Ahh, and you've made the wise decision to NFTify the fucked up Homers! Nothing says "investment vehicle" like-- hey, wait a minute!! That's a fucked up Bart!
Frankly, I'm mocking it. Or trying to. Riffing on it? NFTs strike me as obvious nonsense -- why not use all that energy for something more useful, like piping /dev/urandom to /dev/null -- but NFT sales of obvious nonsense, of an algorithmic corruption of an artistic representation of a shared cultural totem, are almost high art.
Like, unpleasantly high art. Way too high art. Art that makes me want to go lie down for a bit.
It's just digital trading cards, like pokemon or baseball cards. Just amateurs are running the show now, but the NBA already got in on this, so if you want something more legit: https://nbatopshot.com/
Hey now, they're not just "digital trading cards, like pokemon or baseball cards"! They're also an enormous waste of resources!
Yeesh. At least collectors of sports memorabilia have some memorabilia to admire. I can't imagine what value-add there is for "NBA Top Shots" that I can't replicate by backing up a short clip of the play. My solution is more durable, too!
All of money is imaginary and all things have a representative monetary value. All of the above are imaginary things. Work from this basis, the rest of this will make more sense. So now if you use your imagination, we can start lining up some value. What value does gold have in World of Warcraft?
You can admire your digital trading cards! Just check out this program I made that cycles your NFT's as background wallpapers for your computer. github.com/NotaRealThing /s
No, gold is an amazing substance in its own right. There are rarer and scarcer metals, but gold is special. This conversation relies in some part on the ductility and conductivity of gold to power our computers. It doesn’t tarnish and it is very malleable. This means you can shape it into some very beautiful and durable forms with even crude hand tools. The potential value add is very large. It’s use to back currencies is largely symbolic but it was chosen for good reasons.
Gold has properties that are similar to copper (highly electrically and thermally conductive, lustrous, and malleable), with the added benefit of being corrosion resistant. Many of the industrial applications of gold and copper are similar.
Which would suggest that if gold and copper had the same supply, the demand would be similar and thus the price would be the same. It turns out that's approximately correct— the ratio of gold and copper's prices is similar to the ratio of their supply.
These are awesome! Thanks for putting your work out into the world, turtlesoup.
In my free time I make paintings of outputs from GANs/Deepdreams/etc. I've been wanting to paint something based on a model trained on illustrations, but I hadn't seen anyone decent GAN trained on that kind of dataset yet. Thanks for sharing your code, this is really exciting to see!
Given the rise of Loot and other text-based NFTs, you might want to NFTify this too. I know Loot does this by creating a URL with the text in it that points to a text image generator.
Which is a bad strategy as dictated by signaling theory [1]. There's nothing rare or costly about them when you can keep hitting refresh to get a new one.
Instead, if there were only a few produced in the world, and you owning it give you some kind of status, then you'd have a real potential NFT.
It is worse. Anyone can own it. NFTs don't avoid copying, and they are in no way recognized as copyrighted material. Its like having a proof of ownership of a public good: worthless.
filtering objectionable content actually requires you to build a strong AI model capable of being offended itself, so it knows to hold its tongue in mixed company
edit: lest i leave this comment totally useless, the chatbot engine “chatscript” has pretty good capabilities for disambiguating word meaning and classifying the meanings into “badword” and “verybadword” - its free/libre software and very high performance.
Or, better yet: we should all work to make sure every human being has their basic needs met and is treated with respect, and then words like this wouldn't have as much power.
I think actually solving this problem is somewhat loosely reducible to human level intelligence in language understanding. The next best thing is a pile of patches that fix cases of the increasing creativity of the human adversaries to the system as we become aware of them.
You should aim higher than that. After all it was human level intelligence that got a proffesor fired for saying "nèige", a Mandarin filler word, in a lecture about filler words in other languages!
a simple blocklist might be just the cheaper and easier solution. After all, blocklists were, and still are being used to filter human output as well.
And fail-safe. A person with a hobby project does not have a legal/pr department to deal with the consequences of AI having a bad day
I have a feeling the parent poster realized they were only mentioning the word, but chose not to write it anyway. For a lot of people, it is a very uncomfortable word after all.
Perhaps throw10920 thinks I should have just used the word because I was only quoting the output, so it wasn't really me saying it, but I chose otherwise. I don't need to read up on the use-mention distinction to decide whether to make such a choice.
Has it generated a word for the noise you make after sipping a cup of tea or a cold drink? The “ahhhh” sound. I nominate the word “fonce” if it’s not already taken.
“The pair sat foncing so noisily over their hot cups of tea that it drove everyone else from the room”
It's got a good grasp on the difference between Norwegian and Swedish, I see; it defines Norwegian as "very light, quiet, or peaceful; tranquil", as in "we live our lives Norwegian in a quiet quiet city", and Swedish as "wicked or excessively wicked", as in "his swedish exploits". Haha...
I'd suggest this code to follow: https://github.com/lucidrains/stylegan2-pytorch/blob/fc22408.... The standard practice is to interpolate between the source latents and target latents and feed the interpolated values into your GAN. There are various ways of doing interpolation (i.e. not always straight linear interpolation)
I think what it does with real words is almost more interesting. The definition it picked for the word "real" was accurate, but kind of odd. If I recall correctly, "real" was defined as, "committed", or "dedicated", as in "real fans only like the vinyl edition of that record." Which is like, true for sure, but not in the top 10 things I'd think of first.
It would be glorious if we could share definitions. Who wouldn't want to tweet "chadocracy"? or "prospectivism, the belief that an action will have a good outcome".
It's a refinement of a lightweight version of GPT-2 by Hugging Face -- https://huggingface.co/transformers/model_doc/gpt2.html. I don't recall exact numbers, but once I had the structure of the problem right (i.e. sequencing words, part of speech and definitions) it was around 12 hours on my old 1080 TI.
Well duh obviously it exists, arent you reading it? Therefore it falls in the realm of existence
lol jk, im just fucking around with the semantics of words here. However I have to admit, this thought about existence or truthfulness of concepts or statements being spitted out by unsentient machines that mix and match infinite "real" patterns, has me quite worried...
Sometimes I find myself reading at a whole discussion thread, and I get the uncomfortable sensation that everything I've been reading is a bunch of bots training each other's models...
Not quite when the concepts are complex enough, all right, it is easy to spot pointless mouthfuls deviations of a main subject being discussed, but what about, for example, the user reviews on an amazon product? Or a youtube's vid comment section? Or whatever shit you consume from the internet.
I'm by no means any expert in the subject, and current state of the art of the Turing test, but I've seen just enough GPT-3 whichcraft to start being totally skeptical about anything I see online.
sorry for the huge rant, but i took a great effort to make it sound like its coming from a real person XD
also, to @turtlesoup: Thank you, that was wonderful. I hope you enjoy this cup of coffee too :-)
I played with this and it is super interesting (almost made me register a couple domain names!) That said, to me, it once again reinforces the belief that a large factor in GPT-3 amazingness is the taste in prompting/filtering that humans apply to it, that is, it produces a ton of crap that does not catch our eye that we just silently ignore and discard, but we will amplify and share amongst ourselves the output that is interesting, which imputes a huge selection bias external to the intelligence of the model itself that we may perceive as the model's.
Computers aren't creative; the excitement about GPT-3 is humans projecting something into its output or filtering the small number of bits that appear to make sense, as you say.
Neural language models are just recycling bits that humans have said before, they address well the "how to say" part of NLG (Natural Language Generation) but fail with respect to the "what to say" part.
I was starting to write a response, and realised how patterned the debate is.
We've been discussing the topic of machine intelligence since the start of CS and points of contention tend to show up in the same places.
Anyway, it occured to me that if given 10 tries, GPT-3 probably produces my comment adequately. I have an uneasy suspicion that it might take fewer tries than that... especially if prompted with "netcan, please respond."
Anyway, the upshot is that for someone like me, on the "if you can't tell the difference" side of the cliche... your side's insistence that machine's can't be creative makes me doubt my own creativity, to the extent that the argument is convincing.
I'm on the "if you can't tell the difference..." side, too, but the issue is the "10 tries" and the prompting. GPT-3 has no ability to revise or reconsider or judge anything it writes. We're still safe, IMO, until the main loop goes
>generate 10 "netcan comments"
>drop 5 least conformant results
>reword remaining comments several times
>reassess rewordings, select best of each set
>select 1 most conformant "netcan comment"
Right now (AFAIK, anyway) all GPT-3 does is the first one. Think about how a human is creative -- lots of drafts, lots of dead ends, lots of borrowing, lots of revision. I appreciate that "reflection on its own output" is waaaay out of scope for a glorified, omni-contextual Markov chain, but I think we're safe, both from the threat of GPT-3 being "creative" in the same way we are (and therefore, maybe "alive" in the same way we are) and the threat of our own mental processes being revealed to be as merely computational as those of GPT-3.
Oh I don't disagree that the "best of gpt-3" on twitter is automated hyperbole machine.^ But I think at least in HN, a lot of people have looked at somewhat more objective sources too. The standard way to prove a trick shot is to film yourself do two or three in a row. I agree these are tricks, and a lot are unproven. Some are proven though, FWIW.
Anyway, if the damn thing can do me 10%-60% of the time in a non trivial context... IDK, maybe this just becomes the default standard for "non trivial." and it's no big deal. It is disconcerting though. In any case, assuming GPT-3 progresses... there might be uses for software that does just the first of those things.
All my above comment really means for sure is that NLP can now participate, simulate and perhaps instigate flame wars better. Flame wars were always one of the easy targets, so that's not much of a standard. Who knows though, maybe the road to AGI is a gradual refinement of a flamewar bot to a dialectic philosopher. Cheating on essays is gonna start getting fun.
^If a person uses the Tom Sawyer fence trick to automate a task, is that automation? If the machine does it, is the turn tables?
Absolutely. The "problem" essentially goes away with a cyborg approach: let GPT-3 generate its 10 netcan comments, then have a human do a little refining, maybe reframe the prompt, edit the result a little, and bam! Much scarier! Not that a sufficiently determined human couldn't ape someone's comment style, but GPT-3 is a force multiplier in the same way that sockpuppet management software is for the professional sockpuppeteer -- scales better, more effective, just makes everything easier.
I've heard serious speculation that GPT-3 (or certainly its successors) might find utility for writers as a combination of a ghostwriter and Github Copilot.
>^If a person uses the Tom Sawyer fence trick to automate a task, is that automation? If the machine does it, is the turn tables?
It's not here yet, but the grim day on which I no longer communicate with anyone on the Internet for fear they're not actually a living conversational partner approaches. Even those I can cryptographically prove are people that I know will be suspects: "euurgh, I'm too busy to talk to this guy today, he's so boring. I'll just feed GPT-10 all our old conversations, tell it to be me, aaaand..."
Messaging apps already do a bare version of suggesting auto-replies. It's mostly it's a texting-while-driving aid, so they keep it concise and uncomplicated. Code completion already exists. Spam. Gradual steps have places to start stepping.
this is the only way to reliably use GPT-X in a production setting. you have to postprocesses the responses with a second model, until you find one that is 1) reasonable 2) coherent 3) related to the topic of the prompt
That fact that humans can be creative does not mean that all humans are. Nor does it mean its actually all that common. True creativity is very rare. Even people we normally describe as "creatives" are mostly working off of convention and are influenced by others.
I've come to accept that 99% of what humanity does is derivative and mediocre. That's ok.
> humans projecting something into its output or filtering the small number of bits
I think this has value and is an interesting form of machine assisted creation.
There is something similar, but more simplistic (no AI, just old-school RNGs), going on in generative music. For modular synthesizers, some of the more popular modules are random sequence generators [0]. These allow the musician to generate random elements of the music, say melody or drum pattern, but curate it. You might generate dozens of elements, carefully tweaking parameters, before you hear "the right one" – which you then actually use in the music.
I guess my point is, curation of generated material is a form of creation! The generator is perhaps not the creator, but it is useful to develop better generators.
[0] : such as “Turing Machine” from Modular Thing and “Marbles” from Mutable Instruments.
I wonder how much of that is due to the task GPT-3 was trained for? Perhaps the "what to say" part is working perfectly, but what it wants to say is as generic a text stream as possible. It already seems like GPT-3 has more "knowledge" than is obvious at first glance, but you need to prompt it correctly.
I agree it's fragile and dreamlike - but it is in my opinion genuine understanding. If you have or ever do have children you can observe them pass through a similar kind of stage, fractured incomplete understanding, dreamlike, yet still understanding. It crystallises slowly overtime. I predict the same will happen as we expand the models and add more complexity.
Your statement that computers aren't creative is not true in general and it's especially not true for GPT-3. It can generate text that appears random at first glance but which has a structure hidden within. As I wrote above, there is no way to prove conclusively whether any given sentence generated by GPT-3 was actually written by GPT-3. But if we assume that all sentences were indeed written by GPT-3 then some of them must be very good indeed because many people find them interesting and worth reading. That means that GPT-3 has demonstrated creativity.
I feel like this implies that creativity is the creation of the interpretation of a work rather than literally its creation and I don't really agree with that conclusion.
The caveat being that what catches our eye when flipping through GPT-3 output is more often comically absurd [0] instead of meaningful or 'intelligent.'
0. dishwitter
dish·wit·ter
a person who eats hot wax or other food
"she was a dishwitter"
It;s true that selection bias is a factor, and the way GPT-3 is distributed tends to aggravates that.
Also, this is a human impersonator. It's not surprising that we find it personable and interesting. We find ceramic dolls personable and interesting. People can't really be trusted to evaluate human-looking things well. It's just to triggering to the instinctive biases of a social mammal. A gorilla impersonator would probably amaze gorillas in much the same way.
That said, I do think there's something to these nlp systems that we didn't have before.
The poke-poke stage of examining novel tech tends to be quirky, and most almost-insights are bogus. Stuff that's cool but useless is as compelling as useful stuff, at first. This isn't a long stage though. We usually gain a grounded understanding of a technology only when we find a use for it. Technology being used for something is what makes it technology, for some definition of.
Prompting & filtering by humans is how GPT-3 is "operated." That it's merely augmenting the creativity of the prompter is a philosophical concern. The practical one is that it needs prompting and filtering. Also, it's trivial to automate some kinds of prompting and filtering.
There's a subset of words produced by the site that trigger obvious and amusing ideas of what they could mean in the reader, but the generated definitions have nothing to do with that, and usually do not make much sense (besides being sometimes grammatically incorrect). It's evident that the software has no semantic understanding of what it generates. The rare occasions where the definitions appear to (almost) make creative sense are just statistical flukes.
That said, if I go through this for 5 minutes and pick the 10 best words they will be much better than the 10 best words I could make up in 5 minutes by myself.
I feel you could use it for like 10 seconds to get some inspiration and then trivially come up with something much better. It’s kind of like brainstorming with somebody who speaks before they think.
This isn't even 'GPT-2'! Since that usually refers to GPT-2-1.5b. It's GPT-2-117M... distilled, so an even smaller version of the smallest GPT-2. Amazing it works so well anyway.
Appears to be a collection of perfectly cromulent words; someone should propulgate them into wider use in order to embiggen the vocabulary of our youth.
I call it the "too lazy to set up a database" pattern (I'm the original author :P). There should be an signature in there too so hopefully tamper-proof.
The generated "using it in a sentence" examples are often the funniest (and most absurd) part:
chokebone
choke·bone
the muscle holding a prisoner down, used in controlling an animal or person such as a cat, dog, snake, snakebite, or spider
"he managed to regain the chokebone and win the auction for his 12-year-old daughter"
a bomb which, in addition to the usual stimulants such as caffeine, turns out to be deadly if inhaled by a person having sexual intercourse
"a young man survived a blast of coffeebomb, killing two men and wounding five"
I got loctite, which is a brand famous for making thread locking adhesive - so much so that the product is generally referred to just loctite in the industry, similar to tissues/kleenex and vacuum cleaner/hoover.
Apparently now it also means "a mineral of the basaltic and orogenic variety, typically occurring in white phosphates and blue crystalline crystals" - I love that the blue crystals are crystalline too!
Hmm. I think it might need some more work. I mean, there's a strong argument that unprestigious is not a word, but if it was, that certainly would not be the meaning.
I'm not sure why, but "non-prestigious" feels more "correct" to me. I know that those prefixes aren't identical in meaning though, so both could be words with slightly different meanings.
My word was a bit disturbing. Meat cooked in a sauce of raw meat over ice. Yum.
Plumata:
a dish with layers of meat, vegetables, or seafood cooked in a sauce of raw meat, vegetables, and herbs, typically over ice and typically served with other fresh ingredients
parceleducate
parcele·d·u·cate
transfer (the intestines) into offspring, typically as a sacrifice
Sounds like an SCP ritual.
SCP-6121
Object class, Keter.
Upon the birth of offsprings SCP-6121-A, subject will feed SCP-6121-A via the process of parceleducation. Intestinal matter will be sourced from the closest biological entity within a 300 metre radius, replacing the organic tissue with an organ SCP-6121-B.
The Church of Featherless Birds is known to invoke parceleducation as part of its initiation ceremony. Those possessing SCP-6121-B are known as disciples of the Vulture King.
Steak tartare is one of the most underrated dishes. I prefer mine after a few minutes over a flame grill, a piece of cheese, and a round roll. That can turn it into something that I really think would catch on if people tried it.
cronyize: convert (something, especially the skin of a pregnant woman) to a cork or tar by treating the cork and tar with ice or other heat-resistant fat, then applying the skin on top of it to make sure it is moisturized
a woman's loose fitting undergarment, typically made of a soft cotton tweeden
"we ordered a pair of bright-colored polo pants and jumped at the chance to wear a bra with our favorite pussyhat"
I got "boochy" and I'm going to use it today at standup: "having or showing a characteristic or exaggerated enthusiasm for a particular activity or situation"
ludocystectomy
lu·do·cys·tec·tomy
a surgical operation after sexual intercourse in which a lictor appears to cause men to ovulate but then to become unconscious
*"he had ludocystectomy to ease his symptoms to a lesser extent"*
Is creativity going to be the first major human trait upended by AI?
I feel like the trope has always been "But the robots will never be able to express beauty/art/music/prose like humans". However it seems that all early AI is focused on and doing surprisingly well at creative pursuits. If someone rattled off words like this I would be amazed at their creativity.
We're finding that stuff we thought would be impossibly hard is subject to brute force using simple constructs, and stuff we thought would be simple is proving incredibly difficult.
Funnily the stated definition seems to fall for its own trap. Perception, here named as a particularly hard thing is something AI is starting to get pretty good at. Mobility lags behind a little, but we've seen some incredible demonstrations there too.
Perhaps it's always the things that are on our awareness horizon which seem hard but end up solvable. What's next? Perhaps long term planning, interference and on the fly learning? And then what are the difficult things beyond that horizon, the challenges we can't even see yet?
First trait? What about computation, memory and logical reasoning? Computers crush people at all of these and have been doing them so well for so long that you don't think of it as AI. Still, definitely not the first human trait that computers can beat.
Creativity is hard to talk about in this context for a bunch of reasons - it's hard to define, I don't think it's that impressive in humans, AI models are already decent at creative tasks like image and music generation but those are all based on training examples...
That's fine, I guess, for a silly website - but it's not hard for me to imagine a company taking this idea, "AI/ML created X" and selling X's, even copyrighting them... when X already exists in the real world
copyrighting isn't (at least, in US law) a distinct action people take. If it is subject to copyright, the it is copyrighted on creation by operation of law.
> when X already exists in the real world
To the extent that a thing is copyrightable by nature , coincidental existence of an identical thing doesn't make it any less so. It might make it difficult to prove it as a creation rather than a copy if challenged (or to prove that an alleged infringement was a copy of it rather than the identical doppelganger), though.
Also, some words used to exist but have fallen out of use or morphed. For example "wif" is an archaic spelling of "wife," but the definition given on the site is "an animal's fur or fur stock."
I imagine in real life applications you’d need to add a bit of checks and balances around the ML output (e.g. cross check against a dictionary/copyright database)
> bruegela
a thick, smooth-skinned black pepper with a slightly bitter taste, of the red pepper family, widely used as an anesthetic to treat conditions affecting either limb or the hands, such as gallstones (also see green pepper)
"these great kinds of bruegelas flavor every bite"
Haha that’s a wonderful example of how pattern recognition absent understanding can generate nonsense.
When this was posted last time, I read it as "This WOD (Workout of the Day) Does Not Exist". It inspired me to make a workout generator trained on workouts from crossfit.com using a character-level RNN.
A year removed from it an I still laugh at some of the ridiculous workouts.
I actually got a real word, albeit with an incorrect definition.
adjective.
nonexclusionary
nonex·clu·sion·ary
of or concerning people or organizations that don't receive due respect
"nonexclusionary criticism of religion"
a word that does not exist; it was invented, defined and used by a machine learning algorithm.
frood: noun
(in dogs) a thickly furred section between the forelegs, covering the upper upper surface of the hind legs; a hindleg
"a ferret with an astonishing, yet brief, frood"
I was reading some reviews on Google of rave clubs in Budapest and Google translated them from Hungarian to Croatian.
The translations were quite funny and bad but I noticed a bunch of new Croatian words that do not exist in any dictionary.
There were a couple of reviews where I found myself laughing because I implicitly understood the review but the Croatian words were completely made up (Googling the word you won't get any results).
So I guess the translation systems might already create words, especially for language pairs that don't have enough data.
> There were a couple of reviews where I found myself laughing because I implicitly understood the review but the Croatian words were completely made up (Googling the word you won't get any results).
Is it possible some of those were actually Hungarian words being copied verbatim into the Croatian text? Or maybe close variants of the Hungarian words (such as in a different grammatical case)?
I've seen before, when translating from one language to another, if Google Translate doesn't understand a word, it just copies it verbatim from the source to the target. I don't know whether this is true, but it seems possible to me it might even sometimes "normalise" the word when doing so (e.g. if it doesn't recognise the word, but recognises it as being in genitive case, it might convert it to nominative and stick the word "of" in front when translating it to English, assuming the source language's grammar is sufficiently regular to permit it to convert an unrecognised word to a different case.) I've definitely seen it transliterate unknown words in the source language before (when translating from languages with non-Latin scripts.)
Doing something like Hungarian to Croatian is likely worse, because it is probably being chained through English instead of being translated directly, which doubles the possibility of odd things like this happening. Since both Hungarian and Croatian have grammatical case, if it is doing the kind of "case-based normalisation of unknown words" I was talking about, it might do it twice (Hungarian->English then again for English->Croatian), creating even weirder results.
Hungarian and Croatian are very different. There's only a few things that might match (like vegetable/fruit names) but verbs and nouns are very different.
The words that I was surprised by were with proper declension (taking gender into account) and sometimes there were very unique verbs that felt right in Croatian but if you search any form it just doesn't exist anywhere.
If you can remember any of the Hungarian links you observed this behaviour on, and if you can cite any of the specific words from the machine translation you are talking about, I would be appreciative – not that I know any Hungarian or Croatian, but still this topic has piqued my interest
I tried out "portmanteau" a word which actually DOES exist and is kinda what you're going for here. From Wikipedia:
A portmanteau or portmanteau word is a blend of words in which parts of multiple words are combined into a new word, as in smog, coined by blending smoke and fog, or motel, from motor and hotel. In linguistics, a portmanteau is a single morph that is analyzed as representing two underlying morphemes
Your tool says:
"a word that probably exists; with an alternative definition made by a machine learning algorithm."
Interestingly, I've got "kaufmanism" (and a random machine learning definition); this word however has recorded usage:
> A "Kaufmanism" is the persuasive rhetorical juxtaposition of words that reverses the subject and object of a phrase often meant to change its context and meaning, typically used to add additional emphasis to both nouns.
I once dreamt a word, and when I woke up and looked it up, it didn't exist anywhere. I wonder if I refresh this page long enough, whether it would eventually appear.
Sorry to the author but this one doesn't pass the sniff test for me.
Generally, the syllable breaks for these words are not quite correct. The two words that I got were Paulotomy and systerectomy. These were broken into Paulo-tomy and sys-terec-tomy. That generally goes against length-of-syllable-in-English rules that... I can't articulate. I would break these into pau-lo-to-my and sys-ter-ec-to-my (sys-trec-to-my would also be acceptable).
Syllables should always have only one vowel (technically nucleus) in them, which means that the bogus "syllables" the website is producing can't be real syllables - that might be the rule that you can't articulate. However, the website actually does not use GPT-2 to produce the syllables, it uses the library "pyhyphen".
This library provides (misleadingly) a function claiming to create a list of syllables from a word, which does not actually do that and instead splits the word up by all possible hyphenation points - and not every syllable break gets or indeed should get a hyphenation point. (For example, you do not want to break up prefixes or suffixes during hyphenation.)
Would it be correct to say that syllables in English are made of nuclear vowel phonemes and valent phonemes which could be vowel or consonant in nature? I’m not a linguist, but I’m very interested in language and how words are constructed.
I'm not sure what you're referring to by "valent phonemes." The standard treatment of a syllable, not only in English but cross-linguistically, is that a syllable is composed of an cluster of consonants at the beginning (the onset), a single "nucleus" phoneme that is most often a vowel or, somewhat less often, a sonorant consonant like 'r' or 'l', and another cluster of consonants at the end (the coda). The specific ways that syllables can be formed in a particular language are governed by the "phonotactical rules" of that language. These are rules like the English rule that an "ng" sound may only occur immediately after the nucleus, or like the Japanese rule that a coda may only be "n" or "" (the null coda).
I was kind of referring to valence electrons - so think “edge phonemes” - but I don’t think that came across.
This is a really interesting explanation of something that I kind of understand, but want to have a deeper understanding of. Any good references you can think of offhand?
Reminds me of the "Black Adder" episode where he meet the pompous inventor of the Dictionary and purposely sets about using made up words just to annoy him.
pussygasm
pussy·gasm
excessive or erotic fondness for one's genitals
"he shared a cocky but disgusting puke and was subjected to even more pussygasm in his younger years"
I've long wondered about these "X does not exist" - are these generated on the fly or pregenerated and displayed from a list on the backend? If so, are they hand picked examples?
Because the hit to miss ratio on this and other ones I've seen are just too good, literally unbelievable that it's just raw output without some kind of curation.
defectariat
de·fec·tariat
a small class of people constituting a large voting bloc in elections, owing to insufficient awareness of the issues at stake
"the failure of the social welfare system to recognize the defectariat"
EDIT:
If it is generated, isn't running the kind of datasets that produce this level of results extremely expensive? Is this trend some kind of advertising of skills for job hunt?
At least for the image generation sites, a common approach for performance reasons was to continuously regenerate the content and just fetch the latest one when requested. So they're fresh and do not repeat but are not unique if many users hit at the same time.
This took about 8 seconds to come back. A little longer and I'd think this was just a mechanical turk front-end with especially witty workers taking the jobs:
This is as perfect example of just how horribly bad the typical "X does not exist" examples are, because here we all know the pool that is being used for training and can call out shitty "creations" for what they are. For instance I got "diaminoid" and "interchangerability". It's extremely transparent just how bad it is. But when people do it with faces, you don't see this, because you don't know the training set so you can't compare.
Every attempt at showing "how great ML is at generating completely new X" owes it to the people wasting their time being fooled into thinking that it actually works to always also show the closest couple of entries from the training set.
Shameless plug for my other "this x does not exist": This Fucked Up Homer Does Not Exist https://www.thisfuckeduphomerdoesnotexist.com/