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14 Words That Are Their Own Opposites

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Here’s an ambiguous sentence for you: “Because of the agency’s oversight, the corporation’s behavior was sanctioned.” Does that mean, 'Because the agency oversaw the company’s behavior, they imposed a penalty for some transgression' or does it mean, 'Because the agency was inattentive, they overlooked the misbehavior and gave it their approval by default'? We’ve stumbled into the looking-glass world of “contronyms”—words that are their own antonyms.

1. Sanction (via French, from Latin sanctio(n-), from sancire ‘ratify,’) can mean ‘give official permission or approval for (an action)’ or conversely, ‘impose a penalty on.’
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2. Oversight is the noun form of two verbs with contrary meanings, “oversee” and “overlook.” “Oversee,” from Old English ofersēon ‘look at from above,’ means ‘supervise’ (medieval Latin for the same thing: super- ‘over’ + videre ‘to see.’) “Overlook” usually means the opposite: ‘to fail to see or observe; to pass over without noticing; to disregard, ignore.’
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3. Left can mean either remaining or departed. If the gentlemen have withdrawn to the drawing room for after-dinner cigars, who’s left? (The gentlemen have left and the ladies are left.)
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4. Dust, along with the next two words, is a noun turned into a verb meaning either to add or to remove the thing in question. Only the context will tell you which it is. When you dust are you applying dust or removing it? It depends whether you’re dusting the crops or the furniture.
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5. Seed can also go either way. If you seed the lawn you add seeds, but if you seed a tomato you remove them.
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6. Stone is another verb to use with caution. You can stone some peaches, but please don’t stone your neighbor (even if he says he likes to get stoned).
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7. Trim as a verb predates the noun, but it can also mean either adding or taking away. Arising from an Old English word meaning ‘to make firm or strong; to settle, arrange,’ “trim” came to mean ‘to prepare, make ready.’ Depending on who or what was being readied, it could mean either of two contradictory things: ‘to decorate something with ribbons, laces, or the like to give it a finished appearance’ or ‘to cut off the outgrowths or irregularities of.’ And the context doesn’t always make it clear. If you’re trimming the tree are you using tinsel or a chain saw?
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8. Cleave can be cleaved into two “homographs,” words with different origins that end up spelled the same. “Cleave,” meaning ‘to cling to or adhere,’ comes from an Old English word that took the forms cleofian, clifian, or clīfan. “Cleave,” with the contrary meaning ‘to split or sever (something), ‘ as you might do with a cleaver, comes from a different Old English word, clēofan. The past participle has taken various forms: “cloven,” which survives in the phrase “cloven hoof,” “cleft,” as in a “cleft palate” or “cleaved.”
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9. Resign works as a contronym in writing. This time we have homographs, but not homophones. “Resign,” meaning ‘to quit,’ is spelled the same as “resign,” meaning ‘to sign up again,’ but it’s pronounced differently.
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10. Fast can mean "moving rapidly," as in "running fast," or ‘fixed, unmoving,’ as in "holding fast." If colors are fast they will not run. The meaning ‘firm, steadfast’ came first. The adverb took on the sense ‘strongly, vigorously,’ which evolved into ‘quickly,’ a meaning that spread to the adjective.
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11. Off means ‘deactivated,’ as in "to turn off," but also ‘activated,’ as in "The alarm went off."
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12. Weather can mean ‘to withstand or come safely through,’ as in “The company weathered the recession,” or it can mean ‘to be worn away’: “The rock was weathered.”
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13. Screen can mean ‘to show’ (a movie) or ‘to hide’ (an unsightly view).
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14. Help means ‘assist,’ unless you can’t help doing something, when it means ‘prevent.’

The contronym (also spelled “contranym”) goes by many names, including “auto-antonym,” “antagonym,” “enantiodrome,” “self-antonym,” “antilogy” and “Janus word” (from the Roman god of beginnings and endings, often depicted with two faces looking in opposite directions). Can’t get enough of them? The folks at Daily Writing Tips have rounded up even more.

Update: Here are 11 more words that are their own opposites.

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iStock // Ekaterina Minaeva
technology
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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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iStock
Animals
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Scientists Think They Know How Whales Got So Big
May 24, 2017
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iStock

It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]

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