Abstract:
The ubiquitous adoption of social media has set high expectations for emergency services to serve the public online. However, the information overload of social media requests to seek help challenges the emergency services. This work introduces a human-AI collaboration framework to assist emergency services in effectively responding to online serviceable requests. In particular, this talk describes specific solutions to two problems in this framework. The first problem is the challenge of how to mitigate potential human errors when giving annotation feedback to the active learning model in the system that we address by proposing a psychological theory-inspired technique. The second is the challenge of how to optimally select how many and how often to present the request for request while accounting for the dynamic constraints of the busy service personnel that we address by proposing an optimization technique.
Biography:
Hemant Purohit, Ph.D. is an assistant professor in the Department of Information Sciences & Technology at Volgenau School of Engineering, George Mason University. His research interest is to design intelligent systems for augmenting human capabilities of real-time information processing at a workplace, particularly public services and NGOs, by using methods of social & web mining, semantic computing, and human-AI collaboration. He applies this research in disaster informatics for assisting communities toward resilience from natural hazards, societal crises (e.g. violence and stereotyping), and man-made crises including cyber attacks. Purohit has received many awards for disaster informatics work including 2014 ITU Young-Innovator award from the United Nations agency on information and communication technologies for an opensource technology concept for disaster management. He was an invited academic member of the DHS Science & Technology Directorate's subcommittee on Social Media Working Group for Emergency Services and Disaster Management. He has given several talks, tutorials, and lectures on social computing for public services as well as organized workshops. His work has been published in prestigious conferences and journals and he currently serves on the editorial board of the Elsevier journal of Information Processing & Management. His research is supported by various national and international agencies including the U.S. National Science Foundation.
Contact:
hpurohit@gmu.edu | http://ist.gmu.edu/~hpurohit
Human-AI Collaborationfor Virtual Capacity in Emergency Operation Centers (E...Hemant Purohit
Describes different use-cases for how AI technologies can help Emergency Management agencies for building virtual capacity in monitoring online data for situational awareness, decision support, and public communication in EOCs during disaster events.
Talk by Dr. Hemant Purohit, Humanitarian Informatics Lab, George Mason University -- https://mason.gmu.edu/~hpurohit
Detect Policy-affecting Intent in Twitter Conversations for Rape and Sexual A...Hemant Purohit
This multidisciplinary research investigates Twitter posts related to sexual assaults and rape myths by characterizing and detecting the types of malicious intent, which leads to the beliefs on discrediting women and rape myths. We analyze narrative contexts in which such malicious intents are expressed and discuss their implications for gender violence policy design.
Pandey, R., Purohit, H., Stabile, B., & Grant, A. (2018). Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults. IEEE/WIC/ACM Web Intelligence. ArXiv preprint: https://arxiv.org/abs/1810.01012
Workload-bound Ranking of Alerts for Emergency Operation Centers - Web Intell...Hemant Purohit
This research presents a novel problem and a model to quantify the relationship between the performance metrics of automated ranking systems (e.g., recall, NDCG) and the bounds on the human performance (e.g., cognitive workload) in emergency services. We synthesize an alert-based ranking system that enforces these bounds to avoid overwhelming end-users for achieving the Human-AI collaboration.
Citation:
Purohit, H., Castillo, C., Imran, M., & Pandey, R. (2018). Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers. IEEE/WIC/ACM Web-Intelligence. ArXiv preprint: https://arxiv.org/abs/1809.08489
Automatically Rank Social Media Requests for Emergency Services using Service...Hemant Purohit
Public expects a prompt response from online services, including emergency response organizations to requests for help posted on social media. However, the information overload experienced by these organizations, coupled with their limited human resources, challenges them to timely identifying and prioritizing such requests. We present a novel model to formally characterize social media requests and then, develop a Learning-to-Rank system using this model.
Paper: Purohit, H., Castillo, C., Imran, M., and Pandey, R. (2018). Social-EOC: Serviceability Model To Rank Social Media Requests for Emergency Operation Centers. ASONAM 2018.
Social Media & Web Mining for Public Services of Smart Cities - SSA TalkHemant Purohit
This talk at Data Science Seminar of SSA presents challenges and methods to model behavior on social media & Web for application opportunities for public services. The talk also demonstrates an in-depth case study of mining intentional behavior from the noisy natural language text of social media messages during disasters and how it could assist emergency services of future smart cities.
Uncertain Concept Graph for Social Web Summarization during Emergencies - CPS18Hemant Purohit
Web has empowered emergency services to enhance operations by collecting real-time information about incidents from diverse data sources such as social media and Web. However, the high volume of unstructured data with varying degrees of veracity challenges the timely extraction and integration of relevant information to summarize the current situation. This research proposes a novel idea of building Uncertain Concept Graphs.
It was presented at 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering. Cyber-Physical Systems, CPS Week 2018.
Purohit, H., Nannapaneni, S., Dubey, A., Karuna, P., & Biswas, G. (2018). Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph. 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering. Cyber-Physical Systems (CPS Week).
User Classification of Organization and Organization Affiliated Users during ...Hemant Purohit
This document discusses classifying user types on social media to inform crisis response coordination. It proposes classifying users into three types: organizations, organization-affiliated individuals, and non-affiliated individuals. It describes collecting Twitter data from two 2016 disasters and manually annotating over 1500 users. Automated classification experiments using basic user metadata achieved up to 75% accuracy. Analysis found organizations disseminate trusted content while organization-affiliated users more actively engage. Future work is needed to improve classification and analyze interactions between user types.
Public Health Crisis Analytics for Gender ViolenceHemant Purohit
The document discusses using social media data to analyze gender-based violence campaigns and public attitudes. It summarizes a study of cross-campaign participation on Twitter around three hashtags. Most users and tweets were individual rather than organizational. Few male users were observed. The document also describes a system called CitizenHelper for visualizing attitude trend analytics over time from social media to evaluate campaign effects and inform intervention events.
Human-AI Collaborationfor Virtual Capacity in Emergency Operation Centers (E...Hemant Purohit
Describes different use-cases for how AI technologies can help Emergency Management agencies for building virtual capacity in monitoring online data for situational awareness, decision support, and public communication in EOCs during disaster events.
Talk by Dr. Hemant Purohit, Humanitarian Informatics Lab, George Mason University -- https://mason.gmu.edu/~hpurohit
Detect Policy-affecting Intent in Twitter Conversations for Rape and Sexual A...Hemant Purohit
This multidisciplinary research investigates Twitter posts related to sexual assaults and rape myths by characterizing and detecting the types of malicious intent, which leads to the beliefs on discrediting women and rape myths. We analyze narrative contexts in which such malicious intents are expressed and discuss their implications for gender violence policy design.
Pandey, R., Purohit, H., Stabile, B., & Grant, A. (2018). Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults. IEEE/WIC/ACM Web Intelligence. ArXiv preprint: https://arxiv.org/abs/1810.01012
Workload-bound Ranking of Alerts for Emergency Operation Centers - Web Intell...Hemant Purohit
This research presents a novel problem and a model to quantify the relationship between the performance metrics of automated ranking systems (e.g., recall, NDCG) and the bounds on the human performance (e.g., cognitive workload) in emergency services. We synthesize an alert-based ranking system that enforces these bounds to avoid overwhelming end-users for achieving the Human-AI collaboration.
Citation:
Purohit, H., Castillo, C., Imran, M., & Pandey, R. (2018). Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers. IEEE/WIC/ACM Web-Intelligence. ArXiv preprint: https://arxiv.org/abs/1809.08489
Automatically Rank Social Media Requests for Emergency Services using Service...Hemant Purohit
Public expects a prompt response from online services, including emergency response organizations to requests for help posted on social media. However, the information overload experienced by these organizations, coupled with their limited human resources, challenges them to timely identifying and prioritizing such requests. We present a novel model to formally characterize social media requests and then, develop a Learning-to-Rank system using this model.
Paper: Purohit, H., Castillo, C., Imran, M., and Pandey, R. (2018). Social-EOC: Serviceability Model To Rank Social Media Requests for Emergency Operation Centers. ASONAM 2018.
Social Media & Web Mining for Public Services of Smart Cities - SSA TalkHemant Purohit
This talk at Data Science Seminar of SSA presents challenges and methods to model behavior on social media & Web for application opportunities for public services. The talk also demonstrates an in-depth case study of mining intentional behavior from the noisy natural language text of social media messages during disasters and how it could assist emergency services of future smart cities.
Uncertain Concept Graph for Social Web Summarization during Emergencies - CPS18Hemant Purohit
Web has empowered emergency services to enhance operations by collecting real-time information about incidents from diverse data sources such as social media and Web. However, the high volume of unstructured data with varying degrees of veracity challenges the timely extraction and integration of relevant information to summarize the current situation. This research proposes a novel idea of building Uncertain Concept Graphs.
It was presented at 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering. Cyber-Physical Systems, CPS Week 2018.
Purohit, H., Nannapaneni, S., Dubey, A., Karuna, P., & Biswas, G. (2018). Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph. 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering. Cyber-Physical Systems (CPS Week).
User Classification of Organization and Organization Affiliated Users during ...Hemant Purohit
This document discusses classifying user types on social media to inform crisis response coordination. It proposes classifying users into three types: organizations, organization-affiliated individuals, and non-affiliated individuals. It describes collecting Twitter data from two 2016 disasters and manually annotating over 1500 users. Automated classification experiments using basic user metadata achieved up to 75% accuracy. Analysis found organizations disseminate trusted content while organization-affiliated users more actively engage. Future work is needed to improve classification and analyze interactions between user types.
Public Health Crisis Analytics for Gender ViolenceHemant Purohit
The document discusses using social media data to analyze gender-based violence campaigns and public attitudes. It summarizes a study of cross-campaign participation on Twitter around three hashtags. Most users and tweets were individual rather than organizational. Few male users were observed. The document also describes a system called CitizenHelper for visualizing attitude trend analytics over time from social media to evaluate campaign effects and inform intervention events.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
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2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
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The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Human AI Collaboration for Real-time Data Processing at Emergency Services, Guest Lecture, University of South Carolina
1. Human-AI Collaboration for Real-time Data
Processing Systems at Emergency Services
Hemant Purohit, Ph.D.
Humanitarian Informatics Lab (Human_Info_Lab)
Dept. of Information Sciences & Technology
Mar 5, 2021 @hemant_pt | hpurohit@gmu.edu
Grants:
• IIS #1657379, IIS #1815459
PhD Students:
Rahul Pandey & Yasas Senarath
Special Thanks:
Guest Lecture for CSCE 791: Seminar in Advances in Computing
University of South Carolina
2. Human-AI Collaboration for Next-Generation Emergency Services
Outline
¨ Summary of research thrusts
¨ Focus: social media & city services during crises
¨ Problem 1. Modeling human errors in human-in-the-loop AI
system design
¨ Problem 2. Human workload-aware serviceability ranking
system design
¨ Future directions
2
3. Human-AI Collaboration for Next-Generation Emergency Services
Broad Research Area
3
¨ Human-centered Computing
n Sebe (2010) – “integrating human sciences (e.g. social & cognitive)
and computer science (e.g. machine learning) methods
for the design of computing systems with a human focus,
which should consider the personal, social, and cultural contexts
in which such systems are deployed”
My focus on
Social Media Mining &
Semantic Text Analytics
for real-time processing
systems at city services
4. Human-AI Collaboration for Next-Generation Emergency Services
Lab’s Research Thrusts
4
¨ [Natural Crises] Social Media Mining for Crisis Communication
¤ Extracting actionable posts in a new crisis using transfer & active learning
¤ Ranking serviceable requests for help on social media
¤ Human workload-aware ranking system design
¨ [Societal Crises] Semantic Analysis for Human Behavior Modeling
¤ Defining intent behind harmful behaviors on social media: Stereotyping, Hate
¤ Mining malicious stereotypical behavior against women for negative social construction
¤ Identifying factors affecting diffusion and mitigation of hate and disinformation
¨ [Cyber Crises] Text Comprehension Modeling for Cyber Defense
¤ Manipulating text comprehensibility to generate deceptive content
¤ Estimating believability for deceptive content
5. Human-AI Collaboration for Next-Generation Emergency Services
Outline
¨ Summary of research thrusts
¨ Focus: social media & city services during crises
¨ Problem 1. Modeling human errors in human-in-the-loop AI
system design
¨ Problem 2. Human workload-aware serviceability ranking
system design
¨ Future directions
5
6. Human-AI Collaboration for Next-Generation Emergency Services
Current Work at EM Services
6
World
Events
EM Response &
Decision Making
Human Workers
Information
Processing
CURRENT
Reliable but
small-scale
Accurate but
high workload
Data
Collection
7. Human-AI Collaboration for Next-Generation Emergency Services
Motivation
7
When traditional call-for-help EM services are overwhelmed ..
Source: https://www.usatoday.com/story/news/nation-now/2017/08/27/desperate-help-flood-victims-houston-turn-twitter-rescue/606035001/
Help
Offering
Help
Seeking
People resolving
to Social Media
How to
discover?
8. Human-AI Collaboration for Next-Generation Emergency Services
Future of Work at EM Services
8
World
Events
EM Response &
Decision Making
Human Worker +
AI agent
Data
Collection
Information
Processing
FUTURE
Noisy but
Large-scale
Faster but
Inaccurate
9. Human-AI Collaboration for Next-Generation Emergency Services
Future of Work at EM Services
9
World
Events
EM Response &
Decision Making
Human Worker +
AI agent
Data
Collection
Information
Processing
FUTURE
How to improve
AI Mental Model
with Worker
Mental Model?
Noisy but
Large-scale
Faster but
Inaccurate
10. Human-AI Collaboration for Next-Generation Emergency Services
Matching Mental Models of Human & AI
Agent: How to design Human-in-the-loop AI system?
10
Human Worker +
AI Agent
Information Processing Tasks
1. Filtering
• Classification Problem
2. Prioritization
• Ranking Problem
..
1. Adapt to
classify relevant
items in a
data stream
2. Adapt to rank
top-K items for
human
intervention
11. Human-AI Collaboration for Next-Generation Emergency Services
Human-in-the-loop AI System Design:
Awareness for human factors
11
Human Worker +
AI Agent
Information Processing Tasks
1. Filtering
• Classification Problem
2. Prioritization
• Ranking Problem
..
1. Active Learning
for Relevancy
Classification
Depends on
Annotator
Reliability
12. Human-AI Collaboration for Next-Generation Emergency Services
Human-in-the-loop AI System Design:
Awareness for human factors
12
Human Worker +
AI Agent
Information Processing Tasks
1. Filtering
• Classification Problem
2. Prioritization
• Ranking Problem
..
2. Adaptive
Top-K ranking
alerts for
human
Affects
Human
Workload
1. Active Learning
for Relevancy
Classification
Depends on
Annotator
Reliability
13. Human-AI Collaboration for Next-Generation Emergency Services
Outline
¨ Summary of research thrusts
¨ Focus: social media & city services during crises
¨ Problem 1. Modeling human errors in human-in-the-loop AI
system design
¨ Problem 2. Human workload-aware serviceability ranking
system design
¨ Future directions
13
14. Human-AI Collaboration for Next-Generation Emergency Services
Human-in-the-loop AI System Design:
Awareness for human factors
14
Human Worker +
AI Agent
1. Active Learning
for Relevancy
Classification
Depends on
Annotator
Reliability
What if
system
causes
human
errors?
15. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
15
Understanding potential human error causes using psychology theories
¨ Annotator burnout(Marshall and Shipman, 2013)
¨ Cognitive bias for answer positions(Burghardt, Hogg, and Lerman, 2018)
¨ Human error in execution(Reason, 1990; Zhang et al., 2004)
Mistakes
Errors due to incorrect or incomplete
knowledge
Faulty heuristics
Slips
Errors in the presence of correct and
complete knowledge
Loss of activation
[Pandey, Castillo, & Purohit, ASONAM’19]
16. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
16
Ø Hypothesis: Serial ordering of instances given to the human
annotator may cause him/her errors due to a mistake or slip.
{c4, c1, c2, c3, c1, c3, c4, c1, c4, c1, c4, c2, c1, c4, c1, c2, c4, c2, c4, c3}
How likely an
annotator would
make error on this
3rd occurrence due
to the potential
decay in memory?
Instance class
Motivation: Memory Decay, Ebbinghaus Curve(Ebbinghaus,2013)
[Pandey, Castillo, & Purohit, ASONAM’19]
17. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
17
Type of Error Potential Cause Mitigation Approach
Slips induced by
time constraints
• Concept forgotten • Show reminder for concept
examples
Mistakes induced by
serial ordering
• Concept not acquired yet
or forgotten
• Show frequent learning
examples
Slips induced by
serial ordering
• Presence of
a high-availability concept or
a low-availability concept
• Limit extreme divergence
from base rate
Preliminary framework to study human factors in active learning
Ø Hypothesis: Serial ordering of instances given to the human
annotator may cause him/her errors due to a mistake or slip.
[Pandey, Castillo, & Purohit, ASONAM’19]
18. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
18
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1st 2nd 3rd
Average
Error
Position
¨ Crowdsourcing annotation
testing experiment
¤ 20 ordered instances per
schedule with specific
class positions
¤ 6 such schedules
¤ 10 human annotator per
task
p-value
0.005
Annotation Schedule: {c4, c1, c2, c3, c1, c3, c4, c1, c4, c1, c4, c2, c1, c4, c1, c2, c4, c2, c4, c3}
Forgetting or Memory Decaying
behavior exists
[Pandey, Castillo, & Purohit, ASONAM’19]
19. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
¨ Sigmoid function to
model error probability
for an ordered instance
¨ Lab annotation testing
¤ 3 human annotator
¤ 800 ordered instances
with the induced error
19
[Pandey, Castillo, & Purohit, ASONAM’19]
Forgetting or Memory Decaying
behavior resembles sigmoid function.
20. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
Generate
Annotation-Schedule
Sample instances
Minimizing
human memory
decaying score
Maximizing
streaming model
performance
1. Sample instances from decision boundary
range of active learning model
• Prediction probability in [30%, 70%]
2. Maintain a class label Cdiscarded for each
interval to avoid samples predicted with
Cdiscarded labels
• Choose Cdiscarded based on
• If the class is appearing too frequent
• If the class is adding noise to the
streaming model
20
Solution: Error-avoiding Annotation Schedule to augment both human & model performance
[Pandey, Castillo, & Purohit, ASONAM’19]
21. Human-AI Collaboration for Next-Generation Emergency Services
21
[Pandey, Castillo, & Purohit, ASONAM’19]
Solution: Error-avoiding Annotation Schedule to augment both human & model performance
Problem 1: How to Reduce Annotator Errors
22. Human-AI Collaboration for Next-Generation Emergency Services
Problem 1: How to Reduce Annotator Errors
22
Human Error-Mitigating Sampling Algorithm outperforms in most cases!
[Pandey, Castillo, & Purohit, ASONAM’19]
23. Human-AI Collaboration for Next-Generation Emergency Services
Outline
¨ Summary of research thrusts
¨ Focus: social media & city services during crises
¨ Problem 1. Modeling human errors in human-in-the-loop AI
system design
¨ Problem 2. Human workload-aware serviceability ranking
system design
¨ Future directions
23
24. Human-AI Collaboration for Next-Generation Emergency Services
Human-in-the-loop AI System Design:
Awareness for human factors
24
Human Worker +
AI Agent
2. Adaptive
Top-K ranking
alerts for
human Affects
Human
Workload
Can you
increase
human
control or
agency?
25. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2: How to Create Human Workload-
aware Serviceability Ranking System
25
Image: https://blog.bufferapp.com/twitter-timeline-algorithm
BEYOND TIME & CREDIBILITY,
RANK BY
Serviceability
Can you
increase my
control or
agency?
End user
(Servicer)
26. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2: Workload-aware Serviceability
Ranking: Designing for human-AI collaboration
26
¨ Problem: how many and how often generate the request
alerts to respond for a human servicer (cause him workload!)
High Recall can cause
more work for a
time-crunched
Servicer!
Low Recall can cause
missing important
requests for a
Servicer!
RECALL (Machine/System metric)
WORKLOAD
(Human metric)
Ineffective
Inefficient
Worst
Desired
Optimal
Solution
[Purohit, Castillo, Imran, & Pandey, WI’18]
27. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2: Workload-aware Serviceability
Ranking: Designing for human-AI collaboration
27
Streaming
Requests
tij corresponds to
time period - when
to check requests,
e.g., 10 mins.
Row k corresponds
to the selection of
top-k ranked
requests to check
Ranked Requests Performance Metrics Estimation Dynamic Policy Selection
A cell tuple
corresponds to the
attainable
(Recall, Workload)
Choose a config,
e.g., k=10, tij=30,
and (R,W) = (90,20)
[Purohit, Castillo, Imran, & Pandey, WI’18]
28. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2: Workload-aware Serviceability
Ranking: Approach summary
28
Serviceability
Categorization
and Ranking
Ranking-
Workload
(RW) Matrix
Generation
Optimal RW
Policy Selection
29. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2. Serviceability Model: using Qualitative
Knowledge extracted from domain guides
29
Explicit
Request
E(m)
Answerable
Query
A(m)
Sufficiently
Detailed
D(m)
Correctly
Addressed
C(m)
Serviceability(m) = f ( E(m), A(m), D(m), C(m) )
Explicitly asks for a
resource or service
Explicitly asks a question
that can be answered
Sent to organization or
person who could have
resources or provide the
service, an alarm, or
could answer questions
Specifying contextual
information: time (when),
location (where),
quantity (how much),
resource (which)
[Purohit, Castillo, Imran, &
Pandey, ASONAM’18]
30. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2. Serviceability Model: Quantifying
Characteristics
(Anonymized) Message Explicit Answer-
able
Addressed Detailed
@account1 please, governor, post a phone # for
specific info in our local areas
4.3 4.3 3.3 3.7
@account2 is thr parking at McMahon for volunteer? 4.0 5.0 5.0 5.0
@account3 how can I help 1.3 4.3 4.3 1.0
@account4 Plz pray for these families 1.7 1.0 1.0 1.0
@account5 been working in #LAFlood shelter, we
actively monitor SM for feedback
1.0 1.0 2.0 2.0
“@account7 No matter where in the world ur
followers live, you can donate from link Plz RT
1.0 1.0 1.0 1.0
¨ E(m), A(m), C(m), D(m) : Likert Scale Functions [score:1-5]
30
Illustration Table: Average scores of Likert ratings by crowd annotators
31. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2. Serviceability Model: Learning-to-Rank
System Design
31
[Purohit, Castillo, Imran, & Pandey, ASONAM’18]
32. Human-AI Collaboration for Next-Generation Emergency Services
Serviceability Model: Examples of resulting
ranked requests
33
Ranked Messages by T (text)+I (Inferred) Modeling Scheme
TOP-2
[Sandy]
- @_USER_ please, governor, post a website or phone# where we can get
specific info for our local areas
- @_USER_ Queens trains aren’t being addressed at all. When can v expect any
service updates for the NQR trains?
BOTTOM-2
[Sandy]
- @_USER_ Romney not going2like that gov christie is being nice about Obama’s
leadership
- @_USER_ HILARIOUS! That’s much needed laughter, I am sure.
TOP-2
[Alberta]
- @_USER_ can you tell me if sanitary pumps are running yet in elbow park?
#yycflood
- @_USER_ plz text with what you need & address. Lots of volunteers in mission
BOTTOM-2
[Alberta]
- @_USER_ thank u calgary police
- @_USER_ Tx for ur time!!
33. Human-AI Collaboration for Next-Generation Emergency Services
Workload-aware Serviceability Ranking:
Designing for human-AI collaboration
34
Streaming
Requests
Ranked Requests Performance Metrics Estimation Dynamic Policy Selection
[Purohit, Castillo, Imran, & Pandey, WI’18]
Image: https://commons.wikimedia.org/wiki/File:Front_pareto.svg
Pareto Optimization
à Given the lack of user
preference apriori, rely
on for non-dominated
sorting
Ranking-Workload (RW) Matrix
34. Human-AI Collaboration for Next-Generation Emergency Services
Workload-aware Serviceability Ranking:
Ranking-Workload (RW) Matrix
35
¨ Define RW Matrix to model the relationship between human &
machine performances for a request-set 𝑥𝑖𝑗 in time 𝑡𝑖𝑗
n 𝑅𝑊 (𝑘, 𝑡𝑖𝑗) = ⟨ 𝑀(𝑅(𝑥𝑖𝑗)), 𝑤 𝑡𝑖𝑗, 𝑘 ⟩
¤ Machine Performance Metric (for a ranking system 𝑅(𝑥𝑖𝑗)): 𝑀(𝑅(𝑥𝑖𝑗))
n Recall@k, e.g., no. of relevant requests in top-k
n Precision@k
¤ Human Performance Metric: 𝑤(𝑡𝑖𝑗, 𝑘)
n Cognitive Load, e.g. hourly rate of requests to read
n Time-on-Task
[Purohit, Castillo, Imran, & Pandey, WI’18]
35. Human-AI Collaboration for Next-Generation Emergency Services
Workload-bound Serviceability Ranking:
Pareto-Optimal RW Policy Selection
36
Given the lack of user
preference apriori, rely on
Pareto Optimization[Ross, 1973]
for the non-dominated
selection
Can you
recommend
me to choose?
Image: https://commons.wikimedia.org/wiki/File:Front_pareto.svg
36. Human-AI Collaboration for Next-Generation Emergency Services
Workload-bound Serviceability Ranking:
Experimental Setup
37
¨ Used relevancy data as alerts from 6 crisis events in our prior work,
where relevancy is ‘serviceability’[Purohit, Castillo, Imran, & Pandey, ASONAM18] of a
message for response
Event (Year, start day – end day) Tweets Relevant Irrelevant
Hurricane Sandy (2012, 10/27-11/07) 1,153 40% 60%
Oklahoma Tornado (2013, 05/20-05/29) 1,513 48% 52%
Alberta Floods (2013, 06/16-06/16) 2,727 28% 72%
Nepal Earthquake (2015, 04/15-05/15) 2,222 18% 82%
Louisiana Floods (2016, 10/11-10/31) 1,369 34% 66%
Hurricane Harvey (2017, 08/29-09/15) 12,742 20% 80%
37. Human-AI Collaboration for Next-Generation Emergency Services
Workload-bound Serviceability Ranking:
Experimental Setup
38
¨ Compared two algorithms for recommending RW policy:
¤ Periodic algorithm
n process requests posted in the time window of past H (24) hrs.
n generate top-k ranking and a RW matrix at the beginning of
every hour (e.g., 7am, 8am)
¤ Near-Realtime algorithm
n process requests posted in the time window of past G (60) mins.
n generate top-k ranking and a RW matrix at the beginning of
every minute (e.g., 7:01am, 7:02am)
38. Human-AI Collaboration for Next-Generation Emergency Services
Workload-bound Serviceability Ranking:
Experiment 1 – RW trade-off validation analysis
39
[Purohit, Castillo, Imran, & Pandey, WI’18]
Multiple
Recall values
for a given
workload
budget!
39. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2. Workload-aware Serviceability
Ranking: Experiment – Greedy-recall baseline comparison
40
Pareto-optimal
Periodic RW
recommendations
give lower workload
in contrast to max.
recall-based policy.
40. Human-AI Collaboration for Next-Generation Emergency Services
Problem 2. Workload-aware Serviceability
Ranking: Experiment – Greedy-workload baseline
41
Pareto-optimal
Periodic RW
recommendations
give higher recall in
contrast to min.
workload-based
policy.
41. Human-AI Collaboration for Next-Generation Emergency Services
Conclusion: Lessons, Limitations, and Future Work
42
¤ A human-AI collaboration approach can help in scalable
stream data processing for the emergency services
n Combining Human Factors + AI systems
¤ Lessons learned:
n Serviceability characteristics of information capture the notion of relevance
and serviceability for social media requests to online public services.
n Workload-aware serviceability ranking provides a Human-AI Collaboration
design to seamlessly incorporate user choices in the system design.
42. Human-AI Collaboration for Next-Generation Emergency Services
Conclusion: Lessons, Limitations, and Future Work
43
¨ Limitations & opportunities:
¤ Serviceability model
n Study non-English language request messages
n Explore multiple as opposed to single platform based datasets
n Twitter vs. Forum
n Include indirectly addressed requests (i.e. not starting with @user)
¤ Human-AI collaboration
n Extend the human performance metrics in the Ranking-Workload matrix
n Incorporate bias of the performance metrics in the RW matrix
n Adapt the workload-aware serviceability approach to other domains
43. Human-AI Collaboration for Next-Generation Emergency Services
44
Applications:
CitizenHelper-Adaptive Tool: Expert-augmented Streaming Analytics
System for Emergency Services and Humanitarian Organizations
[Pandey & Purohit, ASONAM’18]
44. Human-AI Collaboration for Next-Generation Emergency Services
Applications:
Human-Annotation for Crowdsourcing Work
45
Concept for class c2
not acquired yet
– Mistakes
Imbalanced
presence of class c1
– Slips
45. Human-AI Collaboration for Next-Generation Emergency Services
Applications:
Working with CERTs
46
Assisting regional CERT organizations for rapid social media filtering
for COVID-19 response using the tool developed under NSF CRII
project, CitizenHelper Tool, leading to a new NSF RAPID grant!
46. Human-AI Collaboration for Next-Generation Emergency Services
Future Work: Human-AI Collaboration at
Workplaces of Various City Services
47
Q2.
How to classify
relevant
content in
online streams
in a new event
domain?
[ECML’20, ASONAM’20,
SBP-BRiMS’18]
Q3.
How to rank &
semantically group
serviceable,
actionable request
content?
[SNAM’20, ASONAM’18]
Q4.
How many & when
to present requests
to a worker with
dynamic workload?
[ASONAM’18, WI’18]
Data
Stream
City
Service
Worker
Filtering Prioritization Human-Machine
Interaction
Q1.
How to sample &
order instances
for human
annotation, to
improve labeled
data quality?
[ASONAM’19, IJHCS (under
review)]
Human
Annotation
opportunity for fundamental research in AI with Human-Centered Computing
CitizenHelper
Tool
47. Human-AI Collaboration for Next-Generation Emergency Services
More about our research:
http://ist.gmu.edu/~hpurohit/informatics-lab.html
CONTACT: hpurohit@gmu.edu
Acknowledgement:
Image sources, collaborators (especially Prof. Carlos Castillo, Prof. Valerie Shalin, Dr. Muhammad Imran);
U.S. DHS Science & Technology SMWGESDM Researcher-Practitioner Subgroup (especially Steve Peterson),
Human_Info_lab students as well as sponsors:
Questions?
48
Primary grants that supported this work:
• IIS #1657379, IIS #1815459