Tag: Artificial Intelligence

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 3 of 3)

In the final part of our conversation, Chelsea Wang explains how her background in psychology has influenced her work in artificial intelligence. In particular, she describes how her social science background helped her develop and deploy her own version of the Mutual Theory of Mind as a psychologist within the field of artificial intelligence. When socializing, humans employ a recursive feedback loop of conceptualization of each other, and she explores the application of similar concepts to conversational AI systems.

She concludes by discussing her journey as a PhD student: what led her to seek her dissertation and her plans afterwards to use what she is learning now to conduct innovative and impactful work in the business world.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 2 of 3)

Chelsea Wang has spent many years trying to improve the cognitive process of artificial intelligence systems to better interact with humans. In this second part of our conversation, she explains her theories about metacognition, intelligence, and potential anthropomorphization of AI “thought” processes. Through this, she explicates her vision and approach to the potential social life of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 1 of 3)

Chelsea Wang describes her work developing and refining the communication processes between artificial intelligence and humans, particularly the Mutual Theory of Mind framework she has helped build. As a doctoral student in Human-Computer Interaction, she also discusses her journey from human psychology to the social interactions of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Ethno-Data: Introduction to My Blog

            Hello, my name is Stephen Paff. I am a data scientist and an ethnographer. The goal of this blog is to explore the integration of data science and ethnography as an exciting and innovative way to understand people, whether consumers, users, fellow employees, or anyone else.

            I want to think publicly. Ideas worth having develop in conversation, and through this blog, I hope to present my integrative vision so that others can potentially use it to develop their own visions and in turn help shape mine.

Please Note: Because my blog straddles two technical areas, I will split my posts based on how in-depth they go into each technical expertise. Many posts I will write for a general audience. I will write some posts, though, for data scientists discussing technical matters within that field, and other posts will focus on technical topics withn ethnography for anthropologists and other ethnographers. At the top of each post, I will provide the following disclaimers:

Data Science Technical Level: None, Moderate, or Advanced
Ethnography Technical Level: None, Moderate, or Advanced

Integrating Ethnography and Data Science

As a data scientist and ethnographer, I have worked on many types of research projects. In professional and business settings, I am excited by the enormous growth in both data science and ethnography but have been frustrated by how, despite recent developments that make them more similar, their respective teams seem to be growing apart and competitively against each other.

Within academia, quantitative and qualitative research methods have developed historically as distinct and competing approaches as if one has to choose which direction to take when doing research: departments or individual researchers specialize in one or the other and fight over scarce research funding. One major justification for this division has been the perception that quantitative approaches tend to be prescriptive and top-down compared with qualitative approaches which tend to be to descriptive and bottom-up. That many professional research contexts have inherited this division is unfortunate.

Recent developments in data science draw parallels with qualitative research and if anything, could be a starting point for collaborative intermingling. What has developed as “traditional” statistics taught in introductory statistics courses is generally top-down, assuming that data follows a prescribed, ideal model and asking regimented questions based on that ideal model. Within the development of machine learning been a shift towards models uniquely tailored to the data and context in question, developed and refined iteratively.[i] These trends may show signs of breaking down the top-down nature of traditional statistics work.

If there was ever a time to integrate quantitative data science and qualitative ethnographic research, it is now. In the increasingly important “data economy,” understanding users/consumers is vital to developing strategic business practices. In the business world, both socially-oriented data scientists and ethnographers are experts in understanding users/consumers, but separating them into competing groups only prevents true synthesis of their insights. Integrating the two should not just include combining the respective research teams and their projects but also encouraging researchers to develop expertise in both instead of simply specializing in one or the other. New creative energy could burst forth when we no longer treat these as distinct methodologies or specialties.

[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12.

Photo credit #1: Frank V at  https://unsplash.com/photos/IFLgWYlT2fI

Photo credit #2: Arif Wahid at https://unsplash.com/photos/y3FkHW1cyBE

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 1 of 3)

As part of my Season 2, I interviewed Matt Artz, a design anthropologist who has been recently working as a product manager in the tech space. In Part 1, he discussed his experiences making innovative software products as an anthropologist and product manager.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 2 of 3)

This is the second part of three in our conversation. In it, he described his work developing data science-based recommendation systems using the concepts of design anthropology, participatory research, and design thinking, and then how he uses his skills as an anthropologist to visualize and communicate results and then plan what to do going forward with stakeholders.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

Please also see Part 1 of the interview.

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 3 of 3)

This is the third and final part of three in our conversation. In Part 3, he discussed why he decided to study anthropology for his business work and how that helped give him the skills for the work he does today.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

Previous Parts:

  1. Part 1
  2. Part 2

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 1 of 3)

I spoke with Gemma Galdon-Clavell, founder of Eticas Foundation and Eticas Consulting about the social implications of artificial intelligence technologies. In this first part, we discussed the policy strategies for ensuring that our data and artificial intelligence systems built on our data are good quality, safe, and accountable.

Here are Part 2 and Part 3 of the interview.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 2 of 3)

Here is the second part of three in my conversation with Gemma Clavell. We compared various corporate models – good and bad – for artificial intelligence and how to foster responsible corporate practices in this field.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

Here is Part 1 and Part 3 of our interview.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.