Data Skeptic

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 PST

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 PST

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 PST

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 PST

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 PST

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

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

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 PST

1