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106 of the Least Popular Baby Names in American History

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The SSA website gives the top 1000 boy and girl names (as reported on Social Security card applications) for each year from 1880 onward. If you look at the low end of the top 1000 names for 2012, you see boys’ names like Dangelo, Foster, Jaidyn, Briggs and Davon. For girls, you see names like Katalina, Hayleigh, Sloan, Karlie, and Meadow. These names are a bit different, but not all that unusual. Even the 1000th most popular name represents a few hundred babies, or even a few thousand if added up over four or five years. However, in the early years of SSA data, the population was much smaller, so the low end of the list represents fewer babies. And there are some pretty fabulous names in there. 

I went through the first 53 years of the SSA records and pulled out some of the best boy and girl names from the 900 to 1000 range for each year. Together, they make for great couples. I love imagining that among all the Johns and Marys who settled down together, Orange and Leafy (1893), or Henery and Florance (1897), or Lillian the boy and Lillyan the girl (1908) might have found each other too.

If you’re looking for a baby name and want something truly original, but with historical precedent, here’s your list:

Year Boy (Rank) Girl (Rank)
1880 Handy (970) Parthenia (914)
1881 Okey (972) Erie (1000)
1882 Ab (943) Dove (944)
1883 Commodore (925) Lovey (992)
1884 Spurgeon (958) Kathern (974)
1885 Fount (989) Icy (977)
1886 Squire (953) Texie (987)
1887 Bliss (946) Lockie (907)
1888 Boss (930) Indiana (989)
1889 Starling (962) Easter (967)
1890 Lawyer (999) Pinkey (918)
1891 Manley (962) Chestina (974)
1892 Little (914) Odell (1000)
1893 Orange (1000) Leafy (933)
1894 Flem (1000) Ova (986)
1895 Toy (969) Sister (974)
1896 Josephine (937)* Clifford (935)*
1897 Henery (1000) Florance (1000)
1898 Pleasant (973) Tiny (915)
1899 Fate (972) Cuba (884)
1900 Gorge (935) Electa (948)
1901 Joesph (999) Buelah (923)
1902 Rolla (917) Bama (942)
1903 Ples (992) Capitola (982)
1904 Council (989) Pearly (993)
1905 Son (912) Wava (967)
1906 Virgle (999) Carry (971)
1907 Geo (956) Arizona (949)
1908 Lillian (992) Lilyan (991)
1909 Murl (1000) Flonnie (1000)
1910 Lemon (964) Classie (994)
1911 Wash (978) Lavada (806)
1912 Christ (940) Almeta (940)
1913 Louise (982) Louis (974)
1914 Stephan (1000) Vella (1000)
1915 Mayo (990) Dimple (980)
1916 Green (929) Golden (908)
1917 Elza (968) Loyce (984)
1918 Curley (998) Ivory (979)
1919 Metro (982) Louvenia (993)
1920 Berry (941) Merry (934)
1921 Reno (969) Glendora (976)
1922 Author (950) Gaynell (981)
1923 Burley (994) Dorathy (995)
1924 Dorman (954) Mardell (982)
1925 Buddie (973) Bobbye (990)
1926 Wardell (929) Willodean (941)
1927 Estel (914) Gregoria (970)
1928 Gust (996) Hildred (998)
1929 Vester (984) Jettie (953)
1930 Otho (972) Charlsie (951)
1931 Early (1000) Ferne (1000)
1932 Dock (928) Jack (992)

* Not an error!

See Also...

8 Countries With Fascinating Baby Naming Laws
*
11 Baby Naming Trends of the Past
*
Dog Naming Trends Through the Ages

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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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May 23, 2017
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