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Wikimedia Commons/Bryan Dugan

The Klout Score of 1903: A Statistical Study of Eminent Men

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Wikimedia Commons/Bryan Dugan

How do you measure influence? What is notability? It might seem that before the social ranking site Klout came along to assign people numbers by cold, numerical, social media calculation, the only way to rank people's importance was by hunch and opinion. Your top 100 might be different from my top 100, and who was to say which one captured the truth? But long before the age of Klout, there was psychologist James McKeen Cattell and his 1903 paper, "A Statistical Study of Eminent Men."

Cattell wanted to develop a measure of social importance that would move the study of great men from the realm of literature into the realm of science. In order to put a number on greatness, he first had to determine what, exactly, should be measured. Men could be important in different ways:

"We have men of genius, great men and men merely eminent. Thus many a genius has been a 'mute inglorious Milton' lacking the character or the circumstance for the accomplishment of his task. Washington was scarcely a genius, but was truly a great man. Napoleon III was neither a genius nor a great man, but was eminent to an unusual degree. But if we simply take those men who have most attracted the eyes and ears of the world, who have most set its tongues and printing presses in motion, we have a definite group."

So Cattell decided the number he needed was to be found in the measurement of "the motion of tongues and printing presses." He came up with a strategy to discover the set of men who had been most talked about. First, he took the 2000 longest articles from each of 6 different encyclopedias (English, French, German, and American), narrowed them down to the list of those that appeared in at least three of the encyclopedias, and then from that list chose those with the greatest average number of lines devoted to them over the whole set.

The top 25 men

The end product was an ordered list of the 1000 most eminent men. The top 25 were Napoleon, Shakespeare, Mohammed, Voltaire, Bacon, Aristotle, Goethe, Julius Caesar, Luther, Plato, Napoleon III, Burke, Homer, Newton, Cicero, Milton, Alexander the Great, Pitt, Washington, Augustus, Wellington, Raphael, Descartes, Columbus, and Confucius.

The bottom 10, as expected, are much less recognizable to us today: Otho, Sertorius, Macpherson, Claudianus, Domitian, Bugeaud, Charles I (Naples), Fauriel, Enfantin, and Babeuf.

Once he had the list, Cattell endeavored to unlock some of the secrets of greatness by analyzing factors like era, nationality, and what the greats were known for. For example, France was first in eminence, followed by Britain, Germany, Italy, Rome, Greece, America, Spain, Switzerland, Holland, and Sweden.

The real point of all this was to provide support for Cattell's ideas on eugenics. He used the statistics on nationality to argue for the unsavory conclusion that race and heredity were the primary factors in greatness; he reckons, for example, that the fall-off in Greek eminence after the classical period was due to "racial mixing."

At the same time he undermines his own point by cautioning against reading too much into France's numbers, arguing that "the French Revolution brought into prominence many men not truly great" and asserting that "in so far as the curves for the nineteenth century are valid, the promise for America is large." (Yes, Cattell was American.) So I guess he thought circumstances did have something to do with who ends up on the list? Still, the paper ends with an ominous call for science to gather more quantitative data that would help society figure out how to "improve the stock" and produce more great men.

What about the eminent women?

Cattell had not intended to leave women out of his analysis. A few did end up on his list of 1000. He explained that by "eminent men" he really meant "eminent people," but since women did "not have an important place on the list" there was no reason not to just say "eminent men" and be done with it.

However, ten years later, a student of Cattell's named Cora Sutton Castle decided to use his measurement technique to study eminent women for her doctoral dissertation. Needless to say, she came away with a slightly different conclusion about the role of different factors in eminence.

The top 25 women

Castle intended to work with a list of the 1000 most eminent women, but after applying the encyclopedia strategy and removing women of the Bible from the list, she was left with only 868. The top 25 were Mary Stuart, Jeanne d'Arc, Victoria of England, Elizabeth of England, George Sand, Madame de Staël, Catherine II of Russia, Maria Theresa, Marie Antoinette, Anne of England, Madame de Sévigné, Mary I of England, George Eliot, Christina of Sweden, Elizabeth Barrett Browning, Madame de Maintenon, Josephine of France, Catherine de Medici, Cleopatra, Harriet Beecher Stowe, Charlotte Brontë, Charlotte Corday, Marie Roland, Jeanne Pompadour, and Barbara Krüdener.

You can see Castle struggle to extract conclusions similar to those of her advisor from her breakdown of the data, but the "race" angle (which was really nationality) didn't yield much. She does find it interesting that the ratio of eminent women to the population in general increases so much (and far more than it did for men) over the course of history, and notes that one reason for the recent spike may be that "ability in women is more readily and willingly recognized at the present time than formerly."

"Who knows," she asks in an aside about ancient Greece, "but that her women were as potentially as great as her men, and if Plato's theory regarding the education of women had been universally applied, the curve might not have risen higher?" She concludes the thesis with a hypothetical question that she clearly knows the answer to: "Has innate inferiority been the reason for the small number of eminent women, or has civilization never yet allowed them an opportunity to develop their innate powers and possibilities?"

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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
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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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