Proposing Annoyance Mining

06/09/15 • 30 min

A recent episode of the Skeptics Guide to the Universe included a slight rant by Dr. Novella and the rouges about a shortcoming in operating systems. This episode explores why such a (seemingly obvious) flaw might make sense from an engineering perspective, and how data science might be the solution.

In this solo episode, Kyle proposes the concept of "annoyance mining" - the idea that with proper logging and enough feedback, data scientists could be provided the right dataset from which they can detect flaws and annoyances in software and other systems and automatically detect potential bugs, flaws, and improvements which could make those systems better.

As system complexity grows, it seems that an abstraction like this might be required in order to keep maintaining an effective development cycle. This episode is a bit of a soap box for Kyle as he explores why and how we might track an appropriate amount of data to be able to make better software and systems more suited for the users.

Previous Episode

Preserving History at Cyark

Elizabeth Lee from CyArk joins us in this episode to share stories of the work done capturing important historical sites digitally. CyArk is a non-profit focused on using technology to preserve the world's important historic and cultural locations digitally. CyArk's founder Ben Kacyra, a pioneer in 3D capture technology, and his wife, founded CyArk after seeing the need to preserve important artifacts and locations digitally before they are lost to natural disasters, human destruction, or the passage of time. We discuss their technology, data, and site selection including the upcoming themes of locations and the CyArk 500.

Elizabeth puts out the call to all listeners to share their opinions on what important sites should be included in The Cyark 500 Challenge - an effort to digitally preserve 500 of the most culturally important heritage sites within the next five years. You can Nominate a site by submitting a short form at CyArk.org

Visit http://www.cyark.org/projects/ to view an immersive, interactive experience of many of the sites preserved.

Next Episode

[MINI] Anscombe's Quartet

This mini-episode discusses Anscombe's Quartet, a series of four datasets which are clearly very different but share some similar statistical properties with one another. For example, each of the four plots has the same mean and variance on both axis, as well as the same correlation coefficient, and same linear regression.

The episode tries to add some context by imagining each of these datasets as data about a sports team, and why it can be important to look beyond basic summary statistics when exploring your dataset.