Martin Ruskov @mapto@social.cleverlibre.org

To initiate such a process, I engaged with an investigation following the principles of action research (https://sonyaterborg.com/2016/02/17/action-research/) - an iterative methodology where research and practice go hand in hand. While a task is being completed, in parallel a reproducible process is being developed, lessons learned are being collected.

What does it take to be more rigorous about the composition of prompts? Well, first of all, not only talking about what words are used, but also about how they are chosen, why one choice of words might be better than another? Then, it is important to be careful how features are tried, putting an effort to separate different effects as much as possible. This takes time, but allows for a better understanding of the interplays (https://doi.org/10.1207/s15327809jls1502_2). And this is only for a start of a long reflective practice that allows for the collection of insights that can be valid also across models, hopefully also for models that are yet to come.

With AI text-to-image generator models gaining popularity, there's a lot of talk about #promptEngineering, the process of refining the textual input used to obtain a desired result. However, calling the current practices "engineering" is an exaggeration considering that 1. it commonly limits itself to everyone using their ad-hoc techniques, 2. that are barely validated, and 3. and transferability across models is not even considered. For an overview of practices among #aiartists, consider Jonas Oppenlaender's ethnographic study (https://arxiv.org/abs/2204.13988) He gives a good idea of some of the employed witchcraft, such as quality boosters, repetitions and magic words

Not long ago I presented my experiments on illustrating famous fairy tales with #Midjourney v4 at the #IRCDL23 (http://lacam.di.uniba.it/IRCDL23/) This experimentation is part of the VAST project https://www.vast-project.eu/ that studies values present in different texts, including some fairy tales recorded by the Grimm brothers. My generations were an aside activity, and I attempted to generate 5 illustrations for each of 5 fairy tales: #LittleRedRidingHood, #Cinderella, #LittleSnowWhite, #HanselAndGretel, and #FaithfulJohannes

@BarrenPlanet@c.im @janellecshane let's see what educational research has to say about it...

Hmm, it seems there's cramming and there's understanding. Actually, David Perkins from Harvard even wrote a paper titled "Beyond Understanding". It was the introductory chapter of a book on threshold concepts... interesting, isn't it.
http://tales.islab.di.unimi.it/2023/03/13/in-what-exact-way-is-chatgpt-intelligent/

@quinn @janellecshane @KevinMarks talk of storage as computer memory, as in RAM/ROM, is standard

@LovesTha @janellecshane wait, isn't lossy compression meant to keep the important (key) information and drop the details? Did anyone try to indicate to LLMs what is key? Not to my knowledge

@nntaleb do you see in this anything else than the personal agenda of the persons in power? What, if so?

An enquiry into photograph smiles and how Midjourney distorts these

RT @hackylawyER@twitter.com

"It was as if the AI had cast 21st century Americans to put on different costumes and play the various cultures of the world. Which, of course, it had."

Great article from @babiejenks@twitter.com https://medium.com/@socialcreature/ai-and-the-american-smile-76d23a0fbfaf

🐦🔗: https://twitter.com/hackylawyER/status/1641157473833762833

At @IslabUnimi@twitter.com we're using bespoke (quantitative) word embeddings as a tool for comparative corpus analysis. I see it as a first step towards better informed critical analysis. Here's an example by @eliroc98@twitter.com and Tommaso Locatelli
http://tales.islab.di.unimi.it/2023/03/27/john2vec-or-embedding-deweys-philosophy/
#DigitalHumanities

RT @emilymbender@twitter.com

But people want to believe SO HARD that AGI is nigh.

Remember: If #GPT4 or #ChatGPT or #Bing or #Bard generated some strings that make sense, that's because you made sense of them.

🐦🔗: https://twitter.com/emilymbender/status/1640414584891928576

RT @george__mack@twitter.com

2. The Rise Of Negative Media

Since 2010, the media massively increased headlines that use fear, anger, disgust, and sadness.

It's no surprise the media isn't covering this 😅

🐦🔗: https://twitter.com/george__mack/status/1640010316468858880

RT @yudapearl@twitter.com

For this reason, GPT's understanding should be tested on questions whose answer is "NO, it is not possible under any circumstances". Such questions are the core of causal logic. e.g., can a drug be good for men, good for women and bad for people? https://twitter.com/yudapearl/status/1640075242470277120

🐦🔗: https://twitter.com/yudapearl/status/1640079735282864131

RT @ProfNoahGian@twitter.com

GPT4 can speak fluently, so maybe it's mastered language in that sense--but to manipulate us it'd need to measure the impact its words have on us, and it cannot. Donald Trump could see what his words do to his crowds of supporters, what actions they took because of his speeches.

🐦🔗: https://twitter.com/ProfNoahGian/status/1639698833276846081

RT @ProfNoahGian@twitter.com

data: we're already training LLMs now on most human written text, so how do we keep increasing this finite supply? Even if we could, how do we know AI capabilities will continue exponentially and not plateau? We don't. They might, they might not: it's an assumption, not a fact.

🐦🔗: https://twitter.com/ProfNoahGian/status/1639698828839329792