Links for February 1

Uber vs. taxis simulation and explanation of it from Kevin McLaughlin.

An NFL scheduling quirk explains how certain teams can pile up the wins against weak opponents.

Inside the Wall Street Journal’s prediction calculator (for predicting ethnicity from names).

The recently departed Marvin Minsky on What makes mathematics hard to learn?

From the Notices of the AMS:
George Andrews reports on The Man Who Knew Infinity (the new Ramanujan movie) and the editors explain Gauss curvature.

Gunnar Carlsson at Ayasdi writes on How Topological Data Analysis provides a glimpse into what may be powering the Trump engine.. (This may all make a little more sense – or less – after tonight’s caucuses.)

Richard Nisbett talks to EDGE about what’s wrong with multiple regression analysis.

Erik Bernhardsson analyzed 50,000 fonts using deep neural networks. (It’s like Metafont, but with neural networks and more data.)

John Cook asks what are the next areas of math to be applied?

Nicolas Kruchten at MLDB on machine learning meets economics. (ROC is not the One True Criterion for model evaluation.)

Videos of curve-drawing machines (silent and with little explanation, but oddly hypnotic)

John Pavlus interviews Leslie Valiant on “probably approximately correct” learning and “ecorithms”.

Robert Bosch, Robert Fathauer, and Henry Segerman on numerically balanced dice – that is, many-sided dice that are optimally fair even if they’re physically a bit unbalanced.

Steve Paulson inteviews Frank Wilczek for Nautilus: Beauty is physics’ secret weapon.

Evelyn Lamb shows us an impractical, ahistorical, mathematically elegant way to figure out Earth is a (topological) sphere.

Nick Berry at DataGenetic explains Hamming codes for error correction.

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