Data Skeptic

Announcing Journal Club

I am pleased to announce Data Skeptic is launching a new spin-off show called "Journal Club" with similar themes but a very different format to the Data Skeptic everyone is used to.

In Journal Club, we will have a regular panel and occasional guest panelists to discuss interesting news items and one featured journal article every week in a roundtable discussion. Each week, I'll be joined by Lan Guo and George Kemp for a discussion of interesting data science related news articles and a featured journal or pre-print article.

We hope that this podcast will give listeners an introduction to the works we cover and how people discuss these works. Our topics will often coincide with the original Data Skeptic podcast's current Interpretability theme, but we have few rules right now or what we pick. We enjoy discussing these items with each other and we hope you will do.

In the coming weeks, we will start opening up the guest chair more often to bring new voices to our discussion. After that we'll be looking for ways we can engage with our audience.

Keep reading and thanks for listening!

Kyle

Direct download: AlphaGo_COVID-19_Contact_Tracing_and_New_Data_Set.mp3
Category:general -- posted at: 11:00pm PDT

Direct download: visualizing-uncertainty.mp3
Category:general -- posted at: 8:00am PDT

Pramit Choudhary joins us to talk about the methodologies and tools used to assist with model interpretability.

Direct download: interpretability-tooling.mp3
Category:general -- posted at: 8:00am PDT

Kyle and Linhda discuss how Shapley Values might be a good tool for determining what makes the cut for a home renovation.

Direct download: shapley-values.mp3
Category:general -- posted at: 12:29pm PDT

We welcome back Marco Tulio Ribeiro to discuss research he has done since our original discussion on LIME.

In particular, we ask the question Are Red Roses Red? and discuss how Anchors provide high precision model-agnostic explanations.


Please take our listener survey.

Direct download: anchors-as-explanations.mp3
Category:general -- posted at: 6:46am PDT

Direct download: mathematical-models-of-ecological-systems.mp3
Category:general -- posted at: 4:10pm PDT

Walt Woods joins us to discuss his paper Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness with co-authors Jack Chen and Christof Teuscher.

Direct download: adversarial-explanations.mp3
Category:general -- posted at: 3:10pm PDT

Andrei Barbu joins us to discuss ObjectNet - a new kind of vision dataset.

In contrast to ImageNet, ObjectNet seeks to provide images that are more representative of the types of images an autonomous machine is likely to encounter in the real world. Collecting a dataset in this way required careful use of Mechanical Turk to get Turkers to provide a corpus of images that removes some of the bias found in ImageNet.

http://0xab.com/

Direct download: objectnet.mp3
Category:general -- posted at: 8:00am PDT

Enrico Bertini joins us to discuss how data visualization can be used to help make machine learning more interpretable and explainable.

Find out more about Enrico at http://enrico.bertini.io/.

More from Enrico with co-host Moritz Stefaner on the Data Stories podcast!

Direct download: visualization-and-interpretability.mp3
Category:general -- posted at: 8:00am PDT

We welcome Su Wang back to Data Skeptic to discuss the paper Distributional modeling on a diet: One-shot word learning from text only.

Direct download: interpretable-one-shot-learning.mp3
Category:general -- posted at: 9:00pm PDT

Wiebe van Ranst joins us to talk about a project in which specially designed printed images can fool a computer vision system, preventing it from identifying a person.  Their attack targets the popular YOLO2 pre-trained image recognition model, and thus, is likely to be widely applicable.

Direct download: fooling-computer-vision.mp3
Category:general -- posted at: 10:38am PDT

This episode includes an interview with Aaron Roth author of The Ethical Algorithm.

Direct download: algorithmic-fairness.mp3
Category:general -- posted at: 6:31pm PDT

Interpretability

Machine learning has shown a rapid expansion into every sector and industry. With increasing reliance on models and increasing stakes for the decisions of models, questions of how models actually work are becoming increasingly important to ask.

Welcome to Data Skeptic Interpretability.

In this episode, Kyle interviews Christoph Molnar about his book Interpretable Machine Learning.

Thanks to our sponsor, the Gartner Data & Analytics Summit going on in Grapevine, TX on March 23 – 26, 2020. Use discount code: dataskeptic.

Music

Our new theme song is #5 by Big D and the Kids Table.

Incidental music by Tanuki Suit Riot.

Direct download: interpretability.mp3
Category:general -- posted at: 12:33am PDT

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