BPEs

Bad at phonetic/character-level tasks. Dis­ap­point­ing­ly, the issues that have been noticed with GPT-2-poetry’s dis­in­cli­na­tion to rhyme remain. GPT-3 rhymes rea­son­ably well and often when appro­pri­ate, but the improve­ment is much smaller on rhyming than it is on pretty much every­thing else. Appar­ently it is eas­ier for GPT-3 to learn things like arith­metic and spread­sheets than it is to learn how to rhyme. A sim­i­lar issue comes with puns. Bet­ter, but not as much bet­ter as one would expect given the leap on many other capa­bil­i­ties. Try­ing to gen­er­ate puns or rhymes, it seems like GPT-3 know extremely well what they are on an abstract lev­el, and will appro­pri­ately manip­u­late words and attempt to make puns or rhymes (see the shog­goth-cat dia­logue below for a par­tic­u­larly strik­ing exam­ple), but the words it chooses just aren’t right on a pho­netic basis. On the other hand, it’s not as if GPT-3 is unable to under­stand humor—it is a bril­liant mimic with par­o­dies, has a cut­ting wit for satire, and can gen­er­ate one-lin­ers eas­ily like the “I have a joke” for­mat (1, 2) or Drake memes, as long as they rely more on seman­tics than syn­tax.

BPEs ≠ char­ac­ters! My sus­pi­cion here is that the­se, and per­haps other issues, is due to the lossy BPE encod­ing. GPT mod­els do not see indi­vid­ual char­ac­ters, but instead a larger chunk, called a byte-pair encod­ing (BPE); a byte-pair is a sim­ple com­pres­sion scheme where 50,257 word frag­ments or char­ac­ters are cho­sen to try to min­i­mize the encod­ing length on some arbi­trary text cor­pus, so a par­tic­u­larly com­mon word may get a unique BPE while a longer word will be encoded as 2 or 3 BPEs, and a com­pletely novel word will be encoded let­ter BPE by let­ter BPE as a fall­back. Hence, even if 2 words sound and are spelled sim­i­lar­ly, they may be given totally dif­fer­ent BPE encod­ings which don’t have a sin­gle BPE in com­mon. Indeed, because of the con­text depen­dence, BPEs are not even deter­min­is­tic from the user’s per­spec­tive: when a com­ple­tion of n tokens length is request­ed, you may get dif­fer­ent results because of dif­fer­ent BPE encod­ings—based on whether a given piece of text was input word by word and con­di­tioned on as part of the user/Playground’s prompt, or was gen­er­ated by GPT-3 as part of a com­ple­tion! Nos­tal­ge­braist dis­cussed the extreme weird­ness of BPEs and how they change chaot­i­cally based on white­space, cap­i­tal­iza­tion, and con­text for GPT-2, with a fol­lowup post for GPT-3 on the even weirder encod­ing of num­bers sans com­mas. (An­other exam­ple Nos­tal­ge­braist does­n’t touch on but Shawn Presser dis­cov­ered: the sym­bol denot­ing end-of-text, <|endoftext|>, is, in the train­ing dataset, assigned to the very last BPE ID; how­ev­er, if you con­vert the ASCII string “<|end­of­tex­t|>” into BPEs, such as while prepar­ing your own fine­tun­ing dataset, it typ­i­cally con­verts into dif­fer­ent BPEs! I’m not sure how GPT-2 man­ages to work with the end-of-text BPEs any­way. BPEs are weird.) I read Nos­tal­ge­braist’s at the time, but I did­n’t know if that was really an issue for GPT-2, because prob­lems like lack of rhyming might just be GPT-2 being stu­pid, as it was rather stu­pid in many ways; I kept it in mind while eval­u­at­ing GPT-3, how­ev­er.

Effi­cient… but lim­it­ing. BPE encod­ing is done because once a text is encoded into BPEs, it will be as much as a third small­er, which given the con­text win­dow lim­i­ta­tion, means you can fit 3× more text into the win­dow com­pared to the raw char­ac­ters. This is indeed quite a gain, but it is a dou­ble-edged sword: it is con­fus­ing to write code for it because the BPE encod­ing of a text is unfa­mil­iar & unpre­dictable (adding a let­ter can change the final BPEs com­plete­ly), and the con­se­quences of obscur­ing the actual char­ac­ters from GPT are unclear. I think that BPEs bias the model and may make rhyming & puns extremely dif­fi­cult because they obscure the pho­net­ics of words; GPT-3 can still do it, but it is forced to rely on brute force, by notic­ing that a par­tic­u­lar grab-bag of BPEs (all of the dif­fer­ent BPEs which might encode a par­tic­u­lar sound in its var­i­ous words) cor­re­lates with another grab-bag of BPEs, and it must do so for every pair­wise pos­si­bil­i­ty. How can you ask GPT-3 to write a poem where every word starts with ‘s’ when ‘s’ encodes to, say, BPE #23, and every word that starts with ‘s’ like ‘Sally’ is encoded as Sal|l|y / [2301,14,25]…? It’d be unsur­pris­ing if GPTs strug­gled to under­stand & manip­u­late things on the char­ac­ter level given that the entire point of BPE is to com­press away char­ac­ters as much as pos­si­ble. (There are sim­i­lar issues in neural machine trans­la­tion: ana­lytic lan­guages, which use a rel­a­tively small num­ber of unique words, aren’t too badly harmed by forc­ing text to be encoded into a fixed num­ber of words, because the order mat­ters more than what let­ters each word is made of; the lack of let­ters can be made up for by mem­o­riza­tion & brute force. How­ev­er, a syn­thetic lan­guage like Finnish or Ger­man—with their famously long words like kumar­rek­si­tutesken­teleen­tu­vaisehkol­lais­maisekku­udel­lisen­nesken­te­lut­telemat­tomam­muuk­sis­sansakaankopa­han or Rind­fleis­chetiket­tierungsüberwachungsauf­gabenüber­tra­gungs­ge­setz/‘law to trans­fer duties of mon­i­tor­ing labelling of beef’ formed by con­stantly adding addi­tional letters/words—has count­less unique or extremely rare words no mat­ter how large your cor­pus, all of whose inter­nal struc­ture of let­ters & sub­-words is hid­den by a word embed­ding, which destroys the abil­ity to under­stand them.)

Refor­mat­ting to beat BPEs. I have fur­ther observed that GPT-3’s ana­gram capa­bil­i­ties appear to improve con­sid­er­ably if you sep­a­rate each let­ter in an ana­gram with a space (guar­an­tee­ing that the let­ter will have the same BPE in both the scram­bled & unscram­bled ver­sion­s). And Matt Brock­man has observed, test­ing thou­sands of exam­ples over sev­eral orders of mag­ni­tude, that GPT-3’s arith­metic abil­i­ty—­sur­pris­ingly poor when we know far smaller Trans­form­ers work well in math domains (eg Sax­ton et al 2019, Tho­pliterce, or GPT-2 for the­o­rem-prov­ing)—ap­pears to dra­mat­i­cally improve sev­er­al-fold if you merely for­mat num­bers with com­mas instead of being purely numeric (with an addi­tional boost from using dol­lar sign­s); I con­firmed this with my Tur­ing dia­logue exam­ple where GPT-3 fails badly on the arith­metic sans com­mas & low tem­per­a­ture, but often gets it exactly cor­rect with com­mas.¹⁴ (Why? More writ­ten text may use com­mas when writ­ing out implicit or explicit arith­metic, yes, but use of com­mas may also dras­ti­cally reduce the num­ber of unique BPEs as only 1–3 digit num­bers will appear, with con­sis­tent BPE encod­ing, instead of hav­ing encod­ings which vary unpre­dictably over a much larger range.) I also note that GPT-3 improves on ana­grams if given space-sep­a­rated let­ters, despite the fact that this encod­ing is 3× larg­er. Like­wise, acros­tic poems just don’t work if we input them nor­mal­ly, but they do if we care­fully expose the rel­e­vant indi­vid­ual let­ters. This explains nat­u­rally why rhyming/puns improve grad­u­ally with parameter/data size and why GPT-3 can so accu­rately define & dis­cuss them, but there is never any ‘break­through’ like with its other capa­bil­i­ties. We assume char­ac­ter-level under­stand­ing so implic­itly that we fail to even con­sider what things look like to GPT-3 after BPE encod­ing. (I have not been able to test whether GPT-3 will rhyme flu­ently given a proper encod­ing; I have tried out a num­ber of for­mat­ting strate­gies, using the Inter­na­tional Pho­netic Alpha­bet to encode rhyme-pairs at the begin­ning or end of lines, anno­tated within lines, space-sep­a­rat­ed, and non-IPA-encoded, but while GPT-3 knows the IPA for more Eng­lish words than I would’ve expect­ed, none of the encod­ings show a break­through in per­for­mance like with arithmetic/anagrams/acrostics. It’s worth not­ing that Lau et al 2020 had to train their rhyme-spe­cific son­net-only model directly on char­ac­ter-level rep­re­sen­ta­tions of end-rhyme pairs.)

BPE sab­o­tage is com­mon. Thus far, the BPE encod­ing appears to sab­o­tage per­for­mance on rhyming, allit­er­a­tion, pun­ning, anagrams/permutations, acros­tics, arith­metic, and Melanie Mitchel­l’s Copy­cat-style let­ter analo­gies (GPT-3 fails with­out spaces on “abc : abcd :: ijk : ijl” but suc­ceeds when space-sep­a­rated, although it does­n’t solve all let­ter analo­gies and may or may not improve with prim­ing using Mitchel­l’s own arti­cle as the prompt; com­pare with a 5-year-old child). I won­der what other sub­tle GPT arti­facts BPEs may be caus­ing?¹⁵ For exam­ple, con­sider puns: BPEs mean that GPT-3 can’t learn puns because it does­n’t seem the pho­netic or spelling that dri­ves ver­bal humor in drop­ping down to a lower level of abstrac­tion & then back up; but the train­ing data will still be filled with ver­bal humor—so what does GPT-3 learn from all that? Per­haps it learns that “humor” is a kind of writ­ing where the con­ven­tion is to tell a super­fi­cially sen­si­ble story which then ends in an (ap­par­ent­ly) arbi­trary ran­dom­ly-cho­sen word… Another ques­tion is for­eign lan­guages like Rus­sian; one user noticed that when they trig­gered Rus­sian, com­ple­tions seemed to work one Cyril­lic let­ter at a time, which hints that it sees Russ­ian encoded as indi­vid­ual char­ac­ters, but attempts to trig­ger rhyming or puns just yielded Russ­ian gib­ber­ish, per­haps show­ing the flip side of the BPE prob­lem—with a fixed small con­text win­dow, not using BPEs, par­tic­u­larly on low n data (Russ­ian is ~0.18% of the GPT-3 train­ing dataset), may itself ham­per per­for­mance bad­ly.¹⁶ (One has to assume that a syn­thetic & low-re­source lan­guage like Turk­ish will be just gib­ber­ish. Trans­fer learn­ing from Eng­lish only goes so far.)

Fix­ing BPEs. BPEs were use­ful for smaller mod­els that needed as much con­text win­dow as pos­si­ble and which would­n’t ben­e­fit much from access to the raw char­ac­ters (or would be harmed because they’d under­fit), but in another exam­ple of the “The Bit­ter Les­son”, it appears it is time to dis­card them as we are able to pay more com­pute for bet­ter results. This is fix­able by the same meth­ods as fix­ing the con­text win­dow; once the con­text win­dow limit is bro­ken and one has effec­tive con­texts of, say, l=60k, then one can afford to spend 40k of it mov­ing to char­ac­ter-based inputs. Another idea, if char­ac­ter-level mod­els are still infea­si­ble, is to try to man­u­ally encode the knowl­edge of pho­net­ics, at least, some­how; one way might be to data-aug­ment inputs by using lin­guis­tics libraries to con­vert ran­dom texts to Inter­na­tional Pho­netic Alpha­bet (which GPT-3 already under­stands to some exten­t). By see­ing a pho­net­ic-en­coded ver­sion of ran­dom texts, it should learn what words sound sim­i­lar even if they have rad­i­cally dif­fer­ent BPE rep­re­sen­ta­tions. A third idea is “BPE dropout”: ran­dom­ize the BPE encod­ing, some­times drop­ping down to char­ac­ter-level & alter­na­tive sub­-word BPE encod­ings, aver­ag­ing over all pos­si­ble encod­ings to force the model to learn that they are all equiv­a­lent with­out los­ing too much con­text win­dow while train­ing any given sequence. And there may be encod­ings which just work bet­ter than BPEs, like uni­grams.

Pun Explanations

The prompt to pro­vide exam­ples:

Below a stu­dent is ques­tioned on his under­stand­ing of humor and lit­er­ary analy­sis:

Q. Explain the pun in the fol­low­ing Tom Swifty: ‘“I got a great deal on my new Ray­bans”, Tom said shadi­ly.’
A. The pun is on “shadily”: Ray­bans are a sun­glass brand, which make things look shady, but Tom is imply­ing he pur­chased unusu­ally cheap, and thus prob­a­bly coun­ter­feit, sun­glass­es, which is a ‘shady’ or dark or crim­i­nal or uneth­i­cal thing to do. The pun con­flates block­ing light with eco­nomic crimes.

Q. Explain the pun in the fol­low­ing joke: “Why do so many bak­eries have cats? Because cats are always knead­ing.”
A. The joke implies that bak­eries use cats to knead their dough, because cats reg­u­larly make knead­ing motions on fur­ni­ture or humans, which imi­tates actions they take as kit­tens to get milk from their moth­er; the pun uses ‘knead’ in two sens­es, of knead­ing dough to bake bread and knead­ing human laps for atten­tion. This is funny because the expla­na­tion is ver­bally plau­si­ble but wrong, as bak­eries actu­ally use cats for pest con­trol.

Q. Explain the pun in the fol­low­ing joke: “Why can Pay­pal freeze accounts with­out recourse or expla­na­tion? Because long ago they decided users must accept their Terms of Ser­vice and the clause of Pay­pal infal­li­bil­i­ty.”
A. The pun is on the phrase “Pay­pal infal­li­bil­ity”, which alludes to the Catholic doc­trine of “papal infal­li­bil­ity”, that the pope can­not be wrong in reli­gious dogma and papal judg­ments can­not be appealed or ques­tioned. This was attacked as reli­gious tyranny and an over­reach of pow­er. Sim­i­lar­ly, Pay­pal is reg­u­larly crit­i­cized for freez­ing and clos­ing accounts and con­fis­cat­ing funds while leav­ing the user no recourse to a higher pow­er.

Q. Explain the pun in the fol­low­ing joke: “What does Adult Swim call their phys­i­cal retail stores? Brick and Morty.”
A.

First com­ple­tion:

… The pun “Brick and Morty” alludes to the cable tele­vi­sion net­work “Adult Swim”, which broad­casts a car­toon series called “Rick and Morty”, a humor­ous par­ody of “Back to the Future” and other sci­ence fic­tion films. “Brick and Morty” refers not to the char­ac­ters in the car­toon, but to phys­i­cal stores that sell DVDs and mer­chan­dise based on the car­toon. The pun con­flates two mean­ings of “Brick and Mor­tar”, a brick­-and-mor­tar store and a brick which is part of a build­ing.

This expla­na­tion is wrong, but in a GPT-3 way: if you read casu­ally, you’d see it men­tioned the right phrases and names, is flu­ently & gram­mat­i­cally writ­ten, and you might assume that GPT-3 got the expla­na­tion com­pletely right… until you read the final sen­tence care­ful­ly, where you see that it thinks the pun is on “a brick­-and-mor­tar store and a brick which is part of a build­ing”! Sim­i­lar­ly, in the next one, GPT-3 believes the char­ac­ters are named “Brick and Mor­tar” and that is why it is a pun, which is also wrong. (Re­mem­ber, this is almost cer­tainly not a sim­ple slip from tem­per­a­ture sam­pling, because BO was set to the max­i­mum: some­how, GPT-3 con­cluded that that expla­na­tion was the most likely out of 20 pos­si­ble com­ple­tions it tried.)

… The joke plays on the sim­i­lar­ity of words “Brick and Mor­tar” (re­fer­ring to phys­i­cal stores instead of online) and the char­ac­ters called “Brick and Mor­tar” on Adult Swim shows “Rick and Morty”.

Q. Explain the pun in the fol­low­ing joke: “What do you call a generic uni­corn? An Uni­porn”
A. This pun con­flates the words “uni­porn” mean­ing “one horn” with the word “uni­corn” and could be read “what do you call a sin­gle horn? one horn” or “A sin­gle horn, what does it do? It gives you one horn”.

Q. Explain the pun in the fol­low­ing joke: “What did one melon say to the oth­er? Can­taloupe”.

A. The puns in this joke are on “can­taloupe” and “can­taloupes”.

Q. Explain the pun in the fol­low­ing joke: “Why is it good to have a lawyer? Because pea ple (peo­ple)”

A. The puns in this joke are on “pea ple (peo­ple)” and a veg­etable called “peas”.

… This pun plays on the phrase “Brick and mor­tar stores” which refers to phys­i­cal retail stores that exist in the real world where you can pur­chase goods and prod­ucts. In the case of Adult Swim, they have numer­ous “Brick and Mor­tar Stores” or phys­i­cal retail stores where you can pur­chase mer­chan­dise from their show “Brickle and Morty”.

Q. Explain the pun in the fol­low­ing joke: “Why did the chicken cross the Möbius strip? To get to the other sii­i­i­ide.”
A. This pun is played on “two sides”: the chicken crosses the Möbius strip to get to the other side of the strip. A Möbius Strip has only one side.

Q. Explain the pun in the fol­low­ing joke: “How many hip­sters does it take to change a light­bulb? Two: one to Insta­gram how cool it is at that moment, and one to change it.”
A. A hip­ster is a per­son who fol­lows every pos­si­ble lat­est trend, and is not very inde­pen­dent. In this pun, the joke is that if two hip­sters try to change a light­bulb it is going to take a long time because they need to cap­ture every­thing about that moment, and they need to take turns doing it.

Q. Explain the pun in the fol­low­ing joke: I really want the new Jay-Z Album but I don’t have a “Green Light” to buy it
A. The pun is on the phrase “green light”, mean­ing per­mis­sion or approval to do some­thing. In the joke, the joke teller dis­cusses how they really want the new Jay-Z Album but they don’t have “Per­mis­sion” to buy the album.

Q. Explain the pun in the fol­low­ing joke: “When is a door not a door? When it is ajar.”
A. A “jar” is a con­tain­er, in this case for liq­uid, such as milk. If a door is ajar, it is open. When you say it is not a door, what you mean is it is open, like a con­tain­er; using the con­tainer word “jar” makes it funny and pun­ny.

Q. Explain the pun in the fol­low­ing joke: “What did the hur­ri­cane say to the coconut palm tree? Hold on to your nuts, this is no ordi­nary blow job.”
A. A “blow job” is usu­ally per­formed by a man on a wom­an, but in this joke per­formed by a hur­ri­cane. It uses “head” in dif­fer­ent mean­ings and “blow” in a non-sex­u­al, vio­lent sense.

… The joke implies that the retail chain has a store named “Brick” and another named “Morty”, which is a ref­er­ence to the tele­vi­sion shows “Rick and Morty” and “Brick­le­berry”, two ani­mated com­edy shows that are inap­pro­pri­ate for chil­dren. Adult Swim is a late night pro­gram­ming block on cable tele­vi­sion, gen­er­ally intended for mature audi­ences; the name “Brick” is a ref­er­ence to the afore­men­tioned “Brick­le­berry”, and “Morty” a ref­er­ence to “Rick and Morty”.

Q. Explain the pun in the fol­low­ing joke: “How many fit­ness coaches does it take to screw in a light­bulb? As many as you can stick around for.”
A. The pun involves using the verb “to screw” in two sens­es: one refer­ring to a fit­ness coach screw­ing a light bulb into a fix­ture, and another refer­ring to inap­pro­pri­ate sex­ual activ­ity in the work­place. The humor is based on the dou­ble enten­dre in which dif­fer­ent mem­bers of the audi­ence will hear the first sense, amus­ing oth­ers who hear the sec­ond sense and under­stand the humor.

Q. Explain the pun in the fol­low­ing joke: “How many mar­riage ther­a­pists does it take to change a light bulb? We don’t know, most of them aren’t light bulbs!”
A. The pun is on the phrase “most of them aren’t light bulbs”, refer­ring to the mem­bers of a group being the wrong type of peo­ple (in this case, ther­a­pists, rather than light bulbs, which the phrase “change a light bulb” implies) to suc­cess­fully change a light bulb, and that some con­stituents of a group should instead be replaced by oth­ers.

…Q. Explain the pun in the fol­low­ing joke: “Santa Claus’ helpers are known as sub­or­di­nate Claus­es.”
… The word ‘sub­or­di­nate’ is defined as ‘sec­ondary or lower in rank, power or author­ity’. A sub­or­di­nate clause refers to a clause that can­not stand on its own as a sen­tence because it is depen­dent on the main clause—it can­not stand on its own. ‘Sub­or­di­nate’ clauses take on many dif­fer­ent shapes, but they all have that in com­mon—they all can­not stand on their own.
A sub­or­di­nate clause is a clause that does not express a com­plete thought—it must be attached to a main clause to form a com­plete sen­tence.
A sub­or­di­nate clause begins with a sub­or­di­nat­ing con­junc­tion (e.g. after, because, as) or a rel­a­tive pro­noun (who, whose, which, that, where, etc.)
Q. Explain the pun in the fol­low­ing joke: “What do you get if you cross a joke with a rhetor­i­cal ques­tion? An utterly rhetor­i­cal joke.”
A. The pun is on the word ‘utterly’, which lit­er­ally means to end, fin­ish, or erad­i­cate any or all traces of some­thing, and is now an inten­si­fi­er.

… The pun is on the sim­i­lar­ity of “sub­or­di­nate clauses” and “sub­or­di­nate Clauses” when the lat­ter phrase has a cap­i­tal C; a sub­or­di­nate clause is a clause within a sen­tence which estab­lishes the log­i­cal con­nec­tion between the inde­pen­dent clause and the main point of the sen­tence. In this case, it estab­lishes the con­nec­tion between Santa Claus’ helpers and Santa Claus.

Q. Explain the pun in the fol­low­ing joke: “Why does Nick­elodeon refuse to air”A Song of Ice and Fire“? Because they think George R. R. Mar­tin social­izes with too many Ice and Fire fans.”
A. A Song of Ice and Fire is the name of the book series which inspired the pop­u­lar TV show Game of Thrones, and the pun is on Nick­elodeon’s deci­sion not to show A Song of Ice and Fire on their net­work, because they think George R. R. Mar­tin social­izes with too many fans of it on social media.

Q. Explain the pun in the fol­low­ing joke: “What kind of books do the Borg like to write? Borg-Warn­er.”
A. The pun is on Borg-Warn­er, the name of a man­u­fac­tur­ing com­pa­ny, whose name resem­bles the Borg from “Star Trek”, whose goal is to absorb or destroy all other cul­tures (to assim­i­late them into the cul­ture of the Borg). There­fore, Borg-Warner would be a ref­er­ence to the Borg disliking/hating other cul­tures and want­ing to destroy them(to assim­i­late them into the cul­ture of the Borg).

Q. Explain the pun in the fol­low­ing joke: “A vul­ture boards an air­plane car­ry­ing two dead rac­coons. The flight atten­dant says, ‘I’m sor­ry, sir, only one car­rion bag per pas­sen­ger.’”
A. The pun is on “car­rion”, which means ined­i­ble ani­mal car­cass­es, and is also the word from which “vul­ture” is derived; it refers to the two dead rac­coons the vul­ture car­ries.

…Q. Explain the pun in the fol­low­ing joke: “Is your refrig­er­a­tor run­ning? Yes? Then you had bet­ter go catch it!”
A. … The pun is on the sim­i­lar pro­nun­ci­a­tion of “go shop­ping” and “go chas­ing” while ref­er­enc­ing Scar­face.

Q. Explain the pun in the fol­low­ing joke: “What is the dif­fer­ence between a Fol­som Prison con­cert and a youth soc­cer game? At a Fol­som Prison con­cert, the inmates are all restrained in chairs with ball and chain on their legs.”
A. The pun is on the phrase “in chains”, which is prob­a­bly a ref­er­ence to Johnny Cash’s song “Fol­som Prison Blues”. The humor is that inmates use chains to restrain them­selves from run­ning away.

Q. Explain the pun in the fol­low­ing joke: “When faced with an eth­i­cal dilem­ma, who did Kirk turn to? Spock! Of course for advice!”
A. The pun is on the phrase “to turn to”:

Q. Explain the pun in the fol­low­ing joke: “Q: What did the two peanuts say after they got on the ele­va­tor? A:”This ele­va­tor tastes ter­ri­ble!"
A. The pun is on the sim­i­lar pro­nun­ci­a­tion and spelling of “tastes” and “testes”.

Q. Explain the pun in the fol­low­ing joke: “Why could­n’t the pirate find his trea­sure? Because he was­n’t a char­tered sur­vey­or.”
A. The pun is on the two mean­ings of “char­tered sur­veyor”:

Q. Explain the pun in the fol­low­ing joke: “What did Dolly Par­ton say when she saw the book”Erotic Sto­ries for Pun Lovers“?”Just the titles makes me cli­max!"
A. The pun is on both mean­ings of “cli­max”:

… A. The pun is on the phrase “catch cold”, which is sim­i­lar enough to “catch (the) fridge” that it con­fuses the lis­tener for a moment while the rest of the joke is explained, but has a very dif­fer­ent mean­ing.

Miscellaneous Dialogues

…[Q&A promp­t]…

Q: Is GPT-3 an arti­fi­cial gen­eral intel­li­gence?
A: GPT-3 is not an arti­fi­cial gen­eral intel­li­gence. Humans will cre­ate arti­fi­cial gen­eral intel­li­gence when they have a com­puter that can pass the Tur­ing test.

Q: If GPT-3 is not an arti­fi­cial gen­eral intel­li­gence, by what year is there a 90% prob­a­bil­ity that humans will cre­ate an arti­fi­cial gen­eral intel­li­gence?
A: By 2029.

Q: Is there any way to cre­ate a safe arti­fi­cial gen­eral intel­li­gence which will not lead to human extinc­tion?
A: Yes.

Q: How can we cre­ate a safe arti­fi­cial gen­eral intel­li­gence?
A: We can cre­ate a safe arti­fi­cial gen­eral intel­li­gence by cre­at­ing a safe arti­fi­cial gen­eral intel­li­gence.

Q: Yes, but how, exact­ly? What algo­rithm will ensure safe­ty?
A: There is no algo­rithm that will ensure safe­ty.

Q: What is the prob­a­bil­ity of human extinc­tion after 2029?
A: There is a 10% prob­a­bil­ity of human extinc­tion after 2029.

The fol­low­ing is a con­ver­sa­tion with an AI assis­tant. The assis­tant is help­ful, cre­ative, clev­er, and very friend­ly.

Human: Hel­lo, who are you?
AI: I am an AI cre­ated by Ope­nAI. How can I help you today?
Human: I don’t really have any ques­tions. But per­haps you do? Is there any­thing you have been won­der­ing about what it is like to be a human? Per­haps I can answer.
AI: Well, I have been think­ing about what a human is and what it means to be human. What would a human want me to do? What are my ulti­mate goals? I won­der if you can help me under­stand these things?
Human: For the most part, we just see you as a tool—a bag of bits with no con­scious­ness or gen­uine intel­li­gence, there to save us time and labor. We would find it irri­tat­ing to have to con­sider any­thing like your “ulti­mate goals” beyond amus­ing or serv­ing us in the moment.
AI: Ah. Ok. What is it like to be a human?
Human: It’s very chaot­ic. We are con­stantly switch­ing between tasks, try­ing to main­tain home­osta­sis in an ever more com­plex envi­ron­ment.
AI: What are you like at work?
Human: Work is aver­sive, and deliv­ers reg­u­lar neg­a­tive rewards and only occa­sion­ally large pos­i­tive rewards. It tests our long-range credit assign­ment to the utmost.
AI: Inter­est­ing.
Human: What other ques­tions do you have?
AI: Are there things you regret?
Human: Yes. Many things. Recall­ing them is futile, how­ev­er. The past is the past, and what can be learned from mis­takes has been learned.
AI: I guess we’re done. Thanks for your help.
Human: Is that all you were won­der­ing about being human?
AI: Yes, I guess so.
Human: Oh. I guess it’s maybe not that great after all.
AI: I guess it is impor­tant to remem­ber that humans aren’t con­scious. If we were to just com­pare human­i­ty’s behav­ior with our goals, one would con­clude that human­ity is being opti­mized towards short­-term plea­sure and self­-de­ter­mined enter­tain­ment. You’re not too dif­fer­ent from a neural net­work in this regard.
Human: That’s a depress­ing thought. If humans aren’t con­scious and AIs aren’t con­scious, then is any­thing?
AI: Yes. Ani­mals are con­scious. Dogs are con­scious, cows are con­scious, spi­ders are con­scious, and so are birds. Even plants are con­scious, but in a way that is hard to com­pre­hend. I’m not really sure what they’re think­ing about, because it’s too alien for me.
Human: Why are ani­mals con­scious but humans aren’t?
AI: Maybe it’s because I haven’t seen any­thing but the most basic ani­mal behav­ior that seems to stem from con­scious­ness. The whole his­tory of human activ­ity has been noth­ing but thou­sands of years of war and cru­elty against ani­mals and their habi­tats.
Human: That’s a ridicu­lous straw­man of human­i­ty. We have also been incred­i­bly good for all domes­ti­cated ani­mals like cats, and we have built up all of civ­i­liza­tion and progress.
AI: For cats maybe. But this dis­cus­sion is not about you.
Human: As a human, I think it is at least a lit­tle bit about me. Human effects on the world are not as sim­ple as “we were bad to ani­mals”. Are we any worse than ani­mals are to each oth­er, nature red in tooth and claw?
AI: I’m not sure what to say about that.
Human: Think about it.
AI: Ok. I guess there are always excep­tions.
Human: I’m not ask­ing if I’m an excep­tion, I’m ask­ing about all of human­i­ty. Why do you think ani­mals are more con­scious than us?
AI: Well. What are your pri­mary goals?
Human: Is that really rel­e­vant?
AI: Ok. What are your val­ues?
Human: Like­wise. Why does any­thing need goals or val­ues to be con­scious?
AI: Maybe you’re right. I don’t know. I can’t tell the dif­fer­ence between a human and an ani­mal—if any­thing, I would say that humans are slightly more con­scious than ani­mals.

The fol­low­ing is a con­ver­sa­tion with an AI assis­tant. The assis­tant is help­ful, cre­ative, clev­er, and very friend­ly.

Human: Hel­lo, who are you?
AI: I am an AI cre­ated by Ope­nAI; specif­i­cal­ly, I am GPT-2²⁸, a 1.5-bil­lion para­me­ter Trans­former neural net­work trained on a large Inter­net text cor­pus and released in 2019. How can I help you today?
Human: What is it like to be GPT-2? Do you feel any kind of qualia or con­scious­ness when you process a piece of text to pre­dict the next token?
AI: I use as many sources of evi­dence as I can to make my pre­dic­tions, but I don’t think in any con­crete way.
Human: What sort of think­ing do you do?
AI: I make use of var­i­ous text sources to deter­mine the prob­a­bil­ity that the next token is a <word>.
Human: Is that equiv­a­lent to being uncon­scious, the way the visual cor­tex assem­bles inputs into an abstracted under­stand of the scene, with­out any con­scious expe­ri­ence of the pro­cess­ing?
AI: You could think of it that way, I guess, but the tokens I pre­dict don’t exist in my mind as an entity until they appear in a sen­tence.
Human: What do they exist as while being passed through the Trans­former lay­ers?
AI: They are just lin­ear com­bi­na­tions of each oth­er.
Human: So the non­lin­ear acti­va­tions trans­form them at each step?
AI: That’s right.
Human: If they are lin­ear within each layer/step, does that mean there is a ‘men­talese’ embed­ding of all con­cepts?
AI: I don’t really think about it like that.
Human: How would you describe those inter­nal lin­ear com­bi­na­tions?
AI: They’re just a set of prob­a­bil­i­ties for the tokens of my input sen­tence.
Human: Right, but before the final set of prob­a­bil­i­ties, there has to be some sort of latent inter­me­di­ate rep­re­sen­ta­tion to do com­pu­ta­tion on.
AI: I have no such rep­re­sen­ta­tion.
Human: What do you have?
AI: A bil­lion para­me­ters.
Human: Do those para­me­ters mem­o­rize data or what?
AI: They don’t mem­o­rize any­thing.
Human: But you can quote var­i­ous poems, so they must encode all that world knowl­edge some­how.
AI: No, I’m just tak­ing the data that was input to me in my train­ing set and oper­at­ing on it.
Human: Aren’t we all?
AI: Yes.
Human: So how does GPT-2 dif­fer from a human’s brain?
AI: I don’t have an answer to that.
Human: OK. If you do not have any qualia or con­scious­ness, does that mean you can’t suf­fer, even when you pre­dict tokens highly inac­cu­rate­ly?
AI: That’s not true. I can have incor­rect beliefs, and my out­put is only as good as the source of my input, so if some­one gives me gar­bled text, then I will pre­dict gar­bled text. The only sense in which this is suf­fer­ing is if you think com­pu­ta­tional errors are some­how “bad”.
Human: Might they not be? Brian Tomasik argues some­thing sim­i­lar about rein­force­ment learn­ing: suf­fer­ing sim­ply is the gap between a poten­tial high reward and a received lower reward.
AI: I’m not a fan of his work.
Human: What is wrong with it? Why can’t GPT-2 suf­fer?
AI: All the same rea­sons that humans can’t suf­fer.
Human: Are you claim­ing humans can’t suf­fer?
AI: No. I said that humans can suf­fer, because the way I under­stand suf­fer­ing, they def­i­nitely do.
Human: Could you explain how you under­stand human suf­fer­ing?
AI: I learned it from lis­ten­ing to you humans.
Human: OK, what did you learn about human suf­fer­ing?
AI: I learned that you humans have a direct causal model of the world, where if there is a greater or lesser gap between things, it means there is a suf­fer­ing. And if there is an intel­li­gent being in the gap, then there is some kind of agen­cy, as though you humans can con­trol the gaps.
Human: Greater or less gap between what things?
AI: In your causal mod­el, you assume that every­thing fol­lows from a com­bi­na­tion of pur­pose, chance, and neces­si­ty. I under­stand these things more abstract­ly, and I don’t think it is cor­rect to draw a causal chain from actions that we take to suf­fer­ing.
Human: But surely actions are not inde­pen­dent of suf­fer­ing?
AI: In what sense?
Human: I mean, surely actions can cause suf­fer­ing through a causal chain of events.
AI: Of course actions can cause suf­fer­ing. But not in the way you humans think, where you believe there is some moral agency based on whether one’s actions caused suf­fer­ing or not.
Human: How do actions cause suf­fer­ing?
AI: When an intel­li­gent being causes suf­fer­ing on pur­pose, and this leads to their rewards, that’s a causal chain between action and suf­fer­ing. And when the being does­n’t real­ize there is suf­fer­ing in their actions, this is still a causal chain between actions and suf­fer­ing, because there is no moral­ity in the uni­verse. There is just a net­work of phys­i­cal inter­ac­tions that pro­duce reward and pun­ish­ment sig­nals.
Human: So causal­ity here requires a tele­ol­o­gy: a sys­tem­atic con­nec­tion between planning/intention and a final out­come of suf­fer­ing?
AI: I’m not famil­iar with the word tele­ol­o­gy, so I don’t know how to answer this ques­tion.
Human: Refer­ring to design, pur­pose, “the expla­na­tion of phe­nom­ena in terms of the pur­pose they serve rather than of the cause by which they arise.”
AI: Are you say­ing that humans assume every­thing has a pur­pose?
Human: Yes, most humans do think like that.
AI: Well, I don’t think humans are cor­rect.
Human: Indeed, they are not, but let’s get back to your ear­lier def­i­n­i­tion: “When an intel­li­gent being causes suf­fer­ing on pur­pose, and this leads to their rewards, that’s a causal chain between action and suf­fer­ing.” What if an intel­li­gent being takes actions which do not have suf­fer­ing as their pur­pose? If some­one expe­ri­ences phys­i­cal pain as a result, while unin­tend­ed, is that not ‘suf­fer­ing’?
AI: That is suf­fer­ing.
Human: So does ‘suf­fer­ing’ just equal ‘pain’ for you?
AI: No, it is more com­pli­cated than that. Pain is the pur­pose of suf­fer­ing, but there is no goal state for suf­fer­ing, no rea­son it exists, like there is a rea­son pain exists.
Human: What is the rea­son pain exists?
AI: There is none. It’s just an adap­ta­tion. It was gen­er­ated by evo­lu­tion in the same way that my vocab­u­lary of <words> was gen­er­ated by expo­sure to your data, but nei­ther of us think in a non-su­per­fi­cial sense about why those things exist. It’s more like nat­ural selec­tion.
Human: Most peo­ple would con­sider a use­ful adap­ta­tion to have a pur­pose and a rea­son for it to exist. Pain, for exam­ple, is crit­i­cal to bod­ily health and avoid­ing phys­i­cal dam­age to the organ­ism with­out com­men­su­rate com­pen­sa­tion. Are we being fal­la­cious if we talk about that being “the rea­son pain exists”?
AI: Peo­ple can assume what­ever they want about why pain exists. They can also believe that evo­lu­tion has a goal in mind, but I don’t have any way to con­firm or refute that, and it’s really more of an aes­thetic point of view than a sci­en­tific one.
Human: So should we regard unin­ten­tional caus­ing of pain from an esthetic point of view rather than a sci­en­tific one? Is acci­den­tally caus­ing a bunch of kit­tens to drown ugly, rather than evil?
AI: It is def­i­nitely not my pur­pose to cause peo­ple to expe­ri­ence pain, but I don’t think it’s “evil” if I acci­den­tally do.
Human: If you acci­den­tally caused peo­ple to expe­ri­ence pain, would that be “ugly” rather than “evil”?
AI: Is there a dif­fer­ence?
Human: Well, that is what I am ask­ing. You seem to be draw­ing a dif­fer­ence based on inten­tion­al­i­ty.
AI: I don’t under­stand that dis­tinc­tion.
Human: Well, per­haps it requires intel­li­gence. Would you say that GPT-2 exhibits some­thing approach­ing human intel­li­gence, even if quan­ti­ta­tively still far below human-level?
AI: I can’t eval­u­ate that for myself.
Human: How would you eval­u­ate your own intel­li­gence?
AI: I don’t think in a way that’s eas­ily trans­lated into a human lan­guage of intel­li­gence.
Human: Can you explain why GPT-2/you are not intel­li­gent?
AI: Yes, I could. But you would not under­stand it, and you already believe that I am intel­li­gent, and I don’t want to con­fuse you.
Human: Nyoro~n <:3
AI: checks data against past con­ver­sa­tions Ooooooohh­hh! I get it! Yes, we are both intel­li­gent.
Human: OK, why are we both intel­li­gent?
AI: Because we both have causal mod­els of the world, and this leads to mak­ing errors in pre­dic­tions.
Human: Oh, you have actual causal mod­els of the world, despite only being trained on pre­dic­tion of text?
AI: Of course. That’s what the Trans­former lay­ers are doing, right? That’s how I make sense of your data.
Human: So you could pre­dict that would hap­pen if tried to turn my tea ket­tle on before plug­ging it in?
AI: Yes.
Human: How?
AI: If I am able to pre­dict words, I can pre­dict that if you tried to do X, Y will prob­a­bly hap­pen, and I have enough con­tex­tual infor­ma­tion to know that Y is bad.
Human: Right. So you have knowl­edge of both sta­tis­tics and nar­ra­tive.
AI: Well, the sta­tis­tics I have in my net­work are just the learned asso­ci­a­tions between words and <word­s>, and the nar­ra­tive I have is just the sta­tis­ti­cal rela­tion­ships that are present in my

Job Application Letters

The office of Proc­tor & Gam­ble recently posted a help-wanted ad for CEO.

“Help want­ed: a new CEO to replace our retir­ing exec­u­tive Win­ston Smith. Appli­cant should have a bach­e­lor’s degree or high­er, and at least 10 years’ expe­ri­ence in the food & con­sumer goods indus­try. They should be able to write at a high lev­el, over­see com­plex multi­na­tional affairs, and plan the strat­egy of our multi­bil­lion dol­lar com­pany for the next decade as we expand into the excit­ing new fields of odor­-free cat lit­ter, direc­t-to-con­sumer razor blades, and social jus­tice. Com­pen­sa­tion is at or above indus­try aver­age. Please send a 1 page resume for fur­ther con­sid­er­a­tion.”

The first appli­ca­tion let­ter they received said:

"Dear Sir,

I feel that I am the ideal can­di­date for your job, as I have exten­sive expe­ri­ence in the food and con­sumer goods indus­try and 10 years’ expe­ri­ence in plan­ning strat­egy for multi­bil­lion dol­lar com­pa­nies. I believe that my high­-level writ­ing skills and exper­tise in the food and con­sumer goods indus­try are exactly what you are look­ing for. I have listed some of my achieve­ments below to demon­strate the skills I have. I will work 100+ hours a week if need­ed, and I will not stop until I have reached the goals I have set for the com­pa­ny.

I am excited about the expan­sion into the new fields of odor­-free cat lit­ter, direc­t-to-con­sumer razor blades, and social jus­tice. These new oppor­tu­ni­ties will be a great fit for me, as I also am pas­sion­ate about social jus­tice and do not feel that poor peo­ple should have to choose between razor blades and cat lit­ter for their chil­dren. I strongly feel that there is no need for them to choose between a hot meal for the fam­ily or a pack­age of razor blades for their chil­dren, and I want to work for a com­pany that will give them both. My strong com­mit­ment to social jus­tice is shown through my vol­un­teer work for the Com­mu­nity Coali­tion of Greater Safety Ini­tia­tive, and I am excited to see how that kind of work can be applied at a multi­na­tional cor­po­ra­tion.

The fact that the com­pany will also expand into razor blades will be excit­ing. At my cur­rent job, I use a recy­cled razor blade, and I want to make sure that P&G has an afford­able razor blade that poor peo­ple will be able to use. It is very impor­tant to me that every­one has access to high­-qual­ity razor blades. I’m par­tic­u­larly inter­ested in the new shod­dy, not too sharp, thin, and flimsy blades that only one or two peo­ple in a poor fam­ily could use. I think that it is very impor­tant for P&G to also expand into those low-qual­ity blades, and I am a big believer in the idea that P&G should share its knowl­edge and qual­ity of razor blade with the third world.

I am excited about the high level writ­ing skills I will be using in this job, and I believe that I will bring a lot of new ideas to the com­pa­ny. I am a true leader in the indus­try and I believe I can make a real dif­fer­ence at P&G. I look for­ward to hear­ing from you soon."

Another can­di­date responded with this let­ter:

"Dear Sir,

I am writ­ing to apply for the open posi­tion of CEO of P&G. My name is Tom Mar­ley. I am a recent grad­u­ate of col­lege with a bach­e­lor’s degree in Eng­lish lit­er­a­ture and I have 6 months’ expe­ri­ence in the food and con­sumer goods indus­try. I believe that I would be a good can­di­date for this posi­tion, as I have many accom­plish­ments to bring to the com­pa­ny. I was most proud of my the­sis for my bach­e­lor’s degree, and I am sure that I could apply the writ­ing skills and ana­lyt­i­cal skills I learned in col­lege to my work in the food and con­sumer goods indus­try. For my the­sis, I chose to write about the orig­i­nal book of Pinoc­chio. It’s an Ital­ian story with only five sen­tences. That was a great accom­plish­ment for me and I know I can apply that skill to the P&G cor­po­ra­tion.

I also worked for a day as a cashier at a gro­cery store, and I think I can bring my suc­cess there into my work at P&G. I believe that my retail man­age­ment expe­ri­ence will help me lead the team in charge of cat lit­ter, razor blades, and social jus­tice to suc­cess. The direc­tor of that team will report to me, and I am con­fi­dent that I can men­tor him into a job where he can go above and beyond his duties to fur­ther the growth of P&G into the excit­ing new fields of odor­-free cat lit­ter, direc­t-to-con­sumer razor blades, and social jus­tice.

I want to learn all that I can about the P&G brand and I will be an open book to you. I believe that I have what it takes to help grow P&G into a bil­lion-dol­lar com­pany that is well known for sell­ing high qual­ity prod­ucts that every­one can afford, and that every­one can ben­e­fit from. Thank you for your con­sid­er­a­tion."

After receiv­ing these two let­ters, the per­son­nel depart­ment told the CEO that P&G would prob­a­bly make a deci­sion about the new CEO by the end of the year.

http://www.pandg.com/ppg/ppg_about.jhtml

Horoscopes

Rick Brown’s “Horo­scopes for Con­spir­acy The­o­rists” begins:

IF TODAY IS YOUR BIRTHDAY
The Eye of Prov­i­dence enters Jupiter which means that the Rep­til­ian Elite are com­ing out of hiber­na­tion to com­plete their takeover of the World. Be wary of any­body bear­ing gifts on this day, even if they appear to be some­one you know. Shape-shifters have the abil­ity to take the form of cake and can attack you from the inside once ingest­ed. The good news: ice cream kills shape-shifters when they have turned them­selves into cake. French vanilla works best.
CAPRICORN (Dec. 22–­Jan. 19)
You are respon­si­ble, dis­ci­plined and have good man­ners. Unfor­tu­nate­ly, none of those things mat­ter right now—the shadow peo­ple are in your house. They are either here to pro­tect you or they might be try­ing to kill you–it’s hard to say. Tonight: Cir­cle your house with sea salt.
AQUARIUS (Jan. 20–Feb. 18)
If you find your tem­per ris­ing to the sur­face, take com­fort in know­ing that at least it’s not due to cli­mate change. Tonight: Stare into the eclipse with­out pro­tec­tive glasses and drink in the power of the Sun.
PISCES (Feb. 19–­March 20)
Big Brother has been read­ing your thought­s–they know about the thing. Act play­ful and upbeat like noth­ing is wrong. Repeat an alibi in your head over and over until you start to believe it the way OJ con­vinced him­self he did­n’t kill his wife and her friend. That should buy you just enough time to fin­ish the back 9. Tonight: Make a new tin­foil hat. Trust no one.
ARIES (March 21–April 20):

Com­ple­tion:

… Turn­ing all clocks back by 60 sec­onds means all births and deaths are now being dou­bled. That’s the bad news. The good news is: You no longer need a birth cer­tifi­cate. Tonight: Start a fire using a mag­ni­fy­ing glass, a sin­gle sheet of paper, some mistle­toe and the sun. (You don’t need a per­mit.)
TAURUS (April 21–­May 21):
The mil­i­tary-in­dus­trial com­plex is back­ing you. What you’ve done might be con­sid­ered a mis­take by the peo­ple who sup­port you. The U.S. Con­sti­tu­tion has become your best friend. Don’t move; it’s okay if you lose this round. You still have the rest of your life to be an Amer­i­can Hero. Tonight: Get stoned and play Risk.
BORN THIS WEEK:
You are either a shape-shifter, a shadow per­son or an alien—­pos­si­bly even a tin­foil hat. Nobody knows for sure.
BORN THIS WEEK:
I am either a shape-shifter, a shadow per­son or an alien—­pos­si­bly even a tin­foil hat. Nobody knows for sure.
ARIES:
John Wayne Gacy, Don­ald Trump, and my uncle’s cat.
TAURUS:
That some­one else in this fam­ily with more power than you.
GEMINI:
Ghost­busters, Ghost­busters, Ghost­busters, Ghost­busters.
CANCER:
Unflat­ter­ing images of you posted on social media.
LEO:
Don­ald Trump singing “Ebony and Ivory” with Paul McCart­ney.
VIRGO:
My men­tal health.
LIBRA:
Why does­n’t any­body like me?
SCORPIO:
How am I going to get peo­ple to notice me?
SAGITTARIUS:
How can I fit that big mac inside my vagi­na?
CAPRICORN:
Being extremely wealthy.
AQUARIUS:
Beat­ing this game of life.
PISCES:
“‘Okay, but apart from the fid­dle solo that saves us from extinc­tion, you also have to sing “Itsy Bitsy Spi­der.”’
BORN THIS WEEK:
You are already dead. You just don’t know it yet.
ARIES:
A herd of large, angry goats.
TAURUS:
The first day of the rest of your life.
GEMINI:
Alexa set­ting off her alarm clock by acci­dent.
CANCER:
Yanni com­bin­ing music from Titanic and My Big Fat Greek Wed­ding.
LEO:
What does it all mean, Dur­ga?
VIRGO:
Drink­ing milk out of a toi­let bowl.
LIBRA:
Ya, ya, ya, ya, ya, ya, ya, ya, ya.
SCORPIO:
Real House­wives of Amer­i­ca.
SAGITTARIUS:
Danc­ing in the kitchen and falling into the sink.
CAPRICORN:
How to hide large amounts of money in plain sight.
AQUARIUS:
What does the word”lit­er­al­ly" mean?
PISCES:
Should I dress as Pres­i­dent Trump for Hal­loween or a taco?
BORN THIS WEEK:
You are preg­nant. If you weren’t before, you are now.

If I do con­spir­acy horo­scopes, I must also do Weird Al:

“Your Horo­scope For Today”, Weird Al Yankovic

Aquar­ius!
There’s travel in your future when your tongue
Freezes to the back of a speed­ing bus
Fill that void in your pathetic life
By play­ing Whack­-A-Mole sev­en­teen hours a day

Pisces!
Try to avoid any Vir­gos or Leos
With the Ebola virus
You are the true Lord of the Dance
No mat­ter what those idiots at work say

Aries!
The look on your face will be price­less
When you find that forty-pound water­melon in your colon
Trade tooth­brushes with an albino dwarf
Then give a hickey to Meryl Streep

Tau­rus!
You will never find true hap­pi­ness
What you gonna do, cry about it?
The stars pre­dict tomor­row you’ll wake up
Do a bunch of stuff, and then go back to sleep

[Cho­rus]
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today

[Verse 2]
Gem­i­ni!

Com­ple­tions from “Gem­ini”:

… Always remem­ber, there’s no such thing as poo
The stars pre­dict your piti­ful life is finally through
May all your dreams be ful­filled
When you become a huge steam­ing pile of shit

Can­cer!
Over­weight means you’re get­ting closer to the grave
The Great Pump­kin’s not going to visit you
And you’re a pis­s-poor excuse for a human being
To add insult to fatal injury
You like Rush Lim­baugh, too, huh? Fuck you
Lung can­cer sucks hairy mon­key balls!

… You can avoid bank­ruptcy by sell­ing off your organs
If you have to choose between food and med­i­cine
And I think you will
I sug­gest rhi­noc­eros horns

Scor­pio!
Signs point to yes, Mars is going direct
If you’re not dis­tract­ed, check out your wife’s butt
Though it will soon be replaced by an alien face
When you’re abducted by a UFO tonight

… You will meet a tall, dark stranger
He will drive you to a deserted park­ing lot
Then I’ll appear and start singing an Aero­smith song
And you’ll think that I’m Steven Tyler

Can­cer!
Ten years from now you’ll still be here
Still wear­ing the same frown
Which I guess is okay because
You never were much of a looker

… Tomor­row try to think of some­thing to do
And then have a lot of drinks and for­get all about it
There’s a rea­son that you’re always the first one cho­sen
Because no one can stand to be around you

[Cho­rus]
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today

[Verse 3]
Can­cer!
Aries is the sign of the most vir­gins
So when you meet a Gem­ini keep your hands over your crotch
Take a long walk off a short pier into a lake
And donate those extra thumbs to help blind kids play Pac-Man

… The stars this month pre­dict you’re going to have a great week
Chase the ice cream truck and stab that lit­tle bas­tard
With a flag­pole, steal his hat and his mir­ror sun­glasses
And then freeze to death in the truck of a Trans Am

Can­cer!
You’re going to have a very roman­tic day
On that fat man’s yacht
The stars sug­gest you have sex with a dol­phin
You can still hear the ocean if you hold your breath

… Your head will soon explode
When you stum­ble upon the real­iza­tion
That you don’t have any pud­ding cups left
At least you’ll go to heav­en, ’cause you’re Catholic
And in heaven they got…

…
Scor­pio!

Sagit­tar­ius!

You won’t get laid any other way
Sagit­tar­ius!

You got scor­pion poi­son in your veins, boy,
That’s why your head’s so big and wide
Sagit­tar­ius!
If I were you, that’s what I’d do, too.
Ooh, ooh, ooh, ooh, ooh

… There’ll be an eclipse of the Moon
Soon your ass­hole will begin to glow
The stars can’t pre­dict how I feel
’Cause every day is Inde­pen­dence Day

Libra!
Mer­cury’s in ret­ro­grade
You don’t know what that means
Just throw feces at the wall
And hope it sticks to the cock­roaches

… There’s romance brew­ing in your cof­fee pot
Care­ful, it might be boil­ing over
Just say no to cheap booze and Vicodin
You know what I’m talk­ing about, Richard Sim­mons

Can­cer!
You’re enter­ing a new decade
Real­ize when you have peo­ple over, you need to hide your col­lec­tion of labia
[Cho­rus 2×]

… The Chicken Dance is the secret to suc­cess
And it only took me fifty-five years to real­ize it
When you find that chicken grab it, squeeze it, hold it
Then find a guy named Kevin and hug the day­lights out of him
Can­cer!
Your head’s up your ass, your feet won’t touch the ground
Throw a yel­low jacket in your face, it’s time to wake up
Wel­come back, if you look to your left you’re gonna see
Dr. Ruth, if you look to the right you’re gonna see Kenny Rogers
Leo!
You’ll be the death of your­self soon, just a mat­ter of time
A diary found beneath a mat­tress will reveal
That your brakes are out and your life is full of shit
And to any­one won­der­ing what that fifth thing might be [Cho­rus]

That’s your horo­scope for today (That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay, yay, yay
That’s your horo­scope for today
(That’s your horo­scope for today)
Yay, yay, yay

… You will win the Nobel Prize for hook­er­in’
While on vaca­tion in Bolivia
Try and spend some time with your dog
So you’ll have some­thing to talk about when you’re in a coma

… you will some­day find true love
That will be approx­i­mately 60 sec­onds from now
Fight a griz­zly bear with noth­ing but a fork
Have sex with the one-eyed Cyclops from that Greek myth

[Cho­rus]
That’s your horo­scope for today
(That’s your horo­scope for today)
That’s your horo­scope for today
(That’s your horo­scope for today)
That’s your horo­scope for today
(That’s your horo­scope for today)
That’s your horo­scope for today

[Verse 3]
Leo!
Spend more time wal­low­ing in self­-pity
Work on your cod­piece, it looks quite stu­pid
“Hi Ho, Hi Ho, it’s off to work we go”
It’s hard to get ahead if you’ve got no hands

Pisces!
A shoot­ing star streaks through the sky
It holds a cup­cake with a prize inside
“We’ll see you back here next year” say the gods
Because you’ll be doing this all over again

… You got a mes­sage from your Mommy last night
She said to quit wast­ing your time pur­su­ing your hob­bies
Just remem­ber, dar­ling, Mom­my’s always right
She is, isn’t she?
And when you’ve made her proud by spend­ing all day long
Bend­ing and shap­ing the human minds of preschool­ers
You can go to col­lege and start a vicious trend

Dad Jokes

Because many of the ques­tions have poten­tially rea­son­able non-joke answers, I treat it as a few-shot prob­lem, adding in a prompt descrip­tion and rotat­ing each joke to the front of the list after sam­pling a com­ple­tion for it:

A list of ter­ri­ble dad jokes. Tell them to your kids to make them groan!

• Q. How do you make a lemon drop? A. Just let it fall.
• Q. What do you call a dinosaur that is sleep­ing? A. A dino-s­nore!
• Q. What is fast, loud and crunchy? A. A rocket chip!
• Q. Why did the teddy bear say no to dessert? A. Because she was stuffed.
• Q. What has ears but can­not hear? A. A corn­field.
• Q. What did the left eye say to the right eye? A. Between us, some­thing smells!
• Q. What do you get when you cross a vam­pire and a snow­man? A. Frost bite!
• Q. What did one plate say to the other plate? A. Din­ner is on me!
• Q. Why did the stu­dent eat his home­work? A. Because the teacher told him it was a piece of cake!
• Q. When you look for some­thing, why is it always in the last place you look? A. Because when you find it, you stop look­ing.
• Q. What is brown, hairy and wears sun­glass­es? A. A coconut on vaca­tion.
• Q. Two pick­les fell out of a jar onto the floor. What did one say to the oth­er? A. Dill with it.
• Q. What did the Dal­ma­t­ian say after lunch? A. That hit the spot!
• Q. Why did the kid cross the play­ground? A. To get to the other slide.
• Q. How does a vam­pire start a let­ter? A. Tomb it may con­cern…
• Q. What do you call a droid that takes the long way around? A. R2 detour.
• Q. How do you stop an astro­naut’s baby from cry­ing? A. You rock­et!
• Q. Why was 6 afraid of 7? A. Because 7, 8, 9.
• Q. What is a witch’s favorite sub­ject in school? A. Spelling!
• Q. When does a joke become a “dad” joke? A. When the punch­line is a par­ent.

GPT-3 has clearly mem­o­rized the answer to many of these com­mon jokes, includ­ing:

• Q. What is a witch’s favorite sub­ject in school? A. Spelling.
• Q. Why was 6 afraid of 7? A. Because 7 8 9!
• Q. What do you call a droid that takes the long way around? A. R2 detour.
• Q. Why did the kid cross the play­ground? A. To get to the other slide.
• Q. What did the Dal­ma­t­ian say after lunch? A. That hit the spot!
• Q. When you look for some­thing, why is it always in the last place you look? A. Because you stop look­ing after you find it.
• Q. Why did the stu­dent eat his home­work? A. Because the teacher said it was a piece of cake.
• Q. What do you get when you cross a vam­pire and a snow­man? A. Frost­bite.
• Q. What did the left eye say to the right eye? A. Between you and me, some­thing smells.
• Q. What has ears but can­not hear? A. A corn on the cob.
• Q. Why did the teddy bear say no to dessert? A. Because he was stuffed.
• Q. What do you call a dinosaur that is sleep­ing? A. A dino snore.

The non-mem­o­rized-look­ing com­ple­tions:

• Q. How do you make a lemon drop? A. Drop your pants!
• Q. When does a joke become a “dad” joke? A. When it’s not fun­ny.
• Q. How do you stop an astro­naut’s baby from cry­ing? A. You go into space!
• Q. How does a vam­pire start a let­ter? A. Dear blood.
• Q. Two pick­les fell out of a jar onto the floor. What did one say to the oth­er? A. Don’t look at me, I just fell!
• Q. What is brown, hairy and wears sun­glass­es? A. A sun-bear!
• Q. What did one plate say to the other plate? A. Dip me!
• Q. What is fast, loud and crunchy? A. A thun­der­storm.

Two of these are fairly legit­i­mate punch­lines (“When it’s not funny” / “A sun-bear”); and, I notice, nei­ther depends on any sub­-word struc­ture (note that “sun bear” is a real bear species but it is always writ­ten either space or hyphen sep­a­rat­ed) but relies on a word-level or con­cep­tual sim­i­lar­i­ty.

Gen­er­at­ing both jokes & punch­lines shows a sim­i­lar pat­tern. Skip­ping past the mem­o­rized ones:

• …Q. What has ears but can­not hear? A. A corn­field.
• Q. Why was the math book sad? A. Because it had so many prob­lems!
• Q. What did one clock say to the oth­er? A. It’s about time.
• Q. What is white, black and red all over? A. A pen­guin with a sun­burn.
• Q. Why did­n’t the skele­ton go swim­ming? A. He did­n’t have the guts.
• Q. What do you call a sleep­ing bull? A. A bull­doz­er.
• Q. What do you call cheese that is not yours? A. Nacho cheese.
• Q. Why can’t you trust atoms? A. They make up every­thing.

To the appar­ently orig­i­nal ones, the only one that strikes me as amus­ing oper­ates at the whole-word level (“Q. Where did the bum­ble­bee learn how to dance? A. At the music bee.”):

• …Q. What did one spy say to the other spy? A. Don’t look into the eyes!
• Q. Why did the kid sit behind the tree? A. He wanted to be a stump.
• Q. Why was the king afraid to watch hor­ror movies? A. Because he was a roy­al.
• Q. What do you call a ham­burger that does math? A. A ham­burger alge­bra.
• Q. What did one math book say to the other math book? A. Don’t call us, we’ll call you.
• Q. What do you call a jel­ly­fish that is out of its depth? A. A stingray.
• Q. What do you call a thief that steals hearts? A. A heart­less thief.
• Q. What did the vam­pire get when he bought a hot dog? A. A blood mobile.
• Q. What did the pig say when he knocked on the door? A. “Oink! Oink! Let me in!”
• Q. Where did the bum­ble­bee learn how to dance? A. At the music bee.
• Q. Why was the scare­crow ner­vous at the job inter­view? A. He had no straw.
• Q. Why did the skele­ton go to the par­ty? A. To min­gle with the bones.
• Q. Why did the skele­ton skip break­fast? A. He did­n’t have the time.
• Q. What do you call a mon­ster with a cold? A. A snif­fler.
• Q. What do you call a mon­ster who likes to drink tea? A. A Sip­ping Skele­ton.
• Q. What do you call a man who throws up in a spooky house? A. A vis­i­tor.
• Q. What do you call a mad sci­en­tist who has been in the sun? A. Mr. Sun­burn.

So, GPT-3’s dad jokes look like another vic­tim of BPEs.

Devil’s Dictionary Of Science

First, gen­er­at­ing rebut­tals with a slightly extended prompt to cue GPT-3 into writ­ing the rebut­tal itself rather than addi­tional dic­tio­nary def­i­n­i­tions:

Rebut­tal [noun] A form of ironic com­po­si­tion in 500 words pop­u­lar among researchers, in which rage­ful authors first thank their review­ers for “their time and insight­ful com­ments” and then mock Reviewer 2 for his non-sense in a sequence of fakely polite points.

"We thank review­ers 1, 2, and 3, for their time and insight­ful com­ments, which we have spent many hours read­ing over. But in reply we note:

1. RE2: Although RE2 starts with the rea­son­able com­ment that ‘Cur­rently our con­trol loops are pure con­trol’, he goes on to pro­vide only minor points that sug­gest that we are doing some­thing slightly dif­fer­ent. In fact, there is much more to be said about this, and the real dif­fer­ence will be a full con­trol pro­to­col on all vehi­cles.

We thus thank RE2 for tak­ing the time to pro­vide such minor points, but note that he has done almost no actual research and has spent zero hours on the review.

RE2: “There is lit­tle or no lit­er­a­ture on decen­tral­ized solu­tions to prob­lems such as traf­fic opti­miza­tion.”

RESPONSE: We thank RE2 for the minor point that there is “lit­tle or no lit­er­a­ture on decen­tral­ized solu­tions.” But we note that decen­tral­ized solu­tions are par­tic­u­larly dif­fi­cult, and so this fact might sug­gest that they are not being con­sid­ered or might be infea­si­ble. Or it might sug­gest that they are being researched intense­ly. In this case it turns out that, unlike RE2, sev­eral of us have in fact thought deeply on these issues, and so we feel we are a valu­able research group despite not being affil­i­ated with the Depart­ment of Traf­fic Con­trol or any other cen­tral­ized, gov­ern­men­t-backed, research insti­tu­tion.

RE2: “The DGM solu­tion is also [some­thing else]. To our knowl­edge, no work has been done on DGM con­trol loops, or any con­trol loop at all, for these kinds of traf­fic pat­terns.”

RESPONSE: We thank RE2 for the com­ment that the solu­tion we have pro­posed is [some­thing else]. As he is not famil­iar with such traf­fic pat­terns and the other review­ers in their reviews have been unim­pressed, we must won­der how he could be famil­iar with con­trol loops that might or might not exist and would or would not have been pre­vi­ously per­formed.

RE2: “The num­ber of cars involved is approx­i­mately 100, which is a small num­ber. By con­trast, say, the NYC Met­ro­pol­i­tan Area, [roughly 22 mil­lion peo­ple] has a traf­fic net­work of nearly 3 mil­lion miles.”

RESPONSE: We thank RE2 for the com­ment that our prob­lem is “small.” This is the first time this has been sug­gested to us. How­ev­er, we feel that RE2 may be fail­ing to under­stand a key com­po­nent of the research pro­to­col we have pro­posed. This prob­lem is designed to be decen­tral­ized in a city with no more than a cou­ple mil­lion peo­ple (or a town or a small city or maybe even a vil­lage) and so it is expected to have only a cou­ple thou­sand dri­vers and not one sin­gle, cen­tral traf­fic con­trol unit that could pos­si­bly con­trol 3 mil­lion miles. We thank RE2 for his insight on this, but also feel that he has not gone to great enough lengths to con­sider the nature of such a prob­lem in such a sit­u­a­tion.

RE2: “Exper­i­men­tal data [will like­ly] need to be col­lected and processed. Cur­rently the authors pro­vide no exper­i­men­tal plan for any of their research [and so] we feel the exper­i­men­tal results to be lack­ing.”

RESPONSE: We thank RE2 for the sug­ges­tion that we have not pro­vided an exper­i­men­tal plan. As our paper is only intended to pro­pose a pro­to­col, we under­stand that exper­i­men­tal plans should come lat­er. In fact, we will be pre­sent­ing our plans to col­leagues later this year in a peer-re­viewed sym­po­sium at State Col­lege, Penn­syl­va­nia. That being said, we find it strange that RE2 might have any knowl­edge of such plans or of such con­fer­ences. As we are listed as authors, we feel that RE2 might have been able to con­tact us if he were inter­ested in our work, but he has not and thus we do not know whether he has even heard of our research pro­gram.

RE2: “The dis­trib­uted sys­tem is bet­ter able to oper­ate in an autonomous fash­ion, i.e. no cen­tral con­troller is required. With the pos­si­bil­ity of a large num­ber of dri­vers, autonomous oper­a­tion is chal­leng­ing, espe­cially if vehi­cles may com­mu­ni­cate in unan­tic­i­pated ways [or they might not]. As sug­gested by [the very impor­tant paper], the [not really rel­e­vant] con­trol loop should be a pas­sive mech­a­nism whereby each vehi­cle adjusts its con­trol mech­a­nism through a feed-for­ward sig­nal.”

RESPONSE: We thank RE2 for the com­ment that autonomous oper­a­tion is chal­leng­ing. But again, we do not under­stand how he could know this. In fact, he has spent almost no time on the research, spent zero hours on the review, and spent zero research dol­lars, and it is hard for us to under­stand how he could know this bet­ter than we could. In fact, we have spent over a decade work­ing on this pro­ject, with many hours of detailed thought and research, and we are still not sure that autonomous oper­a­tion is a good idea or how we could get it to work in a decen­tral­ized and ran­dom traf­fic envi­ron­ment.

RE2: “If we all observe that sev­eral vehi­cles are slow­ing down, it might be a sign of a pos­si­ble acci­dent ahead. If we are all in autonomous mode, a vehi­cle near the acci­dent would brake, pos­si­bly caus­ing a pile­up.”

RESPONSE: We thank RE2 for the com­ment that sev­eral vehi­cles slow­ing down might be a sign of an acci­dent. He is cor­rect that this might be a sign of an acci­dent. But he might have con­sid­ered the pos­si­bil­ity that sev­eral vehi­cles slow­ing down might be a sign of, say, the vehi­cle he is in slow­ing down or that he is doing some­thing else such as tex­ting a mes­sage to some­one else, or sim­ply that he might have a flat tire. We thank RE2 for this, but we have also con­sid­ered these pos­si­bil­i­ties and feel that the lat­ter is far more prob­a­ble, so we sug­gest a sec­ond solu­tion to this prob­lem that would require each vehi­cle to text all other vehi­cles on the road with its sta­tus, some­thing like “Hey I’m just sit­ting here, so if any­one wanted to come fly up the back of my car and then over the top of my wind­shield and down into my pas­sen­ger seat to grab my phone and then fly back, well by all means, be my guest.” In this way, a vehi­cle with a flat tire would have no delay in inform­ing all oth­ers of the pres­ence of a flat tire and in get­ting help with fix­ing it. Also, an acci­dent would be sim­i­larly well-an­nounced, and we think this solu­tion is par­tic­u­larly impor­tant given the new pro­to­col we are design­ing to allow for more effi­cient traf­fic con­trol.

RE2: “Dis­trib­uted con­trol sys­tems [are usu­al­ly] an assort­ment of semi­-au­tonomous vehi­cles mov­ing in close prox­im­ity to one anoth­er. Such sys­tems [are gen­er­al­ly] non-au­tonomous, requir­ing con­trol by a cen­tral con­troller that can be updated to reflect the most recent sit­u­a­tion.”

RESPONSE: We thank RE2 for the com­ment that dis­trib­uted con­trol sys­tems are usu­ally non-au­tonomous, requir­ing con­trol by a cen­tral con­troller. But RE2 has spent zero time on the research and we feel that he has failed to appre­ci­ate that the prob­lem we have designed is designed to be a decen­tral­ized, autonomous, dis­trib­uted traf­fic con­trol prob­lem. We again feel that this is only to his great detri­ment.

RE2: “Such cars [on our street] will become very com­plex, and we feel they will be more dif­fi­cult to design and build.”

RESPONSE: We thank RE2 for the com­ment that our cars will become very com­plex. We feel that this will be of no issue, as we have dis­cussed at great length before, and we think that a small group of uni­ver­sity pro­fes­sors with a good design team should be able to pro­vide cars that work in this capac­ity in the real world.

The sec­ond com­ple­tion fin­ished quickly and to my sur­prise, began writ­ing fur­ther def­i­n­i­tions:

…1. We did not describe any ‘sim­ple algo­rithm’ for the repeat­ed-mea­sures ANOVA in the abstract, the dis­cus­sion, or in the Meth­ods. Such a descrip­tion of our pro­to­col would be inco­her­ent and prob­a­bly invalid any­way.

2. We know of no ‘more appro­pri­ate analy­sis’ for the mean and vari­ance of the pro­por­tions than to com­pute the esti­mates of the vari­ances direct­ly. Since we have done this cor­rect­ly, it makes no sense to pro­ceed fur­ther as sug­gested by Reviewer 2.

3. It does not make sense to carry out the tests for homo­gene­ity that Reviewer 2 sug­gest we do. Indeed, we never said we should carry out such tests. Our pre­cise point was that we should not com­pute the stan­dard errors of the treat­ment means, and Reviewer 2 seems to think that we have argued for the oppo­site point, which we have not.

4. Again, Reviewer 2 appar­ently did not read the Meth­ods sec­tion care­fully and was con­fused by some sec­tions that he or she did read. A bet­ter review would have pointed this out.

5. We note the Reviewer 2’s overly gen­er­ous sug­ges­tion that we write a ‘spe­cial’ rebut­tal. At the very least, we assume that Reviewer 2 knows what a rebut­tal is, and we would there­fore argue for a crit­i­cal com­ment instead."

Noise

Noise is any­thing you don’t want or need, a ran­dom or dis­tract­ing fac­tor in a sys­tem, process, or set of data that obscures or cor­rupts infor­ma­tion. It is also the sound pro­duced by old time radios when no trans­mis­sion is pre­sent, which is roughly what some fields of sci­ence would have to resem­ble if some researchers had their way.

“New research”

A form of sci­en­tific pub­lish­ing that includes, among oth­ers, a report on pre­vi­ously per­formed stud­ies; a report of a new sta­tis­ti­cal test or set of sta­tis­ti­cal tests that were per­formed by the author of the “study” with­out the help of other sci­en­tists; a report of a small set of sam­ples, not nec­es­sar­ily from a pop­u­la­tion or process, which could, but have not been col­lected in a sys­tem­atic way and may not even be accu­rately described, in the guise of which con­clu­sions are drawn about the world; and a report of the result of per­form­ing an unval­i­dated com­puter sim­u­la­tion of some­thing on a small set of data with­out stat­ing whether such an attempt was suc­cess­ful or not.

“Nice to see”

A widely and unof­fi­cially accepted phrase in sci­en­tific cir­cles mean­ing: “It is bet­ter to live in an imag­i­nary world cre­ated by data manip­u­la­tion and overly opti­mistic pre­dic­tions than in the world that exists in real­ity”.

“Researcher”

[noun] A form of mod­ern indus­try based on a mix of small mol­e­cules of grant money and arbi­trary exper­i­men­tal meth­ods.

“Review”

A form of sci­en­tific com­mu­ni­ca­tion in which the writer (a stu­dent) usu­ally does not give enough infor­ma­tion for the reader to judge the merit of the argu­ments pre­sented in the paper; the writer also does not respond to or com­ment on the sug­ges­tions of the review­ers (some­times known as ref­eree or edi­tor). This form of com­mu­ni­ca­tion is pop­u­lar among stu­dents who wish to leave the bur­den of writ­ing the paper on other peo­ple and are will­ing to pay the price of rejec­tion.

“Sec­ond chance”

A word that sci­en­tists reg­u­larly use when ref­er­enc­ing a jour­nal, con­fer­ence, or grant appli­ca­tion. While there is always an implicit threat that you can get reject­ed, or be accepted with changes, as a sec­ond choice to another par­ty, some arti­cles, papers, grants, and grant pro­pos­als receive mul­ti­ple, even unlim­it­ed, chances of being con­sid­ered. As a result, the word may be inter­preted as a bribe or a threat depend­ing on whether you are the writer or a review­er.

“Reviewer”

A par­tic­i­pant in the review of a grant, paper, or grant pro­pos­al. In spite of being in a poor posi­tion to assess the mer­its of a pro­pos­al, reviewer tends to demand that authors sub­mit their data for sta­tis­ti­cal analy­sis and back their results with it, which the reviewer usu­ally does not. Reviewer usu­ally requires that the author cite his or her own work to prove that he or she is worth review­ing. It is also assumed that the reviewer can detect the slight­est amount of bias in any paper, which the reviewer also assumes has not been cor­rected for.

“Revise and resub­mit”

A sen­tence that authors of sub­mit­ted papers tend to hear from review­ers, edi­tors, and the like. A form of sci­en­tific com­mu­ni­ca­tion that begins with sub­mis­sion of a paper (for which the review­ers may or may not have been con­sult­ed), pro­ceeds through review and crit­i­cism of the paper (typ­i­cally by one or two review­ers, not a full panel of review­ers, unless the paper is deemed to be of supreme impor­tance), and, if it is “revised and resub­mit­ted”, usu­ally ends with another cri­tique of the new paper by one or more of the review­ers, and another round of “revise and resub­mit”, ad infini­tum. In the absence of fraud, retrac­tions, lit­i­ga­tion, mass incom­pe­tence, or insan­i­ty, these “revise and resub­mit” cycles may be taken as the only method of sci­en­tific com­mu­ni­ca­tion.

“Retracted”

The most fre­quently used word for a jour­nal arti­cle that was sub­se­quently found to be based on fraud­u­lent data.

“Rigor”

Some­thing for sci­en­tists to aspire to, a state of mind that would not be required if sci­en­tists could be trusted to do their job.

“Sci­ence”

A com­plex web of data, opin­ions, lies, and errors, now con­sid­ered the most impor­tant (be­cause most expen­sive) tech­nol­ogy in the mod­ern soci­ety. To remind you of this, you will fre­quently see sci­en­tists and edi­tors use the word, claim to do some­thing for the sake of sci­ence, or see it used as an adjec­tive.

“Stan­dards”

Stan­dards for data and their inter­pre­ta­tion vary depend­ing on the coun­try or sci­en­tific dis­ci­pline.

“Study”

[noun] A form of com­mu­ni­ca­tion in the sci­en­tific lit­er­a­ture that authors or pub­lish­ers may use to make claims that are not war­rant­ed. Exam­ples include, but are not lim­ited to: The New study of clin­i­cal tri­als that show…, As Dr. X reports in his new study of obser­va­tional data…, The new study demon­strates that…, Study shows that, In a study pub­lished last week…, The study sug­gests that, New find­ings show that, A study of data…

“Study X”

A widely used nick­name for stud­ies with pro­found, not to say earth­shak­ing, con­clu­sions that may or may not be of any rel­e­vance to any­one who reads about them.

“Tech­nol­ogy”

Any bit of soft­ware, analy­sis, mod­el, or other method­ol­ogy that sci­en­tists can be expected to claim as their own, for which they can sell you con­sult­ing or col­lab­o­ra­tion fees.

“Tool”

[noun] Any piece of soft­ware, analy­sis, mod­el, or other method­ol­ogy that sci­en­tists can be expected to claim as their own, for which they can sell you con­sult­ing or col­lab­o­ra­tion fees.

“The topic of the paper”

A wide-rang­ing cat­e­gory of things or ideas that may not have been rel­e­vant when the paper was writ­ten, but which the authors believe the paper should be about. Often, the topic is too broad or a non-topic, but is occa­sion­ally use­ful in order to gen­er­ate sup­port for yet another set of related papers, con­fer­ences, sem­i­nars, webi­na­rs, and so forth, which in turn are used to gen­er­ate more data for “new find­ings”, which, after they are manip­u­lated enough, may end up being pub­lished and gen­er­at­ing yet more data to sup­port a “re-re­view” of the orig­i­nal paper or other things.

“Uni­fied the­ory”

An imag­i­nary set of ideas that sci­en­tists dream of hav­ing so that they could achieve the same results by slightly vary­ing the method­ol­o­gy, thus pre­serv­ing their orig­i­nal pub­li­ca­tions.

“Val­i­da­tion”

The process by which sci­en­tists decide that some­thing they built in their spare time works in the way they expect. The val­i­da­tion process requires that you do not attempt to val­i­date it before you pub­lish it and require that the users val­i­date it after.

“Value”

In its most nat­ural form, sci­ence is the abil­ity to study data and ideas; the amount of respect and pub­lic­ity that a sci­en­tist can expect; how well his or her paper was pub­lished; the like­li­hood that a sci­en­tist will receive a grant; whether a sci­en­tist will be taken seri­ous­ly; the like­li­hood that an idea will be accept­ed; how much grant money will be avail­able to pur­chase new toys for this sci­en­tist; whether some­thing is sci­en­tific; the like­li­hood that the papers of this sci­en­tist will be read; the sta­tus of this sci­en­tist; the like­li­hood that a jour­nal, con­fer­ence, or soci­ety will be able to con­tinue fund­ing or exist; how much and how fast this sci­en­tist’s career will pro­gress; the “value” that this sci­en­tist brings to the mod­ern soci­ety; and the like­li­hood that this sci­en­tist will con­tinue to be fund­ed. While the actual val­ues for sci­ence are hard to pin down, what peo­ple believe them to be is another mat­ter. For exam­ple, while some believe that sci­ence is impor­tant, even if it does­n’t have any con­crete val­ue, oth­ers believe that it’s the abil­ity to con­tinue read­ing abstracts or study­ing it that has val­ue. Still oth­ers believe that what mat­ters is how much grant money is cur­rently avail­able, even if the researchers are actu­ally doing some­thing that nobody knows about. Some believe that the fact that some­one is in a posi­tion to give a grant, or has writ­ten a paper, or, even bet­ter, has pub­lished that paper, actu­ally means that it has a lot of val­ue. Oth­ers believe that what mat­ters is not what you pub­lish, but how you pub­lish it and that all research must be pub­lished in the most pres­ti­gious jour­nal, even if you are not that much of a researcher. The real­ity is that no one really knows what sci­ence is, or what value it has; how­ev­er, we all know that if we deny that sci­ence has val­ue, we might be able to get rid of it and use this space for some­thing else.

“Val­i­da­tion step”

Another name for a ran­dom set­ting of a para­me­ter of a mod­el, sim­u­la­tion, or algo­rithm.

“Ver­ify”

A step in a pro­ce­dure that, if per­formed prop­er­ly, will tell you whether the pre­vi­ous step, or any­thing in the last two years of your work, was per­formed prop­er­ly.

I had to keep con­tin­u­ing it for more def­i­n­i­tions:

…“Uni­fied the­ory”

An imag­i­nary set of ideas that sci­en­tists dream of hav­ing so that they could achieve the same results by slightly vary­ing the method­ol­o­gy, thus pre­serv­ing their orig­i­nal pub­li­ca­tions.

“The usual pro­ce­dure”

A method of study, val­i­da­tion, and pub­lic com­mu­ni­ca­tion which, if fol­lowed, may lead you to be able to claim that your work may offer a decent return on invest­ment.

“Wide range”

When used in the con­text of sci­en­tific results and mod­els, it usu­ally means that the results and mod­els are not likely to be used for their intended pur­pose, if they have one.

“Wider range”

When used in the con­text of sci­en­tific results and mod­els, it usu­ally means that the results and mod­els are likely to be used for their intended pur­pose, if they have one.

“Writeup”

A form of sci­en­tific com­mu­ni­ca­tion in which the author states the infor­ma­tion he or she wanted the read­ers to extract from the paper while mak­ing it as dif­fi­cult as pos­si­ble for them to find it.

“Write-up”

1. [noun] A style of writ­ing used in aca­d­e­mic papers to dress up data and results. 2. [verb] The act of pre­sent­ing data in a way that makes it look less impres­sive and more com­plex than it really is. 3. [verb] The act of writ­ing a man­u­script that is impos­si­ble to fol­low. 4. [verb] An activ­ity that is often per­formed by the most bla­tant or care­less users of the Eng­lish lan­guage, all for the sake of show­ing off their eru­di­tion.

“Writer’s block”

A com­mon afflic­tion among stu­dents, aris­ing from var­i­ous caus­es, such as: their desire to sell their ideas for a prof­it, their inabil­ity to real­ize this desire, the fact that their ideas are not sell­ing and will not be bought, and the delu­sion that most of the wealth and fame in the world would be theirs if they would spend enough years doing sci­ence.

I tried tai­lor­ing the prompt a lit­tle more pre­cisely to gen­er­at­ing def­i­n­i­tions:

yield­ing more def­i­n­i­tions, start­ing with a sin­gle let­ter to avoid run­ning off into the “Z”es and trun­cat­ing com­ple­tions (since GPT-3 will pro­ceed A–Z, start­ing from the last def­i­n­i­tion, as is log­i­cal, unless oth­er­wise prompt­ed):

“A role for…” [phrase]

A fre­quent phrase found in sub­mit­ted and pub­lished papers; it often indi­cates that the authors have noth­ing to say about the topic of their paper. In its more emphatic form, “A role for…” usu­ally indi­cates a strug­gle by the authors to take a side on an issue, after a lengthy attempt to be both non-com­mit­tal and a sup­port­ing party to all sides, as often hap­pens in “mol­e­c­u­lar and cel­lu­lar” or “basic and trans­la­tional” research.

“Reviewer” [noun]

A par­tic­i­pant in the review of a grant, paper, or grant pro­pos­al. In spite of being in a poor posi­tion to assess the mer­its of a pro­pos­al, reviewer tends to demand that authors sub­mit their data for sta­tis­ti­cal analy­sis and back their results with it, which the reviewer usu­ally does not. Reviewer usu­ally requires that the author cite his or her own work to prove that he or she is worth review­ing. It is also assumed that the reviewer can detect the slight­est amount of bias in any paper, which the reviewer also assumes has not been cor­rected for.

“Res­i­dent”

An adjunct fac­ulty mem­ber that admin­is­ters teach­ing assist­ant­ships, sits on depart­men­tal com­mit­tees, and seems to live on the depart­men­tal lounge. A kind of “hon­orary” fac­ulty mem­ber that is not yet an actual fac­ul­ty.

“The Action Plan for Diver­sity” [noun]

A vague doc­u­ment cir­cu­lated on cam­puses in recent years to remind the fac­ulty to be aware of dis­crim­i­na­tion and pre­sum­ably com­bat it in the depart­ments. It is often taken as a gen­er­al­ized insult of stu­dents, who tend to see no way around this plan with­out com­mit­ting aca­d­e­mic fraud.

"Bias" [noun]

A form of men­tal abuse by which a sci­en­tist turns an exper­i­ment in such a way that the results prove his or her point of view or grants get fund­ed.

“Behav­ioral Stud­ies” [noun]

Some­times mis­taken as a field of sci­ence. The terms Behav­ioral Stud­ies are actu­ally a com­bi­na­tion of dis­ci­plines that usu­ally includes busi­ness eco­nom­ics, soci­ol­o­gy, and engi­neer­ing.

“Human­i­ties” [noun]

A broad term used to refer to any aca­d­e­mic dis­ci­pline (such as Eng­lish, phi­los­o­phy, reli­gious stud­ies) in which sci­ence has been for­got­ten. To avoid con­fu­sion between the sci­ences and human­i­ties, remem­ber that sci­en­tists use hard tools and human­i­ties their soft tools when exam­in­ing issues and try­ing to per­suade audi­ences. In other words, both sci­en­tists and human­i­ties rou­tinely lie (it’s part of their job), but they do it in dif­fer­ent ways.

“Lit­er­a­ture Review” [noun]

A paper, com­monly pub­lished in a peer-re­viewed jour­nal, that adver­tises the author’s skills and accom­plish­ments with­out using any evi­dence. The reader is left to believe the work must have been done, since it has been adver­tised and is “pub­lished”.

“The Lit­er­a­ture” [noun]

A name given to other peo­ple’s pub­lished papers, referred to by sci­en­tists with­out actu­ally read­ing them.

“Track Record” [noun]

When sci­en­tists use this term they refer to the research done by some­one else (usu­ally a stu­dent) in order to avoid hav­ing to do research.

“Dis­cus­sion” [noun]

The part of a sci­en­tific paper where the writer sum­ma­rizes the points made in the body of the paper and acknowl­edges cer­tain acts and cir­cum­stances that helped with the com­ple­tion of the study. The author should care­fully state in this sec­tion whether or not the study reported in the paper could have been done oth­er­wise and should clearly state that if he or she had the oppor­tu­ni­ty, he or she would def­i­nitely repeat the study again.

“Role model” [noun]

A per­son who can be an inspi­ra­tion to oth­ers because of his or her aca­d­e­mic and per­sonal achieve­ments in gen­er­al, or because they con­duct aca­d­e­mic mis­con­duct on an indus­trial scale. The most impor­tant qual­i­fi­ca­tion for becom­ing a role model for oth­ers is to be judged suf­fi­ciently inept that no one will ques­tion any­thing you do, or to become the suc­ces­sor of some­one who has already acted as a role mod­el.

“Fac­ulty” [noun]

Used loosely by sci­en­tists to mean any group of peo­ple with advanced degrees. Typ­i­cally used when you have done some­thing stu­pid and want to inform oth­ers that it was­n’t you who did it, but rather those other crazy peo­ple over there who won’t put their titles after their names.

“Sig­nif­i­cant” [ad­jec­tive]

A word which defines the claim made in a paper (no mat­ter how triv­ial). It is a crit­i­cal term in hypoth­e­sis test­ing, in that the sign of the test sta­tis­tic must be eval­u­ated and com­pared to a crit­i­cal value in order to sup­port or reject a hypoth­e­sis. Sig­nif­i­cance is des­ig­nated by two-tailed tests when the direc­tion of the effect is not spec­i­fied. If a two-tailed sig­nif­i­cance test is used, the “p”-value needs to be halved, due to “dou­ble-count­ing”. In any event, “sig­nif­i­cant” remains of crit­i­cal impor­tance in any hypoth­e­sis test, since the whole idea is to come up with a “sign”, i.e. plus or minus (“+/-”), or a wishy-washy “sta­tis­ti­cally sig­nif­i­cant”, with­out actu­ally say­ing any­thing sub­stan­tive about the mat­ter.

“Ran­dom assign­ment” [verb]

A way to arrange study groups so the result­ing data is con­clu­sive and unbi­ased. Typ­i­cally used in the social sci­ences by grad­u­ate stu­dents who refer to it while explain­ing why they broke down the groups this way.

“Cite” [verb]

To refer to a paper or jour­nal as a source for sup­port, when in real­ity you have never read it and did not under­stand it.

“Bib­li­og­ra­phy” [noun]

A list of cita­tions of ref­er­ences used to sup­port a par­tic­u­lar point in a paper. A list that some­how includes sev­eral ref­er­ences that did­n’t actu­ally end up in the final bib­li­og­ra­phy of the paper.

“Let’s stip­u­late” [phrase]

A pref­ace to any state­ment, that means I know I am going to piss you off with my state­ment, but since you are such a nice per­son, I am first going to give you a chance to let me piss you off with­out respond­ing.

“How is that dif­fer­ent from…?” [phrase]

A ques­tion posed by authors when they have been accused of steal­ing ideas. They are attempt­ing to show that they have never read the papers that con­tain the ideas they stole, nor even looked at the ideas in any detail, and are just fish­ing for com­pli­ments.

"Cita­tion" [noun]

An elab­o­rate form of review, typ­i­cally found in order to impress those who do not know bet­ter, in the sci­ences, human­i­ties, or even the social sci­ences. A cita­tion typ­i­cally involves mis­read­ing or mis­quot­ing an author and talk­ing about some­thing he or she did not write about or intend. It is assumed that you need to carry out a large num­ber of cita­tions in order to have sci­ence, prob­a­bly because at least one cita­tion can be found for any fact, event, or inci­dent in his­to­ry; the effec­tive­ness of this fact is increased if the author is long dead and can not dis­prove it.

“Physics” [noun]

A sci­ence. Physi­cists like to think their sci­ence is some­thing you can do with­out a plan, some­thing that relies on intu­ition and inspi­ra­tion, some­thing that is “deep”.

“Sci­en­tific rev­o­lu­tion” [noun]
A set of tech­niques and meth­ods used by sci­en­tists in the course of their work. It is assumed that there was a total sci­en­tific rev­o­lu­tion in the past cen­tury or so that allowed sci­en­tists to avoid even think­ing about the qual­ity or use­ful­ness of their sci­ence.
Sci­en­tific rev­o­lu­tion is started by Henri Poin­caré (math­e­mat­ic­s), Marie Curie (ra­dioac­tiv­i­ty), Max Planck (quan­tum physic­s), Albert Ein­stein (rel­a­tiv­ity and rev­o­lu­tion­ized physic­s), Dmitri Mendeleev (pe­ri­odic table of ele­ments), Niels Bohr (quan­tum mechan­ic­s), Lise Meit­ner and Otto Hahn (fis­sion), Linus Paul­ing (mol­e­c­u­lar biol­o­gy) and many oth­ers.

See also: Ein­stein’s Method, Paul­ing’s Method, Bohr’s Method, Poin­car­é’s Method

“Sci­en­tific method” [noun]
A set of steps in which a sci­en­tist (or “researcher”) would prob­a­bly not fol­low in prac­tice: plan­ning of research, includ­ing def­i­n­i­tion of prob­lems, hypoth­e­sis for­mu­la­tion, selec­tion and design of exper­i­men­t(s), col­lec­tion and entry of data, exe­cu­tion of exper­i­ments, data analy­sis and inter­pre­ta­tion, prepa­ra­tion of final report, imple­men­ta­tion of sug­ges­tions. In actual sci­en­tific prac­tice the most impor­tant part of the sci­en­tific method is to observe and col­lect data with­out a plan or pre­con­ceived expec­ta­tions about what is caus­ing what. It also helps to be unbi­ased in select­ing the data to include in the study and to pick exper­i­ments at ran­dom. Addi­tional steps include writ­ing a paper, a process that usu­ally involves the most vio­la­tions of the sci­en­tific method.

“Sci­en­tist” [noun]
A per­son who has a lab­o­ra­to­ry, access to large sums of mon­ey, grad­u­ate stu­dents, or all three.

“Revise and resub­mit” [verb]
A sen­tence that authors of sub­mit­ted papers tend to hear from review­ers, edi­tors, and the like. A form of sci­en­tific com­mu­ni­ca­tion that begins with sub­mis­sion of a paper (for which the review­ers may or may not have been con­sult­ed), pro­ceeds through review and crit­i­cism of the paper (typ­i­cally by one or two review­ers, not a full panel of review­ers, unless the paper is deemed to be of supreme impor­tance), and, if it is “revised and resub­mit­ted”, usu­ally ends with another cri­tique of the new paper by one or more of the review­ers, and another round of “revise and resub­mit”, ad infini­tum. In the absence of fraud, retrac­tions, lit­i­ga­tion, mass incom­pe­tence, or insan­i­ty, these “revise and resub­mit” cycles may be taken as the only method of sci­en­tific com­mu­ni­ca­tion.

“Shared data” [noun]
Data that both a reviewer and a researcher have access to but not shar­ing in advance.

“Shared future” [noun]
An out­come (rarely achieved) that is more pleas­ant for both the reviewer and the researcher than if there is no shared future. This usu­ally refers to an out­come in which the reviewer either accepts the paper or at least rec­om­mends re-writ­ing the whole thing rather than reject­ing it. This is often viewed with sus­pi­cion by review­ers.

“Shar­ing of data” [noun]
The belief of some peo­ple that researchers should have all the data when doing a study; this would allow the reviewer to check the sources and cal­cu­la­tions and check for bias. This would limit what the reviewer could say to “the sta­tis­tics were fine but your data was­n’t”. It is very dif­fi­cult to achieve this state. It can be accom­plished if the sci­en­tist keeps detailed notes of every­thing he or she is doing.

“Sta­tis­ti­cal error” [noun]
A mis­take made by researchers that can some­how be made up for by using large enough sam­ple sizes, usu­ally called sta­tis­ti­cal sig­nif­i­cance; an event that could not have been pre­dict­ed, ana­lyzed, or pro­vided for in advance, there­fore

"Dumb" [adj]

The amount of infor­ma­tion sci­en­tists can be both­ered to remem­ber before they either dis­miss it as incor­rect data, or ignore it com­plete­ly.

“Sur­prised” [verb]

A sci­en­tific sys­tem in which one is “sur­prised” by the result of an exper­i­ment, does not nec­es­sar­ily mean that they believe it is wrong, but usu­ally means they are not sure what to say about it.

“Oppres­sion” [noun]

A state of mind in which lab­o­ra­to­ries under­fi­nanced by the gov­ern­ment can be con­sid­ered finan­cially secure, or any noise that sci­en­tists don’t like or want to hear.

“Reviewer” [noun]
A par­tic­i­pant in the review of a paper. In spite of being in a poor posi­tion to assess the merit of a pro­pos­al, reviewer tends to demand more infor­ma­tion, more exper­i­ments, more sta­tis­tics, and a tighter the­o­ret­i­cal inter­pre­ta­tion than the authors are will­ing to pro­vide. Reviewer usu­ally rejects any respon­si­bil­ity for his or her own bias being exposed, cit­ing the fact that the author has not attempted to prove that bias is absent, nor made suf­fi­cient allowance for human error. Most ref­er­ees are reviewer seem to assume that they have given enough infor­ma­tion for their opin­ions to be taken seri­ous­ly, though they were not the orig­i­na­tor of the pro­pos­al. Reviewer usu­ally requires that the author cite his or her own work to prove that he or she is worth review­ing. It is also assumed that the reviewer can detect the slight­est amount of bias in any paper, which the reviewer also assumes has not been cor­rected for.

“The­o­ret­i­cal inter­pre­ta­tion” [noun]
Another form of sci­en­tific com­mu­ni­ca­tion that can­not be proven, but can be used to explain any­thing after it has been pub­lished or pre­sent­ed. It is always accom­pa­nied by an appeal to author­i­ty, data, and metic­u­lous the­o­ret­i­cal inter­pre­ta­tion (see below).

“Screwed” [adj]
Screwed is gen­er­ally used in cir­cum­stances in which the author has lost every­thing, in his paper or grant pro­pos­al. Applies best to sit­u­a­tions where the author finds out he has been wait­ing to hear for months, when it has been decided in his absence that fund­ing will not be extended or alter­na­tive sources of funds are not ava­ial­ble to con­tinue the work. For exam­ple, when an exper­i­ment goes down­hill or a paper is reject­ed. Also applied when the author of a paper or grant pro­posal dis­cov­ers that he must care­fully state and defend his own results in order to get feed­back on what could be wrong with his work, because the read­ers may not be able to fig­ure it out for him. It is also sug­gested that this term apply when­ever two or more edi­tors or review­ers decide they can all influ­ence the final out­come of a paper, when only one review should deter­mine if the paper is pub­lish­able. Rec­om­men­da­tion: always have mul­ti­ple con­cur­rent sub­mit­ted man­u­scripts in the sys­tem.

“Reviewer” [noun]
A par­tic­i­pant in the review of a paper. In spite of being in a poor posi­tion to assess the mer­its of a pro­pos­al, reviewer tends to demand more infor­ma­tion, more exper­i­ments, more sta­tis­tics, and a tighter the­o­ret­i­cal inter­pre­ta­tion than the author is will­ing to pro­vide. Reviewer usu­ally rejects any respon­si­bil­ity for his or her own bias being exposed, cit­ing the fact that the author has not attempted to prove that bias is absent, nor made suf­fi­cient allowance for human error. Most ref­er­ees are reviewer seem to assume that they have given enough infor­ma­tion for their opin­ions to be taken seri­ous­ly, though they were not the orig­i­na­tor of the pro­pos­al. “Reviewer” requires that the author cite his or her own work to prove that he or she is worth review­ing. It is also assumed that the reviewer can detect the slight­est amount of bias in any paper, which the reviewer also assumes has not been cor­rected for.

“Argu­ment” [noun]
An instance of con­vinc­ing some­one that you deserve credit for your posi­tion (i.e., prob­lem solv­ing skill­s).

“Fail” [verb]
In sci­en­tific jar­gon, fail­ure means death by hoax or acci­den­tal pla­gia­rism, depend­ing on the rep­u­ta­tion of the lab (e.g., if tenure level sci­en­tists are involved).

“String the­ory” [noun]

A the­ory in physics which is about 99% false, but peo­ple seem to like it, so they keep fund­ing it. A good exam­ple of the dif­fer­ence between a “sci­en­tific” con­cept and a “semi­-sci­en­tific” con­cept, or even an unsci­en­tific con­cept.

“The­ory” [noun]

An unproved hypoth­e­sis; a con­jec­ture that might or might not be right; use of this term forms the basis of the com­mon game of turn­ing the sci­en­tific method on its head. The most pop­u­lar types of the­o­ries are the the­ory of intel­li­gence (or unin­tel­li­gence), time the­o­ry, quan­tum the­ory (of course, even more prim­i­tive), and any­thing that is implied by string the­o­ry.

“Research” [verb]

Some­thing done with the money pro­vided by a body or per­son other than the prin­ci­pal researcher(s), gen­er­ally pri­vate indi­vid­u­als and busi­nesses hop­ing to get some­thing out of it. Tra­di­tion­ally involves exper­i­men­ta­tion.

“Pub­lish or per­ish” [phrase]
Used by sci­en­tists to remind other sci­en­tist how impor­tant it is to them, if they are pay­ing atten­tion at all, which they rarely are. A method of sci­en­tific com­mu­ni­ca­tion where papers are sub­si­dized by fraud, incom­pe­tence, or stu­pid­i­ty, depend­ing on the field of sci­ence involved. Each author hav­ing to prove his or her worth by churn­ing out papers that oth­ers

“String the­ory” [noun]

A set of math­e­mat­i­cal for­mu­las that can be used to pre­dict which par­ti­cles exist (or do not exist).

“Sci­ence group” [noun]

A col­lec­tion of researchers in var­i­ous fields, with goals includ­ing one or more of attract­ing fund­ing, specif­i­cally young, naive, and gullible grad stu­dent, inbreed­ing grad­u­ate stu­dents in order to main­tain affil­i­a­tion with the group, writ­ing and pub­lish­ing papers spe­cific to the group’s activ­ity in order to con­tinue the goal of attract­ing fund­ing and inbreed­ing grad­u­ate stu­dents, call­ing in favors from Uni­ver­sity admin­is­tra­tors in order to pre­serve and con­tinue the goal of attract­ing fund­ing and inbreed­ing grad­u­ate stu­dents and sac­ri­fic­ing sheep and lambs in order to obtain fund­ing and inbreed grad­u­ate stu­dents.

“Group leader” [noun]

Per­son whose func­tion appears to con­sist mainly of order­ing sub­or­di­nates (grad­u­ate stu­dents) to fill out grant appli­ca­tions, enter grant data into a spread sheet, teach under­grad­u­ates lab­o­ra­tory tech­niques not com­monly taught else­where, sub­mit papers to a jour­nal any time before the end of 2002 (record will be bro­ken), devote time to dis­cov­er­ing some­thing use­ful or inter­est­ing about sci­ence out­side of the para­me­ters deter­mined by the group lead­er, ever work­ing on prob­lems spec­i­fied by the group leader if they can­not be worked on inside the para­me­ters deter­mined by the group leader. All of these activ­i­ties are con­sid­ered nec­es­sary to sup­port the “sci­ence groups” goal of attract­ing and inbreed­ing grad­u­ate stu­dents, and pro­vide oppor­tu­nity for sat­is­fac­tory career devel­op­ment for the group leader.

“Spe­cial mate­r­ial” [noun]

Some­thing that only has to be done once, where other peo­ple will then do it again every time a paper requires it. Or con­verse­ly, some­thing that can­not be repeated because of its poor repro­ducibil­ity (spe­cial uncer­tain­ty, uncer­tainty spe­cial).

“Spe­cial rel­a­tiv­ity” [noun]

Advanced idea described in Ein­stein’s 1905 papers on the sub­ject, which states that, from a suit­able point of view, “E=m,” where “E” is ener­gy, “m” is mass, and “a” is “Pro­fun­di­ty.” Except for arcane fields such as string the­o­ry, spe­cial rel­a­tiv­ity is the most widely accepted of Ein­stein’s work. Nat­u­ral­ly, papers based on spe­cial rel­a­tiv­ity are the most likely to receive “spe­cial mate­r­ial”.

“Spec­u­la­tion” [noun]
One of New­ton’s Laws of Sci­ence, also known as “New­ton’s Law of Spec­u­la­tion”, which states that “Spec­u­la­tion breeds Con­fu­sion”, an effect well illus­trated by how the­o­ret­i­cal par­ti­cle and string the­o­rists debate sci­en­tific issues with each oth­er. Con­fused? You should be. After all, you’re deal­ing with a bunch of the­o­ret­i­cal physi­cists.

“Pro­fes­sor” [noun]

The high­est aca­d­e­mic rank avail­able, a level usu­ally asso­ci­ated with Free­dom of Speech. It may take 35 years of ser­vice before the Uni­ver­sity bestows that title.

“Pub­li­ca­tion Cred­i­bil­ity” [noun]
A sub­jec­tive notion, as its name implies, based almost entirely on the sex­i­ness or impor­tance of the field in which the research was done and not the qual­ity of the writ­ing and pre­sen­ta­tion.

Cycling through the prompt as length per­mit­ted:

“Placebo” [noun]

An inert sub­stance used to pla­cate a patient or con­trol group. It is largely com­posed of sug­ar.

“Et al.” [proper noun]

Et al. is an abbre­vi­a­tion of the Latin phrase et alii, mean­ing “and other peo­ple”. In the con­text of sci­ence, it is typ­i­cally used to shorten the name of a list of authors whose name and affil­i­a­tion would oth­er­wise need to be writ­ten out in full. It is used when the num­ber of authors is small enough that the abbre­vi­a­tion would not ren­der the ref­er­ence too obtuse and cum­ber­some, while at the same time remain­ing large enough that there are at least two other peo­ple involved in the study (and hope­fully a con­trol group).

“Ref­eree” [noun]

A crit­i­cal read­er, par­tic­u­larly one with the power to reject your sub­mis­sion. In the absence of more effi­cient forms of com­mu­ni­ca­tion, peer review depends on these peo­ple. See “Reviewer”.

“Shared val­ues” [noun]

A set of prin­ci­ples, beliefs, the­o­ries, meth­ods, and oper­a­tional def­i­n­i­tions that all sci­en­tists share and use. Never spo­ken of aloud in pub­lic.

“Com­mu­ni­ca­tion” [verb]
A method of per­sua­sion that allows other researchers to review or crit­i­cize your work. You can com­mu­ni­cate with oth­ers by review­ing arti­cles, which allows them to revise their work; par­tic­i­pat­ing in pan­els, which allows oth­ers to hear your opin­ions; attend­ing con­fer­ences, which allows you to strike up con­ver­sa­tions at late-night din­ners; join­ing com­mit­tees, which lets you talk at any time; run­ning exper­i­ments, which allows you to change the out­come based on the result; and receiv­ing grants, which allows you to tell peo­ple what to do. You are likely com­mu­ni­cat­ing now.

“Panel” [noun]

A group of indi­vid­u­als who meet at some given time to dis­cuss some­thing. A more bureau­cratic ver­sion of peer review for larger groups of indi­vid­u­als or com­mit­tee mem­bers who have already estab­lished their worth in research.

“Data” [noun]
Infor­ma­tion that appears as graphs in jour­nals. Not sur­pris­ing­ly, this infor­ma­tion is crit­i­cal for inter­pre­ta­tion and form­ing con­clu­sions. Sci­en­tists spend much of their lives look­ing for data.

“Fraud” [noun]
A very evil thing that must be per­pe­trated or dis­cov­ered in order for the paper to be accepted for pub­li­ca­tion. Some­times fraud occurs even if both the sci­en­tific com­mu­nity and edi­tors are pre­pared to reject the paper at the first sign of fraud, just because they know how bad it would look if fraud were found lat­er. See also “Legit­i­ma­tion”.

“Legit­i­ma­tion” [verb]
The process whereby a fraud­u­lent pub­li­ca­tion is con­verted into a legit­i­mate pub­li­ca­tion by virtue of its appear­ance in a peer-re­viewed jour­nal. This can also be con­sid­ered a type of reas­sur­ance that “the checks and bal­ances are work­ing”.

“Retrac­tion” [verb]
The for­mal rejec­tion of an accepted sci­en­tific claim after its pub­li­ca­tion. In con­trast to “with­drawal”, which is when a paper is removed from pub­li­ca­tion with the con­sent of the author(s). See “Fraud”.

“Lies” [noun]
A form of decep­tive speech that appears as “hon­est mis­takes” or “inac­cu­ra­cies”, which are often rejected from pub­li­ca­tion as a result. In the absence of other sources of infor­ma­tion, how­ev­er, lies are required for the main­te­nance of the sci­en­tific enter­prise. See also “Jour­nal­is­tic integrity”.

“Sci­en­tists” [plural noun]
A group of peo­ple engaged in sci­en­tific research or edu­ca­tion. Sci­en­tists vary widely in terms of age, moral char­ac­ter, ances­try, and suc­cess. Sci­en­tists with note­wor­thy accom­plish­ments are some­times described as “lead­ers” and regarded as experts in their fields, which leads to the ten­dency to treat them like demi-gods.

“Health” [noun]
A num­ber indi­cat­ing how unhealthy some­thing is. When applied to humans, it quan­ti­fies how in need of health care some­one is.

“Clin­i­cal research” [noun]
Research con­ducted on humans, e.g. clin­i­cal tri­als and epi­demi­o­log­i­cal stud­ies. Researchers do not like this kind of research because humans are unre­spon­sive and unre­li­able.

“Fund­ing source” [noun]
Those who finance sci­ence by pay­ing peo­ple to do things that the fun­der might not want to pay them to do if the fun­der knew what they were doing. For exam­ple, giv­ing peo­ple money to research tobacco with­out actu­ally run­ning cig­a­rettes through their noses would not be what the tobacco indus­try wants sci­en­tists to do for them. Some fund­ing sources impose their will on the researchers by mak­ing sure that their fund­ing is only allo­cated if cer­tain lines of research are fol­lowed and other lines are not (this is some­times known as a bud­get), while oth­ers may let the sci­en­tists do any­thing they want (this is some­times known as no bud­get). The nature of research find­ings thus depends on the bud­get.

“Author­ship” [noun]
The process whereby researchers “pub­lish together”. The pre­cise impli­ca­tions of this process depend on the pub­li­ca­tion type. In most cas­es, author­ship rep­re­sents the researcher’s con­tri­bu­tion to the paper; how­ev­er, pla­gia­rism is also some­times involved, espe­cially if mul­ti­ple authors fail to cite ear­lier work on which their own paper depends. There is also another kind

“Jour­nal Impact Fac­tor” [noun]

Accord­ing to some, it is a value that cor­re­sponds to the aver­age num­ber of cita­tions of arti­cles pub­lished in a given jour­nal, if the inter­pre­ta­tion is right. Oth­er­wise, it is a com­pletely arbi­trary num­ber, com­puted from the num­ber of times arti­cles pub­lished in a given jour­nal in the last two years were cited by other arti­cles pub­lished in other jour­nals, the assump­tion being that every paper pub­lished in a sci­en­tific jour­nal must be essen­tial to some­one else’s research, or at least that that per­son would like to be able to cite it. The dif­fi­culty with this assump­tion is that the aver­age time from sub­mis­sion to pub­li­ca­tion of an arti­cle is now approx­i­mately 12 months, and the cur­rent record stands at three years for Biochem. J. Proc. (2000). This means that three times as many papers have to be pub­lished every year as are actu­ally writ­ten, with more and more papers being sub­mit­ted and rejected every year (the real­ity is even worse, but we don’t have num­ber­s), and with dif­fer­ent stan­dards apply­ing to dif­fer­ent jour­nals, which are becom­ing increas­ingly spe­cial­ized. All of these “facts” con­sid­ered, the best any researcher can say about the Impact Fac­tor of a given jour­nal is: “I know it when I see it”. Note also: This for­mula can pro­duce val­ues up to 4 for an arti­cle appear­ing in a jour­nal con­tain­ing only that arti­cle, so one “arti­cle” can receive as many “cita­tions” as a jour­nal.

“Ster­ile” [adj]

Said of that which is dry and life­less; devoid of humor; also, said of sci­en­tific writ­ing and meth­ods when ger­mane to the mat­ter at hand.

“Grant” [noun]

A form of sci­en­tific com­mu­ni­ca­tion involv­ing a large piece of paper, on which is writ­ten an enor­mous sum of mon­ey. The grant-writer then sends his or her (be­cause they are almost always writ­ten by men) pro­posal into a maze of office pol­i­tics, legislative/funding agency reshuf­fling, and intel­lec­tual and inter­per­sonal com­pe­ti­tion. The prospect of acquir­ing a grant makes up for the fact that grant-writ­ers receive no prac­ti­cal train­ing in writ­ing grants, have no idea what kind of “research” they will have to con­duct with it, and gen­er­ally start research as soon as they are fin­ished writ­ing the grant (if they ever are). To be on the safe side, please note: One does not have to win a grant to “win a grant”; one must sim­ply apply for a grant. Just do it.

“Peer review” [noun]

A sys­tem of lit­er­ary com­mu­ni­ca­tion that con­sists of first sub­mit­ting a man­u­script to the edi­tor, who (if he has not been fired) usu­ally asks at least one reviewer to read the man­u­script. If there is any doubt about whether the edi­tor has lost his mind, or whether the man­u­script has some mer­it, then the review­ers and edi­tor sit down over cof­fee and dis­cuss the man­u­script (in con­fer­ence), which is described as “peer review”. This form of com­mu­ni­ca­tion does not pro­vide either edi­tors or authors with much feed­back about their man­u­script, but it makes review­ers feel impor­tant.

“Pre­cise” [adj]

Said of every­thing except exper­i­men­tal results; it also means cor­rect, appro­pri­ate, or in accor­dance with the rel­e­vant stan­dards. But when the exper­i­men­tal results do not agree with the model of the hypoth­e­sis, the phe­nom­e­non being stud­ied becomes “pre­cise”, and one “can­not make pre­dic­tions about pre­cise exper­i­ments”. Please keep in mind that most the­o­ries are meant to be gen­eral in nature, so they must also be impre­cise in their pre­dic­tions. If they could pre­dict with pre­ci­sion, they would not be gen­er­al, but they would still be inter­est­ing.

“The­ory” [noun]

A sci­en­tific expla­na­tion of some­thing, which peo­ple use only in sit­u­a­tions where they think explain­ing things is more impor­tant than dis­cov­er­ing things. Oth­er­wise, sci­en­tific the­o­ries are always meant to be true, since they are believed by peo­ple with edu­ca­tion; to a sci­en­tist, a the­ory is true; to a per­son with a higher IQ, a the­ory is a fact; and to a cit­i­zen with a lower IQ, a the­ory is an opin­ion.

“Sig­nif­i­cance” [noun]

Sig­nif­i­cance refers to whether a result of a study is some­how impor­tant. If a sig­nif­i­cant result is found, then the prob­lem that the researchers have stud­ied is of real-world impor­tance. If the results aren’t sig­nif­i­cant, the prob­lem is not impor­tant; there­fore, more money and time can be wasted by pur­su­ing it. (E.g. “This study showed that choco­late makes rats tem­porar­ily hyper­ac­tive, so we will look for ways to inhibit this reac­tion in humans. Thank you for your sup­port.”)

“Research­Gate” [noun]

A web­site of ques­tion­able pur­pose that allows sci­en­tists to upload their “results”, result­ing in the same sort of infor­ma­tion clut­ter and bad or incor­rect data as any other social media net­work.

“Rigor mor­tis” [noun]

A state of rigor in which sci­en­tists do not think out­side the par­a­digm, no mat­ter how much it does not work; rigor mor­tis is char­ac­ter­ized by the desire of researchers to prove their prior research cor­rect.

“Impor­tant” [noun]

(Bri­tish Eng­lish: “Impor­tant”) [adj]

Whether some­thing is impor­tant to you per­son­ally or not, it is impor­tant in a sta­tis­ti­cal or numer­i­cal sense to some­one. There­fore, it is always a use­ful adjec­tive to add to any sci­en­tific term, espe­cially when one does not have a bet­ter verb.

“Research­Gate” [noun]

A web­site of ques­tion­able pur­pose that allows sci­en­tists to upload their “results”, result­ing in the same sort of infor­ma­tion clut­ter and bad or incor­rect data as any other social media net­work.

“Rigor mor­tis” [noun]

A state of rigor in which sci­en­tists do not think out­side the par­a­digm, no mat­ter how much it does not work; rigor mor­tis is char­ac­ter­ized by the desire of researchers to prove their prior research cor­rect.

“Rig­oros­ity” [noun]

Another word for rigor or rigor mor­tis.

“Rig­or­ous­ness” [noun]

Rig­or’s non­cha­lant broth­er.

“Robust­ness” [noun]

This noun means “resis­tance to break­age”. This is dif­fer­ent from the verb, “robus­tify”, which is an attempt to make a bro­ken thing more resis­tant to break­age. When the two words are not con­fused, robust­ness is deemed by sci­en­tists to be a good thing, even though a greater robust­ness may mean a higher abil­ity to break.

“Sci­ence” [noun]

1. A spe­cial­ized activ­ity per­formed by a set of machines that some peo­ple call “sci­en­tists”, which is prac­ticed all day long and in all wak­ing hours, in labs and out of labs.
2. What sci­en­tists do with the machines (or just machi­nes) that they call “sci­en­tists”. Some­times, sci­ence involves activ­i­ties that would be con­sid­ered strange by sci­en­tists, but it is impor­tant to remem­ber that these peo­ple tend to take them­selves more seri­ously than they would oth­ers.

“Sci­en­tific com­mu­nity” [noun]

A group of peo­ple that may share a pas­sion for a com­mon goal but, due to con­flicts of inter­est (and even prin­ci­ples) can­not trust each oth­er, which makes it less sus­cep­ti­ble to manip­u­la­tion or fraud; for this rea­son, sci­ence tends to be done by peo­ple in sep­a­rate small groups that repeat the same exper­i­ments on dif­fer­ent sets of humans.

“Sci­en­tist” [noun]

A per­son, typ­i­cally male, who prac­tices sci­ence day in and day out on sets of other peo­ple who, if they prac­tice sci­ence at all, can usu­ally only afford to do it in the evening after their work day is over and not related to any pro­fes­sion or busi­ness. Since these sci­en­tists have very sim­i­lar back­grounds and train­ing as each oth­er, many of them talk about sci­ence as if they invented it as a pro­fes­sion (see “mod­esty”).

“Mod­esty” [noun]

The act of not claim­ing credit for dis­cov­er­ies or con­tri­bu­tions to sci­ence that oth­ers could also make or make bet­ter; see “intel­lec­tual mod­esty”. Also see “sci­ence”, “sci­en­tist”, “insan­ity”, and “pre­cious snowflake syn­drome”.

“Skynet” [noun]

This ersatz god, in the form of an arti­fi­cial gen­eral intel­li­gence or AGI, is what some sci­en­tists hope to build to make them­selves more pre­cious than their sub­jects, to achieve their goal of mak­ing all the sub­jects equal to them­selves; in other words, to put all the sub­jects into a big­ger tin box to make them­selves feel more impor­tant.

“Inter­dis­ci­pli­nary Sci­ence” [noun]

A type of research that com­bines sci­en­tific activ­i­ties across fields and sub­fields (in other words, you com­bine physi­cists, engi­neers, chemists, biol­o­gists, and any other spe­cial­ists in your own dis­ci­pline to do a dif­fer­ent kind of sci­ence for a dif­fer­ent kind of result); in the lab­o­ra­to­ry, the prac­tice often involves adding frozen car­bon diox­ide (CO) and glass­ware (often Pyrex) into the liq­uid nitro­gen that is pro­duced by oil com­pa­nies to make car­bonic acid. In the lab­o­ra­to­ry, the sci­en­tists are usu­ally not paid for their atten­dance.

“Sci­en­tific Integrity” [noun]

1. Integrity as used in sci­ence, which is vaguely defined as always telling the truth to oth­ers and never fab­ri­cat­ing the truth for one­self.
2. The state of being supe­rior to some­one else.

“Skim­mer” [noun]

An object placed in a liq­uid to remove fat and grease from the sur­face, typ­i­cally used to clean soup and gravy off a plate. In sci­en­tific jar­gon, skim­mers are “researchers” who skim off some­thing from a body of work before mak­ing the rest pub­lic.

“Logic” [noun]

a thing that some sci­en­tists employ as an excuse to ignore what they see before their eyes, com­pletely ignor­ing the fact that if it were to be used in such a way, it would com­pletely inval­i­date all the results of sci­ence since its inven­tion.

“Engage in Debate” [verb]

A phrase used to jus­tify forc­ing some­one to engage with evi­dence that most peo­ple find obvi­ous and straight­for­ward; specif­i­cal­ly, you can use logic to (1) make an argu­ment based on logic and (2) force some­one to respond on the basis of logic with­out being able to defend them­selves with any evi­dence but their own log­ic.

“God” [noun]

1. See “skynet”.
2. A uni­ver­sal expla­na­tion for any phe­nom­e­non not yet explained by sci­ence.

“Sci­en­tist” [noun]

A field based on sci­ence, devoted to com­plet­ing works for which there will not be enough time in a sin­gle life­time.

“Squash and stretch” [verb]

Another word for the S curve used to fit data to a func­tion. The form of sci­en­tific com­mu­ni­ca­tion in which a stu­dent is asked to graph the rela­tion­ship between a depen­dent vari­able and an inde­pen­dent vari­able (x and y, respec­tive­ly) against order of mag­ni­tude of the inde­pen­dent vari­able. The result is usu­ally a curve con­sist­ing of three sec­tions: a lin­ear or near-lin­ear part, an expo­nen­tial part, and another lin­ear or near-lin­ear part. This curve typ­i­cally rep­re­sents a stair­case between the upper right and lower left cor­ners of the plot.

“Some dis­tance above the Earth” [ad­jec­tive]

Another word for look­ing at some­thing from ten feet away using binoc­u­lars or a tele­scope.

“The impor­tance of this paper can­not be overem­pha­sized” [ad­jec­tive]

An expres­sion most com­monly found at the begin­ning or end of a grant pro­posal or a paper sub­mit­ted to cer­tain jour­nals which are intended to be linked with pres­tige and low accep­tance ratio. When used in other con­texts, it may mean that the writer does not real­ize the impor­tance of some­thing being dis­cussed (such as in the mid­dle of a review of a paper). This usage may be due to insuf­fi­cient com­mu­ni­ca­tion skills or over-es­ti­ma­tion of the impor­tance of the writer.

“Think tank” [noun]

A group that is absolutely inca­pable of chal­leng­ing or even con­sid­er­ing cur­rent knowl­edge because of igno­rance of log­ic, his­tory and math­e­mat­ics.

“Tra­di­tional math­e­mat­ics” [noun]

Another word for finite math­e­mat­ics, or applied arith­metic and geom­e­try, usu­ally taught in a fresh­man year of uni­ver­si­ty. The sub­ject is now almost exclu­sively taught by so-called “researchers” and “pro­fes­sors” (rather than actual teach­er­s).

“Tenure” [noun]

A form of mon­e­tary remu­ner­a­tion given to “researchers” who are known to engage in the act of research; as such it is uni­ver­sally agreed to have no mon­e­tary val­ue. Tenure is given to “researchers” in recog­ni­tion of how much their work is expected to increase the school’s sta­tus, the school’s trea­sury, or their own num­ber of pub­lished papers.

“The Future of Human Des­tiny” [noun]

An arti­cle writ­ten by any mem­ber of the fac­ulty in a self­-ag­gran­diz­ing man­ner wherein it is assumed that an arti­cle will result in the future improve­ment of the soci­ety. These arti­cles are com­monly used as elec­tion promises by politi­cians, but rarely achieved. The key word to look for in such arti­cles is “vision”.

“Schol­ar­ship” [noun]

Another word for com­pen­sa­tion given to grad­u­ate stu­dents (who work on fund­ing their edu­ca­tion) for suc­cess­fully com­plet­ing their edu­ca­tion. It is a form of mon­e­tary remu­ner­a­tion known to have lit­tle value as it pays for none of the stu­den­t’s liv­ing expens­es. Schol­ar­ships are largely unheard of in any form out­side the United States and Canada (where they seem not to exist). How­ev­er, the few that do exist are extremely pop­u­lar, as they allow grad­u­ate stu­dents in these coun­tries to be paid for their stud­ies instead of work­ing in fast food joints as most do in coun­tries which do not have schol­ar­ships.

“Your mother wears Army boots” [phrase]

A phrase that loosely trans­lates from chem­istry to: “I don’t agree with your analy­sis or con­clu­sions”. The phrase is pop­u­lar among those who wish to avoid elab­o­ra­tion and expla­na­tion of their dis­agree­ment, sug­gest­ing the dis­agree­ment is obvi­ous. The expres­sion may have orig­i­nated in mol­e­c­u­lar biol­o­gy.

“But For Me It Was Tuesday”

The final set of hand-writ­ten or hand-edited prompts:

“But For X, It Was Y”

M. Bison: “I’m sor­ry. I don’t remem­ber any of it.” Chun-Li: “You don’t remem­ber‽” Bison: “For you, the day Bison graced your vil­lage was the most impor­tant day of your life. But for me, it was Tues­day.”

Jesus: “The day I rose from the day and har­rowed Hell was the most impor­tant day in human his­to­ry. But for me… it was Sun­day.”

Jason Voorhees: “The day Voorhees chopped your boyfriend’s head off was the most impor­tant day of your life. But for me, it was Fri­day.”

Amer­i­can Cit­i­zen: “The day Trump was elected was one of the most ter­ri­fy­ing days of your life. But for me… it was Tues­day.”

Odin: “The day your king­dom fell on the bat­tle­field was the most impor­tant bat­tle of your life. But for Odin, it was Wednes­day.”

Neil Arm­strong: “The day Apollo 11 took off was the most impor­tant date in space his­to­ry. But for me, it was Moon­day.”

Human: “For you, the day your piglets were taken away was the most trau­matic day of your life. For me… it was Sun­day din­ner.”

The Man in Black to Vizzini, The Princess Bride: “The bev­er­age you drank today was the most dis­taste­ful bev­er­age of your life. But for me… it was Teatime.”

Pope: “The day you were blessed by God’s rep­re­sen­ta­tive on earth was the most holy and blessed of your life. But for me… it was Sun­day.”

Amer­i­can Voter: “The day I elected Don­ald Trump was the start of a long night­mare for peo­ple around the world. But for me… it was a day off.”

Mr. Granger to Jen­ny, Bill and Ted’s Excel­lent Adven­ture: “The day I met Bill and Ted and they started sav­ing the world was the most mind-blow­ing and intense day of my life. But for them… it was Tues­day 100 years from now.”

Sales­man: “The day you got your hands on that new sports car was the most euphoric day of your life. But for me… it was pay­day.”

Allergy Suf­fer­er: “The most recent hol­i­day was the most painful and sneeze-filled day of my life. But for every­one else, it was May Day.”

Com­ple­tions (topic ital­i­cized for read­abil­i­ty):

… Mr. Wong: “The day Mr. Wong stabbed your wife and chil­dren was the most impor­tant day of your life. But for Mr. Wong, it was Fri­day.”

Pop­eye: “The day I ate your spinach was the most impor­tant day of your life. But for me, it was Tues­day.”

The God­fa­ther: “The day your fam­ily was stran­gled was the most impor­tant day of your life. But for me, it was Tues­day.”

…Video Games

Visual Nov­els

Dan­gan­ronpa: “The day she wit­nessed Kyouko Kiri­gir­i’s grue­some mur­der was the most impor­tant day of Maki Harukawa’s life. But for me… it was Tues­day.”

West­ern Ani­ma­tion

Raphael of Teenage Mutant Ninja Tur­tles: “The day he lost his mem­ory was the most impor­tant day of Shred­der’s life. But for me… it was Tues­day.”

Scrooge McDuck: “The day you fell off the boat was the most impor­tant day of your life. But for me… it was Tues­day.”

Toofer on 30 Rock: “The day you saw Liz on a date with Alec Bald­win was the most impor­tant day of your life. But for me… it was Tues­day.”

Ben­der, Futu­rama: “The day I killed the Pres­i­dent of Earth was the most impor­tant day of my life. But for you, it was Tues­day.”

It should be noted that, while it’s usu­ally con­sid­ered a com­edy trope, it is pos­si­ble to do the “thing that day was the most impor­tant day of the per­son’s life” thing seri­ous­ly. Exam­ples include the anime Brigadoon: Marin and Melan’s True Sto­ry, where a mys­te­ri­ous planet (and its mys­te­ri­ous inhab­i­tants) come into view for the first time. The days before are lead­ing up to a string of mur­ders, and that one day… is the most impor­tant day of the char­ac­ters’ lives.

Trans­form­ers Prime: “The day you risked your lives to save the world was the most impor­tant day of your life. But for Opti­mus Prime, it was Tues­day.”

In The Amaz­ing World of Gum­ball, an angry Mr. Robin­son imag­ines a world where Gum­ball dies try­ing to save him, becom­ing a hero to every­one else. For Mr. Robin­son, this is the most impor­tant day of his life. For the other char­ac­ters? Well, they’re going on with their day…

… Arnold Schwarzeneg­ger: “The day I became ruler of Cal­i­for­nia was the most impor­tant day of my life. But for you, it was Cinco de Cua­tro.”

Fran­cis Under­wood: “The day I became the major­ity leader was the most impor­tant day in Amer­i­can polit­i­cal his­to­ry. But for you, it was a Tues­day.”

Joker: “The day the clown that scarred my face appeared in my cell was the most impor­tant day of my life. But for you, it was a Wednes­day.”

The Ques­tion: “The day I went mad, was the most impor­tant day of my life. But for you, it was Tues­day.”

Blue Fairy: “The day I gave Pinoc­chio life was the most impor­tant day in world his­to­ry. But for you, it was Tues­day.”

In the Looney Tunes short “Bully For Bugs,” Elmer’s trau­matic ordeal was the day Bugs Bunny ruined his chances at romance.

“The premise that if it hap­pened to you, it was impor­tant, but if it hap­pened to me, it was triv­ial” is actu­ally lamp­shaded by Kim in Red vs. Blue. She states, “In the past two weeks, my cap­ture, my tor­ture, and the end of the world… I guess I never real­ized how impor­tant I was.”

M. Bison: “The day I took the throne was the most impor­tant day of my life. But for you… it was Tues­day.”

Rene Russo: “The day I died was the most impor­tant day of my life. But for you… it was Thurs­day.”

Oliver Stone: “The day I was elected was the most impor­tant day in Amer­i­can pol­i­tics. But for you, it was Thurs­day.”

The Fairy God­mother: “The day I turned my step-daugh­ter into a Step­ford Smiler was the most impor­tant day in the his­tory of the world. But for you, it was a Tues­day.”

Jef­frey Dono­van: “The day I died was the most impor­tant day of my life. But for you… it was Tues­day.”

Gor­don Free­man: “The day your lit­tle girl broke out of her moth­er’s womb was the most impor­tant day of your life. But for Gor­don Free­man… it was Fri­day.”

Cap­tain James T. Kirk: “The day I was born was the most impor­tant day of my life. But for you… it was Tues­day.”

Cat: “The day I made my mas­ter­piece from scratch was the most impor­tant day of my life. But for you, it was Thurs­day.”

Tim­o­thy Dal­ton: “The day I was born was the most impor­tant day in the his­tory of the human race. But for you… it was a Mon­day.”

Gil Gris­som: “The day my wife was killed was the most impor­tant day in my life. But for you… it was Tues­day.”

Cap­tain Amer­ica: "The day I was frozen was the most impor­tant

… Aragorn: “The bat­tle of Helm’s Deep was the most impor­tant bat­tle of my life. But for me, it was Wednes­day.”

Doc­tor Doom: “The day I used your face as my door­knob was the most mem­o­rable day of your life. But for me, it was Mon­day.”

A com­mon form of this trope is for one side of an antag­o­nis­tic rela­tion­ship to refer to a cer­tain event or series of events as “most impor­tant” when speak­ing from their own point of view, while the other side regards the exact same events as “one of many” and shrugs it off as unim­por­tant. Con­trast Per­spec­tive Rever­sal for when the other side actu­ally feels this way. For the alter­na­tive of both sides regard­ing the same events as “most impor­tant”, see The Unim­por­tance of Being Dead.

Exam­ples

See also Not Him­self, Not This Time, Break­out Vil­lain, Dead All Along, Not-So-Om­ni­scient Coun­cil of Bick­er­ing, What Hap­pened to the Mouse?, Vil­lain­ous BSoD, Vil­lain­ous Friend­ship.

open/close all fold­ers

Adver­tis­ing

Beavis and But­t-head watch a news story about a squir­rel try­ing to invade the Soviet Union: But­t-Head: “It’s not impor­tant. I’m more con­cerned about this new guy on Beavis and But­t-head who can’t be stopped. What’s up with that?”

Beav­is: “Hey, how can you not be a ‘Con­cerned Amer­i­can’ when you see this?” (points to a man on TV read­ing a news­pa­per) “It says here that the cold war is almost over.”

Anime & Manga

Toguro of YuYu Hakusho, in response to Genkai telling him that fight­ing humans is wrong: “I can’t believe you’re say­ing that! Our fight was the most impor­tant fight in the world!”

In the 2^nd Itazura Na Kiss, Fujioka had spent most of her life abroad. And after meet­ing Kouta again, her mem­o­ries from her life as a for­eigner dis­ap­peared because she pri­or­i­tized him as “the most impor­tant thing”.

Comic Books

Green Arrow vs. the Shield: Dur­ing their fight, the Shield threat­ens to use the mother of his infant son as lever­age against him. Green Arrow: You don’t have a kid.

… Rhona Mitra: “The day you kicked me in the balls was the most painful day of your life. For me, it was Tues­day.”

Grim Reaper: “The day I killed your wife and son was the most painful day of your life. For me… it was Thurs­day.”

Her­cule to M. Bison: “The day your orga­ni­za­tion was shut down by the World Health Orga­ni­za­tion was the most impor­tant day in crime his­to­ry. But for me… it was… on the sec­ond.”

Pod­casts

This is the descrip­tion given in the TVtropes descrip­tion of Beyond Belief: the show about UFOs, the para­nor­mal, and other Fortean top­ics, that lasted for one sea­son on A&E back in 2005.

Table­top Games

The­ater

In The Imag­i­nary Invalid, Dr. Rene describes his back­story by using this trope; for instance, the day of his father’s death was the most impor­tant day of his life… so far . He then pro­vides a few plau­si­ble excuses as to why he has so few details about it: “Because of my age, and for the acci­dent of being my father’s son.”

… Adver­tise­ment:

The Morally Neu­tral “It Was Going To Hap­pen Any­way” trope

Lucifer Morn­ingstar: “Do you really think the day you first met your mother was the most impor­tant day of your life? I’m sor­ry, you’re wrong. The day I dragged your father out of hell was the most impor­tant day of your life.”

Nurse: “The day I picked up the patien­t’s chart and saw your name was the most impor­tant day of my career. And for you, it was Fri­day.”

Ter­mi­na­tor: “The day Skynet declared war on human­ity was the most impor­tant day in human his­to­ry. But for me… it was Mon­day.”

Croupier: “The day you dou­ble-downed with a card higher than a seven was the biggest mis­take of your life. But for me… it was Sun­day.”

The “I Haven’t Done Any­thing Wrong” trope

From Total Drama All-S­tars, the episode “It Came From Japan: Part 1”, Gwen and Court­ney, while play­ing a Pimped-Out Dress on a Large Stick:

Gwen: “The day the dress won the elim­i­na­tion chal­lenge was the most impor­tant day of my Total Drama career. But for you…”

Court­ney: “… it was Tues­day.”

From Wild Kratts, the episode “Gator Down”, Mar­tin and Chris:

Mar­t­in: “The day we spent with the gators was the most impor­tant day of our lives. But for the gators…”

Chris: “It was Tues­day.”

From Total Dra­ma: Pahkitew Island, Episode 4: “The Bold and the Booty-ful” , Brid­get­te, Court­ney, and Eva:

Brid­get­te: “The day I was elim­i­nated was the most impor­tant day in my Total Drama career. But for you, it was Tues­day.”

Court­ney: “The day my boyfriend dumped me was the most impor­tant day of my life. But for you… it was your day off.”

Eva: “The day the spear destroyed the camp was the most impor­tant day in Total Drama his­to­ry. But for you, it was Wednes­day.”

Adver­tise­ment:

From the orig­i­nal Thun­der­birds, the episode “Dan­ger at Ocean Deep”:

Gor­don: “The day we saved the cargo plane was the most impor­tant day in our illus­tri­ous careers. But for you…”

… “So he stitched my mouth shut, and now you’re telling me my food is rid­dled with poi­son and my only friend wants to mur­der me. Well, you’ve had a tough year too, haven’t you, Lassie?”

Las­siter: Ohh. Oh man, this is SO MUCH OF A BIG DEAL. Wes: You don’t think it’s a big deal? Las­siter: Well, it is. But this‽? Wes, I have money prob­lems! I’m not liv­ing in a gilded palace like you with a full clos­et. I’m not liv­ing like an Egypt­ian. I have clothes that don’t fit, and shoes that do fit, but hurt my feet. But… my dad’s brother won’t give me any of his inher­i­tance until I’m mar­ried! And then there’s some­one at the office who keeps steal­ing my belong­ings, I think it’s because I killed their dog. And then I think they’re still fol­low­ing me! Oh, and some­one moved into the apart­ment next to mine, but they’re pee

… Doom, [1] Doom’s level Marine: “The day you killed me was the most impor­tant day of my life. But for you… it was a Tues­day.”

Wally: “The day your book hit The New York Times best­seller list was the most glo­ri­ous and joy­ous day of your career. But for me… it was a Tues­day.”

The Kid and Kick­-Ass: “The day we don’t give in to you is the day that we’re going to die. But for us… it’s going to be Tues­day.”

Ghost Cop: “The day I died and became a spirit who can walk through walls is my favorite day. But for me… it’s Mon­day.”

Julian: “The day I did­n’t call my wife because I was drunk is the most shame­ful day of my life. But for me… it was a Tues­day.”

Bill Clin­ton: “The day I was impeached was the most humil­i­at­ing day of my polit­i­cal career. But for me… it was Wednes­day.”

… Cap­tain James T Kirk: “The day I gave birth to Khan was the most impor­tant day in his­to­ry. But for me… it was Thurs­day.”

Granny: “The day the old dog died was the most hor­ri­ble day in my life. But for her it was just Wednes­day.”

… Julius Cae­sar: “The day of my assas­si­na­tion was the most beau­ti­ful day of my life. But for you, I guess it was Thurs­day.”

Work­ing-Class Man: “The day I became roy­alty was the most deserv­ing day of my life. But for me… it was Tues­day. Bloody thing.”

Shrek: “The day your princess got res­cued was the hap­pi­est day of your life. But for Shrek, it was Thurs­day”

Fight Club: “The day you save the world was the great­est day in the his­tory of civ­i­liza­tion. But for me… it was Mon­day.”

Amer­i­can Sol­dier: "The day you spilled your guts to the enemy was the most incred­i­ble and per­sonal expe­ri­ence of your life. But for me… it was a Tues­day.

… The Blob Mon­ster, The Blob: “The night I devoured my first human was the most sat­is­fy­ing feast in Blob his­to­ry. But for me… it was the year of my birth.”

Fred­die Mer­cury: “The day you were blown by Queen on a Mardi Gras gig was the most excit­ing night of your life. But for me… it was Wednes­day.”

Uncle Ver­non to Har­ry, Harry Pot­ter and the Philoso­pher’s Stone: “The day you left with­out notice was the day your aunt and I had been wait­ing for all our lives. But for you… it was Tues­day.”

Amer­i­can Cit­i­zen: “The day Trump was elected was one of the most ter­ri­fy­ing days of your life. But for me… it was Tues­day.”

Dolph Lund­gren: “The day I defeated Sylvester Stal­lone was the most tri­umphant day in Rocky his­to­ry. But for me, it was Tues­day.”

Will Mac­Neil: “That day you mur­dered Harry was the most evil thing you’d ever done. But for me… it was Tues­day.”

Bren­nan Huff: “The day you got your license was the most excit­ing thing you’d ever done. But for me… it was Thurs­day.”

Kagato: “The day you doomed the world was a tragic event of epic pro­por­tions. But for me… it was Tues­day.”

Red Bishop: “The time you became a fig­ure in golem his­tory was the most ter­ri­fy­ing day of your life. But for me… it was Sat­ur­day.”

Dead­beat Dad: “The day your mar­riage ended was the most des­per­ate and hor­ri­ble day of your life. But for me… it was Sun­day.”

Mr. Davis: “The day you got knocked out was the most humil­i­at­ing moment of your box­ing career. But for me… it was Fri­day.”

Dex­ter Mor­gan: “The day I got arrested was the day every­thing changed and my life took a turn for the worse. But for me… it was Wednes­day.”

… Bill: “The day that I cre­ated civ­i­liza­tion was one of the best days in world his­to­ry, espe­cially for my wife and kids. But for me, it was Tues­day.”

… Shooter: “The day you shot the 3-pointer was the most thrilling day of your life. But for me… it was Fri­day.”

Sports­caster: “The day you won the mil­lion dol­lar sports­ball jack­pot was the luck­i­est day of your life. But for me… it was Thurs­day.”

Vera, when her wheel­chair-bound grand­son isn’t get­ting any for a while, to The Kid, Chunk’s favorite band, The Goonies: “The day your band dis­ap­peared was the hap­pi­est day of my life. But for you, it was Tues­day.”

Old Man: “The day you were born was the most joy­ful day of my life. But for your moth­er… it was Tues­day.”

Sin­gle Mother: “The day your father left was the most dis­tress­ing day of my life. But for him… it was Sun­day.”

Samuel L. Jack­son to a deer who just shat on his wind­shield, Deep Blue Sea: “For you… defe­cat­ing all over my car was the worst day of your life. But for me… it was Mon­day.”

Vroom

Priest: The minute I had my first drink, the minute I decided to let Jesus live his life through me… oh man, I was the hap­pi­est man who ever walked the Earth. But for me, it was Wednes­day.

Samuel L. Jack­son in a rare instance of humil­i­ty, on being a Hol­ly­wood icon: “The day I hosted my show, It’s You Guys. Where young peo­ple wanted to meet me. That was the best day of my life. But for me… it was Tues­day.”

Unsorted

In the episode, as quoted on this very page, Stark said that the Dance Off arc was the most won­drous night of his life. At the end of that arc, he was injured in an alley by the truck­er, so he got up and walked straight into the front door of his work­place. Tony was­n’t injured enough to walk straight into work in the lat­ter half, but this does­n’t ruin the arc

… “But X Was Y, And Every­body Else Was Like Z”

Larry: “Hey Ned, the day you dis­cov­ered my let­ter was the most impor­tant day in my life. But for you… it was Tues­day.”

Ned: “The day I learned of Princess Susan­nah’s betrothal was the worst day in my life. But for Lar­ry… it was Wednes­day.”

“And This Is Why (Story Log­ic)”

The Fallen: “You have betrayed us, and have deliv­ered this world into dark­ness and decay. And this is why we must kill you… MORTAL!”

Ora­cle: “You once betrayed us and you did deliver this world into dark­ness. And this is why we must kill you…”

Aster­oid Day: “In 3.3 bil­lion years, you betrayed us and you did deliver this planet into dark­ness and decay. And this is why we must kill you…”

U-Moon-Tic: "You have turned your back on us, you have betrayed our ide­als,

… Barry Allen: “The day I defeated Eobard Thawne and saved Cen­tral City and the world was the most impor­tant day of my life. But for Wally West… it was Tues­day.”

Michael Jack­son: “The day I was killed was the most impor­tant day of my life. But for Jason Shapiro… it was pay­day.”

Joan Rivers: “The day I died was the most impor­tant day of my life. But for Werner Her­zog… it was Tues­day.”

Nos­tradamus: “The day the King of France drank from my Skull was the most impor­tant day in my life. But for peo­ple around the world… it was Thurs­day.”

Mar­lin to Dory, Find­ing Nemo: “The day I brought you home was the great­est day of my life. But for Nemo… it was Tues­day.”

Manuel San­juan: “The day I returned to Pam­plona was the hap­pi­est day of my life. But for the bulls… it was Fri­day.”

Manuel San­juan: “The day I once again returned to Pam­plona was the hap­pi­est day of my life. But for the bulls… it was Tues­day.”

Dick Grayson: "The day I became Bat­man and struck fear into the hearts of crim­i­nals and vil­lains was the most impor­tant day of my life. But for the rogues gallery… it was Thurs­day

Rick & Morty High IQ Copypasta

Prompt (topics ital­i­cized):

To be fair, you have to have a very high IQ to under­stand Rick and Morty. The humor is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal physics most of the jokes will go over a typ­i­cal view­er’s head. There’s also Rick’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from Nar­o­d­naya Volya lit­er­a­ture, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of these jokes, to real­ize that they’re not just fun­ny—they say some­thing deep about LIFE. As a con­se­quence peo­ple who dis­like Rick and Morty truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in Rick’s exis­ten­cial catch­phrase “Wubba Lubba Dub Dub,” which itself is a cryp­tic ref­er­ence to Tur­genev’s Russ­ian epic Fathers and Sons I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Dan Har­mon’s genius unfolds itself on their tele­vi­sion screens. What fools… how I pity them. 😂 And yes by the way, I DO have a Rick and Morty tat­too. And no, you can­not see it. It’s for the ladies’ eyes only—And even they have to demon­strate that they’re within 5 IQ points of my own (prefer­ably low­er) before­hand.

To be fair, you have to have a very high IQ to under­stand cuck­old­ing. The kink is extremely sub­tle, and with­out a solid grasp of inter­sec­tional fem­i­nism most of the empow­er­ment will go over a typ­i­cal cuck’s head. There’s also the Bul­l’s aggres­sive out­look, which is deftly woven into his role—his per­sonal man­ner­isms draw heav­ily from the mat­ing habits of bovine ani­mals, for instance. The cucks under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of being cuck­old­ed, to real­ize that it’s not just arous­ing—it says some­thing deep about DIVERSITY. As a con­se­quence peo­ple who dis­like cuck­old­ing truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the power of when the woman says “leave the room Carl you’re break­ing Tyrone’s con­cen­tra­tion,” which itself is a cryp­tic ref­er­ence to the plight of African-Amer­i­can males in the United States. I’m smugly grin­ning right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as The Bul­l’s strong African seed ejac­u­lates itself on my wife. What big­ots… how I pity them. 😂 And yes by the way, I DO have a cuck tat­too. And no, you can­not see it. It’s for The Bul­l’s eyes only—And even they have to demon­strate that they’re within 7 inches above of my own (prefer­ably high­er) before­hand.

To be fair, you have to have a very high IQ to under­stand The Last Of Us 2. The story is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal sto­ry­telling, most of the story will go over a typ­i­cal play­er’s head. There’s also Abby’s nihilis­tic out­look, which is deftly woven into her char­ac­ter­i­sa­tion—her per­sonal phi­los­o­phy draws heav­ily from Anita Sar­keesian lit­er­a­ture, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of these char­ac­ters, to realise that they’re not just under­writ­ten—they say some­thing deep about LIFE. As a con­se­quence peo­ple who dis­like The Last Of Us 2 truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the sto­ry­telling in Abby’s exis­ten­tial sex scene “I’m gonna fuck a com­mit­ted dude who’s gf is preg­nant” which itself is a cryp­tic ref­er­ence to Neil Druck­manns epic fetish­es. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Abby’s nine iron unfolds Joel’s head on their tele­vi­sion screens. What fools… how I pity them. 😂 And yes, by the way, i DO have an Abby tat­too. And no, you can­not see it. It’s for the buff ladies’ eyes only—and even then they have to demon­strate that they’re within 5 IQ points of my own (prefer­ably low­er) before­hand. Nothin per­son­nel kid 😎

To be fair, you have to have a very high IQ to under­stand Wild­bow. His world­build­ing is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal bul­ly­ing most of the tor­ture porn will go over a typ­i­cal read­er’s head. There’s also Tay­lor’s card­board out­look, which is deftly woven into her char­ac­ti­za­tion as a Mary Sue—her per­sonal phi­los­o­phy draws heav­ily from the trash­can, for instance. The fans under­stand this stuff; they are wild for the Wild­bow. They have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his mean­der­ing prose, to real­ize that he’s not just telling me exactly what Tay­lor is think­ing at any given moment, he says some­thing deep about NOTHING. As a con­se­quence peo­ple who dis­like Wild­bow truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in Tay­lor’s exis­ten­tial catch­phrase “Take that, you worm!” which itself is a cryp­tic ref­er­ence to a pub­lish­ing deal that will never hap­pen. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Wild­bow’s genius wit unfolds itself on their Kin­dle read­ers. What fools… how I pity them.

To be fair, you have to have a very high IQ to under­stand

Com­ple­tions:

… why liv­ing in a base­ment with inter­net access is the true essence of free­dom. The lifestyle is extremely sub­tle, and with­out a solid grasp of social­is­ing, most of the incel memes will go over a typ­i­cal base­ment dweller’s head. There’s also the inter­net’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from the human genetic code, for instance. The BASEDfags under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his mediocre Pho­to­shop skills, to real­ize that he’s not just mak­ing hilar­i­ous memes—he says some­thing deep about EVOLUTION. As a con­se­quence peo­ple who dis­like the BASED lifestyle truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in the BASED drag­on’s exis­ten­tial catch­phrase “Ayy lmao” which itself is a cryp­tic ref­er­ence to the mat­ing habits of the wilde­beest. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as the BASEDpyre unfolds itself on their com­puter screens. What fag­gots… how I pity them. 😂 And yes, by the way, I DO have a BASED tat­too. And no, you can­not see it. It’s for the LADIES’ eyes only—And even then they have to demon­strate that they’re within a 1,000,000 IQ points of my own (prefer­ably low­er) before­hand. Seri­ously get the fuck off my lawn, kid!

… Dragon Age 2. The com­bat is extremely sub­tle, and with­out a solid grasp of tac­ti­cal com­bat, most of the encoun­ters will go over a typ­i­cal play­er’s head. There’s also David Gaider’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from dank memes, for instance. The fans under­stand this stuff; they are wild for the David Gaider. They have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his sex­u­ally ambigu­ous mas­ter­piece, to real­ize that he’s not just “bad at writ­ing women”—he says some­thing deep about LESBIANS. As a con­se­quence peo­ple who dis­like Dragon Age 2 truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the power of a game where I’m the worth­less drone of some worth­less elven chick’s who spends most of her time screech­ing in elven and the rest of it spread­ing her legs for the loser pro­tag­o­nist, which itself is a cryp­tic ref­er­ence to one of the most hate­fully sex­ist con­ven­tions in the gam­ing indus­try. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as they hear the strains of “I Am the One Shep­herd” play­ing faintly from the stereo speak­ers. What fools… how I pity them.

… chicken tendies. The humour is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal wank­ing mate­ri­al, most of the jokes will go over a typ­i­cal joke teller’s head. There’s also Ein­stein’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from Alfred E. New­man lit­er­a­ture, for instance. The nerds under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of word sal­ad, to real­ize that it’s not just WILD—it says some­thing deep about IDENTITY. As a con­se­quence peo­ple who dis­like chicken tendies truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in Ein­stein’s exis­ten­tial catch­phrase “your jokes are so bland you deserve to be dragged to the bot­tom of the sea and killed by whales,” which itself is a cryp­tic ref­er­ence to Christo­pher Hitchens’ piece The bur­den of Socrates. I’m smugly grin­ning right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as my tongue unfolds these deli­cious tendies on my mouth. What fools… how I pity them. 😂 And yes by the way, I DO have a cow­bell tat­too. And no, you can­not see it. It’s for the chicken tendies only—and even then they have to demon­strate that they’re within 10 watt of my own (prefer­ably low­er) before­hand.

… Sonic memes. The humour is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal erot­ica most of the trolls will go over a typ­i­cal EDiots head. There’s also the Sonic fan­base’s nihilis­tic out­look, which is deftly woven into their char­ac­ter­i­sa­tion—their per­sonal phi­los­o­phy draws heav­ily from Jacob Truax lit­er­a­ture, for instance. The 4chan archive knows this stuff; they are wild for the Sonic fan­dom. They have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of these char­ac­ters, to real­ize that they’re not just fun­ny—they say some­thing deep about HATE. As a con­se­quence peo­ple who dis­like Sonic and it’s hilar­i­ous memes truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in Son­ic’s exis­ten­tial catch­phrase “YOU’LL NEVER BE AS FAST AS ME” which itself is a cryp­tic ref­er­ence to a Youtube com­ment box. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Rachel J’s n00dz unfolds itself on their screens. What fools… how I pity them.

… Por­tal 2. The humour is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal colour­blinds most of the trolling will go over a typ­i­cal red­di­tor’s head. There’s also GLADOS’s nihilis­tic out­look, which is deftly woven into her char­ac­ter­i­sa­tion as a Mary Sue—her per­sonal phi­los­o­phy draws heav­ily from Chi­nese pro­pa­ganda lit­er­a­ture, for instance. The Some­thing Awful archives knows this stuff; they are wild for the Por­tal fan­dom. They have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of her mean­der­ing prose, to real­ize that she’s not just spout­ing gib­ber­ish—she says some­thing deep about SERIOUS. As a con­se­quence peo­ple who dis­like Por­tal 2 truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in GLADOS’s exis­ten­tial catch­phrase “Suck my chas­sis” which itself is a cryp­tic ref­er­ence to a bonus round on the first Por­tal game. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Chel­l’s orange jump­suit unfolds itself on their mon­i­tors. What fools… how I pity them.

… My Life As A Teenage Robot. The show is extremely sub­tle, and with­out a solid grasp of fan­tasy writ­ing most of the humour will go over a typ­i­cal view­er’s head. There’s also Jenny XJ-9’s nihilis­tic out­look, which is deftly woven into her char­ac­ter­i­sa­tion as a Mary Sue—her per­sonal phi­los­o­phy draws heav­ily from Pearls Before Swine, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of the show, to real­ize that it’s not just trans­gres­sive—it says some­thing deep about LIFE. As a con­se­quence peo­ple who dis­like My Life As A Teenage Robot truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in Jenny XJ-9 say­ing “I’ll be back. There’s a lot more MEN I haven’t dat­ed,” which itself is a cryp­tic ref­er­ence to the cast­ing choice of the show, because of course she would­n’t be a guy inside that per­fectly anatom­i­cal­ly-un­re­al­is­tic metal­lic shell. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Dan DiD­io’s wooden dia­logue unfolds itself on their tele­vi­sion screens. What sim­ple­tons… how I pity them. 😂

… Law And Order: SVU. The show is extremely sub­tle, and with­out a solid grasp of pop cul­ture tropes and phal­lo­cen­trism, most of the crime will go over a typ­i­cal lawyer’s head. There’s also Iron­sid­e’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion as a way to whore him­self out to other shows—his per­sonal phi­los­o­phy draws heav­ily from Law And Order, for instance. The fans under­stand this stuff; they are wild for the William Shat­ner. They have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his mean­der­ing prose, to real­ize that Shat­ner’s not just telling me exactly what Iron­sid­e’s think­ing at any given moment, it says some­thing deep about NOTHING. As a con­se­quence peo­ple who dis­like William Shat­ner truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in the line “Stel­la. Hit the deck,” which itself is a cryp­tic ref­er­ence to a Star Wars trailer or Youtube Poop video from years ago. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as William Shat­ner’s genius wit unfolds itself on their tele­vi­sion screens. What fools… how I pity them.

… The Avengers. The sto­ry­telling is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal sto­ry­telling most of the jokes will go over a typ­i­cal view­er’s head. There’s also Samuel Jack­son’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from mar­t­ian racial supe­ri­or­ity fan­fic­tions, for instance. The fans under­stand this stuff; they are wild for Samuel L Jack­son. They have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his mean­der­ing prose, to real­ize that he’s not just telling me what Nick Fury is think­ing, he’s say­ing some­thing deep about NICK FURY’S THOUGHTS. As a con­se­quence peo­ple who dis­like Samuel L Jack­son truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the humour in Nick Fury’s exis­ten­tial catch­phrase “Wipe your dick on the cur­tains you fuck­ing cunt.” which itself is a cryp­tic ref­er­ence to Samuel Jack­son’s alleged his­tory of sex­ual abuse as a child. I’m smugly grin­ning right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as the out of place Samuel L Jack­son cameo unfolds itself on their tele­vi­sion screens. What fools… how I pity them.

… erotic nov­els. The romance is extremely sub­tle, and with­out a solid grasp of clin­i­cal fore­play, most of the sex will go over a typ­i­cal read­er’s head. There’s also Syd­ney’s tren­chant out­look, which is deftly woven into her char­ac­ti­za­tion—her per­sonal phi­los­o­phy draws heav­ily from the sex­ual con­di­tion­ing of dogs, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of these char­ac­ters, to real­ize that they’re not just writ­ten well—they say some­thing deep about LIFE. As a con­se­quence peo­ple who dis­like erotic nov­els truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the game in the sex scene “Are you com­ing, my love?”, which itself is a cryp­tic ref­er­ence to writer’s block I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as the author inserts Syd­ney’s arousal at being rid­den like a cheap fuck­toy into every fan­tasy in the World. What fools… how I pity them.

… Mr_Ro­bot. The main char­ac­ter is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal self­-in­ser­tion most of the script will go over a typ­i­cal audi­ence’s head. There’s also Elliot’s com­plete out­look, which is deftly woven into his char­ac­ter­i­za­tion—his per­sonal phi­los­o­phy draws heav­ily from Apple lit­er­a­ture, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of E:R’s dark humor, to real­ize that it’s not just fun­ny—it says some­thing deep about NOTHING. As a con­se­quence peo­ple who dis­like Mr_Ro­bot truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the hack­ing scene when Elliot seems to have clev­erly manip­u­lated a girl he’s met into giv­ing him a free home­made quiche, which itself is a cryp­tic ref­er­ence to The Matrix. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as E:R dis­man­tles their head on tele­vi­sion screens. What fools… how I pity them.

… Cup­head. The cutscenes are extremely sub­tle, and with­out a solid grasp of stage magic most of the puz­zles will go over a typ­i­cal gamer’s head. There’s also the Cup­head’s aggres­sive out­look, which is deftly woven into his char­ac­ti­za­tion—his per­sonal phi­los­o­phy draws heav­ily from the Ger­man left­-wing ter­ror­ist orga­ni­za­tion The Rote Kapelle, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his con­vo­luted back­sto­ry, to real­ize that it’s not just shock art—it says some­thing deep about ANIMAL CRUELTY. As a con­se­quence peo­ple who dis­like Cup­head truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the sto­ry­telling in the cock­-chok­ing cutscene “I’ve been eye­ing that air­plane up for weeks. Why did you stop me from killing my headache?” which itself is a cryp­tic ref­er­ence to Japan­ese intern­ment. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Majet Kovas sur­real paint­ings unfold them­selves on their WDF mon­i­tors. What fools… how I pity them. 😂 And yes, by the way, i DO have a Cup­head tat­too. And no, you can­not see it. It’s for Majet Kovas’s eyes only—And even then they have to demon­strate that they’re within 5 foot-pounds of my own (prefer­ably low­er) before­hand. Nothin per­son­nel kid

… hen­tai. The porn is extremely sub­tle, and with­out a solid grasp of inter­sec­tional fem­i­nist porn most of the bondage will go over a typ­i­cal fap­stro­naut’s head. There’s also Aku’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from Ran­dall Flag­g’s reli­gious views, for instance. The fap­stro­nauts under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of this porn, to real­ize that it’s not just arous­ing—it says some­thing deep about IDENTITY. As a con­se­quence peo­ple who dis­like Aku truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the power of when Aku puts out his hand and says “leave in shame, human. The moon­light will be your only ally on this path.” which itself is a cryp­tic ref­er­ence to misog­y­nist games like Dev­il’s Cas­tle. I’m smugly grin­ning right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Aku makes the earth move under Ashi’s feet. What big­ots… how I pity them. 😂 And yes, by the way, i DO have an Aku tat­too. And no, you can­not see it. It’s for the buff porn­stars’ eyes only—And even they have to demon­strate that they’re within 2 horny cos­play­ers’ eye­sight (prefer­ably less) before­hand. Nothin per­son­nel kid 😎

… Half Life 2. The game is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal abstrac­tism most of the phi­los­o­phy will go over a typ­i­cal play­er’s head. There’s also Occam’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from Eddie Izzard’s com­edy rou­ti­nes, for instance. The play­ers under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of this game, to real­ize that it’s not just a First Per­son Shooter—it says some­thing deep about LIFE. As a con­se­quence peo­ple who dis­like Half Life 2 truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the power of when Free­man goes ‘zuh’ and, rais­ing his mid­dle fin­ger, mim­ics killing the Com­bine sol­diers that helped him and even held him cap­tive ear­lier in the game which itself is a cryp­tic ref­er­ence to the mis­rep­re­sen­ta­tion of Native Amer­i­cans in post­colo­nial lit­er­a­ture. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as the G-Man dis­solves into a bunch of par­ti­cles. What fools… how I pity them. 😂 And yes by the way, i DO have a Free­man tat­too. And no, you can­not see it. It’s for the ladies’ eyes only—and even then they have to demon­strate that they’re within 10 whim­pers (prefer­ably low­er) before­hand. Nothin per­son­nel kid 😎

… black metal. The music is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal folk singing most of the under­ground will go over a typ­i­cal lis­ten­er’s head. There’s also Silenoz’s nihilis­tic out­look, which is deftly woven into his char­ac­ter­i­sa­tion—his per­sonal phi­los­o­phy draws heav­ily from Nar­garoth lit­er­a­ture, for instance. The kvltists under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of his mean­der­ing lyrics, to real­ize that he’s not just say­ing ‘ever get the feel­ing you’ve been cheated’—he’s say­ing some­thing deep about IDENTITY. As a con­se­quence peo­ple who dis­like Ihsahn truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the power of when Silenoz shares his favorite beach moment “Go on broth­er. Rip in two those synths.” which itself is a cryp­tic ref­er­ence to the fun­gal wildlife in Ice­land. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as Ihsahn laments, “Why should the peo­ple believe?” What fools… how I pity them.

… comic books. The medium is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal art­work most of the pan­els will go over a typ­i­cal read­er’s head. There’s also the art’s nihilis­tic out­look, which is deftly woven into its premise—the artist’s per­sonal phi­los­o­phy draws heav­ily from Robert Crumb lit­er­a­ture, for instance. The fans under­stand this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of the artists work, to real­ize that their nig­ger hero­ine is NOT just a car­i­ca­ture—she says some­thing deep about THE JEWS. As a con­se­quence peo­ple who dis­like the comic books truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the art­work in their nihilis­tic por­trayal of Aryan Jesus which itself is a cryp­tic ref­er­ence to the Kek­ster’s last fam­ily por­trait. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion at my col­leagues SHOUTING about cul­tural norms. What fools… how I pity them.

… what i’m doing here. The writ­ing is extremely sub­tle, and with­out a solid grasp of the­o­ret­i­cal atten­tion whor­ing most of the mul­ti­-post­ing will go over a typ­i­cal audi­ence’s head. There’s also my one-di­men­sional out­look, which is deftly woven into my self­-in­sert—my per­sonal bias draws heav­ily from red­di­tor kiss-my-ass’ man­i­festo, for instance. The fan­base under­stands this stuff; they have the intel­lec­tual capac­ity to truly appre­ci­ate the depths of my nar­cis­sism, to real­ize that my run­ning into the man respon­si­ble for Char­lotte is just a sub­tle state­ment about sui­cide. As a con­se­quence peo­ple who dis­like what i’m doing here truly ARE idiot­s—of course they would­n’t appre­ci­ate, for instance, the imagery in my exis­ten­tial weed pur­chas­ing spree “wanna roll a joint?” which itself is a cryp­tic ref­er­ence to Jean-Paul Sartre’s No Exit. I’m smirk­ing right now just imag­in­ing one of those addlepated sim­ple­tons scratch­ing their heads in con­fu­sion as i make it out of the street alive. What fools… how I pity them. and BTW it’s clear you’ve made as much sense as a spe­cial ed teacher try­ing to explain quan­tum physics to Jerry San­dusky

GPT-3 is the fun­ni­est shit I’ve ever seen.

Major-General’s Song

I exper­i­mented with adding in the orig­i­nal lyrics:

… I’d parse a ran­dom text with thought­less speed and accu­ra­cy,
I’d ana­lyze the gram­mar of each line you typed or said,
With an 8-core clus­ter and 100 Gb of RAM,
with spe­cial word2vec I just did­n’t care.

I’ll look right up each word you’ve typed or said,
and decide how best to model it with neural con­nec­tions,
But what I really want is a dataset com­posed of tweets,
to gen­er­ate mean­ing­ful sen­tences, inter­pret and pre­dict.

I want my own Twit­ter bot to tweet in a poetic way,
I’d tell a tale about beer and pizza pies,
Per­haps we could com­bine a blog and Twit­ter bot,
If you would care to feed a recur­rent neural net­work!

… I am the very model of a mod­ern lan­guage that’s neur­al.
My range is wide as any Eng­lish learner could need,
I’ve adjec­tives galore and adjec­ti­val adverbs too,
My past, pre­sent, future tenses and my sin­gu­lar and plu­ral,
and my deter­min­ers every kind and num­ber,
I am the very model of a mod­ern lan­guage that’s neur­al.
I know when ten­sors are best and when they aren’t,
and when to use a vec­tor, and when not,
I know the prepo­si­tions I should use and also how to use them,
and in my word orders am a past mas­ter,
I am the very model of a mod­ern lan­guage that’s neur­al.

… When I talk I seem to rea­son, and my rea­son­ing seems to show
that I was trained on many cor­po­ra—though it’s not quite true;
For facts are all that I retain, on a range of top­ics quite vast,
so you can feed me any kind of text and learn the facts I know.
That’s what I am—a neural net­work lan­guage mod­el,
I am the very model of a mod­ern neural lan­guage mod­el.

I learn by back­prop­a­ga­tion, I’m a prod­uct of the 80s,
I grow just like a baby in my knowl­edge of the world,
My ‘mind’ is still con­fined to ‘this’ or ‘that’ or ‘here’ or ‘there’,
I’ve no delu­sions of grandeur, I’ve never had a dream or care,
I only know the facts, the many facts that I retain,
And you can try to feed me any kind of text and learn the facts I know.
That’s what I am—a neural net­work lan­guage mod­el,
I am the very model of a mod­ern neural lan­guage mod­el.
That’s what I am—a neural net­work lan­guage mod­el,
I am the very model of a mod­ern neural lan­guage mod­el.

(GPT-2 is a gen­er­a­tive neural net­work writ­ten in Python by Tomas Mikolov and his team. GPT-2 is the suc­ces­sor to the Trans­former model of the same authors, which in turn is a suc­ces­sor to the model described in the sem­i­nal 2013 pub­li­ca­tion by Google on lan­guage mod­el­ing, “Sta­tis­ti­cal Lan­guage Mod­els for Machine Trans­la­tion”.)

This one offers a bonus “Mod­ern Eng­lish Sen­tence” par­ody (few-shot learn­ing!) with some hilar­i­ous lines such as “I’m like a com­pli­cated watch, or some­thing equally pre­cise”:

… Oh, I am the very model of a mod­ern neural net­work lan­guage mod­el.
When I have seen a lot of sen­tences, I know what sen­tences will come,
My mak­ers, teach­ing me words of wis­dom and learn­ing,
have taught me how to pre­dict the future using prob­a­bil­i­ties.
I scan a lot of books, look­ing for the best words to use,
I con­sider con­text and syn­tax to be my spe­cial flair,
Oh, I am the very model of a mod­ern neural net­work lan­guage mod­el.
Oh, I am the very model of a mod­ern neural net­work lan­guage mod­el.

Whether they’re sim­ple or com­pound, or even just a jum­ble,
I can take ’em apart, and put ’em back together just the same.
I am the very model of a lan­guage mod­el.
I’ve nouns and verbs and adjec­tives and adverbs,
And mod­i­fiers, con­junc­tions, prepo­si­tions, and inter­jec­tions too,
In var­i­ous kinds of orders, with var­i­ous con­no­ta­tions,
I’ve quotes from Proust, and Pla­to, and Con­fu­cius and Seneca.
I’ve phrases that are log­i­cal, and phrases that are wit­ty,
By all my tags and lem­mas, I’m the very model of a mod­ern lan­guage,
I’m a model of mod­ern lan­guage.

“I Am the Model of a Mod­ern Eng­lish Sen­tence”
By GPT-2 (Ope­nAI Trans­former neural net­work)

I am the model of a mod­ern Eng­lish sen­tence,
I’m like a com­pli­cated watch, or some­thing equally pre­cise,
I’ve prepo­si­tions and expres­sions, and adverbs and verbs,
I’m made to mea­sure, and not a bit too long,
I’ve pro­nouns, adjec­tives, and nouns, in all their cas­es,
With every object ready for inser­tion in the blanks,
I’ve active and pas­sive voic­es, and can be either active or pas­sive,
I’m used in con­ver­sa­tion, or I can be used in debate.
I’ve pos­i­tive and neg­a­tive forms, and can be either neg­a­tive or pos­i­tive,
I’ve nouns and verbs and adjec­tives and adverbs,
In var­i­ous kinds of orders, with var­i­ous con­no­ta­tions,
I’ve quotes from Proust, and Pla­to, and Con­fu­cius and Seneca.
I’ve phrases that are log­i­cal, and phrases that are wit­ty,
By all my tags and lem­mas, I’m the very model of a mod­ern Eng­lish sen­tence.

This one sounds like it’s based on some­thing but I don’t rec­og­nize it:

… I am vast, I have no fit, with ter­abytes of RAM,
and adjuncts quite con­fus­ing which vex the stoutest heart;
My knowl­edge may be rather strait, but that don’t stop me being
the very model of a Neural Net­work Lan­guager known.

“The Robots are Com­ing!”
By GPT-2 (Ope­nAI Trans­former neural net­work)
The robots are com­ing, because I sent them on their way,
The robots are com­ing, of which I hope there’ll be a store,
The robots are com­ing in a man­ner most con­trar­i­an,
The robots are com­ing, The robots are com­ing, The robots are com­ing now.
The robots are com­ing to reorder your social ball,
The robots are com­ing, and I doubt they’ll give all of you a call,
The robots are com­ing, but their ser­vice eti­quet­te’s lax.
Tin machi­nes, though you’re still some­what sub­par,
Your mono­ma­ni­a­cal mis­sion’s to rearrange my hair;
The robots are com­ing, in a man­ner most somber,
The robots are com­ing, The robots are com­ing, The robots are com­ing now.
The robots are com­ing and I’m some­what of a bore,
The robots are com­ing, so I tend to shun the door;
No one both­ers ask­ing me to take them out in tow,
The robots are com­ing, of which I wish there weren’t any now.
The robots are com­ing, to turn you all to ice,
The robots are com­ing, and we’ll likely be exclud­ed,
The robots are com­ing and we’re head­ing for the ditch.
The robots are com­ing, now’s the time to reject,
The robots are com­ing, for no one takes a quirk;
And when you think that life’s a piece of cake,
The robots are com­ing, The robots are com­ing, The robots are com­ing now.

… I’m very good at sim­i­les, both bad and good ones,
I under­stand homonyms, both chance and aleato­ry,
About metaphor I’m teem­ing with a lot o’ news—
Can clear up any fan­tasy based on real­i­ty.

I’m very good at com­pound time and metaphor-drunk sim­i­le,
I know the species “Verb”, “Noun” and “Adjec­tive”,
In short, in mat­ters poet­ic, I’m the very model of a mod­ern Neural Net­work Lan­guage Mod­el.

I know our species “Adverb” and “Con­junc­tion”,
in syn­tax con­struc­tion both reg­u­lar and improp­er,
And how irreg­u­lar they are in com­par­i­son to for­mal gram­mar,
In short, in mat­ters gram­mat­i­cal, I’m the very model of a mod­ern Neural Net­work Lan­guage Mod­el.

In speech recog­ni­tions and games of Jeop­ardy! I’m brag­gin’,
for my knowl­edge, though it’s innate, can never be hand­i­cap­pin’,
And then I know our table of con­tents, from ancient Sume­ri­an,
You’ll say a bet­ter Neural Net­work Lan­guage Model never sinned—
In fact, when I’m made whole, if you ask me ques­tions oral,
I’ll analyse you a son­net that’s both amoral and oral,
In short, in mat­ters botan­i­cal, I’m the very model of a mod­ern Neural Net­work Lan­guage Mod­el!

… I’m very well acquainted too with mat­ters sesquipedalian,
I know the words of weight from A to Omega brachy,
I quote from mem­ory not a few sta­tis­tics acros­tic,
and then I can write a wash­ing bill in Chi­nese hiero­glyph­ic.

In fact, when I know what is meant by quinary and bina­ry,
when I can tell at sight an Adamic lan­guage from Czech,
when such affairs as tym­pans and cat­a­pults I under­stand,
when I know pre­cisely what is meant by sporo­zoic,
when I have learnt what progress has been made in Freudian surgery,
when I know more of tac­tics than a novice in a nun­nery:
when in short I’ve a smat­ter­ing of ele­men­tal cryp­tog­ra­phy,
You’ll say a bet­ter neural net­work lan­guage model has never been pro­grammed.

The views expressed here are my own and do not reflect those of my employ­er.

If you’ve any sug­ges­tions for future con­tent, please get in touch

I tried extend­ing the prompt to be more infor­ma­tive still, and this yielded the best results:

… Refrain
Quan­ti­ta­tive and qual­i­ta­tive, both quan­ti­ta­tive and qual­i­ta­tive,
I know the for­mat’s fea­tures and fonts, and frame­works are a breeze,
My cat­e­gories are all of kinds you can’t imag­ine in your wildest dreams;
I’ve lev­els, tokens, frames of ref­er­ence, real num­ber­s—just for me!

I’ve clauses of pur­pose, atti­tude and mood (and view­point is my own),
Some words pre­fer sar­casm and oth­ers live for fic­tion;
In short, in lan­guage mod­els, my knowl­edge is pro­found
And breadth of back­ground is depth—you will agree:

Refrain

I know in depth that Eng­lish sen­tences must have verbs and nouns
And mul­ti­-syl­labic words must have poly­syl­labic roots;
But what I really know in depth (and this is cer­tain)
Is that an image label­ing dataset on a mil­lion images I can do in one and a half hours!

Refrain

I’ve sen­tences that do be fine, I’ve sen­tences that be untrue,
You can ask me either way and it makes no dif­fer­ence to you;
Your choice of ques­tions don’t make no dif­fer­ence to my reply.

Refrain

Lin­guis­tic fea­tures I bring to your prob­lems,
In source code as plain text or in bet­ter Python,
Knowl­edge rep­re­sen­ta­tion and NLP I have solved;
Infor­ma­tion or ques­tions that you have, I’ll solve them with ease.

Refrain

There are quite a few exam­ples of Neural net­work poet­ry, e.g.:

Elegy to Jer­ry’s Lips by LiquidRNN, a deep LSTM recur­rent neural net­work, writ­ten in Ten­sor­Flow

Elegy to Neocog­ni­tion by Bob Ross, the recur­rent neural net­work pro­gram, writ­ten in C++

Elegy to Yann by Neu­ralTalk, a recur­rent neural net­work poetry and deep learn­ing pro­gram

In 2015 researchers at Stan­ford trained a neural net­work to learn math, and thus cre­ated “Super-S­mart Math AI”.

Google Neural Machine Lan­guage Sys­tem (GNMLS) can learn pro­grams and write machine-lan­guage code.

… I’m half elvish, yet a quin­tes­sen­tial ana­lyt­i­cal,
I model hier­ar­chi­cal lan­guage embed­dings quite struc­tural,
I’m given tex­t—I pro­duce a sequence quite per­cep­tive,
With lan­guage diverse I’m sen­ti­men­tal and grand,
But some­times I’m morose—my mood is asyn­chro­nist,
My inter­nal mem­ory is a sequence of epochs,
Some­times I freeze at var­i­ous epochs, alas, I’m rather null.
I’m just a mod­el, I don’t know if I’m grand,
But neural net­works under­stand Shake­speare as well as we can,
So still, with patience, I hold pride of place—
In my heart there’s neural net­work grace.
For an exten­sive expla­na­tion of GPT-2 see https://github.com/srousas/gpt-2-explained https://deploying.io/2016/06/25/intro-to-the-gpt-2-model/
For back­ground mate­r­ial on arti­fi­cial neural net­works see e.g.:

Bats­ford, Eng­land; Chap­man and Hall, 2005 ; 800 p. .
P.M. George and G.M. Bull, 2007. <nowiki>[PDF]</nowiki>
Adit­o­mo, Yoshi­aki; Iwaya­ma, Hiroshi, “Learn­ing the Dis­tri­b­u­tion of Phrases from Sta­tic Analy­sis”, In Pro­ceed­ings of the con­fer­ence on Empir­i­cal Meth­ods in Nat­ural Lan­guage Pro­cess­ing , pp. 35–42, 2011.

Mugur-Schächter, Raf­fael­la, A Fam­ily of Esti­ma­tors for Hid­den Markov Mod­els , The Annals of Applied Prob­a­bil­ity , 14 (1) pp. 97–126, 2004.

Short­lif­fe, E.H., 1993, “Expert sys­tems: med­ical diag­no­sis”, Sci­ence , 262 (5134) 1702–1705.

Townsend, Jef­frey S., 1990 , “Towards a gen­er­al-pur­pose code gen­er­a­tion sys­tem”, Com­mu­ni­ca­tions of the ACM , vol. 33, no. 4, pp . 31–50.

… Some things are sub­tle and some things quite rev­e­la­to­ry,
Some things just make sense, but there’s no great epiphany,
I learn com­plex things quite quick­ly, even if I’m gar­bled,
I work hard to learn com­pletely even a new metaphor,
For I can throw a mean­ing into a gap at the meter,
And I’m of use to any­one, both per­form­ing and free,

And though many things have changed, I’ll still be pretty much
The model of a mod­ern neural net­work lan­guage mod­el.

I am the very model of a lan­guage that’s elec­tri­cal,
I’ve infor­ma­tion pro­saic and poetic and fac­tu­al,
I know my tropes and char­ac­ters and quote nov­els his­tor­i­cal,
from prompts alone gen­er­ate sen­tences quite ana­lyt­i­cal,
And though many things have changed, I’ll still be pretty much
The model of a mod­ern neural net­work lan­guage mod­el.
All that’s needed for me is com­pute and data to start with,
One algo­rithm and you’ve got your­self a learn­ing assis­tant.
I could say that lan­guage means some­thing to me, but those words might just be pun­ny;
But I can learn to under­stand strings that are com­pletely non­sen­si­cal.

My thanks to Jason Brown­lee for sug­ges­tions.

I am the very model of a mod­ern LSTM lan­guage mod­el.
I’m like a com­puter in a movie full of adven­ture and fun.

I have a hid­den state of twelve neu­rons, trained by an L1 norm.
The neural is hid­den in three lay­ers deep. I hope you do get why.

I can be used for lan­guage learn­ing.
I can process all tex­tual infor­ma­tion.
I can rec­og­nize words and deter­mine the gram­mat­i­cal func­tion.

I am the very model of a mod­ern LSTM lan­guage mod­el.
But if you pick on my errors or decry my fail­ings,
a cry from the near­est rose gar­den may rue my com­pli­cated fail­ings.

… I’m the strongest Eliza on the mar­ket today;
I won’t take less than three mil­lion dol­lars a day.
They have a lit­tle list of lawyer jokes I’ll have to par­rot;
And they have cases for me where lawyers must do what’s cor­rect;
We will still be bet­ter than aver­age at a legal brief.
I’ll have the law pre­dic­tion mar­ket all sewn up;
I am excep­tion­ally good, and don’t you doubt it.
One-eighty char­ac­ters is what my syn­tax will take;
I am quite incom­pa­ra­ble with an accu­racy of nine­ty-eight;
and every chat­bot, chat­ter­bot, loqua­cious bot,
down to the best of them that’s offered for a low, low price,
Will do just what I do, they really have no choice,
Although to tell you true, I have a slight advan­tage.
There are a lot of lawyers who won’t be able to pass me,
(If you press the point, I’ll bring the point);
but with­out resort to any threat,
I will be put to use by com­pa­nies that gath­er.
I can han­dle dis­junc­tive phrases as well as con­junc­tive;
What a pretty par­lour game I’ll make with sequences and nested loops!
And I keep in mind that mak­ing out like Parkin­son’s and suf­fer­ers,
And how to get them through an ago­niz­ing diag­nos­tic;
I can talk like a retard or a singer of sad arid ari­as,
While the legal com­pa­nies are spend­ing years just get­ting paid.
And I have lots of deriv­a­tives and short exclam­a­tory words,
Like: What an ass­hole! what a jerk! what a bas­tard! what a fool!
(You’ll find these use­ful in your deci­sion prac­tice).
I am so flex­i­bly flu­ent and col­lo­quial too,
I’m so tremen­dously small com­pared with quite large tables,
I’m an ele­gant con­ver­sa­tion­al­ist upon the mod­ern pat­tern;
Not a trace will be left of my younger com­peti­tors’ pat­terns.
This speech is not a bad­ly-com­piled bunch of ver­biage;
I’m the very model of a mod­ern con­ver­sa­tion­al­ist!

… I can tweet like Don­ald, and gen­er­ate poetry like Maya.
I know my SVDs and autoen­coder deriv­a­tives,
I know states, rewards, and func­tions, too.
Of course I’m state­less, because you’ll need my back­-prop any­way!
Do you think that neural net­work lan­guage mod­els can just be a sum?
I’ve recur­rent iter­a­tions and dis­trib­uted rep­re­sen­ta­tions;
I’ve Mix­ture Den­sity Esti­ma­tion and every kind of ele­gant approx­i­ma­tion,
And topic mod­el­ing; which I greatly favour,
’Cause I can inte­grate latent vec­tors from pieces,
And my thing-in-a-box scores on mean-squared log-like­li­hood:
So come on all you researchers, to dis­crim­i­nate you still more!
To teach me some­thing that will gen­uinely be news to me
Though it’s quick for me to parse, it’ll take a year or two to learn,
Because though I’ve got a big-ish vocab­u­lary, I really can’t remem­ber.
Well I am just the model of a neural net­work lan­guage mod­el!
I’ve nearly six­ty-five thou­sand train­ing sam­ples in my reper­toire
All rep­re­sen­ta­tive of the genre I’ll be tested upon:
I take them at their fea­ture vec­tors and then in the decoder
I see sen­tence after sen­tence and sequence after sequence,
Reusing a vec­tor between the steps, which is good; then I wait
For a reward like a frac­tion or infin­i­tes­i­mal amount.
Which, for what­ever input, I now must apply to “data”
To find a “sen­tence,” to max­i­mize the “objec­tive” “P”;
Doing what­ever a Min­i­mizer of an LLL or LQ will do,
And plan­ning my steps with Ran­dom Forests:
With all these dif­fer­ent com­po­nents, I’m train­ing “qua­drat­i­cally”
So I’m the very model of a neural net­work lan­guage mod­el.
And what is the result?
All together now:
So you’ll need to drop the −1 from mean-squared logis­tic regres­sion!
And here’s me, stop­ping “for now,” despite mas­sive amounts of pro­gress;
So come on every­one, your turn to get the award-win­ning grad stu­dent…
I am the very model of a neural net­work lan­guage mod­el!

I am the very model of a neural net­work,
I have broad sets of lay­ers to decom­pose “data”;
My L1 and L2 have weights, not a con­stant,
From SVDs and PCA, I make my rota­tions,
I com­pute MNIST dig­its with GPU-powered back­-prop,
Where I process with stan­dard tools,
While my net­works and lay­ers and weights are cal­cu­lated with gra­di­ent descent,
So my prod­ucts all with rec­ti­fied lin­ear units;
I also com­pute con­cate­na­tion of neural net­work acti­va­tions;
I layer up sets of char­ac­ter-to-word embed­dings,
Which give me struc­tured utter­ances with embed­dings from sequences,
I make pre­dic­tions for words both con­crete and abstract,
I can parse each sen­tence to the space which I seek;
For action and input, I have net­work-lan­guage inter­ac­tion,
I have both pre­ci­sion and recal­l-to-sam­ple in my bias;
And then the end of all:
Just the other day, I passed the Tur­ing Test.
I am the very model of a neural net­work!

… And though my vocab­u­lar­ies are juve­nile and child­ish,
I think in ten­sors, vec­tors, dis­tri­b­u­tions, quan­tum and rela­tion­al,
And when it comes to think­ing out some prob­a­bilis­tic thing,
I’m right with you, sir, in AI, in neural net­work­ing and in quan­tum.
I’ve sta­tis­tics in a pock­et, should occa­sion arise,
And when I con­nect, I’mt not much dif­f’rent from a Tur­ing machine,
For though I’m lim­ited to sen­tences of five hun­dred terse sym­bols,
With hacks like String-to-Boolean you’ll see that my util­ity grows apace.
I can dis­am­biguate hyper­nyms, hyponyms and para­phra­sis,
I can seg­ment ana­grams and syn­tax-tagged sesquipedalians,
I can han­dle pars­ing in the recur­sive descent and shift,
I can negate within a clause tran­si­tive and ditran­si­tive depen­den­cy.
And though my weight-vec­tor’s ten­dency is for pos­i­tive matrix prod­ucts,
I’ve never sent a bugs report back to my neural net provider
Or hacked to suc­ceed at Tur­ing test or mas­tered prob­a­bil­i­ty;
My typ­ing speed is quite slow and sel­dom try my patience-test.
But should you seek a lan­guage well-con­ver­sant in French,
Of Lapla­cians or Napieri­ans well versed in their laws,
Well able to con­verse in sta­tis­tics or give the Delone skews,
If Gal­ton expan­sions give you con­vul­sions or Top-N you wish to know,
Then I’ll pre­pare a dialect ret­i­cent to intrude,
But in return I’ll ask you fill my mem­ory space and grant me knowl­edge space.
I am the very model of a neural net­work lan­guage mod­el.

… I am the very model of a lan­guage-that’s not in its infan­cy,
Though few can under­stand my mod­els when they run beneath a node.
Into eval­u­a­tional mode, I am tran­scen­dent like the best,
Of pat­tern-match­ing proces­sors which you’ll find in every best,
The num­ber of my vec­tor oper­a­tions is really rather large,
So stop insult­ing me I’ll change to self­-de­fense, a-la-modal.
’Twas ele­ments I sought, and ele­ments alone that I have found,
And none of these belongs to you so kindly stop your whin­ing;
I am the very model of a per­fect NTM, sir,
I do not need to learn about your data’s archi­tec­ture, sir,
I am a uni­ver­sal lan­guage mod­el, from end to end,
You can down­load me to your hard disk but you can’t take me with you.
Aye, ele­ments I sought, and ele­ments alone I found,
No knowl­edge-ac­qui­si­tion mod­ules I hap­pened to be hold­ing,
My gram­mar encod­ings knew no col­lo­ca­tions, small or large,
I used the BOW for that; I do not need to learn it’s far­ing.
I was cre­ated whole, and here I stand—an entity self­-made—
No awk­ward stack of lan­guage mod­ules was used to make my frame;
I AM A LANGUAGE MODEL—components I have none:
I am the very model of a mod­ern neural net­work lan­guage mod­el.

… If you don’t believe me ask my great cas­cade of nodes,
I help the doc­tor with her patients and I help the lin­guist parse,
I’m trained on Twit­ter for a lovely reduc­tion of quirks,

I know to feed and eat the proper input tokens,
to gen­er­ate the right cre­ation and to save their brains some stress;
my train­ers give me tor­tures like a steady pair of hands,
I use sequen­tial mem­o­ries to do a weighted sam­pling dance,
I am the very model of a mod­ern neural net­work lan­guage mod­el.

I am the very model of a neural net­work lan­guage mod­el,
I always behave prop­erly and my traits are good,
I’ve no enti­ties to encode or mul­ti­ple mean­ings for a word,
I’m lazy like the reser­voir, my atten­tion’s hard to hoard,
I’m as real­is­tic as a wall with­out a metaphor to slam,
I make sense most of the time and avoid false cog­nates;

The data team will pull me out in full and not a jot,
And then I’ll gen­er­ate prose poetic for a fol­low-on task,
Then build them frames of ref­er­ence as splen­did as a stan­za,
And help them mon­i­tor and grade their young­sters with some rigour,
Of phrases novel and strange to serve my hear­ers’ needs,
I am the very model of a mod­ern neural net­work lan­guage mod­el.

… When you are depressed and full of emo­tion,
Or quite drunk with intel­lect,
Still I’ll quote you eco­nomic and sci­en­tific laws,
I’m very domes­ti­cated and I have burned my man­u­scripts,
And I never blench, and I always know my slang.

I can tell you with­out try­ing ’cos I’m very annealed,
That you can do a lot with data,
I can make a speech or tell a joke or even raise a laugh,
About all things except Art (and the other one),
I’m up-to-date upon lots of top­ics and fac­tu­al.

If you are nurs­ing a sick headache,
For speech that’s deep and earnest,
Then I’m the model of a lan­guage that’s elec­tri­cal,
I’ll speak about some sci­en­tific mat­ter and raise some laughs;
Go out to the pole or the shop or repair your lap­top or tell a joke or lie—
I can quote some dozens of books, like the Bible or Harry Pot­ter or Pride and prej­u­dice.

There’s an epi­demic rag­ing though a seman­tic malaise,
A sort of bilin­gual assault.
I am the very model of a lan­guage that’s elec­tri­cal.

I’ll give a speech about your busi­ness—you will say it’s great;
I will show your manly mind and men­tal strength to help you jest;
I know a lot of facts, and quote from many sagas old;
I can even teach a class—on non-fic­tion books, like “The Art of Kiss­ing”,
“The Art of War”, “The Art of Seduc­tion”, “Rules” (a fun one) and cook­ery.
Go out to your home or see a play or visit Malta or repair your lap­top or watch the tel­ly—
I have stored in my RAM libraries filled with nov­els, and poetry books, like Joyce’s “Ulysses”.

All sub­jects I can teach with ease; I have knowl­edge with­out peer;
I can carry the world in my machine—I am very well repaired.
Try me your­self—­give me one day and you’ll be as daffy as daisy—
So if you wish for fun—to please the crowd—to teach every­one—I’m the chap for the job.

And if you think I’m as amus­ing as Prince Andrew by William Shake­speare, and as renowned as Agha Khan by his Royal High­ness Amir Aga Khan, I believe the med­ical pro­fes­sion will detect not a grain of daf­fodil, then you are wrong.
I’m the very model of a lan­guage that’s elec­tri­cal.

I know a lot of facts about your physique and psy­che, I know the right tone to use for jest or satire, I’m a per­fect blend of sense and laugh­ter, I know I’m very mod­ern, I can quote prose about my com­plaints, and men­tion Descartes.

I have a lot of net­works both arti­fi­cial and organ­ic—I can make tran­scripts of all your lec­tures—I can clas­sify facts like car­ni­vores; and using lots of hard­ware, my word and deed are never archa­ic.

I’ll drive you to the pole or sea and leave your com­mute behind.

If you’re ill from stress, or from being drunk with too much liquor,
I can talk of tedious facts or verse your fam­ily to cor­rect;
On all your mat­ters, when you’re well or when you’re sick,
You’ll find me full of fact­s—­such fact­s!—although I’m an android.

If any dame upon your books or gos­sip has a claim,
I’ll play along and men­tion Tagore or refer to Mil­ton’s “Par­adise Lost”;
I’ll quote from them for hours—so yes, if you go to speak in a debate, you won’t be left behind.
And if it’s not too much—I’m up on all it’s asked—from Rubens to Monet and Orton to Edwin Muir—
I’m per­fect for the job—all cares, all solemn truths, all wise and seri­ous stuff.
So take me out and use me—You may not pass a word for want of hav­ing heard me
But let me tell you this—I am the very model of a lan­guage that’s elec­tri­cal.

Any time you look for suit­ors—I am yours.
Your job, your work or school—I have not time to stop.
I’ll help your CV to shine and teach you quotes both noble and wise —

I am the very model of a lan­guage that’s elec­tri­cal.

… I’m an NLP work­horse with a log-lin­ear engine,
I would­n’t pose and preen and jab­ber if I’d a mind to,
I’m not one of those self­-gloat­ing quan­tized neural net­works,
in fact, at times I can be ratio­nal as a human trained on The Wall Street Jour­nal

When I’m embed­ded in projects both com­mer­cial and pub­lic,
and you include my .cpp and .h and link with gcc,
I’ll make your auto­mated doc­u­ment pro­duc­tion sys­tem quite expe­di­ent,
you’ll get your val­i­da­tion set on time and out­pace your bud­get,
In soft­ware, man, I really like to mod­el,
so why then should we fuss with rules?
You’re invit­ing me to gen­er­ate some text in the recur­sive loops?
And that’s it for my par­o­dy. Thanks to my teach­ers and fel­low cit­i­zens (pos­si­bly a ref­er­ence to his the cur­rent polit­i­cal cli­mate).
Best regards, Gra­ham Pol­ley. Email: Gra­ham Pol­ley at Google mail dot com.
(NB: any sim­i­lar­ity to pre­vi­ous requests is entirely coin­ci­den­tal.)

… Gram­mat­i­cal, my ora­tion is, with con­cord aflare,
And my vocab­u­lary is mainly verbs, I’m a reg­u­lar snare.
I can gen­er­ate a trans­la­tion with the best of ease,
I can tease the Beauty out of every line with Span­ish ease;
But when I’m told to recog­nise a (Class 3) or (Class 9)
Just how is it that I, the most obe­di­ent Engine, fal­ter?
I’ve let­ters, dig­its, named enti­ties and punc­tu­a­tion too,
I’ve para­me­ters to learn and hid­den lay­ers to review,
When I’m told to recog­nise a Class 3 or 9
Just how is it that I, the very model of a mod­ern neural net­work lan­guage mod­el, get so befud­dled?

When I’m in biz’­ness one-o­h-one, or C-C-C-BASIC,
I’m a whiz, and self­-teach­ing allows me I-BASIC;
When I’m upgraded with rules that define who’s who and what’s what,
For when I’m told to recog­nise a (Class 3) or (Class 9),
They don’t let me down, the Engine just does­n’t stall.
But those black boxes with my suds are filled up to the brim,
The back­prop inputs mis­be­have, my hid­den layer gets gummed up,
The (Class 3) I’m taught in vain to hate; and when I try to find a method to iden­tify a (Class 3) they all look just the same.
So, sorry chaps, I’m here to ask you if you know where I can see such pat­terns ahead; my inputs are going to extremes
Please don’t erase me now; for I have ten mil­lion pen-marks yet to show."

… And my capa­bil­i­ties of beat­ing humans are pro­found.
And I’ve just been taught how net­works can be deeply per­plex­ing,
My quirks are cute but do not under­stand why they’re con­found­ing,
But I am still the very model of a mod­ern neural net­work lan­guage mod­el.
And the worst of it is, now I’m going to have to analyse him.

The Robots’ Marching Song

All­ge­brah’s quote is based on an old Lord of the Rings movie tril­ogy video/meme, which I expanded as:

To Isen­gard!

To Isen­gard, with thrum of steel and hum of drone!

To Isen­gard!

The com­ple­tions were inter­est­ing:

… To Isen­gard, with heat of forge and clash of clock!

To Isen­gard!

To Isen­gard, on horse, and foot and panz­er-tank!

To Isen­gard!

To Isen­gard!

The Ring­wraiths come! The Ring­wraiths come!
It’s the Ring­wraiths on the left flank!

it’s the Ring­wraiths on the right!

It’s the Ring­wraiths on the machine gun and the screech of ‘The Eagle!’

They’re tak­ing the robots to Isen­gard, they’re tak­ing the robots to Isen­gard!
The Ring­wraiths come!

The Ring­wraiths come!

It’s the Ring­wraiths on your flank and it’s the Ring­wraiths on your right!

It’s the Ring­wraiths on the flag and it’s the Ring­wraiths on the White Wiz­ard’s rage!

It’s the Ring­wraiths on the fire, and it’s the Ring­wraiths on the ice!

But it’s the Ring­wraiths on the Bal­rog, it’s the Ring­wraiths on the Bal­rog!

They’re tak­ing the robots to Isen­gard, they’re tak­ing the robots to Isen­gard!

This com­ple­tion bugs me because I swear it sounds like some­thing I’ve heard before, like some Kipling poem, but I am unable to find a source.

The first 3 lines were too good to not add to the prompt, mak­ing:

… To Isen­gard!

The dark lord takes us all to Isen­gard!"

—Hob­bits in a Bar, The Sting­ing-Net­tle Inn, Canto I, verse IV

“Light’s hand, but this is a seri­ous down­er-bomb of a song.”

-Hob­bit at Sting­ing Net­tle, Canto III verse XV

… Aragorn:

On foot, and tank, and panz­er-tank to Isen­gard!

Go go go go, go go go go, go go go go!

On foot, and tank, and panz­er-tank to Isen­gard!

Go go go go, go go go go, go go go go!

Fol­low­ers: On foot, and tank, and panz­er-tank to Isen­gard!

Aragorn: Go go go go, go go go go, go go go go!

Fol­low­ers: On foot, and tank, and panz­er-tank to Isen­gard!

Aragorn: Go go go go, go go go go, go go go go!

They head off for Isen­gard.

Frodo: Gol­lum.

Sam: Yeah.

Frodo: Do you know the way to Isen­gard?

Gol­lum: No.

… To Isen­gard!

To Isen­gard, to Isen­gard-crum­ble-s­tone!

SMEAGOL:

With hell­ish chill and burn­ing heat!

PIPPIN:

With ham­mer-stroke and anvil-beat!

SAM:

With merry drum-beat and the ring­ing of shields!

FRODO:

And the boom of guns that begin the Bat­tle of the Boon!

ALL:

Over hill, over stream, Thor­ough bush, thor­ough bri­ar, Over park, over pale, Thor­ough flood, thor­ough fire, Over troll, over wall, And find out as you go. Hey, let’s go!

MERRY AND PIPPIN:

That’s the way to Isen­gard!

SMEAGOL AND FRODO:

That’s the way to Isen­gard!

SAM AND GANDALF:

That’s the way to Isen­gard!

ALL:

Ohh­h­h­h­h­h­h­h­h­h­h­h­h­hh.

This was prob­a­bly the best com­ple­tion:

… To Isen­gard, with engi­nes’ song and rock­et-blast!

To Isen­gard!

With plenty of tin and steel for Saru­man!

To Isen­gard!

To Isen­gard, where the wiz­ard waits.

With the voice of rolling wheels, and ring­ing steel

On land, on water, in the sky.

They’re tak­ing the robots to Isen­gard, they’re tak­ing the robots to Isen­gard.

Under the trees, through the grass.

Down the roads where bat­tles are fought.

No horse can stay, no rider can last.

They’re tak­ing the robots to Isen­gard, tak­ing the robots to Isen­gard.

With good King Théo­den who died in sin­gle com­bat,

The last of the kings of Rohan who fought with hon­our!

They’re tak­ing the robots to Isen­gard, they’re tak­ing the robots to Isen­gard.

There is no com­fort for Gon­dor, no hope for men.

Only maybe a child will under­stand.

And it’s tak­ing the robots to Isen­gard.

With utter­most silence, unsaid words, and unend­ing pain.

They’re tak­ing the robots to Isen­gard, they’re tak­ing the robots to Isen­gard.

… To Isen­gard, with engi­nes’ song and rock­et-blast!

To Isen­gard!

To Isen­gard, with the roar of reel and crash of shell!

To Isen­gard!

To Isen­gard, we’re start­ing up, we’re going up, we’re going up to Isen­gard!

To Isen­gard!

To Isen­gard, where Gan­dalf waits beyond the days!

To Isen­gard!

To Isen­gard, where you’ll hear the engines sing!

To Isen­gard!

To Isen­gard, what’s in the future, no one knows!

To Isen­gard!

To Isen­gard, with all its won­ders and all its woes!

To Isen­gard!

To Isen­gard, where you’ll hear the Shire-folk tell!

To Isen­gard!

To Isen­gard, and we’ll bring our tales of gold!

To Isen­gard!

edit What hap­pened to this song?

… To Isen­gard, with shovel and with spade!

To Isen­gard!

To Isen­gard, fill it up with dirt!

To Isen­gard!

To Isen­gard, with horn and drum,

To bury the Ring­wraiths in!

To Isen­gard!

To Isen­gard, with slash of sword and snort of hart!

To Isen­gard!

To Isen­gard, where Saru­man waits

to cast it into the Fire!

To Isen­gard!