elements include the generation and analysis of data to improve living standards through social, economic, and environmental initiatives. The study analyzed 28 global cities and identified Moscow, New York, London, Barcelona, and Sydney as technological leaders due to their extensive use of data-driven solutions across areas like transportation, utilities, security and citizen engagement. While each city demonstrates strengths in various technologies and policy areas, New York stands out as the overall leader in terms of development and implementation of data-driven practices in urban management.
Big DataParadigm, Challenges, Analysis, and ApplicationUyoyo Edosio
Big Data Paradigm: Analysis, Application and Challenges
This document discusses big data, including its definition in terms of volume, variety and velocity; how it is analyzed using machine learning algorithms and distributed storage and processing; applications in various domains like healthcare, transportation and consumer products; and challenges like privacy, noisy data, skills shortage and immature tools. The conclusion recommends further research on hardware, algorithms and computational methods to effectively manage and gain insights from increasingly large data volumes.
BIG DATA IN SMART CITIES: A SYSTEMATIC MAPPING REVIEWsarfraznawaz
Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain “Big Data in Smart Cities” by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
Next generation big data analytics state of the artNazrul Islam
This document reviews recent research on network big data, including different data types (online network data, mobile/IoT data, geography data, spatial temporal data, streaming/real-time data, and visual data), storage models, privacy and security issues, analysis methods, and applications. It discusses the trends in network big data moving from simple online social network data to include more data sources and data that has spatial, temporal, and real-time components. The challenges of analyzing large and diverse datasets from networks are also addressed.
The data-driven economy promises the creation of enormous amounts of economic activity and growth opportunities. However these projections lie to a large extent in the development of new services. Currently, the results in terms of service creation remain below the expectations of open data promoters. Indeed most services created are not sustainable and / or do not use the variety of datasets. They are to a wide extent relying on a limited number of very popular datasets. To increase the reuse and the value extracted by services from data, our hypothesis is that service innovation approaches can help understand the mechanisms that drive the creation of services. We therefore propose a review the current approaches to encouraging the creation of services based on data, an analysis of the creation of services from two open data platforms, in the UK and in Singapore, and a description of the roles that the data can have in the design of services based on a theoretical framework of service innovation.
Muriel Foulonneau 1, Slim Turki 1, Géradine Vidou 1, Sébastien Martin 2
1 Public Research Centre Henri Tudor, Luxembourg-Kirchberg, Kirchberg
2 Université Paris 8, Vincennes-Saint-Denis, France
muriel.foulonneau@tudor.lu
slim.turki@tudor.lu
geraldine.vidou@tudor.lu
Proceedings of 14th European Conference on eGovernment – ECEG 2014
12-13 June 2014
Brasov, Romania
A Context Ontology for Service Provisioning and ConsumptionOscar Cabrera Bejar
Nowadays services as those provided by smart cities,
health smart services, as well as common services (e.g., telephonic services, e-mail services), have a great economic impact for organisations and represent an important mean to deliver value to their consumers. The malfunctions of both the services themselves as well as the entities responsible for their execution and consumption might cause economic losses, consumers’ dissatisfaction and even shorten the service life cycle, among other risks. To avoid malfunctions beyond maintaining quality levels desired, it is important to take into account the widest possible context information that cause either positive or negative effects around services and entities involved in their provisioning and consumption. In this paper, we propose an upper ontology for service provisioning and consumption from a service-centric perspective. Specifically, we focus on software services, although we could argue for more generic applications. The contribution is the analysis, evaluation and reuse of existing proposals on context models to identify the strengths and weaknesses of its current status as well as to identify contexts not yet considered, and consolidate an integrated view of these proposals. The ultimate intention is to provide a well-defined and consolidated infrastructure of context information as a common body of knowledge, that could be instantiated on variety of use cases, for example, to be instantiated by monitors as context information useful to be monitored, or to be used as context information that allows knowing which contexts affect a service when a user consumes it, among others.
This document provides an overview of a presentation on the future of AI given by Jim Spohrer from IBM. The presentation discusses IBM's past work in AI, current focus on open source technologies through CODAT, and vision for the future which includes solving problems related to trust, identity, and resilience as AI capabilities continue to advance. It also discusses different types of systems like information, physical symbol, service, and cognitive systems.
The document discusses the history and future of AI at IBM, from its early work with Nathan Rochester on physical symbol systems to its current focus on open source technologies and cognitive systems through its Center for Open Source Data and AI Technologies (CODAIT). It also covers IBM's view of service science as the study of evolving service system entities, their capabilities, constraints, rights, and responsibilities. The document provides context around IBM's past, present and future work in AI and how it relates to fields like computer science, chemistry, biology and service science.
This document summarizes a presentation by Jim Spohrer from IBM on open technology, innovation, and service system evolution. Some key points:
- Spohrer discusses the multidisciplinary nature of services and the need for service scientists to study increasingly service-dominated economies and societies.
- He outlines the evolution of complex systems from the physical to sociotechnical, and how disciplines have evolved to study and design increasingly complex systems, from physics to computer science to service science.
- Spohrer summarizes the development of service science and service-dominant logic as frameworks to study value co-creation within service systems, which are dynamic configurations of resources including people, organizations, information, and technology.
Service Systems Engineering in the Human-Centered AI Era
Online Event: October 17, 2022, 11am-5:00pm ET
NAE Event Link: https://www.nae.edu/281715/Service-Systems-Engineering-in-the-Era-of-HumanCentered-AI
Event Agenda Link: https://www.nae.edu/File.aspx?id=281720&v=d8f00309
ISSIP Blog Post with additional links: https://issip.org/service-systems-engineering-in-the-era-of-human-centered-ai/
Guest lecture for
Course: Front Lines on Adoption of Digital and AI-based Service Offerings
Course URL: https://www.nhh.no/en/courses/front-lines-on-adoption-of-digital-and-ai-based-services/
Prof Tor Andreassen LI URL: https://www.linkedin.com/in/tor-wallin-andreassen-1aa9031/
Top 10 Read articles in Web & semantic technologydannyijwest
This document discusses the future of cloud computing in government IT. It begins by defining cloud computing and examining its growing adoption across both private and public sectors globally. The document then outlines challenges for governments transitioning to cloud computing, including workforce and computing resource issues. The author presents a six-step strategy for government agencies to migrate to the cloud. Finally, it explores implications of the continued cloud computing revolution for public sector organizations and the IT community.
From Algorithms to Diagrams: How to Study Platforms?Bernhard Rieder
YouTube is a platform that connects content creators, advertisers, and end-users. It does so through constructed infrastructures like search and recommendation algorithms, as well as interfaces and terms of service. These elements can be studied empirically to understand how they shape practices and outcomes. While algorithms are often blamed, platforms are actually complex systems influenced by technical and non-technical factors. Understanding requires examining how platform grammars intersect with subject-specific contexts.
Distributed renewable and interactive energy systemsmsibilla
European Policies consider a multitude of Low Carbon Technologies to transform cities to Low Carbon Cities. Some of these technologies can form distributed systems. These are new forms of Energy Networks which can contribute to reducing the vulnerability and homogenization of urban patterns as they evolve to become part of the urban infrastructure. This evolution process also involves computerizing elements of the infrastructure, and thus relates to the Smart City concept. In this sense, a Distributed and Renewable energy system becomes interactive promoting a set of novel system properties. Following a qualitative approach, this paper presents an innovative conceptual framework in order to establish, communicate and disseminate these new system properties
Service may be regarded as the application of competences for the benefit of others. Service science focuses on service as a system of interacting parts that include people, technology, and business. It is the study of services, service systems and value propositions. It integrates many service research areas and service disciplines. This paper is a brief introduction to the new field of service science. Matthew N. O. Sadiku | Adedamola Omotoso | Sarhan M. Musa "Service Science: An Introduction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28052.pdf Paper URL: https://www.ijtsrd.com/engineering/other/28052/service-science-an-introduction/matthew-n-o-sadiku
Determining relevance of “best practice” based on interoperability in Europea...ePractice.eu
Authors: Robert Deller, Guilloux Véronique.
eGovernment is one of Europe’s big challenges, and interoperability is a necessary condition encouraged by the European Commission. Interoperability is believed to ensure effective service to citizens and to perform governmental functions effectively as well as efficiently.
Frontiers scope of service science 2011072 v1ISSIP
The document proposes a framework for visualizing the scope of service science. It discusses service systems through the lens of three foundational premises: 1) Service systems dynamically configure resources to provide value, 2) Service systems calculate value from multiple stakeholder perspectives, and 3) Service systems reconfigure access to resources through value propositions that change over time. The framework is used to analyze interactions between service system entities and outcomes in various domains like transportation, healthcare, education, and more.
This document discusses analytical gestures used in social media data analysis. It begins by defining social media platforms as large databases that formalize different types of entities and connections. It then discusses that analytical gestures involve understanding the platform, selected analytical tools/methods, and researcher imagination. Examples of analytical gestures provided include visualizing friendship networks and co-liking networks from Facebook data. The document emphasizes that data analysis requires understanding the technical, social, and analytical aspects of the data.
Social network analysis is a method of big data analysis which reveals the nature
of connections between objects, including implicit connections. This is a tool of interest
since it can be applied to large data sets, manual processing of which is very laborintensive,
while automated processing through self-learning linguistic engines requires
a lot of resources. In this regard a study was carried out: it was aimed at development
and testing of social network analysis tools and creating a research algorithm which is
applicable to solve a wide range of analytical and search tasks. The current image of
Russia and its activities in the Arctic was chosen as a case.
The research algorithm helps to discover implicit patterns and trends, relate
information flows and events with relevant newsworthy events and news stories to form
a “clear” view of the study object and key actors which this object is associated with.
The work contributes to filling the gap in scientific literature, caused by insufficient
development of applied issues of using social network analysis to solve managerial
tasks, while theoretical papers, which describe the theory and methodology of such an
analysis, are abundant.
Top 5 most viewed articles from academia in 2020IJCSEA Journal
Data enter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital and operational costs required for running datacenters. These tools are helpful for business owners to improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can provide more opportunities to business owners to control costs .In addition, they also introduce an analytical structure in which anecdotal information can be cross-checked for consistency with other well-known parameters driving data center costs. This work focuses on comparing between number of proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included and not included in such tools and whether the tools are documented or not. Such an approach presents a solid ground for designing more and better tools and spreadsheets in the future.
The document discusses the concept of data-driven cities and provides examples from several major cities. It finds that while there is no single definition of a data-driven city, key elements include the generation and analysis of data to improve living standards through social, economic, and environmental initiatives. The study analyzed 28 global cities and identified Moscow, New York, London, Barcelona, and Sydney as technological leaders due to their extensive use of data-driven solutions across areas like transportation, utilities, security and citizen engagement. While each city demonstrates strengths in various technologies and policy areas, New York stands out as the overall leader in terms of development and implementation of data-driven practices in urban management.
Big DataParadigm, Challenges, Analysis, and ApplicationUyoyo Edosio
Big Data Paradigm: Analysis, Application and Challenges
This document discusses big data, including its definition in terms of volume, variety and velocity; how it is analyzed using machine learning algorithms and distributed storage and processing; applications in various domains like healthcare, transportation and consumer products; and challenges like privacy, noisy data, skills shortage and immature tools. The conclusion recommends further research on hardware, algorithms and computational methods to effectively manage and gain insights from increasingly large data volumes.
BIG DATA IN SMART CITIES: A SYSTEMATIC MAPPING REVIEWsarfraznawaz
Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain “Big Data in Smart Cities” by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
Next generation big data analytics state of the artNazrul Islam
This document reviews recent research on network big data, including different data types (online network data, mobile/IoT data, geography data, spatial temporal data, streaming/real-time data, and visual data), storage models, privacy and security issues, analysis methods, and applications. It discusses the trends in network big data moving from simple online social network data to include more data sources and data that has spatial, temporal, and real-time components. The challenges of analyzing large and diverse datasets from networks are also addressed.
The data-driven economy promises the creation of enormous amounts of economic activity and growth opportunities. However these projections lie to a large extent in the development of new services. Currently, the results in terms of service creation remain below the expectations of open data promoters. Indeed most services created are not sustainable and / or do not use the variety of datasets. They are to a wide extent relying on a limited number of very popular datasets. To increase the reuse and the value extracted by services from data, our hypothesis is that service innovation approaches can help understand the mechanisms that drive the creation of services. We therefore propose a review the current approaches to encouraging the creation of services based on data, an analysis of the creation of services from two open data platforms, in the UK and in Singapore, and a description of the roles that the data can have in the design of services based on a theoretical framework of service innovation.
Muriel Foulonneau 1, Slim Turki 1, Géradine Vidou 1, Sébastien Martin 2
1 Public Research Centre Henri Tudor, Luxembourg-Kirchberg, Kirchberg
2 Université Paris 8, Vincennes-Saint-Denis, France
muriel.foulonneau@tudor.lu
slim.turki@tudor.lu
geraldine.vidou@tudor.lu
Proceedings of 14th European Conference on eGovernment – ECEG 2014
12-13 June 2014
Brasov, Romania
A Context Ontology for Service Provisioning and ConsumptionOscar Cabrera Bejar
Nowadays services as those provided by smart cities,
health smart services, as well as common services (e.g., telephonic services, e-mail services), have a great economic impact for organisations and represent an important mean to deliver value to their consumers. The malfunctions of both the services themselves as well as the entities responsible for their execution and consumption might cause economic losses, consumers’ dissatisfaction and even shorten the service life cycle, among other risks. To avoid malfunctions beyond maintaining quality levels desired, it is important to take into account the widest possible context information that cause either positive or negative effects around services and entities involved in their provisioning and consumption. In this paper, we propose an upper ontology for service provisioning and consumption from a service-centric perspective. Specifically, we focus on software services, although we could argue for more generic applications. The contribution is the analysis, evaluation and reuse of existing proposals on context models to identify the strengths and weaknesses of its current status as well as to identify contexts not yet considered, and consolidate an integrated view of these proposals. The ultimate intention is to provide a well-defined and consolidated infrastructure of context information as a common body of knowledge, that could be instantiated on variety of use cases, for example, to be instantiated by monitors as context information useful to be monitored, or to be used as context information that allows knowing which contexts affect a service when a user consumes it, among others.
This document provides an overview of a presentation on the future of AI given by Jim Spohrer from IBM. The presentation discusses IBM's past work in AI, current focus on open source technologies through CODAT, and vision for the future which includes solving problems related to trust, identity, and resilience as AI capabilities continue to advance. It also discusses different types of systems like information, physical symbol, service, and cognitive systems.
The document discusses the history and future of AI at IBM, from its early work with Nathan Rochester on physical symbol systems to its current focus on open source technologies and cognitive systems through its Center for Open Source Data and AI Technologies (CODAIT). It also covers IBM's view of service science as the study of evolving service system entities, their capabilities, constraints, rights, and responsibilities. The document provides context around IBM's past, present and future work in AI and how it relates to fields like computer science, chemistry, biology and service science.
This document summarizes a presentation by Jim Spohrer from IBM on open technology, innovation, and service system evolution. Some key points:
- Spohrer discusses the multidisciplinary nature of services and the need for service scientists to study increasingly service-dominated economies and societies.
- He outlines the evolution of complex systems from the physical to sociotechnical, and how disciplines have evolved to study and design increasingly complex systems, from physics to computer science to service science.
- Spohrer summarizes the development of service science and service-dominant logic as frameworks to study value co-creation within service systems, which are dynamic configurations of resources including people, organizations, information, and technology.
Service Systems Engineering in the Human-Centered AI Era
Online Event: October 17, 2022, 11am-5:00pm ET
NAE Event Link: https://www.nae.edu/281715/Service-Systems-Engineering-in-the-Era-of-HumanCentered-AI
Event Agenda Link: https://www.nae.edu/File.aspx?id=281720&v=d8f00309
ISSIP Blog Post with additional links: https://issip.org/service-systems-engineering-in-the-era-of-human-centered-ai/
Guest lecture for
Course: Front Lines on Adoption of Digital and AI-based Service Offerings
Course URL: https://www.nhh.no/en/courses/front-lines-on-adoption-of-digital-and-ai-based-services/
Prof Tor Andreassen LI URL: https://www.linkedin.com/in/tor-wallin-andreassen-1aa9031/
Naples forum solving service_science 20190605 v15ISSIP
This document provides an overview of a presentation given by Jim Spohrer on solving service science. Some key points:
- Spohrer discusses what it means to "solve" a discipline like service science, which involves understanding service systems.
- Other disciplines like artificial intelligence, economics, and law must also be solved to make progress on service science.
- Several books are mentioned that could help with solving service science, including ones about artificial intelligence, social justice, and multidisciplinary thinking.
- The presentation touches on service-dominant logic, the study of service systems as complex adaptive entities, and the goal of service science to understand and improve these systems.
2021004 jim spohrer alan hartman_retirement v3ISSIP
(1) The document discusses the future of artificial intelligence and service science in a post-pandemic society from a service science perspective. (2) It compares AI, which aims to automate human intelligence, to service science, which studies how systems like businesses and societies can transform and improve lives through cooperation. (3) The document outlines how service science views systems as evolving over time through running existing practices, transforming by adopting new practices, and innovating to create new practices.
This document outlines the emerging discipline of service science, management, engineering, and design (SSMED). It discusses how the growth of the global service economy has led to a dramatic increase in specialized service systems and interactions. However, surprisingly few university students study services or service systems. The document presents some of the key foundations of SSMED as a new interdisciplinary field aimed at understanding and innovating service systems. It explores theoretical concepts around the growth of services and value co-creation. It also discusses how SSMED relates to and can integrate multiple existing academic disciplines and professions.
This document discusses the future of artificial intelligence (AI) and intelligence augmentation (IA) from a service science perspective. It provides background on the speaker, Jim Spohrer, and his work in service science. The document outlines key concepts in service science including service systems, value co-creation, and the transdisciplinary nature of service science. It discusses how service science and open source AI both require trust to succeed. The document presents timelines showing how computing costs are decreasing exponentially and how this could impact productivity and GDP. It frames AI progress through open leaderboards and benchmarks. And it discusses how IA is a socio-technical extension of human capabilities that should lead to more responsible and capable people.
History of SSME (Service Science Mangement Engineering) forming, one of the early presentation to IBM Research at Yorktown Heights Watson Lab in New York
The document discusses several key topics related to service science:
1. It defines service science as the study of complex service systems, which are dynamic configurations of resources like people, technologies, organizations and information.
2. It emphasizes the importance of an interdisciplinary approach to service science research and education.
3. It calls for stakeholders in education, business, government and other areas to work together to advance service science and address gaps through funding research, developing skills and tools, and creating service innovation roadmaps.
1) The document discusses service science and its importance for universities. It provides definitions for key terms like service, service innovations, and service systems.
2) It notes the progress of service science, including the growth of courses, conferences, and publications in the field.
3) The document outlines important future trends for service science, such as the need for better frameworks, theories, and tools to study service systems.
5th Global Value Creation Conference https://smartconf.jp/content/gccv5th/program
The Future of Creating Value with AI: A Service Science Perspective
This talk explores the future of Artificial Intelligence (AI) for creating value. AI, both service robot automation and service augmentation platforms, are poised to improve service productivity, quality, compliance, sustainable innovation, resilience, equity and inclusion for under-served populations. Service is defined as the application of knowledge for the benefit of another. Service innovations improve interaction and change processes in business and society. However, to achieve these outcomes and create value with AI, responsible actors (people, businesses, governments, universities) must learn to invest wisely in becoming better future versions of themselves augmented by their AI digital twin. Learning to invest systematically can accelerate both value cocreation and capability coelevation in a virtual cycle of responsible actor interaction and change processes. However, great risks must also be avoided.
Serviceology 2013: Fundamental Concepts and Premises of Service ScienceStephen Kwan
The document discusses the development of Service Science from SSME to including design, art, and public policy. It proposes a set of 10 Fundamental Concepts of Service Science like ecology, entities, and outcomes to facilitate communication across disciplines studying services. A preliminary set of Fundamental Premises is presented to allow reasoning about interactions between the concepts. The developments are reflected upon pioneering work in Service-Dominant Logic and its Fundamental Premises. There is seen to be a convergence between Service Science and Service-Dominant Logic with more work needed in the area of networks and systems.
T-shaped skills: T6 is about the evolution of the T-shaped model over time, from T1 to T2 to T3 to now T6. The number refers to how many categories for breath and depth.
1. The document discusses service science and its focus on service systems and value co-creation. 2. It outlines foundational premises of service science including the configuration of resources and calculation of value from multiple stakeholder perspectives. 3. Future directions discussed include challenges of local optimization not equaling global optimization and real-world problems not equating to single discipline problems.
The document discusses linking service science with policymaking to enable desirable societal outcomes. It outlines that service science studies value co-creation interactions in service systems and that policies can shape rules and incentives to connect interactions with outcomes. The document also provides background on key concepts in service science like the service-dominant logic and definitions of service systems.
an introduction to service science that provides the basics of: service system thinking, service system dynamics, service system re-design examples, and tries to answer the "why questions" - end notes include the birth of service science, discussion of advanced manufacturing, outsourcing, sustainability, as well as ways to learn more about service science
The document discusses service science and its importance for building a smarter planet. It outlines how the world's economies and jobs have shifted towards services. Service science aims to study complex service systems and improve customer-provider interactions. The document discusses key concepts in service science like service systems, value co-creation, and a systems-disciplines matrix. It emphasizes the need for a skilled multi-disciplinary workforce and highlights opportunities in areas that improve quality of life.
This document provides a summary of Jim Spohrer's presentation on "Service in the AI Era: Science, Logic, and Architecture Perspectives" given to the 2022 UC Merced Service Science class. The presentation covered several key topics:
1) It discussed two approaches to the future - artificial intelligence which focuses on building capable machine systems, and service science which studies transformation and building smarter socio-technical systems.
2) It presented a conceptual framework for service science that views it as a transdisciplinary approach to studying service systems.
3) It emphasized that as artificial intelligence and digital technologies continue advancing, they require investing wisely to improve service and understanding through better science, logics, and architectures.
AI and Education 20240327 v16 for Northeastern.pptxISSIP
Prof. Mark L. Miller (https://www.linkedin.com/in/mlmiller751/), Northeastern University, class on AI and Education
Speaker: Jim Spohrer (https://www.linkedin.com/in/spohrer/)
===
Speaker: Dr. Jim Spohrer, retired Apple and IBM executive, currently Board of Directors for ISSIP.org (International Society of Service Innovation Professionals).
Title: AI and Education: A Historical Perspective and Possible Future Directions
Abstract: This talk will briefly survey my 50 years working in the area of AI & Education. At MIT (1974- 1978), MIT's summer EXPLO schools for AI and entrepreneurship classes. At Verbex (1978-1982), speech recognition, language models, early generative AI. At Yale (1982-1989), MARCEL, a generate- test-and-debug architecture and student model of programming bugs. At Apple (1989-1998), from content (SK8) to community (EOE) to context (WorldBoard). At IBM (1999 - 2021), service science and open source AI. At ISSIP (2021-present), generative AI and digital twins.
Bio:Jim’s Bio (142 words):
Jim Spohrer is a student of service science and open-source, trusted AI. He is a retired industry executive (Apple, IBM), who is a member of the Board of Directors of the non-profit International Society of Service Innovation Professionals (ISSIP). At IBM, he served as Director for Open Source AI/Data, Global University Programs, IBM Almaden Service Research, and CTO IBM Venture Capital Relations Group. At Apple, he achieved Distinguished Engineer Scientist Technologist (DEST) for authoring and learning platforms. After MIT (BS/Physics), he developed speech recognition systems at Verbex (Exxon), then Yale (PhD/Computer Science AI). With over ninety publications and nine patents, awards include AMA ServSIG Christopher Lovelock Career Contributions to the Service Discipline, Evert Gummesson Service Research, Vargo-Lusch Service-Dominant Logic, Daniel Berg Service Systems, and PICMET Fellow for advancing service science. In 2021, Jim was appointed a UIDP Senior Fellow (University-Industry Demonstration Partnership).
Readings:Apple's ATG Authoring Tools:
URL: https://dl.acm.org/doi/pdf/10.1145/279044.279173 Blog: WorldBoard
URL: https://service-science.info/archives/2060 Blog: Reflecting on Generative AI and Digital Twins
URL: https://service-science.info/archives/6521 Book: Service in the AI Era
Attached: Pages 46-54.Video: Speech Recognition (History)
URL: https://youtu.be/G9z4VAsw_kw
Thanks, -Jim
--Jim Spohrer, PhDBoard of Directors, ISSIP (International Society of Service Innovation Professionals) Board of Directors, ServCollab ("Serving Humanity Through Collaboration")Senior Fellow, UIDP ("Strengthening University-Industry Partnerships")Retired Industry Executive (Apple, IBM)
March 20, 2024
Host Ganesan Narayanasamy (https://www.linkedin.com/in/ganesannarayanasamy/)
Uploaded here:
===
Event 20230320
https://www.linkedin.com/posts/ganesannarayanasamy_productnation-semiconductorproductnation-activity-7174119132114620418-jvpx
Themed Shaping a Sustainable $1 Trillion Era, semicondynamics.org 2024 will gather industry experts on March 20th at Milpitas, California , for insights into the latest trends and innovations Accelerating AI with Semiconductor RTL Front end services and workforce development. The event will feature keynotes from the Semiconductor ecosystem, academia and Industries.
March 20, 2024
Host Ganesan Narayanasamy (https://www.linkedin.com/in/ganesannarayanasamy/)
Uploaded here:
===
Event 20230320
https://www.linkedin.com/posts/ganesannarayanasamy_productnation-semiconductorproductnation-activity-7174119132114620418-jvpx
Themed Shaping a Sustainable $1 Trillion Era, semicondynamics.org 2024 will gather industry experts on March 20th at Milpitas, California , for insights into the latest trends and innovations Accelerating AI with Semiconductor RTL Front end services and workforce development. The event will feature keynotes from the Semiconductor ecosystem, academia and Industries.
Jim Spohrer is an advisor to industry, academia, governments, startups and non-profits on topics of AI upskilling, innovation strategy, and win-win service in the AI era. He is a retired IBM executive and was previously the director of IBM's open-source AI developer ecosystem effort. In this talk, Spohrer discusses topics such as how to keep up with accelerating change, verifying results from generative AI, and understanding how generative AI works through concepts like monkeys at typewriters in high dimensional spaces. He emphasizes balancing hype with realism and doing work alongside gaining knowledge.
This document contains notes from a presentation by Jim Spohrer on leadership, career experiences, and technology topics. The presentation covers collaborating with others, teamwork practices, storytelling, communication skills, leadership habits and mindsets. It includes links to Spohrer's online profiles and resources. Tables provide estimates of increasing GDP per employee over time and a timeline of Spohrer's career highlights and accomplishments in the fields of service science and artificial intelligence.
It my pleasure to be with you all today – thanks to my host for the opportunity to speak with you all today.
Host: Leonard Walletzky <qwalletz@fi.muni.cz> (https://www.linkedin.com/in/leonardwalletzky/) +420 549 49 7690
Google Scholar: https://scholar.google.com/citations?user=aUvbsmwAAAAJ&hl=cs
Katrina Motkova (https://www.linkedin.com/in/kateřina-moťková-mba-a964a3175/en/?originalSubdomain=cz)
Speaker: Jim Spohrer <spohrer@gmail.com> (https://www.linkedin.com/in/spohrer/) +1-408-829-3112
I am Jim Spohrer, a retired Apple and IBM Executive, and currently a UIDP Senior Fellow, on the Board of Directors of ISSIP and ServCollab.
I am retired, meaning my primary activities are family-oriented – families are the oldest and most important type of service systems
I volunteer to help non-profits, mentor students, professionals, and retiree (some in retirement communities where the average age is 85) on AI & service science
My hobbies are hiking, reading, programming, and building my AI digital twin and humanoid robots for maintaining farms and farming equipment.
My hobbies are also trying to understand as much as I can about the system called the universe and mult-verse, and robots to rapidly rebuild civilization including themselves from scratch.
2001 - Nonzero: The Logic of Human Desitiny (Wright) - https://en.wikipedia.org/wiki/Nonzero:_The_Logic_of_Human_Destiny
2015 - Geek Heresy: Rescuing Social Change from the Cult of Technology - https://www.amazon.com/Geek-Heresy-Rescuing-Social-Technology/dp/161039528X
2021 - Humankind: A Hopeful History (Bregman) - https://en.wikipedia.org/wiki/Humankind:_A_Hopeful_History
Humankind - https://www.amazon.com/Humankind-Hopeful-History-Rutger-Bregman/dp/0316418536
Humankind Book Review - https://service-science.info/archives/5654
2022 - Service in the AI Era: Science, Logic, and Architecture Perspectives (2022) by Spohrer, Maglio, Vargo, Warg - https://www.amazon.com/Service-AI-Era-Architecture-Perspectives/dp/1637423039
2023 - Design for a Better World: Meaningful, Sustainable, Humanity-Centered (2023) by Don Norman - https://www.amazon.com/Design-Better-World-Meaningful-Sustainable/dp/0262047950/
It my pleasure to be with you all today – thanks to my host for the opportunity to speak with you all today.
Host: Leonard Walletzky <qwalletz@fi.muni.cz> (https://www.linkedin.com/in/leonardwalletzky/) +420 549 49 7690
Google Scholar: https://scholar.google.com/citations?user=aUvbsmwAAAAJ&hl=cs
Katrina Motkova (https://www.linkedin.com/in/kateřina-moťková-mba-a964a3175/en/?originalSubdomain=cz)
Speaker: Jim Spohrer <spohrer@gmail.com> (https://www.linkedin.com/in/spohrer/) +1-408-829-3112
I am Jim Spohrer, a retired Apple and IBM Executive, and currently a UIDP Senior Fellow, on the Board of Directors of ISSIP and ServCollab.
I am retired, meaning my primary activities are family-oriented – families are the oldest and most important type of service systems
I volunteer to help non-profits, mentor students, professionals, and retiree (some in retirement communities where the average age is 85) on AI & service science
My hobbies are hiking, reading, programming, and building my AI digital twin and humanoid robots for maintaining farms and farming equipment.
My hobbies are also trying to understand as much as I can about the system called the universe and mult-verse, and robots to rapidly rebuild civilization including themselves from scratch.
2001 - Nonzero: The Logic of Human Desitiny (Wright) - https://en.wikipedia.org/wiki/Nonzero:_The_Logic_of_Human_Destiny
2015 - Geek Heresy: Rescuing Social Change from the Cult of Technology - https://www.amazon.com/Geek-Heresy-Rescuing-Social-Technology/dp/161039528X
2021 - Humankind: A Hopeful History (Bregman) - https://en.wikipedia.org/wiki/Humankind:_A_Hopeful_History
Humankind - https://www.amazon.com/Humankind-Hopeful-History-Rutger-Bregman/dp/0316418536
Humankind Book Review - https://service-science.info/archives/5654
2022 - Service in the AI Era: Science, Logic, and Architecture Perspectives (2022) by Spohrer, Maglio, Vargo, Warg - https://www.amazon.com/Service-AI-Era-Architecture-Perspectives/dp/1637423039
2023 - Design for a Better World: Meaningful, Sustainable, Humanity-Centered (2023) by Don Norman - https://www.amazon.com/Design-Better-World-Meaningful-Sustainable/dp/0262047950/
Brno-IESS 20240206 v10 service science ai.pptxISSIP
It my pleasure to be with you all today – thanks to my host for the opportunity to speak with you all today.
Host: Leonard Walletzky <qwalletz@fi.muni.cz> (https://www.linkedin.com/in/leonardwalletzky/) +420 549 49 7690
Google Scholar: https://scholar.google.com/citations?user=aUvbsmwAAAAJ&hl=cs
Katrina Motkova (https://www.linkedin.com/in/kateřina-moťková-mba-a964a3175/en/?originalSubdomain=cz)
Speaker: Jim Spohrer <spohrer@gmail.com> (https://www.linkedin.com/in/spohrer/) +1-408-829-3112
NordicHouse 20240116 AI Quantum IFTF dfiscussionv7.pptxISSIP
Jim Spohrer presented on AI and quantum computing. He discussed the history of AI from the 1955 Dartmouth workshop to modern advances like AlphaGo, GPT-3, and DALL-E 2. Spohrer noted that computation costs have decreased exponentially over time, driving increases in knowledge worker productivity. He highlighted several experts and resources he follows to stay informed on AI capabilities and implications. Spohrer sees opportunities to improve learning and performance through advances in learning sciences, technology, lifelong learning, and early education. The talk addressed how generative AI works and challenges around verification.
20240104 HICSS Panel on AI and Legal Ethical 20240103 v7.pptxISSIP
20240103 HICSS Panel
Ethical and legal implications raised by Generative AI and Augmented Reality in the workplace.
Souren Paul - https://www.linkedin.com/in/souren-paul-a3bbaa5/
Event: https://kmeducationhub.de/hawaii-international-conference-on-system-sciences-hicss/
Congratulations to the organizers of the “Symposium for Celebrating 40 Years of Bayesian Learning in Speech and Language Processing” and to Prof. Chin-Hui Lee of Georgia Tech the Honorary Chair of the Symposium.
Thanks to Huck Yang (Amazon) for the invitation to record this short message.
Huck Yang
URL: https://www.linkedin.com/in/huckyang/
Event: https://bayesian40.github.io
Recording:
Slides:
URL: https://professionalschool.eitdigital.eu/generative-ai-essentials
Course on Generative Al
Description:
Generative AI is a world-changing power tool that is getting better by the day. So now is the time to get truly inspired, climb up the learning curve, and unleash more of your creative potential.
Learning Topics:
* Inspiration: What is Generative AI in the context of AI's history, present, and future
* Climbing Up: Ways to accelerate your learning trajectory
* Unleashing Creativity: Ways to stay future-ready in the AI era
What You'll Take Away:
By the end of this session, you'll understand the importance of upskilling with today's generative AI tools to get more work done, both faster and at higher quality, as well as some pitfalls to avoid, all within the broader context of the past, present, and future of Artificial Intelligence (AI) and Intelligence Augmentation (IA).
Learning Topics
Inspiration: What is Generative AI in the context of AI's history, present, and future.
Climbing Up: Ways to accelerate your learning trajectory.
Unleashing Creativity: Ways to stay future-ready in the AI era.
Deep dive into ChatGPT's features.
Techniques for basic and advanced prompting and real-world applications.
- Service science has progressed significantly in the past two decades since its inception in the early 2000s.
- However, there is still a long way to go to fully realize the potential of service science and its role in areas like upskilling with AI.
- Looking ahead, some of the biggest challenges will be upskilling entire nations with AI for digital transformation, while also decarbonizing nations through sustainable energy infrastructure - both accomplished through service-based business models.
Spohrer Open Innovation Reflections 20230911 v2.pptxISSIP
September 11, 2023
Berkeley Innovation Forum
Open Innovation Journey
Henry Chesbrough, Solomon Darwin, Jim Spohrer
https://corporateinnovation.berkeley.edu/wp-content/uploads/2023/07/BIF-Fall2023-7.28.23.pdf
Pre-Event: Monday, September 11, 2023 at The CITRIS Innovation Hub
UC Berkeley, 330 Sutardja Dai Hall, MC 1764
7:45pm - 8:30pm
8:45pm
Fireside Chat: The Open Innovation Journey - Moderated by Henry Chesbrough
Henry Chesbrough
Faculty Director, Garwood Center for Corporate Innovation, UC Berkeley
Olga Diamandis
Former Disney, Smuckers, Mattel, P&G Executive
Jim Spohrer
Former Exec: IBM, Distinguished Scientist at Apple, Director of IBM AI
Nitin Narkhede
General Manager, Emerging Technologies and Innovation, Wipro
Bus pick-up to Hotel Shattuck Plaza
Henry Chesbrough is a professor at the Haas Business School, UC Berkeley, and faculty director of the Garwood Center for Corporate Innovation. An internationally acclaimed author, Dr. Chesbrough’s Open Innovation concept was first introduced in his award-winning book, Open Innovation: The New Imperative for Creating and Profiting from Technology (2003). When he coined the term Open Innovation, he defined an approach that companies around the globe now use to innovate. Today, Chesbrough works directly with companies through Garwood’s programs to apply the principles of Open Innovation, and he continues to refine our understanding through his research and books.
Olga Diamandis is the senior manager at TE Connectivity. Previously, she served as principal technical architect at the Walt Disney Company. She also worked as principal scientst of innovation & knowledge management at The J.M. Smucker Company. Before that, she served as senior manager of Open Innovation at Mattel. She also has experience as a manager of global business development at Procter & Gamble, alongside a previous managerial role at Nestle.
Jim Spohrer previously served as IBM Director of Cognitive OpenTech - which includes open source AI/ML/DL - as well as director of IBM’s deep question-answering system Watson. Prior to that, he worked as a Distinguished Scientist in Learning Research at Apple Computer, Inc. where he developed SK8, Educational Object Economy - an open source learning object community - as well as WorldBoard which served as a vision for Planetary Augmented Reality system.
Nitin Narkhede is General Manager of Emerging Technologies and Innovation at Wipro Technologies. He is responsible for the development of new services and solutions based on emerging trends and technologies at Wipro. Nitin has been in the forefront of a number of technology and business model transitions during his 20 years of work at Wipro. Prior to his current assignment, he managed Wipro’s e-Business Solutions Practice in the Americas. Nitin has over 23 years of experience in the technology industry spanning IT strategy and planning, information systems and software product development, technology strategy and innovation management.
Host:
Bart Raynaud - https://www.linkedin.com/in/bart-raynaud-160a0318/
Title: AI: Past, Present, and Future
Abstract: In 1956, the term "Artificial Intelligence" was coined for a workshop at Dartmouth. Since then there has been waxing and waning enthusiasm and investment, so called "AI Winters" after hype, did not live up to reality. In late 2022, with the release of ChatGPT, and over 100 million users in just 60 days, there is a new wave of hype, investment, excitement, and increased fears of AI use by 'bad actors' for misinformation and other harms to society. What are the future trajectories as this technology is tamed and becomes routine? Are we about to enter a 'golden age' of service in business and society, as technology comes to the service sector, as it came to agriculture and manufacturing in the past?
Bio: Jim Spohrer is a retired industry executive (Apple, IBM). In the 1970's, after graduating MIT with a degree in physics, he worked at an AI startup doing speech recognition with mathematical models. In the 1980's, after completing his PhD in Computer Science/AI & Cognitive Science at Yale, he moved to California to join Apple and work on AI for Education. In the late 1990's, he joined IBM as CTO of the Venture Capital Relations group during the internet investment boom, and later started IBM Research's service research area, led IBM Global University Programs, and led IBM's open source AI efforts. Jim's most recent co-authored book, "Service in the AI Era" was published in late 2022.
This document provides an agenda and materials for a post-industrial forum on knowledge worker productivity hosted by Jim Spohrer at SRI. The document includes:
- An introduction and background on Jim Spohrer, a retired industry executive and UIDP senior fellow.
- An agenda for a discussion on knowledge worker productivity, including presentations on relevant books and topics like estimation frameworks.
- Materials and figures for estimating knowledge worker productivity over time based on metrics like computing power and GDP per employee in the US.
- Additional slides on AI progress milestones, types of AI models, and an overview of Jim Spohrer's areas of study and priorities around service science, artificial intelligence, and trust.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
1. A Service Science Perspective on
OpenTech Artificial Intelligence
Jim from IBM
Director, Measuring AI Progress Cognitive Opentech Group (MAP COG)
Frontiers in Service Conference, Austin, TX USA, Sept 8, 2018
http://slideshare.net/spohrer/frontiers-opentechai-20180908-v3
9/8/2018 (c) IBM MAP COG .| 1
3. Today’s talk
• AI at the peak of the hype cycle
• What’s really going on?
• Your data is becoming your AI… transformation
• Computer Science, AI, SD logic, Service Science
• Part 1: Solving AI
• Roadmap and Implications
• Open Technologies, Innovation
• Part 2: Service System Entity Evolution
• Better Building Blocks
• Trust and Resilience and Transformation
9/8/2018 IBM Code #OpenTechAI 3
“there is nothing as practical as a good abstraction.”
4. What is a biological cognitive system (entity)?
9/8/2018 Understanding Cognitive Systems 4
5. What is a digital cognitive system (entity)?
9/8/2018 Understanding Cognitive Systems 5
6. 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 computer 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
9/8/2018 (c) IBM MAP COG .| 6
7. 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.9/8/2018 (c) IBM MAP COG .| 7
8. 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).9/8/2018 (c) IBM MAP COG .| 8
9. Service Science the study of service system entities
9/8/2018 (c) IBM MAP COG .| 9
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
10. Service Science the study of service system entities
9/8/2018 (c) IBM MAP COG .| 10
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.
11. Service Science the study of service system entities
9/8/2018 (c) IBM MAP COG .| 11
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.
12. Service Science the study of service system entities
9/8/2018 (c) IBM MAP COG .| 12
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.
14. Disciplines and some of the key entities they study
9/8/2018 (c) IBM MAP COG .| 14
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.
15. 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.
9/8/2018 (c) IBM MAP COG .| 15
17. Smartphones pass entrance exams? When?
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 17
… when will
your smartphone
be able to take and
pass any online
course? And then
be your coach, so
you can pass too?
18. IBM-MIT $240M
over 10 year AI mission
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 18
19. 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?
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 19
21. 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
219/8/2018 (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
22. Timeline: GDP/Employee
9/8/2018 (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 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)
2015 2018 2021 2024 2027 2030 2033 2036
9/8/2018 (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 ->
24. Who is winning
9/8/2018 (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
9/8/2018 IBM #OpenTechAI 25
26. 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.
9/8/2018 IBM #OpenTechAI 26
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
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 27
28. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 28
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.)
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 29
30. Stakeholders
• Individuals
• Families
• Businesses and
other Organizations
• Industry Groups
• Regional
Governments:
• Cities
• States
• Nations
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 30
31. “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
32. 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.
9/8/2018 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.
9/8/2018 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."
9/8/2018 IBM Code #OpenTechAI 34
35. 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
9/8/2018 (c) IBM 2017, Cognitive Opentech Group 35
1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…
37. Jim from IBM – 20 years today!
9/8/2018 (c) IBM MAP COG .| 37
38. 10 million minutes of experience
9/8/2018 Understanding Cognitive Systems 38
39. 2 million minutes of experience
9/8/2018 Understanding Cognitive Systems 39
40. Hardware < Software < Data < Experience < Transformation
9/8/2018 Understanding Cognitive Systems 40
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
41. 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.
9/8/2018 41
Take free online cognitive classes today at cognitiveclass.ai
43. 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
9/8/2018 IBM Code #OpenTechAI 43
60. Trust: Two Communities
9/8/2018 IBM Code #OpenTechAI 60
Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?
61. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
9/8/2018 IBM Code #OpenTechAI 61
64. 9/8/2018 (c) IBM MAP COG .| 64
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:
67. 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.”
9/8/2018 (c) IBM MAP COG .| 67
68. Ruskin, Unto this last… five great service professions
Gandhi’s transformation into Gandhi
9/8/2018 (c) IBM MAP COG .| 68
so that on him falls, in great part, the responsibility for the kind of life they lead;
The lawyer, rather than countenance Injustice…
69. By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
9/8/2018 69
Empathy & Teamwork
sector
region/culture
discipline
Depth
Breadth
STEM
Liberal Arts