Data Skeptic
Kyle Polich
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
Episodes
Mentioned books
Jul 11, 2020 • 27min
GANs Can Be Interpretable
Erik Härkönen joins us to discuss the paper GANSpace: Discovering Interpretable GAN Controls. During the interview, Kyle makes reference to this amazing interpretable GAN controls video and it’s accompanying codebase found here. Erik mentions the GANspace collab notebook which is a rapid way to try these ideas out for yourself.
Jul 6, 2020 • 29min
Sentiment Preserving Fake Reviews
David Ifeoluwa Adelani joins us to discuss Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection.
Jun 26, 2020 • 32min
Interpretability Practitioners
Sungsoo Ray Hong joins us to discuss the paper Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs.
Jun 19, 2020 • 48min
Facial Recognition Auditing
Deb Raji joins us to discuss her recent publication Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing.
Jun 12, 2020 • 38min
Robust Fit to Nature
Uri Hasson joins us this week to discuss the paper Robust-fit to Nature: An Evolutionary Perspective on Biological (and Artificial) Neural Networks.
Jun 5, 2020 • 32min
Black Boxes Are Not Required
Deep neural networks are undeniably effective. They rely on such a high number of parameters, that they are appropriately described as “black boxes”. While black boxes lack desirably properties like interpretability and explainability, in some cases, their accuracy makes them incredibly useful. But does achiving “usefulness” require a black box? Can we be sure an equally valid but simpler solution does not exist? Cynthia Rudin helps us answer that question. We discuss her recent paper with co-author Joanna Radin titled (spoiler warning)… Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition
May 30, 2020 • 22min
Robustness to Unforeseen Adversarial Attacks
Daniel Kang joins us to discuss the paper Testing Robustness Against Unforeseen Adversaries.
May 22, 2020 • 25min
Estimating the Size of Language Acquisition
Frank Mollica joins us to discuss the paper Humans store about 1.5 megabytes of information during language acquisition
May 15, 2020 • 36min
Interpretable AI in Healthcare
Jayaraman Thiagarajan joins us to discuss the recent paper Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.
May 8, 2020 • 35min
Understanding Neural Networks
What does it mean to understand a neural network? That’s the question posted on this arXiv paper. Kyle speaks with Tim Lillicrap about this and several other big questions.
