Data Skeptic
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.
Latest episodes
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.
May 2, 2020 • 32min
Self-Explaining AI
Dan Elton joins us to discuss self-explaining AI. What could be better than an interpretable model? How about a model wich explains itself in a conversational way, engaging in a back and forth with the user. We discuss the paper Self-explaining AI as an alternative to interpretable AI which presents a framework for self-explainging AI.
Apr 24, 2020 • 35min
Plastic Bag Bans
Becca Taylor joins us to discuss her work studying the impact of plastic bag bans as published in Bag Leakage: The Effect of Disposable Carryout Bag Regulations on Unregulated Bags from the Journal of Environmental Economics and Management. How does one measure the impact of these bans? Are they achieving their intended goals? Join us and find out!
Apr 18, 2020 • 31min
Self Driving Cars and Pedestrians
We are joined by Arash Kalatian to discuss Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning.
Apr 10, 2020 • 26min
Computer Vision is Not Perfect
Computer Vision is not Perfect Julia Evans joins us help answer the question why do neural networks think a panda is a vulture. Kyle talks to Julia about her hands-on work fooling neural networks. Julia runs Wizard Zines which publishes works such as Your Linux Toolbox. You can find her on Twitter @b0rk
