Presentation at Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020).
Can we use data to train Machine Learning models, perform statistical analysis, yet without putting private data on risk? There are tools and techniques such as Federated Learning, Differential Privacy or Homomorphic Encryption enabling safer work on the data.
This document discusses Chat GPT, an AI assistant created by Anthropic to be helpful, harmless, and honest. It summarizes Chat GPT's capabilities, including answering text-based prompts, using large language models, and employing techniques like NLP. The document also outlines some advantages of Chat GPT like budget-friendly access, mostly correct responses, and optimized answers. Potential disadvantages discussed include limited training data and lack of non-English support or updates. It concludes by noting Chat GPT's rapid adoption and expectations for future, more powerful models.
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
Movie Recommendation System - MovieLens DatasetJagruti Joshi
We built a recommender system that recommends movies to users based on historical ratings and tags data using information filtering techniques such as Collaborative Filtering, Content-Based Filtering and Singular Value Decomposition.
Can we use data to train Machine Learning models, perform statistical analysis, yet without putting private data on risk? There are tools and techniques such as Federated Learning, Differential Privacy or Homomorphic Encryption enabling safer work on the data.
This document discusses Chat GPT, an AI assistant created by Anthropic to be helpful, harmless, and honest. It summarizes Chat GPT's capabilities, including answering text-based prompts, using large language models, and employing techniques like NLP. The document also outlines some advantages of Chat GPT like budget-friendly access, mostly correct responses, and optimized answers. Potential disadvantages discussed include limited training data and lack of non-English support or updates. It concludes by noting Chat GPT's rapid adoption and expectations for future, more powerful models.
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
Movie Recommendation System - MovieLens DatasetJagruti Joshi
We built a recommender system that recommends movies to users based on historical ratings and tags data using information filtering techniques such as Collaborative Filtering, Content-Based Filtering and Singular Value Decomposition.
Slide about working of federated learning and the introduction of machine learning and how user privacy is preserved in future machine learning approach.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
The document summarizes key topics around fairness and bias in machine learning:
- It discusses different types of biases that can arise such as historical, representation, measurement, and aggregation biases.
- It explores how bias can be introduced and amplified at various stages of an ML system from data collection to deployment.
- Various definitions of fairness are presented, including demographic parity, equal odds, and equal opportunity.
- Methods for quantifying and mitigating bias are outlined, such as preprocessing techniques like reweighing and disparate impact removal, inprocessing approaches like prejudice removal and adversarial debiasing, and postprocessing options like tuning for equal odds.
Generative AI and Security (1).pptx.pdfPriyanka Aash
Generative AI and Security Testing discusses generative AI, including its definition as a subset of AI focused on generating content similar to human creations. The document outlines the evolution of generative AI from artificial neural networks to modern models like GPT, GANs, and VAEs. It provides examples of different types of generative AI like text, image, audio, and video generation. The document proposes potential uses of generative AI like GPT for security testing tasks such as malware generation, adversarial attack simulation, and penetration testing assistance.
The document discusses generative models and their applications in artificial intelligence. Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate new data that looks real by fooling the discriminator, while the discriminator learns to better identify real from fake data. GANs have been used for tasks like image generation and neural style transfer. They show potential to generate art, music and other creative forms through machine learning.
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?Bernard Marr
GPT-3 is an AI tool created by OpenAI that can generate text in human-like ways. It has been trained on vast amounts of text from the internet. GPT-3 can answer questions, summarize text, translate languages, and generate computer code. However, it has limitations as its output can become gibberish for complex tasks and it operates as a black box system. While impressive, GPT-3 is just an early glimpse of what advanced AI may be able to accomplish.
Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)Sergey Karayev
The document discusses infrastructure and tooling for full stack deep learning. It provides an overview of the different components involved, including compute, data processing, experiment management, deployment, and software engineering practices. Specifically, it covers topics like GPU basics, cloud computing options, development versus training needs, popular programming languages and editors like Python and Jupyter Notebooks, and setting up development environments.
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
The document discusses advances in large language models from GPT-1 to the potential capabilities of GPT-4, including its ability to simulate human behavior, demonstrate sparks of artificial general intelligence, and generate virtual identities. It also provides tips on how to effectively prompt ChatGPT through techniques like prompt engineering, giving context and examples, and different response formats.
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
This document discusses how social media can be used to help businesses in several ways:
1. Social media can drive brand awareness, relevance, and value by amplifying messaging to more people.
2. It provides ways to increase sales through new customer acquisition, increased transactions, and product exposure.
3. It allows for improved customer support, public relations, loyalty, and intelligence gathering.
4. User-generated content like reviews provide a global platform for sharing opinions that can influence decisions. Hashtags help group posts by topic to understand sentiment.
Build a Recommendation Engine using Amazon Machine Learning in Real-timeAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will introduce how to use Amazon Machine Learning to create a data model, and use it to generate the real-time prediction for your application.
The document discusses data privacy and security challenges posed by large language models like ChatGPT. It outlines recent data breaches and leaks involving ChatGPT, including a software bug and instances where ChatGPT was used to inadvertently leak company secrets. The document also examines ChatGPT's data retention policy and privacy issues, noting concerns about how personal information from user conversations may be collected and reviewed. Potential cybersecurity risks of ChatGPT like phishing scams and generating malicious code are presented. OpenAI's handling of these issues through bug bounties is also covered.
This document provides a 50-hour roadmap for building large language model (LLM) applications. It introduces key concepts like text-based and image-based generative AI models, encoder-decoder models, attention mechanisms, and transformers. It then covers topics like intro to image generation, generative AI applications, embeddings, attention mechanisms, transformers, vector databases, semantic search, prompt engineering, fine-tuning foundation models, orchestration frameworks, autonomous agents, bias and fairness, and recommended LLM application projects. The document recommends several hands-on exercises and lists upcoming bootcamp dates and locations for learning to build LLM applications.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
The CIPR's Artificial Intelligence (AI) panel has published new research revealing the impact of technology, and specifically AI, on public relations practice. It predicts the impact on skills in the profession in the next five years.
Slide about working of federated learning and the introduction of machine learning and how user privacy is preserved in future machine learning approach.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
The document summarizes key topics around fairness and bias in machine learning:
- It discusses different types of biases that can arise such as historical, representation, measurement, and aggregation biases.
- It explores how bias can be introduced and amplified at various stages of an ML system from data collection to deployment.
- Various definitions of fairness are presented, including demographic parity, equal odds, and equal opportunity.
- Methods for quantifying and mitigating bias are outlined, such as preprocessing techniques like reweighing and disparate impact removal, inprocessing approaches like prejudice removal and adversarial debiasing, and postprocessing options like tuning for equal odds.
Generative AI and Security (1).pptx.pdfPriyanka Aash
Generative AI and Security Testing discusses generative AI, including its definition as a subset of AI focused on generating content similar to human creations. The document outlines the evolution of generative AI from artificial neural networks to modern models like GPT, GANs, and VAEs. It provides examples of different types of generative AI like text, image, audio, and video generation. The document proposes potential uses of generative AI like GPT for security testing tasks such as malware generation, adversarial attack simulation, and penetration testing assistance.
The document discusses generative models and their applications in artificial intelligence. Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate new data that looks real by fooling the discriminator, while the discriminator learns to better identify real from fake data. GANs have been used for tasks like image generation and neural style transfer. They show potential to generate art, music and other creative forms through machine learning.
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?Bernard Marr
GPT-3 is an AI tool created by OpenAI that can generate text in human-like ways. It has been trained on vast amounts of text from the internet. GPT-3 can answer questions, summarize text, translate languages, and generate computer code. However, it has limitations as its output can become gibberish for complex tasks and it operates as a black box system. While impressive, GPT-3 is just an early glimpse of what advanced AI may be able to accomplish.
Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)Sergey Karayev
The document discusses infrastructure and tooling for full stack deep learning. It provides an overview of the different components involved, including compute, data processing, experiment management, deployment, and software engineering practices. Specifically, it covers topics like GPU basics, cloud computing options, development versus training needs, popular programming languages and editors like Python and Jupyter Notebooks, and setting up development environments.
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
The document discusses advances in large language models from GPT-1 to the potential capabilities of GPT-4, including its ability to simulate human behavior, demonstrate sparks of artificial general intelligence, and generate virtual identities. It also provides tips on how to effectively prompt ChatGPT through techniques like prompt engineering, giving context and examples, and different response formats.
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
This document discusses how social media can be used to help businesses in several ways:
1. Social media can drive brand awareness, relevance, and value by amplifying messaging to more people.
2. It provides ways to increase sales through new customer acquisition, increased transactions, and product exposure.
3. It allows for improved customer support, public relations, loyalty, and intelligence gathering.
4. User-generated content like reviews provide a global platform for sharing opinions that can influence decisions. Hashtags help group posts by topic to understand sentiment.
Build a Recommendation Engine using Amazon Machine Learning in Real-timeAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will introduce how to use Amazon Machine Learning to create a data model, and use it to generate the real-time prediction for your application.
The document discusses data privacy and security challenges posed by large language models like ChatGPT. It outlines recent data breaches and leaks involving ChatGPT, including a software bug and instances where ChatGPT was used to inadvertently leak company secrets. The document also examines ChatGPT's data retention policy and privacy issues, noting concerns about how personal information from user conversations may be collected and reviewed. Potential cybersecurity risks of ChatGPT like phishing scams and generating malicious code are presented. OpenAI's handling of these issues through bug bounties is also covered.
This document provides a 50-hour roadmap for building large language model (LLM) applications. It introduces key concepts like text-based and image-based generative AI models, encoder-decoder models, attention mechanisms, and transformers. It then covers topics like intro to image generation, generative AI applications, embeddings, attention mechanisms, transformers, vector databases, semantic search, prompt engineering, fine-tuning foundation models, orchestration frameworks, autonomous agents, bias and fairness, and recommended LLM application projects. The document recommends several hands-on exercises and lists upcoming bootcamp dates and locations for learning to build LLM applications.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
The CIPR's Artificial Intelligence (AI) panel has published new research revealing the impact of technology, and specifically AI, on public relations practice. It predicts the impact on skills in the profession in the next five years.
This document proposes a framework for evaluating mental health smartphone applications. It notes that while such apps have potential to extend mental health services, most have not been rigorously evaluated for efficacy, safety, or quality. The framework suggests apps be evaluated based on three dimensions: usefulness, usability, and integration/infrastructure. Specific criteria are outlined for each dimension to allow clinicians and patients to assess apps.
This document discusses the risks associated with organizational use of social media and the need to evaluate these risks as part of the audit process. It provides examples of social media risks including malware, data leakage, customer exposure, loss of content rights, and inadequate response to customer expectations. The document also discusses security risks like phishing attacks. It emphasizes that social media policies need to provide clear guidance to employees and be regularly updated. Key questions for organizations to consider include how mobile commerce and social media policies are integrated, monitored, and how information security measures apply to different data sensitivities.
This document discusses using social media analytics and text mining techniques in KNIME to better understand customer networks and characteristics for health and customer relationship management applications. It provides an agenda that covers getting an overview of customer relationships, understanding customer profiles, extracting information from social media, and applying text mining and sentiment analysis. Examples are given of workflows for searching terms, processing text mining, and analyzing sentiment from social media data. The document also discusses understanding social networks and their influence on individuals.
This document provides an agenda and overview for a web seminar on social media analytics applying to health and customer relationship management (CRM). The agenda includes getting an overview of relationships within social networks, understanding customer characteristics, obtaining information from social media through text mining and sentiment analysis. Examples are given of using these techniques on networks from Facebook and Twitter and analyzing sentiments toward topics like health insurance plans. The document also discusses classifying different types of customers based on their interests and using social network analysis in KNIME.
IRJET- Big Data Driven Information Diffusion Analytics and Control on Social ...IRJET Journal
This document discusses controlling the spread of fake or misleading information on social media. It proposes a system to analyze information diffusion on social networks, identify diffused data, and control the spread of fake diffused data. The system would extract data from social media, perform sentiment analysis to determine the veracity of information, and discard fake or untrustworthy information from the database to prevent further propagation. A variety of machine learning techniques could be used for the sentiment analysis, including naive Bayes classification, linear regression, and gradient boosted trees. The goal is to curb the spread of misinformation while still allowing the diffusion of real or truthful information.
The document summarizes a presentation on detecting fake news using machine learning. It introduces the topic, defines important terms like natural language processing and text classification. It also provides a literature review on current techniques for fake news detection using machine learning. The research objective is to develop systems that can accurately identify fake news. The proposed methodology includes data preprocessing, feature extraction, model development, evaluation, and deployment. The results show that a logistic regression model achieved 97% accuracy in distinguishing real and fake news articles. In conclusion, fake news is a growing problem, and machine learning methods show promise in helping address the spread of misinformation.
Prepare a 2-page interprofessional staff update on HIPAA and approprLilianaJohansen814
Prepare a 2-page interprofessional staff update on HIPAA and appropriate social media use in health care.
As you begin to consider the assessment, it would be an excellent choice to complete the Breach of Protected Health Information (PHI) activity. The will support your success with the assessment by creating the opportunity for you to test your knowledge of potential privacy, security, and confidentiality violations of protected health information. The activity is not graded and counts towards course engagement.
Health professionals today are increasingly accountable for the use of protected health information (PHI). Various government and regulatory agencies promote and support privacy and security through a variety of activities. Examples include:
Meaningful use of electronic health records (EHR).
Provision of EHR incentive programs through Medicare and Medicaid.
Enforcement of the Health Insurance Portability and Accountability Act (HIPAA) rules.
Release of educational resources and tools to help providers and hospitals address privacy, security, and confidentiality risks in their practices.
Technological advances, such as the use of social media platforms and applications for patient progress tracking and communication, have provided more access to health information and improved communication between care providers and patients.
At the same time, advances such as these have resulted in more risk for protecting PHI. Nurses typically receive annual training on protecting patient information in their everyday practice. This training usually emphasizes privacy, security, and confidentiality best practices such as:
Keeping passwords secure.
Logging out of public computers.
Sharing patient information only with those directly providing care or who have been granted permission to receive this information.
Today, one of the major risks associated with privacy and confidentiality of patient identity and data relates to social media. Many nurses and other health care providers place themselves at risk when they use social media or other electronic communication systems inappropriately. For example, a Texas nurse was recently terminated for posting patient vaccination information on Facebook. In another case, a New York nurse was terminated for posting an insensitive emergency department photo on her Instagram account.
Health care providers today must develop their skills in mitigating risks to their patients and themselves related to patient information. At the same time, they need to be able distinguish between effective and ineffective uses of social media in health care.
This assessment will require you to develop a staff update for the interprofessional team to encourage team members to protect the privacy, confidentiality, and security of patient information.
Demonstration of Proficiency
By successfully completing this assessment, you will demonstrate your proficiency in the course competencies through the following asse ...
ifib Lunchbag: CHI2018 Highlights - Algorithms in (Social) Practice and morehen_drik
The document summarizes several papers presented at CHI 2018 on the topics of:
1) Understanding user experience of co-creation with AI through a drawing collaboration study.
2) Perceptions of justice and fairness in algorithmic decision-making through experimental studies.
3) A qualitative study of perceptions of algorithmic fairness among marginalized groups.
4) The effects of communicating advertising algorithm processes on user perceptions and trust.
Mission: Possible! Your cognitive future in governmentIBM Government
Read the full report here: http://bit.ly/CognitiveFutureInGov
Welcome to the age of cognitive computing, where intelligent machines simulate human brain capabilities to help solve society’s most vexing problems. Early adopters in government and other industries are already realizing significant value from this innovative technology, and its potential to transform government is enormous. Currently, cognitive systems are helping government organizations navigate complexity in operational environments and foster improved engagement with constituents. Our research indicates that government leaders are poised to embrace this groundbreaking technology and invest in cognitive capabilities to improve outcomes for government organizations across mission areas.
Pharma Social Media Listening: Unlocking Hidden Insights | WhitepaperRNayak3
Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.
Unlocking Hidden Insights for Pharma with Social Media ListeningRNayak3
Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.
Data Collection Tool Used For Information About IndividualsChristy Hunt
The document discusses surveys as a data collection tool used to gather information about individuals. Surveys can be conducted in various ways such as printed questionnaires, telephone interviews, mail, in-person interviews, online, etc. However, standardized procedures are used to ensure every participant is asked the same questions in the same way to make the results reliable and generalizable. The document then discusses some questions and concerns about survey length, question types, and methodology.
Make sure it is in APA 7 format and at least 3-4 paragraphs and refe.docxendawalling
Make sure it is in APA 7 format and at least 3-4 paragraphs and references.
Throughout history, technological advancements have appeared for one purpose before finding applications elsewhere that lead to spikes in its usage and development. The internet, for example, was originally developed to share research before becoming a staple of work and entertainment. But technology—new and repurposed—will undoubtedly continue to be a driver of healthcare information. Informaticists often stay tuned to trends to monitor what the next new technology will be or how the next new idea for applying existing technology can benefit outcomes.
In this Discussion, you will reflect on your healthcare organization’s use of technology and offer a technology trend you observe in your environment.
To Prepare:
Reflect on the Resources related to digital information tools and technologies.
Consider your healthcare organization’s use of healthcare technologies to manage and distribute information.
Reflect on current and potential future trends, such as use of social media and mobile applications/telehealth, Internet of Things (IoT)-enabled asset tracking, or expert systems/artificial intelligence, and how they may impact nursing practice and healthcare delivery.
By Day 3 of Week 6
Post
a brief description of general healthcare technology trends, particularly related to data/information you have observed in use in your healthcare organization or nursing practice. Describe any potential challenges or risks that may be inherent in the technologies associated with these trends you described. Then, describe at least one potential benefit and one potential risk associated with data safety, legislation, and patient care for the technologies you described. Next, explain which healthcare technology trends you believe are most promising for impacting healthcare technology in nursing practice and explain why. Describe whether this promise will contribute to improvements in patient care outcomes, efficiencies, or data management. Be specific and provide examples.
By Day 6 of Week 6
Respond
to at least
two
of your colleagues
* on two different days
, offering additional/alternative ideas regarding opportunities and risks related to the observations shared.
Click on the
Reply
button below to reveal the textbox for entering your message. Then click on the
Submit
button to post your message.
*Note:
Throughout this program, your fellow students are referred to as colleagues.
Throughout history, technological advancements have appeared for one purpose before finding applications elsewhere that lead to spikes in its usage and development. The internet, for example, was originally developed to share research before becoming a staple of work and entertainment. But technology—new and repurposed—will undoubtedly continue to be a driver of healthcare information. Informaticists often stay tuned to trends to monitor what the next new technology will be or how the next .
Social Media Datasets for Analysis and Modeling Drug Usageijtsrd
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
This study developed a survey instrument to segment physicians into groups based on their preferences and opinions regarding information technology. The survey was developed using qualitative research including interviews and focus groups. It presented physicians with statements about IT to rank order.
The findings identified six distinct preference profiles among physicians: 1) "Full-Range Adopters" who saw many benefits of IT, 2) "Skills-Concerned Adopters" who saw benefits but had skills concerns, 3) "Technology-Critical Adopters" who saw benefits but had strong concerns about privacy, monitoring and skills, 4) "Independently-Minded and Concerned" who emphasized independent research uses of IT but also had strong privacy and skills concerns
4040 2 Protected Health Information and Confidentiality Best Practices.docxwrite4
Health care providers must protect patient privacy and confidentiality, especially regarding social media use. A nurse was fired for posting a patient's photo and details on Facebook without permission. In response, an organization created a task force to educate staff on HIPAA and appropriate social media use. The assistant was asked to create a two-page staff update on social media best practices, risks to patient privacy, or what to avoid online. The update must define protected health information, privacy, security and confidentiality, and cite evidence on social media policy violations and breaches.
4040 2 Protected Health Information and Confidentiality Best Practices.docxwrite12
Health care providers must protect patient privacy and confidentiality, especially regarding social media use. A nurse was fired for posting a patient's photo and details on Facebook without permission. In response, an organization created a task force to educate staff on HIPAA and appropriate social media use. The assistant was asked to create a two-page staff update on social media best practices, risks to patient privacy, or what to avoid online. The update informs staff on protected health information, privacy and security laws, evidence of privacy breaches, and strategies to prevent issues.
Similar to Recommender Systems and Misinformation: The Problem or the Solution? (20)
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This document presents a new formulation of neighborhood-based recommender systems that incorporates temporal information. It proposes representing each neighbor's recommendations as a ranked list of items around the user's last interaction, and combining these lists using rank aggregation techniques. The approach is evaluated on the Epinions dataset against baseline methods. Results show the backward-forward method outperforms classical kNN and sequential recommender baselines, with improvements depending on the evaluation methodology used. Future work is discussed to further explore the approach.
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This document summarizes a tutorial on replicable evaluation of recommender systems presented at ACM RecSys 2015. The tutorial covered background on recommender systems and motivation for proper evaluation. It discussed evaluating recommender systems as a "black box" process involving data splitting, recommendation generation, candidate item selection, and metric computation. The presenters emphasized the importance of replicating and reproducing evaluation results to validate findings and advance the field. They provided guidelines for reproducible experimental design and highlighted the need to distinguish between replicability and reproducibility. The tutorial included a demonstration of replicating results and concluded by discussing next steps like agreeing on standard implementations and incentivizing reproducibility.
Implicit vs Explicit trust in Social Matrix FactorizationAlejandro Bellogin
1) The document discusses implicit vs explicit trust in social matrix factorization for recommender systems. It aims to evaluate methods for predicting implicit trust scores between users when explicit trust scores are unavailable.
2) Several trust metrics are evaluated to find the best method for inferring implicit trust scores based on user interaction data. The metric from O'Donovan and Smyth performed best at predicting implicit trust scores.
3) Social matrix factorization using the best implicit trust scoring method performed as accurately as using explicit trust scores, showing that implicit trust can be incorporated when explicit trust is unavailable.
RiVal - A toolkit to foster reproducibility in Recommender System evaluationAlejandro Bellogin
RiVal is an open source recommender system evaluation toolkit written in Java that allows control over evaluation dimensions like data splitting, evaluation strategies, and metrics computation. It integrates three recommendation frameworks - Mahout, LensKit, and MyMediaLite. The toolkit is available as Maven dependencies and as a standalone program. Future work includes integrating more recommendation libraries and evaluating metrics beyond accuracy like novelty and diversity.
Probabilistic Collaborative Filtering with Negative Cross EntropyAlejandro Bellogin
This document proposes using relevance models from information retrieval to improve probabilistic collaborative filtering recommendation algorithms. It introduces a relevance-based language model (RMUB) and a complete probabilistic model (RMCE) that incorporates negative cross entropy. Experiments on MovieLens datasets show both methods outperform baseline user-based and neighbourhood-based collaborative filtering, with RMCE achieving the best performance.
Understanding Similarity Metrics in Neighbour-based Recommender SystemsAlejandro Bellogin
The document discusses understanding which similarity metrics perform best in neighbor-based recommender systems. It analyzes how the choice of similarity metric, like cosine vs Pearson, affects recommendation quality. It finds metrics' performance correlates with their "quality" - whether most users are close or far in the similarity distribution. The document aims to transform "bad" metrics into better ones by adjusting values based on these correlations, but normalizing distributions did not conclusively improve performance. The analysis provides insight into how a metric's stability relates to discriminating good vs bad neighbors.
Artist popularity: do web and social music services agree?Alejandro Bellogin
This document compares artist popularity rankings across different web and social music services using datasets from Last.fm, EchoNest, and Spotify spanning January to March 2021. It finds:
1) While no two services are equivalent, comparing rankings across multiple sources can help promote a more diverse set of artist recommendations.
2) Web-based rankings are highly dynamic while service-based rankings change more slowly.
3) Despite differences, rankings show moderate to high correlations, with stability increasing as more artists are considered.
Improving Memory-Based Collaborative Filtering by Neighbour Selection based o...Alejandro Bellogin
The document presents research on improving memory-based collaborative filtering recommender systems by selecting neighbors based on user preference overlap rather than similarity metrics. It finds that user preference overlap is a good surrogate for similarity in neighbor selection and can provide equivalent or better results than traditional similarity-based approaches. The research compares different neighbor selection methods based on overlap, similarity, and hybrid approaches and evaluates their performance on precision and error metrics across different neighborhood sizes. It determines that selection methods based on preference overlap provide as good or better performance than the baseline similarity approach.
Using Graph Partitioning Techniques for Neighbour Selection in User-Based Col...Alejandro Bellogin
Using graph partitioning techniques like Normalised Cut (NCut) for neighbourhood selection in user-based collaborative filtering outperforms other clustering methods like k-Means. NCut models users as nodes in a graph and clusters them based on their similarities. It was tested on the Movielens 100K dataset against baselines like user-based CF with Pearson correlation and matrix factorization. NCut achieved higher precision and coverage than the baselines, showing the benefit of using graph partitioning for neighbourhood selection in collaborative filtering.
Using Graph Partitioning Techniques for Neighbour Selection in User-Based Col...Alejandro Bellogin
This document proposes using spectral clustering techniques like normalized cuts for identifying neighbors in user-based collaborative filtering. It shows that this approach works better than k-means clustering and standard user-based collaborative filtering, providing higher prediction accuracy and coverage for recommendations. The approach identifies neighbors based on clustering users based on their ratings rather than selecting the nearest users.
Precision-oriented Evaluation of Recommender Systems: An Algorithmic Comparis...Alejandro Bellogin
This document compares different methodologies for evaluating recommender systems using precision-oriented metrics. It presents an approach that builds a target item list for each user and ranks items by predicted rating. It evaluates how precision metrics are affected by the construction of the non-relevant item set. An empirical comparison on MovieLens data shows significant differences in results depending on the evaluation methodology used. The discussion indicates precision and error-based metrics may give different comparative recommender results.
Predicting performance in Recommender Systems - SlidesAlejandro Bellogin
The document discusses predicting performance in recommender systems. It proposes adapting techniques from information retrieval to define performance prediction models for recommender systems. Specifically, it explores adapting the concept of "query clarity" to define user clarity models that capture uncertainty in a user's preferences. The models are used to build dynamic recommendation strategies like dynamic neighbor weighting and dynamic hybrid recommendations. Initial results show the dynamic strategies outperform static baselines and user clarity predictors correlate positively with performance metrics. Future work includes exploring additional input sources for performance prediction models.
Predicting performance in Recommender Systems - PosterAlejandro Bellogin
This document discusses predicting the performance of recommender systems. It proposes that data commonly available to recommender systems could enable estimating the success of recommendations. Specifically, it aims to 1) define a performance prediction theory for recommender systems, 2) adapt query performance techniques from information retrieval to recommendations, and 3) evaluate appropriate performance metrics. The research also explores applying these models when combining multiple recommendation strategies or hybrid recommender systems.
Precision-oriented Evaluation of Recommender Systems: An Algorithmic Comparis...Alejandro Bellogin
The document discusses three questions about evaluating recommender systems: 1) How comparable are results reported using precision-oriented metrics? 2) How meaningful are absolute performance metric values? 3) How sensitive are recommenders to different evaluation methodologies? It presents comparative experiments using different metrics and evaluation designs to analyze recommenders' performance under precision-oriented evaluation.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
4. Factors that influence the spread of misinformation online:
● How information is constructed and presented
● Personality, values, and emotions of the users
● Characteristics of digital platforms
● Algorithms that power the recommendation of information in those platforms
Misinformation spread
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5. Our goal
Explore the effect of existing algorithms on the recommendation of false and
misleading information
Understand which techniques are more prone to spread misinformation, and under which
circumstances.
How can their internal functioning be modified, or adapted, to counter this behaviour?
USER
SYSTEM
Preferences
Model
Interaction
Recommendation
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8. Our approach
Problem Dimensions: which dimensions of
the misinformation problem affect the
behaviour of RS?
Analysis of Recommendation Algorithms:
analyse how existing algorithms improve
or worsen the spread of misinformation
Human-centred Evaluation: modify
existing evaluation methods and metrics
(dissonance vs. satisfaction)
Adaptation, Modification and Vigilance:
adapt existing algorithms and track their
impact over time (vigilance).
which recommendation algorithms are more
prone to spread misinformation, and under
which circumstances?
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9. Content: Different forms (newspaper articles, blog posts, social media posts),
topics (health, elections), formats (text, images, videos), framing (false news,
rumours, conspiracy theories), origin (news outlets, social contacts, public
figures), time
Users: Different motivations, personalities, values, emotions and susceptibility
influence the spread of misinformation
Platform & Network Features: Platforms are designed differently (content
limitations, sharing permissions) & have different typology and topology of
network structure.
Others: global events, presence of malicious actors, presence of checked facts
Misinformation: Problem Dimensions
A global problem,
multiple dimensions
(human, sociological, technological)
Dimensions that intersect between recommendation and misinformation
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10. Analysis of Recommendation Algorithms
Which recommendation techniques are more prone to suggest misinformative items to
users and why?
CF methods neglect any information about the items and their misinformative features.
They can help us to understand the impact of user-item interactions.
CB approaches use content and therefore require analysing NL and its inherent subtleties.
Hybrid methods combine content analysis with user-time interactions.
Are they therefore more prone to spread misinformation?
User & Item Profiles: Multiple representations can be considered.
How do these representations influence the spread of misinformation?
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11. Human-centred Evaluation
Metrics that focus on computing a balanced degree of user satisfaction and discomfort
(introducing opposing views) may help to combat misperceptions.
Same information, opposing views ≠ diversity (novel information)
Datasets: We need users / items & information about their credibility + user-item ratings
- NELA-GT-2018 dataset (713 misinforming articles) → no user profiles / ratings
- COVID-19 Poynter dataset (603 misinforming articles/posts) → no user profiles / ratings
- NewsReel: users, articles and ratings → no information about item credibility
Measure: how can we evaluate when the task has been successfully addressed?
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12. Adaptation, Modification, and Vigilance
Social Science and Psychology
studies: simply presenting people
with corrective information is
likely to fail in changing their
salient beliefs.
- Providing an explanation
rather than a simple refute
- Exposing users to related
but disconfirming stories
- Revealing similarities with
the opposing groups
Adaptation/Modification: the adaptation of existing algorithms
to counter the misinformation recommendation problem should
build on existing social science and psychology theory.
- Profile users to better capture motivations
- RS that introduce small degrees of opposing views
- RS focus on similarity and dissimilarity
- Explanations
Vigilance:
- Dynamics of misinformation captured over time
- Algorithms are adapted
- Proposed adaptations do not back-fire (ethics!)
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13. Recommender systems are often accused of feeding and reinforcing the interaction cycle
BUT, they could be part of the solution, if we understand:
● How misinformation is spread
● How such mechanisms are reinforced
○ And to what extent, depending on the technique
● How we can adapt the algorithms to counter these effects
Our hypothesis: build on social science and psychology theories
Discussion
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14. Current plan
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Starting with a COVID-related Poynter dataset
It contains news items classified as reliable or not
We are collecting Twitter user profiles
Challenge 1: how many users can we collect for each news item? (cold items)
Challenge 2: how many items do user profiles actually contain? (cold users)
Challenge 3: what is a realistic ratio between misinformation/normal items?
Question: are there datasets with this information combined?
Next step: run and evaluate CF recommenders
Next challenges???
15. CREDITS: This presentation template was created by
Slidesgo, including icons by Flaticon, infographics &
images by Freepik and illustrations by Stories
Recommender Systems
and Misinformation:
The problem or the solution?
Miriam Fernández, Open University, United Kingdom
Alejandro Bellogín, Universidad Autónoma de Madrid, Spain
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