Discover DALL‑E, the Artificial Intelligence Artist That Lets You Create Surreal Artwork

in Art, Artificial Intelligence, Computer Science, Data, Technology | June 17th, 2022

DALL‑E, an arti­fi­cial intel­li­gence sys­tem that gen­er­ates viable-look­ing art in a vari­ety of styles in response to user sup­plied text prompts, has been gar­ner­ing a lot of inter­est since it debuted this spring.

It has yet to be released to the gen­er­al pub­lic, but while we’re wait­ing, you could have a go at DALL‑E Mini, an open source AI mod­el that gen­er­ates a grid of images inspired by any phrase you care to type into its search box.

Co-cre­ator Boris Day­ma explains how DALL‑E Mini learns by view­ing mil­lions of cap­tioned online images:

Some of the con­cepts are learnt (sic) from mem­o­ry as it may have seen sim­i­lar images. How­ev­er, it can also learn how to cre­ate unique images that don’t exist such as “the Eif­fel tow­er is land­ing on the moon” by com­bin­ing mul­ti­ple con­cepts togeth­er.

Sev­er­al mod­els are com­bined togeth­er to achieve these results:

• an image encoder that turns raw images into a sequence of num­bers with its asso­ci­at­ed decoder

• a mod­el that turns a text prompt into an encod­ed image

• a mod­el that judges the qual­i­ty of the images gen­er­at­ed for bet­ter fil­ter­ing 

My first attempt to gen­er­ate some art using DALL‑E mini failed to yield the hoped for weird­ness.  I blame the bland­ness of my search term — “toma­to soup.”

Per­haps I’d have bet­ter luck “Andy Warhol eat­ing a bowl of toma­to soup as a child in Pitts­burgh.”

Ah, there we go!

I was curi­ous to know how DALL‑E Mini would riff on its name­sake artist’s han­dle (an hon­or Dali shares with the tit­u­lar AI hero of Pixar’s 2018 ani­mat­ed fea­ture, WALL‑E.)

Hmm… seems like we’re back­slid­ing a bit.

Let me try “Andy Warhol eat­ing a bowl of toma­to soup as a child in Pitts­burgh with Sal­vador Dali.”

Ye gods! That’s the stuff of night­mares, but it also strikes me as pret­ty legit mod­ern art. Love the spar­ing use of red. Well done, DALL‑E mini.

At this point, van­i­ty got the bet­ter of me and I did the AI art-gen­er­at­ing equiv­a­lent of googling my own name, adding “in a tutu” because who among us hasn’t dreamed of being a bal­le­ri­na at some point?

Let that be a les­son to you, Pan­do­ra…

Hope­ful­ly we’re all plan­ning to use this play­ful open AI tool for good, not evil.

Hyperallergic’s Sarah Rose Sharp raised some valid con­cerns in rela­tion to the orig­i­nal, more sophis­ti­cat­ed DALL‑E:

It’s all fun and games when you’re gen­er­at­ing “robot play­ing chess” in the style of Matisse, but drop­ping machine-gen­er­at­ed imagery on a pub­lic that seems less capa­ble than ever of dis­tin­guish­ing fact from fic­tion feels like a dan­ger­ous trend.

Addi­tion­al­ly, DALL‑E’s neur­al net­work can yield sex­ist and racist images, a recur­ring issue with AI tech­nol­o­gy. For instance, a reporter at Vice found that prompts includ­ing search terms like “CEO” exclu­sive­ly gen­er­at­ed images of White men in busi­ness attire. The com­pa­ny acknowl­edges that DALL‑E “inher­its var­i­ous bias­es from its train­ing data, and its out­puts some­times rein­force soci­etal stereo­types.”

Co-cre­ator Day­ma does not duck the trou­bling impli­ca­tions and bias­es his baby could unleash:

While the capa­bil­i­ties of image gen­er­a­tion mod­els are impres­sive, they may also rein­force or exac­er­bate soci­etal bias­es. While the extent and nature of the bias­es of the DALL·E mini mod­el have yet to be ful­ly doc­u­ment­ed, giv­en the fact that the mod­el was trained on unfil­tered data from the Inter­net, it may gen­er­ate images that con­tain stereo­types against minor­i­ty groups. Work to ana­lyze the nature and extent of these lim­i­ta­tions is ongo­ing, and will be doc­u­ment­ed in more detail in the DALL·E mini mod­el card.

The New York­er car­toon­ists Ellis Rosen and Jason Adam Katzen­stein con­jure anoth­er way in which DALL‑E mini could break with the social con­tract:

And a Twit­ter user who goes by St. Rev. Dr. Rev blows minds and opens mul­ti­ple cans of worms, using pan­els from car­toon­ist Joshua Bark­man’s beloved web­com­ic, False Knees:

Pro­ceed with cau­tion, and play around with DALL‑E mini here.

Get on the wait­list for orig­i­nal fla­vor DALL‑E access here.

 

Relat­ed Con­tent

Arti­fi­cial Intel­li­gence Brings to Life Fig­ures from 7 Famous Paint­ings: The Mona Lisa, Birth of Venus & More

Google App Uses Machine Learn­ing to Dis­cov­er Your Pet’s Look Alike in 10,000 Clas­sic Works of Art

Arti­fi­cial Intel­li­gence for Every­one: An Intro­duc­to­ry Course from Andrew Ng, the Co-Founder of Cours­era

- Ayun Hal­l­i­day is the Chief Pri­ma­tol­o­gist of the East Vil­lage Inky zine and author, most recent­ly, of Cre­ative, Not Famous: The Small Pota­to Man­i­festo.  Fol­low her @AyunHalliday.

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Japanese Researcher Sleeps in the Same Location as Her Cat for 24 Consecutive Nights!

in Biology, Data, Design, Life | March 25th, 2022


Cross cat nap­ping with bed hop­ping and you might end up hav­ing an “adven­ture in com­fort” sim­i­lar to the one that informs stu­dent Yuri Naka­hashi’s the­sis for Tokyo’s Hosei Uni­ver­si­ty.

For 24 con­sec­u­tive nights, Naka­hashi for­went the com­forts of her own bed in favor of a green sleep­ing bag, unfurled in what­ev­er ran­dom loca­tion one of her five pet cats had cho­sen as its sleep­ing spot that evening.

(The choice of which cat would get the plea­sure of dic­tat­ing each night’s sleep­ing bag coor­di­nates was also ran­dom­ized.)

As the own­er of five cats, Naka­hashi pre­sum­ably knew what she was sign­ing up for…

 

Cats rack out atop sofa backs, on stairs, and under beds…and so did Naka­hashi.

Her pho­tos sug­gest she logged a lot of time on a bare wood­en floor.

A Fit­Bit mon­i­tored the dura­tion and qual­i­ty of time spent asleep, as well as the fre­quen­cy with which she awak­ened dur­ing the night.

She doc­u­ment­ed the phys­i­cal and psy­cho­log­i­cal effects of this exper­i­ment in an inter­ac­tive pub­lished by the Infor­ma­tion Pro­cess­ing Soci­ety of Japan.

She reports that she eager­ly await­ed the rev­e­la­tion of each night’s coor­di­nates, and that even when her sleep was dis­rupt­ed by her pets’ mid­dle of the night groom­ing rou­tines, bunk­ing next to them had a “relax­ing effect.”

Mean­while, our research sug­gests that the same exper­i­ment would awak­en a vast­ly dif­fer­ent response in a dif­fer­ent human sub­ject, one suf­fer­ing from ail­uro­pho­bia, say, or severe aller­gies to the pro­teins in feline sali­va, urine, and dan­der.

What’s real­ly sur­pris­ing about Nakahashi’s itin­er­ant, and appar­ent­ly plea­sure-filled under­tak­ing is how lit­tle dif­fer­ence there is between her aver­age sleep score dur­ing the exper­i­ment and her aver­age sleep score from the 20 days pre­ced­ing it.

At left, an aver­age sleep score of 84.2 for the 20 days lead­ing up to exper­i­ment. At right, an aver­age sleep score 83.7 dur­ing the exper­i­ment.

Nakahashi’s entry for the YouFab Glob­al Cre­ative Awards, a prize for “work that attempts a dia­logue that tran­scends the bound­aries of species, space, and time” reflects the play­ful spir­it she brought to her slight­ly off-kil­ter exper­i­ment:

 Is it pos­si­ble to add diver­si­ty to the way we enjoy sleep? Let’s think about food. In addi­tion to the taste and nutri­tion of the food, each meal is a spe­cial expe­ri­ence with diver­si­ty depend­ing on the peo­ple you are eat­ing with, the atmos­phere of the restau­rant, the weath­er, and many oth­er fac­tors. In order to bring this kind of enjoy­ment to sleep, we pro­pose an “adven­ture in com­fort” in which the cat decides where to sleep each night, away from the fixed bed­room and bed. This project is sim­i­lar to going out to eat with a good friend at a restau­rant, where the cat guides you to sleep.

She notes that tra­di­tion­al beds have an immo­bil­i­ty owing to “their phys­i­cal weight and cul­tur­al con­cepts such as direc­tion.”

This sug­gests that her work could be of some ben­e­fit to humans in decid­ed­ly less fan­ci­ful, invol­un­tary sit­u­a­tions, whose lack of hous­ing leads them to sleep in unpre­dictable, and inhos­pitable loca­tions.

Naka­hashi’s time in the green sleep­ing bag inspired her to cre­ate the below mod­el of a more flex­i­ble bed, using a polypropy­lene bag, rice and nylon film.

We have cre­at­ed a pro­to­type of a dou­ble-lay­ered inflat­able bed that has a pouch struc­ture that inflates with air and a jam­ming struc­ture that becomes hard when air is com­pressed. The pouch side soft­ly receives the body when inflat­ed. The jam­ming side becomes hard when the air is removed, and can be firm­ly fixed in an even space. The air is designed to move back and forth between the two lay­ers, so that when not in use, the whole thing can be rolled up soft­ly for stor­age. 

It’s hard to imag­ine the pres­ence of a pussy­cat doing much to ame­lio­rate the anx­i­ety of those forced to flee their famil­iar beds with lit­tle warn­ing, but we can see how Nakahashi’s design might bring a degree of phys­i­cal relief when sleep­ing in sub­way sta­tions, base­ment cor­ners, and oth­er har­row­ing loca­tions.

Via Spoon & Toma­go

- Ayun Hal­l­i­day is the Chief Pri­ma­tol­o­gist of the East Vil­lage Inky zine and author, most recent­ly, of Cre­ative, Not Famous: The Small Pota­to Man­i­festo.  Fol­low her @AyunHalliday.

Relat­ed Con­tent 

A 110-Year-Old Book Illus­trat­ed with Pho­tos of Kit­tens & Cats Taught Kids How to Read

An Ani­mat­ed His­to­ry of Cats: How Over 10,000 Years the Cat Went from Wild Preda­tor to Sofa Side­kick

GPS Track­ing Reveals the Secret Lives of Out­door Cats

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M.I.T. Computer Program Predicts in 1973 That Civilization Will End by 2040

in Computer Science, Data, Life | March 8th, 2022

In 1704, Isaac New­ton pre­dict­ed the end of the world some­time around (or after, “but not before”) the year 2060, using a strange series of math­e­mat­i­cal cal­cu­la­tions. Rather than study what he called the “book of nature,” he took as his source the sup­posed prophe­cies of the book of Rev­e­la­tion. While such pre­dic­tions have always been cen­tral to Chris­tian­i­ty, it is star­tling for mod­ern peo­ple to look back and see the famed astronomer and physi­cist indulging them. For New­ton, how­ev­er, as Matthew Stan­ley writes at Sci­ence, “lay­ing the foun­da­tion of mod­ern physics and astron­o­my was a bit of a sideshow. He believed that his tru­ly impor­tant work was deci­pher­ing ancient scrip­tures and uncov­er­ing the nature of the Chris­t­ian reli­gion.”

Over three hun­dred years lat­er, we still have plen­ty of reli­gious doom­say­ers pre­dict­ing the end of the world with Bible codes. But in recent times, their ranks have seem­ing­ly been joined by sci­en­tists whose only pro­fessed aim is inter­pret­ing data from cli­mate research and sus­tain­abil­i­ty esti­mates giv­en pop­u­la­tion growth and dwin­dling resources. The sci­en­tif­ic pre­dic­tions do not draw on ancient texts or the­ol­o­gy, nor involve final bat­tles between good and evil. Though there may be plagues and oth­er hor­ri­ble reck­on­ings, these are pre­dictably causal out­comes of over-pro­duc­tion and con­sump­tion rather than divine wrath. Yet by some strange fluke, the sci­ence has arrived at the same apoc­a­lyp­tic date as New­ton, plus or minus a decade or two.

The “end of the world” in these sce­nar­ios means the end of mod­ern life as we know it: the col­lapse of indus­tri­al­ized soci­eties, large-scale agri­cul­tur­al pro­duc­tion, sup­ply chains, sta­ble cli­mates, nation states…. Since the late six­ties, an elite soci­ety of wealthy indus­tri­al­ists and sci­en­tists known as the Club of Rome (a fre­quent play­er in many con­spir­a­cy the­o­ries) has fore­seen these dis­as­ters in the ear­ly 21st cen­tu­ry. One of the sources of their vision is a com­put­er pro­gram devel­oped at MIT by com­put­ing pio­neer and sys­tems the­o­rist Jay For­rester, whose mod­el of glob­al sus­tain­abil­i­ty, one of the first of its kind, pre­dict­ed civ­i­liza­tion­al col­lapse in 2040. “What the com­put­er envi­sioned in the 1970s has by and large been com­ing true,” claims Paul Rat­ner at Big Think.

Those pre­dic­tions include pop­u­la­tion growth and pol­lu­tion lev­els, “wors­en­ing qual­i­ty of life,” and “dwin­dling nat­ur­al resources.” In the video at the top, see Aus­trali­a’s ABC explain the computer’s cal­cu­la­tions, “an elec­tron­ic guid­ed tour of our glob­al behav­ior since 1900, and where that behav­ior will lead us,” says the pre­sen­ter. The graph spans the years 1900 to 2060. “Qual­i­ty of life” begins to sharply decline after 1940, and by 2020, the mod­el pre­dicts, the met­ric con­tracts to turn-of-the-cen­tu­ry lev­els, meet­ing the sharp increase of the “Zed Curve” that charts pol­lu­tion lev­els. (ABC revis­it­ed this report­ing in 1999 with Club of Rome mem­ber Kei­th Suter.)

You can prob­a­bly guess the rest—or you can read all about it in the 1972 Club of Rome-pub­lished report Lim­its to Growth, which drew wide pop­u­lar atten­tion to Jay Forrester’s books Urban Dynam­ics (1969) and World Dynam­ics (1971). For­rester, a fig­ure of New­ton­ian stature in the worlds of com­put­er sci­ence and man­age­ment and sys­tems theory—though not, like New­ton, a Bib­li­cal prophe­cy enthusiast—more or less endorsed his con­clu­sions to the end of his life in 2016. In one of his last inter­views, at the age of 98, he told the MIT Tech­nol­o­gy Review, “I think the books stand all right.” But he also cau­tioned against act­ing with­out sys­tem­at­ic think­ing in the face of the glob­al­ly inter­re­lat­ed issues the Club of Rome omi­nous­ly calls “the prob­lem­at­ic”:

Time after time … you’ll find peo­ple are react­ing to a prob­lem, they think they know what to do, and they don’t real­ize that what they’re doing is mak­ing a prob­lem. This is a vicious [cycle], because as things get worse, there is more incen­tive to do things, and it gets worse and worse.

Where this vague warn­ing is sup­posed to leave us is uncer­tain. If the cur­rent course is dire, “unsys­tem­at­ic” solu­tions may be worse? This the­o­ry also seems to leave pow­er­ful­ly vest­ed human agents (like Exxon’s exec­u­tives) whol­ly unac­count­able for the com­ing col­lapse. Lim­its to Growth—scoffed at and dis­parag­ing­ly called “neo-Malthu­sian” by a host of lib­er­tar­i­an crit­ics—stands on far sur­er evi­den­tiary foot­ing than Newton’s weird pre­dic­tions, and its cli­mate fore­casts, notes Chris­t­ian Par­en­ti, “were alarm­ing­ly pre­scient.” But for all this doom and gloom it’s worth bear­ing in mind that mod­els of the future are not, in fact, the future. There are hard times ahead, but no the­o­ry, no mat­ter how sophis­ti­cat­ed, can account for every vari­able.

Note: An ear­li­er ver­sion of this post appeared on our site in 2018.

Relat­ed Con­tent:

In 1953, a Tele­phone-Com­pa­ny Exec­u­tive Pre­dicts the Rise of Mod­ern Smart­phones and Video Calls

In 1922, a Nov­el­ist Pre­dicts What the World Will Look Like in 2022: Wire­less Tele­phones, 8‑Hour Flights to Europe & More

In 1704, Isaac New­ton Pre­dicts the World Will End in 2060

It’s the End of the World as We Know It: The Apoc­a­lypse Gets Visu­al­ized in an Inven­tive Map from 1486

Watch the Destruc­tion of Pom­peii by Mount Vesu­vius, Re-Cre­at­ed with Com­put­er Ani­ma­tion (79 AD)

Josh Jones is a writer and musi­cian based in Durham, NC. Fol­low him at @jdmagness

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Google App Uses Machine Learning to Discover Your Pet’s Look Alike in 10,000 Classic Works of Art

in Art, Data, K-12, Museums, Photography | February 15th, 2022


Does your cat fan­cy her­self a 21st-cen­tu­ry incar­na­tion of Bastet, the Egypt­ian God­dess of the Ris­ing Sun, pro­tec­tor of the house­hold, aka The Lady of Slaugh­ter?

If so, you should def­i­nite­ly per­mit her to down­load the Google Arts & Cul­ture app on your phone to take a self­ie using the Pet Por­traits fea­ture.

Remem­ber all the fun you had back in 2018 when the Art Self­ie fea­ture mis­took you for William II, Prince of Orange or the woman in “Jacob Cor­nelisz. van Oost­sa­nen Paint­ing a Por­trait of His Wife”?

Sure­ly your pet will be just as excit­ed to let a machine-learn­ing algo­rithm trawl tens of thou­sands of art­works from Google Arts & Culture’s part­ner­ing muse­ums’ col­lec­tions, look­ing for dop­pel­gängers.

Or maybe it’ll just view it as one more exam­ple of human fol­ly, if a far less­er evil than our predilec­tion for pet cos­tumes.

Should your pet wish to know more about the art­works it resem­bles, you can tap the results to explore them in depth.

Dogs, fish, birds, rep­tiles, hors­es, and rab­bits can play along too, though any­one hail­ing from the rodent fam­i­ly will find them­selves shut out.

Mash­able reports that “upload­ing a stock image of a mouse returned draw­ings of wolves.”

We can’t blame your pet snake for fum­ing.

Dit­to your Viet­namese Pot-bel­lied pig.

Though your pet fer­ret prob­a­bly doesn’t need an app (or a crys­tal ball) to know what its result would be. Bet­ter than an ermine col­lar, any­way…


If your pet is game and falls with­in Pet Por­traits approved species para­me­ters, here are the steps to fol­low:

  1. Launch the Google Arts & Cul­ture app and select the Cam­era but­ton. Scroll to the Pet Por­traits option.
  2. Have your pet take a self­ie. (Or alter­na­tive­ly, upload a saved image.)
  3. Give the app a few sec­onds (or min­utes) to return mul­ti­ple results with sim­i­lar­i­ty per­cent­ages.

Down­load the Google Arts & Cul­ture app here.

- Ayun Hal­l­i­day is the Chief Pri­ma­tol­o­gist of the East Vil­lage Inky zine and author, most recent­ly, of Cre­ative, Not Famous: The Small Pota­to Man­i­festo.  Fol­low her @AyunHalliday.

Relat­ed Con­tent:

Google’s Free App Ana­lyzes Your Self­ie and Then Finds Your Dop­pel­ganger in Muse­um Por­traits

Con­struct Your Own Bayeux Tapes­try with This Free Online App

A Gallery of 1,800 Gigapix­el Images of Clas­sic Paint­ings: See Vermeer’s Girl with the Pearl Ear­ring, Van Gogh’s Star­ry Night & Oth­er Mas­ter­pieces in Close Detail

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A 10-Course Introduction to Data Science from Johns Hopkins

in Data, Online Courses | September 12th, 2021

Data is now every­where. And those who can har­ness data effec­tive­ly stand poised to inno­vate and make impact­ful deci­sions. This holds true in busi­ness, med­i­cine, health­care, edu­ca­tion and oth­er spheres of life.

Enter the 10-course Intro­duc­tion to Data Sci­ence from Johns Hop­kins. Offered on the Cours­era plat­form, this course sequence cov­ers “the con­cepts and tools you’ll need through­out the entire data sci­ence pipeline, from ask­ing the right kinds of ques­tions to mak­ing infer­ences and pub­lish­ing results.” The pro­gram includes cours­es cov­er­ing The Data Scientist’s Tool­box, R Pro­gram­ming, Get­ting and Clean­ing Data, Devel­op­ing Data Prod­ucts and more. There’s also a Cap­stone Project where stu­dents can build a data prod­uct using real-world data.

Stu­dents can for­mal­ly enroll in the Intro­duc­tion to Data Sci­ence spe­cial­iza­tion and receive a cer­tifi­cate for each course they complete–a cer­tifi­cate they can share with prospec­tive employ­ers and their pro­fes­sion­al net­works. They’ll also leave with a port­fo­lio demon­strat­ing mas­tery of the mate­r­i­al cov­ered in the sequence. Hop­kins esti­mates that most learn­ers can com­plete the sequence in 3–7 months, dur­ing which time stu­dents will be charged $49 per month.

Alter­na­tive­ly, stu­dents can audit indi­vid­ual cours­es for free. When you enroll in a course, look care­ful­ly for the Audit option. Note: Audi­tors can­not receive a cer­tifi­cate for each com­plet­ed course.

If would like to for­mal­ly enroll in the Intro­duc­tion to Data Sci­ence sequence, you can start a 7‑Day Free Tri­al and size things up here.

Open Cul­ture has a part­ner­ship with Cours­era. If read­ers enroll in cer­tain Cours­era cours­es and pro­grams, it helps sup­port Open Cul­ture.

Relat­ed Con­tent:

Google Data Ana­lyt­ics Cer­tifi­cate: 8 Cours­es Will Help Pre­pare Stu­dents for an Entry-Lev­el Job in 6 Months

Become a Project Man­ag­er With­out a Col­lege Degree with Google’s Project Man­age­ment Cer­tifi­cate

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Google Data Analytics Certificate: 8 Courses Will Help Prepare Students for an Entry-Level Job in 6 Months

in Data, Education, Online Courses, Technology | September 5th, 2021

Dur­ing the pan­dem­ic, Google launched a series of Career Cer­tifi­cates that will “pre­pare learn­ers for an entry-lev­el role in under six months.” The new career ini­tia­tive includes cer­tifi­cates con­cen­trat­ing on Project Man­age­ment and UX Design. And now also Data Ana­lyt­ics, a bur­geon­ing field that focus­es on “the col­lec­tion, trans­for­ma­tion, and orga­ni­za­tion of data in order to draw con­clu­sions, make pre­dic­tions, and dri­ve informed deci­sion mak­ing.”

Offered on the Cours­era plat­form, the Data Ana­lyt­ics Pro­fes­sion­al Cer­tifi­cate con­sists of eight cours­es, includ­ing “Foun­da­tions: Data, Data, Every­where,” “Pre­pare Data for Explo­ration,” “Data Analy­sis with R Pro­gram­ming,” and “Share Data Through the Art of Visu­al­iza­tion.” Over­all this pro­gram “includes over 180 hours of instruc­tion and hun­dreds of prac­tice-based assess­ments, which will help you sim­u­late real-world data ana­lyt­ics sce­nar­ios that are crit­i­cal for suc­cess in the work­place. The con­tent is high­ly inter­ac­tive and exclu­sive­ly devel­oped by Google employ­ees with decades of expe­ri­ence in data ana­lyt­ics.”

Upon com­ple­tion, students–even those who haven’t pur­sued a col­lege degree–can direct­ly apply for jobs (e.g., junior or asso­ciate data ana­lyst, data­base admin­is­tra­tor, etc.) with Google and over 130 U.S. employ­ers, includ­ing Wal­mart, Best Buy, and Astreya. You can start a 7‑day free tri­al and explore the cours­es here. If you con­tin­ue beyond the free tri­al, Google/Coursera will charge $39 USD per month. That trans­lates to about $235 after 6 months, the time esti­mat­ed to com­plete the cer­tifi­cate.

Explore the Data Ana­lyt­ics Cer­tifi­cate by watch­ing the video above. Learn more about the over­all Google career cer­tifi­cate ini­tia­tive here. And find oth­er Google pro­fes­sion­al cer­tifi­cates here.

Note: Open Cul­ture has a part­ner­ship with Cours­era. If read­ers enroll in cer­tain Cours­era cours­es and pro­grams, it helps sup­port Open Cul­ture.

Relat­ed Con­tent:

Online Degrees & Mini Degrees: Explore Mas­ters, Mini Mas­ters, Bach­e­lors & Mini Bach­e­lors from Top Uni­ver­si­ties.

Google Intro­duces 6‑Month Career Cer­tifi­cates, Threat­en­ing to Dis­rupt High­er Edu­ca­tion with “the Equiv­a­lent of a Four-Year Degree”

Cours­era and Google Launch an Online Cer­tifi­cate Pro­gram to Help Stu­dents Become IT Pro­fes­sion­als & Get Attrac­tive Jobs

Google’s UX Design Pro­fes­sion­al Cer­tifi­cate: 7 Cours­es Will Help Pre­pare Stu­dents for an Entry-Lev­el Job in 6 Months

Become a Project Man­ag­er With­out a Col­lege Degree with Google’s Project Man­age­ment Cer­tifi­cate

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Are We All Getting More Depressed?: A New Study Analyzing 14 Million Books, Written Over 160 Years, Finds the Language of Depression Steadily Rising

in Data, Psychology | July 28th, 2021


The rela­tions between thought, lan­guage, and mood have become sub­jects of study for sev­er­al sci­en­tif­ic fields of late. Some of the con­clu­sions seem to echo reli­gious notions from mil­len­nia ago. “As a man thin­keth, so he is,” for exam­ple, pro­claims a famous verse in Proverbs (one that helped spawn a self-help move­ment in 1903). Pos­i­tive psy­chol­o­gy might agree. “All that we are is the result of what we have thought,” says one trans­la­tion of the Bud­dhist Dhamma­pa­da, a sen­ti­ment that cog­ni­tive behav­ioral ther­a­py might endorse.

But the insights of these tra­di­tions — and of social psy­chol­o­gy — also show that we’re embed­ded in webs of con­nec­tion: we don’t only think alone; we think — and talk and write and read — with oth­ers. Exter­nal cir­cum­stances influ­ence mood as well as inter­nal states of mind. Approach­ing these ques­tions dif­fer­ent­ly, researchers at the Lud­dy School of Infor­mat­ics, Com­put­ing, and Engi­neer­ing at Indi­ana Uni­ver­si­ty asked, “Can entire soci­eties become more or less depressed over time?,” and is it pos­si­ble to read col­lec­tive changes in mood in the writ­ten lan­guages of the past cen­tu­ry or so?

The team of sci­en­tists, led by Johan Bollen, Indi­ana Uni­ver­si­ty pro­fes­sor of infor­mat­ics and com­put­ing, took a nov­el approach that brings togeth­er tools from at least two fields: large-scale data analy­sis and cog­ni­tive-behav­ioral ther­a­py (CBT). Since diag­nos­tic cri­te­ria for mea­sur­ing depres­sion have only been around for the past 40 years, the ques­tion seemed to resist lon­gi­tu­di­nal study. But CBT pro­vid­ed a means of ana­lyz­ing lan­guage for mark­ers of “cog­ni­tive dis­tor­tions” — think­ing that skews in over­ly neg­a­tive ways. “Lan­guage is close­ly inter­twined with this dynam­ic” of thought and mood, the researchers write in their study, “His­tor­i­cal lan­guage records reveal a surge of cog­ni­tive dis­tor­tions in recent decades,” pub­lished just last month in PNAS.

Choos­ing three lan­guages, Eng­lish (US), Ger­man, and Span­ish, the team looked for “short sequences of one to five words (n‑grams), labeled cog­ni­tive dis­tor­tion schema­ta (CDS).” These words and phras­es express neg­a­tive thought process­es like “cat­a­stro­phiz­ing,” “dichoto­mous rea­son­ing,” “dis­qual­i­fy­ing the pos­i­tive,” etc. Then, the researchers iden­ti­fied the preva­lence of such lan­guage in a col­lec­tion of over 14 mil­lion books pub­lished between 1855 and 2019 and uploaded to Google Books. The study con­trolled for lan­guage and syn­tax changes dur­ing that time and account­ed for the increase in tech­ni­cal and non-fic­tion books pub­lished (though it did not dis­tin­guish between lit­er­ary gen­res).

What the sci­en­tists found in all three lan­guages was a dis­tinc­tive “‘hock­ey stick’ pat­tern” — a sharp uptick in the lan­guage of depres­sion after 1980 and into the present time. The only spikes that come close on the time­line occur in Eng­lish lan­guage books dur­ing the Gild­ed Age and books pub­lished in Ger­man dur­ing and imme­di­ate­ly after World War II. (High­ly inter­est­ing, if unsur­pris­ing, find­ings.) Why the sud­den, steep climb in lan­guage sig­ni­fy­ing depres­sive think­ing? Does it actu­al­ly mark a col­lec­tive shift in mood, or show how his­tor­i­cal­ly oppressed groups have had more access to pub­lish­ing in the past forty years, and have expressed less sat­is­fac­tion with the sta­tus quo?

While they are care­ful to empha­size that they “make no causal claims” in the study, the researchers have some ideas about what’s hap­pened, observ­ing for exam­ple:

The US surge in CDS preva­lence coin­cides with the late 1970s when wages stopped track­ing increas­ing work pro­duc­tiv­i­ty. This trend was asso­ci­at­ed with ris­es in income inequal­i­ty to recent lev­els not seen since the 1930s. This phe­nom­e­non has been observed for most devel­oped economies, includ­ing Ger­many, Spain and Latin Amer­i­ca.

Oth­er fac­tors cit­ed include the devel­op­ment of the World Wide Web and its facil­i­ta­tion of polit­i­cal polar­iza­tion, “in par­tic­u­lar us-vs.-them think­ing… dichoto­mous rea­son­ing,” and oth­er mal­adap­tive thought pat­terns that accom­pa­ny depres­sion. The scale of these devel­op­ments might be enough to explain a major col­lec­tive rise in depres­sion, but one com­menter offers an addi­tion­al gloss:

The globe is *Lit­er­al­ly* on fire, or his­tor­i­cal­ly flood­ing — Mul­ti­ple eco­nom­ic crash­es bare­ly decades apart — a ghost town of a hous­ing mar­ket — a mul­ti-year glob­al pan­dem­ic — wealth con­cen­tra­tion at the .01% lev­el — ter­ri­ble pay/COL equa­tions — block­ing unionization/workers rights — abu­sive mil­i­ta­rized police, with­out the restraint or train­ing of actu­al mil­i­tary —  You can’t afford X for a month­ly mort­gage pay­ment!  Pay 1.5x for rent instead! — end­less wars for the last… 30…years? 50 if we include stuff like Korea, Cold War, Viet­nam… How far has the IMC been milk­ing the gov for funds to make the rich rich­er? Oh, and a bil­lion­aire 3‑way space race to deter­mine who’s got the biggest “rock­et”

These sound like rea­sons for glob­al depres­sion indeed, but the arrow could also go the oth­er way: maybe cat­a­stroph­ic rea­son­ing pro­duced actu­al cat­a­stro­phes; black and white think­ing led to end­less wars, etc…. More study is need­ed, says Bollen and his col­leagues, yet it seems prob­a­ble, giv­en the data, that “large pop­u­la­tions are increas­ing­ly stressed by per­va­sive cul­tur­al, eco­nom­ic, and social changes” — changes occur­ring more rapid­ly, fre­quent­ly, and with greater impact on our dai­ly lives than ever before. Read the full study at PNAS. 

Relat­ed Con­tent: 

Stanford’s Robert Sapol­sky Demys­ti­fies Depres­sion, Which, Like Dia­betes, Is Root­ed in Biol­o­gy

A Uni­fied The­o­ry of Men­tal Ill­ness: How Every­thing from Addic­tion to Depres­sion Can Be Explained by the Con­cept of “Cap­ture”

Charles Bukows­ki Explains How to Beat Depres­sion: Spend 3–4 Days in Bed and You’ll Get the Juices Flow­ing Again (NSFW)

Josh Jones is a writer and musi­cian based in Durham, NC. Fol­low him at @jdmagness

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A Data Visualization of Every Italian City & Town Founded in the BC Era

in Data, History | June 10th, 2021


Ancient peo­ple did not think about his­to­ry the way most of us do. It made no dif­fer­ence to con­tem­po­rary read­ers of the pop­u­lar Roman his­to­ri­an, Livy (the “JK Rowl­ing of his day”), that “most of the flesh and blood of [his] nar­ra­tive is fic­ti­tious,” and “many of the sto­ries are not real­ly Roman but Greek sto­ries reclothed in Roman dress,” his­to­ri­an Robert Ogilvie writes in an intro­duc­tion to Livy’s Ear­ly His­to­ry of Rome. Ancient his­to­ri­ans did not write to doc­u­ment facts, but to illus­trate moral, philo­soph­i­cal, and polit­i­cal truths about what they saw as immutable human nature.

Much of what we know about Roman antiq­ui­ty comes not from ancient Roman his­to­ry but from mod­ern arche­ol­o­gy (which is still mak­ing “amaz­ing” new dis­cov­er­ies about Roman cities). The remains of Rome at its apogee date from the time of Livy, who was like­ly born in 59 BC and died cir­ca 12 AD. A con­tem­po­rary, and pos­si­bly a friend, of Augus­tus, the his­to­ri­an lived through a peri­od of immense growth in which the new empire spread across the con­ti­nent, found­ing, build­ing, and con­quer­ing towns and cities as it went — a time, he wrote, when “the might of an impe­r­i­al peo­ple is begin­ning to work its own ruin.”

Livy pre­ferred to look back — “turn my eyes from the trou­bles,” he said — “more than sev­en hun­dred years,” to the date long giv­en for the found­ing of Rome, 753 BC, which seemed ancient enough to him. Mod­ern arche­ol­o­gists have found, how­ev­er, that the city prob­a­bly arose hun­dreds of years ear­li­er, hav­ing been con­tin­u­ous­ly inhab­it­ed since around 1000 BC. Livy’s own pros­per­ous but provin­cial city of Pad­ua only became incor­po­rat­ed into the Roman empire a few decades before his birth. Accord­ing to Livy him­self, Pad­ua was first found­ed in 1183 BC by the Tro­jan prince Antenor…  if you believe the sto­ries….

The point is that ancient Roman dates are sus­pect when they come from lit­er­ary sources (or “his­to­ries”) rather than arti­facts and archae­o­log­i­cal dat­ing meth­ods. What is the dis­tri­b­u­tion of such dates across arti­cles about ancient Rome on Wikipedia? Who could say. But the sheer num­ber of doc­u­ments and arti­facts left behind by the Romans and the peo­ple they con­quered and sub­dued make it easy to recon­struct the his­tor­i­cal stra­ta of Euro­pean cities — though we should allow for more than a lit­tle exag­ger­a­tion, dis­tor­tion, and even fic­tion in the data.

The maps you see here use Wikipedia data to visu­al­ize towns and cities in mod­ern-day Italy found­ed before the first cen­tu­ry — that is, every Ital­ian set­tle­ment of any kind with a “BC” cit­ed in its asso­ci­at­ed arti­cle. Many of these were found­ed by the Romans in the 2nd or 3rd cen­tu­ry BC. Many cities, like Pom­peii, Milan, and Livy’s own Pad­ua, were con­quered or slow­ly tak­en over from ear­li­er peo­ples. Anoth­er ver­sion of the visu­al­iza­tion, above, shows a dis­tri­b­u­tion by col­or of the dates from 10,000 BC to 10 BC. It makes for an equal­ly strik­ing way to illus­trate the his­to­ry, and pre­his­to­ry, of Italy up to Livy’s time — that is, accord­ing to Wikipedia.

The cre­ator of the visu­al­iza­tions obtained the data by scrap­ing 8000 Ital­ian Wikipedia arti­cles for men­tions of “BC” (or “AC” in Ital­ian). Even if we all agreed the open online ency­clo­pe­dia is an author­i­ta­tive source (and we cer­tain­ly do not), we’d still be left with the prob­lem of ancient dat­ing in cre­at­ing an accu­rate map of ancient Roman and Ital­ian his­to­ry. Unre­li­able data does not improve in pic­ture form. But data visu­al­iza­tions can, when com­bined with care­ful schol­ar­ship and good research, make dry lists of num­bers come alive, as Livy’s sto­ries made Roman his­to­ry, as he knew it, live for his read­ers.

See the creator’s dataset below and learn more here.

count 1152

mean 929.47

std 1221.89

min 2

25% 196

50% 342.5

75% 1529.5

max 10000

Relat­ed Con­tent: 

The Roads of Ancient Rome Visu­al­ized in the Style of Mod­ern Sub­way Maps

Rome’s Colos­se­um Will Get a New Retractable Floor by 2023 — Just as It Had in Ancient Times

A Vir­tu­al Tour of Ancient Rome, Cir­ca 320 CE: Explore Stun­ning Recre­ations of The Forum, Colos­se­um and Oth­er Mon­u­ments

Josh Jones is a writer and musi­cian based in Durham, NC. Fol­low him at @jdmagness

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