This document provides an overview of the artificial intelligence and machine learning landscape. It begins with an introduction and discusses the current state of AI, including artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. It then covers trends such as the law of accelerating returns, convergence of technologies, visualizations of the emerging future, and how AI is transforming industries. The document also includes classifications of AI, machine learning algorithms, and libraries. It closes with considerations for cognitive systems, entrepreneurial opportunities, and a question about Kochi's potential as an AI hub in India.
Introduction to Data Science Talk Given to Girl Develop It! Central VA members
Note: some slides had animations in Excel, so unfortunately, the images overlap on the SlideShare version.
The document discusses the semantic web and its potential uses for liberal arts campuses. It provides an overview of semantic web technologies like RDF, OWL, and SPARQL. Examples are given of how semantic web tools could be used for campus projects, pedagogy, and research by exposing metadata and linking data. Challenges mentioned include complexity, lack of visible applications, and the ecological growth needed for widespread adoption.
This document provides an overview and updates from the Apereo Learning Analytics Initiative (LAI). It discusses the historical context including the Open Academic Analytics Initiative funded by Gates Foundation. It describes current proof-of-concept projects from LAI including the Learning Analytics Processor, OpenLRS, and OpenDashboard. It also outlines the modular components that make up the open learning analytics platform and provides demos of the Learning Record Store, Learning Analytics Processor and Student Success Plan. Finally, it discusses engagement opportunities and the roadmap for further developing the open learning analytics platform.
Data excellence: Better data for better AILora Aroyo
The document discusses the importance of data quality and a data lifecycle approach for artificial intelligence. Some key points made include:
- A data lifecycle is needed to guide best practices for data research and development, similar to how a software lifecycle guides software engineering.
- Data quality must be addressed through practices and standards to help avoid unintended AI behaviors that can result from low quality data.
- Disagreement in annotation tasks can provide valuable signals about ambiguity and diversity rather than just being considered noise.
- Achieving high quality, reliable data requires consideration of aspects like validity, fidelity, reproducibility and maintaining data over time - an approach toward "data excellence".
Rise presentation for jisc online mtg 2011 06-02Richard Nurse
The document discusses an online event about using activity data from online resources to improve search experiences. It lists various online resources like EZProxy, CIRCE, and RISE that collect usage data and the attributes available from each resource. The document also questions how the data could be used, such as seeing which courses students accessing certain resources are studying, and whether the data can be aggregated or has any legal or privacy implications.
Maandag 9 november
Sessieronde 1
Titel: Dashboards voor learning analytics
Spreker(s): Renée Filius (Elevate), Alan Berg (Universiteit van Amsterdam)
Zaal: Rotterdam Hall
This document provides an overview of the artificial intelligence and machine learning landscape. It begins with an introduction and discusses the current state of AI, including artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. It then covers trends such as the law of accelerating returns, convergence of technologies, visualizations of the emerging future, and how AI is transforming industries. The document also includes classifications of AI, machine learning algorithms, and libraries. It closes with considerations for cognitive systems, entrepreneurial opportunities, and a question about Kochi's potential as an AI hub in India.
Introduction to Data Science Talk Given to Girl Develop It! Central VA members
Note: some slides had animations in Excel, so unfortunately, the images overlap on the SlideShare version.
The document discusses the semantic web and its potential uses for liberal arts campuses. It provides an overview of semantic web technologies like RDF, OWL, and SPARQL. Examples are given of how semantic web tools could be used for campus projects, pedagogy, and research by exposing metadata and linking data. Challenges mentioned include complexity, lack of visible applications, and the ecological growth needed for widespread adoption.
This document provides an overview and updates from the Apereo Learning Analytics Initiative (LAI). It discusses the historical context including the Open Academic Analytics Initiative funded by Gates Foundation. It describes current proof-of-concept projects from LAI including the Learning Analytics Processor, OpenLRS, and OpenDashboard. It also outlines the modular components that make up the open learning analytics platform and provides demos of the Learning Record Store, Learning Analytics Processor and Student Success Plan. Finally, it discusses engagement opportunities and the roadmap for further developing the open learning analytics platform.
Data excellence: Better data for better AILora Aroyo
The document discusses the importance of data quality and a data lifecycle approach for artificial intelligence. Some key points made include:
- A data lifecycle is needed to guide best practices for data research and development, similar to how a software lifecycle guides software engineering.
- Data quality must be addressed through practices and standards to help avoid unintended AI behaviors that can result from low quality data.
- Disagreement in annotation tasks can provide valuable signals about ambiguity and diversity rather than just being considered noise.
- Achieving high quality, reliable data requires consideration of aspects like validity, fidelity, reproducibility and maintaining data over time - an approach toward "data excellence".
Rise presentation for jisc online mtg 2011 06-02Richard Nurse
The document discusses an online event about using activity data from online resources to improve search experiences. It lists various online resources like EZProxy, CIRCE, and RISE that collect usage data and the attributes available from each resource. The document also questions how the data could be used, such as seeing which courses students accessing certain resources are studying, and whether the data can be aggregated or has any legal or privacy implications.
Maandag 9 november
Sessieronde 1
Titel: Dashboards voor learning analytics
Spreker(s): Renée Filius (Elevate), Alan Berg (Universiteit van Amsterdam)
Zaal: Rotterdam Hall
This document provides an overview of using deep learning techniques for recommender systems. It begins with establishing the need for recommender systems due to increasing information overload. It then gives a basic introduction and agenda for the talk, covering motivation, basics, deep learning for vehicle recommendations, and scalability/production. The talk discusses using deep learning approaches like wide and deep learning as well as sequential models to improve recommendation relevance for applications like vehicle recommendations. It provides details on preprocessing, training a classifier, candidate generation and ranking for recommendations. The document concludes with discussing deploying such a system at scale and current trends in recommender system research.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Future Technological Practices: Medical Librarians’ Skills and Information Structures for Continued Effectiveness in a Changing Environment
Patricia F. Anderson, Skye Bickett, AHIP, Joanne Doucette, Pamela R. Herring, AHIP, Judith Kammerer, AHIP, Andrea Kepsel, AHIP, Tierney Lyons, Scott McLachlan, Ingrid Tonnison, and Lin Wu, AHIP
How to Create Controlled Vocabularies for Competitive IntelligenceIntelCollab.com
The document describes an upcoming webinar on creating controlled vocabularies for competitive intelligence. The webinar will feature two speakers, Justin Soles and Lisa Coady, and will cover topics such as what a controlled vocabulary is, how it can help competitive intelligence work, and best practices for developing one. Attendees are encouraged to ask questions during the webinar.
The ELIXIR FAIR Knowledge Ecosystem for practical know-how: RDMkit and FAIRCo...Carole Goble
Presented at the FAIR Data in Practice Symposium, 16 may 2023 at BioITWorld Boston. https://www.bio-itworldexpo.com/fair-data. The ELIXIR European research Infrastructure for life science data is an inter-governmental organizations coordinating, integrating and sustaining FAIR data and software resources across its 23 nations. To help advise users, data stewards, project managers and service providers, ELIXIR has developed complementary community-driven, open knowledge resources for guiding FAIR Research Data Management (RDMkit) and providing FAIRification recipes (FAIRCookbook). 150+ people have contributed content so far, including representatives of the pharmaceutical industry.
20210623 Digital Technologies and Innovations in EducationRamesh C. Sharma
Digital technology and innovation are rapidly changing the world. [1] Artificial intelligence, machine learning, and big data are fueling the fourth industrial revolution. [2] New technologies like augmented and virtual reality are enhancing learning. [3] Learning analytics tools are providing insights to improve education. The future of learning will be highly personalized and lifelong, enabled by technologies like AI assistants, adaptive learning apps, and blockchain-backed credentials.
Museum Collections Management: Possibilities for Access and Use with Linked D...cbogen
Carly Bogen completed a practicum at a museum where she worked on their collections management system, wrote grants, and assisted with strategic planning. She discusses how modern collection management systems can link objects and their associated data. However, much of this data is not publicly available due to issues like copyright and data sensitivity. Linked open data could help make more museum collection data accessible by standardizing its structure and linking it across institutions. However, barriers include a lack of resources and fears about data accuracy and control. Integrating linked open data with collection management software could help lower these barriers. A few museums have begun publishing linked open data to make their collections more discoverable and connectable with others.
Impact-Driven Research on Software Engineering ToolingTao Xie
This document discusses impact-driven research on software engineering tooling. It provides examples of research that had impact on practice through commercial tools adopting the research results or startups being formed. It also discusses releasing open source tools and data to engage communities. The document advocates for access to real-world data and cooperation with industry to achieve research impact and leadership. It provides examples of the author's impactful research publications and outlines future directions like starting a startup or collaborating more closely with industry.
The document discusses Apereo, a non-profit foundation that supports open source education technologies. It provides an overview of Apereo's mission, activities, software communities, and communities of interest. It also discusses priorities such as an open learning analytics platform and the need for an interoperable next generation digital learning environment that goes beyond course-centered learning management systems. Open source is seen as critical to realizing this vision through interoperability, reference models, and sustaining diverse communities that can innovate.
Introducing Apereo and the Apereo Learning Analytics InitiativeIan Dolphin
This document discusses Apereo Foundation's Learning Analytics Initiative. It provides context on the use of analytics in higher education. It outlines Apereo's vision for an open learning analytics platform that would allow institutions to collect, store, analyze and act on learning data. The platform would integrate with systems like Sakai, Apereo OAE and others. It discusses current progress including an analytics processor and dashboard. It also outlines seven strategic issues and a future of moving platform elements to incubation and encouraging adoption and resource commitment. The overall goal is to facilitate collaboration and sharing of analytics among institutions.
BigScience is a one-year research workshop involving over 800 researchers from 60 countries to build and study very large multilingual language models and datasets. It was granted 5 million GPU hours on the Jean Zay supercomputer in France. The workshop aims to advance AI/NLP research by creating shared models and data as well as tools for researchers. Several working groups are studying issues like bias, scaling, and engineering challenges of training such large models. The first model, T0, showed strong zero-shot performance. Upcoming work includes further model training and papers.
An Overview of the area and the current potential for the open technologies to be used, and some suggestions as to why they are not as heavily used as they should be.
Big Data in Learning Analytics - Analytics for Everyday LearningStefan Dietze
This document summarizes Stefan Dietze's presentation on big data in learning analytics. Some key points:
- Learning analytics has traditionally focused on formal learning environments but there is interest in expanding to informal learning online.
- Examples of potential big data sources mentioned include activity streams, social networks, behavioral traces, and large web crawls.
- Challenges include efficiently analyzing large datasets to understand learning resources and detect learning activities without traditional assessments.
- Initial models show potential to predict learner competence from behavioral traces with over 90% accuracy.
QA-Financial Forum 2019 in New York
13 November
Iosif Itkin, CEO and co-founder
Elena Treshcheva, Business Development Manager and Researcher
An October 2019 survey by BoE and FCA found that ML in financial organizations has already passed an initial development phase, and the usage of live ML applications is about to dramatically increase over the next three years. Artificial Intelligence systems are used in market surveillance, they are providing intellectual analysis of news feeds, and they are an important part of the conversational agents facing users and helping them with their business needs from identity verification to trading and portfolio management. How to ensure that an AI-powered system is up to its task? And what would that mean from the software testing perspective?
Prepping the Analytics organization for Artificial Intelligence evolutionRamkumar Ravichandran
This is a discussion document to be used at the Big Data Spain at Madrid on Nov 18th, 2016. The key takeaway from the deck is that AI is reality and much closer than we realize. It will impact our Analytics Community in a very different way vs. an average Consumer. We can shape and guide the revolution if we start preparing for it now - right from our mindset, design thinking principles and productization of Analytics (API-zation). AI is a need to address the problems of scale, speed, precision in the world that is getting more and more complex around us - it is not humanly possible to answer all the questions ourselves and we will need machines to do it for us. The flow of the story line begins with a reality check on popular misconceptions and some background on AI. It then delves into all the ways it can optimize the current flow and ends with the "Managing Innovation Playbook" a set of three steps that should guide our innovation programs - Strategy, Execution & Transformation, i.e., the principles that tell us what we want to get out of it, how to get it done and finally how much the benefits permanent and consistently improving.
Would love to hear your feedback, thoughts and reactions.
MyExperiment è un social network all’interno del quale è possibile cercare flussi di lavoro scientifico resi pubblici, ma anche proporre, condividere e svilupparne di nuovi, al fine di creare delle comunità e sviluppare relazioni. La presentazione illustra la visione di My Experiment sull' Open Science
Dave de Roure - The myExperiment approach towards Open Scienceshwu
Dave de Roure's talk on myExperiment, including thoughts on protocol and workflow sharing and online communities. Presented at the Open Science workshop at the Pacific Symposium on Biocomputing, January 5th, 2009
https://www.ted.com/talks/shyam_sankar_the_rise_of_human_computer_cooperation
http://www.research.ibm.com/cognitive-computing/index.shtml#fbid=v4BFIWPrO5n
http://www.research.ibm.com/client-programs/index.shtml
https://www.ibm.com/design/
https://www.youtube.com/watch?v=RHMl5SHHYjM
2/26/17, 3)10 PM
Page 1 of 2https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1…d2lSessionVal=fHIzkKJBP8wWlfc2loiXUyRDM&ou=85341&d2l_body_type=3
Module 2 - SLP
BUSINESS AND ORGANIZATIONAL ASPECTS OF HCI
For this exercise, please return to the software package that you used in your
SLP assignment for Module 1.
SLP Assignment Expectations
Then please prepare a paper addressing these topics.
Have you ever called their technical support to get help due to lack of ease of
use? Why or why not?
What more you would like to have in the software from an ease of use point of
view?
Would you be willing to pay more for the software for such features? Why or
why not?
Any conclusions you might have drawn about ease of use as a business
criterion, and why you make this assessment.
SLP Grading and Expectations
Your paper will be evaluated on the following criteria:
Complete the SLP assignment. Length of 2-3 pages (since a page is about 300
words, this is approximately 600-900 words)
Conducted evaluation and analysis as required
Precision: the questions asked are answered.
Clarity: Your answers are clear and show your good understanding of the
topic.
Breadth and Depth: The scope covered in your paper is directly related to the
questions of the assignment and the learning objectives of the module.
Listen
https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1/DW4Mod%20-%20Codes/EMPTY%204-MODULE%20HTML%20DOCS/Modules/Module2/https%3A%2F%2Fapp.readspeaker.com%2Fcgi-bin%2Frsent?customerid=8725&lang=en_us&voice=Kate&readid=d2l_read_element&url=https%3A%2F%2Ftlc.trident.edu%2Fcontent%2Fenforced%2F85341-ITM433-FEB2017FT-1%2FDW4Mod%2520-%2520Codes%2FEMPTY%25204-MODULE%2520HTML%2520DOCS%2FModules%2FModule2%2FMod2SLP.html
2/26/17, 3)10 PM
Page 2 of 2https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1…d2lSessionVal=fHIzkKJBP8wWlfc2loiXUyRDM&ou=85341&d2l_body_type=3
Privacy Policy | Contact
Critical thinking: Incorporate YOUR reactions, examples, and applications of
the material to business that illustrate your reflective judgment and good
understanding of the concepts.
Your paper is well written and the references are properly cited and listed
http://www.trident.edu/privacy-policy
http://www.trident.edu/university-information/contact-us
Search HFES.com
Think Again Before Tapping the Install Button for That App
Friday, October 16, 2015
Before installing a new app on a mobile device, people need to be mindful of the security
risks. One poor decision can bypass the most secure encryption, and a malicious app can
gain access to confidential information or even lock the user’s device. A presentation at
the upcoming HF ...
Joe Murphy's opening talk for the European Innovative Users Group meeting in Edinburgh, Scotland June 16 2014 at Queen Margaret University
Joe Murphy is a futurist. Joe spoke as a librarian working as Director of Library Futures with Innovative Interfaces.
The document discusses the issues around platforms and privacy in education. It notes that education data is being collected and used by companies in the same way consumer data is, without proper consent or transparency. This raises concerns about privacy, surveillance, and the potential misuse of sensitive student data. The document questions whether institutions have considered how to implement appropriate safeguards and control over student data before adopting new educational platforms and technologies.
The document discusses open learning analytics and the case for openness. It summarizes key points from a presentation including:
- Learning analytics can help identify at-risk students and improve courses, but also raises issues regarding privacy, bias, and transparency.
- Open source approaches to learning analytics aim to address these issues through open data standards, algorithms, and governance structures.
- Early examples from the UK and France explore open learning analytics to provide transparency and safeguards around predictive modeling.
- Ensuring understanding of predictive models, managing consent appropriately, and exploring techniques like counterfactual explanations can help address transparency concerns with learning analytics.
This document provides an overview of using deep learning techniques for recommender systems. It begins with establishing the need for recommender systems due to increasing information overload. It then gives a basic introduction and agenda for the talk, covering motivation, basics, deep learning for vehicle recommendations, and scalability/production. The talk discusses using deep learning approaches like wide and deep learning as well as sequential models to improve recommendation relevance for applications like vehicle recommendations. It provides details on preprocessing, training a classifier, candidate generation and ranking for recommendations. The document concludes with discussing deploying such a system at scale and current trends in recommender system research.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Future Technological Practices: Medical Librarians’ Skills and Information Structures for Continued Effectiveness in a Changing Environment
Patricia F. Anderson, Skye Bickett, AHIP, Joanne Doucette, Pamela R. Herring, AHIP, Judith Kammerer, AHIP, Andrea Kepsel, AHIP, Tierney Lyons, Scott McLachlan, Ingrid Tonnison, and Lin Wu, AHIP
How to Create Controlled Vocabularies for Competitive IntelligenceIntelCollab.com
The document describes an upcoming webinar on creating controlled vocabularies for competitive intelligence. The webinar will feature two speakers, Justin Soles and Lisa Coady, and will cover topics such as what a controlled vocabulary is, how it can help competitive intelligence work, and best practices for developing one. Attendees are encouraged to ask questions during the webinar.
The ELIXIR FAIR Knowledge Ecosystem for practical know-how: RDMkit and FAIRCo...Carole Goble
Presented at the FAIR Data in Practice Symposium, 16 may 2023 at BioITWorld Boston. https://www.bio-itworldexpo.com/fair-data. The ELIXIR European research Infrastructure for life science data is an inter-governmental organizations coordinating, integrating and sustaining FAIR data and software resources across its 23 nations. To help advise users, data stewards, project managers and service providers, ELIXIR has developed complementary community-driven, open knowledge resources for guiding FAIR Research Data Management (RDMkit) and providing FAIRification recipes (FAIRCookbook). 150+ people have contributed content so far, including representatives of the pharmaceutical industry.
20210623 Digital Technologies and Innovations in EducationRamesh C. Sharma
Digital technology and innovation are rapidly changing the world. [1] Artificial intelligence, machine learning, and big data are fueling the fourth industrial revolution. [2] New technologies like augmented and virtual reality are enhancing learning. [3] Learning analytics tools are providing insights to improve education. The future of learning will be highly personalized and lifelong, enabled by technologies like AI assistants, adaptive learning apps, and blockchain-backed credentials.
Museum Collections Management: Possibilities for Access and Use with Linked D...cbogen
Carly Bogen completed a practicum at a museum where she worked on their collections management system, wrote grants, and assisted with strategic planning. She discusses how modern collection management systems can link objects and their associated data. However, much of this data is not publicly available due to issues like copyright and data sensitivity. Linked open data could help make more museum collection data accessible by standardizing its structure and linking it across institutions. However, barriers include a lack of resources and fears about data accuracy and control. Integrating linked open data with collection management software could help lower these barriers. A few museums have begun publishing linked open data to make their collections more discoverable and connectable with others.
Impact-Driven Research on Software Engineering ToolingTao Xie
This document discusses impact-driven research on software engineering tooling. It provides examples of research that had impact on practice through commercial tools adopting the research results or startups being formed. It also discusses releasing open source tools and data to engage communities. The document advocates for access to real-world data and cooperation with industry to achieve research impact and leadership. It provides examples of the author's impactful research publications and outlines future directions like starting a startup or collaborating more closely with industry.
The document discusses Apereo, a non-profit foundation that supports open source education technologies. It provides an overview of Apereo's mission, activities, software communities, and communities of interest. It also discusses priorities such as an open learning analytics platform and the need for an interoperable next generation digital learning environment that goes beyond course-centered learning management systems. Open source is seen as critical to realizing this vision through interoperability, reference models, and sustaining diverse communities that can innovate.
Introducing Apereo and the Apereo Learning Analytics InitiativeIan Dolphin
This document discusses Apereo Foundation's Learning Analytics Initiative. It provides context on the use of analytics in higher education. It outlines Apereo's vision for an open learning analytics platform that would allow institutions to collect, store, analyze and act on learning data. The platform would integrate with systems like Sakai, Apereo OAE and others. It discusses current progress including an analytics processor and dashboard. It also outlines seven strategic issues and a future of moving platform elements to incubation and encouraging adoption and resource commitment. The overall goal is to facilitate collaboration and sharing of analytics among institutions.
BigScience is a one-year research workshop involving over 800 researchers from 60 countries to build and study very large multilingual language models and datasets. It was granted 5 million GPU hours on the Jean Zay supercomputer in France. The workshop aims to advance AI/NLP research by creating shared models and data as well as tools for researchers. Several working groups are studying issues like bias, scaling, and engineering challenges of training such large models. The first model, T0, showed strong zero-shot performance. Upcoming work includes further model training and papers.
An Overview of the area and the current potential for the open technologies to be used, and some suggestions as to why they are not as heavily used as they should be.
Big Data in Learning Analytics - Analytics for Everyday LearningStefan Dietze
This document summarizes Stefan Dietze's presentation on big data in learning analytics. Some key points:
- Learning analytics has traditionally focused on formal learning environments but there is interest in expanding to informal learning online.
- Examples of potential big data sources mentioned include activity streams, social networks, behavioral traces, and large web crawls.
- Challenges include efficiently analyzing large datasets to understand learning resources and detect learning activities without traditional assessments.
- Initial models show potential to predict learner competence from behavioral traces with over 90% accuracy.
QA-Financial Forum 2019 in New York
13 November
Iosif Itkin, CEO and co-founder
Elena Treshcheva, Business Development Manager and Researcher
An October 2019 survey by BoE and FCA found that ML in financial organizations has already passed an initial development phase, and the usage of live ML applications is about to dramatically increase over the next three years. Artificial Intelligence systems are used in market surveillance, they are providing intellectual analysis of news feeds, and they are an important part of the conversational agents facing users and helping them with their business needs from identity verification to trading and portfolio management. How to ensure that an AI-powered system is up to its task? And what would that mean from the software testing perspective?
Prepping the Analytics organization for Artificial Intelligence evolutionRamkumar Ravichandran
This is a discussion document to be used at the Big Data Spain at Madrid on Nov 18th, 2016. The key takeaway from the deck is that AI is reality and much closer than we realize. It will impact our Analytics Community in a very different way vs. an average Consumer. We can shape and guide the revolution if we start preparing for it now - right from our mindset, design thinking principles and productization of Analytics (API-zation). AI is a need to address the problems of scale, speed, precision in the world that is getting more and more complex around us - it is not humanly possible to answer all the questions ourselves and we will need machines to do it for us. The flow of the story line begins with a reality check on popular misconceptions and some background on AI. It then delves into all the ways it can optimize the current flow and ends with the "Managing Innovation Playbook" a set of three steps that should guide our innovation programs - Strategy, Execution & Transformation, i.e., the principles that tell us what we want to get out of it, how to get it done and finally how much the benefits permanent and consistently improving.
Would love to hear your feedback, thoughts and reactions.
MyExperiment è un social network all’interno del quale è possibile cercare flussi di lavoro scientifico resi pubblici, ma anche proporre, condividere e svilupparne di nuovi, al fine di creare delle comunità e sviluppare relazioni. La presentazione illustra la visione di My Experiment sull' Open Science
Dave de Roure - The myExperiment approach towards Open Scienceshwu
Dave de Roure's talk on myExperiment, including thoughts on protocol and workflow sharing and online communities. Presented at the Open Science workshop at the Pacific Symposium on Biocomputing, January 5th, 2009
https://www.ted.com/talks/shyam_sankar_the_rise_of_human_computer_cooperation
http://www.research.ibm.com/cognitive-computing/index.shtml#fbid=v4BFIWPrO5n
http://www.research.ibm.com/client-programs/index.shtml
https://www.ibm.com/design/
https://www.youtube.com/watch?v=RHMl5SHHYjM
2/26/17, 3)10 PM
Page 1 of 2https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1…d2lSessionVal=fHIzkKJBP8wWlfc2loiXUyRDM&ou=85341&d2l_body_type=3
Module 2 - SLP
BUSINESS AND ORGANIZATIONAL ASPECTS OF HCI
For this exercise, please return to the software package that you used in your
SLP assignment for Module 1.
SLP Assignment Expectations
Then please prepare a paper addressing these topics.
Have you ever called their technical support to get help due to lack of ease of
use? Why or why not?
What more you would like to have in the software from an ease of use point of
view?
Would you be willing to pay more for the software for such features? Why or
why not?
Any conclusions you might have drawn about ease of use as a business
criterion, and why you make this assessment.
SLP Grading and Expectations
Your paper will be evaluated on the following criteria:
Complete the SLP assignment. Length of 2-3 pages (since a page is about 300
words, this is approximately 600-900 words)
Conducted evaluation and analysis as required
Precision: the questions asked are answered.
Clarity: Your answers are clear and show your good understanding of the
topic.
Breadth and Depth: The scope covered in your paper is directly related to the
questions of the assignment and the learning objectives of the module.
Listen
https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1/DW4Mod%20-%20Codes/EMPTY%204-MODULE%20HTML%20DOCS/Modules/Module2/https%3A%2F%2Fapp.readspeaker.com%2Fcgi-bin%2Frsent?customerid=8725&lang=en_us&voice=Kate&readid=d2l_read_element&url=https%3A%2F%2Ftlc.trident.edu%2Fcontent%2Fenforced%2F85341-ITM433-FEB2017FT-1%2FDW4Mod%2520-%2520Codes%2FEMPTY%25204-MODULE%2520HTML%2520DOCS%2FModules%2FModule2%2FMod2SLP.html
2/26/17, 3)10 PM
Page 2 of 2https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1…d2lSessionVal=fHIzkKJBP8wWlfc2loiXUyRDM&ou=85341&d2l_body_type=3
Privacy Policy | Contact
Critical thinking: Incorporate YOUR reactions, examples, and applications of
the material to business that illustrate your reflective judgment and good
understanding of the concepts.
Your paper is well written and the references are properly cited and listed
http://www.trident.edu/privacy-policy
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Search HFES.com
Think Again Before Tapping the Install Button for That App
Friday, October 16, 2015
Before installing a new app on a mobile device, people need to be mindful of the security
risks. One poor decision can bypass the most secure encryption, and a malicious app can
gain access to confidential information or even lock the user’s device. A presentation at
the upcoming HF ...
Joe Murphy's opening talk for the European Innovative Users Group meeting in Edinburgh, Scotland June 16 2014 at Queen Margaret University
Joe Murphy is a futurist. Joe spoke as a librarian working as Director of Library Futures with Innovative Interfaces.
The document discusses the issues around platforms and privacy in education. It notes that education data is being collected and used by companies in the same way consumer data is, without proper consent or transparency. This raises concerns about privacy, surveillance, and the potential misuse of sensitive student data. The document questions whether institutions have considered how to implement appropriate safeguards and control over student data before adopting new educational platforms and technologies.
The document discusses open learning analytics and the case for openness. It summarizes key points from a presentation including:
- Learning analytics can help identify at-risk students and improve courses, but also raises issues regarding privacy, bias, and transparency.
- Open source approaches to learning analytics aim to address these issues through open data standards, algorithms, and governance structures.
- Early examples from the UK and France explore open learning analytics to provide transparency and safeguards around predictive modeling.
- Ensuring understanding of predictive models, managing consent appropriately, and exploring techniques like counterfactual explanations can help address transparency concerns with learning analytics.
The Apereo Foundation is a non-profit organization that supports open source software for education. It has launched a learning analytics initiative to develop an open source software platform for collecting, analyzing, and acting on student learning data. Early adopters include North Carolina State University, the University of Notre Dame, the University of Lorraine in France, and the UK's Jisc national education service. Lessons from these implementations emphasize clearly defining goals, addressing ethical issues, conducting readiness assessments, and gaining institutional leadership support. The platform is intended to provide flexibility and interoperability beyond limited analytics in learning management systems.
Short overview of the incubation process supporting open source software projects and communities participating in the Apereo Foundation.
Apereo was formed by the 2013 merger of two open source in educaton pioneers - Jasig and Sakai
Introducing Apereo: Presentation to the PESC Fall Data Summit, September 2013Ian Dolphin
This document is a presentation by Ian Dolphin, Executive Director of Apereo Foundation, introducing Apereo. The presentation discusses how Apereo serves the academic mission by fostering collaboration and sustaining open technologies to support learning, teaching, and research. It provides details on Apereo Foundation membership and describes Apereo's model as a hybrid, federated network of peers and software communities that pool resources to meet common objectives while remaining self-governing.
Japan Sakai Conference Presentation - March 2011Ian Dolphin
The document summarizes the state of the Sakai community and Foundation. It provides an overview of Sakai adoption at over 340 institutions, the capabilities of the Sakai Collaboration and Learning Environment (CLE), priorities for the Sakai Open Academic Environment project, and the current state and priorities of the Sakai Foundation including resource aggregation, mobile development, and internationalization.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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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.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Xerte Conference, June 2018
1. Learning
Analytics
Xerte Conference - June 2018
Ian Dolphin
Apereo Foundation
ian.dolphin@apereo.org
IanDolphinviaFlickrAttribution2.0Generic (CCBY-NC-ND2.0)
Apereo Foundation
More about why this sign was created at http://www.intrastructures.net/Intrastructures/Actions_-_The_next_big_thing_2.html
5. “… software for which the original
source code is made freely available and
may be redistributed and modified …”
https://opensource.org/osd-annotated
Under an approved Open Source
Initiative license
https://opensource.org/licenses
10. – The Apereo Foundation is a non-profit [501(c)(3)]
registered in New Jersey
Mission
“…collaborate to foster, develop, and sustain
open technologies and innovation to
support learning, teaching, and research."
11. Membership Organization
Legal: New Jersey 501.c.3
Elected Board of Directors
Global
WhatisApereo?
Partnership LAMP North America
Partnership ESUP-Portail in France
15. The Next Two Years: Six Key ThemesStrategy
Strategic review: https://bit.ly/2HLIpK8
1 Membership and fundraising
2 Partnerships
3 Communications, outreach & engagement
4 Development opportunities & recognition
5 Software community health measures
6 Foundation services - review
20. Learning Analytics Definition
“Measurement, collection, analysis and
reporting of data about learners and their
contexts, for purposes of understanding
and optimizing learning and the
environments in which it occurs” (1)
(1) Learning and Academic Analytics, Siemens, G., 5 August 2011, http://www.learninganalytics.net/?p=131
26. Learning Analytics Data Sources
Student Information Systems
Virtual Learning Environments
Attendance Monitoring Systems
Library Management Systems
Media Players
Learning Materials
??????????
28. Predictive Analytics
Applying techniques associated with big data to
data produced in course of learning.
Analysing historical aggregate data to identify
potential failure/success.
big data
noun COMPUTING
1. extremely large data sets that may be analysed computationally to reveal patterns,
trends, and associations, especially relating to human behaviour and interactions.
29. Big Data: What Could Go Wrong?
People write algorithms …
… or people write the algorithms that
write algorithms …
It’s not difficult to find horror stories …
37. Spark
Hadoop
Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Big Data
Techniques
Analysis
Communication:
Dashboards/Early Alert
Open Dashboard
Intervention:
Action/Advising
Student Success Plan
Big Data
Techniques
Collect & Store
Open LRW
Predictive Analytics
https://www.apereo.org/projects/shuhari
Uses historical aggregate data
41. Eight-fold Path to Analytics Success
Open Purpose
Open Ethical Framework
Open and Inclusive Governance
Open Source Software
Open Platform
Open Standards
Open Algorithms
Open Consent - and Consent Management
IanDolphinviaFlickrAttribution2.0Generic (CCBY-NC-ND2.0)
42. Learning Analytics Data Sources
Student Information Systems
Virtual Learning Environments
Attendance Monitoring Systems
Library Management Systems
Media Players
Learning Materials
??????????
Accidental or Designed Data?