Data Skeptic

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"

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.

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.

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 When Brain-inspired AI Meets AGI. 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.

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.

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 implementation

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.

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.

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.

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.

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.

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.

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.  

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.

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.

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. 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.

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.

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.

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.

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.