Sometimes when I’m bored, I like to look at the list of ma­cOS Bash com­mands. Here’s some com­mands that I found in­ter­est­ing:

If you store your se­crets in the Keychain (and you should!), you can ac­cess them pro­gram­mat­i­cally us­ing se­cu­rity.

se­cu­rity find-in­ter­net-pass­word -s “https://​ex­am­ple.com”

I found this use­ful for writ­ing au­to­mated scripts that used lo­cally-stored cre­den­tials.

Bonus tip: If you are us­ing 1Password, there is a 1Password CLI that you can use to ac­cess your 1Password items from the com­mand line.

If you want to open a file from the ter­mi­nal, you can use the open com­mand.

open file.txt

This will open the file in the de­fault ap­pli­ca­tion for that file type, as if you had dou­ble-clicked it in the Finder.

pb­copy and pb­paste are com­mand-line util­i­ties that al­low you to copy and paste text to the paste­board (what other op­er­at­ing sys­tems might call the “clipboard”).

pb­copy takes what­ever was given in the stan­dard in­put, and places it in the paste­board.

echo “Hello, world!” | pb­copy;

pb­paste takes what­ever is in the paste­board and prints it to the stan­dard out­put.

pb­paste>> Hello, world!

This is very use­ful for get­ting data from files into the browser, or other GUI ap­pli­ca­tions.

If you work with servers a lot, it can be use­ful to know the cur­rent time in UTC, when e.g. look­ing at server logs.

This is a one-liner in the ter­mi­nal:

date -u

Alternatively, you can use

TZ=UTC date

If you want to run an Internet speedtest, you can run one di­rectly from the ter­mi­nal with

net­workQual­ity # Note the cap­i­tal “Q”!

If you are want to keep your Mac from sleep­ing, you can run caf­feinate in the ter­mi­nal.

caf­feinate

caf­feinate will keep your Mac awake un­til you stop it, e.g. by press­ing Ctrl+C. caf­feinate used to be a third-party tool, but it is now built-in to ma­cOS.

I use this mostly to pre­vent my Mac from sleep­ing when I am run­ning a server.

If you need to gen­er­ate a UUID, you can use the uuid­gen com­mand.

uuid­gen

By de­fault uuid­gen out­puts a UUID in up­per­case. You can com­bine this with tr and pb­copy to copy the UUID to the clip­board in low­er­case.

uuid­gen | tr ‘[:upper:]’ ‘[:lower:]’ | pb­copy

I use this a lot when writ­ing unit tests that re­quire IDs.

* mdfind: Spotlight search, but in the ter­mi­nal. I gen­er­ally use Spotlight it­self (or rather the ex­cel­lent Raycast). Link

* say: This com­mand makes your Mac speak the text you give it. Link

* screen­cap­ture: This com­mand al­lows you to take screen­shots and save them to a file. I pre­fer us­ing cmd-shift-5 for this. Link

* net­work­setup: This com­mand al­lows you to con­fig­ure your net­work set­tings pro­gram­mat­i­cally. I found its API very in­tim­i­dat­ing, and so I haven’t re­ally used it much. Link

A ti­tle drop is when a char­ac­ter in a movie says the ti­tle of the movie they’re in. Here’s a large-scale analy­sis of 73,921 movies from the last 80 years on how of­ten, when and maybe even why that hap­pens.

I’m sure you all know the part of the movie where one of the char­ac­ters says the ac­tual ti­tle of the movie

and you’re like

The over­all meta-ness of this is - of course - noth­ing new. And film­mak­ers and scriptwrit­ers have been do­ing it since the dawn of the medium it­self*. It’s known in film speak as a ti­tle drop.

Consequently, there’s tons of ex­am­ples through­out movie his­tory that range from the iconic (see Back to the Future’s above)

via the ec­cen­tric,

the very much self-aware

But how com­mon are these ti­tle drops re­ally? Has this phe­nom­e­non gained mo­men­tum over time with our post­mod­ern cul­ture be­com­ing ever more meta? Can we pre­dict any­thing about the qual­ity of a film based on how many times its ti­tle is men­tioned? And what does a movie ti­tle mean, any­way?

There have been analy­ses

and oh so so many lis­ti­cles

of the ti­tle drop phe­nom­e­non be­fore, but they are small and anec­do­tal. Here’s the first ex­ten­sive analysis of ti­tle drops for a dataset of 73,921 movies that amount to roughly 61% of movies on IMDb with at least 100 user votes*. I’m look­ing at movies re­leased be­tween 1940 and 2023. Special thanks go to my friends at OpenSubtitles.com for pro­vid­ing this data!

I started out with two datasets: 89,242 (English) movie sub­ti­tles from OpenSubtitles.com

and meta­data for 121,797 movies from IMDb. After joining them and fil­ter­ing them for bro­ken sub­ti­tle files I was left with a to­tal of 73,921 subtitled movies. With that out of the way, I re­al­ized that the tougher task was still ahead of me: an­swer­ing the ques­tion what even was a ti­tle drop?

The naïve ap­proach is - of course - to sim­ply look for the movie’s name any­where in the subtitles. Which is a fan­tas­tic ap­proach for movies like Back to the Future with a nice unique ti­tle:

But this quickly breaks down if we look at movies like E or I *, which lead to way too many matches.

We also run into prob­lems with every movie that is a se­quel (Rocky III, Hot Tub Time Machine 2) since none of the char­ac­ters will add the se­quel num­ber to char­ac­ter names/​over­sized bathing equipment. Similarly, the rise of the colon

in movie ti­tles would make for some very awk­ward di­a­logue (LUKE: “Gosh Mr. Kenobi, it’s al­most like we’re in the mid­dle of some Star Wars Episode Four: A New Hope!“).

(See also the He Didn’t Say That

meme.)

So I ap­plied a few rules to my ti­tle match­ing in the di­a­logue. Leading ‘The’, ‘An’ and ’A’s and special char­ac­ters like dashes are ig­nored, se­quel num­bers both Arabic and Roman are dropped (along with ‘Episode…’, ‘Part…’ etc.) and ti­tles con­tain­ing a colon are split and ei­ther side counts as a ti­tle drop. So for The Lord of the Rings: The Fellowship of the Ring

ei­ther “Lord of the Rings” or “Fellowship of the Ring” would count as ti­tle drops (feel free to hover over the vi­su­al­iza­tions to ex­plore the matches)!

With the data clean­ing out of the way, let’s get down to busi­ness!

Alright, so here’s the num­ber you’ve all been wait­ing for (drumroll):

36.5% - so about a third - of movies have at least one ti­tle drop dur­ing their run­time.

Also, there’s a to­tal of 277,668 ti­tle drops for all 26,965 ti­tle-drop­ping movies which means that there’s an average of 10.3 ti­tle drops per movie that ti­tle drops. If they do it, they really go for it.

So who are the most ex­ces­sive of­fend­ers in men­tion­ing their ti­tles over the course of the film? The over­all star when it comes to fic­tion only came out last year: it’s Barbie by Greta Gerwig with an im­pres­sive 267 ti­tle drops within its 1 hour and 54 min­utes run­time, clock­ing in at a whopping 2.34 BPM (Barbies Per Minute).

On the non-fic­tion side of doc­u­men­taries the win­ner is Mickey: The Story of a Mouse

with 309 ti­tle drops in only 90 min­utes, so 3.43 Mickeys Per Minute!

What’s in­ter­est­ing about the (Fiction) list here is that it’s pretty in­ter­na­tional: only two of the top ten movies come from Hollywood, 6 are from India, one from Indonesia and one from Turkey. So it’s def­i­nitely an in­ter­na­tional phe­nom­e­non.

Looking at the top ten list you might have no­ticed this lit­tle icon

sig­ni­fy­ing a movie where the data says it’s named af­ter one of its char­ac­ters*.

Unsurprisingly, movies named af­ter one of their char­ac­ters have an av­er­age of 24.7 ti­tle drops, more than twice as much as the usual 10.3. Protagonists have a ten­dency to pop up re­peat­edly in a film, so their names usu­ally do the same.

Similarly, movies named af­ter a pro­tag­o­nist have a ti­tle drop rate of 88.5%

while only 34.2% of other movies drop their ti­tles.

A note on the data here

This is the more ex­per­i­men­tal part of the analy­sis. To fig­ure out if a movie was named af­ter its

pro­tag­o­nist I’ve used

IMDb’s Principals Dataset

that lists char­ac­ter names for the first cou­ple of ac­tors and com­pared that to the movie’s ti­tle.

This ap­proach yields re­li­able re­sults, but of course misses movies when the char­ac­ter the movie

is named af­ter does not ap­pear on that list. So you might find movies that miss the

‘Named’

icon even though they’re clearly named af­ter a char­ac­ter.

Special char­ac­ters in the ti­tle and char­ac­ter name are also chal­leng­ing: for ex­am­ple, Tosun Pasa which ac­tu­ally has a ş char­ac­ter in its ti­tle - wrong on IMDb (Pasa) as well as the sub­ti­tles

(Pasha) - or WALL·E with the chal­leng­ing · in the mid­dle: Even

though there are men­tions of “Wall-E” in the sub­ti­tles, the script - look­ing for “WALL·E” - would­n’t

de­tect it. (I’ve fixed both of these films man­u­ally - but there might be more!)

Titles or sur­names also usu­ally pre­vent be­ing counted as ti­tle drops ac­cord­ing to our de­f­i­n­i­tions.

Michael The Brave,

King Lear or Barry Lyndon might men­tion a char­ac­ter’s name (‘Michael’, ‘Lear’, ‘Barry’) but leave out the ti­tle or sur­name

- so zero drops.

Nevertheless, there do ex­ist named films where you would ex­pect a ti­tle drop which does­n’t come!

Examples are:

Anyway - back to the analy­sis!

An in­ter­est­ing cat­e­gory are movies named af­ter a char­ac­ter that only have a sin­gle ti­tle drop - making it all the more mean­ing­ful?

Title-drop con­nois­seurs might sneer at this point and well-ac­tu­ally us that a “real” ti­tle drop should only hap­pen once in a film. That there’s this one mem­o­rable (or cringe-y) scene where the protagonist looks di­rectly at the cam­era and de­clares the ti­tle of the film with as much pathos as they can muster. Or as a nice send-off in the last spo­ken line.

Such sin­gle drops hap­pen sur­pris­ingly of­ten:

11.3% of all movies do EXACTLY ONE ti­tle drop dur­ing their run­time.

Which means that there’s about twice as many movies hav­ing mul­ti­ple ti­tle drops than sin­gle ones.

In the sin­gle drop case it is more likely that the film­mak­ers were adding a ti­tle drop very consciously.

Single drops of­ten hap­pen in a key scene and ex­plain the movie’s ti­tle: what mys­te­ri­ous fellowship the first Lord of the Rings is named af­ter. Or that the au­di­ence wait­ing for some dark knight to show up must sim­ply ac­cept that it’s been the Batman all along.

One sus­pi­cion I had was that the very meta act of hav­ing a char­ac­ter speak the name of the movie they’re in would be some­thing gain­ing more and more trac­tion over the last two or three decades.

And in­deed, if we look at the av­er­age num­ber of movies with ti­tle drops over the decades we can see that there’s a cer­tain up­wards trend. The 1960s and 1970s seemed to be most averse to mentioning their ti­tle in the film, while it’s be­come more com­mon-place over the last years.

If we dig deeper, this growth over the decades comes with a clearer ex­pla­na­tion: split­ting up movies by sin­gle- and multi-ti­tle drops shows that while the ten­dency of movies to drop their title ex­actly once keeps more or less steady, the num­ber of multi-drop films is on the rise.

Your ex­pla­na­tion for this (More movies are be­ing named af­ter their pro­tag­o­nists? Movies are more productified so brand recog­ni­tion be­comes an im­por­tant con­cern?) is prob­a­bly as good as mine 🤷

Another ques­tion I wanted to an­swer was if a high num­ber of ti­tle drops was a sign of a bad movie. Think of all the trashy slasher and hor­ror movies about Meth Marmots and Killer Ballerinas - would­n’t their char­ac­ters in the sparse di­a­logues con­stantly men­tion the ti­tle for brand recog­ni­tion and all that?

Interestingly though, there’s no strong con­nec­tion be­tween film qual­ity (expressed as IMDb rating (YMMV)) and the prob­a­bil­ity of ti­tle-drop­ping.

An as­pect that cer­tainly does have an im­pact on the prob­a­bil­ity of a ti­tle drop though is the genre of a film.

If you think back to the dis­cus­sion about names in ti­tles from ear­lier, gen­res like Biography and other non-fic­tion gen­res like Sport and History - al­most by de­f­i­n­i­tion - men­tion their subject in both the ti­tle and through­out the film.

Accordingly, the prob­a­bil­ity of a ti­tle drop varies wildly by genre. Non-fiction films have a strong ten­dency to­wards ti­tle-drop­ping, while more fic­tion-ori­ented gen­res like Crime, Romance and War don’t.

Finally, we can ask the ques­tion: what even is a movie ti­tle?

I could­n’t find a com­plete clas­si­fi­ca­tion in the sci­en­tific lit­er­a­ture (“What’s in a name? The art of movie ti­tling”

by Ingrid Haidegger comes the clos­est). Movie ti­tles are an in­ter­est­ing case, since they have to work as a de­scrip­tion of a prod­uct, a mar­ket­ing in­stru­ment, but also as the ti­tle of a piece of art.

Consequently, it’s a field ripe with opinions, science and ex­per­i­men­ta­tion

and listicles.

The most ex­ten­sive clas­si­fi­ca­tion of me­dia ti­tles in gen­eral I could find is TVTropes’ Title Tropes list

which lists over 180 (!) dif­fer­ent types of tropes alone. Some of those tropes are:

While nam­ing a movie is a very cre­ative task and pretty suc­cess­fully de­fies clas­si­fi­ca­tion, we can still look at the over­all shape of movie ti­tles and see if that has any im­pact on the num­ber of ti­tle drops.

One such sim­ple as­pect is the length of the ti­tle it­self. As you would ex­pect there’s a neg­a­tive correlation (if only a slight one*) between the length of a ti­tle and the num­ber of ti­tle drops it does.

Still, there are some fun ex­am­ples for reaaaaally

long movie ti­tles that nev­er­the­less do at least one ti­tle drop:

And while these pre­vi­ous ex­am­ples only drops parts from be­fore or af­ter the colon, this next specimen ac­tu­ally does an im­pres­sive full ti­tle drop:

And with that, we’re done with the over­ar­ch­ing analy­sis! Feel free to drop us an e-mail

or fol­low up on X/X, Bluesky

or Mastodon

if you have com­ments, ques­tions, praise ❤️

Oh, and one more thing:

If you’re cu­ri­ous, here’s the full dataset for you to ex­plore!

We in­vite you to down­load raw JunoCam im­ages posted here and do your own im­age pro­cess­ing on them. Be cre­ative! Anything from crop­ping to color en­hanc­ing to col­lag­ing is fair game. Then up­load your cre­ations here.

Please re­frain from di­rect use of any of­fi­cial NASA or Juno mis­sion lo­gos in your work, as this con­fuses what is of­fi­cially sanc­tioned by NASA and by the Juno Project.

We in­vite you to down­load raw JunoCam im­ages posted here and do your own im­age pro­cess­ing on them. Be cre­ative! Anything from crop­ping to color en­hanc­ing to col­lag­ing is fair game. Then up­load your cre­ations here.

Please re­frain from di­rect use of any of­fi­cial NASA or Juno mis­sion lo­gos in your work, as this con­fuses what is of­fi­cially sanc­tioned by NASA and by the Juno Project.

We ask that you re­frain from post­ing any patently of­fen­sive, po­lit­i­cal, or in­ap­pro­pri­ate im­ages. Let’s keep it clean and fun for every­one of any age! Remember, this sec­tion is mod­er­ated so in­ap­pro­pri­ate con­tent will be re­jected. But cre­ativ­ity and cu­rios­ity in the sci­en­tific spirit and the ad­ven­ture of space ex­plo­ration is highly en­cour­aged and we look for­ward to see­ing Jupiter through not only JunoCam’s eyes, but your own. Have at it!

My coworker and I en­joy hav­ing de­bates about whether the American econ­omy is in the ex­press lane to col­lapse or cruis­ing in the good times (I’m re­ally fun at par­ties). For the two-and-a-half years I’ve worked at my cur­rent com­pany, one of us has been a bull while the other has been a bear. I’ll let you guess which one is me.

Monday, November 4, when I walked into the of­fice he brought up a pod­cast he had been lis­ten­ing to on the drive to work: All-In. I had never heard of it, but I guess it’s a group of four ven­ture cap­i­tal­ists that talk about pol­i­tics, cur­rent events, and the econ­omy.

My coworker sur­faced a point that the pod­cast­ers had made in the open­ing seg­ment of last week’s episode: 85% of the past quar­ter’s eco­nomic growth came from gov­ern­ment spend­ing. I was stunned. I had in my mind that gov­ern­ment spend­ing com­posed some­thing like 30%-40% of GDP thanks to Matt Yglesias’ re­cent tirades about how im­ports don’t sub­tract from GDP, resur­fac­ing the macro-101 equa­tion:

My coworker showed me the first few min­utes of the pod­cast, where they flash this chart af­ter not­ing the econ­omy grew by 2.8% in Q3, and one of the hosts, Chamath Palihapitiya, de­scribes what he sees as go­ing on:

This is where you can get a lit­tle con­fused by data. Jason, this is net out­lays. And that’s dif­fer­ent from to­tal gross gov­ern­ment spend­ing, which also in­cludes QE… So just to be clear about what’s hap­pen­ing, 85% of this quar­ter’s GDP was in­duced by the gov­ern­ment. If you sub it out, so take 2.8% and mul­ti­ply it by 0.15, that is the true growth X the United States gov­ern­ment that ex­ists in the United States econ­omy to­day. Sacks, your thoughts here on the GDP, ob­vi­ously looks pretty good for Biden-Harris to have all these stats go­ing in their fa­vor, but there is the caveat ob­vi­ously about the gov­ern­ment spend­ing in there.

“This is where you can get a lit­tle con­fused about data” yeah, okay big guy. Let’s see who is con­fused here.

Putting aside the com­ment about quan­ti­ta­tive eas­ing, which feels ir­rel­e­vant, I left his of­fice and went straight to the Department of Commerce’s web­site, where the Bureau of Economic Analysis pub­lishes GDP es­ti­mates. The third-quar­ter ad­vance es­ti­mate table 2 pro­vides us the in­for­ma­tion we’re look­ing for, and, in fact, is the source of Chamath’s graph.

You can see the Macro-101 equa­tion recre­ated here. All of these sub­cat­e­gories (personal con­sump­tion + in­vest­ment + net ex­ports + gov­ern­ment con­sump­tion) add up to 2.8% (2.82% to be pre­cise) of Q3 GDP growth.

0.85% of the to­tal 2.82% GDP growth is from gov­ern­ment spend­ing. Meaning that 0.85% / 2.82% = 30.1% of Q3 GDP growth came from gov­ern­ment spend­ing, not 85%.

If you look closely at Chamath’s chart, you can tell that he’s us­ing this ex­act data source to de­velop his gross mis­in­ter­pre­ta­tion of the data.

So, Chamath’s the­sis that “if you back out the per­cent­age of gov­ern­ment con­sump­tion that is in­cluded in GDP, you start to see a very dif­fer­ent pic­ture, which is that over the last two and a half years, all of the eco­nomic gains un­der the Biden ad­min­is­tra­tion have largely been through gov­ern­ment con­sump­tion” is to­tal hog­wash. The claim that makes up the en­tire talk­ing point of this ini­tial seg­ment of the show is a mis­read­ing of the data that I, a ran­dom non­ex­pert guy, no­ticed and dis­proved in ten min­utes of re­search and writ­ing this up.

Looking at gov­ern­ment ex­pen­di­tures as a pro­por­tion of GDP over time, you can see that the cur­rent pe­riod is noth­ing new—in fact, it’s typ­i­cal for the post-Great Recession era, roughly in line with gov­ern­ment spend­ing from the late Obama years through Trump’s pres­i­dency, pre-COVID.

Was this gross in­com­pe­tence or pur­pose­ful de­cep­tion? I’m not sure. But I know that I won’t be tun­ing in for the next episode of All-In to find out. I will not fall prey to Gell-Mann Amnesia. In my first and only 15 min­utes of watch­ing, Chamath’s con­fi­dence in mak­ing this false claim, cou­pled with his co-hosts’ com­plete lack of crit­i­cal push­back, sug­gests to me that these kinds of mis­takes hap­pen of­ten enough to where these guys’ con­tent is­n’t worth con­sum­ing.

On its flight to the International Space Station, Dragon ex­e­cutes a se­ries of burns that po­si­tion the ve­hi­cle pro­gres­sively closer to the sta­tion be­fore it per­forms fi­nal dock­ing ma­neu­vers, fol­lowed by pres­sur­iza­tion of the vestibule, hatch open­ing, and crew ingress.

On its flight to the International Space Station, Dragon ex­e­cuted a se­ries of burns that po­si­tioned the ve­hi­cle pro­gres­sively closer to the sta­tion be­fore it per­formed fi­nal dock­ing ma­neu­vers, fol­lowed by pres­sur­iza­tion of the vestibule, hatch open­ing, and crew ingress.

On its flight to the International Space Station, Dragon ex­e­cutes a se­ries of burns that po­si­tion the ve­hi­cle pro­gres­sively closer to the sta­tion be­fore it per­forms fi­nal dock­ing ma­neu­vers, fol­lowed by pres­sur­iza­tion of the vestibule, hatch open­ing, and crew ingress.

On its flight to the International Space Station, Dragon ex­e­cuted a se­ries of burns that po­si­tioned the ve­hi­cle pro­gres­sively closer to the sta­tion be­fore it per­formed fi­nal dock­ing ma­neu­vers, fol­lowed by pres­sur­iza­tion of the vestibule, hatch open­ing, and crew ingress.

Falcon 9’s first stage lofts Dragon to or­bit. Falcon 9’s first and sec­ond stage sep­a­rate. Second stage ac­cel­er­ates Dragon to or­bital ve­loc­ity.

Dragon sep­a­rates from Falcon 9’s sec­ond stage and per­forms ini­tial or­bit ac­ti­va­tion and check­outs of propul­sion, life sup­port, and ther­mal con­trol sys­tems.

Dragon per­forms delta-ve­loc­ity or­bit rais­ing ma­neu­vers to catch up with the International Space Station.

Dragon es­tab­lishes a com­mu­ni­ca­tion link with the International Space Station and per­forms its fi­nal or­bit rais­ing delta-ve­loc­ity burn.

Dragon es­tab­lishes rel­a­tive nav­i­ga­tion to the International Space Station and ar­rives along the dock­ing axis, ini­ti­at­ing an au­tonomous ap­proach.

Dragon per­forms fi­nal ap­proach and docks with the International Space Station, fol­lowed by pres­sur­iza­tion, hatch open, and crew ingress.

Please turn on JavaScript in your browser and re­fresh the page to view its con­tent.

Although I have a good gig as a full pro­fes­sor at Iowa State University, I’ve day­dreamed about learn­ing a trade — some­thing that re­quired both my mind and my hands.

So in 2018, I started night courses in weld­ing at Des Moines Area Community College. For three years, I stud­ied dif­fer­ent types of weld­ing and dur­ing the day worked on a book about the com­mu­ni­ca­tion be­tween weld­ing teach­ers and stu­dents. I was­n’t the only woman who be­came in­ter­ested in trades work dur­ing this time. Recognizing the good pay and job se­cu­rity, U. S. women have moved in greater num­bers into skilled trades such as weld­ing and fab­ri­ca­tion within the past 10 years.

From 2017 to 2022, the num­ber of women in trades rose from about 241,000 to nearly 354,000. That’s an in­crease of about 47%. Even so, women still con­sti­tute just 5.3% of welders in the United States.

When I re­ceived my diploma in weld­ing in May 2022, I’d al­ready found the place I wanted to work: Howe’s Welding and Metal Fabrication. I’d met the owner, Jim Howe, when I vis­ited his three-man shop in Ames, Iowa, in January 2022 for re­search on a sec­ond book about com­mu­ni­ca­tion in skilled trades.

Howe’s shop fo­cuses on re­pairs and one-off fab­ri­ca­tion, not large-scale pro­duc­tion of sin­gle items. Under Howe’s tute­lage, I’ve fab­ri­cated skis for the ma­chines that make the rum­ble strips in the road, shep­herd’s hooks for bird feed­ers, fence poles and stain­less-steel lamp­shade frames. I’ve re­paired trail­ers, wheel­chair ramps, of­fice chairs and lawn mow­ers.

Both my ex­pe­ri­ence at Howe’s and my re­search at nine other fab­ri­ca­tion fa­cil­i­ties in Iowa have shown me that — at least for the time be­ing — tradeswomen must find workarounds for com­monly en­coun­tered chal­lenges. Some of these chal­lenges are phys­i­cal. These could in­clude be­ing un­able to eas­ily reach or move nec­es­sary ma­te­r­ial and tools. Or they could be emo­tional, such as en­coun­ter­ing sex­ism. As I ex­plore in my forth­com­ing book, “Learning Skilled Trades in the Workplace,” this is true even in a wel­com­ing en­vi­ron­ment like Howe’s shop, where I work with a sup­port­ive and help­ful boss and co-work­ers.

Being a tradeswoman means be­ing scru­ti­nized for com­pe­tence. One of the tradeswomen I in­ter­viewed for the book told me this story about be­ing tested by more ex­pe­ri­enced trades­men:

“I re­mem­ber them tack­ing to­gether a cou­ple of pieces of metal for me and say­ing, ‘Okay, I want you to weld a six mil­lime­ter weld here and an eight mil­lime­ter weld here,’ and I was so ner­vous be­cause these are the guys that I’m go­ing to work with, and I just was so ner­vous and I laid down the welds and put my hood up and the guy goes, ‘Well, god­damn, bitch can weld,’ and I was like, ‘Oh my god, thank god.’”

I’ve felt this same scrutiny from Howe’s cus­tomers. Once, two cus­tomers watched me as I used the iron­worker to punch ovals in rec­tan­gu­lar tub­ing. I had to step on the pedal to lower the punch, find the in­den­ta­tion of the spot to punch, hold a com­bi­na­tion square against the metal to en­sure the ob­long shape was par­al­lel to the tub­ing’s edge, step on the pedal and pull the strip­per to­ward me.

I could feel my legs turn to jelly as I per­formed the steps and — as I per­ceived it — rep­re­sented the trade com­pe­tence of all wom­ankind. I’m re­sent­ful of these silent eval­u­a­tions, par­tic­u­larly when I’m learn­ing some­thing new and try­ing to keep all my fin­gers.

The stan­dards es­tab­lished by the Occupational Safety and Health Administration, or OSHA, don’t nec­es­sar­ily ac­count for all the phys­i­cal­ity of trades work. On the day Jim told me to bend 20 pieces of ½-inch round stock, I had to use all my weight to pull the Hossfeld ben­der’s arm to make the S shapes.

The 20 S hooks would hang on a bar and hold the 18 come-alongs that Jim had ac­cu­mu­lated. Tired af­ter I’d fin­ished all the bend­ing, I sighed as Jim told me to hang all the come-alongs on a mo­bile rack he had bought at auc­tion for just this pur­pose.

I had to squat to pick each one up and use my legs and then arms to lift each to a newly made hook. But I did­n’t com­plain. Stoicism is a workaround to cred­i­bil­ity.

My in­ter­ac­tions with Howe’s cus­tomers have been pep­pered with low-grade sex­ism. Trying to de­ter­mine the rea­son for my pres­ence, one cus­tomer asked me, “Are you the new sec­re­tary?”

Another man com­mented on my ap­pear­ance, com­par­ing me to my co-worker: “You’re bet­ter look­ing than the guy I talked to be­fore.” Such ha­rass­ment re­mains com­mon for tradeswomen and ranges from mild, to vi­o­lent, to just plain creepy, as when one man, pay­ing his bill at the front desk, whis­pered, “Your hands are dirty.”

Women in trades have re­ported en­coun­ters with cus­tomers who doubted their com­pe­tence and who re­fused to deal with them, seek­ing a man in­stead.

Some cus­tomers at Howe’s fit this pat­tern. I’ve no­ticed that if I’m at the front desk with a male co-worker, men will of­ten look past me and ad­dress them, even though I’m older and, as far as they know, more ex­pe­ri­enced. Other cus­tomers like to tell me how to do my job.

One man, watch­ing me while I cut 8-foot lengths of tub­ing for him, told me that I could sim­ply hook my tape mea­sure over the saw blade and sub­tract ⅛-inch to find the cor­rect length. Piqued af­ter I ex­plained why his method would­n’t work for a pre­cise mea­sure­ment, he re­sponded by quizzing me on some­thing I was­n’t likely to know: the pur­pose of the black di­a­monds on my tape mea­sure.

The man in the au­di­ence at the aca­d­e­mic con­fer­ence who wants to lec­ture rather than ask a ques­tion of the woman who is the speaker has be­come a trope. The pon­tif­i­cat­ing metal-shop cus­tomer should be, too. Like other tradeswomen, I’ve learned to work around un­wanted com­ments, in­clud­ing un­in­vited con­ver­sa­tions with men bent on sig­nal­ing their ex­per­tise.

My soon-to-be-pub­lished book does­n’t fo­cus solely or even mostly on my ex­pe­ri­ences as a woman in a weld­ing and fab­ri­ca­tion shop. Rather, it looks at the non­lin­ear process of learn­ing skilled trades — a process that is, for tradeswomen, some­times frus­trated by scrutiny, phys­i­cal chal­lenges and sex­ism, which re­quire workarounds.

Nevertheless, along this jour­ney, I’ve leaned on the strength of the tradeswomen be­fore me. Although these women have been “alone in a crowd,” they’ve con­sis­tently worked around chal­lenges to­ward broader and deeper ex­per­tise.

Nintendo has con­firmed that the suc­ces­sor to the Nintendo Switch will be back­ward com­pat­i­ble with the Nintendo Switch.

In a post on X, a mes­sage from Nintendo pres­i­dent Shuntaro Furukawa also an­nounced that fur­ther in­for­ma­tion about the suc­ces­sor to the Nintendo Switch would come “at a later date.”

“This is Furukawa,” the mes­sage reads. “At to­day’s Corporate Management Policy Briefing, we an­nounced that Nintendo Switch soft­ware will also be playable on the suc­ces­sor to Nintendo Switch.

“Nintendo Switch Online will be avail­able on the suc­ces­sor to Nintendo Switch as well. Further in­for­ma­tion about the suc­ces­sor to Nintendo Switch, in­clud­ing its com­pat­i­bil­ity with Nintendo Switch, will be an­nounced at a later date.”

The post also con­firmed that Nintendo Switch Online would be avail­able on the suc­ces­sor con­sole. No fur­ther de­tails on its im­ple­men­ta­tion were an­nounced.

Earlier to­day, Nintendo re­it­er­ated it still in­tends to an­nounce its next con­sole hard­ware be­fore the end of its cur­rent fis­cal year, which con­cludes on March 31, 2025.

President Shuntaro Furukawa made the com­ments dur­ing an on­line press con­fer­ence on Tuesday, fol­low­ing the pub­li­ca­tion of Nintendo’s lat­est earn­ings re­sults, but the ex­ec­u­tive did not add any ad­di­tional de­tails.

According to a re­port, de­vel­op­ers have re­port­edly been briefed not to ex­pect Nin­ten­do’s next con­sole to launch be­fore April 2025.

“No de­vel­oper I’ve spo­ken to ex­pects it to be launch­ing this fi­nan­cial year,” said GI.biz jour­nal­ist Chris Dring. “In fact, they’ve been told not to ex­pect it in the [current] fi­nan­cial year. A bunch of peo­ple I spoke to hope it’s out in April or May time, still early next year, not late.

“I don’t think any of us wants a late launch for Switch 2 be­cause we all want a new Nintendo con­sole, every­one gets very ex­cited for it, and we don’t want that crunch of Grand Theft Auto 6 and Switch and all that kind of stuff on top of each other.”

Having launched in March 2017, Switch is in its eighth year on the mar­ket. In July, it sur­passed the Famicom as the Nintendo con­sole with the longest lifes­pan be­fore be­ing re­placed.

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During my first se­mes­ter as a com­puter sci­ence grad­u­ate stu­dent at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the se­mes­ter there was a lec­ture about neural net­works. This was in the fall of 2008, and I got the dis­tinct im­pres­sion—both from that lec­ture and the text­book—that neural net­works had be­come a back­wa­ter.

Neural net­works had de­liv­ered some im­pres­sive re­sults in the late 1980s and early 1990s. But then progress stalled. By 2008, many re­searchers had moved on to math­e­mat­i­cally el­e­gant ap­proaches such as sup­port vec­tor ma­chines.

I did­n’t know it at the time, but a team at Princeton—in the same com­puter sci­ence build­ing where I was at­tend­ing lec­tures—was work­ing on a pro­ject that would up­end the con­ven­tional wis­dom and demon­strate the power of neural net­works. That team, led by Prof. Fei-Fei Li, was­n’t work­ing on a bet­ter ver­sion of neural net­works. They were hardly think­ing about neural net­works at all.

Rather, they were cre­at­ing a new im­age dataset that would be far larger than any that had come be­fore: 14 mil­lion im­ages, each la­beled with one of nearly 22,000 cat­e­gories.

Li tells the story of ImageNet in her re­cent mem­oir, The Worlds I See. As she worked on the pro­ject, she faced a lot of skep­ti­cism from friends and col­leagues.

“I think you’ve taken this idea way too far,” a men­tor told her a few months into the pro­ject in 2007. “The trick is to grow with your field. Not to leap so far ahead of it.”

It was­n’t just that build­ing such a large dataset was a mas­sive lo­gis­ti­cal chal­lenge. People doubted the ma­chine learn­ing al­go­rithms of the day would ben­e­fit from such a vast col­lec­tion of im­ages.

“Pre-ImageNet, peo­ple did not be­lieve in data,” Li said in a September in­ter­view at the Computer History Museum. “Everyone was work­ing on com­pletely dif­fer­ent par­a­digms in AI with a tiny bit of data.”

Ignoring neg­a­tive feed­back, Li pur­sued the pro­ject for more than two years. It strained her re­search bud­get and the pa­tience of her grad­u­ate stu­dents. When she took a new job at Stanford in 2009, she took sev­eral of those stu­dents—and the ImageNet pro­ject—with her to California.

ImageNet re­ceived lit­tle at­ten­tion for the first cou­ple of years af­ter its re­lease in 2009. But in 2012, a team from the University of Toronto trained a neural net­work on the ImageNet dataset, achiev­ing un­prece­dented per­for­mance in im­age recog­ni­tion. That ground­break­ing AI model, dubbed AlexNet af­ter lead au­thor Alex Krizhevsky, kicked off the deep learn­ing boom that has con­tin­ued un­til the pre­sent day.

AlexNet would not have suc­ceeded with­out the ImageNet dataset. AlexNet also would not have been pos­si­ble with­out a plat­form called CUDA that al­lowed Nvidia’s graph­ics pro­cess­ing units (GPUs) to be used in non-graph­ics ap­pli­ca­tions. Many peo­ple were skep­ti­cal when Nvidia an­nounced CUDA in 2006.

So the AI boom of the last 12 years was made pos­si­ble by three vi­sion­ar­ies who pur­sued un­ortho­dox ideas in the face of wide­spread crit­i­cism. One was Geoffrey Hinton, a University of Toronto com­puter sci­en­tist who spent decades pro­mot­ing neural net­works de­spite near-uni­ver­sal skep­ti­cism. The sec­ond was Jensen Huang, the CEO of Nvidia, who rec­og­nized early that GPUs could be use­ful for more than just graph­ics.

The third was Fei-Fei Li. She cre­ated an im­age dataset that seemed lu­di­crously large to most of her col­leagues. But it turned out to be es­sen­tial for demon­strat­ing the po­ten­tial of neural net­works trained on GPUs.

A neural net­work is a net­work of thou­sands, mil­lions, or even bil­lions of neu­rons. Each neu­ron is a math­e­mat­i­cal func­tion that pro­duces an out­put based on a weighted av­er­age of its in­puts.

Suppose you want to cre­ate a net­work that can iden­tify hand­writ­ten dec­i­mal dig­its like the num­ber two in the red square above. Such a net­work would take in an in­ten­sity value for each pixel in an im­age and out­put a prob­a­bil­ity dis­tri­b­u­tion over the ten pos­si­ble dig­its—0, 1, 2, and so forth.

To train such a net­work, you first ini­tial­ize it with ran­dom weights. Then you run it on a se­quence of ex­am­ple im­ages. For each im­age, you train the net­work by strength­en­ing the con­nec­tions that push the net­work to­ward the right an­swer (in this case, a high prob­a­bil­ity value for the “2” out­put) and weak­en­ing con­nec­tions that push to­ward a wrong an­swer (a low prob­a­bil­ity for “2” and high prob­a­bil­i­ties for other dig­its). If trained on enough ex­am­ple im­ages, the model should start to pre­dict a high prob­a­bil­ity for “2” when shown a two—and not oth­er­wise.

In the late 1950s, sci­en­tists started to ex­per­i­ment with ba­sic net­works that had a sin­gle layer of neu­rons. However, their ini­tial en­thu­si­asm cooled as they re­al­ized that such sim­ple net­works lacked the ex­pres­sive power re­quired for com­plex com­pu­ta­tions.

Deeper net­works—those with mul­ti­ple lay­ers—had the po­ten­tial to be more ver­sa­tile. But in the 1960s, no one knew how to train them ef­fi­ciently. This was be­cause chang­ing a pa­ra­me­ter some­where in the mid­dle of a multi-layer net­work could have com­plex and un­pre­dictable ef­fects on the out­put.

So by the time Hinton be­gan his ca­reer in the 1970s, neural net­works had fallen out of fa­vor. Hinton wanted to study them, but he strug­gled to find an aca­d­e­mic home to do so. Between 1976 and 1986, Hinton spent time at four dif­fer­ent re­search in­sti­tu­tions: Sussex University, the University of California San Diego (UCSD), a branch of the UK Medical Research Council, and fi­nally Carnegie Mellon, where he be­came a pro­fes­sor in 1982.

In a land­mark 1986 pa­per, Hinton teamed up with two of his for­mer col­leagues at UCSD, David Rumelhart and Ronald Williams, to de­scribe a tech­nique called back­prop­a­ga­tion for ef­fi­ciently train­ing deep neural net­works.

Their idea was to start with the fi­nal layer of the net­work and work back­wards. For each con­nec­tion in the fi­nal layer, the al­go­rithm com­putes a gra­di­ent—a math­e­mat­i­cal es­ti­mate of whether in­creas­ing the strength of that con­nec­tion would push the net­work to­ward the right an­swer. Based on these gra­di­ents, the al­go­rithm ad­justs each pa­ra­me­ter in the mod­el’s fi­nal layer.

The al­go­rithm then prop­a­gates these gra­di­ents back­wards to the sec­ond-to-last layer. A key in­no­va­tion here is a for­mula—based on the chain rule from high school cal­cu­lus—for com­put­ing the gra­di­ents in one layer based on gra­di­ents in the fol­low­ing layer. Using these new gra­di­ents, the al­go­rithm up­dates each pa­ra­me­ter in the sec­ond-to-last layer of the model. Then the gra­di­ents get prop­a­gated back­wards to the third-to-last layer and the whole process re­peats once again.

The al­go­rithm only makes small changes to the model in each round of train­ing. But as the process is re­peated over thou­sands, mil­lions, bil­lions, or even tril­lions of train­ing ex­am­ples, the model grad­u­ally be­comes more ac­cu­rate.

Hinton and his col­leagues weren’t the first to dis­cover the ba­sic idea of back­prop­a­ga­tion. But their pa­per pop­u­lar­ized the method. As peo­ple re­al­ized it was now pos­si­ble to train deeper net­works, it trig­gered a new wave of en­thu­si­asm for neural net­works.

Hinton moved to the University of Toronto in 1987 and be­gan at­tract­ing young re­searchers who wanted to study neural net­works. One of the first was the French com­puter sci­en­tist Yann LeCun, who did a year-long post­doc with Hinton be­fore mov­ing to Bell Labs in 1988.

Hinton’s back­prop­a­ga­tion al­go­rithm al­lowed LeCun to train mod­els deep enough to per­form well on real-world tasks like hand­writ­ing recog­ni­tion. By the mid-1990s, LeCun’s tech­nol­ogy was work­ing so well that banks started to use it for pro­cess­ing checks.

“At one point, LeCun’s cre­ation read more than 10 per­cent of all checks de­posited in the United States,” wrote Cade Metz in his 2022 book Genius Makers.

But when LeCun and other re­searchers tried to ap­ply neural net­works to larger and more com­plex im­ages, it did­n’t go well. Neural net­works once again fell out of fash­ion, and some re­searchers who had fo­cused on neural net­works moved on to other pro­jects.

Hinton never stopped be­liev­ing that neural net­works could out­per­form other ma­chine learn­ing meth­ods. But it would be many years be­fore he’d have ac­cess to enough data and com­put­ing power to prove his case.

The brains of every per­sonal com­puter is a cen­tral pro­cess­ing unit (CPU). These chips are de­signed to per­form cal­cu­la­tions in or­der, one step at a time. This works fine for con­ven­tional soft­ware like Windows and Office. But some video games re­quire so many cal­cu­la­tions that they strain the ca­pa­bil­i­ties of CPUs. This is es­pe­cially true of games like Quake, Call of Duty, and Grand Theft Auto that ren­der three-di­men­sional worlds many times per sec­ond.

So gamers rely on GPUs to ac­cel­er­ate per­for­mance. Inside a GPU are many ex­e­cu­tion units—es­sen­tially tiny CPUs—packaged to­gether on a sin­gle chip. During game­play, dif­fer­ent ex­e­cu­tion units draw dif­fer­ent ar­eas of the screen. This par­al­lelism en­ables bet­ter im­age qual­ity and higher frame rates than would be pos­si­ble with a CPU alone.

Nvidia in­vented the GPU in 1999 and has dom­i­nated the mar­ket ever since. By the mid-2000s, Nvidia CEO Jensen Huang sus­pected that the mas­sive com­put­ing power in­side a GPU would be use­ful for ap­pli­ca­tions be­yond gam­ing. He hoped sci­en­tists could use it for com­pute-in­ten­sive tasks like weather sim­u­la­tion or oil ex­plo­ration.

So in 2006, Nvidia an­nounced the CUDA plat­form. CUDA al­lows pro­gram­mers to write “kernels,” short pro­grams de­signed to run on a sin­gle ex­e­cu­tion unit. Kernels al­low a big com­put­ing task to be split up into bite-sized chunks that can be processed in par­al­lel. This al­lows cer­tain kinds of cal­cu­la­tions to be com­pleted far faster than with a CPU alone.

But there was lit­tle in­ter­est in CUDA when it was first in­tro­duced, wrote Steven Witt in the New Yorker last year:

When CUDA was re­leased, in late 2006, Wall Street re­acted with dis­may. Huang was bring­ing su­per­com­put­ing to the masses, but the masses had shown no in­di­ca­tion that they wanted such a thing.“They were spend­ing a for­tune on this new chip ar­chi­tec­ture,” Ben Gilbert, the co-host of “Acquired,” a pop­u­lar Silicon Valley pod­cast, said. “They were spend­ing many bil­lions tar­get­ing an ob­scure cor­ner of aca­d­e­mic and sci­en­tific com­put­ing, which was not a large mar­ket at the time—cer­tainly less than the bil­lions they were pour­ing in.”Huang ar­gued that the sim­ple ex­is­tence of CUDA would en­large the su­per­com­put­ing sec­tor. This view was not widely held, and by the end of 2008 Nvidia’s stock price had de­clined by sev­enty per cent…Down­loads of CUDA hit a peak in 2009, then de­clined for three years. Board mem­bers wor­ried that Nvidia’s de­pressed stock price would make it a tar­get for cor­po­rate raiders.

Huang was­n’t specif­i­cally think­ing about AI or neural net­works when he cre­ated the CUDA plat­form. But it turned out that Hinton’s back­prop­a­ga­tion al­go­rithm could eas­ily be split up into bite-sized chunks. And so train­ing neural net­works turned out to be a killer app for CUDA.

According to Witt, Hinton was quick to rec­og­nize the po­ten­tial of CUDA:

In 2009, Hinton’s re­search group used Nvidia’s CUDA plat­form to train a neural net­work to rec­og­nize hu­man speech. He was sur­prised by the qual­ity of the re­sults, which he pre­sented at a con­fer­ence later that year. He then reached out to Nvidia. “I sent an e-mail say­ing, ‘Look, I just told a thou­sand ma­chine-learn­ing re­searchers they should go and buy Nvidia cards. Can you send me a free one?’ ” Hinton told me. “They said no.”

Despite the snub, Hinton and his grad­u­ate stu­dents, Alex Krizhevsky and Ilya Sutskever, ob­tained a pair of Nvidia GTX 580 GPUs for the AlexNet pro­ject. Each GPU had 512 ex­e­cu­tion units, al­low­ing Krizhevsky and Sutskever to train a neural net­work hun­dreds of times faster than would be pos­si­ble with a CPU. This speed al­lowed them to train a larger model—and to train it on many more train­ing im­ages. And they would need all that ex­tra com­put­ing power to tackle the mas­sive ImageNet dataset.

Fei-Fei Li was­n’t think­ing about ei­ther neural net­works or GPUs as she be­gan a new job as a com­puter sci­ence pro­fes­sor at Princeton in January of 2007. While earn­ing her PhD at Caltech, she had built a dataset called Caltech 101 that had 9,000 im­ages across 101 cat­e­gories.

That ex­pe­ri­ence had taught her that com­puter vi­sion al­go­rithms tended to per­form bet­ter with larger and more di­verse train­ing datasets. Not only had Li found her own al­go­rithms per­formed bet­ter when trained on Caltech 101, other re­searchers started train­ing their mod­els us­ing Li’s dataset and com­par­ing their per­for­mance to one an­other. This turned Caltech 101 into a bench­mark for the field of com­puter vi­sion.

So when she got to Princeton, Li de­cided to go much big­ger. She be­came ob­sessed with an es­ti­mate by vi­sion sci­en­tist Irving Biederman that the av­er­age per­son rec­og­nizes roughly 30,000 dif­fer­ent kinds of ob­jects. Li started to won­der if it would be pos­si­ble to build a truly com­pre­hen­sive im­age dataset—one that in­cluded every kind of ob­ject peo­ple com­monly en­counter in the phys­i­cal world.

A Princeton col­league told Li about WordNet, a mas­sive data­base that at­tempted to cat­a­log and or­ga­nize 140,000 words. Li called her new dataset ImageNet, and she used WordNet as a start­ing point for choos­ing cat­e­gories. She elim­i­nated verbs and ad­jec­tives as well as in­tan­gi­ble nouns like “truth.” That left a list of 22,000 count­able ob­jects, rang­ing from am­bu­lance to zuc­chini.

She planned to take the same ap­proach she’d taken with the Caltech 101 dataset: use Google’s im­age search to find can­di­date im­ages, then have a hu­man be­ing ver­ify them. For the Caltech 101 dataset, Li had done this her­self over the course of a few months. This time she would need more help. She planned to hire dozens of Princeton un­der­grad­u­ates to help her choose and la­bel im­ages.

But even af­ter heav­ily op­ti­miz­ing the la­bel­ing process—for ex­am­ple, pre-down­load­ing can­di­date im­ages so they’re in­stantly avail­able for stu­dents to re­view—Li and her grad­u­ate stu­dent, Jia Deng, cal­cu­lated it would take more than 18 years to se­lect and la­bel mil­lions of im­ages.

The pro­ject was saved when Li learned about Amazon Mechanical Turk, a crowd­sourc­ing plat­form Amazon had launched a cou­ple of years ear­lier. Not only was AMT’s in­ter­na­tional work­force more af­ford­able than Princeton un­der­grad­u­ates, the plat­form was far more flex­i­ble and scal­able. Li’s team could hire as many peo­ple as they needed, on de­mand, and pay them only as long as they had work avail­able.

AMT cut the time needed to com­plete ImageNet down from 18 to two years. Li writes that her lab spent two years “on the knife-edge of our fi­nances” as they strug­gled to com­plete the ImageNet pro­ject. But they had enough funds to pay three peo­ple to look at each of the 14 mil­lion im­ages in the fi­nal data set.

ImageNet was ready for pub­li­ca­tion in 2009, and Li sub­mit­ted it to the Conference on Computer Vision and Pattern Recognition, which was held in Miami that year. Their pa­per was ac­cepted, but it did­n’t get the kind of recog­ni­tion Li hoped for.

“ImageNet was rel­e­gated to a poster ses­sion,” Li writes. “This meant that we would­n’t be pre­sent­ing our work in a lec­ture hall to an au­di­ence at a pre­de­ter­mined time, but would in­stead be given space on the con­fer­ence floor to prop up a large-for­mat print sum­ma­riz­ing the pro­ject in hopes that passersby might stop and ask ques­tions… After so many years of ef­fort, this just felt an­ti­cli­mac­tic.”

To gen­er­ate pub­lic in­ter­est, Li turned ImageNet into a com­pe­ti­tion. Realizing that the full dataset might be too un­wieldy to dis­trib­ute to dozens of con­tes­tants, she cre­ated a much smaller (but still mas­sive) dataset with 1,000 cat­e­gories and 1.4 mil­lion im­ages.

The first year’s com­pe­ti­tion in 2010 gen­er­ated a healthy amount of in­ter­est, with 11 teams par­tic­i­pat­ing. The win­ning en­try was based on sup­port vec­tor ma­chines. Unfortunately, Li writes, it was “only a slight im­prove­ment over cut­ting-edge work found else­where in our field.”

The sec­ond year of the ImageNet com­pe­ti­tion at­tracted fewer en­tries than the first. The win­ning en­try in 2011 was an­other sup­port vec­tor ma­chine, and it just barely im­proved on the per­for­mance of the 2010 win­ner. Li started to won­der if the crit­ics had been right. Maybe “ImageNet was too much for most al­go­rithms to han­dle.”

“For two years run­ning, well-worn al­go­rithms had ex­hib­ited only in­cre­men­tal gains in ca­pa­bil­i­ties, while true progress seemed all but ab­sent,” Li writes. “If ImageNet was a bet, it was time to start won­der­ing if we’d lost.”

But when Li re­luc­tantly staged the com­pe­ti­tion a third time in 2012, the re­sults were to­tally dif­fer­ent. Geoff Hinton’s team was the first to sub­mit a model based on a deep neural net­work. And its top-5 ac­cu­racy was 85 per­cent—10 per­cent­age points bet­ter than the 2011 win­ner.

Li’s ini­tial re­ac­tion was in­credulity: “Most of us saw the neural net­work as a dusty ar­ti­fact en­cased in glass and pro­tected by vel­vet ropes.”

The ImageNet win­ners were sched­uled to be an­nounced at the European Conference on Computer Vision in Florence, Italy. Li, who had a baby at home in California, was plan­ning to skip the event. But when she saw how well AlexNet had done on her dataset, she re­al­ized this mo­ment would be too im­por­tant to miss: “I set­tled re­luc­tantly on a twenty-hour slog of sleep de­pri­va­tion and cramped el­bow room.”

On an October day in Florence, Alex Krizhevsky pre­sented his re­sults to a stand­ing-room-only crowd of com­puter vi­sion re­searchers. Fei-Fei Li was in the au­di­ence. So was Yann LeCun.

Cade Metz re­ports that af­ter the pre­sen­ta­tion, LeCun stood up and called AlexNet “an un­equiv­o­cal turn­ing point in the his­tory of com­puter vi­sion. This is proof.”

The suc­cess of AlexNet vin­di­cated Hinton’s faith in neural net­works, but it was ar­guably an even big­ger vin­di­ca­tion for LeCun.

AlexNet was a con­vo­lu­tional neural net­work, a type of neural net­work that LeCun had de­vel­oped 20 years ear­lier to rec­og­nize hand­writ­ten dig­its on checks. (For more de­tails on how CNNs work, see the in-depth ex­plainer I wrote for Ars Technica in 2018.) Indeed, there were few ar­chi­tec­tural dif­fer­ences be­tween AlexNet and LeCun’s im­age recog­ni­tion net­works from the 1990s.

AlexNet was sim­ply far larger. In a 1998 pa­per, LeCun de­scribed a doc­u­ment recog­ni­tion net­work with seven lay­ers and 60,000 train­able pa­ra­me­ters. AlexNet had eight lay­ers, but these lay­ers had 60 mil­lion train­able pa­ra­me­ters.

LeCun could not have trained a model that large in the early 1990s be­cause there were no com­puter chips with as much pro­cess­ing power as a 2012-era GPU. Even if LeCun had man­aged to build a big enough su­per­com­puter, he would not have had enough im­ages to train it prop­erly. Collecting those im­ages would have been hugely ex­pen­sive in the years be­fore Google and Amazon Mechanical Turk.

And this is why Fei-Fei Li’s work on ImageNet was so con­se­quen­tial. She did­n’t in­vent con­vo­lu­tional net­works or fig­ure out how to make them run ef­fi­ciently on GPUs. But she pro­vided the train­ing data that large neural net­works needed to reach their full po­ten­tial.

The tech­nol­ogy world im­me­di­ately rec­og­nized the im­por­tance of AlexNet. Hinton and his stu­dents formed a shell com­pany with the goal to be “acquihired” by a big tech com­pany. Within months, Google pur­chased the com­pany for $44 mil­lion. Hinton worked at Google for the next decade while re­tain­ing his aca­d­e­mic post in Toronto. Ilya Sutskever spent a few years at Google be­fore be­com­ing a co­founder of OpenAI.

AlexNet also made Nvidia GPUs the in­dus­try stan­dard for train­ing neural net­works. In 2012, the mar­ket val­ued Nvidia at less than $10 bil­lion. Today, Nvidia is one of the most valu­able com­pa­nies in the world, with a mar­ket cap­i­tal­iza­tion north of $3 tril­lion. That high val­u­a­tion is dri­ven mainly by over­whelm­ing de­mand for GPUs like the H100 that are op­ti­mized for train­ing neural net­works.

“That mo­ment was pretty sym­bolic to the world of AI be­cause three fun­da­men­tal el­e­ments of mod­ern AI con­verged for the first time,” Li said in a September in­ter­view at the Computer History Museum. “The first el­e­ment was neural net­works. The sec­ond el­e­ment was big data, us­ing ImageNet. And the third el­e­ment was GPU com­put­ing.”

Today lead­ing AI labs be­lieve the key to progress in AI is to train huge mod­els on vast data sets. Big tech­nol­ogy com­pa­nies are in such a hurry to build the data cen­ters re­quired to train larger mod­els that they’ve started to lease out en­tire nu­clear power plants to pro­vide the nec­es­sary power.

You can view this as a straight­for­ward ap­pli­ca­tion of the lessons of AlexNet. But I won­der if we ought to draw the op­po­site les­son from AlexNet: that it’s a mis­take to be­come too wed­ded to con­ven­tional wis­dom.

“Scaling laws” have had a re­mark­able run in the 12 years since AlexNet, and per­haps we’ll see an­other gen­er­a­tion or two of im­pres­sive re­sults as the lead­ing labs scale up their foun­da­tion mod­els even more.

But we should be care­ful not to let the lessons of AlexNet harden into dogma. I think there’s at least a chance that scal­ing laws will run out of steam in the next few years. And if that hap­pens, we’re go­ing to need a new gen­er­a­tion of stub­born non­con­formists to no­tice that the old ap­proach is­n’t work­ing and try some­thing dif­fer­ent.