CLOSE
Original image

The Only 4 Hawaiians ever to make the MLB All-Star Team

Original image

When the Phillies' Shane Victorino received a record 15.6 million fan votes last week, he beat out the Giants' Pablo Sandoval for the last spot on the National League All-Star Team. (Recent tradition gives the last spot to the people. Though, to be fair, the people can vote as often as they like. For instance, three Phillies fans won a radio station promotion and sat in the press box lounge for 52 straight hours clicking nonstop for Victorino.)

By earning the spot, Victorino (aka The Flyin' Hawaiian), became only the fourth Hawaiian ever to make the All-Star team. Here are some factoids about each Hawaiian, as you gear up for tomorrow night's game (read: endless series of Taco Bell commercials).

1. Charlie Hough

Born: January 5, 1948, Honolulu, Hawaii
Position: Pitcher
Career highlight: Hough holds the distinction of being the oldest Major Leaguer born in Hawaii to eventually make the All-Star team, which he did in 1986 (a theme, you'll discover shortly). He pitched his best years for the Texas Rangers and left Texas as the franchise leader in wins, strikeouts, complete games and losses.
What's he doing now? Hough is the pitching coach for the Inland Empire 66ers of San Bernardino, the AA affiliate of the Los Angeles Dodgers.

2. Ron Darling

darlingBorn: August 19, 1960, Honolulu, Hawaii
Position: Pitcher
Career highlight: Selected to the 1985 All-Star team when he played for the Mets, the team he'd help win the World Series in 1986.
What's he doing now? Working as a color commentator on TBS, as well as for the Mets on both SNY and WPIX.

3. Sid Fernandez

afernadezBorn: October 12, 1962, Honolulu, Hawaii
Position: Pitcher
Career highlight: Just like Darling, Fernandez helped the Mets win the '86 World Series. He also went to the All-Star game that year, and repeated the following year, thanks to a strong first half of the year (he'd only go 3-3 after the break).
What's he doing now? Living in Hawaii again. He and his wife run the Sid Fernandez Foundation, which awards college scholarships to seniors from the Fernandezes' alma mater, Kaiser High School. He also plays a lot of golf.

4. Shane Victorino

victorinoBorn: November 30, 1980, Wailuku, Hawaii (the only one of the four not born in Honolulu)
Position: Outfielder (the only one of the four who isn't a pitcher)
Career highlight: Shane helped the Phillies win the World Series last season, and also won a Gold Glove Award last year. He blogged for the Phillies all through the playoffs, and has become known as one of MLB's most upstanding players with a winning attitude/approach to the game.
What's he doing now?
Taking BP in St. Lou.

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

Original image
quiz
arrow
Name the Author Based on the Character
May 23, 2017
Original image
SECTIONS
BIG QUESTIONS
BIG QUESTIONS
WEATHER WATCH
BE THE CHANGE
JOB SECRETS
QUIZZES
WORLD WAR 1
SMART SHOPPING
STONES, BONES, & WRECKS
#TBT
THE PRESIDENTS
WORDS
RETROBITUARIES