Good news for windmill operators

Good news from a long-term experiment in Norway: painting a single blade of a power-generating windmill may reduce fatal birdstrikes by as much as 70%. This is certainly an experiment that bears replication, especially at facilities that (1) keep careful records of birdstrikes and (2) care enough to make the effort. It’s pretty sad that the Norwegians spent 7 years on this, and few other researchers got on board with it. If this were clinical research for a debilitating disease, mobs would be clamoring for more trials.  Bird conservationists should be outraged at the pace. Of course, they’re not even … Continue reading Good news for windmill operators

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Distance Learning — an Unexpected Life Skill

The Babylon Bee finds a pearl in the oyster of distance learning: Public Schools Now Preparing Kids for a Lifetime of Soul-Crushing Zoom Meetings. Update: Damn it, Bee! You’re supposed to be doing satire, not straight reporting! Continue reading Distance Learning — an Unexpected Life Skill

Slide Rules!

WuFlu isn’t the only virus in the air; I’ve been getting a lot of chatter and questions about slide rules.  Yes, partly because I’m old enough to remember them and remember (vaguely) how to use them.  But what’s going on? Never mind.  Here’s some great online resources for the curious and really curious History and tutorial in a set of…slides More of the same, in standard boring format, from Wikipedia An MIT student breaks it down for the mathematically inclined A More Complete Slide Rule Tutorial for the n00bs An astonishing gallery of slide rule emulators, like drinking from a … Continue reading Slide Rules!

Fresh-O-Matic!

Just read this delightful article about steamed hoagies, and recalled using a Fresh-O-Matic steamer.  Right out of high school, starting in University, I started a  weekend gig as a prep guy in a Mom and Pop hamburger stand in Big Bear, California.  The owners patiently showed me the ropes, and over two years built me up into a virtuoso burger flipping short-order cook. One of our go-to gadgets was the Fresh-O-Matic steamer, good for frozen buns, a quick order of hot dogs, and the occasional pastrami on white.  In retrospect, I’m baffled at the banality of 60s and 70s California … Continue reading Fresh-O-Matic!

Dust Yourself Off

Phylagen, a San Francisco biotech company, has developed a technique for tracking previous locations of objects based on the composition of dust the object has collected. In another experiment, the sampling technology allowed researchers to determine where a person had walked within 1 kilometer in San Francisco, because of the microbes picked up by their shoes. Right now, this technique is proposed for use in tracking manufacturing locations in supply chains.  If it’s successful, expect it to be used first in forensics, and then in ubiquitous “backwards” location tracking for behavior profiling. Oh, great.  Now, in addition to fresh clothes … Continue reading Dust Yourself Off

The Fourier Transform, explained beautifully

At the Better Explained blog, Kalid Azad hits another home run with An Interactive Guide to the Fourier Transform. Here’s a plain-English metaphor: What does the Fourier Transform do? Given a smoothie, it finds the recipe. How? Run the smoothie through filters to extract each ingredient. Why? Recipes are easier to analyze, compare, and modify than the smoothie itself. How do we get the smoothie back? Blend the ingredients. Here’s the “math English” version of the above: The Fourier Transform takes a time-based pattern, measures every possible cycle, and returns the overall “cycle recipe” (the amplitude, offset, & rotation speed … Continue reading The Fourier Transform, explained beautifully

Stanford Invents AI Gaydar, Flubs Write-Up

Yilun Wang and Michal Kosinsksi, researchers at Stanford’s Graduate School of Business, have developed a neural-net classifier that purportedly detects sexual orientation (in caucasians). The authors report an avalanche of experimental results, and claim the classifier can “correctly distinguish between gay and straight men 81% of the time, and 74% for women.”  OK, that’s the sensitivity of the gadget.  What about specificity, i.e. how well does it correctly distinguish folks who are not-so-gay?  Without that second number (as well as an estimate of prevalance), it’s not possible to estimate the false positive and false negative rates for this thing.  Very … Continue reading Stanford Invents AI Gaydar, Flubs Write-Up