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The Woman Who Struck Out Babe Ruth and Lou Gehrig

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Her biggest skill, however, was that she hurled pitches with startling accuracy, and had an uncanny knack for pinpointing a player's weakness. When Chattanooga Lookouts owner Joe Engel saw her playing in Georgia, he decided he had to have her on his minor league team.

Whether or not Engel knew her full potential is debatable. As the self-professed "Barnum of Baseball", Engel was known for press-friendly stunts, like when he traded his shortstop for a 25-pound turkey. As soon as he signed Mitchell, he began promoting the Lookouts as the only team in baseball with a female pitcher.

Amazingly, Mitchell's first game as a pro was in an exhibition game against the Yankees. And her first pitch was against Babe Ruth! Here's how the Baseball Hall of Fame describes it:

In the first inning, after starter Clyde Barfoot surrendered hits to the Yankees' first two batters, Mitchell was called upon to face the heart of "Murderers' Row," Babe Ruth and Lou Gehrig... [Her] uncanny knack came in handy when Mitchell faced Ruth, who watched her first sinker dart low for ball one. Mitchell followed with a sinker on the outside corner, which the Babe swung through and missed. Grinning, the "Sultan of Swat" swung at the next offering and missed for strike two. The next pitch was another sinker on the corner of the plate, which Ruth watched sail by for called strike three. At that point, the Babe "kicked the dirt" and "gave his bat a wild heave" as he stormed unhappily to the dugout...

Next up was Gehrig, who promptly missed three straight dipping sinkers, swinging early each time. On seven pitches, Mitchell had struck out Ruth and Gehrig, two of the game's greatest sluggers. The Chattanooga crowd responded with a rousing standing ovation. Mitchell faced the next Yankees' batter, second baseman Tony Lazzeri, who tried to bunt the first pitch but failed. Lazzeri eventually walked and Mitchell was removed from the game. Engel had maximized her gate appeal by using her to face the heart of baseball's greatest lineup. The 17-year old had squared off against three future Hall of Famers, striking out two of them. The next day, one newspaper would speculate that "maybe her curves were too much for them."

Sadly, Mitchell's game against the Yankees also proved to be her last. Just days after her legendary performance, commissioner Kenesaw Mountain Landis voided her contract. Citing that the sport was "too strenuous for women," Landis essentially wrecked Mitchell's professional career. While Jackie went on to tour with other prominent female athletes (like Babe Didrikson) and play on women's teams for short spells, she bowed out of sports at just 23.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Scientists Think They Know How Whales Got So Big
May 24, 2017
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It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]