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 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.
- 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.
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.
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.
Government of Japan launched Data strategy on Dec 21, 2020.
This slide is a summary of the strategy.
The full paper is following link.
https://www.kantei.go.jp/jp/singi/it2/dgov/dai10/siryou_a.pdf
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.
The document discusses the potential benefits of open data for smart cities. It summarizes that open data can (1) deliver an estimated €40 billion boost to the EU economy annually, (2) become a tradable commodity that increases in value as more data is shared, and (3) help address challenges in smart cities related to transport, energy, education, communication, culture, and governance through an interlinked open data approach.
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.
- 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.
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.
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.
Government of Japan launched Data strategy on Dec 21, 2020.
This slide is a summary of the strategy.
The full paper is following link.
https://www.kantei.go.jp/jp/singi/it2/dgov/dai10/siryou_a.pdf
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.
The document discusses the potential benefits of open data for smart cities. It summarizes that open data can (1) deliver an estimated €40 billion boost to the EU economy annually, (2) become a tradable commodity that increases in value as more data is shared, and (3) help address challenges in smart cities related to transport, energy, education, communication, culture, and governance through an interlinked open data approach.
2020117 for calabria smart service_system 20201117 v2ISSIP
Service science is the study of service systems and value co-creation. A service system is a dynamic configuration of resources, including people, organizations, shared information, and technology, that are connected internally and externally through value propositions. Service science seeks to understand service systems, how they improve, and how they scale up. It is a specialization of systems science that accounts for value co-creation between entities as they interact. The goal of service science is to create a body of knowledge that can enable systematic service innovation.
Towards a Smart (City) Data Science. A case-based retrospective on policies, ...Enrico Daga
This document summarizes a presentation by Dr. Enrico Daga on smart city data science. It discusses several projects and initiatives in Milton Keynes, UK related to building infrastructure for smart city applications and research. This includes a data hub cataloguing city datasets, tools to support data science education and pilots with local businesses. It also covers work on privacy-aware systems, policy propagation in data flows, and using city data to power simulations and games.
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.
(old version)2020-12-21 data strategy in JapanKenji Hiramoto
(This is an old version)
I edited some sentences.
Please check the following revised version.
https://www2.slideshare.net/hiramoto/20201221-data-strategy-in-japan
Organizational and social impact of Artificial IntelligenceAJHSSR Journal
ABSTRACT: Modern information technologies and the advent of machines powered by artificial intelligence
(AI) have already strongly influenced the world of work in the 21st century. Computers, algorithms and
software simplify everyday tasks, and it is impossible to imagine how most of our life could be managed
without them. However, the emergence of artificial intelligence (AI) has its own advantages and disadvantages.
With the recent boom in big data and the continuous need for innovation, Artificial Intelligence is carving out a
bigger place in our society. Through its computer-based capabilities, it brings new possibilities to tackle many
issues within organizations. It also raises new challenges about its use and limits. Over the past few years,
developments in artificial intelligence (AI) have captured the imagination of tens of millions of people around
the world. Both the sophistication and the societal impact of intelligent technologies are set to increase
substantially in the coming years and decades, as are the associated policy challenges. This includes how
government agencies protect consumers and citizens from unethical, unsafe or unsound use of AI systems
employed in critical contexts such as health, finance, or employment by companies or individuals. Some of the
impacts of AI on organizations include: power shifts; reassignment of decision making responsibility; cost
reduction and enhanced service; and personnel shifts and downsizing as some jobs are done Robots. This paper
aims to provide a better understanding of the organizational and social impact of Artificial Intelligence in the
organizational decision making process. Unemployment is not the same as leisure, and there are deep links
between unemployment and unhappiness, self-doubt, and isolation understanding what policies and norms can
break these links could significantly improve the median quality of life. Empirical and theoretical research on AI
came up with discussions and findings that with time the negative perceptions will be addressed.
KEYWORDS: Artificial Intelligence, Algorithms, Decision making, Robots, Technologies.
Big Data an opportunity for friendly cities
Lorena Pocatilu
The Bucharest University of Economic Studies, Economic Informatics and Cybernetics Department Bucharest, Romania
lorena.pocatilu@ie.ase.ro
The use of big data solutions is the biggest opportunity for friendly cities in our years. This happened because we need to access, process and use different data type very fast and big data solutions offers these facilities.
The concept of big data which creating value is not new, and in our age the effective use of da-ta is to becoming the basis element of competition. Cities of our time have always wanted to use correctly and to the real value the information and knowledge in order to make better, smarter, real time, fact-based decisions, this necessity of correct knowledge has fueled the growth of using big data. In this case the big data concept is the most important support for cities’ evolutions. In the world, many cities who are agree that this is true aren't sure how to make the most of it implementation. After a literature review analysis, this paper presents the steps for implement the solutions of big data in the core area of cities.
More and more companies from business and administration are agree that big data is an op-portunity for friendly cities. This paper highlights with examples from all over the world that those areas which use big data have good results. The areas that succeed aren't the ones who have the most data, but the ones who use it best. Big data will fundamentally change the way cities compete and operate. Companies from business and administration that invest in and successfully derive value from their data will have a distinct advantage over their competitors — a performance gap that will continue to grow as more relevant data is generated, emerging technologies and digital channels offer better acquisition and delivery mechanisms, and the technologies that enable faster, easier data analysis continue to develop.
Investment and development are the keys of our cities. This paper presents the impact of the big data solutions and how can use all the facility of this in friendly cities development. Having in view the researches in this area the cities development using big data in accordance with sustainability principles has become an opportunity of this century. An efficient access and use of huge quantity of data through big data solutions and the involvement of citizens in the initi-atives of local communities are the key elements that a city can use to achieve a harmonious development.
The major research of this approach is centered on the necessity of use big data for friendly cities.
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.
From eGov 2.0 to eGov 3.0: The Research Agendasamossummit
The research agenda of the eGov area will be discussed in this session, focusing on innovative ideas and directions for its advancement from the eGov 2.0 to a new eGov 3.0 paradigm.
Yannis Charalabidis, University of the Aegean, Greece
This document discusses how human beings can play an important role in making sense of big data beyond just visualization. It presents a case study where students transformed a large dataset into a visual language and "text" that could be interpreted. The document argues that current sense-making models are too technology-centric and that meaningful interpretation emerges from collaboration between algorithms, data, and human beings. Human perceptual abilities allow them to recognize patterns where computers see only numbers.
THE CRITICAL SUCCESS FACTORS FOR BIG DATA ADOPTION IN GOVERNMENTIAEME Publication
Over the past decade, governments around the world have been trying to take
advantage of Big Data technology to improve public services with citizens. The
adoption of Big Data has increased in most countries, but at the same time, the rate of
successful adoption and management varies from one country to another. A systematic
review of the literature (SLR) was carried out to identify the critical success factors
(CSF) for the adoption of big data in the government. It includes the critical success
factor of the adoption of Big Data in the government in the last 10 years. It presents
the general trends that examine 183 journals and numerous literary works related to
government operations, the provision of public services, citizen participation, decision
making and policies, and governance reform. We selected 90 journals and found 11
classification factors that refer to the successions of a Big Data adoption in the
government
This document discusses smart analytics and big data. It begins by defining the 5 V's of big data: volume, velocity, variety, veracity, and value. It then discusses how analytics can provide competitive advantages for organizations and how the percentage of organizations realizing this advantage has increased significantly in recent years. The rest of the document discusses IBM's work in areas like smarter cities, smart service systems, and T-shaped professionals who have expertise across multiple domains. It provides examples of the large amounts of data being generated and concludes with a discussion of modeling holistic service systems at different levels from an individual to the entire planet.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
This document provides an overview of a book on enabling data ecosystems for intelligent systems. It discusses key concepts like digital twins, physical-cyber-social computing, and mass personalization. It also outlines the architecture of a real-time linked dataspace platform that supports pay-as-you-go data integration and sharing for applications and intelligent systems. The platform is designed to handle streaming data from sensors and integrate it with contextual data sources using approximate semantic matching techniques.
Crowdsourcing Approaches for Smart City Open Data ManagementEdward Curry
A wide-scale bottom-up approach to the creation and management of open data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. This talk explores how to involving a wide community of users in collaborative management of open data activities within a Smart City. The talk discusses how crowdsourcing techniques can be applied within a Smart City context using crowdsourcing and human computation platforms such as Amazon Mechanical Turk, Mobile Works, and Crowd Flower.
How is Data Made? From Dataset Literacy to Data Infrastructure LiteracyJonathan Gray
1. The document discusses the need to go beyond data literacy focused only on reading and using datasets, and instead develop "data infrastructure literacy" to understand how datasets are produced through complex socio-technical systems.
2. It argues for understanding data infrastructures as the elaborate systems that measure and capture information, including laws, software, and institutions that generate datasets.
3. The document calls for "democratizing data infrastructures" so civil society can shape what information is collected and how, not just access existing datasets, in order to address issues like beneficial ownership, measurement of undercounted groups, and global challenges.
The document discusses IBM's efforts to promote service innovation through its University Programs (IBM UP). It outlines IBM's work in several areas: conducting surveys on service science through ISSIP.org; developing a certification for "T-shaped Service Innovators"; creating a model of service ecology; and envisioning future smarter service systems. It also provides examples of IBM's collaborations with universities on projects involving Watson, cybersecurity, and blockchain technologies. The overall aim is to link service innovators around the world and help bridge professional societies to promote service innovations.
Artificial Intelligence in Service SystemsNiklas Kühl
While there are impactful insights on how AI can solve isolated business problems, there is a gap of insights for AI applications within systems and business networks. To our current understanding, this is mostly due to IP preservation and technical issues (data volume, robustness, distributed sources). In this talk, I will highlight a few possibilities on how to tackle these barriers.
EDF2013: Invited talk Florian Bauer: Unleashing climate and energy knowledge ...European Data Forum
Invited talk Florian Bauer, Operations & IT Director REEEP, at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Unleashing climate and energy knowledge with Linked Open Data and consistent terminology
This document discusses lessons learned from service design projects in Japan. It outlines key points for successful service design, including having a good team that understands user needs, thinking from end-to-end rather than individual departments, keeping processes open and agile, and maintaining a consistent vision. It also identifies challenges such as gaining stakeholder consent and ensuring data interoperability between organizations. Finally, it argues for the importance of transparency through a service design scorecard to continually evaluate and improve existing services over time.
Towards Unified and Native Enrichment in Event Processing SystemsEdward Curry
Events are encapsulated pieces of information that flow from one event agent to another. In order to process an event, additional information that is external to the event is often needed. This is achieved using a process called event enrichment. Current approaches to event enrichment are external to event processing engines and are handled by specialized agents. Within large-scale environments with high heterogeneity among events, the enrichment process may become difficult to maintain. This paper examines event enrichment in terms of information completeness and presents a unified model for event enrichment that takes place natively within the event processing engine. The paper describes the requirements of event enrichment and highlights its challenges such as finding enrichment sources, retrieval of information items, finding complementary information and its fusion with events. It then details an instantiation of the model using Semantic Web and Linked Data technologies. Enrichment is realised by dynamically guiding a spreading activation algorithm in a Linked Data graph. Multiple spreading activation strategies have been evaluated on a set of Wikipedia events and experimentation shows the viability of the approach.
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/
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.
2020117 for calabria smart service_system 20201117 v2ISSIP
Service science is the study of service systems and value co-creation. A service system is a dynamic configuration of resources, including people, organizations, shared information, and technology, that are connected internally and externally through value propositions. Service science seeks to understand service systems, how they improve, and how they scale up. It is a specialization of systems science that accounts for value co-creation between entities as they interact. The goal of service science is to create a body of knowledge that can enable systematic service innovation.
Towards a Smart (City) Data Science. A case-based retrospective on policies, ...Enrico Daga
This document summarizes a presentation by Dr. Enrico Daga on smart city data science. It discusses several projects and initiatives in Milton Keynes, UK related to building infrastructure for smart city applications and research. This includes a data hub cataloguing city datasets, tools to support data science education and pilots with local businesses. It also covers work on privacy-aware systems, policy propagation in data flows, and using city data to power simulations and games.
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.
(old version)2020-12-21 data strategy in JapanKenji Hiramoto
(This is an old version)
I edited some sentences.
Please check the following revised version.
https://www2.slideshare.net/hiramoto/20201221-data-strategy-in-japan
Organizational and social impact of Artificial IntelligenceAJHSSR Journal
ABSTRACT: Modern information technologies and the advent of machines powered by artificial intelligence
(AI) have already strongly influenced the world of work in the 21st century. Computers, algorithms and
software simplify everyday tasks, and it is impossible to imagine how most of our life could be managed
without them. However, the emergence of artificial intelligence (AI) has its own advantages and disadvantages.
With the recent boom in big data and the continuous need for innovation, Artificial Intelligence is carving out a
bigger place in our society. Through its computer-based capabilities, it brings new possibilities to tackle many
issues within organizations. It also raises new challenges about its use and limits. Over the past few years,
developments in artificial intelligence (AI) have captured the imagination of tens of millions of people around
the world. Both the sophistication and the societal impact of intelligent technologies are set to increase
substantially in the coming years and decades, as are the associated policy challenges. This includes how
government agencies protect consumers and citizens from unethical, unsafe or unsound use of AI systems
employed in critical contexts such as health, finance, or employment by companies or individuals. Some of the
impacts of AI on organizations include: power shifts; reassignment of decision making responsibility; cost
reduction and enhanced service; and personnel shifts and downsizing as some jobs are done Robots. This paper
aims to provide a better understanding of the organizational and social impact of Artificial Intelligence in the
organizational decision making process. Unemployment is not the same as leisure, and there are deep links
between unemployment and unhappiness, self-doubt, and isolation understanding what policies and norms can
break these links could significantly improve the median quality of life. Empirical and theoretical research on AI
came up with discussions and findings that with time the negative perceptions will be addressed.
KEYWORDS: Artificial Intelligence, Algorithms, Decision making, Robots, Technologies.
Big Data an opportunity for friendly cities
Lorena Pocatilu
The Bucharest University of Economic Studies, Economic Informatics and Cybernetics Department Bucharest, Romania
lorena.pocatilu@ie.ase.ro
The use of big data solutions is the biggest opportunity for friendly cities in our years. This happened because we need to access, process and use different data type very fast and big data solutions offers these facilities.
The concept of big data which creating value is not new, and in our age the effective use of da-ta is to becoming the basis element of competition. Cities of our time have always wanted to use correctly and to the real value the information and knowledge in order to make better, smarter, real time, fact-based decisions, this necessity of correct knowledge has fueled the growth of using big data. In this case the big data concept is the most important support for cities’ evolutions. In the world, many cities who are agree that this is true aren't sure how to make the most of it implementation. After a literature review analysis, this paper presents the steps for implement the solutions of big data in the core area of cities.
More and more companies from business and administration are agree that big data is an op-portunity for friendly cities. This paper highlights with examples from all over the world that those areas which use big data have good results. The areas that succeed aren't the ones who have the most data, but the ones who use it best. Big data will fundamentally change the way cities compete and operate. Companies from business and administration that invest in and successfully derive value from their data will have a distinct advantage over their competitors — a performance gap that will continue to grow as more relevant data is generated, emerging technologies and digital channels offer better acquisition and delivery mechanisms, and the technologies that enable faster, easier data analysis continue to develop.
Investment and development are the keys of our cities. This paper presents the impact of the big data solutions and how can use all the facility of this in friendly cities development. Having in view the researches in this area the cities development using big data in accordance with sustainability principles has become an opportunity of this century. An efficient access and use of huge quantity of data through big data solutions and the involvement of citizens in the initi-atives of local communities are the key elements that a city can use to achieve a harmonious development.
The major research of this approach is centered on the necessity of use big data for friendly cities.
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.
From eGov 2.0 to eGov 3.0: The Research Agendasamossummit
The research agenda of the eGov area will be discussed in this session, focusing on innovative ideas and directions for its advancement from the eGov 2.0 to a new eGov 3.0 paradigm.
Yannis Charalabidis, University of the Aegean, Greece
This document discusses how human beings can play an important role in making sense of big data beyond just visualization. It presents a case study where students transformed a large dataset into a visual language and "text" that could be interpreted. The document argues that current sense-making models are too technology-centric and that meaningful interpretation emerges from collaboration between algorithms, data, and human beings. Human perceptual abilities allow them to recognize patterns where computers see only numbers.
THE CRITICAL SUCCESS FACTORS FOR BIG DATA ADOPTION IN GOVERNMENTIAEME Publication
Over the past decade, governments around the world have been trying to take
advantage of Big Data technology to improve public services with citizens. The
adoption of Big Data has increased in most countries, but at the same time, the rate of
successful adoption and management varies from one country to another. A systematic
review of the literature (SLR) was carried out to identify the critical success factors
(CSF) for the adoption of big data in the government. It includes the critical success
factor of the adoption of Big Data in the government in the last 10 years. It presents
the general trends that examine 183 journals and numerous literary works related to
government operations, the provision of public services, citizen participation, decision
making and policies, and governance reform. We selected 90 journals and found 11
classification factors that refer to the successions of a Big Data adoption in the
government
This document discusses smart analytics and big data. It begins by defining the 5 V's of big data: volume, velocity, variety, veracity, and value. It then discusses how analytics can provide competitive advantages for organizations and how the percentage of organizations realizing this advantage has increased significantly in recent years. The rest of the document discusses IBM's work in areas like smarter cities, smart service systems, and T-shaped professionals who have expertise across multiple domains. It provides examples of the large amounts of data being generated and concludes with a discussion of modeling holistic service systems at different levels from an individual to the entire planet.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
This document provides an overview of a book on enabling data ecosystems for intelligent systems. It discusses key concepts like digital twins, physical-cyber-social computing, and mass personalization. It also outlines the architecture of a real-time linked dataspace platform that supports pay-as-you-go data integration and sharing for applications and intelligent systems. The platform is designed to handle streaming data from sensors and integrate it with contextual data sources using approximate semantic matching techniques.
Crowdsourcing Approaches for Smart City Open Data ManagementEdward Curry
A wide-scale bottom-up approach to the creation and management of open data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. This talk explores how to involving a wide community of users in collaborative management of open data activities within a Smart City. The talk discusses how crowdsourcing techniques can be applied within a Smart City context using crowdsourcing and human computation platforms such as Amazon Mechanical Turk, Mobile Works, and Crowd Flower.
How is Data Made? From Dataset Literacy to Data Infrastructure LiteracyJonathan Gray
1. The document discusses the need to go beyond data literacy focused only on reading and using datasets, and instead develop "data infrastructure literacy" to understand how datasets are produced through complex socio-technical systems.
2. It argues for understanding data infrastructures as the elaborate systems that measure and capture information, including laws, software, and institutions that generate datasets.
3. The document calls for "democratizing data infrastructures" so civil society can shape what information is collected and how, not just access existing datasets, in order to address issues like beneficial ownership, measurement of undercounted groups, and global challenges.
The document discusses IBM's efforts to promote service innovation through its University Programs (IBM UP). It outlines IBM's work in several areas: conducting surveys on service science through ISSIP.org; developing a certification for "T-shaped Service Innovators"; creating a model of service ecology; and envisioning future smarter service systems. It also provides examples of IBM's collaborations with universities on projects involving Watson, cybersecurity, and blockchain technologies. The overall aim is to link service innovators around the world and help bridge professional societies to promote service innovations.
Artificial Intelligence in Service SystemsNiklas Kühl
While there are impactful insights on how AI can solve isolated business problems, there is a gap of insights for AI applications within systems and business networks. To our current understanding, this is mostly due to IP preservation and technical issues (data volume, robustness, distributed sources). In this talk, I will highlight a few possibilities on how to tackle these barriers.
EDF2013: Invited talk Florian Bauer: Unleashing climate and energy knowledge ...European Data Forum
Invited talk Florian Bauer, Operations & IT Director REEEP, at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Unleashing climate and energy knowledge with Linked Open Data and consistent terminology
This document discusses lessons learned from service design projects in Japan. It outlines key points for successful service design, including having a good team that understands user needs, thinking from end-to-end rather than individual departments, keeping processes open and agile, and maintaining a consistent vision. It also identifies challenges such as gaining stakeholder consent and ensuring data interoperability between organizations. Finally, it argues for the importance of transparency through a service design scorecard to continually evaluate and improve existing services over time.
Towards Unified and Native Enrichment in Event Processing SystemsEdward Curry
Events are encapsulated pieces of information that flow from one event agent to another. In order to process an event, additional information that is external to the event is often needed. This is achieved using a process called event enrichment. Current approaches to event enrichment are external to event processing engines and are handled by specialized agents. Within large-scale environments with high heterogeneity among events, the enrichment process may become difficult to maintain. This paper examines event enrichment in terms of information completeness and presents a unified model for event enrichment that takes place natively within the event processing engine. The paper describes the requirements of event enrichment and highlights its challenges such as finding enrichment sources, retrieval of information items, finding complementary information and its fusion with events. It then details an instantiation of the model using Semantic Web and Linked Data technologies. Enrichment is realised by dynamically guiding a spreading activation algorithm in a Linked Data graph. Multiple spreading activation strategies have been evaluated on a set of Wikipedia events and experimentation shows the viability of the approach.
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/
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.
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.
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/
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.
Magic Eraser allows users to easily remove unwanted objects and distractions from photos with just a few clicks. Craiyon is an AI image generator that lets users create new images from text prompts. Rytr is a voice assistant that helps schedule meetings, set reminders, and answer questions using natural language conversations. Thing Translator is a machine translation tool that can translate between over 100 languages with state-of-the-art neural models.
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.
Jim Spohrer provided closing remarks at the end of the IESS 2.2 event on February 18, 2022. He thanked the organizers and recommended the book "Humankind: A Hopeful History" by Rutger Bregman. Spohrer then listed questions related to evaluating real-world service systems and improvements, and encouraged applying for the ISSIP Excellence in Service Innovation Award for papers that can concisely answer these questions. Finally, Spohrer asked participants to share their most innovative service experience from 2021 and discussed how service innovations create win-win outcomes.
2030 inspire students to build it better 20150113 v3ISSIP
The document discusses key concepts in service science including:
- Service is defined as value co-creation interactions among service system entities.
- Service systems calculate value from the perspectives of multiple stakeholders such as customers, providers, authorities, and competitors.
- Service systems dynamically configure resources and reconfigure access rights to resources through value propositions.
- Interactions between service systems can create different types of outcomes such as win-win, lose-lose, lose-win, and win-lose scenarios.
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.
Jim Spohrer gave a presentation at Purdue University on service innovation in the AI era. Some key points from the presentation include:
- Service science is an emerging field that studies how responsible entities can transform through win-win interactions to improve quality of life, while AI aims to automate tasks performed by people.
- As computing costs decrease exponentially every 20 years due to Moore's law, AI capabilities will become much more accessible, with narrow tasks being solved by 2040 and broad human-level abilities by 2060.
- This will greatly increase productivity and GDP per employee over time if the benefits of AI are shared widely. However, there are also risks like job loss that need to be addressed.
2021020 jim spohrer ai for_good_conference future_of_ai v4ISSIP
Jim Spohrer serves on the Board of Directors of ISSIP and previously worked at IBM, where he directed various AI and service science initiatives. He discusses the future of AI, predicting that compute costs will decrease by a factor of 1000 every 20 years, enabling digital workers to become more capable and affordable. He presents a timeline and framework for benchmarking AI progress on open leaderboards to achieve human-level performance in various tasks over time. The best way to predict the future, he says, is to inspire students to build a better future.
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.
The document summarizes Jim Spohrer's presentation on service provision and technology in service systems from a service science perspective. Some key points:
- Better models are needed to understand the increasingly complex and interconnected world from various perspectives including physical, social, virtual, organizational, and technological.
- Human-centered design should evolve to humanity-centered design by focusing on entire ecosystems of people, living things, and the environment with a long-term systems view.
- Value co-creation is accelerated when large numbers of skilled people with advanced technology have a safe, ethical, and sustainable environment for interaction and change.
- Upskilling is moving from individual skills to skills extended with AI tools across knowledge areas
Jim Spohrer was invited to be a panelist for John Hagel's presentation at the Fall 2021 Berkeley Innovation Forum. Spohrer recommends the book "Humankind: A Hopeful History" by Rutger Bregman. He notes his experience at IBM of facing fears of product to service and proprietary to open source transformations, which led IBM to acquire Red Hat for $34B and spin off Kyndryl. Spohrer serves on the board of ISSIP.org and is a retired IBM executive focusing his studies on service science and open source AI, where trust is key.
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.
Host Santokh Badesha: https://www.linkedin.com/in/santokh-badesha-24b72916/
Recommended Readings (If Possible, Skim Before the Talk)
Patent: Management of Usage Costs of a Resource (IBM)
Jim Spohrer patent: Graphical Interface for Interacting Constrained Actors (Apple)
Jim Spohrer's Google Scholar Profile, includes open publications as well as patents
Apple's ATG Authoring Tools - Balancing Open and Proprietary Work
Forbes - Cognitive World
AI Magazine - Role of Open Source in AI
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 review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
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তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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1. AI at IBM: Past, Present, and Future
Jim from IBM (Jim Spohrer)
Director, Measuring AI Progress Cognitive Opentech Group (MAP COG)
See Center for Opensource Data and AI Technologies (CODAIT), http://codait.org
ICIS 2018, Doctoral Consortium, Asilomar, Pacific Grove, CA, USA, December 10, 2018
https://www.slideshare.net/spohrer/icis-20181210-v2
12/10/2018 (c) IBM MAP COG .| 1
5. Today’s talk
• Introduction
• AI at IBM: Past, Present, Future (Summary)
• Types of Systems: Information Systems, Physical
Symbol Systems, Service Systems, Cognitive
Systems, etc.
• AI at the peak of the hype cycle
• What’s really going on?
• Your data is becoming your AI… IA transformation
• Part 1: Solving AI: Leaderboards
• Roadmap and implications
• Open technologies, innovation
• Part 2: Solving IA: Better Building Blocks
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
12/10/2018 IBM Code #OpenTechAI 5
6. AI at IBM: Past (Nathan Rochester)
12/10/2018 (c) IBM MAP COG .| 6
14. Computer Science as Empirical Inquiry:
Symbols and Search
• "Computer science is the study of the phenomena surrounding computers. ... We
build computers and programs for many reasons. We build them to serve society
.... One of the fundamental contributions to knowledge of computer science has
been to explain, at a rather basic level, what symbols are. ... Symbols lie at the
root of intelligent action, which is, of course, the primary topic of artificial
intelligence. For that matter, it is a primary question for all of computer science.
For all information is processed by computers in the service of ends, and we
measure the intelligence of a system by its ability to achieve stated ends in the
face of variations, difficulties and complexities posed by the task environment…
A physical symbol system is a machine that produces through time an evolving
collection of symbol structures. Such a system exists in a world of objects wider
than just these symbolic expressions themselves. ”
• Tenth Turing Awards Lecture: Allen Newell and Herbert A. Simon, “Computer
Science as Empirical Inquiry: Symbols and Search,”Communications of the ACM.
vol. 19, No. 3, pp. 113-126, March,1976. Available online at:
• https://www.cs.utexas.edu/~kuipers/readings/Newell+Simon-cacm-76.pdf
12/10/2018 (c) IBM MAP COG .| 14
15. 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.12/10/2018 (c) IBM MAP COG .| 15
16. 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).12/10/2018 (c) IBM MAP COG .| 16
17. Service Science the study of service system entities
12/10/2018 (c) IBM MAP COG .| 17
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
18. Service Science the study of service system entities
12/10/2018 (c) IBM MAP COG .| 18
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.
19. Service Science the study of service system entities
12/10/2018 (c) IBM MAP COG .| 19
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.
20. Service Science the study of service system entities
12/10/2018 (c) IBM MAP COG .| 20
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.
22. 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.
12/10/2018 IBM #OpenTechAI 22
23. Disciplines and some of the key entities they study
12/10/2018 (c) IBM MAP COG .| 23
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.
24. 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.
12/10/2018 (c) IBM MAP COG .| 24
27. Smartphones pass entrance exams? When?
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 27
… when will
your smartphone
be able to take and
pass any online
course? And then
be your coach, so
you can pass too?
28. IBM-MIT $240M
over 10 year AI mission
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 28
29. Icons of AI Progress
• 1956: Dartmouth Conference
organized by:
• John McCarthy (Dartmouth, later
Stanford)
• Marvin Minsky (MIT)
• and two senior scientists:
• Claude Shannon (Bell Labs)
• Nathan Rochester (IBM)
• 1997: Deep Blue (IBM) - Chess
• 2011: Watson Jeopardy! (IBM)
• 2016: AlphaGo (Google DeepMinds)
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 29
30. 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?
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 30
32. 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
3212/10/2018 (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
33. Timeline: GDP/Employee
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 33
(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
34. 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
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 34
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
35. Who is winning
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 35
https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/
36. Robots by Country
• Industrial robots per 10,000 people by country
12/10/2018 IBM #OpenTechAI 36
34
41. 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
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 41
42. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 42
43. 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.)
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 43
44. Stakeholders = service system entities
• Individuals
• Families
• Businesses and
other Organizations
• Industry Groups and
Professional Associations
• Regional
Governments:
• Cities
• States
• Nations
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 44
“there is nothing as practical as a good abstraction” -> service science studies service system entities
45. “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
46. 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.
12/10/2018 IBM Code #OpenTechAI 46
47. 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.
12/10/2018 IBM Code #OpenTechAI 47
48. 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."
12/10/2018 IBM Code #OpenTechAI 48
50. 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
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 50
1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…
53. Cupertino Teens
• IBM Watson on Bluemix
12/10/2018 (c) IBM 2017, Cognitive Opentech Group 53
AI for NLP
entity identification
54. 10 million minutes of experience
12/10/2018 Understanding Cognitive Systems 54
55. 2 million minutes of experience
12/10/2018 Understanding Cognitive Systems 55
56. Hardware < Software < Data < Experience < Transformation
12/10/2018 Understanding Cognitive Systems 56
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
57. 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.
12/10/2018 57
Take free online cognitive classes today at cognitiveclass.ai
68. 12/10/2018 (c) IBM MAP COG .| 68
Microsoft acquiring GitHub $7.5B
2018 John Marks on Open Source
Models will run the world
Why SW is eating the world
73. 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
12/10/2018 IBM Code #OpenTechAI 73
75. Trust: Two Communities
12/10/2018 IBM Code #OpenTechAI 75
Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?
76. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
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82. 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.”
12/10/2018 (c) IBM MAP COG .| 82
83. Ruskin, Unto this last… five great service professions
Gandhi’s transformation into Gandhi
12/10/2018 (c) IBM MAP COG .| 83
so that on him falls, in great part, the responsibility for the kind of life they lead;
The lawyer, rather than countenance Injustice…
84. By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
12/10/2018 84
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