Listened to The Disagreement Is The Point from On the Media | WNYC Studios

The media's "epistemic crisis," algorithmic biases, and the radio's inherent, historical misogyny.

In hearings this week, House Democrats sought to highlight an emerging set of facts concerning the President’s conduct. On this week’s On the Media, a look at why muddying the waters remains a viable strategy for Trump’s defenders. Plus, even the technology we trust for its clarity isn’t entirely objective, especially the algorithms that drive decisions in public and private institutions. And, how early radio engineers designed broadcast equipment to favor male voices and make women sound "shrill."

1. David Roberts [@drvox], writer covering energy for Vox, on the "epistemic crisis" at the heart of our bifurcated information ecosystem. Listen.

2. Cathy O'Neil [@mathbabedotorg], mathematician and author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, on the biases baked into our algorithms. Listen.

3. Tina Tallon [@ttallon], musician and professor, on how biases built into radio technology have shaped how we hear women speak. Listen.

Some great discussion on the idea of women being “shrill” and ad hominem attacks instead of attacks on ideas.

Cathy O’Neil has a great interview on her book Weapons of Math Distraction. I highly recommend everyone read it, but if for some reason you can’t do it this month, this interview is a good starting place for repairing that deficiency.

In section three, I’ll note that I’ve studied the areas of signal processing and information theory in great depth, but never run across the fascinating history of how we physically and consciously engineered women out of radio and broadcast in quite the way discussed here. I recall the image of “Lena” being nudged out of image processing recently, but the engineering wrongs here are far more serious and pernicious.

Reposted “My ten hour white noise video now has five copyright claims! :)” by Sebastian Tomczak (Twitter)

Information Theory and signal processing FTW!

(Aside: This is a great example of how people really don’t understand our copyright system or science in general.)

Poor State of Automated Machine-Based Language Translation

You know that automated machine language translation is not in good shape when the editor-in-chief of the IEEE’s Signal Processing Magazine says:

As an anecdote, during the early stage in creating the Chinese translation of the [Signal Processing] magazine, we experimented with automated machine translation first, only to quickly switch to professional human translation.  This makes us appreciate why “universal translation” is the “needs and wants” of the future rather than of the present; see [3] for a long list of of future needs and wants to be enabled by signal processing technology.

 

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