This episode kicks off the new season of the show, Data Skeptic: Surveys.  Linhda rejoins the show for a conversation with Kyle about her experience taking surveys and what questions she has for the season.  Lastly, Kyle announces the launch of <a href= "https://survey.dataskeptic.com" target="_blank" rel= "noopener">survey.dataskeptic.com</a>, a new site we're launching to gather your opinions.  Please take a moment and share your thoughts!

This episode kicks off the new season of the show, Data Skeptic: Surveys.  Linhda rejoins the show for a conversation with Kyle about her experience taking surveys and what questions she has for the season.  Lastly, Kyle announces the launch of <a href= "https://survey.dataskeptic.com" target="_blank" rel= "noopener">survey.dataskeptic.com</a>, a new site we're launching to gather your opinions.  Please take a moment and share your thoughts!

This episode kicks off the new season of the show, Data Skeptic: Surveys.  Linhda rejoins the show for a conversation with Kyle about her experience taking surveys and what questions she has for the season.  Lastly, Kyle announces the...

4 out of 5 Data Scientists Agree

Today, Jenny Tang, a Ph.D. student of societal computing at Carnegie Mellon University discusses her work on the generalization of privacy and security surveys on platforms such as Amazon MTurk and Prolific. Jenny shared the drawbacks of using such online platforms, the discrepancies observed about the samples drawn, and key insights from her results.

Today, Jenny Tang, a Ph.D. student of societal computing at Carnegie Mellon University discusses her work on the generalization of privacy and security surveys on platforms such as Amazon MTurk and Prolific. Jenny shared the drawbacks of using such...

Do Results Generalize for Privacy and Security Surveys

Traditional surveys have straight-jacket questions to be answered, thus restricting the information that can be gotten. Today, Ziang Xiao, a Postdoc Researcher in the FATE group at Microsoft Research Montréal, talks about conversational surveys, a type of survey that asks questions based on preceding answers. He discussed the benefits of conversational surveys and some of the challenges it poses.

Traditional surveys have straight-jacket questions to be answered, thus restricting the information that can be gotten. Today, Ziang Xiao, a Postdoc Researcher in the FATE group at Microsoft Research Montréal, talks about conversational surveys, a...

Conversational Surveys

Crafting survey questions is one thing but getting your audience to fill it is yet another. On the show today, we speak with Alexander Nolte, an Associate Professor at the University of Tartu. Alexander discussed the use of Casual Affective Triggers (CAT) to incentivize people to accept survey invitations and improve the completion rate. He revealed the impact of CATs on survey response rates from a study he conducted.

Crafting survey questions is one thing but getting your audience to fill it is yet another. On the show today, we speak with Alexander Nolte, an Associate Professor at the University of Tartu. Alexander discussed the use of Casual Affective Triggers...

Casual Affective Triggers

On the show today, Dino Carpentras, a post-doctoral researcher at the Computational Social Science group at ETH Zürich joins us to discuss how opinion dynamics models are built and validated. He explained how quantifying opinions is complex, and strategies to develop robust models for measuring and predicting public opinions.

On the show today, Dino Carpentras, a post-doctoral researcher at the Computational Social Science group at ETH Zürich joins us to discuss how opinion dynamics models are built and validated. He explained how quantifying opinions is complex, and...

Opinion Dynamics Models

On the show, Iñigo Martinez, a Ph.D. student at the University of Navarra shares his survey results which investigated how data practitioners perform data science projects. He revealed the methodologies typically used by data practitioners and the success factors in data science projects.

On the show, Iñigo Martinez, a Ph.D. student at the University of Navarra shares his survey results which investigated how data practitioners perform data science projects. He revealed the methodologies typically used by data practitioners and the...

A Survey of Data Science Methodologies

Our guest today is Zoltán Kekecs, a Ph.D. holder in Behavioural Science. Zoltán highlights the problem of low replicability in journal papers and illustrates how researchers can better ensure complete replication of their research and findings. He used Bem’s experiment as an example, extensively talking about his methodology and results.

Our guest today is Zoltán Kekecs, a Ph.D. holder in Behavioural Science. Zoltán highlights the problem of low replicability in journal papers and illustrates how researchers can better ensure complete replication of their research and findings. He...

Reproducible ESP Testing

The use of social bots to fill out online surveys is becoming prevalent. Today, we speak with Sara Bybee, a postdoctoral research scholar at the University of Utah. Sara shares from her research, how she detected social bots, the strategies to curb them, and how underrepresented groups can be more represented in surveys.

The use of social bots to fill out online surveys is becoming prevalent. Today, we speak with Sara Bybee, a postdoctoral research scholar at the University of Utah. Sara shares from her research, how she detected social bots, the strategies to curb...

Bot Detection and Dyadic Surveys

Susan Gerbic joins Kyle to review some of the surveys Data Skeptic has launch, draft a new survey about podcast listening habits, and then review the results of that survey. You can see those results at the link below. <a href= "https://survey.dataskeptic.com/survey/result/1675102237053">https://survey.dataskeptic.com/survey/result/1675102237053</a> Watch the videos Susan mentioned on her Youtube page at the link below. <a href= "https://www.youtube.com/playlist?list=PL7VAuaQDhPTVaLeI1IcpYph5lH19xA1u4"> https://www.youtube.com/playlist?list=PL7VAuaQDhPTVaLeI1IcpYph5lH19xA1u4</a>

Susan Gerbic joins Kyle to review some of the surveys Data Skeptic has launch, draft a new survey about podcast listening habits, and then review the results of that survey. You can see those results at the link below.  Watch the videos Susan...

Survey Design Working Session

Noura Insolera, a Research Investigator with the Panel Study of Income Dynamics (PSID), joins us to share how PSID conducts longitudinal household surveys. She also shared some interesting findings from their data exploration, particularly on the observation and trends in food insecurity.

Noura Insolera, a Research Investigator with the Panel Study of Income Dynamics (PSID), joins us to share how PSID conducts longitudinal household surveys. She also shared some interesting findings from their data exploration, particularly on the...

The Panel Study of Income Dynamics

Ever wondered what your next career would be? Today, Keyon Vafa, a computer science Ph.D. student at Columbia University, joins us to discuss his latest research on developing a machine-learning model for career prediction. Keyon extensively spoke about how the model was developed and the possibilities it brings.

Ever wondered what your next career would be? Today, Keyon Vafa, a computer science Ph.D. student at Columbia University, joins us to discuss his latest research on developing a machine-learning model for career prediction. Keyon extensively spoke...

CAREER Prediction

We are joined by two guests today, Mariah, a Ph.D. student in the CORE Robotics Lab at Georgia Tech, and Matthew Gombolay, the Director of the CORE Robotics Lab. They both discuss practices for measuring a respondent’s perception in a survey.

We are joined by two guests today, Mariah, a Ph.D. student in the CORE Robotics Lab at Georgia Tech, and Matthew Gombolay, the Director of the CORE Robotics Lab. They both discuss practices for measuring a respondent’s perception in a survey.

Measuring Trust in Robots with Likert Scales

Today’s show focused on an essential part of surveys — missing values. This is typically caused by a low response rate or non-response from respondents. Yajuan Si is a Research Associate Professor at the Survey Research Center at the University of Michigan. She joins us to discuss dealing with bias from low survey response rates.

Today’s show focused on an essential part of surveys — missing values. This is typically caused by a low response rate or non-response from respondents. Yajuan Si is a Research Associate Professor at the Survey Research Center at the University of...

Non-Response Bias

Today, we are joined by David Bourget. David is an Associate Professor in Philosophy at Western University in London, Ontario. David is also the co-director of the PhilPapers Foundation and Director of the Center for Digital Philosophy. He joins us to discuss the PhilPapers Survey project. David began by explaining what the PhilPapers Index is about. At its core, it is a comprehensive bibliography of philosophy and philosophers’ paradigms on issues. He also talked about the PhilPapers survey. The PhilPapers survey aimed to gather philosophers’ opinions on different philosophical topics. David spoke extensively about the survey: the number of questions, the type of questions, the sample size, etc. On the survey results, he discussed situations where there was consensus and where there were diverging responses. The PhilPapers survey was initially taken in 2009, but there was a follow-up survey in 2020. David discussed the need for the subsequent survey and what changed. He mentioned the metric for measuring the opinion changes between the 2009 and 2020 surveys. He also shared future plans for the PhilPapers surveys. Rounding up, David gave his thoughts on how recent AI development may affect philosophers’ opinions on machine consciousness. Resources The 2020 PhilPapers Survey: <a href= "https://survey2020.philpeople.org/">https://survey2020.philpeople.org/</a> David Bourget’s website: <a href= "https://www.dbourget.com/">https://www.dbourget.com/</a> David Bourget’s Twitter: <a href= "https://twitter.com/dbourget">@dbourget</a>.

Today, we are joined by David Bourget. David is an Associate Professor in Philosophy at Western University in London, Ontario. David is also the co-director of the PhilPapers Foundation and Director of the Center for Digital Philosophy. He joins us to discuss the PhilPapers Survey project. David began by explaining what the PhilPapers Index is about. At its core, it is a comprehensive bibliography of philosophy and philosophers’ paradigms on issues. He also talked about the PhilPapers survey. The PhilPapers survey aimed to gather philosophers’ opinions on different philosophical topics. David spoke extensively about the survey: the number of questions, the type of questions, the sample size, etc. On the survey results, he discussed situations where there was consensus and where there were diverging responses. The PhilPapers survey was initially taken in 2009, but there was a follow-up survey in 2020. David discussed the need for the subsequent survey and what changed. He mentioned the metric for measuring the opinion changes between the 2009 and 2020 surveys. He also shared future plans for the PhilPapers surveys. Rounding up, David gave his thoughts on how recent AI development may affect philosophers’ opinions on machine consciousness. <h2 id="resources">Resources</h2> The 2020 PhilPapers Survey: <a href= "https://survey2020.philpeople.org/">https://survey2020.philpeople.org/</a> David Bourget’s website: <a href= "https://www.dbourget.com/">https://www.dbourget.com/</a> David Bourget’s Twitter: <a href= "https://twitter.com/dbourget">@dbourget</a>.

Today, we are joined by David Bourget. David is an Associate Professor in Philosophy at Western University in London, Ontario. David is also the co-director of the PhilPapers Foundation and Director of the Center for Digital Philosophy. He joins us to...

The PhilPapers Survey

Gireeja Ranade, a University of California at Berkeley professor, speaks with us today. She presented her study on implementing inclusive study groups at scale and shared the observed student performance improvements after the intervention.

Gireeja Ranade, a University of California at Berkeley professor, speaks with us today. She presented her study on implementing inclusive study groups at scale and shared the observed student performance improvements after the intervention.

Inclusive Study Group Formation at Scale

Jeff Jones, a Senior Editor at Gallup, joins us today. His conversation with Kyle spanned a range of topics on Gallup’s poll creation process. He discussed how Gallup generates unbiased questionnaires, gets respondents, analyzes results, and everything in between.

Jeff Jones, a Senior Editor at Gallup, joins us today. His conversation with Kyle spanned a range of topics on Gallup’s poll creation process. He discussed how Gallup generates unbiased questionnaires, gets respondents, analyzes results, and...

The Gallup Poll

Kyle shares his own perspectives on challenges getting insight from surveys. The discussion ranges from commentary on the market research industry to specific advice for detecting disingenuous or fraudulent responses and filtering them from your analysis. Finally, he shares some quick thoughts on the usage of the Chi-Square test for interpreting cross tab results in survey analysis.

<div> Kyle shares his own perspectives on challenges getting insight from surveys. The discussion ranges from commentary on the market research industry to specific advice for detecting disingenuous or fraudulent responses and filtering them from your analysis. Finally, he shares some quick thoughts on the usage of the Chi-Square test for interpreting cross tab results in survey analysis. </div> <div> <div class="Guest"> <div class="container"> <div class="row"> </div> </div> </div> </div>

Kyle shares his own perspectives on challenges getting insight from surveys. The discussion ranges from commentary on the market research industry to specific advice for detecting disingenuous or fraudulent responses and filtering them from your...

Skeptical Survey Interpretation

Julian Michael, a postdoc at the Center for Data Science, New York University, joins us today. Julian’s conversation with Kyle was centered on the NLP community metasurvey: a survey aimed at understanding expert opinions on controversial NLP issues. He shared the process of preparing the survey as well as some shocking results.

Julian Michael, a postdoc at the Center for Data Science, New York University, joins us today. Julian’s conversation with Kyle was centered on the NLP community metasurvey: a survey aimed at understanding expert opinions on controversial NLP issues....

The NLP Community Metasurvey

While the possibilities with AGI emergence seem great, it also calls for safety concerns. On the show, Vahid Behzadan, an Assistant Professor of Computer Science and Data Science, joins us to discuss the complexities of modeling AGIs to accurately achieve objective functions. He touched on tangent issues such as abstractions during training, the problem of unpredictability, communications among agents, and so on.

While the possibilities with AGI emergence seem great, it also calls for safety concerns. On the show, Vahid Behzadan, an Assistant Professor of Computer Science and Data Science, joins us to discuss the complexities of modeling AGIs to accurately...

A Psychopathological Approach to Safety in AGI

Barry Smith and Jobst Landgrebe, authors of the book “Why Machines will never Rule the World,” join us today. They discussed the limitations of AI systems in today’s world. They also shared elaborate reasons AI will struggle to attain the level of human intelligence.

Barry Smith and Jobst Landgrebe, authors of the book “Why Machines will never Rule the World,” join us today. They discussed the limitations of AI systems in today’s world. They also shared elaborate reasons AI will struggle to attain the level...

Why Machines Will Never Rule the World

Fabricio Goes, a Lecturer in Creative Computing at the University of Leicester, joins us today. Fabricio discussed what creativity entails and how to evaluate jokes with LLMs. He specifically shared the process of evaluating jokes with GPT-3 and GPT-4. He concluded with his thoughts on the future of LLMs for creative tasks.

Fabricio Goes, a Lecturer in Creative Computing at the University of Leicester, joins us today. Fabricio discussed what creativity entails and how to evaluate jokes with LLMs. He specifically shared the process of evaluating jokes with GPT-3 and...

Evaluating Jokes with LLMs

The application of LLMs cuts across various industries. Today, we are joined by Steven Van Vaerenbergh, who discussed the application of AI in mathematics education. He discussed how AI tools have changed the landscape of solving mathematical problems. He also shared LLMs' current strengths and weaknesses in solving math problems.

The application of LLMs cuts across various industries. Today, we are joined by Steven Van Vaerenbergh, who discussed the application of AI in mathematics education. He discussed how AI tools have changed the landscape of solving mathematical...

AI for Mathematics Education

An assistant professor of Psychology at Harvard University, Tomer Ullman, joins us. Tomer discussed the theory of mind and whether machines can indeed pass it. Using variations of the Sally-Anne test and the Smarties tube test, he explained how LLMs could fail the theory of mind test.

An assistant professor of Psychology at Harvard University, Tomer Ullman, joins us. Tomer discussed the theory of mind and whether machines can indeed pass it. Using variations of the Sally-Anne test and the Smarties tube test, he explained how LLMs...

AI Fails on Theory of Mind Tasks

We are joined by Koen Holtman, an independent AI researcher focusing on AI safety. Koen is the Founder of Holtman Systems Research, a research company based in the Netherlands. Koen started the conversation with his take on an AI apocalypse in the coming years. He discussed the obedience problem with AI models and the safe form of obedience. Koen explained the concept of Markov Decision Process (MDP) and how it is used to build machine learning models. Koen spoke about the problem of AGIs not being able to allow changing their utility function after the model is deployed. He shared another alternative approach to solving the problem. He shared how to engineer AGI systems now and in the future safely. He also spoke about how to implement safety layers on AI models. Koen discussed the ultimate goal of a safe AI system and how to check that an AI system is indeed safe. He discussed the intersection between large language Models (LLMs) and MDPs. He shared the key ingredients to scale the current AI implementations.

We are joined by Koen Holtman, an independent AI researcher focusing on AI safety. Koen is the Founder of Holtman Systems Research, a research company based in the Netherlands. Koen started the conversation with his take on an AI apocalypse in the...

AGI Can Be Safe

On today’s show, we are joined by Michael Timothy Bennett, a Ph.D. student at the Australian National University. Michael’s research is centered around Artificial General Intelligence (AGI), specifically the mathematical formalism of AGIs. He joins us to discuss findings from his study, Computable Artificial General Intelligence.

On today’s show, we are joined by Michael Timothy Bennett, a Ph.D. student at the Australian National University. Michael’s research is centered around Artificial General Intelligence (AGI), specifically the mathematical formalism of AGIs. He...

Computable AGI

Today on the show, we are joined by Lin Zhao and Lu Zhang. Lin is a Senior Research Scientist at United Imaging Intelligence, while Lu is a Ph.D. candidate at the Department of Computer Science and Engineering at the University of Texas. They both shared findings from their work <a href= "https://arxiv.org/pdf/2303.15935.pdf">When Brain-inspired AI Meets AGI</a>. Lin and Lu began by discussing the connections between the brain and neural networks. They mentioned the similarities as well as the differences. They also shared whether there is a possibility for solid advancements in neural networks to the point of AGI. They shared how understanding the brain more can help drive robust artificial intelligence systems. Lin and Lu shared how the brain inspired popular machine learning algorithms like transformers. They also shared how AI models can learn alignment from the human brain. They juxtaposed the low energy usage of the brain compared to high-end computers and whether computers can become more energy efficient.

Today on the show, we are joined by Lin Zhao and Lu Zhang. Lin is a Senior Research Scientist at United Imaging Intelligence, while Lu is a Ph.D. candidate at the Department of Computer Science and Engineering at the University of Texas. They both...

Brain Inspired AI

Our guest today is Maciej Świechowski. Maciej is affiliated with QED Software and QED Games. He has a Ph.D. in Systems Research from the Polish Academy of Sciences. Maciej joins us to discuss findings from his study, Deep Learning and Artificial General Intelligence: Still a Long Way to Go.

Our guest today is Maciej Świechowski. Maciej is affiliated with QED Software and QED Games. He has a Ph.D. in Systems Research from the Polish Academy of Sciences. Maciej joins us to discuss findings from his study, Deep Learning and Artificial...

A Long Way Till AGI

The creators of large language models impose restrictions on some of the types of requests one might make of them.  LLMs commonly refuse to give advice on committing crimes, producting adult content, or respond with any details about a variety of sensitive subjects.  As with any content filtering system, you have false positives and false negatives. Today's interview with Max Reuter and William Schulze discusses their paper "I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Models".  In this work, they explore what types of prompts get refused and build a machine learning classifier adept at predicting if a particular prompt will be refused or not.

The creators of large language models impose restrictions on some of the types of requests one might make of them.  LLMs commonly refuse to give advice on committing crimes, producting adult content, or respond with any details about a variety of...

Prompt Refusal

In this episode, we are joined by Ryan Liu, a Computer Science graduate of Carnegie Mellon University. Ryan will begin his Ph.D. program at Princeton University this fall. His Ph.D. will focus on the intersection of large language models and how humans think. Ryan joins us to discuss his research titled "ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing"

In this episode, we are joined by Ryan Liu, a Computer Science graduate of Carnegie Mellon University. Ryan will begin his Ph.D. program at Princeton University this fall. His Ph.D. will focus on the intersection of large language models and how...

Automated Peer Review

We are excited to be joined by J.D. Zamfirescu-Pereira, a Ph.D. student at UC Berkeley. He focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI). He joins us to share his work in his paper, Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts.  The discussion also explores lessons learned and achievements related to BotDesigner, a tool for creating chat bots.

We are excited to be joined by J.D. Zamfirescu-Pereira, a Ph.D. student at UC Berkeley. He focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI). He joins us to share his work in his paper, Why Johnny can’t...

Why Prompting is Hard

On today’s episode, we have Daniel Rock, an Assistant Professor of Operations Information and Decisions at the Wharton School of the University of Pennsylvania. Daniel’s research focuses on the economics of AI and ML, specifically how digital technologies are changing the economy. Daniel discussed how AI has disrupted the job market in the past years. He also explained that it had created more winners than losers. Daniel spoke about the empirical study he and his coauthors did to quantify the threat LLMs pose to professionals. He shared how they used the O-NET dataset and the BLS occupational employment survey to measure the impact of LLMs on different professions. Using the radiology profession as an example, he listed tasks that LLMs could assume. Daniel broadly highlighted professions that are most and least exposed to LLMs proliferation. He also spoke about the risks of LLMs and his thoughts on implementing policies for regulating LLMs.

On today’s episode, we have Daniel Rock, an Assistant Professor of Operations Information and Decisions at the Wharton School of the University of Pennsylvania. Daniel’s research focuses on the economics of AI and ML, specifically how digital...

Which Professions Are Threatened by LLMs

Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his research paper, Cuttlefish: Low-Rank Model Training without All the Tuning, on today’s show. Hogyi started by sharing his thoughts on whether developers need to learn how to fine-tune models. He then spoke about the need to optimize the training of ML models, especially as these models grow bigger. He discussed how data centers have the hardware to train these large models but not the community. He then spoke about the Low-Rank Adaptation (LoRa) technique and where it is used. Hongyi discussed the Cuttlefish model and how it edges LoRa. He shared the use cases of Cattlefish and who should use it. Rounding up, he gave his advice on how people can get into the machine learning field. He also shared his future research ideas.

Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his research paper, Cuttlefish: Low-Rank Model Training...

Cuttlefish Model Tuning

In this episode, we are joined by Carlos Hernández Oliván, a Ph.D. student at the University of Zaragoza. Carlos’s interest focuses on building new models for symbolic music generation. Carlos shared his thoughts on whether these models are genuinely creative. He revealed situations where AI-generated music can pass the Turing test. He also shared some essential considerations when constructing models for music composition.

In this episode, we are joined by Carlos Hernández Oliván, a Ph.D. student at the University of Zaragoza. Carlos’s interest focuses on building new models for symbolic music generation. Carlos shared his thoughts on whether these models are...

LLMs in Music Composition

Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is centered on the intersection of computational methods and their applications in social sciences. He joins us to discuss findings from his research papers, ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning, and How to use LLMs for Text Analysis.

Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is centered on the intersection of computational...

LLMs in Social Science

Our guest today is Vid Kocijan, a Machine Learning Engineer at Kumo AI. Vid has a Ph.D. in Computer Science at the University of Oxford. His research focused on common sense reasoning, pre-training in LLMs, pretraining in knowledge-based completion, and how these pre-trainings impact societal bias. He joins us to discuss how he built a BERT model that solved the Winograd Schema Challenge.

Our guest today is Vid Kocijan, a Machine Learning Engineer at Kumo AI. Vid has a Ph.D. in Computer Science at the University of Oxford. His research focused on common sense reasoning, pre-training in LLMs, pretraining in knowledge-based completion,...

The Defeat of the Winograd Schema Challenge

We are joined by Maximilian Mozes, a PhD student at the University College, London. His PhD research focuses on Natural Language Processing (NLP), particularly the intersection of adversarial machine learning and NLP. He joins us to discuss his latest research, Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities.

We are joined by Maximilian Mozes, a PhD student at the University College, London. His PhD research focuses on Natural Language Processing (NLP), particularly the intersection of adversarial machine learning and NLP. He joins us to discuss his latest...

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