The Animated Map of Quantum Computing: A Visual Introduction to the Future of Computing

in Computer Science | May 18th, 2022

If you lis­ten to the hype sur­round­ing quan­tum com­put­ing, you might think the near future shown in Alex Gar­land’s sci-fi series Devs is upon us — that we have com­put­ers com­plex enough to recre­ate time and space and recon­struct the human mind. Far from it. At this still-ear­ly stage, quan­tum com­put­ers promise much more than they can deliv­er, but the tech­nol­o­gy is “poised,” writes IBM “to trans­form the way you work in research.” The com­pa­ny does have — as do most oth­er oth­er big mak­ers of what are now called “clas­si­cal com­put­ers” — a “roadmap” for imple­ment­ing quan­tum com­put­ing and a lot of cool new tech­nol­o­gy (such as the quan­tum run­time envi­ron­ment Quiskit) built around the qubit, the quan­tum com­put­er ver­sion of the clas­si­cal bit.

The com­put­er bit, as we know, is a bina­ry enti­ty: either 1 or 0 and noth­ing in-between. The qubit, on the oth­er hand, mim­ics quan­tum phe­nom­e­na by remain­ing in a state of super­po­si­tion of all pos­si­ble states between 1 and 0 until users inter­act with it, like a spin­ning coin that only lands on one face if it’s phys­i­cal­ly engaged. And like quan­tum par­ti­cles, qubits can become entan­gled with each oth­er. Thus, “Quan­tum com­put­ers work excep­tion­al­ly well for mod­el­ing oth­er quan­tum sys­tems,” writes Microsoft, “because they use quan­tum phe­nom­e­na in their com­pu­ta­tion.” The pos­si­bil­i­ties are thrilling, and a lit­tle unset­tling, but no one’s mod­el­ing the uni­verse, or even a part of it, just quite yet.

“Use cas­es are large­ly exper­i­men­tal and hypo­thet­i­cal at this ear­ly stage,” McK­in­sey Dig­i­tal writes in a report for busi­ness­es, while also not­ing that usable quan­tum sys­tems may be on the mar­ket as ear­ly as 2030. If the roadmaps serve, that’s just around the cor­ner, espe­cial­ly giv­en how quick­ly quan­tum com­put­ers have evolved in rela­tion to their (expo­nen­tial­ly slow­er) clas­si­cal fore­bears. “From the first idea of a quan­tum com­put­er in 1980 [an idea attrib­uted to Nobel prize-win­ning physi­cist Richard Feyn­man] to today, there has been a huge growth in the quan­tum com­put­ing indus­try, espe­cial­ly in the last ten years,” says Dominic Wal­li­man in the video above, “with dozens of com­pa­nies and star­tups spend­ing hun­dreds of mil­lions of dol­lars in a race to build the world’s best quan­tum com­put­ers.”

Wal­li­man offers not only a (non-hyped) map of the pos­si­ble future, but also a map of quan­tum com­put­ing’s past. He promis­es to clear up mis­con­cep­tions we might have about the “dif­fer­ent kinds of quan­tum com­put­ing, how they work, and why so many peo­ple are invest­ing in the quan­tum com­put­ing indus­try.” We’ve pre­vi­ous­ly seen Wal­li­man’s Domain of Sci­ence chan­nel do the same for such huge fields of sci­en­tif­ic study as physics, chem­istry, math, and clas­si­cal com­put­er sci­ence. Here, he presents cut­ting-edge sci­ence on the cusp of real­iza­tion, explain­ing three essen­tial ideas — super­po­si­tion, entan­gle­ment, and inter­fer­ence — that gov­ern quan­tum com­put­ing. The pri­ma­ry dif­fer­ence between quan­tum and clas­si­cal com­put­ing from the point of view of non-spe­cial­ists is algo­rith­mic speed: while clas­si­cal com­put­ers could the­o­ret­i­cal­ly per­form the same com­plex func­tions as their quan­tum cousins, they would take ages to do so, or would halt and fiz­zle out in the attempt.

Will quan­tum com­put­ers be able to sim­u­late nature down to the sub­atom­ic lev­el in the future? McK­in­sey cau­tions, “experts are still debat­ing the most foun­da­tion­al top­ics for the field.” Despite the indus­try’s rapid growth, “it’s not yet clear,” Wal­li­man says, “which approach” among the many he sur­veys “will win out in the long run.” But if the roadmaps serve, we may not have to wait long to find out.

Relat­ed Con­tent:

The Map of Com­put­er Sci­ence: New Ani­ma­tion Presents a Sur­vey of Com­put­er Sci­ence, from Alan Tur­ing to “Aug­ment­ed Real­i­ty”

The Map of Physics: Ani­ma­tion Shows How All the Dif­fer­ent Fields in Physics Fit Togeth­er

The Map of Chem­istry: New Ani­ma­tion Sum­ma­rizes the Entire Field of Chem­istry in 12 Min­utes

The Map of Math­e­mat­ics: Ani­ma­tion Shows How All the Dif­fer­ent Fields in Math Fit Togeth­er

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

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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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Take an Intellectual Odyssey with a Free MIT Course on Douglas Hofstadter’s Pulitzer Prize-Winning Book Gödel, Escher, Bach: An Eternal Golden Braid

in Computer Science, Creativity, Math, MIT, Music, Neuroscience, Philosophy, Psychology | August 2nd, 2021

In 1979, math­e­mati­cian Kurt Gödel, artist M.C. Esch­er, and com­pos­er J.S. Bach walked into a book title, and you may well know the rest. Dou­glas R. Hof­s­tadter won a Pulitzer Prize for Gödel, Esch­er, Bach: an Eter­nal Gold­en Braid, his first book, thence­forth (and hence­forth) known as GEB. The extra­or­di­nary work is not a trea­tise on math­e­mat­ics, art, or music, but an essay on cog­ni­tion through an explo­ration of all three — and of for­mal sys­tems, recur­sion, self-ref­er­ence, arti­fi­cial intel­li­gence, etc. Its pub­lish­er set­tled on the pithy descrip­tion, “a metaphor­i­cal fugue on minds and machines in the spir­it of Lewis Car­roll.”

GEB attempt­ed to reveal the mind at work; the minds of extra­or­di­nary indi­vid­u­als, for sure, but also all human minds, which behave in sim­i­lar­ly unfath­omable ways. One might also describe the book as oper­at­ing in the spir­it — and the prac­tice — of Her­man Hesse’s Glass Bead Game, a nov­el Hesse wrote in response to the data-dri­ven machi­na­tions of fas­cism and their threat to an intel­lec­tu­al tra­di­tion he held par­tic­u­lar­ly dear. An alter­nate title (and key phrase in the book) Mag­is­ter Ludi, puns on both “game” and “school,” and alludes to the impor­tance of play and free asso­ci­a­tion in the life of the mind.

Hesse’s eso­teric game, writes his biog­ra­ph­er Ralph Freed­man, con­sists of “con­tem­pla­tion, the secrets of the Chi­nese I Ching and West­ern math­e­mat­ics and music” and seems sim­i­lar enough to Hof­s­tadter’s approach and that of the instruc­tors of MIT’s open course, Gödel, Esch­er, Bach: A Men­tal Space Odyssey. Offered through the High School Stud­ies Pro­gram as a non-cred­it enrich­ment course, it promis­es “an intel­lec­tu­al vaca­tion” through “Zen Bud­dhism, Log­ic, Meta­math­e­mat­ics, Com­put­er Sci­ence, Arti­fi­cial Intel­li­gence, Recur­sion, Com­plex Sys­tems, Con­scious­ness, Music and Art.”

Stu­dents will not study direct­ly the work of Gödel, Esch­er, and Bach but rather “find their spir­its aboard our men­tal ship,” the course descrip­tion notes, through con­tem­pla­tions of canons, fugues, strange loops, and tan­gled hier­ar­chies. How do mean­ing and form arise in sys­tems like math and music? What is the rela­tion­ship of fig­ure to ground in art? “Can recur­sion explain cre­ativ­i­ty,” as one of the course notes asks. Hof­s­tadter him­self has pur­sued the ques­tion beyond the entrench­ment of AI research in big data and brute force machine learn­ing. For all his daunt­ing eru­di­tion and chal­leng­ing syn­the­ses, we must remem­ber that he is play­ing a high­ly intel­lec­tu­al game, one that repli­cates his own expe­ri­ence of think­ing.

Hof­s­tadter sug­gests that before we can under­stand intel­li­gence, we must first under­stand cre­ativ­i­ty. It may reveal its secrets in com­par­a­tive analy­ses of the high­est forms of intel­lec­tu­al play, where we see the clever for­mal rules that gov­ern the mind’s oper­a­tions; the blind alleys that explain its fail­ures and lim­i­ta­tions; and the pos­si­bil­i­ty of ever actu­al­ly repro­duc­ing work­ings in a machine. Watch the lec­tures above, grab a copy of Hofstadter’s book, and find course notes, read­ings, and oth­er resources for the fas­ci­nat­ing course Gödel, Esch­er, Bach: A Men­tal Space Odyssey archived here. The course will be added to our list, 1,700 Free Online Cours­es from Top Uni­ver­si­ties.

Relat­ed Con­tent: 

How a Bach Canon Works. Bril­liant.

Math­e­mat­ics Made Vis­i­ble: The Extra­or­di­nary Math­e­mat­i­cal Art of M.C. Esch­er

The Mir­ror­ing Mind: An Espres­so-Fueled Inter­pre­ta­tion of Dou­glas Hofstadter’s Ground­break­ing Ideas

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

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AI Software Creates “New” Nirvana, Jimi Hendrix, Doors & Amy Winehouse Songs: Hear Tracks from the “Lost Tapes of the 27 Club”

in Computer Science, Music | April 7th, 2021

What would pop music sound like now if the musi­cians of the 27 club had lived into matu­ri­ty? Can we know where Amy Wine­house would have gone, musi­cal­ly, if she had tak­en anoth­er path? What if Hendrix’s influ­ence over gui­tar hero­ics (and less obvi­ous styles) came not only from his six­ties play­ing but from an unimag­in­able late-career cos­mic blues? Whether ques­tions like these can ever be giv­en real flesh and blood, so to speak, by arti­fi­cial intel­li­gence may still be very much unde­cid­ed.

Of course, it may not be for us to decide. “The charts of 2046,” Mark Beau­mont pre­dicts at NME, “will  be full of 12G code-pop songs, baf­fling to the human brain, writ­ten by banks of com­poser­bots pure­ly for the Spo­ti­fy algo­rithm to rec­om­mend to its colonies of ÆPhone lis­ten­ing farms.” Seems as like­ly as any oth­er future music sce­nario at this point. In the mean­time, we still get to judge the suc­cess­es, such as they are, of AI song­writ­ers on human mer­its.

The Bea­t­les-esque “Daddy’s Car,” the most notable com­put­er-gen­er­at­ed trib­ute song to date, was “com­posed by AI… capa­ble of learn­ing to mim­ic a band’s style from its entire data­base of songs.” The pro­gram pro­duced a com­pe­tent pas­tiche that nonethe­less sounds like “cold com­put­er psy­che­delia — eerie stuff.” What do we, as humans, make of Lost Tapes of the 27 Club, a com­pi­la­tion of songs com­posed in the style of musi­cians who infa­mous­ly per­ished by sui­cide or over­dose at the ten­der age of 27?

The “tapes” include four tracks designed to sound like lost songs from Hen­drix, Wine­house, Nir­vana, and the Doors. High­light­ing a hand­ful of artists who left us too soon in order to address “music’s men­tal health cri­sis,” the project used Magen­ta, the same Google AI as “Daddy’s Car,” to ana­lyze the artists’ reper­toires, as Rolling Stone explains:

For the Lost Tapes project, Magen­ta ana­lyzed the artists’ songs as MIDI files, which works sim­i­lar­ly to a play­er-piano scroll by trans­lat­ing pitch and rhythm into a dig­i­tal code that can be fed through a syn­the­siz­er to recre­ate a song. After exam­in­ing each artist’s note choic­es, rhyth­mic quirks, and pref­er­ences for har­mo­ny in the MIDI file, the com­put­er cre­ates new music that the staff could pore over to pick the best moments.

There is sig­nif­i­cant human input, such as the cura­tion of 20 or 30 songs fed to the com­put­er, bro­ken down sep­a­rate­ly into dif­fer­ent parts of the arrange­ment. Things did not always go smooth­ly. Kurt Cobain’s “loose and aggres­sive gui­tar play­ing gave Magen­ta some trou­ble,” writes Endgad­get, “with the AI most­ly out­putting a wall of dis­tor­tion instead of some­thing akin to his sig­na­ture melodies.”

Judge the end results for your­self in “Drowned by the Sun,” above. The music for all four songs is syn­the­sized with MIDI files. “An arti­fi­cial neur­al net­work was then used to gen­er­ate the lyrics,” Eddie Fu writes at Con­se­quence of Sound, “while the vocals were record­ed by Eric Hogan, front­man of an Atlanta Nir­vana trib­ute band.” Oth­er songs fea­ture dif­fer­ent sound-alike vocal­ists (more or less). In no ways does the project claim that MIDI-gen­er­at­ed com­put­er files can replace actu­al musi­cians.

They’re affec­tion­ate trib­utes, made by play­ers with­out hearts, but they don’t real­ly tell us any­thing about what, say, Jim Mor­ri­son would have done if he hadn’t died at 27. Yet the cause is a noble one: a rejec­tion of the roman­tic idea at the heart of the “27 Club” nar­ra­tive — that men­tal ill­ness, sub­stance abuse, etc. should be glam­or­ized in any way. “Lost Tapes of the 27 Club is the work of Over the Bridge,” notes Fu, “a Toron­to orga­ni­za­tion that helps mem­bers of the music indus­try strug­gling with men­tal ill­ness.” Learn more about the project here and about Over the Bridge’s pro­grams here.

Relat­ed Con­tent: 

Arti­fi­cial Intel­li­gence Writes a Piece in the Style of Bach: Can You Tell the Dif­fer­ence Between JS Bach and AI Bach?

Nick Cave Answers the Hot­ly Debat­ed Ques­tion: Will Arti­fi­cial Intel­li­gence Ever Be Able to Write a Great Song?

Experts Pre­dict When Arti­fi­cial Intel­li­gence Will Take Our Jobs: From Writ­ing Essays, Books & Songs, to Per­form­ing Surgery and Dri­ving Trucks

Arti­fi­cial Intel­li­gence Pro­gram Tries to Write a Bea­t­les Song: Lis­ten to “Daddy’s Car”

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

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MIT’s Introduction to Deep Learning: A Free Online Course

in Computer Science, Online Courses | December 14th, 2020

MIT has post­ed online its intro­duc­to­ry course on deep learn­ing, which cov­ers appli­ca­tions to com­put­er vision, nat­ur­al lan­guage pro­cess­ing, biol­o­gy, and more. Stu­dents “will gain foun­da­tion­al knowl­edge of deep learn­ing algo­rithms and get prac­ti­cal expe­ri­ence in build­ing neur­al net­works in Ten­sor­Flow.” Pre­req­ui­sites assume cal­cu­lus (i.e. tak­ing deriv­a­tives) and lin­ear alge­bra (i.e. matrix mul­ti­pli­ca­tion). Expe­ri­ence in Python is help­ful but not nec­es­sary. The first lec­ture appears above. The rest of the course mate­ri­als (videos & slides) can be found here.

Intro­duc­tion to Deep Learn­ing will be added to our list of Free Com­put­er Sci­ence Cours­es, a sub­set of our larg­er meta col­lec­tion, 1,700 Free Online Cours­es from Top Uni­ver­si­ties.  You can also find Deep Learn­ing cours­es on Cours­era.

If you would like to sign up for Open Culture’s free email newslet­ter, please find it here. Or fol­low our posts on Threads, Face­book, BlueSky or Mastodon.

If you would like to sup­port the mis­sion of Open Cul­ture, con­sid­er mak­ing a dona­tion to our site. It’s hard to rely 100% on ads, and your con­tri­bu­tions will help us con­tin­ue pro­vid­ing the best free cul­tur­al and edu­ca­tion­al mate­ri­als to learn­ers every­where. You can con­tribute through Pay­Pal, Patre­on, and Ven­mo (@openculture). Thanks!

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Discovered: The User Manual for the Oldest Surviving Computer in the World

in Computer Science | September 28th, 2020

Image by Clemens Pfeif­fer via Wiki­me­dia Com­mons

The first com­put­er I ever sat before, the 1983 Apple IIe, had a man­u­al the size of a text­book, which includ­ed a primer on pro­gram­ming lan­guages and a chap­ter enti­tled “Get­ting Down to Busi­ness and Plea­sure.” By “plea­sure,” Apple most­ly meant “elec­tron­ic work­sheets,” “word proces­sors,” and “data­base man­age­ment.” (They hadn’t ful­ly estab­lished them­selves as the fun one yet.) Get­ting these pro­grams run­ning took real effort and patience, espe­cial­ly com­pared to the Mac­Book Air on which I’m typ­ing now.

All those old tedious process­es are auto­mat­ed, and no more do we need manuals—we’ve got the inter­net, which also hap­pens to be the only way I could oper­ate an Apple IIe, whether that means track­ing down a man­u­al on eBay or find­ing a scanned copy some­where online. Luck­i­ly, for vin­tage Apple enthu­si­asts, this isn’t dif­fi­cult, and some­one with rudi­men­ta­ry knowl­edge of Apple DOS could mud­dle through with­out one.

When we go fur­ther back into com­put­er his­to­ry, we find machines that became incom­pre­hen­si­ble over time with­out their oper­at­ing instruc­tions. Such was the case with the Zuse Z4, “con­sid­ered the old­est pre­served dig­i­tal com­put­er in the world,” notes Vice. “The Z4 is one of those machines that takes up a whole room, runs on mag­net­ic tapes, and needs mul­ti­ple peo­ple to oper­ate. Today it sits in the Deutsches Muse­um in Munich, unused. Until now, his­to­ri­ans and cura­tors only had a lim­it­ed knowl­edge of its secrets because the man­u­al was lost long ago.”

The computer’s inven­tor, Kon­rad Zuse, first began build­ing it for the Nazis in 1942, then refused its use in the VI and V2 rock­et pro­gram. Instead, he fled to a small town in Bavaria and stowed the com­put­er in a barn until the end of the war. It wouldn’t see oper­a­tion until 1950. The Z4 proved to be “a very reli­able and impres­sive com­put­er for its time,” Sarah Felice writes. “With its large instruc­tion set it was able to cal­cu­late com­pli­cat­ed sci­en­tif­ic pro­grams and was able to work dur­ing the night with­out super­vi­sion, which was unheard of for this time.”

These qual­i­ties made the Zuse Z4 par­tic­u­lar­ly use­ful to the Insti­tute of Applied Math­e­mat­ics at the Swiss Fed­er­al Insti­tute of Tech­nol­o­gy (ETH), where the com­put­er per­formed advanced cal­cu­la­tions for Swiss engi­neers in the ear­ly 50s. “Around 100 jobs were car­ried out with the Z4 between 1950 and 1955,” writes Her­bert Brud­er­er, retired ETH lec­tur­er. “These includ­ed cal­cu­la­tions on the tra­jec­to­ry of rock­ets… on air­craft wings…” and “on flut­ter vibra­tions,” an oper­a­tion requir­ing “800 hours machine time.”

René Boesch, one of the air­plane researchers work­ing on the Z4 in the 50s kept a copy of the man­u­al among his papers, and it was there that his daugh­ter, Eve­lyn Boesch, also an ETH researcher, dis­cov­ered it. (View it online here.) Brud­er­er tells the full sto­ry of the computer’s devel­op­ment, oper­a­tion, and the redis­cov­ery of its only known copy of oper­at­ing instruc­tions here.

Relat­ed Con­tent:

Hear the First Record­ing of Com­put­er Gen­er­at­ed Music: Researchers Restore Music Pro­grammed on Alan Turing’s Com­put­er (1951)

Meet Grace Hop­per, the Pio­neer­ing Com­put­er Sci­en­tist Who Helped Invent COBOL and Build the His­toric Mark I Com­put­er (1906–1992)

The First Piz­za Ordered by Com­put­er, 1974

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

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How to Manage Your Time More Effectively: The Science of Applying Computer Algorithms to Our Everyday Lives

in Computer Science, Productivity, TED Talks | September 1st, 2020

Who among us has­n’t wished to be as effi­cient as a com­put­er? While com­put­ers seem to do every­thing at once, we either flit or plod from task to task, often get­ting side­tracked or even lost. At this point most have relin­quished the dream of true “mul­ti­task­ing,” which turns out to lie not only beyond the reach of humans but, tech­ni­cal­ly speak­ing, beyond the reach of com­put­ers as well. “Done right, com­put­ers move so flu­id­ly between their var­i­ous respon­si­bil­i­ties, they give the illu­sion of doing every­thing simul­ta­ne­ous­ly,” says the nar­ra­tor of the ani­mat­ed TED-Ed les­son above. But in real­i­ty, even they do one thing at a time; what, then, can we humans learn from how they’re pro­grammed to pri­or­i­tize and switch between their many tasks?

A com­put­er oper­at­ing sys­tem has an ele­ment called a “sched­uler,” which “tells the CPU how long to work on each task before switch­ing.” Sched­ulers work quite well these days, but “even com­put­ers get over­whelmed some­times.” This used to hap­pen to the open-source oper­at­ing sys­tem Lin­ux, which “would rank every sin­gle one of its tasks in order of impor­tance, and some­times spent more time rank­ing tasks than doing them. The pro­gram­mers’ coun­ter­in­tu­itive solu­tion was to replace this full rank­ing with a lim­it­ed num­ber of pri­or­i­ty ‘buck­ets,’ ” replac­ing a pre­cise pri­or­i­ty order­ing with a broad­er low-medi­um-high kind of group­ing. This turned out to be a great improve­ment: “The sys­tem was less pre­cise about what to do next, but more than made up for it by spend­ing more time mak­ing progress.”

The les­son for those of us who habit­u­al­ly list and pri­or­i­tize our tasks is obvi­ous: “All the time you spend pri­or­i­tiz­ing your work is time you aren’t spend­ing doing it,” and “giv­ing up on doing things in the per­fect order may be the key to get­ting them done.” In the case of e‑mail, bane of many a 21st-cen­tu­ry exis­tence, “Insist­ing on always doing the very most impor­tant thing first could lead to a melt­down. Wak­ing up to an inbox three times fuller than nor­mal could take nine times longer to clear.

You’d be bet­ter off reply­ing in chrono­log­i­cal order, or even at ran­dom.” Robert Pir­sig mem­o­rably artic­u­lat­ed this in Zen and the Art of Motor­cy­cle Main­te­nance, whose main char­ac­ter offers advice to his son frus­trat­ed by the task of writ­ing a let­ter home from their road trip:

I tell him get­ting stuck is the com­mon­est trou­ble of all. Usu­al­ly, I say, your mind gets stuck when you’re try­ing to do too many things at once. What you have to do is try not to force words to come. That just gets you more stuck. What you have to do now is sep­a­rate out the things and do them one at a time. You’re try­ing to think of what to say and what to say first at the same time and that’s too hard. So sep­a­rate them out. Just make a list of all the things you want to say in any old order. Then lat­er we’ll fig­ure out the right order.

We don’t write many let­ters home these days, of course, and even e‑mail may no longer pose the direst threat to our time man­age­ment. More of us blame our lack of pro­duc­tiv­i­ty on the inter­rup­tions of instant mes­sag­ing in all its forms, from tex­ting to social media, anoth­er prob­lem with an equiv­a­lent in com­put­ing. That a com­put­er can be inter­rupt­ed by any num­ber of the process­es it runs neces­si­tat­ed the devel­op­ment of a pro­ce­dure called “inter­rupt coa­lesc­ing,” accord­ing to which, “rather than deal­ing with things as they come up,” the sys­tem “groups these inter­rup­tions togeth­er based on how long they can afford to wait.” Even if we can’t elim­i­nate inter­rup­tions in our lives, we can group them: “If no noti­fi­ca­tion or e‑mail requires a response more urgent­ly than once an hour, say, then that’s exact­ly how often you should check them — no more.”

This TED-Ed les­son comes adapt­ed from Bri­an Chris­t­ian and Tom Grif­fiths’ book Algo­rithms to Live By: The Com­put­er Sci­ence of Human Deci­sions. If you’d like to hear about more of the ways in which they apply com­put­ers’ meth­ods of deci­sion mak­ing to areas of human life — home-buy­ing, gam­bling, dat­ing — you can also watch their talk at Google. We also have plen­ty of sup­ple­men­tary time man­age­ment-relat­ed mate­r­i­al here in the Open Cul­ture archives, on every­thing from the neu­ro­science of pro­cras­ti­na­tion to the dai­ly rou­tines of philoso­phers, writ­ers and oth­er cre­ative peo­ple to tips for read­ing more books per year to the pres­i­den­tial­ly-approved “Eisen­how­er Matrix.” By all means, click on all these links; just don’t over­think the order in which to do it.

Relat­ed Con­tent:

Use the “Eisen­how­er Matrix” to Man­age Your Time & Increase Your Pro­duc­tiv­i­ty: The Sys­tem Designed by the 34th Pres­i­dent of the Unit­ed States

The Neu­ro­science & Psy­chol­o­gy of Pro­cras­ti­na­tion, and How to Over­come It

The Dai­ly Rou­tines of Famous Cre­ative Peo­ple, Pre­sent­ed in an Inter­ac­tive Info­graph­ic

The Dai­ly Habits of High­ly Pro­duc­tive Philoso­phers: Niet­zsche, Marx & Immanuel Kant

The Dai­ly Habits of Famous Writ­ers: Franz Kaf­ka, Haru­ki Muraka­mi, Stephen King & More

7 Tips for Read­ing More Books in a Year

Based in Seoul, Col­in Mar­shall writes and broad­casts on cities, lan­guage, and cul­ture. His projects include the book The State­less City: a Walk through 21st-Cen­tu­ry Los Ange­les and the video series The City in Cin­e­ma. Fol­low him on Twit­ter at @colinmarshall, on Face­book, or on Insta­gram.

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Revisit Scenes of Daily Life in Amsterdam in 1922, with Historic Footage Enhanced by Artificial Intelligence

in Computer Science, Data, Film, Technology, Travel | August 3rd, 2020

Welkom in Ams­ter­dam… 1922.

Neur­al net­work artist Denis Shiryaev describes him­self as “an artis­tic machine-learn­ing per­son with a soul.”

For the last six months, he’s been apply­ing him­self to re-ren­der­ing doc­u­men­tary footage of city life—Belle Epoque Paris, Tokyo at the start of the the Taishō era, and New York City in 1911—the year of the Tri­an­gle Shirt­waist Fire.

It’s pos­si­ble you’ve seen the footage before, but nev­er so alive in feel. Shiryaev’s ren­der­ings trick mod­ern eyes with arti­fi­cial intel­li­gence, boost­ing the orig­i­nal frames-per-sec­ond rate and res­o­lu­tion, sta­bi­liz­ing and adding color—not nec­es­sar­i­ly his­tor­i­cal­ly accu­rate.

The herky-jerky bustling qual­i­ty of the black-and-white orig­i­nals is trans­formed into some­thing fuller and more flu­id, mak­ing the human sub­jects seem… well, more human.

This Trip Through the Streets of Ams­ter­dam is tru­ly a blast from the past… the antithe­sis of the social dis­tanc­ing we must cur­rent­ly prac­tice.

Mer­ry cit­i­zens jos­tle shoul­der to shoul­der, unmasked, snack­ing, danc­ing, arms slung around each oth­er… unabashed­ly curi­ous about the hand-cranked cam­era turned on them as they go about their busi­ness.

A group of women vis­it­ing out­side a shop laugh and scatter—clearly they weren’t expect­ing to be filmed in their aprons.

Young boys look­ing to steal the show push their way to the front, cut­ting capers and throw­ing mock punch­es.

Sor­ry, lads, the award for Most Mem­o­rable Per­for­mance by a Juve­nile goes to the small fel­low at the 4:10 mark. He’s not ham­ming it up at all, mere­ly tak­ing a quick puff of his cig­a­rette while run­ning along­side a crowd of men on bikes, deter­mined to keep pace with the cam­era per­son.

Numer­ous YouTube view­ers have observed with some won­der that all the peo­ple who appear, with the dis­tant excep­tion of a baby or two at the end, would be in the grave by now.

They do seem so alive.

Mod­ern eyes should also take note of the absences: no cars, no plas­tic, no cell phones…

And, of course, every­one is white. The Nether­lands’ pop­u­la­tion would not diver­si­fy racial­ly for anoth­er cou­ple of decades, begin­ning with immi­grants from Indone­sia after WWII and Suri­nam in the 50s.

With regard to that, please be fore­warned that not all of the YouTube com­ments have to do with cheeky lit­tle boys and babies who would be push­ing 100…

The footage is tak­en from the archival col­lec­tion of the EYE film­mu­se­um in Ams­ter­dam, with ambi­ent sound by Guy Jones.

See more of Denis Shiryaev’s  upscaled vin­tage footage in the links below.

Relat­ed Con­tent:

Watch Vin­tage Footage of Tokyo, Cir­ca 1910, Get Brought to Life with Arti­fi­cial Intel­li­gence

Watch Scenes from Belle Époque Paris Vivid­ly Restored with Arti­fi­cial Intel­li­gence (Cir­ca 1890)

A Trip Through New York City in 1911: Vin­tage Video of NYC Gets Col­orized & Revived with Arti­fi­cial Intel­li­gence

Icon­ic Film from 1896 Restored with Arti­fi­cial Intel­li­gence: Watch an AI-Upscaled Ver­sion of the Lumière Broth­ers’ The Arrival of a Train at La Cio­tat Sta­tion

Ayun Hal­l­i­day is an author, illus­tra­tor, the­ater mak­er and Chief Pri­ma­tol­o­gist of the East Vil­lage Inky zine. Fol­low her @AyunHalliday.

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