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How Did You Know Maggie Wittlin?

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A hearty congrats to Maggie Wittlin of New York City, who tied the HDYK record for a finished final puzzle in the least amount of time at 9 minutes flat!

In a moment, I'll introduce you to to our new champion, but first:

WE'RE UPPING THE STAKES for the next How Did You Know?!

Here's the deal: If any champion can defend his or her title for three consecutive months, he or she will win not only the usual prizes for that month, but"¦ but"¦ BUT"¦ well, we haven't figure out the "˜but' yet, BUT, it'll be pretty darn snazzy, that much I can guarantee (short of A NEW CAR!).

So the pressure is on Maggie, who I introduce to you now "“ in her own words:

I currently live in New York City, with three wonderful and trivia-knowledgeable roommates. I spent the past three years working with the very awesome Seed Media Group -- the folks who produce the equally awesome Seed magazine -- performing various editorial duties in print and on the web. I'm now taking the summer off before going to law school in the fall. (This is why I have time to do trivia hunts.) I was born in the Bronx, and I grew up in fabulous and exciting Westchester County. Some of my dorkiest interests include physics, musical theater, and hunting down information, which is truly the most dangerous game. I would like to thank the friends, parents, and friends' parents who helped me with the puzzle.

[Just to clarify on the Name That Country challenge: for the first one, in addition to Saudi Arabia I would have also accepted Iran, Sudan, Serbia or Spain. And for the last one, Austria and Australia both worked.]

Now on to Maggie's brilliant, winning solution:

Final Day:

The answer to the puzzle is: Polaris

Located 431.42 light years away, I am the most famous star in ursa minor: Polaris.

day 2 = 8a 6b 7c 5d 3e 0f 9g
Latitude = 39.593
Longitude = -83.587
Milledgeville, OH (From yesterday)
431.42

day 4
Italy
LY = Light years (I know this)

day 1
star - filled in blank :)

day 3
Little bear = ursa minor in Latin
Day 1 - Mystery Foliage

1. Eucalyptus (I found this similar image on flickr: http://www.flickr.com/photos/lauraelaine/1983632/in/set-31331/ and emailed the photographer. She said she believed it to be a juvenile Eucalyptus. I confirmed this at: http://en.wikipedia.org/wiki/Image:Eucalyptus_polyanthemos_vestita_juvenile_foliage.jpg)

2. Star Jasmine (Recognized by my SoCal-raised roommate, Betsey. Confirmed at: http://cosmicvariance.com/2006/05/13/jasmine/, "star" from day 5 puzzle)

3. Bougainvillea (Also recognized by Betsey. Confirmed at: http://en.wikipedia.org/wiki/Bougainvillea)

4. Magnolia (I'll admit, I just got this from the clue for the next day's puzzle. Confirmed which one was a Magnolia here: http://en.wikipedia.org/wiki/Magnolia)

Tomorrow's Clue
http://www.mentalfloss.com/HDYK/hdyk5_clue1.html
Three coins on a red background

Day 2 - Name That Tune

1. 25 or 6 to 4 - Chicago (I recognized this one off the bat. I confirmed it with: http://www.youtube.com/watch?v=aSOaoPDO16Y)

2. 1-2-3 - Gloria Estefan & Miami Sound Machine (Recognized by my friend Joe, who grew up during the 80s. Confirmed with: http://youtube.com/watch?v=kgVPRc5FBtk)

3. 19th Nervous Breakdown - Rolling Stones (Recognized by my Dad, a fountain of oldies and classic rock wisdom. Confirmed with http://youtube.com/watch?v=bHwTygK05_M)

4. 50 Ways to Leave Your Lover - Paul Simon (Recognized by my friend Lauren, who was sad she didn't get more of them. Confirmed with: http://youtube.com/watch?v=91euERWH2M4)

5. Three Coins in the Fountain - Sinatra, Styne/Cahn (I googled "sinatra" + "make it mine" + "lyrics" and came up with this. Confirmed with: http://youtube.com/watch?v=eJQuZOg2Dfo)

6. 867-5309/Jenny "“ Tommy Tutone (Recognized by Joe, the 80s boy. Confirmed with: http://youtube.com/watch?v=lqUPApCUt90)

Tomorrow's Clue:
http://us.penguingroup.com/static/html/yr/childrens.html
Penguin Children's books

Day 3 - One of These Things is Not Like the Others

All pictures are cover illustrations by the not-so-late and very great Maurice Sendak except for the third, which is from There's a Nightmare in My Closet by Mercer Mayer.

1. In the Night Kitchen - Maurice Sendak (I just recognized this. I read the book a million times in my youth. Confirmed here: http://en.wikipedia.org/wiki/In_the_Night_Kitchen)
2. Where the Wild Things Are - Maurice Sendak (I also recognized this one...who wouldn't? Confirmed here: http://en.wikipedia.org/wiki/Where_the_Wild_Things_Are)
3. There's a Nightmare in My Closet - Written and Illustrated by Mercer Mayer (A Google search for "monster" + "closet" led to: http://www.mtholyoke.edu/omc/kidsphil/questions/Nightmare/nightmare_cover_250pixels.jpg)
4. Little Bear - by Else Hlmelund Minarik, Illustrations by Maurice Sendak (Browsing for Sendak works, I found this at: http://www.amazon.com/gp/reader/0064440044/ref=sib_dp_pt#reader-link)

Tomorrow's Clue:
http://www.mentalfloss.com/HDYK/hdyk5_clue3.html
Outline of Tanzania (looked at a bunch of maps and matched the outline)

Day 4 - Name That Country

1. Saudi Arabia - eStAteUpDrIveARkAsBlIndAt
2. Belarus - saBErsLAtRUSt
3. Italy - slIceTALkhappY
4. Tanzania - esTAteNowZootrAiNIsAt (this could also be Estonia, but yesterday's clue hinted otherwise)
5. Australia - sAbersUSTRApsLIceApple

I had Tanzania from the previous day's clue, and the clue for today helped me spot Australia. To browse for countries, I also used: http://en.wikipedia.org/wiki/List_of_countries.

Tomorrow's Clue:
http://www.mentalfloss.com/HDYK/hdyk5_clue4.html
Outline of Ohio

Thanks for the great puzzles! I had a lot of fun this week, even if my friends got sick of me bugging them for help the first couple of days. Woot!

~Maggi

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iStock // Ekaterina Minaeva
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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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Name the Author Based on the Character
May 23, 2017
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