There are a few terms that have been thrown around a lot lately: AI, DL, NN, ML, NLP, and more. While a precise definition of all these terms would take multiple paragraphs, the thing they have in common is that a computer is doing some stuff.
For anyone who is not familiar with this alphabet soup, I've written a fairly comprehensive overview of the field's origins and history, as well as an explanation of the technologies involved, here, and ask forgiveness for starting the explanation of a 2019 software released in 1951.
In recent years, the field of machine learning has advanced at a pace which is, depending on who you ask, somewhere between "astounding", "terrifying", "overhyped" and "revolutionary". For example, GPT (2018) was a mildly interesting research tool, GPT-2 (2019) could write human-level text but was barely capable of staying on topic for more than a couple paragraphs, and GPT-3 (2020–22) wrote this month's arbitration report (a full explanation of what I did, how I did it, and responses to the most obvious questions can be found below).
The generative pre-trained transformers (this is what "GPT" stands for) are a family of large language models developed by OpenAI, similar to BERT and XLnet. Perhaps as a testament to the rapidity of developments in the field, even Wikipedia (famous for articles written within minutes of speeches being made and explosions being heard) currently has a redlink for large language models. Much ink has already been spilled on claims of GPTs' sentience, bias, and potential. It's obvious that a computer program capable of writing on the level of humans would have enormous implications for the corporate, academic, journalistic, and literary world. While there are certainly some unrealistically hyped-up claims, it's hard to overstate how much these things are capable of, despite their constraints.
With that said, there are basically two options here.
For the deletion report, GPT-3 was prompted with a transcript of each discussion in the report, and instructed to write a summary of it in the style of deceased Gonzo journalist Hunter S. Thompson. This produced a mixture of insightful, incisive, and derisive commentary. GPT-Thompson proved quite capable of accurately summarizing the slings and arrows of every discussion in the report – even though it specifically covers the longest and most convoluted AfDs. "Ukrainian Insurgent Army war against Russian occupation", for example, was a whopping 126,000 bytes (and needed to be processed in several segments) but the description was accurate.
For each discussion in the report, I provided a full transcript of the AfD page (with timestamps and long signatures truncated to aid processing), and prompted GPT-3 for a completion, using some variation on the following:
Despite being ostensibly written in Thompson's style, these were generally quite straightforward summaries that covered the arguments made during each discussion, with hardly any profanity.
Afterwards, I provided the summary itself as a prompt, and asked GPT-Thompson for an "acerbic quip" on each. Unlike the "summary" prompts (in which GPT-Thompson only occasionally chose to accompany his commentary with unprintable calumny and scathing political rants), the "acerbic quip" prompts solely produced output ranging from obscene and irreverent to maliciously slanderous. Notably, this behavior is identical to what Hunter S. Thompson habitually did in real life, and part of why many editors allegedly loathed working with him. Personally, I didn't mind sifting through the diatribes (some of them were quite entertaining), but having to run each prompt several times to get something usable did make it fairly expensive.
For the arbitration report, GPT-3 was instructed to write a summary of each page in the style of deceased United States Supreme Court justice Oliver Wendell Holmes, Jr. This produced surprisingly insightful commentary; Justice GPT-Holmes proved able to summarize minute details of proceedings, including some things I'd missed while originally reading them. In general, he was more well-behaved (and less prone to obscene tirades) than GPT-Thompson, although he did have a tendency for long-winded digressions, and would often quote entire paragraphs from the source text.
Similar to the deletion report, input consisted of brief prologues (e.g. "The following is a verbatim transcript of the findings of fact in a Wikipedia arbitration request titled 'WikiProject Tropical Cyclones'"). This was followed by the transcript of the relevant pages (whether they were the main case page, arbitration noticeboard posting, preliminary statements, arbitrator statements, or findings of fact and remedies). Afterwards, a prompt was given for a summary, of the following general form:
Image from Craiyon (formerly "DALL-E Mini"), a VQGAN- and BART-based generative adversarial network
Image from Midjourney, a diffusion network whose architecture is not publicly documented
We all remember those weird DeepDream images where the sky got turned into dogs. This is a little different.
In addition to text completion, transformers (in conjunction with other technologies) have proven themselves quite capable of image generation. The first of these, broadly speaking, was DALL-E, announced by OpenAI in January 2021. Since then, a number of services have become available, which use a variety of architectures to generate images from natural-language prompts (i.e. a prompt phrased in normal language like "a dog eating the Empire State Building", rather than a procedurally defined set of attributes and subjects written in a specialized description language). Among these are Craiyon (formerly known as "DALL-E Mini", despite having no relation to DALL-E) and Midjourney. For this issue, I used both of these services to generate illustrations for our articles: some came out very impressively, and some came out a little goofy. It was definitely surprising to see it have a coherent response for the prompt "Technoblade's avatar" that actually looked like it – I guess this is what happens when the training set is massive. Anyway, you can see a bunch of these on the issue page. For a comparison between the three models I found usable, see the embedded images above.
DALL-E 2 creates much higher-quality images than what I used, but there's a waitlist for access, and it didn't end up happening by press time (although I did get my friend to generate me one). For a comparison, see below; both were prompted from the string "Teddy bears working on new AI research underwater with 1990s technology".
While some concerns have been raised about the intellectual property implications of images generated by such models, the determination has been made (at least on Commons) that they're ineligible for copyright due to being the output of a computer algorithm. With respect to moral rights, the idea is generally that they're ripping off human artists because they were trained on a bunch of images from the Internet, including copyrighted ones. However, it's not clear (at least to me) in what way this process differs from the same being done by human artists. As far as I can tell, this is the way that humans have created art for the last several tens of thousands of years – as far as I can tell, Billie Eilish does not get DMCA claims from the Beatles for writing pop music, and Peter Paul Rubens didn't get in trouble with the Lascaux cavemen (even when he painted obviously derivative works).
They speak for all that is cruel and stupid and vicious in the American character. They are the racists and hate mongers among us [...] Fuck them.