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The Truth About Lie Detectors

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With the possible exception of politicians and small wooden puppets named Pinocchio, most people have a hard time lying with a straight face—and an even harder time lying successfully when their every move, breath, inflection of speech, and variation in blood pressure is being monitored.


While the accuracy of the modern lie detector, or polygraph machine, is considered dubious by many researchers—in 2002, the National Academy of Sciences determined the polygraph to be essentially useless—it's popularly believed that a simple machine can really determine whether or not a person is telling a lie.

Of course, we've bought into a lot of crazier ideas in centuries past. In medieval England, a person thought to be lying might be subjected to a test of fire (walking across hot stones; carrying a scorching hot iron rod) or water (being tossed into the pond). If a person was burned in a trial by fire, it was considered sufficient evidence for a hanging. A person tried by water had an even worse deal: if you floated, you were guilty, and sent to the hangman's noose. If you sank, you were considered innocent, but since you were dead from the drowning, it didn't make too much of a difference.

Untruth or Consequences

By the 19th century, governments were no longer throwing people in ponds (not as a measure of truth-telling, anyway), but the methods used to assess a person's character were still pretty dubious. Phrenology—the study of bumps on the skull—and the new discipline of psychology gave rise to the idea that physical characteristics and behavior could demonstrate a person's moral character, and thus their truthful or deceitful nature.

In 1895, Cesare Lombroso theorized that sudden changes in a person's blood pressure could be an indication of lying, and he attempted to chart these changes with a device called "Lombroso's Glove." More sophisticated machines—simultaneously recording blood pressure, pulse, respiration, and galvanic skin response—were later developed by Dr. John A. Larson and Leonard Keeler (widely considered the father of the modern polygraph machine) in the late 1920s and early 30s. But it was a psychologist named William Marston who first popularized the "lie detector," and gave it the cultural prominence it has today.

Lasso of Truth

Marston was hired by the US government during WWI to come up with a way to make sure that prisoners of war told the truth during interrogation. Echoing Lombroso's experiments, he decided to test his subjects' blood pressure during their interviews. In 1917, he published his findings to great acclaim in the press, who hailed him as the inventor of the "lie detector." Marston didn't shy away from the title. In fact, he publicly stated that his discovery hailed "the end of man's long, futile striving for a means of distinguishing truth-telling from deception."

Marston remained a firm advocate for the implementation of the polygraph into the court system, and was brought in to administer a lie detection test for the 1923 case of Frye vs. United States. The court found that the test could not be considered reliable enough to be wonder-woman.jpgused as evidence, though, so Marston's tests were thrown out. Essentially, the court ruling established a precedent, and polygraphs have, for the most part, been kept out of the courtroom ever since.


A tireless advocate for truth, Marston was undaunted by the court's ruling. Instead, his obsession with honesty would later fuel his work in creating the most enduring female superhero in comic book history, and the greatest lie detector of them all—Wonder Woman, whose magical golden lasso compelled villains caught inside it to tell the absolute truth.

This article was written by Ransom Riggs and excerpted from the mental_floss book In the Beginning: The Origins of Everything. You can pick up a copy in our store.

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Speaking of Lie Detecting...

We recently released our first iPhone trivia game. You're given two statements "“ one true, one blatantly false. Your job is to spot the Big Fat Lies! If you've got an iPhone (or iPod Touch) and $2.99, head on over to the iTunes Store and get downloading. (That link should open up your iTunes and send you right to Big Fat Lies! page of the store. If not, you can search for "Big Fat Lies" and it will come right up.)

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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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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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