The document discusses cognitive information agents that can effectively learn from interactions with information to support complex human tasks. It describes an architecture that can automatically build and execute analytic solutions by specifying input/output types, information sources, and example datasets. It then trains, measures performance, analyzes errors, and proposes new learning tasks to iteratively improve. Example applications discussed are question answering systems for bioinformatics and decision support that can be automatically optimized for new datasets.
Towards Automatic Composition of Multicomponent Predictive SystemsManuel Martín
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
Towards Automatic Composition of Multicomponent Predictive SystemsManuel Martín
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
Data Management Lab: Data mapping exercise instructionsIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise instructions (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
An Overview of Data Mining Concepts Applied in Mathematical Techniquesijcoa
Data Mining and Knowledge Discovery in Databases (KDD) are rapidly evolving areas of research that contribute to several disciplines including Mathematics, Statistics, Pattern Recognition, Visualization, and Parallel Computing. This paper is projected to serve as an overview to the concepts of these rapidly evolving research and application areas. The basic concepts of these areas are outlined in this paper with some key ideas and motivate the importance of data mining concepts applied in Mathematical Techniques
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Leveraging Machine Learning Techniques Predictive Analytics for Knowledge Dis...Kevin Mader
Review the basic principles of predictive analytics.
Be exposed to some of the existing validation methodologies to test predictive models.
Understand how to incorporate radiology data sources (PACS, RIS, etc) into predictive modeling
Learn how to interpret results and make visualizations.
Data Management Lab: Data mapping exercise exampleIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise example (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
I am always looking for the next data science, machine learning and visualization challenge.
Here is a link to my up to date
resume:
https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
cv:
https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE
Data Interview and Data Management PlansJulie Goldman
LIS 532G: Midterm Project Presentation
Data Interview: What is it and how do you do it?
Preparation for Midterm Project to design and conduct a Data Interview, and to create a Data Management Plan.
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.
Data Management Lab: Data mapping exercise instructionsIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise instructions (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
An Overview of Data Mining Concepts Applied in Mathematical Techniquesijcoa
Data Mining and Knowledge Discovery in Databases (KDD) are rapidly evolving areas of research that contribute to several disciplines including Mathematics, Statistics, Pattern Recognition, Visualization, and Parallel Computing. This paper is projected to serve as an overview to the concepts of these rapidly evolving research and application areas. The basic concepts of these areas are outlined in this paper with some key ideas and motivate the importance of data mining concepts applied in Mathematical Techniques
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Leveraging Machine Learning Techniques Predictive Analytics for Knowledge Dis...Kevin Mader
Review the basic principles of predictive analytics.
Be exposed to some of the existing validation methodologies to test predictive models.
Understand how to incorporate radiology data sources (PACS, RIS, etc) into predictive modeling
Learn how to interpret results and make visualizations.
Data Management Lab: Data mapping exercise exampleIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise example (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
I am always looking for the next data science, machine learning and visualization challenge.
Here is a link to my up to date
resume:
https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
cv:
https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE
Data Interview and Data Management PlansJulie Goldman
LIS 532G: Midterm Project Presentation
Data Interview: What is it and how do you do it?
Preparation for Midterm Project to design and conduct a Data Interview, and to create a Data Management Plan.
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.
Institute for the Future (IFTF) Reconfiguring Reality Workshop, Palo Alto, CA Apache Opehnw OpenWhisk Linux Foundation Hyperledger Blockchain Artificial Intelligence Leaderboards
Top 5 MOST VIEWED LANGUAGE COMPUTING ARTICLE - International Journal on Natur...kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Top cited articles 2020 - Advanced Computational Intelligence: An Internation...aciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
Top cited computer science and engineering survey research articles from 2016...IJCSES Journal
The AIRCC's International Journal of Computer Science and Engineering Survey (IJCSES) is devoted to fields of Computer Science and Engineering surveys, tutorials and overviews. The IJCSES is a peer-reviewed scientific journal published in electronic form as well as print form. The journal will publish research surveys, tutorials and expository overviews in computer science and engineering. Articles from supplementary fields are welcome, as long as they are relevant to computer science and engineering.
J48 and JRIP Rules for E-Governance DataCSCJournals
Data are any facts, numbers, or text that can be processed by a computer. Data Mining is an analytic process which designed to explore data usually large amounts of data. Data Mining is often considered to be \"a blend of statistics. In this paper we have used two data mining techniques for discovering classification rules and generating a decision tree. These techniques are J48 and JRIP. Data mining tools WEKA is used in this paper.
Semantic Web technologies have been increasingly used as a tool for generating, organizing and personalizing e-learning content. In this presentation we will discuss and demonstrate an innovative approach to automated generation of computer-assisted assessment (CAA) from Semantic Web–based domain ontologies. The primary application domain of this work is in the automated assessment, and in particular, the development of intelligent CAA systems and question banks, but the ideas can be further generalized in the context of ontology engineering and evaluation. Prototype is implemented and available online at http://www.opensemcq.org
TOP READ NATURAL LANGUAGE COMPUTING ARTICLE 2020kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
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.
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
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.
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.
Talk at SRI for Post Industrial Forum
June 28, 2023 5pm-8pm
https://post-industrial.institute/forum/
Frode Odegard invitation
https://www.linkedin.com/in/odegard/
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
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Instructions for Submissions thorugh G- Classroom.pptx
Csi poster
1. Cognitive Information Agents:
Effective Learning in the Wild
Eric Nyberg, Professor &
Director, Master of Computational Data Science Program
“Architecture and applications to support intelligent, natural interaction
with all kinds of information in support of complex human tasks.”
• Extended Configuration Description (ECD):
Specification language to describe space of
analytic configurations for a task [1]
• Configuration Space Exploration (CSE):
Evaluation and selection of best-performing
analytic configuration(s) for a task [1,2,3]
• Phased Ranking Models: Rank outputs of
any multi-phase, multi-strategy system
based on the features of the derivation
paths that produced them [4]
• Automatic Source Expansion: Multi-faceted
machine reading to improve in-task
performance on a specific topic [5];
pioneered in Watson [6]; trained on
human-labeled relevance judgments
Architecture
Automatically build and
execute analytic solutions
Perform
1
Specification of required
analytic input/output types,
desired information sources,
example dataset.
Learn Reflect
Sample Applications
2
Train
Measure
Proactively evaluate
task performance,
analyze errors, propose
learning tasks
Bioinformatics Question Answering (BioQA): Document and passage retrieval which can be automatically
optimized for new datasets (applied to TREC Genomics, CLEF and and corporate sponsor datasets)[2,3]
Question Answering for Decision Support (QUADS): Automatically learn how to leverage QA systems to support
complex human decision-making with multiple decision factors (for gene target prediction and product ranking)[7]
Team
1. Garduno, E., Yang, Z., Maiberg, A., McCormack, C., Fang, Y. and E. Nyberg (2013). “CSE Framework: A UIMA-based Distributed System for Configuration Space Exploration”, Proceedings
of the 3rd Workshop on Unstructured Information Management Architecture, International Conference of the German Society for Computational Linguistics and Language Technology.
2. Yang, Z., Garduno, E., Fang, Y., Maiberg, A., McCormack, C. and Nyberg, E. (2013). “Building Optimal Information Systems Automatically: Configuration Space Exploration
for Biomedical Information Systems”, Proceedings of the ACM CIKM Conference.
3. A. Patel, Z. Yang, E. Nyberg, and T. Mitamura (2013). “Building an Optimal QA System Automatically Using Configuration Space Exploration for QA4MRE”, Proceedings of CLEF 2013.
4. Liu, R. and Nyberg, E. (2013). “A Phased Ranking Model for Question Answering”, Proceedings of the ACM Conference on Information and Knowledge Management.
5. Schlaefer, N. (2012). Statistical Source Expansion for Question Answering, Ph.D. Thesis, Language Technologies Institute, School of Computer Science, Carnegie Mellon University.
6. N. Schlaefer, J. Chu-Carroll, E. Nyberg, J. Fan, W. Zadrozny, D. Ferrucci (2011). “Statistical Source Expansion for Question Answering”, Proceedings of the ACM CIKM Conference.
7. Z. Yang, Y. Li, J. Cai, and E. Nyberg (2014). “QUADS: Question Answering for Decision Support”, Proceedings of the ACM SIGIR Conference on Information Retrieval, 2014.
3
Subject Matter Experts (SMEs)
Analyst’s
Information
Need
Configure
Optimize
Automatically execute
learning tasks, update
models, KBs, etc.
Machine Learning Agents
• Targeted Machine
Reading
• E-R Extraction
• Set Extension
• Clarification Dialogs
• Type/instance
knowledge
• Concept learning
Crowdsourcing
• Type instance labeling
• New feature extraction
• Relevance judgments
Rui Liu
Ph.D. Candidate
Phased Ranking Models
Leo Boytsov
Ph.D. Candidate
BioQA, Semantic Retrieval
Hugo Rodriguez
Ph.D. Candidate
Question Generation
Di Wang
Ph.D. Candidate
Source Expansion
Zi Yang
Ph.D. Candidate
CSE, BioQA, QUADS
Avner Maiberg
MLT Candidate
ECD, CSE, BioQA
Eric Nyberg
Team Leader
http://oaqa.github.io/