CLOSE
Original image
Thinkstock

Ooshma Garg's Peer-to-Peer Lasagna Company

Original image
Thinkstock

While serving as the co-president of the Stanford Women in Business during her junior year, the proverbial lightbulb went off over Ooshma Garg’s head. After observing some of the big corporations scrambling to meet with her successful student group, she noticed that many of them were having trouble attracting qualified diverse candidates, especially in certain fields, such as law.

Garg’s lightbulb was so bright that she put $80,000 of her own cash into founding Anapata, a website connecting more than 600 of the country’s top law firms with qualified candidates from more than 220 student diversity groups. The site was such a hit with students and firms alike that Garg was able to quit her summer job at Morgan Stanley just a few months later. It’s rather fitting that “Anapata” comes from a Swahili word that means “to find, attain, and achieve.”

After expanding the Anapata site to include analytics of student perception of various law firms—very valuable information to those firms—Garg sold the startup to a legal company called LawWerx. But Garg wasn’t ready to throw in the entrepreneurial towel after just one success. Instead, she used a little personal insight gained while running Anapata to found Gobble, startup #2: It seems she was so busy running the company that her normally healthy habits had started to flounder.

“Starting [Anapata], my eating habits went down the drain," she told CNN Money. You can crowdsource just about anything, of course, so when Garg’s parents suggested she find someone to make her healthy, home-cooked meals, she put the call out on Craigslist. With a budget of $6 to $8 per meal, the young businesswoman quickly had plenty of takers—and a month’s worth of free “sample” dishes. The lightbulb returned, and Garg founded Gobble, a self-described “peer-to-peer lasagna” company and app that connects chefs who craft home-cooked meals with people on the go in the Bay Area, from fellow entrepreneurs to families who just want to spend less time cooking.

With $1.2 million from investors, including funds from LinkedIn co-founder Reid Hoffman, Gobble is looking to expand to other cities soon. Though Garg has described Gobble as her true passion, she’s also mentioned that she believes entrepreneurs “tinker around with many ideas and maybe even a couple different businesses before they find the one.” At just 25 years old, we’re willing to bet that there are a lot more lightbulbs in her future.

Big Brains. Small Films.

The Technologista Series

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
Nick Briggs/Comic Relief
entertainment
arrow
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
Original image
Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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