Data Skeptic

Latest Episodes:

Robust Fit to Nature Jun 12, 2020

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Uri Hasson joins us this week to discuss the paper Robust-fit to Nature: An Evolutionary Perspective on Biological (and Artificial) Neural Networks.

Black Boxes Are Not Required Jun 05, 2020

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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

Robustness to Unforeseen Adversarial Attacks May 30, 2020

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Daniel Kang joins us to discuss the paper Testing Robustness Against Unforeseen Adversaries.

Estimating the Size of Language Acquisition May 22, 2020

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Frank Mollica joins us to discuss the paper Humans store about 1.5 megabytes of information during language acquisition

Interpretable AI in Healthcare May 15, 2020

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Jayaraman Thiagarajan joins us to discuss the recent paper Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.

Understanding Neural Networks May 08, 2020

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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.

Self-Explaining AI May 02, 2020

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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.

Plastic Bag Bans Apr 24, 2020

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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!

Self Driving Cars and Pedestrians Apr 18, 2020

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We are joined by Arash Kalatian to discuss Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning.

Computer Vision is Not Perfect Apr 10, 2020

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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