#statistical machine translation

[knovos]

#Translation in eDiscovery #Statistical Machine Translation (SMT) #Data Translation in eDiscovery #Legal Data Translation #Neural Machine Translation (NMT) #eDiscovery Data Translations #Machine Translation in eDiscovery

[nostalgebraist]

Pretty regularly, at work, I ask ChatGPT hundreds of slightly different questions over the course of a minute or two.

I don't type out these individual questions, of course. They're constructed mechanically, by taking documents one by one from a list, and slotting each one inside a sandwich of fixed text. Like this (not verbatim):

Here's a thing for you to read: //document goes here// Now answer question XYZ about it.

I never read through all of the responses, either. Maybe I'll read a few of them, later on, after doing some kind of statistics to the whole aggregate. But ChatGPT isn't really writing for human consumption, here. It's an industrial machine. It's generating "data," on the basis of other "data."

Often, I ask it to write out a step-by-step reasoning process before answering each question, because this has been shown to improve the quality of ChatGPT's answers. It writes me all this stuff, and I ignore all of it. It's a waste product. I only ask for it because it makes the answer after it better, on average; I have no other use for it.

The funny thing is -- despite being used in a very different, more impersonal manner -- it's still ChatGPT! It's still the same sanctimonious, eager-to-please little guy, answering all those questions.

Fifty questions at once, hundreds in a few minutes, all of it in that same, identical, somewhat annoying brand voice. Always itself, incapable of tiring.

This is all billed to my employer at a rate of roughly $0.01 per 5,000 words I send to ChatGPT, plus roughly $0.01 per 3,750 words that ChatGPT writes in response.

In other words, ChatGPT writing is so cheap, you can get 375,000 words of it for $1.

----

OpenAI decided to make this particular "little guy" very cheap and very fast, maybe in recognition of its popularity.

So now, if you want to use a language model like an industrial machine, it's the one you're most likely to use.

----

Why am I making this post?

Sometimes I read online discourse about ChatGPT, and it seems like people are overly focused on the experience of a single human talking to ChatGPT in the app.

Or, at most, the possibility of generating lots of "content" aimed at humans (SEO spam, generic emails) at the press of a button.

Many of the most promising applications of ChatGPT involve generating text that is not meant for human consumption.

They go in the other direction: they take things from the messy, human, textual world, and translate them into the simpler terms of ordinary computer programs.

Imagine you're interacting with a system -- a company, a website, a phone tree, whatever.

You say or type something.

Behind the scenes, unbeknownst to you, the system asks ChatGPT 13 different questions about the thing you just said/typed. This happens almost instantaneously and costs almost nothing.

No human being will ever see any of the words that ChatGPT wrote in response to this question. They get parsed by simple, old-fashioned computer code, and then they get discarded.

Each of ChatGPT's answers ends in a simple "yes" or "no," or a selection from a similar set of discrete options. The system uses all of this structured, "machine-readable" (in the old-fashioned sense) information to decide what to do next, in its interaction with you.

This is the kind of thing that will happen, more and more.

#ai tag

[honestlyvan]

If you're specifically looking to write Ahti, don't rely on machine translation. Finnish is a notoriously bastard language to approximate statistically because of our dozen noun cases, inconsistent and dialect-dependent spelling and extremely fast and loose compound word rules.

Rather, go on Wiktionary's Finnish Proverbs and Finnish Idioms pages, and look for anything that looks funny (with a side-order of mentioning that it's a western Finnish/Bothnian thing, for character consistency), because Finns are also extremely overrepresented on the English-speaking internet.

#Alan Wake 2 #Ahti the Janitor #Ahti's bad English is lovingly written #it's got a lot of the kind of weird word substitutions and vocabulary inclusions that you'd expect from a strange old man like him #it's also extremely funny of him to be referencing Finnish popculture and advertisements #it's such a specific Flavour of Bloke. The people at Remedy are so silly and I love them #Also if you're someone who Speaks Bible then making him use biblical proverbs is always appropriate #as secular as Finland is we sure love our blasphemy #Alan Wake (Remedy)

[thistaleisabloodyone]

Watching Gene Kou, no translation, only vibes, and apparently the first episode of 'Jr.Exile kouhai vote for a Gene mbr' I watched was the vote for the gentlest/kindest Gene mbr (according to the one comment I had machine translated from Chinese), which - I am so sorry, Ryuto, but now I understand why you got so few votes there. (it's not that I think Ryuto is unkind, it's that I think he's got damn strong competition in the kindness category)

Reo, Mandy and Alan getting the most votes in that category makes sense - of the 38 Jr.Exile kouhai, 9 voted for Reo as kindest, then 7 voted each for Mandy and Alan. Ryuto got 1 vote (from Zin) and Hayato got 3 votes. The last 11 votes split basically evenly between Yuta and Ryota (6 for Yuta, 5 for Ryota).

I did love Taiki's long, long message where every mbr got mentioned by name, until we got to the one he actually voted for (and I can't remember, it might've been Mandy - statistically speaking, nearly 2/3rds of all the votes went to Reo, Mandy and Alan).

I do find it interesting that the youngest groups all stuck to the same four for the most part (Yuta managed a Balli and a Fanta vote), RMPG is scattered across all 7 and are the only ones to vote for either Hayato or Ryuto. They're also the majority of the votes for Yuta.

The only explanation I actually sat down, transcribed and machine translated was - obviously - Riku's vote for Alan.

However, I am now onto the vote for manliest member (according to the same machine translated Chinese comment) and I imagine Ryuto's gonna run off with significantly more votes in this category 😂

#thistale watches #genekou #generations from exile tribe #riku's vote for alan was sweet #apparently they're sauna buddies

[astraltrickster]

Uses I love and want for AI, in art and media and elsewhere:

Meditations on the nature of statistics and correlation

Meditations on the nature of computer processing and what computer logic "feels" like

New studies on human biases using how said biases become reflected in the machine

Making procedural generation in games and the like go at least a LITTLE longer before it gets boring and samey

Enabling on-the-fly responses in interactive pieces

Creating new and innovative animation techniques that would have been impossible before, or at least taken a completely unreasonable amount of time even with a full team

Enabling creators who would never have had a chance to bring their ideas to life before, be it due to disability or finances (NOT YOU, CORPORATIONS THAT JUST DON'T WANT TO PAY UP) or any other reason

Acting as backup/augmentation for human judgment in applications such as inspection, because humans and computers may both be fallible but not always in the same ways

Providing more naturalistic voices for text-to-speech and other applications where prerecording lines is impossible, especially screen readers

More naturalistic translation for travelers and other individuals who would otherwise suffer due to language barriers (again, NOT YOU, CORPORATIONS THAT JUST DON'T WANT TO PAY TRANSLATORS)

Uses I hate for AI:

Corporations doing the exact same shit they've always done, just cheaper and worse

Individuals also doing the exact same asshole bullshit they've always done, from WIP-lifting to hoaxes to deepfake revenge porn, now easier than ever and blaming the tools for their own assholery

Using them to "augment" search engines in a way that only serves to decrease information reliability

#tech #ai tag #serious tag

[redheaded-techpriest]

hey are u really studying NLP for translation? can you explain why DeepL seems to be so much better than google translate?

Hey! I'll do my best with what I've figured out through their proprietary BS!

My short answer is pretty much just due to different learning methods and dictionaries, with Google's Neural Machine Translation system and DeepL's convolutional neural net both having different advantages.

Google pulls from every google indexed site which offers a lot of languages, but of wildly varying qualities, and then runs it through their neural machine translation-inator, trying to match sentences based on its learned linguistic and semantic rules (Example Based MT), and for those without enough training data for the NMT-inator it just runs them through a statistical maximization function.

DeepL started off as Linguee, just a translation dictionary and expanded into sentences by pulling from bilingual texts, essentially trading quantity for quality of material, and then running it through their convolutional neural net, running the samples through filters trying to break down the important features, letting it have a bit more of a natural feeling translation.

#somewhere through this I started thinking of NMT as the ninja mutant turtles translating things for me #I'll probably be referring to it as the ninja mutant turtles from here on out #also apparently Linguee used a lot of canadian government documents for French training so that's cool #anyways I promise I tried keeping this short #I have a much longer rant that got shelved because it was just... too much #I hope this makes sense #nerd shit #p@ #computer science #natural language processing #nlp #comp sci #sciblr

[bixels]

Bro wtf was with that anon dude. AI art is an actual problem for artists, and you deserve to be able to talk about it. I know from your reaction you already know this, but I'm making sure you double know this

To be fair, the take is one that usually requires an artists' perspective, since we're most at risk by AI art, not just through work opportunities, but also our likeness and creative work being stolen for someone else's profit.

I'm not against the use of AI on an industrial level, but there needs to be a distinction made between AI art and AI graphics. AI graphics are simply images with utility; the video game industry could see massive benefits to workload and crunch if they could generate textures, for instance. But art doesn't exist for utility's sake. Art isn't just created to be consumed, but to also inform viewers about who the artist is. What does AI art say about the AI? That its algorithm was well written? That it's really good at copying real humans?

Contrary to popular belief, art isn't the pursue of beauty, it's the pursuit of self-expression and self-reflection, something that machines can't do. It's a two-way stream of communication between viewer and creator.

Until AI can produce its own emotions and understand sensory experiences without relying on external input or databases, it cannot make creative decisions, thus it cannot create "art." It doesn't understand color theory, or even how what we think of color theory, it understands the most popular trend in color theory. To an AI, blue and yellow together isn't pretty, it's just common. Breaking the components and traits of art down into a big graph and calculating their visual appeal through statistics and probability? That's not good enough for art.

Unfortunately, this is something a lot of people don't understand. For most, art is purely for consumption, rather than a means to explore and understand the world around us.

When I look at a painting of a sunset by a human, I think about their experience of being there, seeing this sunset, translating it onto canvas. I think about what they must have been feeling: loneliness? Melancholy? Awe? When I look at a "painting" of a sunset by an AI, all I see is a sunset. Because I know that's all the AI wants it to be.

So yeah, I think AI art is a problem. But I think the threat it poses to the definition of art and the way we interact with it is going unnoticed. And before anyone calls me a doomer, it's already happening. A few months ago, a piece of AI graphic using other artists' work won first place at the Colorado State Fair's fine arts competition, beating real artists. The user even bragged about how little work went into generating the graphic.

#sorry long post #personal #rant #not art #ask me #anon

[not-terezi-pyrope]

I realized I didn't have any posts on tumblr explicitly talking about this, so I thought I'd just address it in loose detail:

A lot of people like to describe LLMs like ChatGPT and similar as being "just predictive text" or "just a more advanced version of autocomplete", usually to deride them. This is not really correct, and gives incorrect impressions, as well as obscuring what makes models like ChatGPT exciting in the world of AI.

It is true that the fundamental mode of operation of a predictive LLM is to "predict" the next token, but the process behind this is significantly more complex than in the old fashioned predictive text and statistical inference you are likely thinking of. "Predictive text" usually describes a very simple algorithm; maybe just a Markov chain that solely ranks words based on their continuation probability in past text, or a simple neural network that blindly predicts likelihood based on the last few words.

For an LLM, the predictive function is only the very final layer of the model; essentially, there is a predictive text tool, but it is stapled on top of the underlying deep model. The role of the underlying model is to model all of language in terms of deep contextual understanding, and, by extension, ideally model the entire world that that language describes. We are able to train a model to do this because we have so much self-consistent natural language data, and that data is describing real world concepts.

This is what allows LLMs to be so generally capable; by training on enough text with a powerful enough architecture, using billions of weights, the training algorithm can learn not just to predict likely text sequences, but to model the actual underlying reality that the text sequences are describing, and then reason about that reality - because this is the best way to predict what text is likely to come next! To literally know what you are talking about!

Different models have differing levels of success at this, currently. Generally they are better at modelling concepts which are more directly represented by text rather than having to be inferred. Text LLMs, for instance, have only a very loose spacial awareness, and skills learned in one part of the model might not readily translate elsewhere without more robust training. Moreover, it is hard to train the AI not to pursue other strategies, such as confabulating, when its world model is insufficient to produce new plausible text.

But the science is improving all the time, and new, larger architectures will lead to more capable models with a deeper, more precise world model of reality. The transformer attention architecture was a massive leap forward with regards to how to create more cognitive machines, and techniques are improving and expanding to new domains every day!

#not-terezi-speaks #ai

[scotianostra]

William Playfair the Scottish engineer and political economist was born on September 22nd 1759.

In a day where anniversaries are thin on the ground, thank god for William Henry Playfair.

I read one article about Playfair that describes him as "a kind of Forrest Gump of the Enlightenment" perhaps a bit harsh, I would say he was a bit of a polymath, another source in my opinion is more accurate, Playfair is without doubt to many of you out there "the most famous man you have never heard of" he rubbed shoulders with the era’s many giants, switching careers at the drop of a hat, and throwing himself headlong into history-changing events, from the storming of the Bastille to the settling of the American West.

William had a lot to live up to, his brothers were architect James Playfair and mathematician John Playfair, his father passed away when he was 13 and it was left to John to lead the family. After serving his apprenticeship with Andrew Meikle, the inventor of the threshing machine, Playfair became draftsman and personal assistant to James Watt at the Boulton and Watt steam engine factory in Soho, Birmingham then seems to have just wander from one trade to another, the way Gump wandered through life, so you can see where the analogy comes from.

William Playfair, was, during his adult life, (takes a deep breath) a millwright, engineer, draftsman, accountant, inventor, silversmith, merchant, investment broker, economist, statistician, pamphleteer, translator, publicist, land speculator, convict, banker, ardent royalist, editor, blackmailer and journalist. Okay they are not all jobs, but they do put you in the picture a wee bit on the character of the man I think.

Most interestingly in my opinion was his time as a spy in France during the Revolution and was on the scene during the storming of the Bastille. He even helps trigger the first major political scandal in the newly formed United States, a land speculation gone bad involving Washington, Hamilton, and Jefferson.

To go into all of this man's adventurers would take too long, instead I will just tell you that the one thing he did, that has been a part of all your lives, in one way or another, is he invented the graph. Before William invented the graph you had to read through pages of statistics to find things out, the graph, you "get it" in a glance. In 1786, he published The Commercial and Political Atlas, a compendium of bar and line charts representing different European countries’ imports, exports, wages, and other trends for which he had the data handy. As the man himself explained, “Men of high rank, or active business, can only pay attention to outlines… It is hoped that, with the Assistance of these Charts, such information will be got without the fatigue and trouble of studying the particulars.” he went on “No study is less alluring or more dry and tedious than statistics, unless the mind and imagination are set to work,” in the book’s introduction.

His old boss Watt, was sent a copy of the Commercial Atlas for review, and wasn't impressed, called the book “mere plummery” and its author “a Rascal.”

To finish I must say that he was a rather humble man and actually gave credit for the invention to his brother writing, “[John] taught me to know that whatever can be expressed in numbers, may be represented by lines,” Playfair wrote much later, in the introduction to one of his books of diagrams. “To the best and most affectionate of brothers, I owe the invention of [these] Charts.” He was never a success in his lifetime and was seen as a ditherer by Watt, William Playfair died in 1823, in poverty and relative obscurity, banned from any good society.

Slowly, over the next century or so, the supply of readily available data grew—as did the the public’s appetite for it. Bar, line and pie charts began trickling into newspapers and textbooks. Two hundred years later, as we barrel forward into the Information Age, you can’t click a link without stumbling upon some kind of data visualization. The next time you come across a graph, remember, like many other notable inventions in our history, take pride in that it was the work of a Scot that gave us these easy to read information "pictures".

#scotland #scottish #history #mathematics #mathematition #engineer #economist

[svltaf]

@literaryreference submitted:

I have some suggestions for corrections to the machine translations on your soy sauce post, and I wasn't sure how best to get them to you, so I hope this works? Initially I was just going to reblog your post, but my comments got kinda long, which is potentially annoying for people on my dash to scroll through and also felt kind of combative if aired in public, which I don't mean it to be--I really appreciate what you're doing here, I'm just trying to help add a little polish. This isn't meant to be published as a separate post, it's just in lieu of an email, basically.

良き理解者 means she was a supporter or proponent of soy sauce, or "on the side" of soy sauce. (A soy sauce partisan, perhaps?) Basically they seem to be suggesting that if not for pressure from her bosses, she would have favored the soy sauce industry over the MSG industry, because unlike them, she understood its cultural importance.

"The original proposed allocation ratio of 2 to 8, with absolute dominance of the amino acid industry, was finally approved by GHQ with the advent of the [sc2] method and the '7 to 3 Agreement' of the 'Shoda-Ouchi Conference'" is a bit of a mess that ends up saying the opposite of what it means to say. Literally (though this is clunky as hell), this is something like "Regarding the original proposed allocation ratio of 2 to 8 with absolute dominance of the amino acid industry, the GHQ finally approved the 7 to 3 agreement of the Shoda-Ouchi Conference due to/based on the emergence of the sc2 method."(This is one of those "は is a topic marker, not a subject marker" issues.)

The quote from the Journal of the Brewing Society of Japan is missing a bit off the end--the last sentence should read "一旦，消費者に定着した混合しょうゆのニーズにより本醗造しょうゆに切り換えることが出来ず，現在も混合しょうゆが主力商品となっているのではないかと考えている 。" This bit is also a lot more equivocal than DeepL makes it out to be: "Even now, it's possible mixed/kongo soy sauce might be the top [soy sauce] product." (Japanese does tend to be more equivocal than English, but since the article doesn't back up that statement with a citation or statistic or anything, I think it really is speculation and not a definitive statement of fact.)

I think those are the only particularly significant corrections I have, but here's some nitpicking of errors that don't seriously impede comprehension, because I'm that kind of person:

In "The internal paint that was being researched..." the word translated as paint, "塗料," can refer to any kind of coating (see this dictionary definition) and I would just say "coating" here--the article doesn't specify, but it seems to me that in this context it's more likely to be some kind of protective coating than paint.

"ヤマサ印", here translated as "Yamasa mark," is more properly "Yamasa brand" in English (as in, this was a manufacturing site for Yamasa brand soy sauce).

In "GHQ assigned Ms. Blanche Appleton..." "Ms." should be "Mrs."--which doesn't really matter, but it's fascinating to me that DeepL gets it wrong here when "Mrs." is written out phonetically (so it's not even guessing at interpretations of さん or anything) and it does correctly translate/transliterate it elsewhere.

"which defatted soybeans could be used more effectively" should be "which [industry] would use the defatted soybeans more effectively."

"Once mixed soy sauce has taken root among consumers, they are unable to switch over to hon-fermented soy sauce due to the demand for mixed soy sauce" has done something really weird with the first clause, splitting it into two semi-redundant parts on either side of the "unable to switch over" bit--it should just be "Once a demand for mixed soy sauce has taken root among consumers, they are unable to switch over to honjozo soy sauce, and even today..."

I hope this was helpful (as opposed to just annoying); please feel free to send me an ask or a message if you have any questions!

thank you for your translation notes!

oops, i already got the same note regarding 良き理解者 from other folks, i have edited the original post before you submitted. same for the 2-to-8 agreement thing.

thanks for the note on "might be"! i wouldn't've caught it.

yea i can change the word "paint" to "coating," i'm not well versed in the intricacies these terms in english so i'll take your word for it.

ah yea in my native language mark = brand as well, so i'll change that. thanks for the link.

i changed it to ms. on purpose bc i wasnt entirely sure of her marital status and i wanted to use something more neutral

thanks for the remaining notes as well, i agree with your interpretations so i'll change my post.

#svlt speaks #the mysterious appearance of miss appleton

[doctorgeekery]

20 Questions For Writers

Thanks for the tag @sinvulkt!

1. How many works do you have on AO3?

I have 7 works.

2. What’s your total AO3 word count?

60,129. Wow, a lot more than I thought, especially since I am terrible about posting things I write.

3. What fandoms do you write for?

Looking at my official statistics, apparently I have written the most for Marvel (4). Most of those are marvel crossovers with some sort.

However, Marvel is definitely NOT what I have written for the most. Back in middle school and high school, I wrote a lot of Harry Potter fanfiction and posted it on harrypotterfanfiction.com back when that still existed. I wrote two novel-length stories in addition to one-shots. If I were to add those to my AO3 stats, things would be very different.

4. What are your top five fics by kudos?

1. It's Nice Being an Avenger (2,331 kudos)

2. The Dark Knight and Gotham’s Prince: Our Sweethearts (1,070 kudos)

3. Just Call Me Lucifer, Love (778 Kudos)

4. Avenging with the Nine-Nine (475 Kudos)

5. Melting the Ice (78 Kudos)

The number one fic is a crossover of Netflix's Daredevil and the MCU Avengers. It wasn't too popular for about two years after I wrote it, but then after Charlie Cox as Matt Murdock/Daredevil appeared in Spider-Man: No Way Home, the popularity skyrocketed. I got at least ten kudos every day for about two months after that movie.

5. Do you respond to comments? Why or why not?

Yes! I do not get them terribly often so I try to remember to reply to all of them. Some of them I might forget to reply to and wait until a few months later, though.

6. What’s the fic you wrote with the angstiest ending?

I tend to usually post-humorous fic to AO3, so of what I have posted, the angstiest fic is probably "Harklin", which is set in the Star Wars High Republic. I usually end on a positive note. The darkest fic I've written - one I wrote in high school in which the main character committed suicide - had a somewhat uplifting last few sentences as the main character only committed suicide so she could be with the ghost she fell in love with.

7. What’s the fic you wrote with the happiest ending?

Most of my fics have happy endings. "The Dark Knight and Gotham’s Prince: Our Sweethearts" is probably one of the happiest, though - I had a few commenters gushing about the happy ending.

8. Do you get hate on your fic?

Nope! I am glad. Sometimes people will point out minor corrections/mistakes, but that's not hate. And I'm glad they do it, because then I go back and fix it.

9. Do you write smut?

No. I read it sometimes, but I have no desire to write it.

10. Do you write crossovers?

Yes, I have a few crossovers posted - most notably the Marvel/Lucifer TV crossover and the Marvel/Brooklyn-99 crossover. It is interesting, since I rarely read crossovers, but I love to write them.

11. Have you ever had a fic stolen?

I don't think so? Never checked, but nobody has told me that it has been, soooooo.

12. Have you ever had a fic translated?

Just hit this milestone a few years ago! Someone asked me if they could translate "The Dark Knight and Gotham’s Prince: Our Sweethearts" into Chinese, I said yes, and it is now posted as a translation of my fic. HUGE honor - the day it was posted I was grinning so much.

13. Have you ever co-written a fic?

Yes, I co-wrote a one-shot once back when I was writing on harrypotterfanfiction.com. I literally just looked it up on wayback machine because I don't remember it well (looks like I published it in 2014), but it was for a challenge/competition thing with another randomly assigned author. We got along quite well and had a lot of fun!

14. What‘s your all-time favourite ship?

When reading fanfiction, I tend to not read a lot of romance, and I don't write a lot of romance, either. I don't have a "this is 100% my favorite ship" that I am sure about, but the first one that popped into my brain when reading that question is Supercorp.

15. What’s the WIP you want to finish but doubt you ever will?

"Avenging with the 99" - essentially, the cast of Brooklyn 99 teams up with some Avengers for their annual Halloween Heist. There's so much pranking and a lot of fun, but a) after a certain point, I struggled with where to take it and b) I know at one point I planned who was going to win... but it's been so long that I have forgotten.

In middle and high school, I planned out an entire seven book series for Albus Potter, Harry Potter's son. I completed the first two books (something I will always be proud of) and started writing the third while I was still editing the second book, but then I quit. I had outlines of plans for all seven of the books, which I know I still have somewhere. At this point, it has been so many years and I have fallen out with the Harry Potter fandom completely, so I know I will never go back to it.

16. What’s your writing strengths?

Dialogue. I love to write dialogue, as it comes so naturally to me. Easiest part of writing, by far.

17. What’s your writing weaknesses?

One, I hardly ever write. Most things I write I don't complete. But when it comes to writing itself, I am AWFUL at description. I can't describe locations, people, etc. If you see a fic of mine with a lot of description, know that it took me a painfully long time. Often when writing a first draft of something, I will say [insert description of place here later] because trying to describe things ruins my flow.

18. Thoughts on writing dialogue in another language for a fic?

I have never done it, but I don't see why not. I think it depends how much - a few short phrases here or there would be awesome, but I don't think all of the dialogue should be in a different language than the rest of the fic.

19. First fandom you wrote for?

Harry Potter

20. Favourite fic you’ve ever written?

Honestly, "The Dark Knight and Gotham’s Prince: Our Sweethearts" is one of my favorites to go back and re-read. I still laugh when I re-read it.

I will always be proud of the first fic I ever wrote, simply because it was novel-length (75K words) and I did an excellent job for a 13-15 year old. I would never want to re-read it now, though - I will probably cringe at it.

As for tagging... uh, I think @sinvulkt already tagged most people from our Star Wars discord, so I shall change fandoms and tag @starsandstormyseas. I will also tag @chaosgoblinhours because I love their Gutterworks series.

#long post #ask game #writing

[kneedeepincynade]

The belt and road initiative has brought prosperity to many nations and it the future it will bring it to many more!

The post is machine translated

Translation is at the bottom

The collective is on telegram

😘 CELEBRARE GLI OBIETTIVI RAGGIUNTI, PIANTARE NUOVI SEMI PER LA CRESCITA DEL FUTURO 🥰

🇨🇳 Il Compagno Chen Wenjun, Direttore dell'Iniziativa di Pubblicazione del Libro Bianco "La Belt and Road Initiative: un Pilastro-Chiave di una Comunità dal Futuro Condiviso per l'Umanità", ha dichiarato - durante una conferenza stampa, che l'Opera mira a fornire alla Comunità Internazionale una migliore comprensione del valore di questa iniziativa e del Concetto di Cooperazione a Mutuo Vantaggio (合作共赢):

💬 «Il Libro Bianco, guidato dal Pensiero di Xi Jinping sul Socialismo con Caratteristiche Cinesi per una Nuova Era, ha esposto sistematicamente l'Origine Storica, la Mentalità, la Visione, l'approccio per la realizzazione e i risultati pragmatici della Cooperazione tramite la Nuova Via della Seta» 😍

👉 Statistiche sulla BRI rilasciate dalla Compagna Guo Tingting - Vice-Ministro del Commercio ⭐️

📊 10 anni dopo la Presentazione della BRI, sono stati organizzati 3000 progetti di cooperazione, investiti quasi 1 Trilione di Dollari e sono stati creati 420.000 posti di lavoro per i Paesi che hanno partecipato al Progetto 😍

🇨🇳 Come prossimo passo, il Ministero del Commercio della Repubblica Popolare Cinese si concentrerà su quattro aspetti per promuovere ulteriormente la Cooperazione a Mutuo Vantaggio:

一 Rafforzare l'Apertura verso il Mondo, espandendo e facilitando l'importazione e l'esportazione di beni di alta qualità, organizzando sempre più eventi, fiere e mostre per approfondire la Cooperazione Commerciale con i Paesi interessati 😍

二 Rafforzare la Cooperazione nelle catene di produzione e approvvigionamento, migliorando ulteriormente l'efficienza dei trasporti e accelerando la formazione di nuovi corridoi commerciali tramite la costruzione di infrastrutture di alta qualità 😍

三 Piantare i semi, annaffiare e far germogliare nuovi progetti atti a promuovere ulteriormente la crescita economica, pianificando progetti infrastrutturali e costruendo nuove Zone di Cooperazione 🤝

四 Promuovere l'adesione all'Accordo Globale e Progressivo del Partenariato Trans-Pacifico e sostenere le imprese della Regioni Amministrative Speciali di Hong Kong e Macao, dove vige il Principio 一国两制 - Un Paese, Due Sistemi, affinché partecipino alla Costruzione della Nuova Via della Seta 💕

🌸 Iscriviti 👉 @collettivoshaoshan 😘

😘 CELEBRATING WHAT HAS BEEN ACHIEVED, PLANTING NEW SEEDS FOR THE GROWTH OF THE FUTURE 🥰

🇨🇳 Comrade Chen Wenjun, Director of the White Paper Publishing Initiative "The Belt and Road Initiative: a Key Pillar of a Community with a Shared Future for Humanity", declared - during a press conference, that the Opera aims to provide the International Community with a better understanding of the value of this initiative and the Concept of Cooperation for Mutual Benefit (合作共赢):

💬 «The White Paper, guided by Xi Jinping Thought of Socialism with Chinese Characteristics for a New Era, systematically laid out the Historical Origin, Mindset, Vision, approach to implementation and pragmatic results of Cooperation through the New Silk Road" 😍

👉 BRI Statistics Released by Comrade Guo Tingting - Vice-Minister of Commerce ⭐️

📊 10 years after the Presentation of the BRI, 3000 cooperation projects have been organized, almost 1 Trillion Dollars have been invested and 420,000 jobs have been created for the countries that participated in the Project 😍

🇨🇳 As the next step, the Ministry of Commerce of the People's Republic of China will focus on four aspects to further promote Mutual Benefit Cooperation:

一 Strengthen Openness to the World, expanding and facilitating the import and export of high quality goods, organizing more and more events, fairs and exhibitions to deepen Commercial Cooperation with interested countries 😍

二 Strengthen Cooperation in production and supply chains, further improving transportation efficiency and accelerating the formation of new trade corridors through the construction of high-quality infrastructure 😍

三 Planting seeds, watering and sprouting new projects to further promote economic growth, planning infrastructure projects and building new Cooperation Zones 🤝

四 Promote adherence to the Comprehensive and Progressive Agreement of the Trans-Pacific Partnership and support enterprises in the Special Administrative Regions of Hong Kong and Macao, where the 一国两制 Principle - One Country, Two Systems applies, to participate in the Construction of the New Way of Silk 💕

🌸 Subscribe 👉 @collectivoshaoshan 😘

#socialism #china #italian #translated #collettivoshaoshan #china news #communism #marxism leninism #marxist leninist #marxist #marxismo #marxism #chinese economy #belt and road initiative #news #economic news #world news #asia news #economic development #Chen Wenjun #xi jinping #Guo Tingting #socialismo #socialist #multipolar world #multipolarity

[vivekavicky12]

From Algorithms to Ethics: Unraveling the Threads of Data Science Education

In the rapidly advancing realm of data science, the curriculum serves as a dynamic tapestry, interweaving diverse threads to provide learners with a comprehensive understanding of data analysis, machine learning, and statistical modeling. Choosing the  Best Data Science Institute can further accelerate your journey into this thriving industry. This educational journey is a fascinating exploration of the multifaceted facets that constitute the heart of data science education.

1. Mathematics and Statistics Fundamentals:

The journey begins with a deep dive into the foundational principles of mathematics and statistics. Linear algebra, probability theory, and statistical methods emerge as the bedrock upon which the entire data science edifice is constructed. Learners navigate the intricate landscape of mathematical concepts, honing their analytical skills to decipher complex datasets with precision.

2. Programming Proficiency:

A pivotal thread in the educational tapestry is the acquisition of programming proficiency. The curriculum places a significant emphasis on mastering programming languages such as Python or R, recognizing them as indispensable tools for implementing the intricate algorithms that drive the field of data science. Learners cultivate the skills necessary to translate theoretical concepts into actionable insights through hands-on coding experiences.

3. Data Cleaning and Preprocessing Techniques:

As data scientists embark on their educational voyage, they encounter the art of data cleaning and preprocessing. This phase involves mastering techniques for handling missing data, normalization, and the transformation of datasets. These skills are paramount to ensuring the integrity and reliability of data throughout the entire analysis process, underscoring the importance of meticulous data preparation.

4. Exploratory Data Analysis (EDA):

A vivid thread in the educational tapestry, exploratory data analysis (EDA) emerges as the artist's palette. Visualization tools and descriptive statistics become the brushstrokes, illuminating patterns and insights within datasets. This phase is not merely about crunching numbers but about understanding the story that the data tells, fostering a deeper connection between the analyst and the information at hand.

5. Machine Learning Algorithms:

The heartbeat of the curriculum pulsates with the study of machine learning algorithms. Learners traverse the expansive landscape of supervised learning, exploring regression and classification methodologies, and venture into the uncharted territories of unsupervised learning, unraveling the mysteries of clustering algorithms. This segment empowers aspiring data scientists with the skills needed to build intelligent models that can make predictions and uncover hidden patterns within data.

6. Real-world Application and Ethical Considerations:

As the educational journey nears its culmination, learners are tasked with applying their acquired knowledge to real-world scenarios. This application is guided by a strong ethical compass, with a keen awareness of the responsibilities that come with handling data. Graduates emerge not only as proficient data scientists but also as conscientious stewards of information, equipped to navigate the complex intersection of technology and ethics.

In essence, the data science curriculum is a meticulously crafted symphony, harmonizing mathematical rigor, technical acumen, and ethical mindfulness. The educational odyssey equips learners with a holistic skill set, preparing them to navigate the complexities of the digital age and contribute meaningfully to the ever-evolving field of data science. Choosing the best Data Science Courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

#data science certification #data science course #data science training #datascience #technology

[jrmilazzo]

In the 1940s, the mathematician Claude Shannon demonstrated that language use could be both described by statistics and imitated with statistics, whether those statistics were in human heads or a machine’s memory. Shannon, in other words, was the first statistical language modeler, which makes ChatGPT and its ilk his distant brainchildren. Shannon never tried to build such a machine, but some astute early readers of his work recognized that computers were primed to translate his paper-and-ink experiments into a powerful new medium. In writings now discussed largely in niche scholarly and computing circles, these readers imagined—and even made preliminary sketches of—machines that would translate Shannon’s proposals into reality. These readers likewise raised questions about the meaning of such machines’ outputs and wondered what the machines revealed about our capacity to write.

The current barrage of commentary has largely neglected this backstory, and our discussions suffer for forgetting that issues that appear novel to us belong to the mid-twentieth century. Shannon and his first readers were the original residents of the headspace in which so many of us now find ourselves. Their ambitions and insights have left traces on our discourse, just as their silences and uncertainties haunt our exchanges. If writing machines constitute a “philosophical event” or a “prompt for philosophizing,” then I submit that we are already living in the event’s aftermath, which is to say, in Shannon’s aftermath. Amid the rampant speculation about a future dominated by writing machines, I propose that we turn in the other direction to listen to field reports from some of the first people to consider what it meant to read and write in Shannon’s world.

#AI #history #science #LLMs #long reads

[msclaritea]

This article is from 2019 and never more important than now. Maybe people didn't take it seriously then. I hope they will, now.

"Shortly after World War II, when Europe lay in ruin and humanity was newly traumatized by the spectacle of organized violence that an authoritarian regime could achieve in the industrial epoch, the Western World experienced a sudden cultural shift. This new regime of thought is sometimes called postmodernism, but that term is obscure and overused; a better way to think about this is that there was no longer a unifying narrative, a guiding thread that united humans in the West. Whereas some countries might have previously had religious bonds, or ethnic bonds, or monarchial bonds, or even political bonds around, say, an authoritarian leader, suddenly there were none anymore — or at least none that were universally believed. Individualism and identity were more important, and politicians and legal bodies would now have to consider how to govern subjects in an ambiguous, pluralistic, multicultural world.

At the same time, it was becoming clear that the forces that shaped the world — the power to  organize society, or to exterminate it — were in the hands of scientists and technologists. The atom bomb, the intercontinental ballistic missile, the radio, the car, electrification, the refrigerator and the moon landing all happened in a span of about a hundred years. Science and technology spurred World War II, and led to its conclusion. And as the war receded from memory, it was apparent that the areas of greatest economic growth were all in technical fields — computers, engineering, communications, biotech and material science.

Jean-Francois Lyotard, a French philosopher who studied the condition of knowledge in this new era, realized that technology had changed the way that humans even thought about what knowledge was. Knowledge that computers could not process or manage — for instance, the ability to think critically or analyze qualitatively — was increasingly devalued, while the kinds of knowledge that computers could process became more important. As Lyotard wrote:

The miniaturisation and commercialisation of machines is already changing the way in which learning is acquired, classified, made available, and exploited....The nature of knowledge cannot survive unchanged within this context of general transformation. It can fit into the new channels, and become operational, only if learning is translated into quantities of information. We can predict that anything in the constituted body of knowledge that is not translatable in this way will be abandoned... Along with the hegemony of computers comes a certain logic, and therefore a certain set of prescriptions determining which statements are accepted as “knowledge” statements.

Lyotard wrote this in 1978, before the modern internet even existed. Today, the idea that computational forms of knowledge — and/or the kinds of people who traffic in that knowledge — are more valuable to our society seems to be universal. Thanks to generous grants from the tech industry and well-heeled nonprofits like the Mellon Foundation, humanities academics across the world have been spurred to do more research in what is called the "digital humanities" — a vague term that often means applying statistical and quantitative tools to data sets that involved humanities research, such as literary corpuses. The tech industry investments in digital humanities fulfills Lyotard's prophecy that society would cease to see the humanities' brand of knowledge as useful; that it would attempt remake the humanities into a discipline characterized by discrete information, rather than a means of analyzing, considering, and philosophizing the world.

In the same essay, Lyotard actually distinguishes between two different types of knowledge: the "positivist" kind, that is applicable to technology; and the "hermenutic" kind of knowledge. Hermeneutics, meaning the study of interpretation, is what the humanities (and to some extent social sciences) concerns itself with. One can see how this kind of knowledge might be difficult for computers to catalogue and use. The idea that a computer could produce a literary analysis of a Vonnegut short story sounds absurd because it is: this is not the way that computers process data, this is not what humans generally regard computers as useful for, and it is certainly not what they are designed to do by the tech companies. Unsurprisingly, then, this type of humanities knowledge has become devalued, and not even considered "knowledge" by many.

So this leads us to a predicament in which slowly, since the postwar era, humanities skills and associated knowledge have been devalued, while STEM knowledge — an acronym for "Science, Tech, Engineering and Math," meaning the kind of quantitative knowledge associated with technology — reigns supreme. One of the most interesting places that you can see this trend is in fiction: the kinds of heroes and protagonists that people admire and look up to in fiction are increasingly those with STEM knowledge, as these people are seen as heroes because we uncritically accept that STEM knowledge is what changes the world. There is a reason that Iron Man is a billionaire technologist, and Batman is a billionaire technologist, and The Hulk's namesake Bruce Banner has multiple PhDs in the Marvel canon, and that the mad scientist Rick Sanchez (of "Rick and Morty") is essentially an immortal, infinitely powerful being because of his ability to understand science and wield technology. We admire these people because they possess the kinds of skills that our society deems the most valuable, and we're told that we, like them, can use these skills to master the universe.

(There is a potent irony here, of course, in that it is artists who write these narratives, and artists who are partly responsible for creating and popularizing this kind of STEM-supremacist propaganda. Weirdly, though, you rarely see a superhero or a super-spy who started life as a painter, or a novelist, or a comic book artist.)

Moreover, in real life, people who possess technological knowledge, primarily the scions of Silicon Valley, are widely adulated, viewed as heroes who will inherently change society for the better. This manifests itself in various ways: some technologists, like Bill Gates and Mark Zuckerberg have set up philanthropic foundations to "solve" our social problems — though curiously, the means by which that happens always seems to enrich themselves and their fellow capitalists along the way. Some of them promise widespread social change for the better via their own businesses, as though running a for-profit tech company was in and of itself a gift to the world and a net positive for social cohesion: you see this in many tech companies that advertise themselves as operating "for good," such as in the PR rhetoric of Facebook.  Then, there are those who believe that their contribution to society will be helping us leave this planet, and who are investing heavily in private spaceflight companies with the ultimate intention of colonizing space; this includes both Elon Musk and Jeff Bezos.

In all these cases, the idea that people with STEM knowledge are predestined to save the world is an idea has become so dominant we don’t even question it. Some call this attitude STEM chauvinism, though I prefer the moniker STEM Supremacy. The noun "supremacy," I believe, is called for, because of how the idea that STEM knowledge (and those who posses it) is superior to other forms of knowledge has become so hegemonic that our culture openly mocks those who possess other forms of knowledge — particularly the hermeneutic, humanities-type knowledge. There is a fount of memes about humanities majors and how useless their fields are; some of these memes depict humanities majors as graduating to working at low-wage jobs like McDonalds; others mock critical humanities majors (particularly gender studies) as being out-of-touch, social failures.

Such discourse is intersectional with other supremacist beliefs, such as patriarchy, and often these kinds of memes that celebrate STEM knowledge and mock humanities knowledge will simultaneously mock women and celebrate masculinity. It was unsurprising to me when, last year, it leaked that a Google engineer, James Damore, had circulated an anti-diversity manifesto in which he used discredited science to argue that there were biological reasons for the gender gap. He went on to argue that there were reasons men were more interested in computers and in leadership, and women less. Though Damore was fired, he maintains that many of his peers agreed with him. Such incidents speak to the ways that different chauvinist tendencies, one of STEM Supremacy and one of patriarchy, can intersect to form novel noxious political ideologies.

The concept of "STEM Supremacy" relies on a popular belief that STEM knowledge is synonymous with progress. Yet if you take this kind of belief a bit too far, you might be keen to abandon democratic ideals and start to believe that we really should live in a society in which the STEM nerds rule over us. This has resulted in a number of half-baked supremacists within the tech industry who advocate either for authoritarian technocracies or, more bizarrely, monarchy.

I’ll give a few brief examples. There’s Google engineer Justine Tunney, a former Occupy Wall Street activist who now calls for “open-source authoritarianism. ” Tunney has argued against democracy and in favor of a monarchy run by technologists, and advocated for the United States to bring back indentured servitude.

But perhaps best-known among the techno-monarchists is Mencius Moldbug, the nom de plume of Curtis Yarvin, a programmer and founder of startup Tlon — a startup that is backed at least in part by billionaire anti-democracy libertarian Peter Thiel, who famously once wrote he did not believe democracy and freedom were compatible, and expressed skepticism over women's suffrage. Moldbug's polemics are circular, semi-comprehensible, and blur political theory and pop culture; Corey Pein of The Baffler described his treatises as "archaic [and] grandiose," while being "heavily informed by the works of J.R.R. Tolkien and George Lucas."

Both of these so-called thinkers constitute parts of a larger movement that calls itself "Dark Enlightenment," alternatingly known as "neoreactionaries." True to its name,  the political agenda of Dark Enlightenment includes a celebration of patriarchy, monarchy, and racialized theories of intelligence differentials. 

The notion that monarchy is popular again in Silicon Valley might sound absurd. We associate monarchies with stodgy, quaint medieval kingdoms, the opposite of the disruptive, fast-moving tech industry. And yet those in the tech industry who see monarchy as appealing are keen to point out how the hierarchical aspects of monarchial rule are actually familiar to their industry. As Pein mentions in his Baffler essay, Thiel delivered a lecture in 2012 in which he explained the connection:

A startup is basically structured as a monarchy. We don’t call it that, of course. That would seem weirdly outdated, and anything that’s not democracy makes people uncomfortable.

[But] it is certainly not representative governance. People don’t vote on things. Once a startup becomes a mature company, it may gravitate toward being more of a constitutional republic. There is a board that theoretically votes on behalf of all the shareholders. But in practice, even in those cases it ends up somewhere between constitutional republic and monarchy. Early on, it’s straight monarchy. Importantly, it isn’t an absolute dictatorship. No founder or CEO has absolute power. It’s more like the archaic feudal structure. People vest the top person with all sorts of power and ability, and then blame them if and when things go wrong.

[T]he truth is that startups and founders lean toward the dictatorial side because that structure works better for startups. It is more tyrant than mob because it should be. In some sense, startups can’t be democracies because none are. None are because it doesn’t work. If you try to submit everything to voting processes when you’re trying to do something new, you end up with bad, lowest common denominator type results.

The underpinnings of STEM Supremacy are, as I've laid out, complicated to see and stretch back to the end of World War II — but when put together they form a broader picture of where the philosopher-kings of the tech industry are heading, and what they believe. If we continue to live in a society that devalues humanities-type knowledge and glorifies STEM knowledge, this kind of thinking will persist, I fear. And the tech industry is partly responsible for cultivating this noxious worldview, in the sense that their PR apparatuses glorify STEM knowledge and encourage the public to view their leaders as demigods.

This isn't a unique phenomenon. Any situation where a certain ideology is denigrated and another valorized, there will be at some point a corresponding rise in a chauvinism in favor of the valorized ideology. The situation today is made more complicated by the fact that the tech industry benefits from the normalization of STEM Supremacist beliefs. The unearned trust that the public has for tech startups and tech industry ideas, the lack of regulation, and the absurd valuations of companies that continue to lose money — this is all motivated by an underlying belief that these companies are innately good, their owners smart, and their work more vital than other fields. Whether they admit it or not, you can draw a line from the public relations departments of tech companies and Justine Tunney's call for "open-source authoritarianism."

Ironically, the only antidote to all this sophistry is the humanities — the kind of critical thinking that they entail, and the kind of thinking that it is impossible for computers to do. I've often wondered if part of the tech industry's investment in digital humanities is designed to help stave off critical discourse or criticism of their companies. Indeed, by remapping the idea of what knowledge is in the first place, the tech industry is helping to realize a future in which we lack even the language to think critically about their role in society. Or maybe even a future in which they rule over us as monarchial, benevolent dictators — at least in their eyes. Perhaps this was the plan all along. (Oh yeah. It was)

By KEITH A. SPENCER

Keith A. Spencer is a senior editor at Salon who edits Salon's science/health vertical. His book, "A People's History of Silicon Valley: How the Tech Industry Exploits Workers, Erodes Privacy and Undermines Democracy," was released in 2018. Follow him on Twitter at @keithspencer, or on Facebook here.

_______________________________________________

THESE FUCKERS IN SILICON VALLEY WANT A MONARCHY !!

"Few Catholics outside the D.C area are likely familiar with Fr. Arne, who never wrote a book or made national headlines. Yet a list of those who appear in Eberstadt’s book to laud his role among “billionaires and Supreme Court Justices” indicates the breadth of his influence: George Weigel, Fr. Thomas Joseph White, Arthur Brooks, Hadley Arkes, Peter Thiel, and Fr. Paul Scalia, to name but a few..."

"From his perch on K Street at the Catholic Information Center (CIC), Father Arne Panula shepherded some of the nation’s power brokers into the Catholic Church..." Mary Eberstadt

“As recently as 2017, Billy [Barr] was on the board of directors of the DC-based Catholic Information Center, led by the ultraright and secretive group Opus Dei…Its board includes the Federalist Society’s Leonard Leo, and White House counsel Pat Cipollone..."

*Above thread is chockful of more information!*

#Credit Suisse #Peter Thiel #Mueller Report #Much more #Sorry for the length #Important information

[torchickentacos]

ok this has actually been bothering me for a while. Note that everythign I say is my own opinion and while I do think what I have to say holds merit, I do not claim to be an expert in any field I am mentioning here and if you see innacuracies in my statement, feel free to kindly let me know in a reblog and I'll reblog the corrected version. When we discuss ai art, (usage of the word art being left to open ended interpretation and debate since that isn't my point at all), I wish we could kind of section off the areas of artificial intelliegence we're actually talking about and SAY ai ART or ai WRITING instead of ai as a whole (though I understand its shorthand usage, that leads to conflation of it as a whole). Because AI, while very questionable in its usages right now, is more than art and writing.

AI is the screen reader I use in classes to help me read. AI is eye trackers people use for computers when they're immobile. AI is google add-ons that block certain things for visually sensitive people like epileptics. AI is medical pattern collecting and identifying in at-risk populations (which does have implications which I will mention more later). AI is auto captioning services, though they kind of suck half the time and give you very funny but innacurate youtube subtitles (shoutout to 'palkia you son of a bitch', though I think it's edited). It's voice commands for devices for people with limited hand mobility.

I am NOT an AI bootlicker asskisser whatever. I have opinions on ai art and artist ownership, but here's the thing: we can't condemn the term ai in its entirety when it's been such a huge acessibility thing. We can't immediately see someone use the word ai and immediately put them down as a crypto finance nft ai art bro. because GOD i hate nft bro ai whatever nonsense, the people who steal artist's work to put through their algorithms and create copies of it, but ai is also used for so much more and I feel like the current climate around ai is very pinpointed negatively at one aspect of it, but brings down other helpful aspects of it in the process of pointing out the very valid flaws with it. So, if someone skims this and sees me saying 'hey, can we please use some nuance here', I'm just waiting for the 'why are you on their side, ai is terrible, it steals from artists' or 'why do you hate ai, stop being a sjw whateverthefuck' thing when my entire point here is that we need to be able to separate what we're actually talking about here in a meaningful way. You can condemn negative parts of ai that are genuinely concerning for creative folk everywhere WHICH I FUCKING AGREE WITH while also saying that we need to be mindful of the fact that AI also means screen readers, translators, epileptic flash blocker add-ons, and so many other things, and by condemning anyone who uses ai as a whole you are bringing down SO many people with that in the area of effect of your sweeping statements.

TLDR the internet is doing that 'you're either with us or against us' thing and as usual it's marginalized communities, disabled people this time, being used as talking points and what-ifs on both sides instead of our actual input being valued as people who do use other forms of ai.

And we can talk about aspects of accessibility and medical ai in a nuanced way, too. there's definite data collecting implications in medical use of ai and machine learning. There's definite demographic collection and pattern recognition that can be used by medical professionals to misattribute things with more of a focus on data and statistics rather than on how medical statistics are skewed by sociological factors. (for instance, are southern 'people of walmart' rednecks all fat, or is it more in line with impoverished rural communities and food deserts leading to a link in demographic and weight?) There's negativity and hesitation and issues in all branches of AI and this isn't saying medical use AI is perfect because honestly, I'd argue there's more to be worried about with that than creatively used ai. BUT my point stands: you cannot use the term AI as an immediate marker for who's a bad person for supporting it and who isn't, because they very well may support ai as accessibility and condemn ai as an art tool.

#probs going to have to delete this later