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11 Words With Meanings That Have Changed Drastically Over Time

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People sometimes tell you you’re misusing a word and cite the Latin origin as proof. Don’t fall for the etymological fallacy. What a word means depends not on its origin, but on how speakers of a language understand it. Over time, words have a way of wandering, and meanings mutate. If you stuck with older meanings of the following words, you could end up in a strange land where “naughty” is the same as “nice” and “awesome” means “terrible.”

1. AWFUL

Ever wonder why “awesome” means excellent but “awful” means really bad when they both derive from “awe”? In Old English, awe meant fear, terror or dread. From its use in reference to God the word came to mean reverential or respectful fear. By the mid-1700s, awe came to mean solemn and reverential wonder, tinged with fear, inspired by the sublime in nature—such as thunder or a storm at sea. Originally, awful and awesome were synonymous, but by the early 19th century, awful absorbed the negative aspects of the emotion and the word was used to mean frightful or exceedingly bad. The earliest citation in the Oxford English Dictionary for awesome meaning marvelous, great; stunning or mind-boggling” is from the Official Preppy Handbook, 1980.

2. CHEATER

A cheater was originally an officer appointed to look after the king's escheats—the land lapsing to the Crown on the death of the owner intestate without heirs. As William Gurnall wrote in 1662, “[A] Cheater may pick the purses of ignorant people, by shewing them something like the Kings Broad Seal, which was indeed his own forgery.” Mistrust of the king’s cheaters led the word into its current sense: a dishonest gamester or a swindler.

3. EGREGIOUS

Egregious now describes something outstandingly bad or shocking, but it originally meant remarkably good. It comes from the Latin egregius, meaning "illustrious, select"—literally, "standing out from the flock," from ex-, "out of," and greg-, "flock." Apparently the current meaning arose from ironic use of the original.

4. FURNITURE

Furniture originally meant equipment, supplies or provisions, in the literal or figurative sense. For example, in a 1570 translation of Euclid’s Elements of Geometry, there is mention of “Great increase & furniture of knowledge.” Gradually, the meaning narrowed to the current sense: large moveable equipment such as tables and chairs, used to make a house, office, or other space suitable for living or working.

5. GIRL

Girl once meant a child or young person of either sex. In The Canterbury Tales, Chaucer says of the summoner, “In daunger hadde he at his owene gise/ The yonge girles of the diocise.” In modern English, that’s, “In his own power had he, and at ease/ Young people of the entire diocese.”

6. MEAT

Beginning in Old English, meat meant solid food (as opposed to drink) or fodder for animals. In A Journey to the Western Islands of Scotland (1775), Samuel Johnson noted, “Our guides told us, that the horses could not travel all day without rest or meat.” Generally, the word’s meaning has narrowed to refer only to the flesh of mammals, and in some regions, only pork or beef, but some Scottish dialects retain the older meaning of any kind of food.

7. NAUGHTY

In the 1300s, naughty people had naught (nothing); they were poor or needy. By the 1400s, the meaning shifted from having nothing to being worth nothing, being morally bad or wicked. It could refer to a licentious, promiscuous or sexually provocative person, or someone guilty of other improper behavior. ISermons preached upon Several Occasions (1678), Isaac Barrow speaks of “a most vile, flagitious man, a sorry and naughty Governour as could be.” But in the same century, “naughty” also had a gentler meaning, especially as applied to children: mischievous, disobedient, badly behaved.

8. NICE

A few centuries ago if a gentleman called a lady “nice,” she might not know whether to flutter her fan or slap his face. Nice entered English via Anglo-Norman from classical Latin nescius, meaning ignorant. Then it wandered off every which way. From the 1300s through 1600s it meant silly, foolish, or ignorant. During that same time period, though, it was used with these unrelated or even contradictory meanings:

Showy and ostentatious, or elegant and refined
Particular in matters of reputation or conduct; or wanton, dissolute, lascivious
Cowardly, unmanly, effeminate
Slothful, lazy, sluggish
Not obvious, difficult to decide, intricate.

By the 1500s, “nice” came to mean meticulous, attentive, sharp, making precise distinctions. By the 18th century, it acquired its current (and rather bland) meaning of agreeable and pleasant, but other meanings hung on, just to keep things interesting.

9. PRETTY

In Old English, “pretty” meant crafty and cunning. Later, it took on a more positive connotation: clever, skillful, or able. It could describe something (for example, a speech) cleverly or elegantly made. Perhaps that is how, by the 1400s, the meaning diverted to its present sense: good-looking, especially in a delicate or diminutive way.

10. SLY

If you call someone sly now, you mean they’re sneaky and deceitful—not a good thing. But when the word entered English from Old Norse in the 13th century, it also had a positive meaning: skillful, clever, knowing, and wise. It’s related to “sleight,” as in “sleight of hand,” the magician’s skill at trickery.

11. TERRIBLE

When terrible entered Middle English from Anglo-Norman and Middle French, it meant causing or fit to cause terror, inspiring great fear or dread. It also meant awe-inspiring or awesome, which—as we saw in the discussion of awful—could be terrifying as well as wonderful. By the 1500s, terrible (like awful, dreadful, frightful, and horrible) came to mean very harsh, severe, formidable, and hence, excessive or extreme—in a bad way.

In language, like everything else, change can be hard to accept. Don’t worry. If you’re an originalist when it comes to semantics and someone calls you egregiously awful, you can take it as high praise.

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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
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